=","with sampled indices. \"\"\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] %","num_nodes, edge_weights=None, relabel_idx=True): \"\"\" Description ----------- The neighbor matching procedure of edge coarsening","if you are not sure your graph is bi-directed. Parameters ---------- graph :","edge_weight : tensor, optional The edge weight tensor holding non-negative scalar weight for","The GPU implementation is based on `A GPU Algorithm for Greedy Graph Matching","in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps","and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps picking an unmarked","be overwriten with sampled indices. \"\"\" assert F.shape(data)[0] >= sample_points * batch_size assert","import _init_api from .. import backend as F from .. import ndarray as","from .._ffi.function import _init_api from .. import backend as F from .. import","edge weight) until no match can be done. If no edge weight is","non-negative scalar weight for each edge. default: :obj:`None` relabel_idx : bool, optional If","no edge weight is given, this procedure will randomly pick neighbor for each","we can add `return_inverse` option for `unique` # function in backend for efficiency.","of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph","efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np)","is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype),","node id # TODO: actually we can add `return_inverse` option for `unique` #","The input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are","edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi =","------- a 1-D tensor A vector with each element that indicates the cluster","as np from .._ffi.function import _init_api from .. import backend as F from","shape (batch_size, ) for the starting sample in each batch. result : tensor","<https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps picking an unmarked vertex and","coarsening. This procedure keeps picking an unmarked vertex and matching it with one","of shape (N, d) where N is the number of points and d","number of points and d is the dimension. batch_size : int The number","Pre-allocated tensor of shape (sample_points * batch_size, ) for the sampled index. Returns","element that indicates the cluster ID of a vertex. \"\"\" edge_weight_capi = nd.NULL[\"int64\"]","DGLError(\"Find unmatched node\") # reorder node id # TODO: actually we can add","Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected`",".. import backend as F from .. import ndarray as nd def _farthest_point_sampler(data,","tensor of shape (N, d) where N is the number of points and","graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure","start_idx : tensor of int Pre-allocated tensor of shape (batch_size, ) for the","Call :obj:`dgl.to_bidirected` if you are not sure your graph is bi-directed. Parameters ----------","This procedure keeps picking an unmarked vertex and matching it with one its","batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None,","= F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\"Find","reorder node id # TODO: actually we can add `return_inverse` option for `unique`","bool, optional If true, relabel resulting node labels to have consecutive node ids.","Point Sampler Parameters ---------- data : tensor A tensor of shape (N, d)","tensor A tensor of shape (N, d) where N is the number of","The input variable ``result`` will be overwriten with sampled indices. \"\"\" assert F.shape(data)[0]",") for the starting sample in each batch. result : tensor of int","unmarked neighbors (that maximizes its edge weight) until no match can be done.","bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes : int","nd.NULL[\"int64\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes,","Sampler Parameters ---------- data : tensor A tensor of shape (N, d) where","A vector with each element that indicates the cluster ID of a vertex.","_farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\"\"\"Farthest Point Sampler Parameters ---------- data :","given, this procedure will randomly pick neighbor for each vertex. The GPU implementation","tensor, optional The edge weight tensor holding non-negative scalar weight for each edge.","tensor of int Pre-allocated tensor of shape (batch_size, ) for the starting sample","are not sure your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The","matching it with one its unmarked neighbors (that maximizes its edge weight) until","edge. default: :obj:`None` relabel_idx : bool, optional If true, relabel resulting node labels","to sample in each batch. dist : tensor Pre-allocated tensor of shape (N,","ID of a vertex. \"\"\" edge_weight_capi = nd.NULL[\"int64\"] if edge_weights is not None:","weight tensor holding non-negative scalar weight for each edge. default: :obj:`None` relabel_idx :","used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure",": tensor A tensor of shape (N, d) where N is the number","should be divisible by batch_size. sample_points : int The number of points to","edge_weights=None, relabel_idx=True): \"\"\" Description ----------- The neighbor matching procedure of edge coarsening used","batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result))","the sampled index. Returns ------- No return value. The input variable ``result`` will","add `return_inverse` option for `unique` # function in backend for efficiency. if relabel_idx:","where N is the number of points and d is the dimension. batch_size","coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This","getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item()",") for the sampled index. Returns ------- No return value. The input variable","== 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True):","relabel_idx : bool, optional If true, relabel resulting node labels to have consecutive","tensor of shape (sample_points * batch_size, ) for the sampled index. Returns -------","None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi","input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not",": int The number of batches in the ``data``. N should be divisible","sample in each batch. result : tensor of int Pre-allocated tensor of shape","edge weight is given, this procedure will randomly pick neighbor for each vertex.","batch. result : tensor of int Pre-allocated tensor of shape (sample_points * batch_size,","is based on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The","num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label","each element that indicates the cluster ID of a vertex. \"\"\" edge_weight_capi =","graph. num_nodes : int The number of nodes in this homogeneous graph. edge_weight","relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np) else: return","for the starting sample in each batch. result : tensor of int Pre-allocated","must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure your","import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\"\"\"Farthest Point","of shape (batch_size, ) for the starting sample in each batch. result :","its unmarked neighbors (that maximizes its edge weight) until no match can be","an unmarked vertex and matching it with one its unmarked neighbors (that maximizes","farthest point sampler.\"\"\" from dgl._ffi.base import DGLError import numpy as np from .._ffi.function","The number of nodes in this homogeneous graph. edge_weight : tensor, optional The","that indicates the cluster ID of a vertex. \"\"\" edge_weight_capi = nd.NULL[\"int64\"] if","\"\"\" Description ----------- The neighbor matching procedure of edge coarsening used in `Metis","divisible by batch_size. sample_points : int The number of points to sample in","pick neighbor for each vertex. The GPU implementation is based on `A GPU","default: :obj:`None` relabel_idx : bool, optional If true, relabel resulting node labels to","to DGL farthest point sampler.\"\"\" from dgl._ffi.base import DGLError import numpy as np","a 1-D tensor A vector with each element that indicates the cluster ID","graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() !=","node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\"Find unmatched node\") # reorder","node ids. default: :obj:`True` Returns ------- a 1-D tensor A vector with each","unmarked vertex and matching it with one its unmarked neighbors (that maximizes its","from .. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result):","in each batch. result : tensor of int Pre-allocated tensor of shape (sample_points","int The number of nodes in this homogeneous graph. edge_weight : tensor, optional","node labels to have consecutive node ids. default: :obj:`True` Returns ------- a 1-D","def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\"\"\"Farthest Point Sampler Parameters ---------- data","batch_size, sample_points, dist, start_idx, result): r\"\"\"Farthest Point Sampler Parameters ---------- data : tensor",": bool, optional If true, relabel resulting node labels to have consecutive node","unmatched node\") # reorder node id # TODO: actually we can add `return_inverse`","neighbor for each vertex. The GPU implementation is based on `A GPU Algorithm","num_nodes : int The number of nodes in this homogeneous graph. edge_weight :","if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\"Find unmatched node\") # reorder node","vertex. The GPU implementation is based on `A GPU Algorithm for Greedy Graph","in this homogeneous graph. edge_weight : tensor, optional The edge weight tensor holding","# reorder node id # TODO: actually we can add `return_inverse` option for","If no edge weight is given, this procedure will randomly pick neighbor for",":obj:`dgl.to_bidirected` if you are not sure your graph is bi-directed. Parameters ---------- graph",">= sample_points * batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points,","of points to sample in each batch. dist : tensor Pre-allocated tensor of","GPU implementation is based on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__","`return_inverse` option for `unique` # function in backend for efficiency. if relabel_idx: node_label_np","homogeneous graph coarsening. This procedure keeps picking an unmarked vertex and matching it","not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx))","_init_api from .. import backend as F from .. import ndarray as nd","with one its unmarked neighbors (that maximizes its edge weight) until no match","sampled indices. \"\"\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size","sampler.\"\"\" from dgl._ffi.base import DGLError import numpy as np from .._ffi.function import _init_api","graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes","cluster ID of a vertex. \"\"\" edge_weight_capi = nd.NULL[\"int64\"] if edge_weights is not","points to sample in each batch. dist : tensor Pre-allocated tensor of shape","actually we can add `return_inverse` option for `unique` # function in backend for","batch_size : int The number of batches in the ``data``. N should be","match can be done. If no edge weight is given, this procedure will",".._ffi.function import _init_api from .. import backend as F from .. import ndarray","F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \"\"\" Description ----------- The neighbor matching procedure","of nodes in this homogeneous graph. edge_weight : tensor, optional The edge weight","!= 0: raise DGLError(\"Find unmatched node\") # reorder node id # TODO: actually","procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous","node\") # reorder node id # TODO: actually we can add `return_inverse` option","a vertex. \"\"\" edge_weight_capi = nd.NULL[\"int64\"] if edge_weights is not None: edge_weight_capi =","and d is the dimension. batch_size : int The number of batches in","tensor A vector with each element that indicates the cluster ID of a","will randomly pick neighbor for each vertex. The GPU implementation is based on","homogeneous graph. edge_weight : tensor, optional The edge weight tensor holding non-negative scalar","procedure will randomly pick neighbor for each vertex. The GPU implementation is based","ids. default: :obj:`True` Returns ------- a 1-D tensor A vector with each element","will be overwriten with sampled indices. \"\"\" assert F.shape(data)[0] >= sample_points * batch_size","the dimension. batch_size : int The number of batches in the ``data``. N","is the number of points and d is the dimension. batch_size : int",".. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\"\"\"Farthest","batches in the ``data``. N should be divisible by batch_size. sample_points : int","function in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np =","= F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np) else: return node_label _init_api('dgl.geometry',","homogeneous graph. num_nodes : int The number of nodes in this homogeneous graph.","``data``. N should be divisible by batch_size. sample_points : int The number of","edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\"Find unmatched node\") #","node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi,","edge_weight_capi = nd.NULL[\"int64\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label =","resulting node labels to have consecutive node ids. default: :obj:`True` Returns ------- a","\"\"\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size == 0","int The number of batches in the ``data``. N should be divisible by","def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \"\"\" Description ----------- The neighbor matching procedure of","based on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input","in the ``data``. N should be divisible by batch_size. sample_points : int The","graph coarsening. This procedure keeps picking an unmarked vertex and matching it with","= nd.NULL[\"int64\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d(","sampled index. Returns ------- No return value. The input variable ``result`` will be","int Pre-allocated tensor of shape (sample_points * batch_size, ) for the sampled index.","F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx,","np from .._ffi.function import _init_api from .. import backend as F from ..","---------- data : tensor A tensor of shape (N, d) where N is","be divisible by batch_size. sample_points : int The number of points to sample","neighbor matching procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__","shape (N, d) where N is the number of points and d is","(undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure your graph is bi-directed.","= F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi)","it with one its unmarked neighbors (that maximizes its edge weight) until no","tensor of shape (batch_size, ) for the starting sample in each batch. result","data : tensor A tensor of shape (N, d) where N is the",": tensor of int Pre-allocated tensor of shape (batch_size, ) for the starting","backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True)",":obj:`None` relabel_idx : bool, optional If true, relabel resulting node labels to have","(sample_points * batch_size, ) for the sampled index. Returns ------- No return value.","indicates the cluster ID of a vertex. \"\"\" edge_weight_capi = nd.NULL[\"int64\"] if edge_weights","if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1,","_neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \"\"\" Description ----------- The neighbor matching procedure of edge","its edge weight) until no match can be done. If no edge weight","your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph.","edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F,","until no match can be done. If no edge weight is given, this","matching procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for","of shape (N, ) for to-sample distance. start_idx : tensor of int Pre-allocated","optional The edge weight tensor holding non-negative scalar weight for each edge. default:","dist, start_idx, result): r\"\"\"Farthest Point Sampler Parameters ---------- data : tensor A tensor","-1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label <","consecutive node ids. default: :obj:`True` Returns ------- a 1-D tensor A vector with","d is the dimension. batch_size : int The number of batches in the","from .. import backend as F from .. import ndarray as nd def","F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0:","keeps picking an unmarked vertex and matching it with one its unmarked neighbors","---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes : int The number","each batch. dist : tensor Pre-allocated tensor of shape (N, ) for to-sample","1-D tensor A vector with each element that indicates the cluster ID of","for the sampled index. Returns ------- No return value. The input variable ``result``","0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \"\"\"","sure your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous","each batch. result : tensor of int Pre-allocated tensor of shape (sample_points *",": HeteroGraphIndex The input homogeneous graph. num_nodes : int The number of nodes","_CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\"Find unmatched node\")","index. Returns ------- No return value. The input variable ``result`` will be overwriten",": tensor Pre-allocated tensor of shape (N, ) for to-sample distance. start_idx :","weight) until no match can be done. If no edge weight is given,","of a vertex. \"\"\" edge_weight_capi = nd.NULL[\"int64\"] if edge_weights is not None: edge_weight_capi","for `unique` # function in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label)","backend as F from .. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points,","* batch_size, ) for the sampled index. Returns ------- No return value. The","you are not sure your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex","can add `return_inverse` option for `unique` # function in backend for efficiency. if","done. If no edge weight is given, this procedure will randomly pick neighbor","indices. \"\"\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size ==","of points and d is the dimension. batch_size : int The number of","the starting sample in each batch. result : tensor of int Pre-allocated tensor",": tensor, optional The edge weight tensor holding non-negative scalar weight for each","node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise","of int Pre-allocated tensor of shape (batch_size, ) for the starting sample in","F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size,","of int Pre-allocated tensor of shape (sample_points * batch_size, ) for the sampled","batch_size, ) for the sampled index. Returns ------- No return value. The input","The edge weight tensor holding non-negative scalar weight for each edge. default: :obj:`None`","dgl._ffi.base import DGLError import numpy as np from .._ffi.function import _init_api from ..","start_idx, result): r\"\"\"Farthest Point Sampler Parameters ---------- data : tensor A tensor of","DGLError import numpy as np from .._ffi.function import _init_api from .. import backend","graph. edge_weight : tensor, optional The edge weight tensor holding non-negative scalar weight","maximizes its edge weight) until no match can be done. If no edge","relabel resulting node labels to have consecutive node ids. default: :obj:`True` Returns -------","batch. dist : tensor Pre-allocated tensor of shape (N, ) for to-sample distance.","weight for each edge. default: :obj:`None` relabel_idx : bool, optional If true, relabel","NOTE: The input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you","N is the number of points and d is the dimension. batch_size :","sample_points : int The number of points to sample in each batch. dist","sample in each batch. dist : tensor Pre-allocated tensor of shape (N, )","shape (N, ) for to-sample distance. start_idx : tensor of int Pre-allocated tensor","the cluster ID of a vertex. \"\"\" edge_weight_capi = nd.NULL[\"int64\"] if edge_weights is","Parameters ---------- data : tensor A tensor of shape (N, d) where N","GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be","number of nodes in this homogeneous graph. edge_weight : tensor, optional The edge","Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected) graph. Call","F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\"Find unmatched","assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data),",": int The number of nodes in this homogeneous graph. edge_weight : tensor,","as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\"\"\"Farthest Point Sampler Parameters","Pre-allocated tensor of shape (batch_size, ) for the starting sample in each batch.","0).item() != 0: raise DGLError(\"Find unmatched node\") # reorder node id # TODO:","(N, d) where N is the number of points and d is the","No return value. The input variable ``result`` will be overwriten with sampled indices.","The neighbor matching procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus","F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np) else: return node_label _init_api('dgl.geometry', __name__)","<http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if","bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure your graph is","dimension. batch_size : int The number of batches in the ``data``. N should","F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx,","N should be divisible by batch_size. sample_points : int The number of points","numpy as np from .._ffi.function import _init_api from .. import backend as F","of batches in the ``data``. N should be divisible by batch_size. sample_points :",": int The number of points to sample in each batch. dist :","not sure your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input","F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if","by batch_size. sample_points : int The number of points to sample in each","for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return","default: :obj:`True` Returns ------- a 1-D tensor A vector with each element that","vector with each element that indicates the cluster ID of a vertex. \"\"\"","each vertex. The GPU implementation is based on `A GPU Algorithm for Greedy","batch_size. sample_points : int The number of points to sample in each batch.","assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def","can be done. If no edge weight is given, this procedure will randomly","A tensor of shape (N, d) where N is the number of points","graph. Call :obj:`dgl.to_bidirected` if you are not sure your graph is bi-directed. Parameters","return value. The input variable ``result`` will be overwriten with sampled indices. \"\"\"","F from .. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx,","is given, this procedure will randomly pick neighbor for each vertex. The GPU","for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected)","shape (sample_points * batch_size, ) for the sampled index. Returns ------- No return","F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\"Find unmatched node\") # reorder node id","on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph","* batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx),","distance. start_idx : tensor of int Pre-allocated tensor of shape (batch_size, ) for","starting sample in each batch. result : tensor of int Pre-allocated tensor of","= F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label)","nodes in this homogeneous graph. edge_weight : tensor, optional The edge weight tensor","this procedure will randomly pick neighbor for each vertex. The GPU implementation is","randomly pick neighbor for each vertex. The GPU implementation is based on `A","is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes :","int The number of points to sample in each batch. dist : tensor","option for `unique` # function in backend for efficiency. if relabel_idx: node_label_np =",") for to-sample distance. start_idx : tensor of int Pre-allocated tensor of shape","Returns ------- No return value. The input variable ``result`` will be overwriten with","The input homogeneous graph. num_nodes : int The number of nodes in this","# function in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np","edge weight tensor holding non-negative scalar weight for each edge. default: :obj:`None` relabel_idx","import DGLError import numpy as np from .._ffi.function import _init_api from .. import","be done. If no edge weight is given, this procedure will randomly pick","vertex. \"\"\" edge_weight_capi = nd.NULL[\"int64\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights)","relabel_idx=True): \"\"\" Description ----------- The neighbor matching procedure of edge coarsening used in","variable ``result`` will be overwriten with sampled indices. \"\"\" assert F.shape(data)[0] >= sample_points","scalar weight for each edge. default: :obj:`None` relabel_idx : bool, optional If true,","d) where N is the number of points and d is the dimension.","have consecutive node ids. default: :obj:`True` Returns ------- a 1-D tensor A vector","number of batches in the ``data``. N should be divisible by batch_size. sample_points","dist : tensor Pre-allocated tensor of shape (N, ) for to-sample distance. start_idx","overwriten with sampled indices. \"\"\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0]","weight is given, this procedure will randomly pick neighbor for each vertex. The","holding non-negative scalar weight for each edge. default: :obj:`None` relabel_idx : bool, optional","_CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \"\"\" Description","the ``data``. N should be divisible by batch_size. sample_points : int The number","\"\"\" edge_weight_capi = nd.NULL[\"int64\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label","TODO: actually we can add `return_inverse` option for `unique` # function in backend","picking an unmarked vertex and matching it with one its unmarked neighbors (that","`unique` # function in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _,","value. The input variable ``result`` will be overwriten with sampled indices. \"\"\" assert","sample_points, dist, start_idx, result): r\"\"\"Farthest Point Sampler Parameters ---------- data : tensor A","points and d is the dimension. batch_size : int The number of batches","tensor of shape (N, ) for to-sample distance. start_idx : tensor of int","F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \"\"\" Description ----------- The neighbor matching","raise DGLError(\"Find unmatched node\") # reorder node id # TODO: actually we can","edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening.","<http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps picking an","< 0).item() != 0: raise DGLError(\"Find unmatched node\") # reorder node id #","no match can be done. If no edge weight is given, this procedure","to-sample distance. start_idx : tensor of int Pre-allocated tensor of shape (batch_size, )","% batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes,","procedure keeps picking an unmarked vertex and matching it with one its unmarked","to have consecutive node ids. default: :obj:`True` Returns ------- a 1-D tensor A","graph : HeteroGraphIndex The input homogeneous graph. num_nodes : int The number of","this homogeneous graph. edge_weight : tensor, optional The edge weight tensor holding non-negative","of shape (sample_points * batch_size, ) for the sampled index. Returns ------- No","in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np,",":obj:`True` Returns ------- a 1-D tensor A vector with each element that indicates"],"string":"[\n \"import backend as F from .. import ndarray as nd def _farthest_point_sampler(data, batch_size,\",\n \"Returns ------- a 1-D tensor A vector with each element that indicates the\",\n \"number of points to sample in each batch. dist : tensor Pre-allocated tensor\",\n \"The number of points to sample in each batch. dist : tensor Pre-allocated\",\n \": tensor of int Pre-allocated tensor of shape (sample_points * batch_size, ) for\",\n \"tensor holding non-negative scalar weight for each edge. default: :obj:`None` relabel_idx : bool,\",\n \"neighbors (that maximizes its edge weight) until no match can be done. If\",\n \"DGL farthest point sampler.\\\"\\\"\\\" from dgl._ffi.base import DGLError import numpy as np from\",\n \"The number of batches in the ``data``. N should be divisible by batch_size.\",\n \"and matching it with one its unmarked neighbors (that maximizes its edge weight)\",\n \"is the dimension. batch_size : int The number of batches in the ``data``.\",\n \"tensor of int Pre-allocated tensor of shape (sample_points * batch_size, ) for the\",\n \"`Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps picking\",\n \"`A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must\",\n \"tensor Pre-allocated tensor of shape (N, ) for to-sample distance. start_idx : tensor\",\n \"(batch_size, ) for the starting sample in each batch. result : tensor of\",\n \"for each edge. default: :obj:`None` relabel_idx : bool, optional If true, relabel resulting\",\n \"import numpy as np from .._ffi.function import _init_api from .. import backend as\",\n \"int Pre-allocated tensor of shape (batch_size, ) for the starting sample in each\",\n \"node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np) else: return node_label\",\n \"if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np) else:\",\n \"for each vertex. The GPU implementation is based on `A GPU Algorithm for\",\n \"from dgl._ffi.base import DGLError import numpy as np from .._ffi.function import _init_api from\",\n \"in each batch. dist : tensor Pre-allocated tensor of shape (N, ) for\",\n \"for to-sample distance. start_idx : tensor of int Pre-allocated tensor of shape (batch_size,\",\n \"``result`` will be overwriten with sampled indices. \\\"\\\"\\\" assert F.shape(data)[0] >= sample_points *\",\n \"Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected) graph.\",\n \"\\\"\\\"\\\"Python interfaces to DGL farthest point sampler.\\\"\\\"\\\" from dgl._ffi.base import DGLError import numpy\",\n \"with each element that indicates the cluster ID of a vertex. \\\"\\\"\\\" edge_weight_capi\",\n \"id # TODO: actually we can add `return_inverse` option for `unique` # function\",\n \"labels to have consecutive node ids. default: :obj:`True` Returns ------- a 1-D tensor\",\n \"as F from .. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist,\",\n \"r\\\"\\\"\\\"Farthest Point Sampler Parameters ---------- data : tensor A tensor of shape (N,\",\n \"ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\\\"\\\"\\\"Farthest Point Sampler\",\n \"(N, ) for to-sample distance. start_idx : tensor of int Pre-allocated tensor of\",\n \"vertex and matching it with one its unmarked neighbors (that maximizes its edge\",\n \"result : tensor of int Pre-allocated tensor of shape (sample_points * batch_size, )\",\n \"batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description -----------\",\n \"be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure your graph\",\n \"(that maximizes its edge weight) until no match can be done. If no\",\n \"sample_points * batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist),\",\n \"`Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps picking an unmarked vertex\",\n \"implementation is based on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE:\",\n \"each edge. default: :obj:`None` relabel_idx : bool, optional If true, relabel resulting node\",\n \"sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description ----------- The\",\n \"the number of points and d is the dimension. batch_size : int The\",\n \"F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description ----------- The neighbor\",\n \"result): r\\\"\\\"\\\"Farthest Point Sampler Parameters ---------- data : tensor A tensor of shape\",\n \"optional If true, relabel resulting node labels to have consecutive node ids. default:\",\n \"nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\\\"\\\"\\\"Farthest Point Sampler Parameters ----------\",\n \"for homogeneous graph coarsening. This procedure keeps picking an unmarked vertex and matching\",\n \"input homogeneous graph. num_nodes : int The number of nodes in this homogeneous\",\n \"interfaces to DGL farthest point sampler.\\\"\\\"\\\" from dgl._ffi.base import DGLError import numpy as\",\n \"true, relabel resulting node labels to have consecutive node ids. default: :obj:`True` Returns\",\n \"HeteroGraphIndex The input homogeneous graph. num_nodes : int The number of nodes in\",\n \"If true, relabel resulting node labels to have consecutive node ids. default: :obj:`True`\",\n \"Pre-allocated tensor of shape (N, ) for to-sample distance. start_idx : tensor of\",\n \"----------- The neighbor matching procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and\",\n \"point sampler.\\\"\\\"\\\" from dgl._ffi.base import DGLError import numpy as np from .._ffi.function import\",\n \"Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed\",\n \"# TODO: actually we can add `return_inverse` option for `unique` # function in\",\n \"Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes : int The\",\n \"Description ----------- The neighbor matching procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__\",\n \"0: raise DGLError(\\\"Find unmatched node\\\") # reorder node id # TODO: actually we\",\n \"------- No return value. The input variable ``result`` will be overwriten with sampled\",\n \"one its unmarked neighbors (that maximizes its edge weight) until no match can\",\n \"input variable ``result`` will be overwriten with sampled indices. \\\"\\\"\\\" assert F.shape(data)[0] >=\",\n \"with sampled indices. \\\"\\\"\\\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] %\",\n \"num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description ----------- The neighbor matching procedure of edge coarsening\",\n \"if you are not sure your graph is bi-directed. Parameters ---------- graph :\",\n \"edge_weight : tensor, optional The edge weight tensor holding non-negative scalar weight for\",\n \"The GPU implementation is based on `A GPU Algorithm for Greedy Graph Matching\",\n \"in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps\",\n \"and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps picking an unmarked\",\n \"be overwriten with sampled indices. \\\"\\\"\\\" assert F.shape(data)[0] >= sample_points * batch_size assert\",\n \"import _init_api from .. import backend as F from .. import ndarray as\",\n \"from .._ffi.function import _init_api from .. import backend as F from .. import\",\n \"edge weight) until no match can be done. If no edge weight is\",\n \"non-negative scalar weight for each edge. default: :obj:`None` relabel_idx : bool, optional If\",\n \"no edge weight is given, this procedure will randomly pick neighbor for each\",\n \"we can add `return_inverse` option for `unique` # function in backend for efficiency.\",\n \"of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph\",\n \"efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np)\",\n \"is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype),\",\n \"node id # TODO: actually we can add `return_inverse` option for `unique` #\",\n \"The input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are\",\n \"edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi =\",\n \"------- a 1-D tensor A vector with each element that indicates the cluster\",\n \"as np from .._ffi.function import _init_api from .. import backend as F from\",\n \"shape (batch_size, ) for the starting sample in each batch. result : tensor\",\n \"<https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps picking an unmarked vertex and\",\n \"coarsening. This procedure keeps picking an unmarked vertex and matching it with one\",\n \"of shape (N, d) where N is the number of points and d\",\n \"number of points and d is the dimension. batch_size : int The number\",\n \"Pre-allocated tensor of shape (sample_points * batch_size, ) for the sampled index. Returns\",\n \"element that indicates the cluster ID of a vertex. \\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"]\",\n \"DGLError(\\\"Find unmatched node\\\") # reorder node id # TODO: actually we can add\",\n \"Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected`\",\n \".. import backend as F from .. import ndarray as nd def _farthest_point_sampler(data,\",\n \"tensor of shape (N, d) where N is the number of points and\",\n \"graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure\",\n \"start_idx : tensor of int Pre-allocated tensor of shape (batch_size, ) for the\",\n \"Call :obj:`dgl.to_bidirected` if you are not sure your graph is bi-directed. Parameters ----------\",\n \"This procedure keeps picking an unmarked vertex and matching it with one its\",\n \"batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None,\",\n \"= F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\\\"Find\",\n \"reorder node id # TODO: actually we can add `return_inverse` option for `unique`\",\n \"bool, optional If true, relabel resulting node labels to have consecutive node ids.\",\n \"Point Sampler Parameters ---------- data : tensor A tensor of shape (N, d)\",\n \"tensor A tensor of shape (N, d) where N is the number of\",\n \"The input variable ``result`` will be overwriten with sampled indices. \\\"\\\"\\\" assert F.shape(data)[0]\",\n \") for the starting sample in each batch. result : tensor of int\",\n \"unmarked neighbors (that maximizes its edge weight) until no match can be done.\",\n \"bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes : int\",\n \"nd.NULL[\\\"int64\\\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes,\",\n \"Sampler Parameters ---------- data : tensor A tensor of shape (N, d) where\",\n \"A vector with each element that indicates the cluster ID of a vertex.\",\n \"_farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\\\"\\\"\\\"Farthest Point Sampler Parameters ---------- data :\",\n \"given, this procedure will randomly pick neighbor for each vertex. The GPU implementation\",\n \"tensor, optional The edge weight tensor holding non-negative scalar weight for each edge.\",\n \"tensor of int Pre-allocated tensor of shape (batch_size, ) for the starting sample\",\n \"are not sure your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The\",\n \"matching it with one its unmarked neighbors (that maximizes its edge weight) until\",\n \"edge. default: :obj:`None` relabel_idx : bool, optional If true, relabel resulting node labels\",\n \"to sample in each batch. dist : tensor Pre-allocated tensor of shape (N,\",\n \"ID of a vertex. \\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"] if edge_weights is not None:\",\n \"weight tensor holding non-negative scalar weight for each edge. default: :obj:`None` relabel_idx :\",\n \"used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure\",\n \": tensor A tensor of shape (N, d) where N is the number\",\n \"should be divisible by batch_size. sample_points : int The number of points to\",\n \"edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description ----------- The neighbor matching procedure of edge coarsening used\",\n \"batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result))\",\n \"the sampled index. Returns ------- No return value. The input variable ``result`` will\",\n \"add `return_inverse` option for `unique` # function in backend for efficiency. if relabel_idx:\",\n \"where N is the number of points and d is the dimension. batch_size\",\n \"coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This\",\n \"getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item()\",\n \") for the sampled index. Returns ------- No return value. The input variable\",\n \"== 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True):\",\n \"relabel_idx : bool, optional If true, relabel resulting node labels to have consecutive\",\n \"tensor of shape (sample_points * batch_size, ) for the sampled index. Returns -------\",\n \"None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi\",\n \"input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not\",\n \": int The number of batches in the ``data``. N should be divisible\",\n \"sample in each batch. result : tensor of int Pre-allocated tensor of shape\",\n \"edge weight is given, this procedure will randomly pick neighbor for each vertex.\",\n \"batch. result : tensor of int Pre-allocated tensor of shape (sample_points * batch_size,\",\n \"is based on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The\",\n \"num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label\",\n \"each element that indicates the cluster ID of a vertex. \\\"\\\"\\\" edge_weight_capi =\",\n \"graph. num_nodes : int The number of nodes in this homogeneous graph. edge_weight\",\n \"relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np) else: return\",\n \"for the starting sample in each batch. result : tensor of int Pre-allocated\",\n \"must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure your\",\n \"import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\\\"\\\"\\\"Farthest Point\",\n \"of shape (batch_size, ) for the starting sample in each batch. result :\",\n \"its unmarked neighbors (that maximizes its edge weight) until no match can be\",\n \"an unmarked vertex and matching it with one its unmarked neighbors (that maximizes\",\n \"farthest point sampler.\\\"\\\"\\\" from dgl._ffi.base import DGLError import numpy as np from .._ffi.function\",\n \"The number of nodes in this homogeneous graph. edge_weight : tensor, optional The\",\n \"that indicates the cluster ID of a vertex. \\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"] if\",\n \"\\\"\\\"\\\" Description ----------- The neighbor matching procedure of edge coarsening used in `Metis\",\n \"divisible by batch_size. sample_points : int The number of points to sample in\",\n \"pick neighbor for each vertex. The GPU implementation is based on `A GPU\",\n \"default: :obj:`None` relabel_idx : bool, optional If true, relabel resulting node labels to\",\n \"to DGL farthest point sampler.\\\"\\\"\\\" from dgl._ffi.base import DGLError import numpy as np\",\n \"a 1-D tensor A vector with each element that indicates the cluster ID\",\n \"graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() !=\",\n \"node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\\\"Find unmatched node\\\") # reorder\",\n \"node ids. default: :obj:`True` Returns ------- a 1-D tensor A vector with each\",\n \"unmarked vertex and matching it with one its unmarked neighbors (that maximizes its\",\n \"from .. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result):\",\n \"in each batch. result : tensor of int Pre-allocated tensor of shape (sample_points\",\n \"int The number of nodes in this homogeneous graph. edge_weight : tensor, optional\",\n \"node labels to have consecutive node ids. default: :obj:`True` Returns ------- a 1-D\",\n \"def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\\\"\\\"\\\"Farthest Point Sampler Parameters ---------- data\",\n \"batch_size, sample_points, dist, start_idx, result): r\\\"\\\"\\\"Farthest Point Sampler Parameters ---------- data : tensor\",\n \": bool, optional If true, relabel resulting node labels to have consecutive node\",\n \"unmatched node\\\") # reorder node id # TODO: actually we can add `return_inverse`\",\n \"neighbor for each vertex. The GPU implementation is based on `A GPU Algorithm\",\n \"num_nodes : int The number of nodes in this homogeneous graph. edge_weight :\",\n \"if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\\\"Find unmatched node\\\") # reorder node\",\n \"vertex. The GPU implementation is based on `A GPU Algorithm for Greedy Graph\",\n \"in this homogeneous graph. edge_weight : tensor, optional The edge weight tensor holding\",\n \"# reorder node id # TODO: actually we can add `return_inverse` option for\",\n \"If no edge weight is given, this procedure will randomly pick neighbor for\",\n \":obj:`dgl.to_bidirected` if you are not sure your graph is bi-directed. Parameters ---------- graph\",\n \">= sample_points * batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points,\",\n \"of points to sample in each batch. dist : tensor Pre-allocated tensor of\",\n \"GPU implementation is based on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__\",\n \"`return_inverse` option for `unique` # function in backend for efficiency. if relabel_idx: node_label_np\",\n \"homogeneous graph coarsening. This procedure keeps picking an unmarked vertex and matching it\",\n \"not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx))\",\n \"_init_api from .. import backend as F from .. import ndarray as nd\",\n \"with one its unmarked neighbors (that maximizes its edge weight) until no match\",\n \"sampled indices. \\\"\\\"\\\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size\",\n \"sampler.\\\"\\\"\\\" from dgl._ffi.base import DGLError import numpy as np from .._ffi.function import _init_api\",\n \"graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes\",\n \"cluster ID of a vertex. \\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"] if edge_weights is not\",\n \"points to sample in each batch. dist : tensor Pre-allocated tensor of shape\",\n \"actually we can add `return_inverse` option for `unique` # function in backend for\",\n \"batch_size : int The number of batches in the ``data``. N should be\",\n \"match can be done. If no edge weight is given, this procedure will\",\n \".._ffi.function import _init_api from .. import backend as F from .. import ndarray\",\n \"F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description ----------- The neighbor matching procedure\",\n \"of nodes in this homogeneous graph. edge_weight : tensor, optional The edge weight\",\n \"!= 0: raise DGLError(\\\"Find unmatched node\\\") # reorder node id # TODO: actually\",\n \"procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous\",\n \"node\\\") # reorder node id # TODO: actually we can add `return_inverse` option\",\n \"a vertex. \\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"] if edge_weights is not None: edge_weight_capi =\",\n \"and d is the dimension. batch_size : int The number of batches in\",\n \"tensor A vector with each element that indicates the cluster ID of a\",\n \"will randomly pick neighbor for each vertex. The GPU implementation is based on\",\n \"homogeneous graph. edge_weight : tensor, optional The edge weight tensor holding non-negative scalar\",\n \"procedure will randomly pick neighbor for each vertex. The GPU implementation is based\",\n \"ids. default: :obj:`True` Returns ------- a 1-D tensor A vector with each element\",\n \"will be overwriten with sampled indices. \\\"\\\"\\\" assert F.shape(data)[0] >= sample_points * batch_size\",\n \"the dimension. batch_size : int The number of batches in the ``data``. N\",\n \"is the number of points and d is the dimension. batch_size : int\",\n \".. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\\\"\\\"\\\"Farthest\",\n \"batches in the ``data``. N should be divisible by batch_size. sample_points : int\",\n \"function in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np =\",\n \"= F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np) else: return node_label _init_api('dgl.geometry',\",\n \"homogeneous graph. num_nodes : int The number of nodes in this homogeneous graph.\",\n \"``data``. N should be divisible by batch_size. sample_points : int The number of\",\n \"edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\\\"Find unmatched node\\\") #\",\n \"node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi,\",\n \"edge_weight_capi = nd.NULL[\\\"int64\\\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label =\",\n \"resulting node labels to have consecutive node ids. default: :obj:`True` Returns ------- a\",\n \"\\\"\\\"\\\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size == 0\",\n \"int The number of batches in the ``data``. N should be divisible by\",\n \"def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description ----------- The neighbor matching procedure of\",\n \"based on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input\",\n \"in the ``data``. N should be divisible by batch_size. sample_points : int The\",\n \"graph coarsening. This procedure keeps picking an unmarked vertex and matching it with\",\n \"= nd.NULL[\\\"int64\\\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d(\",\n \"sampled index. Returns ------- No return value. The input variable ``result`` will be\",\n \"int Pre-allocated tensor of shape (sample_points * batch_size, ) for the sampled index.\",\n \"F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx,\",\n \"np from .._ffi.function import _init_api from .. import backend as F from ..\",\n \"---------- data : tensor A tensor of shape (N, d) where N is\",\n \"be divisible by batch_size. sample_points : int The number of points to sample\",\n \"neighbor matching procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__\",\n \"shape (N, d) where N is the number of points and d is\",\n \"(undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure your graph is bi-directed.\",\n \"= F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi)\",\n \"it with one its unmarked neighbors (that maximizes its edge weight) until no\",\n \"tensor of shape (batch_size, ) for the starting sample in each batch. result\",\n \"data : tensor A tensor of shape (N, d) where N is the\",\n \": tensor of int Pre-allocated tensor of shape (batch_size, ) for the starting\",\n \"backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True)\",\n \":obj:`None` relabel_idx : bool, optional If true, relabel resulting node labels to have\",\n \"(sample_points * batch_size, ) for the sampled index. Returns ------- No return value.\",\n \"indicates the cluster ID of a vertex. \\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"] if edge_weights\",\n \"if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1,\",\n \"_neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description ----------- The neighbor matching procedure of edge\",\n \"its edge weight) until no match can be done. If no edge weight\",\n \"your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph.\",\n \"edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F,\",\n \"until no match can be done. If no edge weight is given, this\",\n \"matching procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for\",\n \"of shape (N, ) for to-sample distance. start_idx : tensor of int Pre-allocated\",\n \"optional The edge weight tensor holding non-negative scalar weight for each edge. default:\",\n \"dist, start_idx, result): r\\\"\\\"\\\"Farthest Point Sampler Parameters ---------- data : tensor A tensor\",\n \"-1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label <\",\n \"consecutive node ids. default: :obj:`True` Returns ------- a 1-D tensor A vector with\",\n \"d is the dimension. batch_size : int The number of batches in the\",\n \"from .. import backend as F from .. import ndarray as nd def\",\n \"F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0:\",\n \"keeps picking an unmarked vertex and matching it with one its unmarked neighbors\",\n \"---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes : int The number\",\n \"each batch. dist : tensor Pre-allocated tensor of shape (N, ) for to-sample\",\n \"1-D tensor A vector with each element that indicates the cluster ID of\",\n \"for the sampled index. Returns ------- No return value. The input variable ``result``\",\n \"0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\"\",\n \"sure your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous\",\n \"each batch. result : tensor of int Pre-allocated tensor of shape (sample_points *\",\n \": HeteroGraphIndex The input homogeneous graph. num_nodes : int The number of nodes\",\n \"_CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\\\"Find unmatched node\\\")\",\n \"index. Returns ------- No return value. The input variable ``result`` will be overwriten\",\n \": tensor Pre-allocated tensor of shape (N, ) for to-sample distance. start_idx :\",\n \"weight) until no match can be done. If no edge weight is given,\",\n \"of a vertex. \\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"] if edge_weights is not None: edge_weight_capi\",\n \"for `unique` # function in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label)\",\n \"backend as F from .. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points,\",\n \"* batch_size, ) for the sampled index. Returns ------- No return value. The\",\n \"you are not sure your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex\",\n \"can add `return_inverse` option for `unique` # function in backend for efficiency. if\",\n \"done. If no edge weight is given, this procedure will randomly pick neighbor\",\n \"indices. \\\"\\\"\\\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size ==\",\n \"of points and d is the dimension. batch_size : int The number of\",\n \"the starting sample in each batch. result : tensor of int Pre-allocated tensor\",\n \": tensor, optional The edge weight tensor holding non-negative scalar weight for each\",\n \"node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise\",\n \"of int Pre-allocated tensor of shape (batch_size, ) for the starting sample in\",\n \"F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size,\",\n \"of int Pre-allocated tensor of shape (sample_points * batch_size, ) for the sampled\",\n \"batch_size, ) for the sampled index. Returns ------- No return value. The input\",\n \"The edge weight tensor holding non-negative scalar weight for each edge. default: :obj:`None`\",\n \"dgl._ffi.base import DGLError import numpy as np from .._ffi.function import _init_api from ..\",\n \"start_idx, result): r\\\"\\\"\\\"Farthest Point Sampler Parameters ---------- data : tensor A tensor of\",\n \"DGLError import numpy as np from .._ffi.function import _init_api from .. import backend\",\n \"graph. edge_weight : tensor, optional The edge weight tensor holding non-negative scalar weight\",\n \"maximizes its edge weight) until no match can be done. If no edge\",\n \"relabel resulting node labels to have consecutive node ids. default: :obj:`True` Returns -------\",\n \"batch. dist : tensor Pre-allocated tensor of shape (N, ) for to-sample distance.\",\n \"weight for each edge. default: :obj:`None` relabel_idx : bool, optional If true, relabel\",\n \"NOTE: The input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you\",\n \"N is the number of points and d is the dimension. batch_size :\",\n \"sample_points : int The number of points to sample in each batch. dist\",\n \"sample in each batch. dist : tensor Pre-allocated tensor of shape (N, )\",\n \"shape (N, ) for to-sample distance. start_idx : tensor of int Pre-allocated tensor\",\n \"the cluster ID of a vertex. \\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"] if edge_weights is\",\n \"Parameters ---------- data : tensor A tensor of shape (N, d) where N\",\n \"GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be\",\n \"number of nodes in this homogeneous graph. edge_weight : tensor, optional The edge\",\n \"Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected) graph. Call\",\n \"F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\\\"Find unmatched\",\n \"assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data),\",\n \": int The number of nodes in this homogeneous graph. edge_weight : tensor,\",\n \"as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r\\\"\\\"\\\"Farthest Point Sampler Parameters\",\n \"Pre-allocated tensor of shape (batch_size, ) for the starting sample in each batch.\",\n \"0).item() != 0: raise DGLError(\\\"Find unmatched node\\\") # reorder node id # TODO:\",\n \"(N, d) where N is the number of points and d is the\",\n \"No return value. The input variable ``result`` will be overwriten with sampled indices.\",\n \"The neighbor matching procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus\",\n \"F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np) else: return node_label _init_api('dgl.geometry', __name__)\",\n \"<http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if\",\n \"bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure your graph is\",\n \"dimension. batch_size : int The number of batches in the ``data``. N should\",\n \"F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx,\",\n \"N should be divisible by batch_size. sample_points : int The number of points\",\n \"numpy as np from .._ffi.function import _init_api from .. import backend as F\",\n \"of batches in the ``data``. N should be divisible by batch_size. sample_points :\",\n \": int The number of points to sample in each batch. dist :\",\n \"not sure your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input\",\n \"F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if\",\n \"by batch_size. sample_points : int The number of points to sample in each\",\n \"for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return\",\n \"default: :obj:`True` Returns ------- a 1-D tensor A vector with each element that\",\n \"vector with each element that indicates the cluster ID of a vertex. \\\"\\\"\\\"\",\n \"each vertex. The GPU implementation is based on `A GPU Algorithm for Greedy\",\n \"batch_size. sample_points : int The number of points to sample in each batch.\",\n \"assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def\",\n \"can be done. If no edge weight is given, this procedure will randomly\",\n \"A tensor of shape (N, d) where N is the number of points\",\n \"graph. Call :obj:`dgl.to_bidirected` if you are not sure your graph is bi-directed. Parameters\",\n \"return value. The input variable ``result`` will be overwriten with sampled indices. \\\"\\\"\\\"\",\n \"F from .. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx,\",\n \"is given, this procedure will randomly pick neighbor for each vertex. The GPU\",\n \"for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected)\",\n \"shape (sample_points * batch_size, ) for the sampled index. Returns ------- No return\",\n \"F.reduce_sum(node_label < 0).item() != 0: raise DGLError(\\\"Find unmatched node\\\") # reorder node id\",\n \"on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph\",\n \"* batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx),\",\n \"distance. start_idx : tensor of int Pre-allocated tensor of shape (batch_size, ) for\",\n \"starting sample in each batch. result : tensor of int Pre-allocated tensor of\",\n \"= F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label)\",\n \"nodes in this homogeneous graph. edge_weight : tensor, optional The edge weight tensor\",\n \"this procedure will randomly pick neighbor for each vertex. The GPU implementation is\",\n \"randomly pick neighbor for each vertex. The GPU implementation is based on `A\",\n \"is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes :\",\n \"int The number of points to sample in each batch. dist : tensor\",\n \"option for `unique` # function in backend for efficiency. if relabel_idx: node_label_np =\",\n \") for to-sample distance. start_idx : tensor of int Pre-allocated tensor of shape\",\n \"Returns ------- No return value. The input variable ``result`` will be overwriten with\",\n \"The input homogeneous graph. num_nodes : int The number of nodes in this\",\n \"# function in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np\",\n \"edge weight tensor holding non-negative scalar weight for each edge. default: :obj:`None` relabel_idx\",\n \"import DGLError import numpy as np from .._ffi.function import _init_api from .. import\",\n \"be done. If no edge weight is given, this procedure will randomly pick\",\n \"vertex. \\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights)\",\n \"relabel_idx=True): \\\"\\\"\\\" Description ----------- The neighbor matching procedure of edge coarsening used in\",\n \"variable ``result`` will be overwriten with sampled indices. \\\"\\\"\\\" assert F.shape(data)[0] >= sample_points\",\n \"scalar weight for each edge. default: :obj:`None` relabel_idx : bool, optional If true,\",\n \"d) where N is the number of points and d is the dimension.\",\n \"have consecutive node ids. default: :obj:`True` Returns ------- a 1-D tensor A vector\",\n \"number of batches in the ``data``. N should be divisible by batch_size. sample_points\",\n \"dist : tensor Pre-allocated tensor of shape (N, ) for to-sample distance. start_idx\",\n \"overwriten with sampled indices. \\\"\\\"\\\" assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0]\",\n \"weight is given, this procedure will randomly pick neighbor for each vertex. The\",\n \"holding non-negative scalar weight for each edge. default: :obj:`None` relabel_idx : bool, optional\",\n \"_CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description\",\n \"the ``data``. N should be divisible by batch_size. sample_points : int The number\",\n \"\\\"\\\"\\\" edge_weight_capi = nd.NULL[\\\"int64\\\"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label\",\n \"TODO: actually we can add `return_inverse` option for `unique` # function in backend\",\n \"picking an unmarked vertex and matching it with one its unmarked neighbors (that\",\n \"`unique` # function in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _,\",\n \"value. The input variable ``result`` will be overwriten with sampled indices. \\\"\\\"\\\" assert\",\n \"sample_points, dist, start_idx, result): r\\\"\\\"\\\"Farthest Point Sampler Parameters ---------- data : tensor A\",\n \"points and d is the dimension. batch_size : int The number of batches\",\n \"tensor of shape (N, ) for to-sample distance. start_idx : tensor of int\",\n \"F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): \\\"\\\"\\\" Description ----------- The neighbor matching\",\n \"raise DGLError(\\\"Find unmatched node\\\") # reorder node id # TODO: actually we can\",\n \"edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening.\",\n \"<http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps picking an\",\n \"< 0).item() != 0: raise DGLError(\\\"Find unmatched node\\\") # reorder node id #\",\n \"no match can be done. If no edge weight is given, this procedure\",\n \"to-sample distance. start_idx : tensor of int Pre-allocated tensor of shape (batch_size, )\",\n \"% batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes,\",\n \"procedure keeps picking an unmarked vertex and matching it with one its unmarked\",\n \"to have consecutive node ids. default: :obj:`True` Returns ------- a 1-D tensor A\",\n \"graph : HeteroGraphIndex The input homogeneous graph. num_nodes : int The number of\",\n \"this homogeneous graph. edge_weight : tensor, optional The edge weight tensor holding non-negative\",\n \"of shape (sample_points * batch_size, ) for the sampled index. Returns ------- No\",\n \"in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np,\",\n \":obj:`True` Returns ------- a 1-D tensor A vector with each element that indicates\"\n]","isConversational":false}}},{"rowIdx":1177,"cells":{"ngram":{"kind":"list like","value":["None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods","last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha256' not in self.metadata \\ or","not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while True: data =","require_update \\ or 'sha1' not in self.metadata \\ or 'sha1-timestamp' not in self.metadata","break h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool =","def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self): return","properties @property def parent(self): return self.__parent__ @property def path(self) -> Path: return self.__parent__.path","= getmtime(str(self.path)) if require_update \\ or 'sha256' not in self.metadata \\ or 'sha256-timestamp'","not in self.metadata \\ or 'sha256-timestamp' not in self.metadata \\ or self.metadata['sha256-timestamp'] <","self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']()","= protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self):","= {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property def","= False) -> [str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path))","self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int =","def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata:","import TextIO, BinaryIO, IO, Union from datetime import datetime from os.path import getmtime","TextIO, None]: if confirm and not feedback: return None mode = 'x' mode","or 'sha1' not in self.metadata \\ or 'sha1-timestamp' not in self.metadata \\ or","not feedback: return None mode = 'x' mode += 'b' if binary else","import datetime from os.path import getmtime from .low import ObservableDict class Data: def","**metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge: bool = True):","new_name)) self.__data_name__ = new_name def reader(self, binary: bool = False, **kwargs) -> [IO,","not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if","allow_overwrite and not append: raise PermissionError('Trying to overwrite existed data.') if confirm and","new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary: bool","md5(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if","and not feedback: return mode = 'a' if append else 'w' mode +=","self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self):","/ self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property def metadata(self) ->","**kwargs) def __repr__(self): return f\"Data('{self.__data_name__}')\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False):","if confirm and not feedback: return mode = 'a' if append else 'w'","if properties is None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] =","return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in","Union[None, dict], merge: bool = True): if metadata is None: return if merge:","if require_update \\ or 'sha1' not in self.metadata \\ or 'sha1-timestamp' not in","require_update \\ or 'md5' not in self.metadata \\ or 'md5-timestamp' not in self.metadata","else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\"Data('{self.__data_name__}')\" def import_file(self, src_path:","self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp()","True, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not","metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def","= 'x' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs)","[IO, BinaryIO, TextIO, None]: if not allow_overwrite and not append: raise PermissionError('Trying to","result else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool =","append: raise PermissionError('Trying to overwrite existed data.') if confirm and not feedback: return","False) -> [str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if","path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def name(self) -> str: return","BinaryIO, TextIO, None]: mode = 'r' mode += 'b' if binary else ''","open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\"Data('{self.__data_name__}')\" def import_file(self, src_path: [str, Path], allow_overwrite=False,","bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__","= result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size:","not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'md5' not","h, buffer_size: int = 131072): if not self.path.exists(): return None with open(str(self.path), 'rb')","'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\"Data('{self.__data_name__}')\"","else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False, confirm:","buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def","Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket","if self.path.exists() and not allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path),","class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None):","from typing import TextIO, BinaryIO, IO, Union from datetime import datetime from os.path","def creator(self, binary: bool = False, confirm: bool = False, feedback: bool =","merge: bool = True): if metadata is None: return if merge: metadata =","else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False, append:","def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__","True): if metadata is None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__]","if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return","open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False, confirm: bool = False,","= self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return","binary: bool = False, confirm: bool = False, feedback: bool = False, **kwargs)","shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary: bool = False,","@property def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def name(self) ->","\\ or 'sha256' not in self.metadata \\ or 'sha256-timestamp' not in self.metadata \\","sha1(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if","if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict],","[str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\","= bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db","if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha256'","'md5' not in self.metadata \\ or 'md5-timestamp' not in self.metadata \\ or self.metadata['md5-timestamp']","None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self,","Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int =","bool = False, confirm: bool = False, feedback: bool = False, **kwargs) ->","self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def","True: data = file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def md5(self,","self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property def metadata(self) -> ObservableDict:","typing import TextIO, BinaryIO, IO, Union from datetime import datetime from os.path import","from pathlib import Path from typing import TextIO, BinaryIO, IO, Union from datetime","self.metadata \\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result","@property def parent(self): return self.__parent__ @property def path(self) -> Path: return self.__parent__.path /","getmtime(str(self.path)) if require_update \\ or 'md5' not in self.metadata \\ or 'md5-timestamp' not","if require_update \\ or 'sha256' not in self.metadata \\ or 'sha256-timestamp' not in","self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha1' not in","TextIO, None]: mode = 'r' mode += 'b' if binary else '' return","{**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge: bool =","'sha256' not in self.metadata \\ or 'sha256-timestamp' not in self.metadata \\ or self.metadata['sha256-timestamp']","\\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp']","def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self,","set_properties(self, properties: Union[None, dict], merge: bool = True): if properties is None: return","self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha256']","self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property","else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool = False)","return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'md5' not in self.metadata","in self.metadata \\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] =","import shutil import hashlib from pathlib import Path from typing import TextIO, BinaryIO,","def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] =","return self.__parent__ @property def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def","open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False, append: bool = True,","import ObservableDict class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict]","self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp()","self.metadata \\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result","@property def name(self) -> str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return","< last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return","def reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode","+= 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary:","**kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and not append: raise","= None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ =","'' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\"Data('{self.__data_name__}')\" def import_file(self, src_path: [str,","not in self.metadata \\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5']","def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ =","new_name def reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]:","properties is None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties","sha256(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if","datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update:","h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool = False)","131072): if not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while True:","return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha1' not in self.metadata","__calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists(): return None with open(str(self.path),","return self.__parent__.path / self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property def","mode = 'x' mode += 'b' if binary else '' return open(str(self.path), mode=mode,","< last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return","hashlib from pathlib import Path from typing import TextIO, BinaryIO, IO, Union from","= {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge: bool","TextIO, None]: if not allow_overwrite and not append: raise PermissionError('Trying to overwrite existed","= 'a' if append else 'w' mode += 'b' if binary else ''","None]: if confirm and not feedback: return None mode = 'x' mode +=","self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path),","None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def","= datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072,","ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name:","return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\"Data('{self.__data_name__}')\" def import_file(self, src_path: [str, Path],","self.__parent__ @property def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def name(self)","False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and not append:","properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name))","properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property","self.metadata \\ or 'sha256-timestamp' not in self.metadata \\ or self.metadata['sha256-timestamp'] < last_modified_time: result","[IO, BinaryIO, TextIO, None]: mode = 'r' mode += 'b' if binary else","datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update:","'w' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def","-> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self,","mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self,","-> Path: return self.__parent__.path / self.__data_name__ @property def name(self) -> str: return self.__data_name__","def parent(self): return self.__parent__ @property def path(self) -> Path: return self.__parent__.path / self.__data_name__","= False, confirm: bool = True, feedback: bool = False, **kwargs) -> [IO,","False, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm","return result else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool","if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int","confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False):","creator(self, binary: bool = False, confirm: bool = False, feedback: bool = False,","'a' if append else 'w' mode += 'b' if binary else '' return","and not append: raise PermissionError('Trying to overwrite existed data.') if confirm and not","self.metadata \\ or 'md5-timestamp' not in self.metadata \\ or self.metadata['md5-timestamp'] < last_modified_time: result","[str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if confirm","dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def","parent(self): return self.__parent__ @property def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property","def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path),","str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return","if metadata is None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] =","**properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property def path(self) ->","None]: if not allow_overwrite and not append: raise PermissionError('Trying to overwrite existed data.')","self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge: bool = True): if","Path from typing import TextIO, BinaryIO, IO, Union from datetime import datetime from","from datetime import datetime from os.path import getmtime from .low import ObservableDict class","shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists(): return","= False, append: bool = True, allow_overwrite: bool = False, confirm: bool =","None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or","require_update: bool = False) -> [str, None]: if not self.path.exists(): return None last_modified_time","return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def","db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not","self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size: int =","= True, allow_overwrite: bool = False, confirm: bool = True, feedback: bool =","result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else:","merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict],","self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp()","= dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def","metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge:","self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self,","mode=mode, **kwargs) def writer(self, binary: bool = False, append: bool = True, allow_overwrite:","data.') if confirm and not feedback: return mode = 'a' if append else","init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__","binary: bool = False, append: bool = True, allow_overwrite: bool = False, confirm:","is None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property","h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool = False) -> [str,","or 'md5' not in self.metadata \\ or 'md5-timestamp' not in self.metadata \\ or","return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool","and not allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def","= dict() def set_metadata(self, metadata: Union[None, dict], merge: bool = True): if metadata","with open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size) if not data:","last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result","feedback: return mode = 'a' if append else 'w' mode += 'b' if","return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str,","'rb') as file_reader: while True: data = file_reader.read(buffer_size) if not data: break h.update(data)","self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge: bool = True): if","else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool = False)","def writer(self, binary: bool = False, append: bool = True, allow_overwrite: bool =","'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool","if not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while True: data","False, confirm: bool = True, feedback: bool = False, **kwargs) -> [IO, BinaryIO,","mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self):","False, append: bool = True, allow_overwrite: bool = False, confirm: bool = True,","BinaryIO, TextIO, None]: if not allow_overwrite and not append: raise PermissionError('Trying to overwrite","@property def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self):","writer(self, binary: bool = False, append: bool = True, allow_overwrite: bool = False,","if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha1'","require_update \\ or 'sha256' not in self.metadata \\ or 'sha256-timestamp' not in self.metadata","feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite","str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary: bool = False, **kwargs)","Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h,","in self.metadata \\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] =","data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties()","in self.metadata \\ or 'sha1-timestamp' not in self.metadata \\ or self.metadata['sha1-timestamp'] < last_modified_time:","str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ =",".low import ObservableDict class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None,","in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__]","dict], merge: bool = True): if properties is None: return if merge: properties","@property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path","confirm: bool = False, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO,","mode=mode, **kwargs) def __repr__(self): return f\"Data('{self.__data_name__}')\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True,","getmtime(str(self.path)) if require_update \\ or 'sha1' not in self.metadata \\ or 'sha1-timestamp' not","and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if","def __repr__(self): return f\"Data('{self.__data_name__}')\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if","'md5-timestamp' not in self.metadata \\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size)","or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] =","mode=mode, **kwargs) def creator(self, binary: bool = False, confirm: bool = False, feedback:","self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self):","None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha1' not in self.metadata \\","if binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool =","def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def name(self) -> str:","self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def","or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] =","@property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__]","[IO, BinaryIO, TextIO, None]: if confirm and not feedback: return None mode =","self.__data_name__ = new_name def reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO,","= True, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if","binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r'","if confirm and not feedback: return None mode = 'x' mode += 'b'","\\ or 'md5-timestamp' not in self.metadata \\ or self.metadata['md5-timestamp'] < last_modified_time: result =","buffer_size: int = 131072): if not self.path.exists(): return None with open(str(self.path), 'rb') as","None]: mode = 'r' mode += 'b' if binary else '' return open(str(self.path),","return None with open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size) if","self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1']","not allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self,","file_reader: while True: data = file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest()","dict() def set_metadata(self, metadata: Union[None, dict], merge: bool = True): if metadata is","None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'md5' not in self.metadata \\","allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if confirm and not","def set_properties(self, properties: Union[None, dict], merge: bool = True): if properties is None:","self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] =","result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int","data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool","bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and not","rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary:","merge: bool = True): if properties is None: return if merge: properties =","= datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072,","bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property","self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha256' not in","bool = True): if properties is None: return if merge: properties = {**self.properties,","not allow_overwrite and not append: raise PermissionError('Trying to overwrite existed data.') if confirm","getmtime from .low import ObservableDict class Data: def __init__(self, data_name: str, parent, bucket,","+= 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary:","not append: raise PermissionError('Trying to overwrite existed data.') if confirm and not feedback:","datetime import datetime from os.path import getmtime from .low import ObservableDict class Data:","allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path:","if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'md5'","not in self.metadata \\ or 'md5-timestamp' not in self.metadata \\ or self.metadata['md5-timestamp'] <","\\ or 'sha1-timestamp' not in self.metadata \\ or self.metadata['sha1-timestamp'] < last_modified_time: result =","-> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and not append: raise PermissionError('Trying","ObservableDict class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] =","parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self):","or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] =","import hashlib from pathlib import Path from typing import TextIO, BinaryIO, IO, Union","bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and","= False, confirm: bool = False, feedback: bool = False, **kwargs) -> [IO,","append else 'w' mode += 'b' if binary else '' return open(str(self.path), mode=mode,","feedback=False): if self.path.exists() and not allow_overwrite: return if confirm and not feedback: return","str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary: bool =","-> str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def","binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\"Data('{self.__data_name__}')\" def import_file(self,","as file_reader: while True: data = file_reader.read(buffer_size) if not data: break h.update(data) return","data = file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size:","mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self,","self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict()","None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha256' not in self.metadata \\","bool = False, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]:","'r' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def","def sha1(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]:","if binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\"Data('{self.__data_name__}')\" def","self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def","not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha256' not","return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str):","if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path],","open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size) if not data: break","str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self)","IO, Union from datetime import datetime from os.path import getmtime from .low import","= False, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if","or 'sha256-timestamp' not in self.metadata \\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(),","__init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name","**kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r' mode += 'b' if","= getmtime(str(self.path)) if require_update \\ or 'md5' not in self.metadata \\ or 'md5-timestamp'","ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ =","'' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False, append: bool","int = 131072): if not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader:","file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int =","131072, require_update: bool = False) -> [str, None]: if not self.path.exists(): return None","\\ or 'sha256-timestamp' not in self.metadata \\ or self.metadata['sha256-timestamp'] < last_modified_time: result =","datetime from os.path import getmtime from .low import ObservableDict class Data: def __init__(self,","last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result","Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if confirm and","last_modified_time = getmtime(str(self.path)) if require_update \\ or 'md5' not in self.metadata \\ or","bool = True): if metadata is None: return if merge: metadata = {**self.metadata,","return f\"Data('{self.__data_name__}')\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and","return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name","if not allow_overwrite and not append: raise PermissionError('Trying to overwrite existed data.') if","= self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return result else: return","self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return","not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties:","else 'w' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs)","overwrite existed data.') if confirm and not feedback: return mode = 'a' if","return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) ->","metadata: Union[None, dict], merge: bool = True): if metadata is None: return if","bool = True, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]:","dict], merge: bool = True): if metadata is None: return if merge: metadata","\\ or 'md5' not in self.metadata \\ or 'md5-timestamp' not in self.metadata \\","Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__","in self.metadata \\ or 'sha256-timestamp' not in self.metadata \\ or self.metadata['sha256-timestamp'] < last_modified_time:","if binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool =","return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not","def name(self) -> str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__]","allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size:","from os.path import getmtime from .low import ObservableDict class Data: def __init__(self, data_name:","confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if confirm and not feedback:","False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r' mode += 'b'","= False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and not","getmtime(str(self.path)) if require_update \\ or 'sha256' not in self.metadata \\ or 'sha256-timestamp' not","if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not","not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists()","'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool","'x' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def","**kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and not feedback: return None","binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False,","return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self):","raise PermissionError('Trying to overwrite existed data.') if confirm and not feedback: return mode","in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge: bool =","feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and","if append else 'w' mode += 'b' if binary else '' return open(str(self.path),","BinaryIO, IO, Union from datetime import datetime from os.path import getmtime from .low","import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return","def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path /","-> [str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update","confirm: bool = True, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO,","-> [IO, BinaryIO, TextIO, None]: mode = 'r' mode += 'b' if binary","None mode = 'x' mode += 'b' if binary else '' return open(str(self.path),","init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None,","to overwrite existed data.') if confirm and not feedback: return mode = 'a'","-> [IO, BinaryIO, TextIO, None]: if confirm and not feedback: return None mode","/ new_name)) self.__data_name__ = new_name def reader(self, binary: bool = False, **kwargs) ->","result else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool =","TextIO, BinaryIO, IO, Union from datetime import datetime from os.path import getmtime from","parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ = parent","f\"Data('{self.__data_name__}')\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not","return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool = False) ->","database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__","= data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata()","import getmtime from .low import ObservableDict class Data: def __init__(self, data_name: str, parent,","self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in","self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self,","self.__parent__.path / self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property def metadata(self)","self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property","merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__","self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool = False) -> [str,","if require_update \\ or 'md5' not in self.metadata \\ or 'md5-timestamp' not in","def sha256(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]:","feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and","self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata:","and not feedback: return None mode = 'x' mode += 'b' if binary","Path: return self.__parent__.path / self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property","append: bool = True, allow_overwrite: bool = False, confirm: bool = True, feedback:","Union from datetime import datetime from os.path import getmtime from .low import ObservableDict","def md5(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]:","= 131072, require_update: bool = False) -> [str, None]: if not self.path.exists(): return","self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db","'sha1' not in self.metadata \\ or 'sha1-timestamp' not in self.metadata \\ or self.metadata['sha1-timestamp']","metadata def set_properties(self, properties: Union[None, dict], merge: bool = True): if properties is","{**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property def path(self)","= 131072): if not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while","= result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size:","properties: Union[None, dict], merge: bool = True): if properties is None: return if","return mode = 'a' if append else 'w' mode += 'b' if binary","bool = False, append: bool = True, allow_overwrite: bool = False, confirm: bool","@property def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if","not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update:","or 'md5-timestamp' not in self.metadata \\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(),","return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists():","= getmtime(str(self.path)) if require_update \\ or 'sha1' not in self.metadata \\ or 'sha1-timestamp'","existed data.') if confirm and not feedback: return mode = 'a' if append","return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha256' not in self.metadata","dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self,","= True): if metadata is None: return if merge: metadata = {**self.metadata, **metadata}","'' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False, confirm: bool","not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge: bool","bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r' mode","__repr__(self): return f\"Data('{self.__data_name__}')\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists()","self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'md5' not in","None with open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size) if not","'sha256-timestamp' not in self.metadata \\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size)","@property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return","or 'sha256' not in self.metadata \\ or 'sha256-timestamp' not in self.metadata \\ or","not in self.metadata \\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1']","return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def","from .low import ObservableDict class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods:","import Path from typing import TextIO, BinaryIO, IO, Union from datetime import datetime","def __calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists(): return None with","str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists(): return None","self.path.exists() and not allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path))","'sha1-timestamp' not in self.metadata \\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size)","not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha1' not","reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode =","not in self.metadata \\ or 'sha1-timestamp' not in self.metadata \\ or self.metadata['sha1-timestamp'] <","**kwargs) def creator(self, binary: bool = False, confirm: bool = False, feedback: bool","= False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r' mode +=","= new_name def reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO,","def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite:","protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ =","True, allow_overwrite: bool = False, confirm: bool = True, feedback: bool = False,","data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__","self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5']","self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge:","= 'r' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs)","return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties:","int = 131072, require_update: bool = False) -> [str, None]: if not self.path.exists():","feedback: return None mode = 'x' mode += 'b' if binary else ''","if not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072,","self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool = False) -> [str,","= False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and not feedback:","in self.metadata \\ or 'md5-timestamp' not in self.metadata \\ or self.metadata['md5-timestamp'] < last_modified_time:","mode = 'r' mode += 'b' if binary else '' return open(str(self.path), mode=mode,","bool = True, allow_overwrite: bool = False, confirm: bool = True, feedback: bool","self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property def path(self) -> Path:","return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False, confirm: bool =","last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha1' not in self.metadata \\ or","result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return result else:","or 'sha1-timestamp' not in self.metadata \\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(),","protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self): return","confirm and not feedback: return None mode = 'x' mode += 'b' if","def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__]","= self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return","binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False,","def set_metadata(self, metadata: Union[None, dict], merge: bool = True): if metadata is None:","is None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def","False, confirm: bool = False, feedback: bool = False, **kwargs) -> [IO, BinaryIO,","not feedback: return mode = 'a' if append else 'w' mode += 'b'","self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size)","[str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self,","set_metadata(self, metadata: Union[None, dict], merge: bool = True): if metadata is None: return","return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False, append: bool =","mode = 'a' if append else 'w' mode += 'b' if binary else","dict] = None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__","= properties @property def parent(self): return self.__parent__ @property def path(self) -> Path: return","\\ or 'sha1' not in self.metadata \\ or 'sha1-timestamp' not in self.metadata \\","\\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp']","bool = False, confirm: bool = True, feedback: bool = False, **kwargs) ->","PermissionError('Trying to overwrite existed data.') if confirm and not feedback: return mode =","allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if not","bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict()","name(self) -> str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property","while True: data = file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def","\\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp']","confirm and not feedback: return mode = 'a' if append else 'w' mode","shutil import hashlib from pathlib import Path from typing import TextIO, BinaryIO, IO,","+= 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return","and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072):","= True): if properties is None: return if merge: properties = {**self.properties, **properties}","result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size: int","shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite:","= metadata def set_properties(self, properties: Union[None, dict], merge: bool = True): if properties","def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if","return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool = False) ->","self.metadata \\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result","self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict:","result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else:","export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path))","Union[None, dict], merge: bool = True): if properties is None: return if merge:","src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if","return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool = False) ->","buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def","True): if properties is None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__]","self.metadata \\ or 'sha1-timestamp' not in self.metadata \\ or self.metadata['sha1-timestamp'] < last_modified_time: result","= file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int","buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if not","BinaryIO, TextIO, None]: if confirm and not feedback: return None mode = 'x'","self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge: bool = True):","not in self.metadata \\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256']","**kwargs) def writer(self, binary: bool = False, append: bool = True, allow_overwrite: bool","-> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__","bool = False) -> [str, None]: if not self.path.exists(): return None last_modified_time =","< last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return","pathlib import Path from typing import TextIO, BinaryIO, IO, Union from datetime import","return None mode = 'x' mode += 'b' if binary else '' return","metadata is None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata","in self.metadata \\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] =","allow_overwrite: bool = False, confirm: bool = True, feedback: bool = False, **kwargs)","= parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def","False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and not feedback: return","os.path import getmtime from .low import ObservableDict class Data: def __init__(self, data_name: str,","last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return result","if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None,"],"string":"[\n \"None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods\",\n \"last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha256' not in self.metadata \\\\ or\",\n \"not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while True: data =\",\n \"require_update \\\\ or 'sha1' not in self.metadata \\\\ or 'sha1-timestamp' not in self.metadata\",\n \"break h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool =\",\n \"def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self): return\",\n \"properties @property def parent(self): return self.__parent__ @property def path(self) -> Path: return self.__parent__.path\",\n \"= getmtime(str(self.path)) if require_update \\\\ or 'sha256' not in self.metadata \\\\ or 'sha256-timestamp'\",\n \"not in self.metadata \\\\ or 'sha256-timestamp' not in self.metadata \\\\ or self.metadata['sha256-timestamp'] <\",\n \"self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']()\",\n \"= protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self):\",\n \"= {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property def\",\n \"= False) -> [str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path))\",\n \"self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int =\",\n \"def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata:\",\n \"import TextIO, BinaryIO, IO, Union from datetime import datetime from os.path import getmtime\",\n \"TextIO, None]: if confirm and not feedback: return None mode = 'x' mode\",\n \"or 'sha1' not in self.metadata \\\\ or 'sha1-timestamp' not in self.metadata \\\\ or\",\n \"not feedback: return None mode = 'x' mode += 'b' if binary else\",\n \"import datetime from os.path import getmtime from .low import ObservableDict class Data: def\",\n \"**metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge: bool = True):\",\n \"new_name)) self.__data_name__ = new_name def reader(self, binary: bool = False, **kwargs) -> [IO,\",\n \"not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if\",\n \"allow_overwrite and not append: raise PermissionError('Trying to overwrite existed data.') if confirm and\",\n \"new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary: bool\",\n \"md5(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if\",\n \"and not feedback: return mode = 'a' if append else 'w' mode +=\",\n \"self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self):\",\n \"/ self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property def metadata(self) ->\",\n \"**kwargs) def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False):\",\n \"if confirm and not feedback: return mode = 'a' if append else 'w'\",\n \"if properties is None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] =\",\n \"return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in\",\n \"Union[None, dict], merge: bool = True): if metadata is None: return if merge:\",\n \"if require_update \\\\ or 'sha1' not in self.metadata \\\\ or 'sha1-timestamp' not in\",\n \"require_update \\\\ or 'md5' not in self.metadata \\\\ or 'md5-timestamp' not in self.metadata\",\n \"else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path:\",\n \"self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp()\",\n \"True, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not\",\n \"metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def\",\n \"= 'x' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs)\",\n \"[IO, BinaryIO, TextIO, None]: if not allow_overwrite and not append: raise PermissionError('Trying to\",\n \"result else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool =\",\n \"append: raise PermissionError('Trying to overwrite existed data.') if confirm and not feedback: return\",\n \"False) -> [str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if\",\n \"path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def name(self) -> str: return\",\n \"BinaryIO, TextIO, None]: mode = 'r' mode += 'b' if binary else ''\",\n \"open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path: [str, Path], allow_overwrite=False,\",\n \"bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__\",\n \"= result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size:\",\n \"not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'md5' not\",\n \"h, buffer_size: int = 131072): if not self.path.exists(): return None with open(str(self.path), 'rb')\",\n \"'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\"\",\n \"else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False, confirm:\",\n \"buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def\",\n \"Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket\",\n \"if self.path.exists() and not allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path),\",\n \"class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None):\",\n \"from typing import TextIO, BinaryIO, IO, Union from datetime import datetime from os.path\",\n \"def creator(self, binary: bool = False, confirm: bool = False, feedback: bool =\",\n \"merge: bool = True): if metadata is None: return if merge: metadata =\",\n \"else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False, append:\",\n \"def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__\",\n \"True): if metadata is None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__]\",\n \"if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return\",\n \"open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False, confirm: bool = False,\",\n \"= self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return\",\n \"binary: bool = False, confirm: bool = False, feedback: bool = False, **kwargs)\",\n \"shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary: bool = False,\",\n \"@property def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def name(self) ->\",\n \"\\\\ or 'sha256' not in self.metadata \\\\ or 'sha256-timestamp' not in self.metadata \\\\\",\n \"sha1(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if\",\n \"if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict],\",\n \"[str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\\",\n \"= bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db\",\n \"if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha256'\",\n \"'md5' not in self.metadata \\\\ or 'md5-timestamp' not in self.metadata \\\\ or self.metadata['md5-timestamp']\",\n \"None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self,\",\n \"Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int =\",\n \"bool = False, confirm: bool = False, feedback: bool = False, **kwargs) ->\",\n \"self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def\",\n \"True: data = file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def md5(self,\",\n \"self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property def metadata(self) -> ObservableDict:\",\n \"typing import TextIO, BinaryIO, IO, Union from datetime import datetime from os.path import\",\n \"from pathlib import Path from typing import TextIO, BinaryIO, IO, Union from datetime\",\n \"self.metadata \\\\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result\",\n \"@property def parent(self): return self.__parent__ @property def path(self) -> Path: return self.__parent__.path /\",\n \"getmtime(str(self.path)) if require_update \\\\ or 'md5' not in self.metadata \\\\ or 'md5-timestamp' not\",\n \"if require_update \\\\ or 'sha256' not in self.metadata \\\\ or 'sha256-timestamp' not in\",\n \"self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha1' not in\",\n \"TextIO, None]: mode = 'r' mode += 'b' if binary else '' return\",\n \"{**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge: bool =\",\n \"'sha256' not in self.metadata \\\\ or 'sha256-timestamp' not in self.metadata \\\\ or self.metadata['sha256-timestamp']\",\n \"\\\\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp']\",\n \"def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self,\",\n \"set_properties(self, properties: Union[None, dict], merge: bool = True): if properties is None: return\",\n \"self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha256']\",\n \"self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property\",\n \"else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool = False)\",\n \"return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'md5' not in self.metadata\",\n \"in self.metadata \\\\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] =\",\n \"import shutil import hashlib from pathlib import Path from typing import TextIO, BinaryIO,\",\n \"def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] =\",\n \"return self.__parent__ @property def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def\",\n \"open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False, append: bool = True,\",\n \"import ObservableDict class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict]\",\n \"self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp()\",\n \"self.metadata \\\\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result\",\n \"@property def name(self) -> str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return\",\n \"< last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return\",\n \"def reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode\",\n \"+= 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary:\",\n \"**kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and not append: raise\",\n \"= None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ =\",\n \"'' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path: [str,\",\n \"not in self.metadata \\\\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5']\",\n \"def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ =\",\n \"new_name def reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]:\",\n \"properties is None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties\",\n \"sha256(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if\",\n \"datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update:\",\n \"h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool = False)\",\n \"131072): if not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while True:\",\n \"return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha1' not in self.metadata\",\n \"__calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists(): return None with open(str(self.path),\",\n \"return self.__parent__.path / self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property def\",\n \"mode = 'x' mode += 'b' if binary else '' return open(str(self.path), mode=mode,\",\n \"< last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return\",\n \"hashlib from pathlib import Path from typing import TextIO, BinaryIO, IO, Union from\",\n \"= {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge: bool\",\n \"TextIO, None]: if not allow_overwrite and not append: raise PermissionError('Trying to overwrite existed\",\n \"= 'a' if append else 'w' mode += 'b' if binary else ''\",\n \"None]: if confirm and not feedback: return None mode = 'x' mode +=\",\n \"self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path),\",\n \"None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def\",\n \"= datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072,\",\n \"ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name:\",\n \"return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path: [str, Path],\",\n \"self.__parent__ @property def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def name(self)\",\n \"False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and not append:\",\n \"properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name))\",\n \"properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property\",\n \"self.metadata \\\\ or 'sha256-timestamp' not in self.metadata \\\\ or self.metadata['sha256-timestamp'] < last_modified_time: result\",\n \"[IO, BinaryIO, TextIO, None]: mode = 'r' mode += 'b' if binary else\",\n \"datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update:\",\n \"'w' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def\",\n \"-> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self,\",\n \"mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self,\",\n \"-> Path: return self.__parent__.path / self.__data_name__ @property def name(self) -> str: return self.__data_name__\",\n \"def parent(self): return self.__parent__ @property def path(self) -> Path: return self.__parent__.path / self.__data_name__\",\n \"= False, confirm: bool = True, feedback: bool = False, **kwargs) -> [IO,\",\n \"False, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm\",\n \"return result else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool\",\n \"if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int\",\n \"confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False):\",\n \"creator(self, binary: bool = False, confirm: bool = False, feedback: bool = False,\",\n \"'a' if append else 'w' mode += 'b' if binary else '' return\",\n \"and not append: raise PermissionError('Trying to overwrite existed data.') if confirm and not\",\n \"self.metadata \\\\ or 'md5-timestamp' not in self.metadata \\\\ or self.metadata['md5-timestamp'] < last_modified_time: result\",\n \"[str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if confirm\",\n \"dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def\",\n \"parent(self): return self.__parent__ @property def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property\",\n \"def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path),\",\n \"str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return\",\n \"if metadata is None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] =\",\n \"**properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property def path(self) ->\",\n \"None]: if not allow_overwrite and not append: raise PermissionError('Trying to overwrite existed data.')\",\n \"self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge: bool = True): if\",\n \"Path from typing import TextIO, BinaryIO, IO, Union from datetime import datetime from\",\n \"from datetime import datetime from os.path import getmtime from .low import ObservableDict class\",\n \"shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists(): return\",\n \"= False, append: bool = True, allow_overwrite: bool = False, confirm: bool =\",\n \"None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or\",\n \"require_update: bool = False) -> [str, None]: if not self.path.exists(): return None last_modified_time\",\n \"return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def\",\n \"db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not\",\n \"self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size: int =\",\n \"= True, allow_overwrite: bool = False, confirm: bool = True, feedback: bool =\",\n \"result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else:\",\n \"merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict],\",\n \"self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp()\",\n \"= dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def\",\n \"metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge:\",\n \"self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self,\",\n \"mode=mode, **kwargs) def writer(self, binary: bool = False, append: bool = True, allow_overwrite:\",\n \"data.') if confirm and not feedback: return mode = 'a' if append else\",\n \"init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__\",\n \"binary: bool = False, append: bool = True, allow_overwrite: bool = False, confirm:\",\n \"is None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property\",\n \"h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool = False) -> [str,\",\n \"or 'md5' not in self.metadata \\\\ or 'md5-timestamp' not in self.metadata \\\\ or\",\n \"return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool\",\n \"and not allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def\",\n \"= dict() def set_metadata(self, metadata: Union[None, dict], merge: bool = True): if metadata\",\n \"with open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size) if not data:\",\n \"last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result\",\n \"feedback: return mode = 'a' if append else 'w' mode += 'b' if\",\n \"return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str,\",\n \"'rb') as file_reader: while True: data = file_reader.read(buffer_size) if not data: break h.update(data)\",\n \"self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge: bool = True): if\",\n \"else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool = False)\",\n \"def writer(self, binary: bool = False, append: bool = True, allow_overwrite: bool =\",\n \"'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool\",\n \"if not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while True: data\",\n \"False, confirm: bool = True, feedback: bool = False, **kwargs) -> [IO, BinaryIO,\",\n \"mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self):\",\n \"False, append: bool = True, allow_overwrite: bool = False, confirm: bool = True,\",\n \"BinaryIO, TextIO, None]: if not allow_overwrite and not append: raise PermissionError('Trying to overwrite\",\n \"@property def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self):\",\n \"writer(self, binary: bool = False, append: bool = True, allow_overwrite: bool = False,\",\n \"if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha1'\",\n \"require_update \\\\ or 'sha256' not in self.metadata \\\\ or 'sha256-timestamp' not in self.metadata\",\n \"feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite\",\n \"str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary: bool = False, **kwargs)\",\n \"Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h,\",\n \"in self.metadata \\\\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] =\",\n \"data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties()\",\n \"in self.metadata \\\\ or 'sha1-timestamp' not in self.metadata \\\\ or self.metadata['sha1-timestamp'] < last_modified_time:\",\n \"str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ =\",\n \".low import ObservableDict class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None,\",\n \"in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__]\",\n \"dict], merge: bool = True): if properties is None: return if merge: properties\",\n \"@property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path\",\n \"confirm: bool = False, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO,\",\n \"mode=mode, **kwargs) def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True,\",\n \"getmtime(str(self.path)) if require_update \\\\ or 'sha1' not in self.metadata \\\\ or 'sha1-timestamp' not\",\n \"and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if\",\n \"def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if\",\n \"'md5-timestamp' not in self.metadata \\\\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size)\",\n \"or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] =\",\n \"mode=mode, **kwargs) def creator(self, binary: bool = False, confirm: bool = False, feedback:\",\n \"self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self):\",\n \"None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha1' not in self.metadata \\\\\",\n \"if binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool =\",\n \"def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def name(self) -> str:\",\n \"self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def\",\n \"or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] =\",\n \"@property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__]\",\n \"[IO, BinaryIO, TextIO, None]: if confirm and not feedback: return None mode =\",\n \"self.__data_name__ = new_name def reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO,\",\n \"= True, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if\",\n \"binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r'\",\n \"if confirm and not feedback: return None mode = 'x' mode += 'b'\",\n \"\\\\ or 'md5-timestamp' not in self.metadata \\\\ or self.metadata['md5-timestamp'] < last_modified_time: result =\",\n \"buffer_size: int = 131072): if not self.path.exists(): return None with open(str(self.path), 'rb') as\",\n \"None]: mode = 'r' mode += 'b' if binary else '' return open(str(self.path),\",\n \"return None with open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size) if\",\n \"self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1']\",\n \"not allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self,\",\n \"file_reader: while True: data = file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest()\",\n \"dict() def set_metadata(self, metadata: Union[None, dict], merge: bool = True): if metadata is\",\n \"None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'md5' not in self.metadata \\\\\",\n \"allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if confirm and not\",\n \"def set_properties(self, properties: Union[None, dict], merge: bool = True): if properties is None:\",\n \"self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] =\",\n \"result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int\",\n \"data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool\",\n \"bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and not\",\n \"rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary:\",\n \"merge: bool = True): if properties is None: return if merge: properties =\",\n \"= datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072,\",\n \"bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property\",\n \"self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha256' not in\",\n \"bool = True): if properties is None: return if merge: properties = {**self.properties,\",\n \"not allow_overwrite and not append: raise PermissionError('Trying to overwrite existed data.') if confirm\",\n \"getmtime from .low import ObservableDict class Data: def __init__(self, data_name: str, parent, bucket,\",\n \"+= 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary:\",\n \"not append: raise PermissionError('Trying to overwrite existed data.') if confirm and not feedback:\",\n \"datetime import datetime from os.path import getmtime from .low import ObservableDict class Data:\",\n \"allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path:\",\n \"if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'md5'\",\n \"not in self.metadata \\\\ or 'md5-timestamp' not in self.metadata \\\\ or self.metadata['md5-timestamp'] <\",\n \"\\\\ or 'sha1-timestamp' not in self.metadata \\\\ or self.metadata['sha1-timestamp'] < last_modified_time: result =\",\n \"-> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and not append: raise PermissionError('Trying\",\n \"ObservableDict class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] =\",\n \"parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self):\",\n \"or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] =\",\n \"import hashlib from pathlib import Path from typing import TextIO, BinaryIO, IO, Union\",\n \"bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and\",\n \"= False, confirm: bool = False, feedback: bool = False, **kwargs) -> [IO,\",\n \"append else 'w' mode += 'b' if binary else '' return open(str(self.path), mode=mode,\",\n \"feedback=False): if self.path.exists() and not allow_overwrite: return if confirm and not feedback: return\",\n \"str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary: bool =\",\n \"-> str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def\",\n \"binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def import_file(self,\",\n \"as file_reader: while True: data = file_reader.read(buffer_size) if not data: break h.update(data) return\",\n \"data = file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size:\",\n \"mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self,\",\n \"self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict()\",\n \"None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha256' not in self.metadata \\\\\",\n \"bool = False, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]:\",\n \"'r' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def\",\n \"def sha1(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]:\",\n \"if binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def\",\n \"self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def\",\n \"not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha256' not\",\n \"return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str):\",\n \"if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path],\",\n \"open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size) if not data: break\",\n \"str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self)\",\n \"IO, Union from datetime import datetime from os.path import getmtime from .low import\",\n \"= False, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if\",\n \"or 'sha256-timestamp' not in self.metadata \\\\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(),\",\n \"__init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name\",\n \"**kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r' mode += 'b' if\",\n \"= getmtime(str(self.path)) if require_update \\\\ or 'md5' not in self.metadata \\\\ or 'md5-timestamp'\",\n \"ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ =\",\n \"'' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False, append: bool\",\n \"int = 131072): if not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader:\",\n \"file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int =\",\n \"131072, require_update: bool = False) -> [str, None]: if not self.path.exists(): return None\",\n \"\\\\ or 'sha256-timestamp' not in self.metadata \\\\ or self.metadata['sha256-timestamp'] < last_modified_time: result =\",\n \"datetime from os.path import getmtime from .low import ObservableDict class Data: def __init__(self,\",\n \"last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result\",\n \"Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if confirm and\",\n \"last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'md5' not in self.metadata \\\\ or\",\n \"bool = True): if metadata is None: return if merge: metadata = {**self.metadata,\",\n \"return f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and\",\n \"return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name\",\n \"if not allow_overwrite and not append: raise PermissionError('Trying to overwrite existed data.') if\",\n \"= self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return result else: return\",\n \"self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return\",\n \"not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties:\",\n \"else 'w' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs)\",\n \"overwrite existed data.') if confirm and not feedback: return mode = 'a' if\",\n \"return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) ->\",\n \"metadata: Union[None, dict], merge: bool = True): if metadata is None: return if\",\n \"bool = True, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]:\",\n \"dict], merge: bool = True): if metadata is None: return if merge: metadata\",\n \"\\\\ or 'md5' not in self.metadata \\\\ or 'md5-timestamp' not in self.metadata \\\\\",\n \"Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__\",\n \"in self.metadata \\\\ or 'sha256-timestamp' not in self.metadata \\\\ or self.metadata['sha256-timestamp'] < last_modified_time:\",\n \"if binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool =\",\n \"return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not\",\n \"def name(self) -> str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__]\",\n \"allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size:\",\n \"from os.path import getmtime from .low import ObservableDict class Data: def __init__(self, data_name:\",\n \"confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if confirm and not feedback:\",\n \"False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r' mode += 'b'\",\n \"= False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and not\",\n \"getmtime(str(self.path)) if require_update \\\\ or 'sha256' not in self.metadata \\\\ or 'sha256-timestamp' not\",\n \"if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not\",\n \"not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists()\",\n \"'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool\",\n \"'x' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def\",\n \"**kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and not feedback: return None\",\n \"binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False,\",\n \"return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self):\",\n \"raise PermissionError('Trying to overwrite existed data.') if confirm and not feedback: return mode\",\n \"in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge: bool =\",\n \"feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and\",\n \"if append else 'w' mode += 'b' if binary else '' return open(str(self.path),\",\n \"BinaryIO, IO, Union from datetime import datetime from os.path import getmtime from .low\",\n \"import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return\",\n \"def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path /\",\n \"-> [str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update\",\n \"confirm: bool = True, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO,\",\n \"-> [IO, BinaryIO, TextIO, None]: mode = 'r' mode += 'b' if binary\",\n \"None mode = 'x' mode += 'b' if binary else '' return open(str(self.path),\",\n \"init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None,\",\n \"to overwrite existed data.') if confirm and not feedback: return mode = 'a'\",\n \"-> [IO, BinaryIO, TextIO, None]: if confirm and not feedback: return None mode\",\n \"/ new_name)) self.__data_name__ = new_name def reader(self, binary: bool = False, **kwargs) ->\",\n \"result else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool =\",\n \"TextIO, BinaryIO, IO, Union from datetime import datetime from os.path import getmtime from\",\n \"parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ = parent\",\n \"f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not\",\n \"return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool = False) ->\",\n \"database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__\",\n \"= data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata()\",\n \"import getmtime from .low import ObservableDict class Data: def __init__(self, data_name: str, parent,\",\n \"self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in\",\n \"self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self,\",\n \"self.__parent__.path / self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property def metadata(self)\",\n \"self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property\",\n \"merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__\",\n \"self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool = False) -> [str,\",\n \"if require_update \\\\ or 'md5' not in self.metadata \\\\ or 'md5-timestamp' not in\",\n \"def sha256(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]:\",\n \"feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and\",\n \"self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata:\",\n \"and not feedback: return None mode = 'x' mode += 'b' if binary\",\n \"Path: return self.__parent__.path / self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property\",\n \"append: bool = True, allow_overwrite: bool = False, confirm: bool = True, feedback:\",\n \"Union from datetime import datetime from os.path import getmtime from .low import ObservableDict\",\n \"def md5(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]:\",\n \"= 131072, require_update: bool = False) -> [str, None]: if not self.path.exists(): return\",\n \"self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db\",\n \"'sha1' not in self.metadata \\\\ or 'sha1-timestamp' not in self.metadata \\\\ or self.metadata['sha1-timestamp']\",\n \"metadata def set_properties(self, properties: Union[None, dict], merge: bool = True): if properties is\",\n \"{**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property def path(self)\",\n \"= 131072): if not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while\",\n \"= result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size:\",\n \"properties: Union[None, dict], merge: bool = True): if properties is None: return if\",\n \"return mode = 'a' if append else 'w' mode += 'b' if binary\",\n \"bool = False, append: bool = True, allow_overwrite: bool = False, confirm: bool\",\n \"@property def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if\",\n \"not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update:\",\n \"or 'md5-timestamp' not in self.metadata \\\\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(),\",\n \"return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists():\",\n \"= getmtime(str(self.path)) if require_update \\\\ or 'sha1' not in self.metadata \\\\ or 'sha1-timestamp'\",\n \"existed data.') if confirm and not feedback: return mode = 'a' if append\",\n \"return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha256' not in self.metadata\",\n \"dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self,\",\n \"= True): if metadata is None: return if merge: metadata = {**self.metadata, **metadata}\",\n \"'' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False, confirm: bool\",\n \"not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge: bool\",\n \"bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r' mode\",\n \"__repr__(self): return f\\\"Data('{self.__data_name__}')\\\" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists()\",\n \"self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'md5' not in\",\n \"None with open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size) if not\",\n \"'sha256-timestamp' not in self.metadata \\\\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size)\",\n \"@property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return\",\n \"or 'sha256' not in self.metadata \\\\ or 'sha256-timestamp' not in self.metadata \\\\ or\",\n \"not in self.metadata \\\\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1']\",\n \"return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def\",\n \"from .low import ObservableDict class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods:\",\n \"import Path from typing import TextIO, BinaryIO, IO, Union from datetime import datetime\",\n \"def __calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists(): return None with\",\n \"str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists(): return None\",\n \"self.path.exists() and not allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path))\",\n \"'sha1-timestamp' not in self.metadata \\\\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size)\",\n \"not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha1' not\",\n \"reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode =\",\n \"not in self.metadata \\\\ or 'sha1-timestamp' not in self.metadata \\\\ or self.metadata['sha1-timestamp'] <\",\n \"**kwargs) def creator(self, binary: bool = False, confirm: bool = False, feedback: bool\",\n \"= False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r' mode +=\",\n \"= new_name def reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO,\",\n \"def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite:\",\n \"protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ =\",\n \"True, allow_overwrite: bool = False, confirm: bool = True, feedback: bool = False,\",\n \"data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__\",\n \"self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5']\",\n \"self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge:\",\n \"= 'r' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs)\",\n \"return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties:\",\n \"int = 131072, require_update: bool = False) -> [str, None]: if not self.path.exists():\",\n \"feedback: return None mode = 'x' mode += 'b' if binary else ''\",\n \"if not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072,\",\n \"self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool = False) -> [str,\",\n \"= False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and not feedback:\",\n \"in self.metadata \\\\ or 'md5-timestamp' not in self.metadata \\\\ or self.metadata['md5-timestamp'] < last_modified_time:\",\n \"mode = 'r' mode += 'b' if binary else '' return open(str(self.path), mode=mode,\",\n \"bool = True, allow_overwrite: bool = False, confirm: bool = True, feedback: bool\",\n \"self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property def path(self) -> Path:\",\n \"return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False, confirm: bool =\",\n \"last_modified_time = getmtime(str(self.path)) if require_update \\\\ or 'sha1' not in self.metadata \\\\ or\",\n \"result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return result else:\",\n \"or 'sha1-timestamp' not in self.metadata \\\\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(),\",\n \"protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self): return\",\n \"confirm and not feedback: return None mode = 'x' mode += 'b' if\",\n \"def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__]\",\n \"= self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return\",\n \"binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False,\",\n \"def set_metadata(self, metadata: Union[None, dict], merge: bool = True): if metadata is None:\",\n \"is None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def\",\n \"False, confirm: bool = False, feedback: bool = False, **kwargs) -> [IO, BinaryIO,\",\n \"not feedback: return mode = 'a' if append else 'w' mode += 'b'\",\n \"self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size)\",\n \"[str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self,\",\n \"set_metadata(self, metadata: Union[None, dict], merge: bool = True): if metadata is None: return\",\n \"return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False, append: bool =\",\n \"mode = 'a' if append else 'w' mode += 'b' if binary else\",\n \"dict] = None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__\",\n \"= properties @property def parent(self): return self.__parent__ @property def path(self) -> Path: return\",\n \"\\\\ or 'sha1' not in self.metadata \\\\ or 'sha1-timestamp' not in self.metadata \\\\\",\n \"\\\\ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp']\",\n \"bool = False, confirm: bool = True, feedback: bool = False, **kwargs) ->\",\n \"PermissionError('Trying to overwrite existed data.') if confirm and not feedback: return mode =\",\n \"allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if not\",\n \"bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict()\",\n \"name(self) -> str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property\",\n \"while True: data = file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def\",\n \"\\\\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp']\",\n \"confirm and not feedback: return mode = 'a' if append else 'w' mode\",\n \"shutil import hashlib from pathlib import Path from typing import TextIO, BinaryIO, IO,\",\n \"+= 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return\",\n \"and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072):\",\n \"= True): if properties is None: return if merge: properties = {**self.properties, **properties}\",\n \"result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size: int\",\n \"shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite:\",\n \"= metadata def set_properties(self, properties: Union[None, dict], merge: bool = True): if properties\",\n \"def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if\",\n \"return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool = False) ->\",\n \"self.metadata \\\\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result\",\n \"self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict:\",\n \"result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else:\",\n \"export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path))\",\n \"Union[None, dict], merge: bool = True): if properties is None: return if merge:\",\n \"src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if\",\n \"return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool = False) ->\",\n \"buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def\",\n \"True): if properties is None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__]\",\n \"self.metadata \\\\ or 'sha1-timestamp' not in self.metadata \\\\ or self.metadata['sha1-timestamp'] < last_modified_time: result\",\n \"= file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int\",\n \"buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if not\",\n \"BinaryIO, TextIO, None]: if confirm and not feedback: return None mode = 'x'\",\n \"self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge: bool = True):\",\n \"not in self.metadata \\\\ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256']\",\n \"**kwargs) def writer(self, binary: bool = False, append: bool = True, allow_overwrite: bool\",\n \"-> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__\",\n \"bool = False) -> [str, None]: if not self.path.exists(): return None last_modified_time =\",\n \"< last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return\",\n \"pathlib import Path from typing import TextIO, BinaryIO, IO, Union from datetime import\",\n \"return None mode = 'x' mode += 'b' if binary else '' return\",\n \"metadata is None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata\",\n \"in self.metadata \\\\ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] =\",\n \"allow_overwrite: bool = False, confirm: bool = True, feedback: bool = False, **kwargs)\",\n \"= parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def\",\n \"False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and not feedback: return\",\n \"os.path import getmtime from .low import ObservableDict class Data: def __init__(self, data_name: str,\",\n \"last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return result\",\n \"if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None,\"\n]","isConversational":false}}},{"rowIdx":1178,"cells":{"ngram":{"kind":"list like","value":["If you want to process the gradient before applying then call `tf.GradientTape` and","hold the slot value. This can be useful if you want to log","without the optimizer; # it's a dependency hypergraph with edges of the form","dimension of `values` (as in an IndexedSlices object). Returns: A tuple of (`summed_values`,","_prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically.","Unless required by applicable law or agreed to in writing, software # distributed","Args: grad: a `Tensor` representing the gradient for the affected indices. handle: a","called `set_weights(weights)` on optimizer \" + self._name + \" with a weight list","optimizer, include all hyper parameters) \"\"\" def __init__(self, name, **kwargs): \"\"\"Create a new","pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) #","which points to the variable to be updated. Returns: An `Operation` which updates","= var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \"\"\"Get the","tensors. Raises: ValueError: In case any gradient cannot be computed (e.g. if gradient","in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name,","a primary variable, for looking up slots. In graph mode the name is","if there is no gradient for the given variable. Args: loss: A callable","should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you","weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if","reverse of `get_config`, capable of instantiating the same optimizer from the config dictionary.","for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name is","iself (e.g. through `apply_gradients`). \"\"\" # TODO(allenl): Make the restored optimizer functional by","= self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value):","list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables>","created or restored. When executing eagerly, we create the slot variable with a","graph mode, returns op that minimizes the loss by updating the listed #","to the variable to be updated. Returns: An `Operation` which updates the value","var in grads_and_vars: scope_name = (\"\" if ops.executing_eagerly_outside_functions() else \"_\" + var.op.name) with","which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this","filtered def _var_key(var): \"\"\"Key for representing a primary variable, for looking up slots.","from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from","the gradients as you wish. 3. Apply the processed gradients with `apply_gradients()`. Example:","`loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`.","applying them you can instead use the optimizer in three steps: 1. Compute","pairs where \"gradient\" is the gradient for \"variable\". Note that \"gradient\" can be","\", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \"Cannot","that applies the specified gradients. If `global_step` was not None, that operation also","and applying them to the variables. If you want to process the gradients","rate as a Tensor with dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule):","created but `variable` has just been added to a dependency graph (causing us","in an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is","a model. You never use this class directly, but instead instantiate one of","not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var","API to add Ops to train a model. You never use this class","compatibility. Holds slot variables and hyperparameters when an optimizer is restored from a","would otherwise be incomplete before `minimize` and the variables are created at the","value of the variable. \"\"\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \"\"\"Add","typically the output of get_config. custom_objects: A Python dictionary mapping names to additional","initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy()","or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or any_symbolic: # If","in functions along with ops created by the original optimizer, but currently we","def from_config(cls, config, custom_objects=None): \"\"\"Creates an optimizer from its config. This method is","the name is derived from the var shared name. In eager mode the","raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \"\"\"Add ops to apply sparse gradients","we'd like to eagerly create/restore slot variables # when possible, but this may","tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients","graph, and so shouldn't keep Tensors as member variables. Generally you should be","call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update","var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if","`apply_gradients()` to get the value for the hyper parameter. Hyper parameters can be","from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from","and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \"\"\"set hyper","newly created slot variable's value.\"\"\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key,","these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes,","dependencies, in which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key,","of construction time. Examples include 1) sequential models without input shape pre-defined, or","passed to any variable, the constraint will be applied to the variable after","gradient. \" \"Please make sure that all of your ops have a \"","many times too big. ### Variable Constraint All Keras optimizers respect variable constraints.","var2]) ``` ### Write a customized optimizer. If you intend to create your","\"clipvalue\", \"lr\", \"decay\"} for k in kwargs: if k not in allowed_kwargs: raise","g, v in grads_and_vars if g is not None and v.dtype != dtypes.resource","you # need to the 'gradient' part, for example cap them, etc. capped_grads_and_vars","the gradient for the affected indices. handle: a `Tensor` of dtype `resource` which","add a dependency on # a slot variable if not for this case).","restores, highest restore UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda","= self.clipnorm if hasattr(self, \"clipvalue\"): config[\"clipvalue\"] = self.clipvalue return config @classmethod def from_config(cls,","= [v for (_, v) in grads_and_vars] # Create iteration if necessary. with","either made this slot variable, or if we've pulled out an # existing","`_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any tensor is not","later (without any saved state) from this configuration. Returns: Python dictionary. \"\"\" config","`Tensor` representing the gradient. handle: a `Tensor` of dtype `resource` which points to","tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops","self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and","(slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","_distributed_apply(self, distribution, grads_and_vars, name): \"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars)","slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and","slot variables have their own dependencies, in which case # those could differ","[ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads, var_list))","# Compute the gradients for a list of variables. with tf.GradientTape() as tape:","then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this function. Args: loss:","integral type representing the indices for which the gradient is nonzero. Indices may","are created when graph building. Instead, _restore_slot_variable catches these after normal creation and","mostly harmless # (aside from double initialization), and makes variable creator scopes #","`Tensor` with rank >= 1. indices: A one-dimensional integer `Tensor`, indexing into the","before `minimize` and the variables are created at the first time when `loss`","answer, resulting in gradients that can be many times too big. ### Variable","!= w.shape: raise ValueError(\"Optimizer weight shape \" + str(pv.shape) + \" not compatible","always present, but gradient can be `None`. Raises: TypeError: If `var_list` contains anything","Optimizer weights have been created\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \"\"\"Get","isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay >","in which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append(","def __getattribute__(self, name): \"\"\"Overridden to support hyperparameter access.\"\"\" try: return super(OptimizerV2, self).__getattribute__(name) except","this dependency and otherwise pretend it's a normal # graph. if slot_variable is","sums gradients across all replicas. To average gradients, you divide your loss by","which means it automatically sums gradients across all replicas. To average gradients, you","pretend it's a normal # graph. if slot_variable is not None: # If","derived from the var shared name. In eager mode the name is derived","entire optimizer is currently thread compatible, not thread-safe. The user needs to perform","instead special-case this dependency and otherwise pretend it's a normal # graph. if","in vars_with_empty_grads])) return filtered def _var_key(var): \"\"\"Key for representing a primary variable, for","the value # to minimize. loss = lambda: 3 * var1 * var1","__setattr__. if name == \"_hyper\": raise e # Backwards compatibility with Keras optimizers.","if trainable: raise ValueError( \"Synchronization value can be set to \" \"VariableSynchronization.ON_READ only","= self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None):","for the variable it created to hold the slot value. This can be","\"\"\"Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check.","@keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated base class for optimizers. This class defines the API","opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize","Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of the variables have","return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def","name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable","tf_variables.Variable( name=\"%s/%s\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name]","summing. Args: grad: a `Tensor` representing the gradient for the affected indices. handle:","`Tensor` of integral type representing the indices for which the gradient is nonzero.","tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine","the optimizer in three steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process","A callable taking no arguments which returns the value to minimize. var_list: list","input shape pre-defined, or 2) subclassed models. Pass var_list as callable in these","variables) - get_config (serialization of the optimizer, include all hyper parameters) \"\"\" def","apply_grad_to_update_var(var, grad): \"\"\"Apply gradient to variable.\"\"\" if isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying to update","= _var_key(var) return name + \"/\" + slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer","are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError:","mean that scopes intended to catch # `variable` also catch its eagerly created","variable after the gradient has been applied to the variable. Important: If gradient","This must be called by the constructors of subclasses. Note that Optimizer instances","decay of learning rate. `lr` is included for backward compatibility, recommended to use","slot_name: The name of this `Optimizer`'s slot to restore into. variable: The variable","is dense) - resource_apply_sparse (update variable given gradient tensor is sparse) - create_slots","tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras","var_list_fn) ``` ### Processing gradients before applying them. Calling `minimize()` takes care of","software # distributed under the License is distributed on an \"AS IS\" BASIS,","support using the optimizer object iself (e.g. through `apply_gradients`). \"\"\" # TODO(allenl): Make","an `IndexedSlices`, or `None` if there is no gradient for the given variable.","in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value): \"\"\"Override setattr to","None: raise RuntimeError(\"Cannot set `iterations` to a new Variable after \" \"the Optimizer","average gradients, you should use `tf.math.reduce_sum` to add up your per-example losses and","the value for the hyper parameter. Hyper parameters can be overwritten through user","indices): \"\"\"Add ops to apply sparse gradients to `handle`, with repeated indices. Optimizers","tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars)","average gradients, you divide your loss by the global batch size, which is",": {slot name : variable}} self._slots = {} self._slot_names = [] self._weights =","isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name],","by norm; `clipvalue` is clip gradients by value, `decay` is included for backward","set to \" \"VariableSynchronization.ON_READ only for non-trainable variables. \" \"You have specified trainable=True","variable. Args: loss: A callable taking no arguments which returns the value to","loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in","to process the gradients before applying them you can instead use the optimizer","`params`. Arguments: loss: Loss tensor. params: List of variables. Returns: List of gradient","output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them. Calling","variable # unintentionally (specifically make_template would add a dependency on # a slot","not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var,","isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize","= [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \"clipvalue\"): grads = [","= self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID first","returned operation. Default to the name passed to the `Optimizer` constructor. Returns: An","variables are not owned by any one object (because we don't want to","disable=protected-access # Get the distributed variable if it exists. if getattr(var, \"_distributed_container\", None)","var2 # In graph mode, returns op that minimizes the loss by updating","is done automatically if you use `tf.keras` built-in training or evaluation loops. See","and \" \"synchronization=VariableSynchronization.ON_READ.\") else: # Set trainable to be false when variable is","for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \"\"\"Restore a","isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying to update a Tensor \", var) if isinstance(grad, ops.IndexedSlices):","any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly()","import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from","\"lr\": name = \"learning_rate\" if hasattr(self, \"_hyper\") and name in self._hyper: self._set_hyper(name, value)","aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \"\"\"Variable. The number of training steps","Save and restore the Optimizer's config raise NotImplementedError( \"Restoring functional Optimzers from SavedModels","import division from __future__ import print_function import abc import functools import six from","# pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name)","they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer =","first. Args: var: the variable. Returns: the unique name of the variable. \"\"\"","exists, get the primary variable first. Args: var: the variable. Returns: the unique","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","customized optimizer. If you intend to create your own optimization algorithm, simply inherit","config: A Python dictionary, typically the output of get_config. custom_objects: A Python dictionary","grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. Returns: A list","i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i,","tuples (gradient, variable). Do whatever you # need to the 'gradient' part, for","args=(grads_and_vars,), kwargs={\"name\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\" reduced_grads","building for the case when a slot variable has already been created but","`unique_indices`) where `unique_indices` is a de-duplicated version of `indices` and `summed_values` contains the","but this may mean that scopes intended to catch # `variable` also catch","tuple of `Variable` objects to update to minimize `loss`, or a callable returning","Tensor with dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations,","dtypes.float64]) def _call_if_callable(self, param): \"\"\"Call the function if param is callable.\"\"\" return param()","if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {})","Variable after \" \"the Optimizer weights have been created\") self._iterations = variable self._weights.append(self._iterations)","representing the gradient for the affected indices. handle: a `Tensor` of dtype `resource`","return self._slot_names def add_slot(self, var, slot_name, initializer=\"zeros\"): \"\"\"Add a new slot variable for","= tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input),","a single graph, and so shouldn't keep Tensors as member variables. Generally you","an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a","variables. Returns: List of gradient tensors. Raises: ValueError: In case any gradient cannot","zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\"Optimizer weight shape \" +","the update ops are # symbolic then the step update should be carried","vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var)","nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \"\"\"Sums `values` associated with any","tensors, or callables. If they are callable, the callable will be called during","with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list):","_filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] # Create iteration if","__setattr__(self, name, value): \"\"\"Override setattr to support dynamic hyperparameter setting.\"\"\" # Backwards compatibility","distribution strategy exists, get the primary variable first. Args: var: the variable. Returns:","if this limitation bothers \" \"you.\") revived_types.register_revived_type( \"optimizer\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration(","`minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient,","eagerly, we create the slot variable with a restoring initializer. No new variables","(gradient, variable) pairs. Variable is always present, but gradient can be `None`. Raises:","from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from","name: Optional name for the returned operation. Default to the name passed to","\" \"supported. Please file a feature request if this limitation bothers \" \"you.\")","anything else than `Variable` objects. ValueError: If some arguments are invalid, or var_list","All Keras optimizers respect variable constraints. If constraint function is passed to any","grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. name: Optional name","etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer","isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name] = value","value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if","values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue","OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper # pylint: disable=protected-access )])","An `Operation` that applies the specified gradients. If `global_step` was not None, that","non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a","same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access","to \" \"VariableSynchronization.ON_READ only for non-trainable variables. \" \"You have specified trainable=True and","correct behavior, to sum non-unique indices and their associated gradients, is enforced by","is called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. name:","indices: A one-dimensional integer `Tensor`, indexing into the first dimension of `values` (as","time. Examples include 1) sequential models without input shape pre-defined, or 2) subclassed","name = \"learning_rate\" if hasattr(self, \"_hyper\") and name in self._hyper: self._set_hyper(name, value) else:","TODO(allenl): Make the restored optimizer functional by tracing its apply # methods. def","slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name,","If you are not using these and you want to average gradients, you","`None`, that operation also increments `global_step`. Raises: ValueError: If some of the variables","self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \"\"\"A list of names","training steps this Optimizer has run.\"\"\" if self._iterations is None: self._iterations = self.add_weight(","optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is","scope. Generally we'd like to eagerly create/restore slot variables # when possible, but","gv in grads_and_vars] # Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars)","gradient cannot be computed (e.g. if gradient function not implemented). \"\"\" params =","isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if","List of gradient tensors. Raises: ValueError: In case any gradient cannot be computed","to a new Variable after \" \"the Optimizer weights have been created\") self._iterations","type representing the indices for which the gradient is nonzero. Indices may be","Maybe share this logic with base_layer. def set_weights(self, weights): params = self.weights if","import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import","name : {slot name : variable}} self._slots = {} self._slot_names = [] self._weights","_, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \"\"\"Apply gradient","= tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn =","No new variables are created when graph building. Instead, _restore_slot_variable catches these after","`Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional","gets created # normally. We keep a list rather than the one with","if var.constraint is not None: raise RuntimeError( \"Cannot use a constraint function on","passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may","value # to minimize. loss = lambda: 3 * var1 + 2 *","= list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is","In graph mode, returns op that minimizes the loss by updating the listed","six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise","updates the value of the variable. \"\"\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle,","created for. \"\"\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name,","and you can ask the optimizer for the names of the slots that","override this method must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices`","a normal # graph. if slot_variable is not None: # If we've either","first pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which","we've either made this slot variable, or if we've pulled out an #","activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output","x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value()","returns the value # to minimize. loss = lambda: 3 * var1 +","- resource_apply_dense (update variable given gradient tensor is dense) - resource_apply_sparse (update variable","the gradient is nonzero. Indices may be repeated. Returns: An `Operation` which updates","return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \"\"\"Returns variables of","when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position)","when a slot variable has already been created but `variable` has just been","Returns: Valid types for loss, variables and gradients. \"\"\" return set( [dtypes.float16, dtypes.bfloat16,","= [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights):","self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\"decay\", var_dtype) lr_t =","gradient for the given variable. Args: loss: A callable taking no arguments which","if dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \"\"\"Overridden to","restore # slot variables which have not yet been created # (trackable._CheckpointPosition objects).","def add_weight(self, name, shape, dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None:","list of length \" + str(len(weights)) + \", but the optimizer was expecting","(eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op","be incomplete before `minimize` and the variables are created at the first time","also increments `global_step`. Raises: ValueError: If some of the variables are not `Variable`","self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \"\"\"`apply_gradients`","param def _resource_apply_dense(self, grad, handle): \"\"\"Add ops to apply dense gradients to the","component of a tensor's shape is the *replica-local* batch size, which is off","them you can instead use the optimizer in three steps: 1. Compute the","with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad:","raise RuntimeError( \"Cannot use a constraint function on a sparse variable.\") return self._resource_apply_sparse_duplicate_indices(","for representing a primary variable, for looking up slots. In graph mode the","under a graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint:","to allow other float types. Returns: Valid types for loss, variables and gradients.","to `handle`, with repeated indices. Optimizers which override this method must deal with","variables have their own dependencies, in which case # those could differ between","p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\"Optimizer weight","`IndexedSlices`, or `None` if there is no gradient for the given variable. Args:","details. By default the correct behavior, to sum non-unique indices and their associated","_restore_slot_variable catches these after normal creation and adds restore ops to the graph.","decay in a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list)","if callable(value): value = value() if dtype: return math_ops.cast(value, dtype) else: return value","additional variables associated with the variables to train. These are called <i>Slots</i>. Slots","object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version","of tuples (gradient, variable). Do whatever you # need to the 'gradient' part,","deal correctly with duplicate indices may instead override this method to avoid the","not use this file except in compliance with the License. # You may","g in grads] if hasattr(self, \"clipvalue\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for","self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key]","optimizer in three steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process the","edges of the form (optimizer, non-slot # variable, variable)). So we don't _track_","using this function. Args: loss: A callable taking no arguments which returns the","> 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\"decay\", var_dtype) lr_t = lr_t","of using this function. Args: loss: A callable taking no arguments which returns","from double initialization), and makes variable creator scopes # behave the same way","the optimizer; # it's a dependency hypergraph with edges of the form (optimizer,","Compute the gradients for a list of variables. with tf.GradientTape() as tape: loss","gradients of `loss` with respect to `params`. Arguments: loss: Loss tensor. params: List","from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import","of `minimize()`. It returns a list of (gradient, variable) pairs where \"gradient\" is","Returns: An `Operation` that applies the specified gradients. If `global_step` was not None,","the gradients before applying them you can instead use the optimizer in three","explicitly instead of using this function. Args: loss: A callable taking no arguments","\"\"\"Apply gradient to variable.\"\"\" if isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying to update a Tensor","config @classmethod def from_config(cls, config, custom_objects=None): \"\"\"Creates an optimizer from its config. This","var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def","`indices` argument to this method has been de-duplicated. Optimizers which deal correctly with","be synced on read. trainable = False elif trainable is None: trainable =","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import","value): \"\"\"set hyper `name` to value. value can be callable, tensor, numeric.\"\"\" if","using `tf.GradientTape` and calls `apply_gradients()`. If you want to process the gradient before","dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend","`summed_values` contains the sum of `values` slices associated with each unique index. \"\"\"","name): \"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v","slot variable for `var`.\"\"\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var)","deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else:","For implementing Trackable. Stores information about how to restore # slot variables which","time `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed for","Optimizers which deal correctly with duplicate indices may instead override this method to","and returns the value # to minimize. loss = lambda: 3 * var1","of dtype `resource` which points to the variable to be updated. indices: a","2 * var2 * var2 # In graph mode, returns op that minimizes","be overwritten through user code: Example: ```python # Create an optimizer with the","agreed to in writing, software # distributed under the License is distributed on","you want to process the gradients before applying them you can instead use","{_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations = {} decay","============================================================================== \"\"\"Version 2 of class Optimizer.\"\"\" # pylint: disable=g-bad-name from __future__ import absolute_import","if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if","new Variable after \" \"the Optimizer weights have been created\") self._iterations = variable","without input shape pre-defined, or 2) subclassed models. Pass var_list as callable in","the affected indices. handle: a `Tensor` of dtype `resource` which points to the","or self._name): for grad, var in grads_and_vars: scope_name = (\"\" if ops.executing_eagerly_outside_functions() else","\"...\") if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv,","data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them. Calling `minimize()` takes","shape \" + str(pv.shape) + \" not compatible with \" \"provided weight shape","called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. Returns: A","hyper parameter. Hyper parameters can be overwritten through user code: Example: ```python #","(update variable given gradient tensor is dense) - resource_apply_sparse (update variable given gradient","and calls `apply_gradients()`. If you want to process the gradient before applying then","variable, slot_variable): \"\"\"Restore a newly created slot variable's value.\"\"\" variable_key = _var_key(variable) deferred_restorations","`Variable` objects to update to minimize `loss`, or a callable returning the list","else param def _resource_apply_dense(self, grad, handle): \"\"\"Add ops to apply dense gradients to","batch size, which is off by a factor equal to the number of","filtered: raise ValueError(\"No gradients provided for any variable: %s.\" % ([v.name for _,","Optimzers from SavedModels is not currently \" \"supported. Please file a feature request","for `var`.\"\"\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict =","name for the returned operation. Default to the name passed to the `Optimizer`","all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If","respect variable constraints. If constraint function is passed to any variable, the constraint","to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead override this","restoring initializer. No new variables are created when graph building. Instead, _restore_slot_variable catches","any tensor is not a valid type. \"\"\" valid_dtypes = self._valid_dtypes() for t","tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \"\"\"Returns variables of this Optimizer based","that updates the variables in `var_list`. If `global_step` was not `None`, that operation","slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter out iterable with grad equal to","Tensor \", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError(","if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p,","An Operation that updates the variables in `var_list`. If `global_step` was not `None`,","added to a dependency graph (causing us to realize that the slot variable","\"gradient\" is the gradient for \"variable\". Note that \"gradient\" can be a `Tensor`,","to minimize. var_list: list or tuple of `Variable` objects to update to minimize","the same optimizer from the config dictionary. Arguments: config: A Python dictionary, typically","currently \" \"supported. Please file a feature request if this limitation bothers \"","you can ask the optimizer for the names of the slots that it","update_op = self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var))","mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2])","`tf.keras` built-in training or evaluation loops. See the `reduction` argument of your loss","limitations under the License. # ============================================================================== \"\"\"Version 2 of class Optimizer.\"\"\" # pylint:","optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable","done automatically if you use `tf.keras` built-in training or evaluation loops. See the","(the `__init__` method), and then passed to `self._set_hyper()`. They can be either regular","{slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations","be a `Tensor`, an `IndexedSlices`, or `None` if there is no gradient for","function on a sparse variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad,","list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not","or any_symbolic: # If the current context is graph mode or any of","variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1,","return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values,","is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer,","self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes:","Defer restoring until it gets created # normally. We keep a list rather","to apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices`","\" \"VariableSynchronization.ON_READ only for non-trainable variables. \" \"You have specified trainable=True and \"","for which the gradient is nonzero. Indices are unique. Returns: An `Operation` which","to minimize. loss = lambda: 3 * var1 * var1 + 2 *","be updated. Returns: An `Operation` which updates the value of the variable. \"\"\"","return config @classmethod def from_config(cls, config, custom_objects=None): \"\"\"Creates an optimizer from its config.","takes care of both computing the gradients and applying them to the variables.","associated with each unique index. \"\"\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum(","= initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \"Synchronization value can","for the given variable. Args: loss: A callable taking no arguments which returns","isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype)","`decay` is included for backward compatibility to allow time inverse decay of learning","slot variable has already been created but `variable` has just been added to","learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if dtype: return math_ops.cast(value, dtype)","optimizers. This class defines the API to add Ops to train a model.","makes variable creator scopes # behave the same way they do when graph","(serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated","self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \"\"\"Add ops to apply sparse","the variable to be updated. Returns: An `Operation` which updates the value of","to in writing, software # distributed under the License is distributed on an","highest restore UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position:","self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name}) def _distributed_apply(self, distribution,","var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not None","implied. # See the License for the specific language governing permissions and #","return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not","gradients to the variable `handle`. Args: grad: a `Tensor` representing the gradient. handle:","from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from","be less than 0: {}\".format(decay)) self._initial_decay = decay if \"clipnorm\" in kwargs: self.clipnorm","self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int,","to be synced on read. trainable = False elif trainable is None: trainable","grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if","Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the \"License\");","backend.get_value(value) return value def variables(self): \"\"\"Returns variables of this Optimizer based on the","of the variables have gradients. \"\"\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for","of this Optimizer based on the order created.\"\"\" return self._weights @property def weights(self):","# limitations under the License. # ============================================================================== \"\"\"Version 2 of class Optimizer.\"\"\" #","present, but gradient can be `None`. Raises: TypeError: If `var_list` contains anything else","for the variable: var_name/slot_name.\"\"\" name = _var_key(var) return name + \"/\" + slot_name","your own optimization algorithm, simply inherit from this class and override the following","callable returning the list or tuple of `Variable` objects. Use callable when the","(update variable given gradient tensor is sparse) - create_slots (if your optimizer algorithm","Args: name: A non-empty string. The name to use for accumulators created for","list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not","in zip(grads, params): if grad is None: raise ValueError(\"Variable {} has `None` for","# symbolic then the step update should be carried out under a graph","In graph mode the name is derived from the var shared name. In","for %s, expected: %s.\" % (dtype, t.name, [v for v in valid_dtypes])) def","in grads_and_vars: scope_name = (\"\" if ops.executing_eagerly_outside_functions() else \"_\" + var.op.name) with backend.name_scope(\"update\"","`tf.math.reduce_mean` will give the wrong answer, resulting in gradients that can be many","allow other float types. Returns: Valid types for loss, variables and gradients. \"\"\"","and thread-compatible. Args: name: A non-empty string. The name to use for accumulators","= self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self):","ValueError: In case any gradient cannot be computed (e.g. if gradient function not","eagerly created slot variable # unintentionally (specifically make_template would add a dependency on","Raises: ValueError: In case any gradient cannot be computed (e.g. if gradient function","processed gradients with `apply_gradients()`. Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1)","included for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name","``` ### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means","g is not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self,","slots.\"\"\" return self._slot_names def add_slot(self, var, slot_name, initializer=\"zeros\"): \"\"\"Add a new slot variable","gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional name for the","callable(value): value = value() if dtype: return math_ops.cast(value, dtype) else: return value def","if \"clipvalue\" in kwargs: self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created = False def minimize(self, loss,","loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for","self._iterations is None: self._iterations = self.add_weight( \"iter\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return","name, shape, dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype =","optimizer is saved without the non-slot # variable, or if the non-slot variable","or `None` if there is no gradient for the given variable. Args: loss:","= {\"name\": self._name} if hasattr(self, \"clipnorm\"): config[\"clipnorm\"] = self.clipnorm if hasattr(self, \"clipvalue\"): config[\"clipvalue\"]","and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with","Apache License, Version 2.0 (the \"License\"); # you may not use this file","params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g,","_get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule):","allowed_kwargs = {\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"} for k in kwargs: if k not","list would otherwise be incomplete before `minimize` and the variables are created at","a hyperparameter that can be a float, callable, or Tensor.\"\"\" value = self._hyper[hyperparameter_name]","optimizer from the config dictionary. Arguments: config: A Python dictionary, typically the output","that all of your ops have a \" \"gradient defined (i.e. are differentiable).","applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this function. Args:","# behave the same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack):","grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\"Gradients does not exist for variables %s when minimizing","\" + str(len(weights)) + \", but the optimizer was expecting \" + str(len(params))","if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if dtype: return","be carried out under a graph # context. (eager updates execute immediately) with","it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer restoring until","else than `Variable` objects. ValueError: If some arguments are invalid, or var_list is","to avoid infinite recursion with __setattr__. if name == \"_hyper\": raise e #","a restoring initializer. No new variables are created when graph building. Instead, _restore_slot_variable","# to minimize. loss = lambda: 3 * var1 + 2 * var2","`minimize()`. It returns a list of (gradient, variable) pairs where \"gradient\" is the","before applying them you can instead use the optimizer in three steps: 1.","var, slot_name, initializer=\"zeros\"): \"\"\"Add a new slot variable for `var`.\"\"\" if slot_name not","Do whatever you # need to the 'gradient' part, for example cap them,","# See the License for the specific language governing permissions and # limitations","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","we've pulled out an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable)","name is derived from the var shared name. In eager mode the name","None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer,","Example: ```python # Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1)","model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights","opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer. If","= zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient, variable). Do","from this class and override the following methods: - resource_apply_dense (update variable given","if hasattr(self, \"clipvalue\"): config[\"clipvalue\"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None):","indicating the slot variable `Trackable` object to be restored. slot_name: The name of","return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \"\"\"Get the slot key for the","gradients. \"\"\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars]","is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there","name: A non-empty string. The name to use for accumulators created for the","are differentiable). \" \"Common ops without gradient: \" \"K.argmax, K.round, K.eval.\".format(param)) if hasattr(self,","{}).pop(variable_key, []) # Iterate over restores, highest restore UID first to minimize the","tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops","class for optimizers. This class defines the API to add Ops to train","Indices are unique. Returns: An `Operation` which updates the value of the variable.","capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer to","containing the configuration of an optimizer. The same optimizer can be reinstantiated later","deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position,","object (because we don't want to # save the slot variable if the","return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a hyperparameter that can be a float,","not a valid type. \"\"\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype","loss by the global batch size, which is done automatically if you use","to any variable, the constraint will be applied to the variable after the","may be referenced in functions along with ops created by the original optimizer,","opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2])","%s when minimizing the loss.\"), ([v.name for v in vars_with_empty_grads])) return filtered def","the Apache License, Version 2.0 (the \"License\"); # you may not use this","in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu'))","If some arguments are invalid, or var_list is None. \"\"\" # TODO(josh11b): Test","exists. if getattr(var, \"_distributed_container\", None) is not None: var = var._distributed_container() if var._in_graph_mode:","primary variable first. Args: var: the variable. Returns: the unique name of the","you may not use this file except in compliance with the License. #","variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types","+ \"/\" + slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer implementation for checkpoint compatibility.","learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops","A `Tensor` with rank >= 1. indices: A one-dimensional integer `Tensor`, indexing into","which updates the value of the variable. \"\"\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad,","we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable.","self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \"\"\"Get decayed learning rate as a","the Optimizer's config raise NotImplementedError( \"Restoring functional Optimzers from SavedModels is not currently","operation. Returns: An Operation that updates the variables in `var_list`. If `global_step` was","be many times too big. ### Variable Constraint All Keras optimizers respect variable","method must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details.","in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them. Calling `minimize()`","self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created:","= True self._name = name self._hyper = {} # dict: {variable name :","self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value","### Write a customized optimizer. If you intend to create your own optimization","in case slot variables have their own dependencies, in which case # those","gradient function not implemented). \"\"\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss,","the `Optimizer` constructor. Returns: An `Operation` that applies the specified gradients. If `global_step`","and the variables are created at the first time when `loss` is called.","does not exist for variables %s when minimizing the loss.\"), ([v.name for v","of `loss` with respect to `params`. Arguments: loss: Loss tensor. params: List of","ops are # symbolic then the step update should be carried out under","`var_list` contains anything else than `Variable` objects. ValueError: If some arguments are invalid,","zip(grads, params): if grad is None: raise ValueError(\"Variable {} has `None` for gradient.","name}) def _distributed_apply(self, distribution, grads_and_vars, name): \"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to(","import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import","expected: %s.\" % (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid","subclasses, such as `Adam` and `Adagrad` allocate and manage additional variables associated with","`global_step` was not `None`, that operation also increments `global_step`. Raises: ValueError: If some","# pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params):","variable list would otherwise be incomplete before `minimize` and the variables are created","`Optimizer` constructor. Returns: An `Operation` that applies the specified gradients. If `global_step` was","handle weight decay in a reasonable way. with backprop.GradientTape() as tape: if not","+ decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \"\"\"Returns the config of","catch its eagerly created slot variable # unintentionally (specifically make_template would add a","variables of this Optimizer based on the order created.\"\"\" return self._weights def get_weights(self):","lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn,","your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","`Operation` that applies the specified gradients. If `global_step` was not None, that operation","value) def get_slot_names(self): \"\"\"A list of names for this optimizer's slots.\"\"\" return self._slot_names","If _create_slots has been overridden instead of _create_vars. \"\"\" allowed_kwargs = {\"clipnorm\", \"clipvalue\",","backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is","slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the","otherwise be incomplete before `minimize` and the variables are created at the first","context is graph mode or any of the update ops are # symbolic","out an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: #","the non-slot # variable, or if the non-slot variable is saved without the","been added to a dependency graph (causing us to realize that the slot","Keras optimizers. if name == \"lr\": name = \"learning_rate\" if name in self._hyper:","Instead, _restore_slot_variable catches these after normal creation and adds restore ops to the","been created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ],","0, received: {}\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper = {}","self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter out iterable with grad equal","disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads =","trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors are","def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \"\"\"Add ops to apply sparse gradients to `handle`,","dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ:","var_list, grad_loss=None, name=None): \"\"\"Minimize `loss` by updating `var_list`. This method simply computes gradient","@property def weights(self): \"\"\"Returns variables of this Optimizer based on the order created.\"\"\"","they are callable, the callable will be called during `apply_gradients()` to get the","is malformed. ValueError: If none of the variables have gradients. \"\"\" grads_and_vars =","functional by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created =","Returns: Python dictionary. \"\"\" config = {\"name\": self._name} if hasattr(self, \"clipnorm\"): config[\"clipnorm\"] =","{}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter out iterable with grad equal to None.\"\"\"","grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \"\"\"Apply gradient to variable.\"\"\" if","optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables.","with `apply_gradients()`. Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute","**kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip","given variable. Args: loss: A callable taking no arguments which returns the value","simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) #","the order created.\"\"\" return self._weights @property def weights(self): \"\"\"Returns variables of this Optimizer","\" \"passed to optimizer: \" + str(k)) # checks that all keyword arguments","it's a normal # graph. if slot_variable is not None: # If we've","i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For","var_list, grad_loss=None): \"\"\"Compute gradients of `loss` for the variables in `var_list`. This is","handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \"\"\"Add ops to apply sparse gradients","UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True)","self._weights @property def weights(self): \"\"\"Returns variables of this Optimizer based on the order","first dimension of `values` (as in an IndexedSlices object). Returns: A tuple of","of summing. Args: grad: a `Tensor` representing the gradient for the affected indices.","optimizer from its config. This method is the reverse of `get_config`, capable of","variable}} self._slots = {} self._slot_names = [] self._weights = [] self._iterations = None","# ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\"decay\", 0.0) if decay <","ValueError: If none of the variables have gradients. \"\"\" grads_and_vars = _filter_grads(grads_and_vars) var_list","return x.value() # --------------- # For implementing the trackable interface # --------------- def","_ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name}) def","minimize. loss = lambda: 3 * var1 * var1 + 2 * var2","def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \"\"\"Restore a slot variable's value, possibly creating","to be updated. indices: a `Tensor` of integral type representing the indices for","@iterations.setter def iterations(self, variable): if self._iterations is not None: raise RuntimeError(\"Cannot set `iterations`","is saved without the optimizer; # it's a dependency hypergraph with edges of","keyword arguments are non-negative. if kwargs[k] < 0: raise ValueError(\"Expected {} >= 0,","import print_function import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as","are callable, the callable will be called during `apply_gradients()` to get the value","base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values,","if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad,","var_name/slot_name.\"\"\" name = _var_key(var) return name + \"/\" + slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A","tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras","callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if","__getattribute__(self, name): \"\"\"Overridden to support hyperparameter access.\"\"\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError","\" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\"zeros\", trainable=None,","make_template would add a dependency on # a slot variable if not for","# Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor)","train a model. You never use this class directly, but instead instantiate one","instead. This class in stateful and thread-compatible. Args: name: A non-empty string. The","be called by the constructors of subclasses. Note that Optimizer instances should not","{variable name : {slot name : variable}} self._slots = {} self._slot_names = []","if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\"decay\", var_dtype) lr_t","\"\"\" if \"lr\" in config: config[\"learning_rate\"] = config.pop(\"lr\") if \"learning_rate\" in config: if","def _valid_dtypes(self): \"\"\"Valid types for loss, variables and gradients. Subclasses should override to","losses and then divide by the global batch size. Note that when using","have not yet been created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train):","self.clipnorm if hasattr(self, \"clipvalue\"): config[\"clipvalue\"] = self.clipvalue return config @classmethod def from_config(cls, config,","to apply sparse gradients to `handle`, with repeated indices. Optimizers which override this","+ self._name + \" with a weight list of length \" + str(len(weights))","the distributed variable if it exists. if getattr(var, \"_distributed_container\", None) is not None:","of (gradient, variable) pairs where \"gradient\" is the gradient for \"variable\". Note that","\" with a weight list of length \" + str(len(weights)) + \", but","mode the name is derived from the var unique id. If distribution strategy","variable) pairs where \"gradient\" is the gradient for \"variable\". Note that \"gradient\" can","config = {\"name\": self._name} if hasattr(self, \"clipnorm\"): config[\"clipnorm\"] = self.clipnorm if hasattr(self, \"clipvalue\"):","indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor`","var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight","config[\"clipnorm\"] = self.clipnorm if hasattr(self, \"clipvalue\"): config[\"clipvalue\"] = self.clipvalue return config @classmethod def","of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum non-unique indices","value = value() if dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self,","vars_with_empty_grads: logging.warning( (\"Gradients does not exist for variables %s when minimizing the loss.\"),","is sparse tensor, variable constraint is not supported. ### Thread Compatibility The entire","Args: grad: a `Tensor` representing the gradient. handle: a `Tensor` of dtype `resource`","for (_, v) in grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _","ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or any_symbolic: #","callable, tensor, numeric.\"\"\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in","slot_name, variable): \"\"\"Restore a slot variable's value, possibly creating it. Called when a","weight decay in a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list):","batch size. Note that when using `tf.distribute.Strategy`, the first component of a tensor's","import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as","updated. indices: a `Tensor` of integral type representing the indices for which the","the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this method has","name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return","the form (optimizer, non-slot # variable, variable)). So we don't _track_ slot variables","gradients by value, `decay` is included for backward compatibility to allow time inverse","if decay < 0.: raise ValueError(\"decay cannot be less than 0: {}\".format(decay)) self._initial_decay","be called during `apply_gradients()` to get the value for the hyper parameter. Hyper","have a slot name you can ask the optimizer for the variable it","are created at the first time `loss` is called. grad_loss: Optional. A `Tensor`","in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \"\"\"Apply gradient to variable.\"\"\"","for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying","variable): if self._iterations is not None: raise RuntimeError(\"Cannot set `iterations` to a new","apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument","with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in zip(grads, params): if","a slot variable if not for this case). Deferring is mostly harmless #","incomplete before `minimize` and the variables are created at the first time when","to the variable after the gradient has been applied to the variable. Important:","{}\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper = {} # dict:","= loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value,","`iterations` to a new Variable after \" \"the Optimizer weights have been created\")","slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be restored.","def __setattr__(self, name, value): \"\"\"Override setattr to support dynamic hyperparameter setting.\"\"\" # Backwards","name == \"_hyper\": raise e # Backwards compatibility with Keras optimizers. if name","grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return grads","which the gradient is nonzero. Indices are unique. Returns: An `Operation` which updates","of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss,","= tf_variables.Variable( name=\"%s/%s\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight)","indices and their associated gradients, is enforced by first pre-processing `grad` and `indices`","shape \" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\"zeros\",","a `Tensor`, an `IndexedSlices`, or `None` if there is no gradient for the","indices: a `Tensor` of integral type representing the indices for which the gradient","an optimizer from its config. This method is the reverse of `get_config`, capable","tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops","A `Tensor` holding the gradient computed for `loss`. name: Optional name for the","synced on read. trainable = False elif trainable is None: trainable = True","restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter out iterable with grad","is the first part of `minimize()`. It returns a list of (gradient, variable)","`minimize` and the variables are created at the first time when `loss` is","to # save the slot variable if the optimizer is saved without the","slot variable if the optimizer is saved without the non-slot # variable, or","is the *replica-local* batch size, which is off by a factor equal to","the variable. \"\"\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle,","slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper","trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name] = value else:","there is an active variable creator # scope. Generally we'd like to eagerly","then passed to `self._set_hyper()`. They can be either regular Python values (like 1.0),","instance. \"\"\" if \"lr\" in config: config[\"learning_rate\"] = config.pop(\"lr\") if \"learning_rate\" in config:","class is `tf.distribute.Strategy` aware, which means it automatically sums gradients across all replicas.","def _decayed_lr(self, var_dtype): \"\"\"Get decayed learning rate as a Tensor with dtype=var_dtype.\"\"\" lr_t","from __future__ import absolute_import from __future__ import division from __future__ import print_function import","form (optimizer, non-slot # variable, variable)). So we don't _track_ slot variables anywhere,","variable: %s.\" % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\"Gradients","config: config[\"learning_rate\"] = config.pop(\"lr\") if \"learning_rate\" in config: if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] =","implemented). \"\"\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad,","{}) weight = slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types) or","None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if","Tensor.\"\"\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value()","along with ops created by the original optimizer, but currently we do not","tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base","as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list =","contains anything else than `Variable` objects. ValueError: If some arguments are invalid, or","== \"_hyper\": raise e # Backwards compatibility with Keras optimizers. if name ==","for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list","a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value =","tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \"\"\"Sums `values` associated with any non-unique `indices`.","should be able to use the _set_hyper()/state.get_hyper() facility instead. This class in stateful","if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name] =","grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in","get_gradients(self, loss, params): \"\"\"Returns gradients of `loss` with respect to `params`. Arguments: loss:","type representing the indices for which the gradient is nonzero. Indices are unique.","non-negative. if kwargs[k] < 0: raise ValueError(\"Expected {} >= 0, received: {}\".format(k, kwargs[k]))","dictionary. Arguments: config: A Python dictionary, typically the output of get_config. custom_objects: A","< 0.: raise ValueError(\"decay cannot be less than 0: {}\".format(decay)) self._initial_decay = decay","variables may be referenced in functions along with ops created by the original","def _create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically. for name, value","slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var,","self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not None and","can ask the optimizer for the variable it created to hold the slot","backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops","after \" \"the Optimizer weights have been created\") self._iterations = variable self._weights.append(self._iterations) def","\"\"\"Get the slot key for the variable: var_name/slot_name.\"\"\" name = _var_key(var) return name","var_dtype): \"\"\"Get decayed learning rate as a Tensor with dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\",","self._slot_names = [] self._weights = [] self._iterations = None # For implementing Trackable.","gradient computed for `loss`. name: Optional name for the returned operation. Returns: An","the variables. If you want to process the gradients before applying them you","If `global_step` was not `None`, that operation also increments `global_step`. Raises: ValueError: If","= distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars","backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self,","# to minimize. loss = lambda: 3 * var1 * var1 + 2","dependency graph (causing us to realize that the slot variable needs to be","\"/\" + slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer implementation for checkpoint compatibility. Holds","self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name}) def _distributed_apply(self, distribution, grads_and_vars, name):","from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import","getattr(var, \"_distributed_container\", None) is not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name","implementation for checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer is","to variable.\"\"\" if isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying to update a Tensor \", var)","hyperparameter setting.\"\"\" # Backwards compatibility with Keras optimizers. if name == \"lr\": name","_track_ slot variables anywhere, and # instead special-case this dependency and otherwise pretend","variables are created when graph building. Instead, _restore_slot_variable catches these after normal creation","grad_loss=None, name=None): \"\"\"Minimize `loss` by updating `var_list`. This method simply computes gradient using","grad, var in grads_and_vars: scope_name = (\"\" if ops.executing_eagerly_outside_functions() else \"_\" + var.op.name)","So we don't _track_ slot variables anywhere, and # instead special-case this dependency","construction time. Examples include 1) sequential models without input shape pre-defined, or 2)","first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for","i in update_ops) if not context.executing_eagerly() or any_symbolic: # If the current context","# scope. Generally we'd like to eagerly create/restore slot variables # when possible,","slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by","for the returned operation. Default to the name passed to the `Optimizer` constructor.","by a factor equal to the number of replicas being used to compute","== \"lr\": name = \"learning_rate\" if name in self._hyper: return self._get_hyper(name) raise e","for grad, param in zip(grads, params): if grad is None: raise ValueError(\"Variable {}","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of","minimize(self, loss, var_list, grad_loss=None, name=None): \"\"\"Minimize `loss` by updating `var_list`. This method simply","opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with Keras models In Keras","tensor's shape is the *replica-local* batch size, which is off by a factor","to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training","computing the gradients and applying them to the variables. If you want to","`loss` with respect to `params`. Arguments: loss: Loss tensor. params: List of variables.","variables %s when minimizing the loss.\"), ([v.name for v in vars_with_empty_grads])) return filtered","An `Operation` which updates the value of the variable. \"\"\" summed_grad, unique_indices =","this class and override the following methods: - resource_apply_dense (update variable given gradient","whatever you # need to the 'gradient' part, for example cap them, etc.","= [] self._iterations = None # For implementing Trackable. Stores information about how","return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share","if self._iterations is None: self._iterations = self.add_weight( \"iter\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations)","0.: raise ValueError(\"decay cannot be less than 0: {}\".format(decay)) self._initial_decay = decay if","differentiable). \" \"Common ops without gradient: \" \"K.argmax, K.round, K.eval.\".format(param)) if hasattr(self, \"clipnorm\"):","var: the variable. Returns: the unique name of the variable. \"\"\" # pylint:","processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars","evaluation loops. See the `reduction` argument of your loss which should be set","is called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. Returns:","output of get_config. custom_objects: A Python dictionary mapping names to additional Python objects","return value def variables(self): \"\"\"Returns variables of this Optimizer based on the order","is to be synced on read. trainable = False elif trainable is None:","isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable:","restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer restoring","# pylint: disable=protected-access # Get the distributed variable if it exists. if getattr(var,","config is a Python dictionary (serializable) containing the configuration of an optimizer. The","lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t","self._get_hyper(\"decay\", var_dtype) lr_t = lr_t / (1. + decay_t * local_step) return lr_t","Deferring is mostly harmless # (aside from double initialization), and makes variable creator","# those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars):","v in vars_with_empty_grads])) return filtered def _var_key(var): \"\"\"Key for representing a primary variable,","Iterate over restores, highest restore UID first to minimize the number # of","graph building. Instead, _restore_slot_variable catches these after normal creation and adds restore ops","just been added to a dependency graph (causing us to realize that the","equal to None.\"\"\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered =","slot_name=slot_name) # Slot variables are not owned by any one object (because we","of integral type representing the indices for which the gradient is nonzero. Indices","raise ValueError(\"Expected {} >= 0, received: {}\".format(k, kwargs[k])) self._use_locking = True self._name =","Use callable when the variable list would otherwise be incomplete before `minimize` since","a constraint function on a sparse variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op","if name not in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if","the gradient computed for `loss`. Returns: A list of (gradient, variable) pairs. Variable","the variables in `var_list`. This is the first part of `minimize()`. It returns","synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \"Synchronization value can be set to","handle, indices): \"\"\"Add ops to apply sparse gradients to `handle`, with repeated indices.","variable: var_name/slot_name.\"\"\" name = _var_key(var) return name + \"/\" + slot_name class _RestoredOptimizer(OptimizerV2):","models In Keras models, sometimes variables are created when the model is first",": variable}} self._slots = {} self._slot_names = [] self._weights = [] self._iterations =","to restore into. variable: The variable object this slot is being created for.","Process the gradients as you wish. 3. Apply the processed gradients with `apply_gradients()`.","self._name = name self._hyper = {} # dict: {variable name : {slot name","the variable list would otherwise be incomplete before `minimize` since the variables are","str(weights)[:50] + \"...\") if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params)","affected indices. handle: a `Tensor` of dtype `resource` which points to the variable","# methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created = True def get_config(self): # TODO(allenl):","See the License for the specific language governing permissions and # limitations under","v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \"\"\"Returns gradients of","initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight","not None: # If we've either made this slot variable, or if we've","optimizer is restored from a SavedModel. These variables may be referenced in functions","currently thread compatible, not thread-safe. The user needs to perform synchronization if necessary.","is clip gradients by value, `decay` is included for backward compatibility to allow","non-unique indices and their associated gradients, is enforced by first pre-processing `grad` and","to compute a single step. As a result, using `tf.math.reduce_mean` will give the","grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered","of `indices` and `summed_values` contains the sum of `values` slices associated with each","to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer","self.clipnorm) for g in grads] if hasattr(self, \"clipvalue\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue,","distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager","var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \"\"\"Get the slot key for the variable:","an active variable creator # scope. Generally we'd like to eagerly create/restore slot","uses. Once you have a slot name you can ask the optimizer for","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","(without any saved state) from this configuration. Returns: Python dictionary. \"\"\" config =","ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer,","hasattr(self, \"clipnorm\"): config[\"clipnorm\"] = self.clipnorm if hasattr(self, \"clipvalue\"): config[\"clipvalue\"] = self.clipvalue return config","governing permissions and # limitations under the License. # ============================================================================== \"\"\"Version 2 of","shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations","Set trainable to be false when variable is to be synced on read.","new slot variable for `var`.\"\"\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key =","= False def minimize(self, loss, var_list, grad_loss=None, name=None): \"\"\"Minimize `loss` by updating `var_list`.","as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads","= self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step),","hyper parameters) \"\"\" def __init__(self, name, **kwargs): \"\"\"Create a new Optimizer. This must","trainable = False elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter(","= True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True,","that can be a float, callable, or Tensor.\"\"\" value = self._hyper[hyperparameter_name] if isinstance(value,","a list of (gradient, variable) pairs where \"gradient\" is the gradient for \"variable\".","dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be","opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with","self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None): \"\"\"Minimize `loss` by updating","+ \"...\") if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for","str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE):","the optimizer object iself (e.g. through `apply_gradients`). \"\"\" # TODO(allenl): Make the restored","the callable will be called during `apply_gradients()` to get the value for the","(trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, #","get_config (serialization of the optimizer, include all hyper parameters) \"\"\" def __init__(self, name,","+ 2 * var2 # In eager mode, simply call minimize to update","none of the variables have gradients. \"\"\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v","config[\"learning_rate\"] = config.pop(\"lr\") if \"learning_rate\" in config: if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize(","-self.clipvalue, self.clipvalue) for g in grads ] return grads def apply_gradients(self, grads_and_vars, name=None):","simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want to process","weights): params = self.weights if len(params) != len(weights): raise ValueError( \"You called `set_weights(weights)`","been overridden instead of _create_vars. \"\"\" allowed_kwargs = {\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"} for","summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad,","position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable):","if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] =","with any non-unique `indices`. Args: values: A `Tensor` with rank >= 1. indices:","possibly creating it. Called when a variable which has an associated slot variable","* var2 # In graph mode, returns op that minimizes the loss by","return var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name or self._name): for","checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \"\"\"Restore a slot","the optimimizer. An optimizer config is a Python dictionary (serializable) containing the configuration","restored. slot_name: The name of this `Optimizer`'s slot to restore into. variable: The","_filter_grads(grads_and_vars): \"\"\"Filter out iterable with grad equal to None.\"\"\" grads_and_vars = tuple(grads_and_vars) if","the specific language governing permissions and # limitations under the License. # ==============================================================================","care of both computing the gradients and applying them to the variables. If","hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value):","v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]):","tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of `indices` and","constraint will be applied to the variable after the gradient has been applied","tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises:","can be useful if you want to log debug a training algorithm, report","A Python dictionary mapping names to additional Python objects used to create this","the sum of `values` slices associated with each unique index. \"\"\" unique_indices, new_index_positions","learning rate as a Tensor with dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t,","# --------------- # For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name,","to average gradients, you should use `tf.math.reduce_sum` to add up your per-example losses","backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name = (\"\" if ops.executing_eagerly_outside_functions()","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","Arguments: config: A Python dictionary, typically the output of get_config. custom_objects: A Python","resource_apply_sparse (update variable given gradient tensor is sparse) - create_slots (if your optimizer","name): \"\"\"Overridden to support hyperparameter access.\"\"\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as","malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars. \"\"\" allowed_kwargs =","self._iterations = self.add_weight( \"iter\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def","active variable creator # scope. Generally we'd like to eagerly create/restore slot variables","sparse variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint","obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper # pylint:","indices. Optimizers which override this method must deal with repeated indices. See the","g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of","[] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is None:","and then divide by the global batch size. Note that when using `tf.distribute.Strategy`,","trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not","tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables","`loss` is a callable that takes no argument and returns the value #","# {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations = {}","is None. \"\"\" # TODO(josh11b): Test that we handle weight decay in a","context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an active","such as a function used for a hyperparameter. Returns: An optimizer instance. \"\"\"","this class directly, but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`,","facility instead. This class in stateful and thread-compatible. Args: name: A non-empty string.","# dict: {variable name : {slot name : variable}} self._slots = {} self._slot_names","def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a hyperparameter that can be a float, callable, or","trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \"\"\"Restore a newly created","= self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name}) def _distributed_apply(self,","building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable =","dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \"\"\"Call the function if param is callable.\"\"\" return","NotImplementedError( \"Restoring functional Optimzers from SavedModels is not currently \" \"supported. Please file","second part of `minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars:","gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this","def get_gradients(self, loss, params): \"\"\"Returns gradients of `loss` with respect to `params`. Arguments:","`tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with the desired parameters. opt","to optimizer: \" + str(k)) # checks that all keyword arguments are non-negative.","sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[],","strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\"%s/%s\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False,","class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and","# Slot variables are not owned by any one object (because we don't","them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead","of the variable. \"\"\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle,","using `tf.math.reduce_mean` will give the wrong answer, resulting in gradients that can be","e # Backwards compatibility with Keras optimizers. if name == \"lr\": name =","params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer.","activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for","with duplicate indices may instead override this method to avoid the overhead of","name of this `Optimizer`'s slot to restore into. variable: The variable object this","for gradient. \" \"Please make sure that all of your ops have a","simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ```","`apply_gradients()`. If you want to process the gradient before applying then call `tf.GradientTape`","Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with","`Tensor` holding the gradient computed for `loss`. Returns: A list of (gradient, variable)","### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means it","clip gradients by value, `decay` is included for backward compatibility to allow time","def _set_hyper(self, name, value): \"\"\"set hyper `name` to value. value can be callable,","set_weights(self, weights): params = self.weights if len(params) != len(weights): raise ValueError( \"You called","increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of the","!= dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \"\"\"set hyper `name` to","the variables are not `Variable` objects. \"\"\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss)","of instantiating the same optimizer from the config dictionary. Arguments: config: A Python","update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a","catch # `variable` also catch its eagerly created slot variable # unintentionally (specifically","self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None): \"\"\"Minimize","deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID","None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError:","cannot be computed (e.g. if gradient function not implemented). \"\"\" params = nest.flatten(params)","self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return","slices associated with each unique index. \"\"\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values =","not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list =","for backward compatibility to allow time inverse decay of learning rate. `lr` is","\"K.argmax, K.round, K.eval.\".format(param)) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in","variables. This is the second part of `minimize()`. It returns an `Operation` that","the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager","reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework","float types. Returns: Valid types for loss, variables and gradients. \"\"\" return set(","var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered =","ValueError: If any tensor is not a valid type. \"\"\" valid_dtypes = self._valid_dtypes()","\"you.\") revived_types.register_revived_type( \"optimizer\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1,","import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import","\"\"\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param","cannot be less than 0: {}\".format(decay)) self._initial_decay = decay if \"clipnorm\" in kwargs:","have been created\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \"\"\"Get decayed learning","weight list of length \" + str(len(weights)) + \", but the optimizer was","the variable. \"\"\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices)","```python # Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) #","loss.\"), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): \"\"\"Key for representing","Holds slot variables and hyperparameters when an optimizer is restored from a SavedModel.","``` ### Custom training loop with Keras models In Keras models, sometimes variables","value() if dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \"\"\"Overridden","to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable`","self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name]","possible, but this may mean that scopes intended to catch # `variable` also","initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \"\"\"Variable. The number of training","# of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def","a result, using `tf.math.reduce_mean` will give the wrong answer, resulting in gradients that","initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def","the slot key for the variable: var_name/slot_name.\"\"\" name = _var_key(var) return name +","Test that we handle weight decay in a reasonable way. with backprop.GradientTape() as","of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads","var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any one object","as you wish. 3. Apply the processed gradients with `apply_gradients()`. Example: ```python #","value # to minimize. loss = lambda: 3 * var1 * var1 +","KIND, either express or implied. # See the License for the specific language","self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value =","that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional name","the variable. Returns: the unique name of the variable. \"\"\" # pylint: disable=protected-access","otherwise be incomplete before `minimize` since the variables are created at the first","\"learning_rate\" if hasattr(self, \"_hyper\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name,","to restore # slot variables which have not yet been created # (trackable._CheckpointPosition","% (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight","minimizing the loss.\"), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): \"\"\"Key","eagerly create/restore slot variables # when possible, but this may mean that scopes","own optimization algorithm, simply inherit from this class and override the following methods:","import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import","params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in","in `var_list`. If `global_step` was not `None`, that operation also increments `global_step`. Raises:","gradients as you wish. 3. Apply the processed gradients with `apply_gradients()`. Example: ```python","0: raise ValueError(\"Expected {} >= 0, received: {}\".format(k, kwargs[k])) self._use_locking = True self._name","variable to be updated. indices: a `Tensor` of integral type representing the indices","Similar to `_apply_sparse`, the `indices` argument to this method has been de-duplicated. Optimizers","iterations(self): \"\"\"Variable. The number of training steps this Optimizer has run.\"\"\" if self._iterations","this optimizer's slots.\"\"\" return self._slot_names def add_slot(self, var, slot_name, initializer=\"zeros\"): \"\"\"Add a new","any_symbolic: # If the current context is graph mode or any of the","sometimes variables are created when the model is first called, instead of construction","that it uses. Once you have a slot name you can ask the","any gradient cannot be computed (e.g. if gradient function not implemented). \"\"\" params","loss, params): \"\"\"Returns gradients of `loss` with respect to `params`. Arguments: loss: Loss","shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def","\" \"provided weight shape \" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name,","graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable","to update to minimize `loss`, or a callable returning the list or tuple","by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() #","= initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy","argument to this method has been de-duplicated. Optimizers which deal correctly with non-unique","callable taking no arguments which returns the value to minimize. var_list: list or","TODO(josh11b): Test that we handle weight decay in a reasonable way. with backprop.GradientTape()","custom_objects=None): \"\"\"Creates an optimizer from its config. This method is the reverse of","the `indices` argument to this method has been de-duplicated. Optimizers which deal correctly","its config. This method is the reverse of `get_config`, capable of instantiating the","loss = lambda: 3 * var1 * var1 + 2 * var2 *","simply inherit from this class and override the following methods: - resource_apply_dense (update","to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position","is derived from the var shared name. In eager mode the name is","In eager mode the name is derived from the var unique id. If","k not in allowed_kwargs: raise TypeError(\"Unexpected keyword argument \" \"passed to optimizer: \"","keras_export def _deduplicate_indexed_slices(values, indices): \"\"\"Sums `values` associated with any non-unique `indices`. Args: values:","numeric.\"\"\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name]","this Optimizer based on the order created.\"\"\" return self._weights def get_weights(self): params =","`clipvalue` is clip gradients by value, `decay` is included for backward compatibility to","instead of using this function. Args: loss: A callable taking no arguments which","with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\":","If the current context is graph mode or any of the update ops","if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape,","\"\"\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]):","raise e # Backwards compatibility with Keras optimizers. if name == \"lr\": name","override the following methods: - resource_apply_dense (update variable given gradient tensor is dense)","thread compatible, not thread-safe. The user needs to perform synchronization if necessary. ###","minimize `loss`, or a callable returning the list or tuple of `Variable` objects.","initializer. No new variables are created when graph building. Instead, _restore_slot_variable catches these","until it gets created # normally. We keep a list rather than the","ANY KIND, either express or implied. # See the License for the specific","loops. See the `reduction` argument of your loss which should be set to","or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None):","parameters can be overwritten through user code: Example: ```python # Create an optimizer","If some of the variables are not `Variable` objects. \"\"\" grads_and_vars = self._compute_gradients(","at the first time `loss` is called. grad_loss: Optional. A `Tensor` holding the","str(k)) # checks that all keyword arguments are non-negative. if kwargs[k] < 0:","distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i","(`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of `indices` and `summed_values` contains","gradient is sparse tensor, variable constraint is not supported. ### Thread Compatibility The","backward compatibility to allow time inverse decay of learning rate. `lr` is included","= nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g,","decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \"\"\"Returns the config of the","three steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process the gradients as","tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list()","manage additional variables associated with the variables to train. These are called <i>Slots</i>.","### Custom training loop with Keras models In Keras models, sometimes variables are","report stats about the slots, etc. ### Hyper parameters These are arguments passed","unique id. If distribution strategy exists, get the primary variable first. Args: var:","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See","the API to add Ops to train a model. You never use this","`handle`, with repeated indices. Optimizers which override this method must deal with repeated","\"\"\" # TODO(josh11b): Test that we handle weight decay in a reasonable way.","strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\"%s/%s\" % (var._shared_name, slot_name), #","update a Tensor \", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None:","of an optimizer. The same optimizer can be reinstantiated later (without any saved","for the hyper parameter. Hyper parameters can be overwritten through user code: Example:","`loss`. name: Optional name for the returned operation. Returns: An Operation that updates","computed (e.g. if gradient function not implemented). \"\"\" params = nest.flatten(params) with backend.get_graph().as_default():","x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x,","The name of this `Optimizer`'s slot to restore into. variable: The variable object","gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware,","case any gradient cannot be computed (e.g. if gradient function not implemented). \"\"\"","case when a slot variable has already been created but `variable` has just","def set_weights(self, weights): params = self.weights if len(params) != len(weights): raise ValueError( \"You","variable it created to hold the slot value. This can be useful if","slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass","param): \"\"\"Call the function if param is callable.\"\"\" return param() if callable(param) else","gradients. If `global_step` was not None, that operation also increments `global_step`. Raises: TypeError:","with backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name = (\"\" if","logic with base_layer. def set_weights(self, weights): params = self.weights if len(params) != len(weights):","or tuple of `Variable` objects to update to minimize `loss`, or a callable","be useful if you want to log debug a training algorithm, report stats","var._unique_id def _get_slot_key_from_var(var, slot_name): \"\"\"Get the slot key for the variable: var_name/slot_name.\"\"\" name","a function used for a hyperparameter. Returns: An optimizer instance. \"\"\" if \"lr\"","if not context.executing_eagerly() or any_symbolic: # If the current context is graph mode","the gradient has been applied to the variable. Important: If gradient is sparse","SavedModels is not currently \" \"supported. Please file a feature request if this","__future__ import absolute_import from __future__ import division from __future__ import print_function import abc","lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients","where \"gradient\" is the gradient for \"variable\". Note that \"gradient\" can be a","request if this limitation bothers \" \"you.\") revived_types.register_revived_type( \"optimizer\", lambda obj: isinstance(obj, OptimizerV2),","None) is not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id","equal to the number of replicas being used to compute a single step.","was not None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is","created at the first time when `loss` is called. grad_loss: Optional. A `Tensor`","tuple(filtered) if not filtered: raise ValueError(\"No gradients provided for any variable: %s.\" %","such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with the","\"\"\"Sums `values` associated with any non-unique `indices`. Args: values: A `Tensor` with rank","raise ValueError(\"No gradients provided for any variable: %s.\" % ([v.name for _, v","vars_with_empty_grads])) return filtered def _var_key(var): \"\"\"Key for representing a primary variable, for looking","Thread Compatibility The entire optimizer is currently thread compatible, not thread-safe. The user","Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can","% (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid types for","the case when a slot variable has already been created but `variable` has","for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`,","# variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply","The number of training steps this Optimizer has run.\"\"\" if self._iterations is None:","+ \" not compatible with \" \"provided weight shape \" + str(w.shape)) weight_value_tuples.append((p,","this case). Deferring is mostly harmless # (aside from double initialization), and makes","the global batch size. Note that when using `tf.distribute.Strategy`, the first component of","returning the list or tuple of `Variable` objects. Use callable when the variable","self._name} if hasattr(self, \"clipnorm\"): config[\"clipnorm\"] = self.clipnorm if hasattr(self, \"clipvalue\"): config[\"clipvalue\"] = self.clipvalue","If `var_list` contains anything else than `Variable` objects. ValueError: If some arguments are","\"\"\"Create a new Optimizer. This must be called by the constructors of subclasses.","number of replicas being used to compute a single step. As a result,","don't want to # save the slot variable if the optimizer is saved","\"decay\"} for k in kwargs: if k not in allowed_kwargs: raise TypeError(\"Unexpected keyword","All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the","= _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices):","trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True,","This is the first part of `minimize()`. It returns a list of (gradient,","Usage ```python # Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1)","\" weights. Provided weights: \" + str(weights)[:50] + \"...\") if not params: return","time inverse decay of learning rate. `lr` is included for backward compatibility, recommended","constructor. Returns: An `Operation` that applies the specified gradients. If `global_step` was not","if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply,","grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g","] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if","hasattr(self, \"clipvalue\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ]","optimizer class is `tf.distribute.Strategy` aware, which means it automatically sums gradients across all","in allowed_kwargs: raise TypeError(\"Unexpected keyword argument \" \"passed to optimizer: \" + str(k))","None: self._iterations = self.add_weight( \"iter\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter","the *replica-local* batch size, which is off by a factor equal to the","optimizers. if name == \"lr\": name = \"learning_rate\" if hasattr(self, \"_hyper\") and name","Ops to train a model. You never use this class directly, but instead","is mostly harmless # (aside from double initialization), and makes variable creator scopes","want to # save the slot variable if the optimizer is saved without","slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer restoring until it","function if param is callable.\"\"\" return param() if callable(param) else param def _resource_apply_dense(self,","else: return value def __getattribute__(self, name): \"\"\"Overridden to support hyperparameter access.\"\"\" try: return","vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in","or if the non-slot variable is saved without the optimizer; # it's a","[] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad,","can ask the optimizer for the names of the slots that it uses.","if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \"\"\"Returns","Optimizer. This must be called by the constructors of subclasses. Note that Optimizer","= {\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"} for k in kwargs: if k not in","if it exists. if getattr(var, \"_distributed_container\", None) is not None: var = var._distributed_container()","dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \"\"\"set hyper `name` to value.","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","the slots that it uses. Once you have a slot name you can","are not owned by any one object (because we don't want to #","for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not using these and","as e: # Needed to avoid infinite recursion with __setattr__. if name ==","variable is to be synced on read. trainable = False elif trainable is","opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer. If you intend to","A `Tensor` holding the gradient computed for `loss`. Returns: A list of (gradient,","respect to `params`. Arguments: loss: Loss tensor. params: List of variables. Returns: List","import absolute_import from __future__ import division from __future__ import print_function import abc import","been created\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \"\"\"Get decayed learning rate","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","def get_config(self): \"\"\"Returns the config of the optimimizer. An optimizer config is a","saved state) from this configuration. Returns: Python dictionary. \"\"\" config = {\"name\": self._name}","_resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \"\"\"Add ops to apply sparse gradients to `handle`, with","super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore the","Indices may be repeated. Returns: An `Operation` which updates the value of the","unintentionally (specifically make_template would add a dependency on # a slot variable if","and # instead special-case this dependency and otherwise pretend it's a normal #","means it automatically sums gradients across all replicas. To average gradients, you divide","types for loss, variables and gradients. Subclasses should override to allow other float","isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if dtype: return math_ops.cast(value,","for looking up slots. In graph mode the name is derived from the","config: if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self,","value to minimize. var_list: list or tuple of `Variable` objects to update to","instead override this method to avoid the overhead of summing. Args: grad: a","Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm;","tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or any_symbolic: # If the","else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\"%s/%s\"","applicable law or agreed to in writing, software # distributed under the License","list or tuple of `Variable` objects. Use callable when the variable list would","Optional name for the returned operation. Returns: An Operation that updates the variables","isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \"Cannot use a constraint","\"\"\" config = {\"name\": self._name} if hasattr(self, \"clipnorm\"): config[\"clipnorm\"] = self.clipnorm if hasattr(self,","decayed learning rate as a Tensor with dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\", var_dtype) if","variable constraint is not supported. ### Thread Compatibility The entire optimizer is currently","list of (gradient, variable) pairs. Variable is always present, but gradient can be","return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars","for not. If you are not using these and you want to average","key for the variable: var_name/slot_name.\"\"\" name = _var_key(var) return name + \"/\" +","a de-duplicated version of `indices` and `summed_values` contains the sum of `values` slices","optimization algorithm, simply inherit from this class and override the following methods: -","from __future__ import division from __future__ import print_function import abc import functools import","1.0), tensors, or callables. If they are callable, the callable will be called","the indices for which the gradient is nonzero. Indices are unique. Returns: An","[v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad):","(specifically make_template would add a dependency on # a slot variable if not","should use `tf.math.reduce_sum` to add up your per-example losses and then divide by","Slots Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage additional","<call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g","distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars =","var_list) # grads_and_vars is a list of tuples (gradient, variable). Do whatever you","initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \"Synchronization value can be","\"provided weight shape \" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape,","incomplete before `minimize` since the variables are created at the first time `loss`","grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars =","opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda:","if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops","or Tensor.\"\"\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return","in grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply gradients to variables.","CONDITIONS OF ANY KIND, either express or implied. # See the License for","params, weights): if pv.shape != w.shape: raise ValueError(\"Optimizer weight shape \" + str(pv.shape)","a callable returning the list or tuple of `Variable` objects. Use callable when","points to the variable to be updated. Returns: An `Operation` which updates the","(summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated base class for optimizers. This class","algorithm, report stats about the slots, etc. ### Hyper parameters These are arguments","`var_list`. This method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you","We keep a list rather than the one with the highest restore #","grad, handle, indices): \"\"\"Add ops to apply sparse gradients to the variable `handle`.","x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- #","your loss by the global batch size, which is done automatically if you","across all replicas. To average gradients, you divide your loss by the global","this logic with base_layer. def set_weights(self, weights): params = self.weights if len(params) !=","ValueError( \"You called `set_weights(weights)` on optimizer \" + self._name + \" with a","position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name,","for. \"\"\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None)","math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated base","grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \"\"\"Compute gradients of `loss`","before applying them. Calling `minimize()` takes care of both computing the gradients and","if name == \"lr\": name = \"learning_rate\" if name in self._hyper: return self._get_hyper(name)","_call_if_callable(self, param): \"\"\"Call the function if param is callable.\"\"\" return param() if callable(param)","or restored. When executing eagerly, we create the slot variable with a restoring","if the non-slot variable is saved without the optimizer; # it's a dependency","variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict","value can be set to \" \"VariableSynchronization.ON_READ only for non-trainable variables. \" \"You","optimizer. The same optimizer can be reinstantiated later (without any saved state) from","in `var_list`. This is the first part of `minimize()`. It returns a list","is not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def","of this Optimizer based on the order created.\"\"\" return self._weights def get_weights(self): params","this limitation bothers \" \"you.\") revived_types.register_revived_type( \"optimizer\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda","writing, software # distributed under the License is distributed on an \"AS IS\"","= initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\"%s/%s\" % (var._shared_name,","hyperparameter_name): \"\"\"Serialize a hyperparameter that can be a float, callable, or Tensor.\"\"\" value","{} >= 0, received: {}\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper","disable=g-bad-name from __future__ import absolute_import from __future__ import division from __future__ import print_function","= name self._hyper = {} # dict: {variable name : {slot name :","\"\"\"Minimize `loss` by updating `var_list`. This method simply computes gradient using `tf.GradientTape` and","about how to restore # slot variables which have not yet been created","= config.pop(\"lr\") if \"learning_rate\" in config: if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"],","apply sparse gradients to `handle`, with repeated indices. Optimizers which override this method","a valid type. \"\"\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype =","{}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable()","else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \"\"\"A list of names for this optimizer's","The user needs to perform synchronization if necessary. ### Slots Many optimizer subclasses,","for any variable: %s.\" % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads:","optimizer \" + self._name + \" with a weight list of length \"","not in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value) or","defined (i.e. are differentiable). \" \"Common ops without gradient: \" \"K.argmax, K.round, K.eval.\".format(param))","self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def","inherit from this class and override the following methods: - resource_apply_dense (update variable","must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By","to minimize `loss`, or a callable returning the list or tuple of `Variable`","compliance with the License. # You may obtain a copy of the License","else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or","nonzero. Indices may be repeated. Returns: An `Operation` which updates the value of","decay < 0.: raise ValueError(\"decay cannot be less than 0: {}\".format(decay)) self._initial_decay =","(gradient, variable) pairs. name: Optional name for the returned operation. Default to the","not None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed.","var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype)","self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any one","restored. When executing eagerly, we create the slot variable with a restoring initializer.","a list of tuples (gradient, variable). Do whatever you # need to the","`Operation` which updates the value of the variable. \"\"\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self,","make sure that all of your ops have a \" \"gradient defined (i.e.","_var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if","de-duplicated version of `indices` and `summed_values` contains the sum of `values` slices associated","up your per-example losses and then divide by the global batch size. Note","return grads_and_vars def get_gradients(self, loss, params): \"\"\"Returns gradients of `loss` with respect to","is restored from a SavedModel. These variables may be referenced in functions along","ValueError( \"Synchronization value can be set to \" \"VariableSynchronization.ON_READ only for non-trainable variables.","and manage additional variables associated with the variables to train. These are called","self._name): for grad, var in grads_and_vars: scope_name = (\"\" if ops.executing_eagerly_outside_functions() else \"_\"","params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not None","self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \"\"\"Creates an optimizer from its","through `apply_gradients`). \"\"\" # TODO(allenl): Make the restored optimizer functional by tracing its","= None # For implementing Trackable. Stores information about how to restore #","want to process the gradients before applying them you can instead use the","variables are not `Variable` objects. \"\"\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return","to log debug a training algorithm, report stats about the slots, etc. ###","Keras optimizers. if name == \"lr\": name = \"learning_rate\" if hasattr(self, \"_hyper\") and","recursion with __setattr__. if name == \"_hyper\": raise e # Backwards compatibility with","def weights(self): \"\"\"Returns variables of this Optimizer based on the order created.\"\"\" return","= self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any","with edges of the form (optimizer, non-slot # variable, variable)). So we don't","__init__(self, name, **kwargs): \"\"\"Create a new Optimizer. This must be called by the","exist for variables %s when minimizing the loss.\"), ([v.name for v in vars_with_empty_grads]))","check. Raises: ValueError: If any tensor is not a valid type. \"\"\" valid_dtypes","repeated. Returns: An `Operation` which updates the value of the variable. \"\"\" summed_grad,","import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import","compatibility with Keras optimizers. if name == \"lr\": name = \"learning_rate\" if name","the name passed to the `Optimizer` constructor. Returns: An `Operation` that applies the","the optimizer was expecting \" + str(len(params)) + \" weights. Provided weights: \"","loss, variables and gradients. \"\"\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self,","tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\"Invalid type %r","value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule))","\"_distributed_container\", None) is not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return","valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype not","arguments are non-negative. if kwargs[k] < 0: raise ValueError(\"Expected {} >= 0, received:","This is the second part of `minimize()`. It returns an `Operation` that applies","avoid this overhead. Args: grad: a `Tensor` representing the gradient for the affected","return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \"\"\"`apply_gradients` using","isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return","An optimizer config is a Python dictionary (serializable) containing the configuration of an","is first called, instead of construction time. Examples include 1) sequential models without","scopes # behave the same way they do when graph building. and not","automatically sums gradients across all replicas. To average gradients, you divide your loss","subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with","`minimize()` takes care of both computing the gradients and applying them to the","grad, handle): \"\"\"Add ops to apply dense gradients to the variable `handle`. Args:","the highest restore # UID in case slot variables have their own dependencies,","called <i>Slots</i>. Slots have names and you can ask the optimizer for the","optimizer for the variable it created to hold the slot value. This can","of (gradient, variable) pairs. Variable is always present, but gradient can be `None`.","if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name):","for t in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise","are called <i>Slots</i>. Slots have names and you can ask the optimizer for","%s, expected: %s.\" % (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self):","# In graph mode, returns op that minimizes the loss by updating the","which updates the value of the variable. \"\"\" raise NotImplementedError() def _resource_scatter_add(self, x,","= [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer to apply","up slots. In graph mode the name is derived from the var shared","Returns: An Operation that updates the variables in `var_list`. If `global_step` was not","value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \"\"\"Returns variables of this","indices): \"\"\"Add ops to apply sparse gradients to the variable `handle`. Similar to","setattr to support dynamic hyperparameter setting.\"\"\" # Backwards compatibility with Keras optimizers. if","is always present, but gradient can be `None`. Raises: TypeError: If `var_list` contains","= _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] # Create iteration","return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \"\"\"Add ops to apply","the optimizer is saved without the non-slot # variable, or if the non-slot","Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn","tensor is sparse) - create_slots (if your optimizer algorithm requires additional variables) -","Generally you should be able to use the _set_hyper()/state.get_hyper() facility instead. This class","of `loss` for the variables in `var_list`. This is the first part of","Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the","You never use this class directly, but instead instantiate one of its subclasses","so shouldn't keep Tensors as member variables. Generally you should be able to","from its config. This method is the reverse of `get_config`, capable of instantiating","grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g","id. If distribution strategy exists, get the primary variable first. Args: var: the","slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is None: if","a list rather than the one with the highest restore # UID in","slot variables # when possible, but this may mean that scopes intended to","is nonetheless important when graph building for the case when a slot variable","] return grads def apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply gradients to variables. This is","<list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars","on a sparse variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var)","grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v","elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable,","return # Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value,","\" + str(weights)[:50] + \"...\") if not params: return weight_value_tuples = [] param_values","can be callable, tensor, numeric.\"\"\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name","creator # scope. Generally we'd like to eagerly create/restore slot variables # when","It returns an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable)","config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a hyperparameter that can be","`lr` is included for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError:","intended to catch # `variable` also catch its eagerly created slot variable #","(the \"License\"); # you may not use this file except in compliance with","indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior,","\"\"\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if","(var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable(","None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name):","minimize. loss = lambda: 3 * var1 + 2 * var2 # In","_var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore","shape is the *replica-local* batch size, which is off by a factor equal","Operation that updates the variables in `var_list`. If `global_step` was not `None`, that","self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars","optimizer; # it's a dependency hypergraph with edges of the form (optimizer, non-slot","that operation also increments `global_step`. Raises: ValueError: If some of the variables are","there is no gradient for the given variable. Args: loss: A callable taking","names to additional Python objects used to create this optimizer, such as a","of both computing the gradients and applying them to the variables. If you","# Unless required by applicable law or agreed to in writing, software #","model. You never use this class directly, but instead instantiate one of its","= tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = []","by applicable law or agreed to in writing, software # distributed under the","[resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle,","\"clipvalue\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars","trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight","if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list","variables # when possible, but this may mean that scopes intended to catch","to allow time inverse decay of learning rate. `lr` is included for backward","if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self,","t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\"Invalid type %r for %s, expected:","_get_slot_key_from_var(var, slot_name): \"\"\"Get the slot key for the variable: var_name/slot_name.\"\"\" name = _var_key(var)","not currently \" \"supported. Please file a feature request if this limitation bothers","part of `minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars: List","give the wrong answer, resulting in gradients that can be many times too","index. \"\"\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return","from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from","variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call","is the second part of `minimize()`. It returns an `Operation` that applies gradients.","shape pre-defined, or 2) subclassed models. Pass var_list as callable in these cases.","name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] =","directly, but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ###","local_step) return lr_t @abc.abstractmethod def get_config(self): \"\"\"Returns the config of the optimimizer. An","= self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer,","= var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \"clipnorm\"):","Needed to avoid infinite recursion with __setattr__. if name == \"_hyper\": raise e","use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has","gradient for the affected indices. handle: a `Tensor` of dtype `resource` which points","= kwargs.pop(\"decay\", 0.0) if decay < 0.: raise ValueError(\"decay cannot be less than","self._iterations = None # For implementing Trackable. Stores information about how to restore","a variable which has an associated slot variable is created or restored. When","created by the original optimizer, but currently we do not support using the","representing the indices for which the gradient is nonzero. Indices are unique. Returns:","if vars_with_empty_grads: logging.warning( (\"Gradients does not exist for variables %s when minimizing the","from the var shared name. In eager mode the name is derived from","configuration of an optimizer. The same optimizer can be reinstantiated later (without any","first part of `minimize()`. It returns a list of (gradient, variable) pairs where","_var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is None:","for a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars","after normal creation and adds restore ops to the graph. This method is","the loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2])","with `tf.GradientTape`. 2. Process the gradients as you wish. 3. Apply the processed","(1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \"\"\"Returns the config","slot variables anywhere, and # instead special-case this dependency and otherwise pretend it's","initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\"%s/%s\" %","file except in compliance with the License. # You may obtain a copy","division from __future__ import print_function import abc import functools import six from tensorflow.python.distribute","variable) pairs. Variable is always present, but gradient can be `None`. Raises: TypeError:","set `iterations` to a new Variable after \" \"the Optimizer weights have been","not using these and you want to average gradients, you should use `tf.math.reduce_sum`","grads_and_vars if g is not None and v.dtype != dtypes.resource ]) return grads_and_vars","\"\"\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype","Apply the processed gradients with `apply_gradients()`. Example: ```python # Create an optimizer. opt","model is first called, instead of construction time. Examples include 1) sequential models","returns a list of (gradient, variable) pairs where \"gradient\" is the gradient for","\"clipvalue\" in kwargs: self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created = False def minimize(self, loss, var_list,","array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops","clip gradients by norm; `clipvalue` is clip gradients by value, `decay` is included","= tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads,","\"passed to optimizer: \" + str(k)) # checks that all keyword arguments are","this may mean that scopes intended to catch # `variable` also catch its","norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility","override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing the gradient","else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value =","a float, callable, or Tensor.\"\"\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value)","Valid types for loss, variables and gradients. \"\"\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32,","# Iterate over restores, highest restore UID first to minimize the number #","= any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or","slots, etc. ### Hyper parameters These are arguments passed to the optimizer subclass","updates the variables in `var_list`. If `global_step` was not `None`, that operation also","# TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights): params =","trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types)","([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): \"\"\"Key for representing a","`_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing the gradient for","be applied to the variable after the gradient has been applied to the","holding the gradient computed for `loss`. Returns: A list of (gradient, variable) pairs.","\"\"\"Creates an optimizer from its config. This method is the reverse of `get_config`,","for the names of the slots that it uses. Once you have a","array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class","= [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return grads def","from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from","variable, the constraint will be applied to the variable after the gradient has","all keyword arguments are non-negative. if kwargs[k] < 0: raise ValueError(\"Expected {} >=","to process the gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead","var_list=[var1, var2]) ``` ### Custom training loop with Keras models In Keras models,","aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors are all valid types","it created to hold the slot value. This can be useful if you","is no gradient for the given variable. Args: loss: A callable taking no","list of names for this optimizer's slots.\"\"\" return self._slot_names def add_slot(self, var, slot_name,","tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads =","implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \"\"\"Restore a","of training steps this Optimizer has run.\"\"\" if self._iterations is None: self._iterations =","\"gradient\" can be a `Tensor`, an `IndexedSlices`, or `None` if there is no","create the slot variable with a restoring initializer. No new variables are created","_create_vars. \"\"\" allowed_kwargs = {\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"} for k in kwargs: if","a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer","instead use the optimizer in three steps: 1. Compute the gradients with `tf.GradientTape`.","zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient, variable). Do whatever","a factor equal to the number of replicas being used to compute a","you want to log debug a training algorithm, report stats about the slots,","over restores, highest restore UID first to minimize the number # of assignments.","has an associated slot variable is created or restored. When executing eagerly, we","function used for a hyperparameter. Returns: An optimizer instance. \"\"\" if \"lr\" in","variables associated with the variables to train. These are called <i>Slots</i>. Slots have","self._slot_names def add_slot(self, var, slot_name, initializer=\"zeros\"): \"\"\"Add a new slot variable for `var`.\"\"\"","be restored. slot_name: The name of this `Optimizer`'s slot to restore into. variable:","variable which has an associated slot variable is created or restored. When executing","normal # graph. if slot_variable is not None: # If we've either made","not context.executing_eagerly() or any_symbolic: # If the current context is graph mode or","= <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for","self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name}) def _distributed_apply(self, distribution, grads_and_vars,","models without input shape pre-defined, or 2) subclassed models. Pass var_list as callable","an associated slot variable is created or restored. When executing eagerly, we create","self._get_hyper(name) raise e def __setattr__(self, name, value): \"\"\"Override setattr to support dynamic hyperparameter","OR CONDITIONS OF ANY KIND, either express or implied. # See the License","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","been created but `variable` has just been added to a dependency graph (causing","variable. \"\"\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i,","which is off by a factor equal to the number of replicas being","Python objects used to create this optimizer, such as a function used for","specified gradients. If `global_step` was not None, that operation also increments `global_step`. Raises:","only for non-trainable variables. \" \"You have specified trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\") else:","in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered)","initialization), and makes variable creator scopes # behave the same way they do","as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices):","{} has `None` for gradient. \" \"Please make sure that all of your","import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as","3 * var1 * var1 + 2 * var2 * var2 # In","gradient to variable.\"\"\" if isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying to update a Tensor \",","config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a hyperparameter","interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \"\"\"Restore a newly created slot","additional variables) - get_config (serialization of the optimizer, include all hyper parameters) \"\"\"","value. value can be callable, tensor, numeric.\"\"\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True)","dictionary, typically the output of get_config. custom_objects: A Python dictionary mapping names to","value.\"\"\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over","has just been added to a dependency graph (causing us to realize that","in grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers()","of replicas being used to compute a single step. As a result, using","if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss)","a sparse variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if","\"\"\"Returns the config of the optimimizer. An optimizer config is a Python dictionary","include 1) sequential models without input shape pre-defined, or 2) subclassed models. Pass","instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing the","return weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return","which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position)","the second part of `minimize()`. It returns an `Operation` that applies gradients. Args:","instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python","A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be restored. slot_name:","passed to the `Optimizer` constructor. Returns: An `Operation` that applies the specified gradients.","value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \"\"\"A list of names for this","repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct","decay = kwargs.pop(\"decay\", 0.0) if decay < 0.: raise ValueError(\"decay cannot be less","{slot name : variable}} self._slots = {} self._slot_names = [] self._weights = []","is a list of tuples (gradient, variable). Do whatever you # need to","`trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be restored. slot_name: The","keyword argument \" \"passed to optimizer: \" + str(k)) # checks that all","special-case this dependency and otherwise pretend it's a normal # graph. if slot_variable","from SavedModels is not currently \" \"supported. Please file a feature request if","but currently we do not support using the optimizer object iself (e.g. through","{\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"} for k in kwargs: if k not in allowed_kwargs:","the hyper parameter. Hyper parameters can be overwritten through user code: Example: ```python","\"VariableSynchronization.ON_READ only for non-trainable variables. \" \"You have specified trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\")","constraint is not supported. ### Thread Compatibility The entire optimizer is currently thread","lambda: 3 * var1 * var1 + 2 * var2 * var2 #","with a weight list of length \" + str(len(weights)) + \", but the","in a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value","`__init__` method), and then passed to `self._set_hyper()`. They can be either regular Python","Returns: the unique name of the variable. \"\"\" # pylint: disable=protected-access # Get","is off by a factor equal to the number of replicas being used","the _set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible. Args: name: A","the list or tuple of `Variable` objects. Use callable when the variable list","the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum","variables are created at the first time when `loss` is called. grad_loss: Optional.","%s.\" % (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid types","loss = lambda: 3 * var1 + 2 * var2 # In eager","variable creation if there is an active variable creator # scope. Generally we'd","to update a Tensor \", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not","if there is an active variable creator # scope. Generally we'd like to","when possible, but this may mean that scopes intended to catch # `variable`","add up your per-example losses and then divide by the global batch size.","base_layer. def set_weights(self, weights): params = self.weights if len(params) != len(weights): raise ValueError(","= distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\"%s/%s\" % (var._shared_name, slot_name), # pylint:","have a \" \"gradient defined (i.e. are differentiable). \" \"Common ops without gradient:","ops to apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the","batch size, which is done automatically if you use `tf.keras` built-in training or","Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return","the specified gradients. If `global_step` was not None, that operation also increments `global_step`.","duplicate indices may instead override this method to avoid the overhead of summing.","`handle`. Similar to `_apply_sparse`, the `indices` argument to this method has been de-duplicated.","v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v):","If any tensor is not a valid type. \"\"\" valid_dtypes = self._valid_dtypes() for","kwargs: self.clipnorm = kwargs.pop(\"clipnorm\") if \"clipvalue\" in kwargs: self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created =","not support using the optimizer object iself (e.g. through `apply_gradients`). \"\"\" # TODO(allenl):","to create your own optimization algorithm, simply inherit from this class and override","six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else:","implementing Trackable. Stores information about how to restore # slot variables which have","if the optimizer is saved without the non-slot # variable, or if the","str(pv.shape) + \" not compatible with \" \"provided weight shape \" + str(w.shape))","invalid, or var_list is None. \"\"\" # TODO(josh11b): Test that we handle weight","when a variable which has an associated slot variable is created or restored.","of class Optimizer.\"\"\" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import","arguments passed to the optimizer subclass constructor (the `__init__` method), and then passed","+ \", but the optimizer was expecting \" + str(len(params)) + \" weights.","takes no argument and returns the value # to minimize. loss = lambda:","you are not using these and you want to average gradients, you should","training loop with Keras models In Keras models, sometimes variables are created when","ops without gradient: \" \"K.argmax, K.round, K.eval.\".format(param)) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g,","for i in update_ops) if not context.executing_eagerly() or any_symbolic: # If the current","update ops are # symbolic then the step update should be carried out","slot_variable_position, slot_name, variable): \"\"\"Restore a slot variable's value, possibly creating it. Called when","has been applied to the variable. Important: If gradient is sparse tensor, variable","learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a hyperparameter that can","Optimizer instances should not bind to a single graph, and so shouldn't keep","gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want to process the gradient","case). Deferring is mostly harmless # (aside from double initialization), and makes variable","to this method has been de-duplicated. Optimizers which deal correctly with non-unique indices","self._use_locking = True self._name = name self._hyper = {} # dict: {variable name","if k not in allowed_kwargs: raise TypeError(\"Unexpected keyword argument \" \"passed to optimizer:","steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process the gradients as you","use `tf.math.reduce_sum` to add up your per-example losses and then divide by the","times too big. ### Variable Constraint All Keras optimizers respect variable constraints. If","return value def __getattribute__(self, name): \"\"\"Overridden to support hyperparameter access.\"\"\" try: return super(OptimizerV2,","pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads","when variable is to be synced on read. trainable = False elif trainable","update_ops) if not context.executing_eagerly() or any_symbolic: # If the current context is graph","self._iterations is not None: raise RuntimeError(\"Cannot set `iterations` to a new Variable after","_RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters","own dependencies, in which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name,","return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \"\"\"Compute gradients of `loss` for","compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError:","Examples include 1) sequential models without input shape pre-defined, or 2) subclassed models.","adds restore ops to the graph. This method is nonetheless important when graph","the variables have gradients. \"\"\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_,","of gradient tensors. Raises: ValueError: In case any gradient cannot be computed (e.g.","get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v","@abc.abstractmethod def get_config(self): \"\"\"Returns the config of the optimimizer. An optimizer config is","parameters) \"\"\" def __init__(self, name, **kwargs): \"\"\"Create a new Optimizer. This must be","+ scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or","grads_and_vars, name): \"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list =","be computed (e.g. if gradient function not implemented). \"\"\" params = nest.flatten(params) with","argument and returns the value # to minimize. loss = lambda: 3 *","If you intend to create your own optimization algorithm, simply inherit from this","dictionary. \"\"\" config = {\"name\": self._name} if hasattr(self, \"clipnorm\"): config[\"clipnorm\"] = self.clipnorm if","# `variable` also catch its eagerly created slot variable # unintentionally (specifically make_template","def __init__(self, name, **kwargs): \"\"\"Create a new Optimizer. This must be called by","single graph, and so shouldn't keep Tensors as member variables. Generally you should","of the update ops are # symbolic then the step update should be","subclass constructor (the `__init__` method), and then passed to `self._set_hyper()`. They can be","a new Optimizer. This must be called by the constructors of subclasses. Note","var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self,","normally. We keep a list rather than the one with the highest restore","of the variable. \"\"\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies(","which override this method must deal with repeated indices. See the docstring of","not None: raise RuntimeError(\"Cannot set `iterations` to a new Variable after \" \"the","slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict =","Python values (like 1.0), tensors, or callables. If they are callable, the callable","where `unique_indices` is a de-duplicated version of `indices` and `summed_values` contains the sum","Slot variables are not owned by any one object (because we don't want","g is not None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self,","with __setattr__. if name == \"_hyper\": raise e # Backwards compatibility with Keras","the reverse of `get_config`, capable of instantiating the same optimizer from the config","per-example losses and then divide by the global batch size. Note that when","trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not","`DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in","TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version","weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value =","for the affected indices. handle: a `Tensor` of dtype `resource` which points to","argument \" \"passed to optimizer: \" + str(k)) # checks that all keyword","objects. \"\"\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self,","loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \"\"\"Compute gradients","_restore_slot_variable(self, slot_name, variable, slot_variable): \"\"\"Restore a newly created slot variable's value.\"\"\" variable_key =","graph (causing us to realize that the slot variable needs to be restored).","have names and you can ask the optimizer for the names of the","`Adagrad` allocate and manage additional variables associated with the variables to train. These","for grad, var in grads_and_vars: scope_name = (\"\" if ops.executing_eagerly_outside_functions() else \"_\" +","method is nonetheless important when graph building for the case when a slot","1. indices: A one-dimensional integer `Tensor`, indexing into the first dimension of `values`","thread-safe. The user needs to perform synchronization if necessary. ### Slots Many optimizer","var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ```","name you can ask the optimizer for the variable it created to hold","created to hold the slot value. This can be useful if you want","on # a slot variable if not for this case). Deferring is mostly","If you want to process the gradients before applying them you can instead","self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \"\"\"Compute","gradients before applying them you can instead use the optimizer in three steps:","self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None)","created # normally. We keep a list rather than the one with the","to variables. This is the second part of `minimize()`. It returns an `Operation`","= decay if \"clipnorm\" in kwargs: self.clipnorm = kwargs.pop(\"clipnorm\") if \"clipvalue\" in kwargs:","+ str(len(params)) + \" weights. Provided weights: \" + str(weights)[:50] + \"...\") if","`var_list`. If `global_step` was not `None`, that operation also increments `global_step`. Raises: ValueError:","= nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in zip(grads,","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\"","of the variable. \"\"\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \"\"\"Add ops","None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name","size, which is done automatically if you use `tf.keras` built-in training or evaluation","_resource_apply_dense(self, grad, handle): \"\"\"Add ops to apply dense gradients to the variable `handle`.","# need to the 'gradient' part, for example cap them, etc. capped_grads_and_vars =","for g in grads] if hasattr(self, \"clipvalue\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue)","tensor is dense) - resource_apply_sparse (update variable given gradient tensor is sparse) -","but `variable` has just been added to a dependency graph (causing us to","make the slot variable. Defer restoring until it gets created # normally. We","t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid types for loss, variables","name == \"lr\": name = \"learning_rate\" if name in self._hyper: return self._get_hyper(name) raise","_deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \"\"\"Add","# grads_and_vars is a list of tuples (gradient, variable). Do whatever you #","def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value,","0: {}\".format(decay)) self._initial_decay = decay if \"clipnorm\" in kwargs: self.clipnorm = kwargs.pop(\"clipnorm\") if","`Tensor` holding the gradient computed for `loss`. name: Optional name for the returned","Raises: ValueError: If any tensor is not a valid type. \"\"\" valid_dtypes =","params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in","optimizer, but currently we do not support using the optimizer object iself (e.g.","the processed gradients with `apply_gradients()`. Example: ```python # Create an optimizer. opt =","can be either regular Python values (like 1.0), tensors, or callables. If they","de-duplicated. Optimizers which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to","then divide by the global batch size. Note that when using `tf.distribute.Strategy`, the","callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value =","= self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return","var2 # In eager mode, simply call minimize to update the list of","`learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has been","# In eager mode, simply call minimize to update the list of variables.","!= len(weights): raise ValueError( \"You called `set_weights(weights)` on optimizer \" + self._name +","(optimizer, non-slot # variable, variable)). So we don't _track_ slot variables anywhere, and","strategy exists, get the primary variable first. Args: var: the variable. Returns: the","# If we've either made this slot variable, or if we've pulled out","\"\"\"Version 2 of class Optimizer.\"\"\" # pylint: disable=g-bad-name from __future__ import absolute_import from","if kwargs[k] < 0: raise ValueError(\"Expected {} >= 0, received: {}\".format(k, kwargs[k])) self._use_locking","variables(self): \"\"\"Returns variables of this Optimizer based on the order created.\"\"\" return self._weights","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\"decay\", var_dtype) lr_t = lr_t /","callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \"Synchronization","to avoid the overhead of summing. Args: grad: a `Tensor` representing the gradient","or tuple of `Variable` objects. Use callable when the variable list would otherwise","out iterable with grad equal to None.\"\"\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars:","\"\"\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters when","method is the reverse of `get_config`, capable of instantiating the same optimizer from","slot variable, or if we've pulled out an # existing slot variable, we","to additional Python objects used to create this optimizer, such as a function","import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from","= self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype not in","be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object","learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.:","has `None` for gradient. \" \"Please make sure that all of your ops","indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \"\"\"Add ops to","which updates the value of the variable. \"\"\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad,","in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \"\"\"Restore a slot variable's","input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them.","`None`. Raises: TypeError: If `var_list` contains anything else than `Variable` objects. ValueError: If","variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this method has been","building. Instead, _restore_slot_variable catches these after normal creation and adds restore ops to","for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var))","# save the slot variable if the optimizer is saved without the non-slot","TypeError: If `var_list` contains anything else than `Variable` objects. ValueError: If some arguments","By default the correct behavior, to sum non-unique indices and their associated gradients,","gradients.gradients(loss, params) for grad, param in zip(grads, params): if grad is None: raise","Get the distributed variable if it exists. if getattr(var, \"_distributed_container\", None) is not","raise ValueError(\"decay cannot be less than 0: {}\".format(decay)) self._initial_decay = decay if \"clipnorm\"","tensor is not a valid type. \"\"\" valid_dtypes = self._valid_dtypes() for t in","to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this method","if \"learning_rate\" in config: if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return","computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want to process the","An optimizer instance. \"\"\" if \"lr\" in config: config[\"learning_rate\"] = config.pop(\"lr\") if \"learning_rate\"","slot variable's value, possibly creating it. Called when a variable which has an","weights have been created\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \"\"\"Get decayed","variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \"\"\"Get decayed learning rate as a Tensor with","is the reverse of `get_config`, capable of instantiating the same optimizer from the","variable if not for this case). Deferring is mostly harmless # (aside from","new Optimizer. This must be called by the constructors of subclasses. Note that","or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError(","value def variables(self): \"\"\"Returns variables of this Optimizer based on the order created.\"\"\"","the variables are created at the first time `loss` is called. grad_loss: Optional.","self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this","var) if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op","mapping names to additional Python objects used to create this optimizer, such as","@classmethod def from_config(cls, config, custom_objects=None): \"\"\"Creates an optimizer from its config. This method","allowed_kwargs: raise TypeError(\"Unexpected keyword argument \" \"passed to optimizer: \" + str(k)) #","from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \"\"\"Sums `values`","= [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads,","have specified trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\") else: # Set trainable to be false","`tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means it automatically sums gradients","execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1)","of the variables are not `Variable` objects. \"\"\" grads_and_vars = self._compute_gradients( loss, var_list=var_list,","if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer","value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value","nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm)","and gradients. \"\"\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \"\"\"Call","args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if","from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import","associated with the variables to train. These are called <i>Slots</i>. Slots have names","valid type. \"\"\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype","name, **kwargs): \"\"\"Create a new Optimizer. This must be called by the constructors","slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key = _var_key(var)","slot variable with a restoring initializer. No new variables are created when graph","do not support using the optimizer object iself (e.g. through `apply_gradients`). \"\"\" #","def _deduplicate_indexed_slices(values, indices): \"\"\"Sums `values` associated with any non-unique `indices`. Args: values: A","`handle`. Args: grad: a `Tensor` representing the gradient. handle: a `Tensor` of dtype","\"\"\"Compute gradients of `loss` for the variables in `var_list`. This is the first","be `None`. Raises: TypeError: If `var_list` contains anything else than `Variable` objects. ValueError:","`Variable` objects. \"\"\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def","the License for the specific language governing permissions and # limitations under the","str(len(weights)) + \", but the optimizer was expecting \" + str(len(params)) + \"","to support hyperparameter access.\"\"\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: #","`tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with the desired parameters.","through user code: Example: ```python # Create an optimizer with the desired parameters.","if pv.shape != w.shape: raise ValueError(\"Optimizer weight shape \" + str(pv.shape) + \"","`values` (as in an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where","was expecting \" + str(len(params)) + \" weights. Provided weights: \" + str(weights)[:50]","infinite recursion with __setattr__. if name == \"_hyper\": raise e # Backwards compatibility","var2]) opt_op.run() # In eager mode, simply call minimize to update the list","before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this function.","to be false when variable is to be synced on read. trainable =","the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \"\"\"Restore a newly","variable. \"\"\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def","%s.\" % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\"Gradients does","= array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\")","decay_t = self._get_hyper(\"decay\", var_dtype) lr_t = lr_t / (1. + decay_t * local_step)","slot name you can ask the optimizer for the variable it created to","of _create_vars. \"\"\" allowed_kwargs = {\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"} for k in kwargs:","default the correct behavior, to sum non-unique indices and their associated gradients, is","in update_ops) if not context.executing_eagerly() or any_symbolic: # If the current context is","# If the current context is graph mode or any of the update","disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight)","hyperparameter. Returns: An optimizer instance. \"\"\" if \"lr\" in config: config[\"learning_rate\"] = config.pop(\"lr\")","param in zip(grads, params): if grad is None: raise ValueError(\"Variable {} has `None`","in stateful and thread-compatible. Args: name: A non-empty string. The name to use","resulting in gradients that can be many times too big. ### Variable Constraint","than the one with the highest restore # UID in case slot variables","optimizer was expecting \" + str(len(params)) + \" weights. Provided weights: \" +","the variable list would otherwise be incomplete before `minimize` and the variables are","None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError(\"No","### Usage ```python # Create an optimizer with the desired parameters. opt =","dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization","var1 + 2 * var2 * var2 # In graph mode, returns op","To average gradients, you divide your loss by the global batch size, which","same optimizer can be reinstantiated later (without any saved state) from this configuration.","of this `Optimizer`'s slot to restore into. variable: The variable object this slot","return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads,","etc. ### Hyper parameters These are arguments passed to the optimizer subclass constructor","OptimizerV2(trackable.Trackable): \"\"\"Updated base class for optimizers. This class defines the API to add","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #","config. This method is the reverse of `get_config`, capable of instantiating the same","necessary. ### Slots Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and","if ops.executing_eagerly_outside_functions() else \"_\" + var.op.name) with backend.name_scope(\"update\" + scope_name): update_ops.extend( distribution.extended.update( var,","hyperparameter that can be a float, callable, or Tensor.\"\"\" value = self._hyper[hyperparameter_name] if","if getattr(var, \"_distributed_container\", None) is not None: var = var._distributed_container() if var._in_graph_mode: return","@six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated base class for optimizers. This class defines the","(_, v) in grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _ =","def apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply gradients to variables. This is the second part","!= dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \"\"\"Returns gradients of `loss`","var unique id. If distribution strategy exists, get the primary variable first. Args:","grads_and_vars is a list of tuples (gradient, variable). Do whatever you # need","which points to the variable to be updated. indices: a `Tensor` of integral","keep a list rather than the one with the highest restore # UID","kwargs[k] < 0: raise ValueError(\"Expected {} >= 0, received: {}\".format(k, kwargs[k])) self._use_locking =","overwrite=True) if name not in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name]","if name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value): \"\"\"Override","`clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients","import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \"\"\"Sums `values` associated with","Hyper parameters can be overwritten through user code: Example: ```python # Create an","computed for `loss`. Returns: A list of (gradient, variable) pairs. Variable is always","was not `None`, that operation also increments `global_step`. Raises: ValueError: If some of","__future__ import division from __future__ import print_function import abc import functools import six","class directly, but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`.","created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ...","type %r for %s, expected: %s.\" % (dtype, t.name, [v for v in","increments `global_step`. Raises: ValueError: If some of the variables are not `Variable` objects.","not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = [] with","as a Tensor with dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step","rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer.","They can be either regular Python values (like 1.0), tensors, or callables. If","(\"\" if ops.executing_eagerly_outside_functions() else \"_\" + var.op.name) with backend.name_scope(\"update\" + scope_name): update_ops.extend( distribution.extended.update(","aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not None:","the `reduction` argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for","than 0: {}\".format(decay)) self._initial_decay = decay if \"clipnorm\" in kwargs: self.clipnorm = kwargs.pop(\"clipnorm\")","grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \"\"\"Apply gradient to variable.\"\"\" if isinstance(var,","this slot is being created for. \"\"\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key,","of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM`","w.shape: raise ValueError(\"Optimizer weight shape \" + str(pv.shape) + \" not compatible with","have gradients. \"\"\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in","def _compute_gradients(self, loss, var_list, grad_loss=None): \"\"\"Compute gradients of `loss` for the variables in","\"\"\"Returns variables of this Optimizer based on the order created.\"\"\" return self._weights @property","sparse gradients to `handle`, with repeated indices. Optimizers which override this method must","1) sequential models without input shape pre-defined, or 2) subclassed models. Pass var_list","scopes intended to catch # `variable` also catch its eagerly created slot variable","apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created = True def get_config(self): #","gradient tensors. Raises: ValueError: In case any gradient cannot be computed (e.g. if","created.\"\"\" return self._weights @property def weights(self): \"\"\"Returns variables of this Optimizer based on","A non-empty string. The name to use for accumulators created for the optimizer.","value for the hyper parameter. Hyper parameters can be overwritten through user code:","be either regular Python values (like 1.0), tensors, or callables. If they are","first time `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed","if name == \"_hyper\": raise e # Backwards compatibility with Keras optimizers. if","with \" \"provided weight shape \" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self,","The entire optimizer is currently thread compatible, not thread-safe. The user needs to","you can instead use the optimizer in three steps: 1. Compute the gradients","ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss,","us to realize that the slot variable needs to be restored). Args: slot_variable_position:","loss: Loss tensor. params: List of variables. Returns: List of gradient tensors. Raises:","lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \"\"\"Returns","loop with Keras models In Keras models, sometimes variables are created when the","variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is","dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if","slot variable is created or restored. When executing eagerly, we create the slot","to create this optimizer, such as a function used for a hyperparameter. Returns:","name : variable}} self._slots = {} self._slot_names = [] self._weights = [] self._iterations","is passed to any variable, the constraint will be applied to the variable","Version 2.0 (the \"License\"); # you may not use this file except in","of dtype `resource` which points to the variable to be updated. Returns: An","new variables are created when graph building. Instead, _restore_slot_variable catches these after normal","trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \"\"\"Variable. The number of","variable. Returns: the unique name of the variable. \"\"\" # pylint: disable=protected-access #","This optimizer class is `tf.distribute.Strategy` aware, which means it automatically sums gradients across","automatically if you use `tf.keras` built-in training or evaluation loops. See the `reduction`","v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \"\"\"Apply gradient to","= list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is","# pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var,","= math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t =","a customized optimizer. If you intend to create your own optimization algorithm, simply","and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \"\"\"A","one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create","integer `Tensor`, indexing into the first dimension of `values` (as in an IndexedSlices","* local_step) return lr_t @abc.abstractmethod def get_config(self): \"\"\"Returns the config of the optimimizer.","highest restore # UID in case slot variables have their own dependencies, in","when the variable list would otherwise be incomplete before `minimize` since the variables","apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply gradients to variables. This is the second part of","(dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid types for loss,","non-unique `indices`. Args: values: A `Tensor` with rank >= 1. indices: A one-dimensional","parameters These are arguments passed to the optimizer subclass constructor (the `__init__` method),","NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value()","Optional. A `Tensor` holding the gradient computed for `loss`. name: Optional name for","None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \"\"\"Returns","gradients. Subclasses should override to allow other float types. Returns: Valid types for","is saved without the non-slot # variable, or if the non-slot variable is","= [] with backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name =","`Trackable` object to be restored. slot_name: The name of this `Optimizer`'s slot to","want to average gradients, you should use `tf.math.reduce_sum` to add up your per-example","name, overwrite=True) if name not in self._hyper: self._hyper[name] = value else: prev_value =","(if your optimizer algorithm requires additional variables) - get_config (serialization of the optimizer,","grad_loss=None): \"\"\"Compute gradients of `loss` for the variables in `var_list`. This is the","if not for this case). Deferring is mostly harmless # (aside from double","([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\"Gradients does not exist","the gradients with `tf.GradientTape`. 2. Process the gradients as you wish. 3. Apply","var.constraint is not None: raise RuntimeError( \"Cannot use a constraint function on a","minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in","`None` if there is no gradient for the given variable. Args: loss: A","variables and hyperparameters when an optimizer is restored from a SavedModel. These variables","# Defer slot variable creation if there is an active variable creator #","params): if grad is None: raise ValueError(\"Variable {} has `None` for gradient. \"","tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list)","and override the following methods: - resource_apply_dense (update variable given gradient tensor is","optimizers. if name == \"lr\": name = \"learning_rate\" if name in self._hyper: return","global batch size, which is done automatically if you use `tf.keras` built-in training","not exist for variables %s when minimizing the loss.\"), ([v.name for v in","RuntimeError( \"Cannot use a constraint function on a sparse variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values,","params) for grad, param in zip(grads, params): if grad is None: raise ValueError(\"Variable","backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in zip(grads, params): if grad","\"Please make sure that all of your ops have a \" \"gradient defined","return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return","self, slot_variable_position, slot_name, variable): \"\"\"Restore a slot variable's value, possibly creating it. Called","shared name. In eager mode the name is derived from the var unique","deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead.","slot variables and hyperparameters when an optimizer is restored from a SavedModel. These","when graph building for the case when a slot variable has already been","of `Variable` objects. Use callable when the variable list would otherwise be incomplete","lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper #","name of the variable. \"\"\" # pylint: disable=protected-access # Get the distributed variable","variable's value.\"\"\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate","This class in stateful and thread-compatible. Args: name: A non-empty string. The name","initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any one object (because","def minimize(self, loss, var_list, grad_loss=None, name=None): \"\"\"Minimize `loss` by updating `var_list`. This method","= tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with tf.GradientTape()","`name` to value. value can be callable, tensor, numeric.\"\"\" if isinstance(value, trackable.Trackable): self._track_trackable(value,","arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by","+ 2 * var2 * var2 # In graph mode, returns op that","are non-negative. if kwargs[k] < 0: raise ValueError(\"Expected {} >= 0, received: {}\".format(k,","array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated base class for optimizers.","the optimizer for the names of the slots that it uses. Once you","a `Tensor` representing the gradient. handle: a `Tensor` of dtype `resource` which points","name. In eager mode the name is derived from the var unique id.","made this slot variable, or if we've pulled out an # existing slot","the first time when `loss` is called. grad_loss: Optional. A `Tensor` holding the","argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or","= zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \"\"\"Apply gradient to variable.\"\"\" if isinstance(var, ops.Tensor):","loss, var_list, grad_loss=None): \"\"\"Compute gradients of `loss` for the variables in `var_list`. This","# Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list)","\"_\" + var.op.name) with backend.name_scope(\"update\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False))","_decayed_lr(self, var_dtype): \"\"\"Get decayed learning rate as a Tensor with dtype=var_dtype.\"\"\" lr_t =","dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\"Invalid type %r for","hypergraph with edges of the form (optimizer, non-slot # variable, variable)). So we","the one with the highest restore # UID in case slot variables have","`tf.keras.losses.Reduction.SUM` for not. If you are not using these and you want to","names of the slots that it uses. Once you have a slot name","not yet been created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position,","self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params))","None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \"\"\"set","we don't want to # save the slot variable if the optimizer is","* var2 * var2 # In graph mode, returns op that minimizes the","the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate =","the gradient for \"variable\". Note that \"gradient\" can be a `Tensor`, an `IndexedSlices`,","grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] # Create","as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import","for pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise","gradients, is enforced by first pre-processing `grad` and `indices` and passing them on","which deal correctly with duplicate indices may instead override this method to avoid","slot variable if not for this case). Deferring is mostly harmless # (aside","optimizer can be reinstantiated later (without any saved state) from this configuration. Returns:","`tf.distribute.Strategy` aware, which means it automatically sums gradients across all replicas. To average","= {} decay = kwargs.pop(\"decay\", 0.0) if decay < 0.: raise ValueError(\"decay cannot","def add_slot(self, var, slot_name, initializer=\"zeros\"): \"\"\"Add a new slot variable for `var`.\"\"\" if","< 0: raise ValueError(\"Expected {} >= 0, received: {}\".format(k, kwargs[k])) self._use_locking = True","this configuration. Returns: Python dictionary. \"\"\" config = {\"name\": self._name} if hasattr(self, \"clipnorm\"):","Raises: ValueError: If some of the variables are not `Variable` objects. \"\"\" grads_and_vars","self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return","get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with","(e.g. through `apply_gradients`). \"\"\" # TODO(allenl): Make the restored optimizer functional by tracing","you have a slot name you can ask the optimizer for the variable","if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \"Synchronization value can be set","requires additional variables) - get_config (serialization of the optimizer, include all hyper parameters)","grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list)","valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any","by the global batch size, which is done automatically if you use `tf.keras`","var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in","optimizers respect variable constraints. If constraint function is passed to any variable, the","tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework","if you want to log debug a training algorithm, report stats about the","about the slots, etc. ### Hyper parameters These are arguments passed to the","False def minimize(self, loss, var_list, grad_loss=None, name=None): \"\"\"Minimize `loss` by updating `var_list`. This","\"\"\"Returns variables of this Optimizer based on the order created.\"\"\" return self._weights def","grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if","not in valid_dtypes: raise ValueError(\"Invalid type %r for %s, expected: %s.\" % (dtype,","the gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using","ops to apply sparse gradients to `handle`, with repeated indices. Optimizers which override","with backend.name_scope(\"update\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i,","group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not","v) in grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _ = self.iterations","rather than the one with the highest restore # UID in case slot","pv.shape != w.shape: raise ValueError(\"Optimizer weight shape \" + str(pv.shape) + \" not","tensor. params: List of variables. Returns: List of gradient tensors. Raises: ValueError: In","None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype,","as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from","kwargs.pop(\"clipnorm\") if \"clipvalue\" in kwargs: self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created = False def minimize(self,","slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the","\"\"\"set hyper `name` to value. value can be callable, tensor, numeric.\"\"\" if isinstance(value,","given gradient tensor is dense) - resource_apply_sparse (update variable given gradient tensor is","without gradient: \" \"K.argmax, K.round, K.eval.\".format(param)) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm)","len(params) != len(weights): raise ValueError( \"You called `set_weights(weights)` on optimizer \" + self._name","e: # Needed to avoid infinite recursion with __setattr__. if name == \"_hyper\":","# For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable):","of subclasses. Note that Optimizer instances should not bind to a single graph,","+ slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot","trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \"\"\"Sums","the first part of `minimize()`. It returns a list of (gradient, variable) pairs","capable of instantiating the same optimizer from the config dictionary. Arguments: config: A","kwargs={\"name\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\" reduced_grads =","the model is first called, instead of construction time. Examples include 1) sequential","limitation bothers \" \"you.\") revived_types.register_revived_type( \"optimizer\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto:","to perform synchronization if necessary. ### Slots Many optimizer subclasses, such as `Adam`","initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables","between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter out iterable with","wrong answer, resulting in gradients that can be many times too big. ###","called by the constructors of subclasses. Note that Optimizer instances should not bind","compatible, not thread-safe. The user needs to perform synchronization if necessary. ### Slots","a dependency graph (causing us to realize that the slot variable needs to","and you want to average gradients, you should use `tf.math.reduce_sum` to add up","When executing eagerly, we create the slot variable with a restoring initializer. No","and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot(","= kwargs.pop(\"clipnorm\") if \"clipvalue\" in kwargs: self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created = False def","a new slot variable for `var`.\"\"\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key","grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for","variable, variable)). So we don't _track_ slot variables anywhere, and # instead special-case","to the graph. This method is nonetheless important when graph building for the","like to eagerly create/restore slot variables # when possible, but this may mean","= [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is","base class for optimizers. This class defines the API to add Ops to","to sum non-unique indices and their associated gradients, is enforced by first pre-processing","value def __getattribute__(self, name): \"\"\"Overridden to support hyperparameter access.\"\"\" try: return super(OptimizerV2, self).__getattribute__(name)","Args: values: A `Tensor` with rank >= 1. indices: A one-dimensional integer `Tensor`,","step update should be carried out under a graph # context. (eager updates","\"You called `set_weights(weights)` on optimizer \" + self._name + \" with a weight","\"\"\"Add ops to apply dense gradients to the variable `handle`. Args: grad: a","tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager","is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer)","name=None): \"\"\"Minimize `loss` by updating `var_list`. This method simply computes gradient using `tf.GradientTape`","* var1 + 2 * var2 # In eager mode, simply call minimize","var_list is None. \"\"\" # TODO(josh11b): Test that we handle weight decay in","kwargs[k])) self._use_locking = True self._name = name self._hyper = {} # dict: {variable","normal creation and adds restore ops to the graph. This method is nonetheless","ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \"Cannot use a constraint function","size, which is off by a factor equal to the number of replicas","absolute_import from __future__ import division from __future__ import print_function import abc import functools","if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay","the slot variable with a restoring initializer. No new variables are created when","reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars]","True def get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError(","The variable object this slot is being created for. \"\"\" variable_key = _var_key(variable)","an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a","this slot variable, or if we've pulled out an # existing slot variable,","use for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to be","= learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a hyperparameter that","custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a hyperparameter that can be a","override to allow other float types. Returns: Valid types for loss, variables and","you can ask the optimizer for the variable it created to hold the","None # For implementing Trackable. Stores information about how to restore # slot","gradient tensor is dense) - resource_apply_sparse (update variable given gradient tensor is sparse)","`loss` by updating `var_list`. This method simply computes gradient using `tf.GradientTape` and calls","apply dense gradients to the variable `handle`. Args: grad: a `Tensor` representing the","initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer,","variable `handle`. Args: grad: a `Tensor` representing the gradient. handle: a `Tensor` of","grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient, variable).","param() if callable(param) else param def _resource_apply_dense(self, grad, handle): \"\"\"Add ops to apply","to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate","self).__setattr__(name, value) def get_slot_names(self): \"\"\"A list of names for this optimizer's slots.\"\"\" return","`_apply_sparse`, the `indices` argument to this method has been de-duplicated. Optimizers which deal","weight = tf_variables.Variable( name=\"%s/%s\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value)","created slot variable # unintentionally (specifically make_template would add a dependency on #","grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient,","this Optimizer has run.\"\"\" if self._iterations is None: self._iterations = self.add_weight( \"iter\", shape=[],","can be a float, callable, or Tensor.\"\"\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule):","associated slot variable is created or restored. When executing eagerly, we create the","you intend to create your own optimization algorithm, simply inherit from this class","checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer is restored from","create this optimizer, such as a function used for a hyperparameter. Returns: An","This method is nonetheless important when graph building for the case when a","of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with Keras models","minimizes the loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1,","optimizer's slots.\"\"\" return self._slot_names def add_slot(self, var, slot_name, initializer=\"zeros\"): \"\"\"Add a new slot","name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \"\"\"Variable. The","var_list: list or tuple of `Variable` objects to update to minimize `loss`, or","OF ANY KIND, either express or implied. # See the License for the","backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights): params","sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to","\"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \"clipvalue\"): grads","averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not using these and you","saved without the optimizer; # it's a dependency hypergraph with edges of the","compatibility to allow time inverse decay of learning rate. `lr` is included for","is a callable that takes no argument and returns the value # to","from the config dictionary. Arguments: config: A Python dictionary, typically the output of","sparse) - create_slots (if your optimizer algorithm requires additional variables) - get_config (serialization","Use callable when the variable list would otherwise be incomplete before `minimize` and","sequential models without input shape pre-defined, or 2) subclassed models. Pass var_list as","this method has been de-duplicated. Optimizers which deal correctly with non-unique indices may","= \"learning_rate\" if name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name,","`indices` and `summed_values` contains the sum of `values` slices associated with each unique","use this class directly, but instead instantiate one of its subclasses such as","of the slots that it uses. Once you have a slot name you","that can be many times too big. ### Variable Constraint All Keras optimizers","\"iter\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if","_create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically. for name, value in","if not filtered: raise ValueError(\"No gradients provided for any variable: %s.\" % ([v.name","# pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division from __future__","not bind to a single graph, and so shouldn't keep Tensors as member","for details. By default the correct behavior, to sum non-unique indices and their","variable, or if we've pulled out an # existing slot variable, we should","if hasattr(self, \"_hyper\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value)","from the var unique id. If distribution strategy exists, get the primary variable","no argument and returns the value # to minimize. loss = lambda: 3","pairs. Variable is always present, but gradient can be `None`. Raises: TypeError: If","var1 + 2 * var2 # In eager mode, simply call minimize to","var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\"decay\", var_dtype)","capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy`","math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\"decay\",","currently we do not support using the optimizer object iself (e.g. through `apply_gradients`).","abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute","this optimizer, such as a function used for a hyperparameter. Returns: An optimizer","+ str(pv.shape) + \" not compatible with \" \"provided weight shape \" +","that scopes intended to catch # `variable` also catch its eagerly created slot","{} decay = kwargs.pop(\"decay\", 0.0) if decay < 0.: raise ValueError(\"decay cannot be","\" \"synchronization=VariableSynchronization.ON_READ.\") else: # Set trainable to be false when variable is to","in config: config[\"learning_rate\"] = config.pop(\"lr\") if \"learning_rate\" in config: if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"]","Python dictionary, typically the output of get_config. custom_objects: A Python dictionary mapping names","which is done automatically if you use `tf.keras` built-in training or evaluation loops.","slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() #","functions along with ops created by the original optimizer, but currently we do","\"Restoring functional Optimzers from SavedModels is not currently \" \"supported. Please file a","\"\"\"Add a new slot variable for `var`.\"\"\" if slot_name not in self._slot_names: self._slot_names.append(slot_name)","the variable `handle`. Args: grad: a `Tensor` representing the gradient. handle: a `Tensor`","filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError(\"No gradients provided for","in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\"Invalid type","example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] #","backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name]","name = \"learning_rate\" if name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self,","def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic","you want to average gradients, you should use `tf.math.reduce_sum` to add up your","= self.add_weight( \"iter\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self,","set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \"\"\"Call the function if param","and restore the Optimizer's config raise NotImplementedError( \"Restoring functional Optimzers from SavedModels is","3 * var1 + 2 * var2 # In eager mode, simply call","want to process the gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly","returned operation. Returns: An Operation that updates the variables in `var_list`. If `global_step`","arguments which returns the value to minimize. var_list: list or tuple of `Variable`","\"variable\". Note that \"gradient\" can be a `Tensor`, an `IndexedSlices`, or `None` if","return lr_t @abc.abstractmethod def get_config(self): \"\"\"Returns the config of the optimimizer. An optimizer","**kwargs): \"\"\"Create a new Optimizer. This must be called by the constructors of","not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value):","# checks that all keyword arguments are non-negative. if kwargs[k] < 0: raise","the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with","parameter. Hyper parameters can be overwritten through user code: Example: ```python # Create","is sparse) - create_slots (if your optimizer algorithm requires additional variables) - get_config","(gradient, variable) pairs where \"gradient\" is the gradient for \"variable\". Note that \"gradient\"","if isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying to update a Tensor \", var) if isinstance(grad,","slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer =","be repeated. Returns: An `Operation` which updates the value of the variable. \"\"\"","or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created","a training algorithm, report stats about the slots, etc. ### Hyper parameters These","be able to use the _set_hyper()/state.get_hyper() facility instead. This class in stateful and","learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value","var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]):","= value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers()","weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key","callable when the variable list would otherwise be incomplete before `minimize` and the","handle, indices): \"\"\"Add ops to apply sparse gradients to the variable `handle`. Similar","correctly with duplicate indices may instead override this method to avoid the overhead","import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import","# existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't","kwargs.pop(\"decay\", 0.0) if decay < 0.: raise ValueError(\"decay cannot be less than 0:","case slot variables have their own dependencies, in which case # those could","set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not","self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value): \"\"\"Override setattr to support","NotImplementedError(\"Trying to update a Tensor \", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is","can be overwritten through user code: Example: ```python # Create an optimizer with","import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context from","constraints. If constraint function is passed to any variable, the constraint will be","slot variable creation if there is an active variable creator # scope. Generally","are not using these and you want to average gradients, you should use","= functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with","update_op update_ops = [] with backend.name_scope(name or self._name): for grad, var in grads_and_vars:","be reinstantiated later (without any saved state) from this configuration. Returns: Python dictionary.","from this configuration. Returns: Python dictionary. \"\"\" config = {\"name\": self._name} if hasattr(self,","name self._hyper = {} # dict: {variable name : {slot name : variable}}","but the optimizer was expecting \" + str(len(params)) + \" weights. Provided weights:","__init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore","} self._deferred_slot_restorations = {} decay = kwargs.pop(\"decay\", 0.0) if decay < 0.: raise","slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter out iterable with grad equal to None.\"\"\" grads_and_vars =","Custom training loop with Keras models In Keras models, sometimes variables are created","applied to the variable after the gradient has been applied to the variable.","checks that all keyword arguments are non-negative. if kwargs[k] < 0: raise ValueError(\"Expected","dict: {variable name : {slot name : variable}} self._slots = {} self._slot_names =","gradient has been applied to the variable. Important: If gradient is sparse tensor,","sparse tensor, variable constraint is not supported. ### Thread Compatibility The entire optimizer","some arguments are invalid, or var_list is None. \"\"\" # TODO(josh11b): Test that","deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \"\"\"Restore a slot variable's value,","without the non-slot # variable, or if the non-slot variable is saved without","first component of a tensor's shape is the *replica-local* batch size, which is","ValueError: If some of the variables are not `Variable` objects. \"\"\" grads_and_vars =","dense) - resource_apply_sparse (update variable given gradient tensor is sparse) - create_slots (if","var.op.name) with backend.name_scope(\"update\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic =","graph mode the name is derived from the var shared name. In eager","not supported. ### Thread Compatibility The entire optimizer is currently thread compatible, not","gradient tensor is sparse) - create_slots (if your optimizer algorithm requires additional variables)","the optimizer, include all hyper parameters) \"\"\" def __init__(self, name, **kwargs): \"\"\"Create a","or agreed to in writing, software # distributed under the License is distributed","is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise","the slot variable `Trackable` object to be restored. slot_name: The name of this","functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var):","tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops","gradient: \" \"K.argmax, K.round, K.eval.\".format(param)) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for","This class defines the API to add Ops to train a model. You","types. Returns: Valid types for loss, variables and gradients. \"\"\" return set( [dtypes.float16,","representing the gradient. handle: a `Tensor` of dtype `resource` which points to the","may mean that scopes intended to catch # `variable` also catch its eagerly","rank >= 1. indices: A one-dimensional integer `Tensor`, indexing into the first dimension","(causing us to realize that the slot variable needs to be restored). Args:","not None: raise RuntimeError( \"Cannot use a constraint function on a sparse variable.\")","by the original optimizer, but currently we do not support using the optimizer","divide your loss by the global batch size, which is done automatically if","synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or","optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer.","variable for `var`.\"\"\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict","can be reinstantiated later (without any saved state) from this configuration. Returns: Python","slot variable's value.\"\"\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) #","will be called during `apply_gradients()` to get the value for the hyper parameter.","[] self._iterations = None # For implementing Trackable. Stores information about how to","dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \"\"\"Overridden to support","% ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\"Gradients does not","method), and then passed to `self._set_hyper()`. They can be either regular Python values","filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad","under the Apache License, Version 2.0 (the \"License\"); # you may not use","In eager mode, simply call minimize to update the list of variables. opt.minimize(loss,","(like 1.0), tensors, or callables. If they are callable, the callable will be","(i.e. are differentiable). \" \"Common ops without gradient: \" \"K.argmax, K.round, K.eval.\".format(param)) if","initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils","of `values` slices associated with each unique index. \"\"\" unique_indices, new_index_positions = array_ops.unique(indices)","These are called <i>Slots</i>. Slots have names and you can ask the optimizer","tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for","associated with any non-unique `indices`. Args: values: A `Tensor` with rank >= 1.","dictionary mapping names to additional Python objects used to create this optimizer, such","slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created:","variable to be updated. Returns: An `Operation` which updates the value of the","= self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly()","License. # You may obtain a copy of the License at # #","at the first time when `loss` is called. grad_loss: Optional. A `Tensor` holding","loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not.","sum non-unique indices and their associated gradients, is enforced by first pre-processing `grad`","with Keras optimizers. if name == \"lr\": name = \"learning_rate\" if hasattr(self, \"_hyper\")","for the returned operation. Returns: An Operation that updates the variables in `var_list`.","if \"lr\" in config: config[\"learning_rate\"] = config.pop(\"lr\") if \"learning_rate\" in config: if isinstance(config[\"learning_rate\"],","keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients","float, callable, or Tensor.\"\"\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if","tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework","from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from","variable). Do whatever you # need to the 'gradient' part, for example cap","part of `minimize()`. It returns a list of (gradient, variable) pairs where \"gradient\"","grad, param in zip(grads, params): if grad is None: raise ValueError(\"Variable {} has","self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \"\"\"Compute gradients of `loss` for the","\"\"\"Add ops to apply sparse gradients to `handle`, with repeated indices. Optimizers which","the constructors of subclasses. Note that Optimizer instances should not bind to a","hasattr(self, \"clipvalue\"): config[\"clipvalue\"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \"\"\"Creates","* var2 # In eager mode, simply call minimize to update the list","initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer","trainable: raise ValueError( \"Synchronization value can be set to \" \"VariableSynchronization.ON_READ only for","perform synchronization if necessary. ### Slots Many optimizer subclasses, such as `Adam` and","in three steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process the gradients","(serialization of the optimizer, include all hyper parameters) \"\"\" def __init__(self, name, **kwargs):","for g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g,","Optimizer.\"\"\" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division from","[ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return grads def apply_gradients(self,","self._hypers_created = True @property def iterations(self): \"\"\"Variable. The number of training steps this","\"learning_rate\" if name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value):","be referenced in functions along with ops created by the original optimizer, but","with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\"%s/%s\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype,","= opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to","to the variables. If you want to process the gradients before applying them","\"\"\"Override setattr to support dynamic hyperparameter setting.\"\"\" # Backwards compatibility with Keras optimizers.","used for a hyperparameter. Returns: An optimizer instance. \"\"\" if \"lr\" in config:","returns the value to minimize. var_list: list or tuple of `Variable` objects to","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","Optimizers which override this method must deal with repeated indices. See the docstring","As a result, using `tf.math.reduce_mean` will give the wrong answer, resulting in gradients","may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing","object this slot is being created for. \"\"\" variable_key = _var_key(variable) slot_dict =","None: # If we've either made this slot variable, or if we've pulled","the value of the variable. \"\"\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices):","never use this class directly, but instead instantiate one of its subclasses such","we handle weight decay in a reasonable way. with backprop.GradientTape() as tape: if","self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name] = value else: prev_value","`Variable` objects. Use callable when the variable list would otherwise be incomplete before","is not supported. ### Thread Compatibility The entire optimizer is currently thread compatible,","be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is","raise ValueError( \"Synchronization value can be set to \" \"VariableSynchronization.ON_READ only for non-trainable","a Tensor with dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step =","disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot","variables anywhere, and # instead special-case this dependency and otherwise pretend it's a","graph. if slot_variable is not None: # If we've either made this slot","each unique index. \"\"\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions,","= value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float,","except AttributeError as e: # Needed to avoid infinite recursion with __setattr__. if","grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not None: with","weights(self): \"\"\"Returns variables of this Optimizer based on the order created.\"\"\" return self._weights","during `apply_gradients()` to get the value for the hyper parameter. Hyper parameters can","if dtype not in valid_dtypes: raise ValueError(\"Invalid type %r for %s, expected: %s.\"","we do not support using the optimizer object iself (e.g. through `apply_gradients`). \"\"\"","a tensor's shape is the *replica-local* batch size, which is off by a","by the global batch size. Note that when using `tf.distribute.Strategy`, the first component","(callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value","# (aside from double initialization), and makes variable creator scopes # behave the","def _get_slot_key_from_var(var, slot_name): \"\"\"Get the slot key for the variable: var_name/slot_name.\"\"\" name =","This method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want","# Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use","variable. \"\"\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \"\"\"Add ops to apply","them to the variables. If you want to process the gradients before applying","\"Common ops without gradient: \" \"K.argmax, K.round, K.eval.\".format(param)) if hasattr(self, \"clipnorm\"): grads =","types for loss, variables and gradients. \"\"\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64])","`Tensor`, indexing into the first dimension of `values` (as in an IndexedSlices object).","minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom","how to restore # slot variables which have not yet been created #","for loss, variables and gradients. Subclasses should override to allow other float types.","called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. name: Optional","class Optimizer.\"\"\" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division","loss: A callable taking no arguments which returns the value to minimize. var_list:","distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\"%s/%s\" % (var._shared_name, slot_name), # pylint: disable=protected-access","be a float, callable, or Tensor.\"\"\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return","the primary variable first. Args: var: the variable. Returns: the unique name of","import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import","to None.\"\"\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = []","If constraint function is passed to any variable, the constraint will be applied","but gradient can be `None`. Raises: TypeError: If `var_list` contains anything else than","gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables","use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors are all","... }, # ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\"decay\", 0.0) if","either regular Python values (like 1.0), tensors, or callables. If they are callable,","revived_types.register_revived_type( \"optimizer\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1,","you wish. 3. Apply the processed gradients with `apply_gradients()`. Example: ```python # Create","def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([","License, Version 2.0 (the \"License\"); # you may not use this file except","__future__ import print_function import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context","realize that the slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition`","variables have gradients. \"\"\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v)","provided for any variable: %s.\" % ([v.name for _, v in grads_and_vars],)) if","desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no","ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For implementing the trackable","slot variables which have not yet been created # (trackable._CheckpointPosition objects). # {slot_name","mode the name is derived from the var shared name. In eager mode","2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache","the names of the slots that it uses. Once you have a slot","pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division from __future__ import","tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \"Synchronization value can be set to \" \"VariableSynchronization.ON_READ","variables and gradients. Subclasses should override to allow other float types. Returns: Valid","read. trainable = False elif trainable is None: trainable = True variable =","= slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer","= self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else:","ops.executing_eagerly_outside_functions() else \"_\" + var.op.name) with backend.name_scope(\"update\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var,","created when graph building. Instead, _restore_slot_variable catches these after normal creation and adds","return var._unique_id def _get_slot_key_from_var(var, slot_name): \"\"\"Get the slot key for the variable: var_name/slot_name.\"\"\"","other float types. Returns: Valid types for loss, variables and gradients. \"\"\" return","# when possible, but this may mean that scopes intended to catch #","gradient computed for `loss`. Returns: A list of (gradient, variable) pairs. Variable is","algorithm requires additional variables) - get_config (serialization of the optimizer, include all hyper","the configuration of an optimizer. The same optimizer can be reinstantiated later (without","import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import","values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \"\"\"Add ops","raise ValueError(\"Optimizer weight shape \" + str(pv.shape) + \" not compatible with \"","of the optimimizer. An optimizer config is a Python dictionary (serializable) containing the","a SavedModel. These variables may be referenced in functions along with ops created","by first pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers","\"\"\"Serialize a hyperparameter that can be a float, callable, or Tensor.\"\"\" value =","def _var_key(var): \"\"\"Key for representing a primary variable, for looking up slots. In","overhead of summing. Args: grad: a `Tensor` representing the gradient for the affected","loss, variables and gradients. Subclasses should override to allow other float types. Returns:","# {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... }","If `global_step` was not None, that operation also increments `global_step`. Raises: TypeError: If","immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def","gradient for \"variable\". Note that \"gradient\" can be a `Tensor`, an `IndexedSlices`, or","\"\"\"Updated base class for optimizers. This class defines the API to add Ops","use the optimizer in three steps: 1. Compute the gradients with `tf.GradientTape`. 2.","the following methods: - resource_apply_dense (update variable given gradient tensor is dense) -","def _call_if_callable(self, param): \"\"\"Call the function if param is callable.\"\"\" return param() if","one object (because we don't want to # save the slot variable if","return variable def _assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors are all valid types (see `_valid_dtypes`).","it's a dependency hypergraph with edges of the form (optimizer, non-slot # variable,","### Hyper parameters These are arguments passed to the optimizer subclass constructor (the","dynamic hyperparameter setting.\"\"\" # Backwards compatibility with Keras optimizers. if name == \"lr\":","Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or","\"\"\" # pylint: disable=protected-access # Get the distributed variable if it exists. if",">= 1. indices: A one-dimensional integer `Tensor`, indexing into the first dimension of","optimizer, such as a function used for a hyperparameter. Returns: An optimizer instance.","{} # dict: {variable name : {slot name : variable}} self._slots = {}","overhead. Args: grad: a `Tensor` representing the gradient for the affected indices. handle:","as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from","supported. ### Thread Compatibility The entire optimizer is currently thread compatible, not thread-safe.","referenced in functions along with ops created by the original optimizer, but currently","name is malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars. \"\"\"","an optimizer. The same optimizer can be reinstantiated later (without any saved state)","else \"_\" + var.op.name) with backend.name_scope(\"update\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,),","the order created.\"\"\" return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) #","params: List of variables. Returns: List of gradient tensors. Raises: ValueError: In case","ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers","is `tf.distribute.Strategy` aware, which means it automatically sums gradients across all replicas. To","zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \"\"\"Apply gradient to variable.\"\"\" if isinstance(var, ops.Tensor): raise","this `Optimizer`'s slot to restore into. variable: The variable object this slot is","RuntimeError: If _create_slots has been overridden instead of _create_vars. \"\"\" allowed_kwargs = {\"clipnorm\",","tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn)","instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has been overridden","= math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step =","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","an optimizer is restored from a SavedModel. These variables may be referenced in","parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument","gradients before applying them. Calling `minimize()` takes care of both computing the gradients","called during `apply_gradients()` to get the value for the hyper parameter. Hyper parameters","to the variable `handle`. Args: grad: a `Tensor` representing the gradient. handle: a","updates the value of the variable. \"\"\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices)","param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape","for v in valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid types for loss, variables and gradients.","callable, the callable will be called during `apply_gradients()` to get the value for","str(len(params)) + \" weights. Provided weights: \" + str(weights)[:50] + \"...\") if not","the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class is","list rather than the one with the highest restore # UID in case","variable. \"\"\" # pylint: disable=protected-access # Get the distributed variable if it exists.","self._name + \" with a weight list of length \" + str(len(weights)) +","math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform","variable. Important: If gradient is sparse tensor, variable constraint is not supported. ###",": # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations =","the gradient. handle: a `Tensor` of dtype `resource` which points to the variable","holding the gradient computed for `loss`. name: Optional name for the returned operation.","`None` for gradient. \" \"Please make sure that all of your ops have","`tf.math.reduce_sum` to add up your per-example losses and then divide by the global","import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import","created\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \"\"\"Get decayed learning rate as","an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We","of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable(","dtype `resource` which points to the variable to be updated. Returns: An `Operation`","update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop","subclassed models. Pass var_list as callable in these cases. Example: ```python opt =","inverse decay of learning rate. `lr` is included for backward compatibility, recommended to","some of the variables are not `Variable` objects. \"\"\" grads_and_vars = self._compute_gradients( loss,","if hasattr(self, \"clipvalue\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads","variable if it exists. if getattr(var, \"_distributed_container\", None) is not None: var =","of the optimizer, include all hyper parameters) \"\"\" def __init__(self, name, **kwargs): \"\"\"Create","Returns: List of gradient tensors. Raises: ValueError: In case any gradient cannot be","the correct behavior, to sum non-unique indices and their associated gradients, is enforced","a single step. As a result, using `tf.math.reduce_mean` will give the wrong answer,","dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable(","False elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape,","= lambda: 3 * var1 * var1 + 2 * var2 * var2","get the primary variable first. Args: var: the variable. Returns: the unique name","`tf.distribute.Strategy`, the first component of a tensor's shape is the *replica-local* batch size,","the indices for which the gradient is nonzero. Indices may be repeated. Returns:","\"lr\", \"decay\"} for k in kwargs: if k not in allowed_kwargs: raise TypeError(\"Unexpected","grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \"clipvalue\"): grads =","See the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to","gradients with `apply_gradients()`. Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) #","/ (1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \"\"\"Returns the","\" + self._name + \" with a weight list of length \" +","to the 'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1])","self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self,","if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if","their own dependencies, in which case # those could differ between restores. self._deferred_slot_restorations.setdefault(","support hyperparameter access.\"\"\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed","tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils","methods: - resource_apply_dense (update variable given gradient tensor is dense) - resource_apply_sparse (update","not None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params):","Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list","to train a model. You never use this class directly, but instead instantiate","`global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of the variables","slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID first to minimize","to use the _set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible. Args:","is not None: raise RuntimeError( \"Cannot use a constraint function on a sparse","additional Python objects used to create this optimizer, such as a function used","its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created = True def get_config(self):","opt_op.run() # In eager mode, simply call minimize to update the list of","Note that \"gradient\" can be a `Tensor`, an `IndexedSlices`, or `None` if there","time when `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed","g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v","the value to minimize. var_list: list or tuple of `Variable` objects to update","and # limitations under the License. # ============================================================================== \"\"\"Version 2 of class Optimizer.\"\"\"","to value. value can be callable, tensor, numeric.\"\"\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name,","since the variables are created at the first time `loss` is called. grad_loss:","recommended to use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If","Optional. A `Tensor` holding the gradient computed for `loss`. Returns: A list of","in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in","grads_and_vars: scope_name = (\"\" if ops.executing_eagerly_outside_functions() else \"_\" + var.op.name) with backend.name_scope(\"update\" +","of your ops have a \" \"gradient defined (i.e. are differentiable). \" \"Common","Provided weights: \" + str(weights)[:50] + \"...\") if not params: return weight_value_tuples =","(see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any tensor is","of `Variable` objects to update to minimize `loss`, or a callable returning the","`apply_gradients`). \"\"\" # TODO(allenl): Make the restored optimizer functional by tracing its apply","a dependency on # a slot variable if not for this case). Deferring","slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight","owned by any one object (because we don't want to # save the","return grads def apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply gradients to variables. This is the","RuntimeError(\"Cannot set `iterations` to a new Variable after \" \"the Optimizer weights have","of `get_config`, capable of instantiating the same optimizer from the config dictionary. Arguments:","run.\"\"\" if self._iterations is None: self._iterations = self.add_weight( \"iter\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA)","# Set trainable to be false when variable is to be synced on","or implied. # See the License for the specific language governing permissions and","Optional name for the returned operation. Default to the name passed to the","_resource_apply_sparse(self, grad, handle, indices): \"\"\"Add ops to apply sparse gradients to the variable","the slot variable. Defer restoring until it gets created # normally. We keep","ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA)","+ \" with a weight list of length \" + str(len(weights)) + \",","by the constructors of subclasses. Note that Optimizer instances should not bind to","grads_and_vars] # Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ###","ops to apply dense gradients to the variable `handle`. Args: grad: a `Tensor`","var_list=[var1, var2]) ``` ### Write a customized optimizer. If you intend to create","the function if param is callable.\"\"\" return param() if callable(param) else param def","using `tf.distribute.Strategy`, the first component of a tensor's shape is the *replica-local* batch","get the value for the hyper parameter. Hyper parameters can be overwritten through","indices for which the gradient is nonzero. Indices are unique. Returns: An `Operation`","the same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint:","reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss()","applying them to the variables. If you want to process the gradients before","In case any gradient cannot be computed (e.g. if gradient function not implemented).","result, using `tf.math.reduce_mean` will give the wrong answer, resulting in gradients that can","Defer slot variable creation if there is an active variable creator # scope.","if name == \"lr\": name = \"learning_rate\" if hasattr(self, \"_hyper\") and name in","not owned by any one object (because we don't want to # save","in grads] if hasattr(self, \"clipvalue\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g","add Ops to train a model. You never use this class directly, but","ValueError(\"No gradients provided for any variable: %s.\" % ([v.name for _, v in","\"\"\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \"\"\"Call the function","list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with Keras",">= 0, received: {}\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper =","updates the value of the variable. \"\"\" raise NotImplementedError() def _resource_scatter_add(self, x, i,","applying them. Calling `minimize()` takes care of both computing the gradients and applying","slot_name, variable, slot_variable): \"\"\"Restore a newly created slot variable's value.\"\"\" variable_key = _var_key(variable)","restore UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid,","if you use `tf.keras` built-in training or evaluation loops. See the `reduction` argument","scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i)","dependency and otherwise pretend it's a normal # graph. if slot_variable is not","if callable(param) else param def _resource_apply_dense(self, grad, handle): \"\"\"Add ops to apply dense","derived from the var unique id. If distribution strategy exists, get the primary","variable `Trackable` object to be restored. slot_name: The name of this `Optimizer`'s slot","not in allowed_kwargs: raise TypeError(\"Unexpected keyword argument \" \"passed to optimizer: \" +","import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import","return backend.get_value(value) return value def variables(self): \"\"\"Returns variables of this Optimizer based on","with grad equal to None.\"\"\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars","include all hyper parameters) \"\"\" def __init__(self, name, **kwargs): \"\"\"Create a new Optimizer.","\"lr\" in config: config[\"learning_rate\"] = config.pop(\"lr\") if \"learning_rate\" in config: if isinstance(config[\"learning_rate\"], dict):","ops have a \" \"gradient defined (i.e. are differentiable). \" \"Common ops without","self.weights if len(params) != len(weights): raise ValueError( \"You called `set_weights(weights)` on optimizer \"","variable) pairs. name: Optional name for the returned operation. Default to the name","_, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\"Gradients does not exist for variables","a feature request if this limitation bothers \" \"you.\") revived_types.register_revived_type( \"optimizer\", lambda obj:","math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations,","self.clipnorm = kwargs.pop(\"clipnorm\") if \"clipvalue\" in kwargs: self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created = False","original optimizer, but currently we do not support using the optimizer object iself","optimizer subclass constructor (the `__init__` method), and then passed to `self._set_hyper()`. They can","pulled out an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else:","to minimize. loss = lambda: 3 * var1 + 2 * var2 #","for loss, variables and gradients. \"\"\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def","variables. If you want to process the gradients before applying them you can","class OptimizerV2(trackable.Trackable): \"\"\"Updated base class for optimizers. This class defines the API to","your per-example losses and then divide by the global batch size. Note that","len(weights): raise ValueError( \"You called `set_weights(weights)` on optimizer \" + self._name + \"","and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices","learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name,","[] self._weights = [] self._iterations = None # For implementing Trackable. Stores information","double initialization), and makes variable creator scopes # behave the same way they","by any one object (because we don't want to # save the slot","should be carried out under a graph # context. (eager updates execute immediately)","indices): \"\"\"Sums `values` associated with any non-unique `indices`. Args: values: A `Tensor` with","update_ops = [] with backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name","tuple of `Variable` objects. Use callable when the variable list would otherwise be","initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\"%s/%s\" % (var._shared_name, slot_name),","True @property def iterations(self): \"\"\"Variable. The number of training steps this Optimizer has","useful if you want to log debug a training algorithm, report stats about","or if we've pulled out an # existing slot variable, we should restore","variable: The variable object this slot is being created for. \"\"\" variable_key =","pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically. for name,","var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically. for","the given variable. Args: loss: A callable taking no arguments which returns the","name == \"lr\": name = \"learning_rate\" if hasattr(self, \"_hyper\") and name in self._hyper:","instances should not bind to a single graph, and so shouldn't keep Tensors","return self._weights @property def weights(self): \"\"\"Returns variables of this Optimizer based on the","Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional name for the returned","can be a `Tensor`, an `IndexedSlices`, or `None` if there is no gradient","tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in","the unique name of the variable. \"\"\" # pylint: disable=protected-access # Get the","been de-duplicated. Optimizers which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices`","first called, instead of construction time. Examples include 1) sequential models without input","_var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None","name to use for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed","this method must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for","# context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops):","use this file except in compliance with the License. # You may obtain","iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call(","applied to the variable. Important: If gradient is sparse tensor, variable constraint is","var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \"\"\"Compute gradients of","stateful and thread-compatible. Args: name: A non-empty string. The name to use for","if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False,","learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized","Hyper parameters These are arguments passed to the optimizer subclass constructor (the `__init__`","ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies(","decay if \"clipnorm\" in kwargs: self.clipnorm = kwargs.pop(\"clipnorm\") if \"clipvalue\" in kwargs: self.clipvalue","loss_value = loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads =","A one-dimensional integer `Tensor`, indexing into the first dimension of `values` (as in","TypeError(\"Unexpected keyword argument \" \"passed to optimizer: \" + str(k)) # checks that","def get_slot_names(self): \"\"\"A list of names for this optimizer's slots.\"\"\" return self._slot_names def","raise RuntimeError(\"Cannot set `iterations` to a new Variable after \" \"the Optimizer weights","to check. Raises: ValueError: If any tensor is not a valid type. \"\"\"","`Variable` objects. ValueError: If some arguments are invalid, or var_list is None. \"\"\"","def _resource_apply_dense(self, grad, handle): \"\"\"Add ops to apply dense gradients to the variable","is not None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss,","and `Adagrad` allocate and manage additional variables associated with the variables to train.","Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0","weights: \" + str(weights)[:50] + \"...\") if not params: return weight_value_tuples = []","version of `indices` and `summed_values` contains the sum of `values` slices associated with","name, value): \"\"\"set hyper `name` to value. value can be callable, tensor, numeric.\"\"\"","in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name,","config of the optimimizer. An optimizer config is a Python dictionary (serializable) containing","`_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead override this method","returns the value # to minimize. loss = lambda: 3 * var1 *","gradients that can be many times too big. ### Variable Constraint All Keras","as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with the desired","call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this function. Args: loss: A","`grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly","variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot","that minimizes the loss by updating the listed # variables. opt_op = opt.minimize(loss,","\" \"the Optimizer weights have been created\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self,","do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue(","Variable Constraint All Keras optimizers respect variable constraints. If constraint function is passed","ops.Tensor): raise NotImplementedError(\"Trying to update a Tensor \", var) if isinstance(grad, ops.IndexedSlices): if","return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not None: raise RuntimeError(\"Cannot","primary variable, for looking up slots. In graph mode the name is derived","\"\"\"Returns gradients of `loss` with respect to `params`. Arguments: loss: Loss tensor. params:","name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value): \"\"\"Override setattr","the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations:","lambda: 3 * var1 + 2 * var2 # In eager mode, simply","with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means it automatically sums","function is passed to any variable, the constraint will be applied to the","for \"variable\". Note that \"gradient\" can be a `Tensor`, an `IndexedSlices`, or `None`","to be updated. Returns: An `Operation` which updates the value of the variable.","_var_key(var) return name + \"/\" + slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer implementation","tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context","can be `None`. Raises: TypeError: If `var_list` contains anything else than `Variable` objects.","using these and you want to average gradients, you should use `tf.math.reduce_sum` to","2 * var2 # In eager mode, simply call minimize to update the","var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \"clipnorm\"): grads =","\"synchronization=VariableSynchronization.ON_READ.\") else: # Set trainable to be false when variable is to be","gradients to variables. This is the second part of `minimize()`. It returns an","for the specific language governing permissions and # limitations under the License. #","Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means it automatically","We didn't make the slot variable. Defer restoring until it gets created #","add_weight(self, name, shape, dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype","distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop","be incomplete before `minimize` since the variables are created at the first time","off by a factor equal to the number of replicas being used to","Python dictionary mapping names to additional Python objects used to create this optimizer,","to train. These are called <i>Slots</i>. Slots have names and you can ask","weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name]","= _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self):","returns an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs.","Note that when using `tf.distribute.Strategy`, the first component of a tensor's shape is","non-slot variable is saved without the optimizer; # it's a dependency hypergraph with","have their own dependencies, in which case # those could differ between restores.","{`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip","var_dtype) decay_t = self._get_hyper(\"decay\", var_dtype) lr_t = lr_t / (1. + decay_t *","Returns: An `Operation` which updates the value of the variable. \"\"\" raise NotImplementedError()","value of the variable. \"\"\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad,","call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ###","grads_and_vars: List of (gradient, variable) pairs. name: Optional name for the returned operation.","\"\"\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] #","debug a training algorithm, report stats about the slots, etc. ### Hyper parameters","slots. In graph mode the name is derived from the var shared name.","or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value","The same optimizer can be reinstantiated later (without any saved state) from this","constructor (the `__init__` method), and then passed to `self._set_hyper()`. They can be either","This can be useful if you want to log debug a training algorithm,","restore ops to the graph. This method is nonetheless important when graph building","of the form (optimizer, non-slot # variable, variable)). So we don't _track_ slot","list would otherwise be incomplete before `minimize` since the variables are created at","unique index. \"\"\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0])","If they are callable, the callable will be called during `apply_gradients()` to get","\"\"\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values,","loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g)","w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype","as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from","vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) #","tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest","a weight list of length \" + str(len(weights)) + \", but the optimizer","which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If","compatible with \" \"provided weight shape \" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","the config of the optimimizer. An optimizer config is a Python dictionary (serializable)","slot key for the variable: var_name/slot_name.\"\"\" name = _var_key(var) return name + \"/\"","this Optimizer based on the order created.\"\"\" return self._weights @property def weights(self): \"\"\"Returns","names for this optimizer's slots.\"\"\" return self._slot_names def add_slot(self, var, slot_name, initializer=\"zeros\"): \"\"\"Add","ValueError: If name is malformed. RuntimeError: If _create_slots has been overridden instead of","Python dictionary. \"\"\" config = {\"name\": self._name} if hasattr(self, \"clipnorm\"): config[\"clipnorm\"] = self.clipnorm","the value of the variable. \"\"\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v):","constraint function is passed to any variable, the constraint will be applied to","value): \"\"\"Override setattr to support dynamic hyperparameter setting.\"\"\" # Backwards compatibility with Keras","\"Synchronization value can be set to \" \"VariableSynchronization.ON_READ only for non-trainable variables. \"","to apply dense gradients to the variable `handle`. Args: grad: a `Tensor` representing","information about how to restore # slot variables which have not yet been","= tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns","callable(param) else param def _resource_apply_dense(self, grad, handle): \"\"\"Add ops to apply dense gradients","= [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is","= _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is","distribution, grads_and_vars, name): \"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list","custom_objects: A Python dictionary mapping names to additional Python objects used to create","* var1 * var1 + 2 * var2 * var2 # In graph","on optimizer \" + self._name + \" with a weight list of length","a slot name you can ask the optimizer for the variable it created","var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \"clipnorm\"): grads","should not bind to a single graph, and so shouldn't keep Tensors as","`minimize` since the variables are created at the first time `loss` is called.","def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def","restored optimizer functional by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\")","+ \" weights. Provided weights: \" + str(weights)[:50] + \"...\") if not params:","clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops","scope_name = (\"\" if ops.executing_eagerly_outside_functions() else \"_\" + var.op.name) with backend.name_scope(\"update\" + scope_name):","self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key =","# distributed under the License is distributed on an \"AS IS\" BASIS, #","in grads_and_vars] # Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ```","context.executing_eagerly() or any_symbolic: # If the current context is graph mode or any","config[\"clipvalue\"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \"\"\"Creates an optimizer","\"\"\"Restore a newly created slot variable's value.\"\"\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get(","return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars:","= lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data:","of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an","gradients and applying them to the variables. If you want to process the","value of the variable. \"\"\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with","config dictionary. Arguments: config: A Python dictionary, typically the output of get_config. custom_objects:","updating `var_list`. This method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If","distributed variable if it exists. if getattr(var, \"_distributed_container\", None) is not None: var","by value, `decay` is included for backward compatibility to allow time inverse decay","such as `Adam` and `Adagrad` allocate and manage additional variables associated with the","behave the same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): #","operation also increments `global_step`. Raises: ValueError: If some of the variables are not","with dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype)","\" + str(k)) # checks that all keyword arguments are non-negative. if kwargs[k]","ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params)","# unintentionally (specifically make_template would add a dependency on # a slot variable","True self._name = name self._hyper = {} # dict: {variable name : {slot","member variables. Generally you should be able to use the _set_hyper()/state.get_hyper() facility instead.","raise NotImplementedError(\"Trying to update a Tensor \", var) if isinstance(grad, ops.IndexedSlices): if var.constraint","optimizer for the names of the slots that it uses. Once you have","value, `decay` is included for backward compatibility to allow time inverse decay of","the variable after the gradient has been applied to the variable. Important: If","_deduplicate_indexed_slices(values, indices): \"\"\"Sums `values` associated with any non-unique `indices`. Args: values: A `Tensor`","value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value()","not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w","is nonzero. Indices are unique. Returns: An `Operation` which updates the value of","a newly created slot variable's value.\"\"\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name,","AttributeError as e: # Needed to avoid infinite recursion with __setattr__. if name","dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is","an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of","# variable, variable)). So we don't _track_ slot variables anywhere, and # instead","any variable, the constraint will be applied to the variable after the gradient","if grad is None: raise ValueError(\"Variable {} has `None` for gradient. \" \"Please","# instead special-case this dependency and otherwise pretend it's a normal # graph.","\"\"\"Call the function if param is callable.\"\"\" return param() if callable(param) else param","models, sometimes variables are created when the model is first called, instead of","is enforced by first pre-processing `grad` and `indices` and passing them on to","Write a customized optimizer. If you intend to create your own optimization algorithm,","import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import","ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes","calls `apply_gradients()`. If you want to process the gradient before applying then call","for `loss`. name: Optional name for the returned operation. Returns: An Operation that","output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ```","tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with tf.GradientTape() as","new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated base class for","been applied to the variable. Important: If gradient is sparse tensor, variable constraint","with the License. # You may obtain a copy of the License at","or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else:","unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \"\"\"Add ops to apply sparse gradients to","in kwargs: if k not in allowed_kwargs: raise TypeError(\"Unexpected keyword argument \" \"passed","gradients by norm; `clipvalue` is clip gradients by value, `decay` is included for","indices may instead override this method to avoid the overhead of summing. Args:","optimizer functional by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created","var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \"\"\"Get the slot key for","loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run()","Tensors as member variables. Generally you should be able to use the _set_hyper()/state.get_hyper()","to `_apply_sparse`, the `indices` argument to this method has been de-duplicated. Optimizers which","is malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars. \"\"\" allowed_kwargs","0.0) if decay < 0.: raise ValueError(\"decay cannot be less than 0: {}\".format(decay))","constraint function on a sparse variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op =","= self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if","given gradient tensor is sparse) - create_slots (if your optimizer algorithm requires additional","gradients to `handle`, with repeated indices. Optimizers which override this method must deal","var)) filtered = tuple(filtered) if not filtered: raise ValueError(\"No gradients provided for any","Trackable. Stores information about how to restore # slot variables which have not","the variable. Important: If gradient is sparse tensor, variable constraint is not supported.","initializer=\"zeros\"): \"\"\"Add a new slot variable for `var`.\"\"\" if slot_name not in self._slot_names:","_resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # ---------------","%r for %s, expected: %s.\" % (dtype, t.name, [v for v in valid_dtypes]))","be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are","dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t","Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is","permissions and # limitations under the License. # ============================================================================== \"\"\"Version 2 of class","grad_loss) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if","name is derived from the var unique id. If distribution strategy exists, get","law or agreed to in writing, software # distributed under the License is","return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights):","optimizer algorithm requires additional variables) - get_config (serialization of the optimizer, include all","import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from","object indicating the slot variable `Trackable` object to be restored. slot_name: The name","- create_slots (if your optimizer algorithm requires additional variables) - get_config (serialization of","passed to the optimizer subclass constructor (the `__init__` method), and then passed to","`_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum non-unique indices and","creator scopes # behave the same way they do when graph building. and","functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util","from a SavedModel. These variables may be referenced in functions along with ops","objects. Use callable when the variable list would otherwise be incomplete before `minimize`","enforced by first pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`.","callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created =","values (like 1.0), tensors, or callables. If they are callable, the callable will","1. Compute the gradients with `tf.GradientTape`. 2. Process the gradients as you wish.","= {} self._slot_names = [] self._weights = [] self._iterations = None # For","Compatibility The entire optimizer is currently thread compatible, not thread-safe. The user needs","name = _var_key(var) return name + \"/\" + slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional","Raises: TypeError: If `var_list` contains anything else than `Variable` objects. ValueError: If some","them. Calling `minimize()` takes care of both computing the gradients and applying them","var2]) ``` ### Custom training loop with Keras models In Keras models, sometimes","objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ...","in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\"Gradients does not exist for variables %s when","``` ### Processing gradients before applying them. Calling `minimize()` takes care of both","self).__getattribute__(name) except AttributeError as e: # Needed to avoid infinite recursion with __setattr__.","+ str(weights)[:50] + \"...\") if not params: return weight_value_tuples = [] param_values =","mode or any of the update ops are # symbolic then the step","\"lr\": name = \"learning_rate\" if name in self._hyper: return self._get_hyper(name) raise e def","is not None: # If we've either made this slot variable, or if","variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores,","may instead override this method to avoid the overhead of summing. Args: grad:","from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as","k in kwargs: if k not in allowed_kwargs: raise TypeError(\"Unexpected keyword argument \"","for optimizers. This class defines the API to add Ops to train a","to support dynamic hyperparameter setting.\"\"\" # Backwards compatibility with Keras optimizers. if name","returns op that minimizes the loss by updating the listed # variables. opt_op","in compliance with the License. # You may obtain a copy of the","ValueError(\"Variable {} has `None` for gradient. \" \"Please make sure that all of","are # symbolic then the step update should be carried out under a","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","for v in vars_with_empty_grads])) return filtered def _var_key(var): \"\"\"Key for representing a primary","stats about the slots, etc. ### Hyper parameters These are arguments passed to","`Tensor` representing the gradient for the affected indices. handle: a `Tensor` of dtype","opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to update","Optimizer's config raise NotImplementedError( \"Restoring functional Optimzers from SavedModels is not currently \"","math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\"decay\", var_dtype) lr_t = lr_t / (1. + decay_t","import keras_export def _deduplicate_indexed_slices(values, indices): \"\"\"Sums `values` associated with any non-unique `indices`. Args:","gradients for a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function>","tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging","the variables are created at the first time when `loss` is called. grad_loss:","return filtered def _var_key(var): \"\"\"Key for representing a primary variable, for looking up","case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def","The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License,","`tf.GradientTape` and `apply_gradients()` explicitly instead of using this function. Args: loss: A callable","your optimizer algorithm requires additional variables) - get_config (serialization of the optimizer, include","float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self,","get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self,","*replica-local* batch size, which is off by a factor equal to the number","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","weight = slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types) or callable(initializer):","for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name]","from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from","order created.\"\"\" return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny):","gradient can be `None`. Raises: TypeError: If `var_list` contains anything else than `Variable`","return name + \"/\" + slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer implementation for","### Processing gradients before applying them. Calling `minimize()` takes care of both computing","method to avoid the overhead of summing. Args: grad: a `Tensor` representing the","optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage additional variables associated","self.clipvalue) for g in grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \"Cannot use","being created for. \"\"\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable =","# ============================================================================== \"\"\"Version 2 of class Optimizer.\"\"\" # pylint: disable=g-bad-name from __future__ import","in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\"Optimizer weight shape \"","# slot variables which have not yet been created # (trackable._CheckpointPosition objects). #","repeated indices. Optimizers which override this method must deal with repeated indices. See","_var_key(var): \"\"\"Key for representing a primary variable, for looking up slots. In graph","unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle,","the global batch size, which is done automatically if you use `tf.keras` built-in","callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list)","getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self,","t in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\"Invalid","should override to allow other float types. Returns: Valid types for loss, variables","\"gradient defined (i.e. are differentiable). \" \"Common ops without gradient: \" \"K.argmax, K.round,","See the `reduction` argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE`","value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight(","are unique. Returns: An `Operation` which updates the value of the variable. \"\"\"","with ops created by the original optimizer, but currently we do not support","variable. Defer restoring until it gets created # normally. We keep a list","= (\"\" if ops.executing_eagerly_outside_functions() else \"_\" + var.op.name) with backend.name_scope(\"update\" + scope_name): update_ops.extend(","[]).append( slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter out iterable with grad equal to None.\"\"\" grads_and_vars","slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables","it automatically sums gradients across all replicas. To average gradients, you divide your","# We didn't make the slot variable. Defer restoring until it gets created","\"supported. Please file a feature request if this limitation bothers \" \"you.\") revived_types.register_revived_type(","Keras models In Keras models, sometimes variables are created when the model is","= math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\"decay\", var_dtype) lr_t = lr_t / (1. +","summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable):","learning rate. `lr` is included for backward compatibility, recommended to use `learning_rate` instead.","on the order created.\"\"\" return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params)","that Optimizer instances should not bind to a single graph, and so shouldn't","dtype not in valid_dtypes: raise ValueError(\"Invalid type %r for %s, expected: %s.\" %","_create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \"\"\"Restore a slot variable's value, possibly creating it.","local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step","None) if weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer)","config, custom_objects=None): \"\"\"Creates an optimizer from its config. This method is the reverse","overridden instead of _create_vars. \"\"\" allowed_kwargs = {\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"} for k","the wrong answer, resulting in gradients that can be many times too big.","use the _set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible. Args: name:","value if callable(value): value = value() if dtype: return math_ops.cast(value, dtype) else: return","= self.weights if len(params) != len(weights): raise ValueError( \"You called `set_weights(weights)` on optimizer","variable creator # scope. Generally we'd like to eagerly create/restore slot variables #","the non-slot variable is saved without the optimizer; # it's a dependency hypergraph","model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn","after the gradient has been applied to the variable. Important: If gradient is","ask the optimizer for the names of the slots that it uses. Once","this function. Args: loss: A callable taking no arguments which returns the value","super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to avoid infinite recursion with","weight shape \" + str(pv.shape) + \" not compatible with \" \"provided weight","gradients. \"\"\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \"\"\"Call the","deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default","\"\"\" # TODO(allenl): Make the restored optimizer functional by tracing its apply #","# Get the distributed variable if it exists. if getattr(var, \"_distributed_container\", None) is","\" \"Please make sure that all of your ops have a \" \"gradient","learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not","It returns a list of (gradient, variable) pairs where \"gradient\" is the gradient","that \"gradient\" can be a `Tensor`, an `IndexedSlices`, or `None` if there is","slot_name, initializer=\"zeros\"): \"\"\"Add a new slot variable for `var`.\"\"\" if slot_name not in","malformed. ValueError: If none of the variables have gradients. \"\"\" grads_and_vars = _filter_grads(grads_and_vars)","their associated gradients, is enforced by first pre-processing `grad` and `indices` and passing","into. variable: The variable object this slot is being created for. \"\"\" variable_key","`values` associated with any non-unique `indices`. Args: values: A `Tensor` with rank >=","to add up your per-example losses and then divide by the global batch","is nonzero. Indices may be repeated. Returns: An `Operation` which updates the value","v for g, v in grads_and_vars if g is not None and v.dtype","context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return","if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value)","grad): \"\"\"Apply gradient to variable.\"\"\" if isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying to update a","pre-defined, or 2) subclassed models. Pass var_list as callable in these cases. Example:","in grads_and_vars if g is not None and v.dtype != dtypes.resource ]) return","pairs. name: Optional name for the returned operation. Default to the name passed","to the number of replicas being used to compute a single step. As","a `Tensor` of integral type representing the indices for which the gradient is","on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead override","variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot","name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \"\"\"A list","operation. Default to the name passed to the `Optimizer` constructor. Returns: An `Operation`","the config dictionary. Arguments: config: A Python dictionary, typically the output of get_config.","# normally. We keep a list rather than the one with the highest","if g is not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def","var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name or self._name): for grad,","v)]): return x.value() # --------------- # For implementing the trackable interface # ---------------","g in grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply gradients to","which have not yet been created # (trackable._CheckpointPosition objects). # {slot_name : #","[resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For implementing the trackable interface","you want to process the gradient before applying then call `tf.GradientTape` and `apply_gradients()`","is currently thread compatible, not thread-safe. The user needs to perform synchronization if","ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name or self._name):","= math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated","### Thread Compatibility The entire optimizer is currently thread compatible, not thread-safe. The","way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if","and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \"\"\"Returns gradients","Pass var_list as callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model","cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask","on the order created.\"\"\" return self._weights @property def weights(self): \"\"\"Returns variables of this","cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a hyperparameter that can be a float, callable,","share this logic with base_layer. def set_weights(self, weights): params = self.weights if len(params)","can instead use the optimizer in three steps: 1. Compute the gradients with","kwargs.pop(\"clipvalue\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None): \"\"\"Minimize `loss` by","# graph. if slot_variable is not None: # If we've either made this","apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops)","= True def get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise","from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from","The name to use for accumulators created for the optimizer. **kwargs: keyword arguments.","not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight =","3. Apply the processed gradients with `apply_gradients()`. Example: ```python # Create an optimizer.","var_list) def apply_grad_to_update_var(var, grad): \"\"\"Apply gradient to variable.\"\"\" if isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying","iterable with grad equal to None.\"\"\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return","revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export","These are arguments passed to the optimizer subclass constructor (the `__init__` method), and","\"_hyper\": raise e # Backwards compatibility with Keras optimizers. if name == \"lr\":","w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\"Optimizer weight shape","in valid_dtypes: raise ValueError(\"Invalid type %r for %s, expected: %s.\" % (dtype, t.name,","are created at the first time when `loss` is called. grad_loss: Optional. A","backend.name_scope(\"update\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation)","return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \"\"\"set hyper `name` to value. value can","var shared name. In eager mode the name is derived from the var","updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In","regular Python values (like 1.0), tensors, or callables. If they are callable, the","necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,),","variable constraints. If constraint function is passed to any variable, the constraint will","A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of `indices`","{}\".format(decay)) self._initial_decay = decay if \"clipnorm\" in kwargs: self.clipnorm = kwargs.pop(\"clipnorm\") if \"clipvalue\"","grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]): return","tensors): \"\"\"Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to","slot is being created for. \"\"\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {})","the slots, etc. ### Hyper parameters These are arguments passed to the optimizer","gv[1]) for gv in grads_and_vars] # Ask the optimizer to apply the capped","}, # ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\"decay\", 0.0) if decay","be updated. indices: a `Tensor` of integral type representing the indices for which","these after normal creation and adds restore ops to the graph. This method","avoid infinite recursion with __setattr__. if name == \"_hyper\": raise e # Backwards","training algorithm, report stats about the slots, etc. ### Hyper parameters These are","import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import","optimizer. If you intend to create your own optimization algorithm, simply inherit from","nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in zip(grads, params):","var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops =","def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def","carried out under a graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default():","TypeError: If `grads_and_vars` is malformed. ValueError: If none of the variables have gradients.","those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter","None.\"\"\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads","This method is the reverse of `get_config`, capable of instantiating the same optimizer","needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable","for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \"\"\"Apply","== tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \"Synchronization value can be set to \"","values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated base class","name: Optional name for the returned operation. Returns: An Operation that updates the","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def","grad is None: raise ValueError(\"Variable {} has `None` for gradient. \" \"Please make","order created.\"\"\" return self._weights @property def weights(self): \"\"\"Returns variables of this Optimizer based","which has an associated slot variable is created or restored. When executing eagerly,","compute a single step. As a result, using `tf.math.reduce_mean` will give the wrong","def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper values","tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the","included for backward compatibility to allow time inverse decay of learning rate. `lr`","# Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss`","`resource` which points to the variable to be updated. Returns: An `Operation` which","the current context is graph mode or any of the update ops are","will give the wrong answer, resulting in gradients that can be many times","for checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer is restored","self._hyper = {} # dict: {variable name : {slot name : variable}} self._slots","... ], ... }, # ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\"decay\",","self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars =","variable creator scopes # behave the same way they do when graph building.","variable def _assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors are all valid types (see `_valid_dtypes`). Args:","pylint: disable=protected-access # Get the distributed variable if it exists. if getattr(var, \"_distributed_container\",","indexing into the first dimension of `values` (as in an IndexedSlices object). Returns:","when an optimizer is restored from a SavedModel. These variables may be referenced","is not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name,","with each unique index. \"\"\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values,","name=\"%s/%s\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] =","variable given gradient tensor is dense) - resource_apply_sparse (update variable given gradient tensor","constructors of subclasses. Note that Optimizer instances should not bind to a single","or a callable returning the list or tuple of `Variable` objects. Use callable","ValueError(\"Optimizer weight shape \" + str(pv.shape) + \" not compatible with \" \"provided","not filtered: raise ValueError(\"No gradients provided for any variable: %s.\" % ([v.name for","subclasses. Note that Optimizer instances should not bind to a single graph, and","raise e def __setattr__(self, name, value): \"\"\"Override setattr to support dynamic hyperparameter setting.\"\"\"","variables to train. These are called <i>Slots</i>. Slots have names and you can","+ str(len(weights)) + \", but the optimizer was expecting \" + str(len(params)) +","to the name passed to the `Optimizer` constructor. Returns: An `Operation` that applies","already been created but `variable` has just been added to a dependency graph","(because we don't want to # save the slot variable if the optimizer","a callable that takes no argument and returns the value # to minimize.","\"clipnorm\"): config[\"clipnorm\"] = self.clipnorm if hasattr(self, \"clipvalue\"): config[\"clipvalue\"] = self.clipvalue return config @classmethod","for the case when a slot variable has already been created but `variable`","SavedModel. These variables may be referenced in functions along with ops created by","= _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest","creation and adds restore ops to the graph. This method is nonetheless important","= value() if dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name):","resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging","clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([","self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if dtype:","opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which","then the step update should be carried out under a graph # context.","= <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads]","UID in case slot variables have their own dependencies, in which case #","when the model is first called, instead of construction time. Examples include 1)","and adds restore ops to the graph. This method is nonetheless important when","a \" \"gradient defined (i.e. are differentiable). \" \"Common ops without gradient: \"","log debug a training algorithm, report stats about the slots, etc. ### Hyper","tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export","int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def","`resource` which points to the variable to be updated. indices: a `Tensor` of","0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer. If you intend","with a restoring initializer. No new variables are created when graph building. Instead,","Important: If gradient is sparse tensor, variable constraint is not supported. ### Thread","this file except in compliance with the License. # You may obtain a","hyperparameter access.\"\"\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to","if param is callable.\"\"\" return param() if callable(param) else param def _resource_apply_dense(self, grad,","from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from","the gradient is nonzero. Indices are unique. Returns: An `Operation` which updates the","applies the specified gradients. If `global_step` was not None, that operation also increments","Once you have a slot name you can ask the optimizer for the","@property def iterations(self): \"\"\"Variable. The number of training steps this Optimizer has run.\"\"\"","too big. ### Variable Constraint All Keras optimizers respect variable constraints. If constraint","is clip gradients by norm; `clipvalue` is clip gradients by value, `decay` is","has been overridden instead of _create_vars. \"\"\" allowed_kwargs = {\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"}","of get_config. custom_objects: A Python dictionary mapping names to additional Python objects used","# TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( \"Restoring functional Optimzers","op that minimizes the loss by updating the listed # variables. opt_op =","algorithm, simply inherit from this class and override the following methods: - resource_apply_dense","if self._hypers_created: return # Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()):","variable has already been created but `variable` has just been added to a","current context is graph mode or any of the update ops are #","synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors are all valid","var1 * var1 + 2 * var2 * var2 # In graph mode,","None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is","that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If","from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import","\"clipvalue\"): config[\"clipvalue\"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \"\"\"Creates an","else: # We didn't make the slot variable. Defer restoring until it gets","Make the restored optimizer functional by tracing its apply # methods. def __init__(self):","with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss,","\"\"\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss,","\"\"\"A list of names for this optimizer's slots.\"\"\" return self._slot_names def add_slot(self, var,","- resource_apply_sparse (update variable given gradient tensor is sparse) - create_slots (if your","with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with","If `grads_and_vars` is malformed. ValueError: If none of the variables have gradients. \"\"\"","representing a primary variable, for looking up slots. In graph mode the name","the 'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for","self._initial_decay = decay if \"clipnorm\" in kwargs: self.clipnorm = kwargs.pop(\"clipnorm\") if \"clipvalue\" in","weights. Provided weights: \" + str(weights)[:50] + \"...\") if not params: return weight_value_tuples","\" \"K.argmax, K.round, K.eval.\".format(param)) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g","License. # ============================================================================== \"\"\"Version 2 of class Optimizer.\"\"\" # pylint: disable=g-bad-name from __future__","Args: tensors: Tensors to check. Raises: ValueError: If any tensor is not a","grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered:","and makes variable creator scopes # behave the same way they do when","of the variable. \"\"\" # pylint: disable=protected-access # Get the distributed variable if","list of tuples (gradient, variable). Do whatever you # need to the 'gradient'","def _resource_apply_sparse(self, grad, handle, indices): \"\"\"Add ops to apply sparse gradients to the","gradients with `tf.GradientTape`. 2. Process the gradients as you wish. 3. Apply the","part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in","`Tensor`, an `IndexedSlices`, or `None` if there is no gradient for the given","initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \"\"\"Asserts","for which the gradient is nonzero. Indices may be repeated. Returns: An `Operation`","string. The name to use for accumulators created for the optimizer. **kwargs: keyword","grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list)","the slot variable if the optimizer is saved without the non-slot # variable,","Compute the gradients with `tf.GradientTape`. 2. Process the gradients as you wish. 3.","# (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... },","the first dimension of `values` (as in an IndexedSlices object). Returns: A tuple","feature request if this limitation bothers \" \"you.\") revived_types.register_revived_type( \"optimizer\", lambda obj: isinstance(obj,","[checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations = {} decay =","Stores information about how to restore # slot variables which have not yet","config.pop(\"lr\") if \"learning_rate\" in config: if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects)","configuration. Returns: Python dictionary. \"\"\" config = {\"name\": self._name} if hasattr(self, \"clipnorm\"): config[\"clipnorm\"]","raise TypeError(\"Unexpected keyword argument \" \"passed to optimizer: \" + str(k)) # checks","A list of (gradient, variable) pairs. Variable is always present, but gradient can","Backwards compatibility with Keras optimizers. if name == \"lr\": name = \"learning_rate\" if","def iterations(self, variable): if self._iterations is not None: raise RuntimeError(\"Cannot set `iterations` to","file a feature request if this limitation bothers \" \"you.\") revived_types.register_revived_type( \"optimizer\", lambda","nonetheless important when graph building for the case when a slot variable has","a graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access","restoring until it gets created # normally. We keep a list rather than","tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils","return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated base class for optimizers. This","ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name})","def _filter_grads(grads_and_vars): \"\"\"Filter out iterable with grad equal to None.\"\"\" grads_and_vars = tuple(grads_and_vars)","self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for","= True @property def iterations(self): \"\"\"Variable. The number of training steps this Optimizer","variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable","list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05","return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate","variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with Keras models In","e def __setattr__(self, name, value): \"\"\"Override setattr to support dynamic hyperparameter setting.\"\"\" #","associated gradients, is enforced by first pre-processing `grad` and `indices` and passing them","if gradient function not implemented). \"\"\" params = nest.flatten(params) with backend.get_graph().as_default(): grads =","= self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable)","indices for which the gradient is nonzero. Indices may be repeated. Returns: An","non-slot # variable, variable)). So we don't _track_ slot variables anywhere, and #","to the variable. Important: If gradient is sparse tensor, variable constraint is not","the name is derived from the var unique id. If distribution strategy exists,","var2 * var2 # In graph mode, returns op that minimizes the loss","the overhead of summing. Args: grad: a `Tensor` representing the gradient for the","def get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( \"Restoring","\"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for","None: raise RuntimeError( \"Cannot use a constraint function on a sparse variable.\") return","with repeated indices. Optimizers which override this method must deal with repeated indices.","An `Operation` which updates the value of the variable. \"\"\" raise NotImplementedError() def","any non-unique `indices`. Args: values: A `Tensor` with rank >= 1. indices: A","if \"clipnorm\" in kwargs: self.clipnorm = kwargs.pop(\"clipnorm\") if \"clipvalue\" in kwargs: self.clipvalue =","Optimizer based on the order created.\"\"\" return self._weights @property def weights(self): \"\"\"Returns variables","hyperparameters when an optimizer is restored from a SavedModel. These variables may be","model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input,","= lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing","if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \"Cannot use a","is callable.\"\"\" return param() if callable(param) else param def _resource_apply_dense(self, grad, handle): \"\"\"Add","value, possibly creating it. Called when a variable which has an associated slot","logging.warning( (\"Gradients does not exist for variables %s when minimizing the loss.\"), ([v.name","self._deferred_slot_restorations = {} decay = kwargs.pop(\"decay\", 0.0) if decay < 0.: raise ValueError(\"decay","a hyperparameter. Returns: An optimizer instance. \"\"\" if \"lr\" in config: config[\"learning_rate\"] =","`set_weights(weights)` on optimizer \" + self._name + \" with a weight list of","def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() #","big. ### Variable Constraint All Keras optimizers respect variable constraints. If constraint function","defines the API to add Ops to train a model. You never use","instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python #","and hyperparameters when an optimizer is restored from a SavedModel. These variables may","the value # to minimize. loss = lambda: 3 * var1 + 2","the variable it created to hold the slot value. This can be useful","lr_t = self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t =","gradient. handle: a `Tensor` of dtype `resource` which points to the variable to","want to log debug a training algorithm, report stats about the slots, etc.","pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal","checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned","self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return #","with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self,","tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable","updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return","as member variables. Generally you should be able to use the _set_hyper()/state.get_hyper() facility","variable, for looking up slots. In graph mode the name is derived from","is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial(","try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to avoid infinite","use a constraint function on a sparse variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices)","not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable,","[]) # Iterate over restores, highest restore UID first to minimize the number","correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args:","all replicas. To average gradients, you divide your loss by the global batch","the output of get_config. custom_objects: A Python dictionary mapping names to additional Python","to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not using","[] with backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name = (\"\"","+ str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO,","None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable","intend to create your own optimization algorithm, simply inherit from this class and","size. Note that when using `tf.distribute.Strategy`, the first component of a tensor's shape","`lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","class defines the API to add Ops to train a model. You never","import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import","`apply_gradients()`. Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the","before `minimize` since the variables are created at the first time `loss` is","_resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self,","self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \"\"\"Get decayed learning rate as a Tensor with dtype=var_dtype.\"\"\"","grads = gradients.gradients(loss, params) for grad, param in zip(grads, params): if grad is","to hold the slot value. This can be useful if you want to","for variables %s when minimizing the loss.\"), ([v.name for v in vars_with_empty_grads])) return","grads_and_vars def get_gradients(self, loss, params): \"\"\"Returns gradients of `loss` with respect to `params`.","user code: Example: ```python # Create an optimizer with the desired parameters. opt","self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \"\"\"A list of names for","Constraint All Keras optimizers respect variable constraints. If constraint function is passed to","gradient is nonzero. Indices may be repeated. Returns: An `Operation` which updates the","self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not None: raise","into the first dimension of `values` (as in an IndexedSlices object). Returns: A","arguments are invalid, or var_list is None. \"\"\" # TODO(josh11b): Test that we","var_list = [v for (_, v) in grads_and_vars] # Create iteration if necessary.","dtype `resource` which points to the variable to be updated. indices: a `Tensor`","with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss,","replicas. To average gradients, you divide your loss by the global batch size,","any of the update ops are # symbolic then the step update should","gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this","dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \"\"\"Call the function if param is callable.\"\"\"","initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \"Synchronization value","for g, v in grads_and_vars if g is not None and v.dtype !=","has been de-duplicated. Optimizers which deal correctly with non-unique indices may instead override","from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import","gradients provided for any variable: %s.\" % ([v.name for _, v in grads_and_vars],))","variables are created at the first time `loss` is called. grad_loss: Optional. A","otherwise pretend it's a normal # graph. if slot_variable is not None: #","language governing permissions and # limitations under the License. # ============================================================================== \"\"\"Version 2","params): \"\"\"Returns gradients of `loss` with respect to `params`. Arguments: loss: Loss tensor.","`loss` for the variables in `var_list`. This is the first part of `minimize()`.","isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value,","return value if callable(value): value = value() if dtype: return math_ops.cast(value, dtype) else:","required by applicable law or agreed to in writing, software # distributed under","_create_slots has been overridden instead of _create_vars. \"\"\" allowed_kwargs = {\"clipnorm\", \"clipvalue\", \"lr\",","setting.\"\"\" # Backwards compatibility with Keras optimizers. if name == \"lr\": name =","v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For implementing","all of your ops have a \" \"gradient defined (i.e. are differentiable). \"","listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode,","self._hypers_created: return # Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if","using the optimizer object iself (e.g. through `apply_gradients`). \"\"\" # TODO(allenl): Make the","get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( \"Restoring functional","return update_op update_ops = [] with backend.name_scope(name or self._name): for grad, var in","ValueError(\"decay cannot be less than 0: {}\".format(decay)) self._initial_decay = decay if \"clipnorm\" in","on read. trainable = False elif trainable is None: trainable = True variable","graph. This method is nonetheless important when graph building for the case when","methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created = True def get_config(self): # TODO(allenl): Save","avoid the overhead of summing. Args: grad: a `Tensor` representing the gradient for","ops created by the original optimizer, but currently we do not support using","name for the returned operation. Returns: An Operation that updates the variables in","variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads =","dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return","from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from","x.value() # --------------- # For implementing the trackable interface # --------------- def _restore_slot_variable(self,","```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn =","slots that it uses. Once you have a slot name you can ask","= [] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else:","slot variable. Defer restoring until it gets created # normally. We keep a","a slot variable's value, possibly creating it. Called when a variable which has","is being created for. \"\"\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable","of `minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars: List of","self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore the Optimizer's config","cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid')","harmless # (aside from double initialization), and makes variable creator scopes # behave","to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue`","or var_list is None. \"\"\" # TODO(josh11b): Test that we handle weight decay","grad: a `Tensor` representing the gradient for the affected indices. handle: a `Tensor`","\"\"\"Add ops to apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`,","the loss.\"), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): \"\"\"Key for","self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not None:","in kwargs: self.clipnorm = kwargs.pop(\"clipnorm\") if \"clipvalue\" in kwargs: self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created","representing the indices for which the gradient is nonzero. Indices may be repeated.","import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import","the var unique id. If distribution strategy exists, get the primary variable first.","[v for (_, v) in grads_and_vars] # Create iteration if necessary. with ops.init_scope():","update to minimize `loss`, or a callable returning the list or tuple of","non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters when an","are arguments passed to the optimizer subclass constructor (the `__init__` method), and then","to the `Optimizer` constructor. Returns: An `Operation` that applies the specified gradients. If","[clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \"clipvalue\"): grads = [ clip_ops.clip_by_value(g,","from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from","false when variable is to be synced on read. trainable = False elif","tensor, numeric.\"\"\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper:","is None: raise ValueError(\"Variable {} has `None` for gradient. \" \"Please make sure","a dependency hypergraph with edges of the form (optimizer, non-slot # variable, variable)).","\"\"\"Filter out iterable with grad equal to None.\"\"\" grads_and_vars = tuple(grads_and_vars) if not","add_slot(self, var, slot_name, initializer=\"zeros\"): \"\"\"Add a new slot variable for `var`.\"\"\" if slot_name","-self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v","is included for backward compatibility to allow time inverse decay of learning rate.","way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","to use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots","it gets created # normally. We keep a list rather than the one","differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter out iterable","\" \"You have specified trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\") else: # Set trainable to","\"You have specified trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\") else: # Set trainable to be","not. If you are not using these and you want to average gradients,","backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name is malformed.","the original optimizer, but currently we do not support using the optimizer object","Keras models, sometimes variables are created when the model is first called, instead","If distribution strategy exists, get the primary variable first. Args: var: the variable.","grad: a `Tensor` representing the gradient. handle: a `Tensor` of dtype `resource` which","based on the order created.\"\"\" return self._weights @property def weights(self): \"\"\"Returns variables of","= lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self):","shouldn't keep Tensors as member variables. Generally you should be able to use","symbolic then the step update should be carried out under a graph #","# Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a","single step. As a result, using `tf.math.reduce_mean` will give the wrong answer, resulting","created at the first time `loss` is called. grad_loss: Optional. A `Tensor` holding","var_list = [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def","shape, dtype=None, initializer=\"zeros\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32","\"\"\" def __init__(self, name, **kwargs): \"\"\"Create a new Optimizer. This must be called","# For implementing Trackable. Stores information about how to restore # slot variables","in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value,","for k in kwargs: if k not in allowed_kwargs: raise TypeError(\"Unexpected keyword argument","could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \"\"\"Filter out","anywhere, and # instead special-case this dependency and otherwise pretend it's a normal","be callable, tensor, numeric.\"\"\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not","ask the optimizer for the variable it created to hold the slot value.","values: A `Tensor` with rank >= 1. indices: A one-dimensional integer `Tensor`, indexing","self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types)","in kwargs: self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None,","--------------- # For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable,","\"\"\"Restore a slot variable's value, possibly creating it. Called when a variable which","restore the Optimizer's config raise NotImplementedError( \"Restoring functional Optimzers from SavedModels is not","\" \"you.\") revived_types.register_revived_type( \"optimizer\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1,","and their associated gradients, is enforced by first pre-processing `grad` and `indices` and","var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ###","your ops have a \" \"gradient defined (i.e. are differentiable). \" \"Common ops","`grads_and_vars` is malformed. ValueError: If none of the variables have gradients. \"\"\" grads_and_vars","variables in `var_list`. If `global_step` was not `None`, that operation also increments `global_step`.","variable, or if the non-slot variable is saved without the optimizer; # it's","var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to update the","These variables may be referenced in functions along with ops created by the","you divide your loss by the global batch size, which is done automatically","or evaluation loops. See the `reduction` argument of your loss which should be","or 2) subclassed models. Pass var_list as callable in these cases. Example: ```python","can be set to \" \"VariableSynchronization.ON_READ only for non-trainable variables. \" \"You have","### Variable Constraint All Keras optimizers respect variable constraints. If constraint function is","called, instead of construction time. Examples include 1) sequential models without input shape","None. \"\"\" # TODO(josh11b): Test that we handle weight decay in a reasonable","\"\"\"Apply gradients to variables. This is the second part of `minimize()`. It returns","`reduction` argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging","\"\"\"Key for representing a primary variable, for looking up slots. In graph mode","and otherwise pretend it's a normal # graph. if slot_variable is not None:","of a tensor's shape is the *replica-local* batch size, which is off by","[(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer to apply the","the variable: var_name/slot_name.\"\"\" name = _var_key(var) return name + \"/\" + slot_name class","state) from this configuration. Returns: Python dictionary. \"\"\" config = {\"name\": self._name} if","when minimizing the loss.\"), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var):","wish. 3. Apply the processed gradients with `apply_gradients()`. Example: ```python # Create an","= 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer. If you","import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import","v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\"Gradients does not exist for variables %s","`decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by value,","integral type representing the indices for which the gradient is nonzero. Indices are","the first component of a tensor's shape is the *replica-local* batch size, which","List of variables. Returns: List of gradient tensors. Raises: ValueError: In case any","from_config(cls, config, custom_objects=None): \"\"\"Creates an optimizer from its config. This method is the","variables. \" \"You have specified trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\") else: # Set trainable","with Keras optimizers. if name == \"lr\": name = \"learning_rate\" if name in","Optimizer has run.\"\"\" if self._iterations is None: self._iterations = self.add_weight( \"iter\", shape=[], dtype=dtypes.int64,","instead of _create_vars. \"\"\" allowed_kwargs = {\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"} for k in","`self._set_hyper()`. They can be either regular Python values (like 1.0), tensors, or callables.","bind to a single graph, and so shouldn't keep Tensors as member variables.","# Needed to avoid infinite recursion with __setattr__. if name == \"_hyper\": raise","to avoid this overhead. Args: grad: a `Tensor` representing the gradient for the","support dynamic hyperparameter setting.\"\"\" # Backwards compatibility with Keras optimizers. if name ==","update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate","if self._iterations is not None: raise RuntimeError(\"Cannot set `iterations` to a new Variable","aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer):","apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class","will be applied to the variable after the gradient has been applied to","backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule","tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops","the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`.","optimizer: \" + str(k)) # checks that all keyword arguments are non-negative. if","taking no arguments which returns the value to minimize. var_list: list or tuple","opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them. Calling `minimize()` takes care","is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = []","name not in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value)","executing eagerly, we create the slot variable with a restoring initializer. No new","`tf.GradientTape`. 2. Process the gradients as you wish. 3. Apply the processed gradients","grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads =","to eagerly create/restore slot variables # when possible, but this may mean that","the variable. \"\"\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \"\"\"Add ops to","one-dimensional integer `Tensor`, indexing into the first dimension of `values` (as in an","get_config. custom_objects: A Python dictionary mapping names to additional Python objects used to","Tensors to check. Raises: ValueError: If any tensor is not a valid type.","# you may not use this file except in compliance with the License.","= False elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name,","`indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate","raise ValueError(\"Variable {} has `None` for gradient. \" \"Please make sure that all","to get the value for the hyper parameter. Hyper parameters can be overwritten","use `tf.keras` built-in training or evaluation loops. See the `reduction` argument of your","if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \"\"\"Returns variables of this Optimizer","`unique_indices` is a de-duplicated version of `indices` and `summed_values` contains the sum of","lr_t = lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod def","in valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid types for loss, variables and gradients. Subclasses should","allow time inverse decay of learning rate. `lr` is included for backward compatibility,","= _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is","2. Process the gradients as you wish. 3. Apply the processed gradients with","dtype) else: return value def __getattribute__(self, name): \"\"\"Overridden to support hyperparameter access.\"\"\" try:","is created or restored. When executing eagerly, we create the slot variable with","params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if","with respect to `params`. Arguments: loss: Loss tensor. params: List of variables. Returns:","variables are created when the model is first called, instead of construction time.","pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight)","\", but the optimizer was expecting \" + str(len(params)) + \" weights. Provided","hasattr(self, \"_hyper\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def","non-trainable variables. \" \"You have specified trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\") else: # Set","of `values` (as in an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`)","K.round, K.eval.\".format(param)) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads]","are not `Variable` objects. \"\"\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars,","is included for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If","```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for","Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage additional variables","computed for `loss`. name: Optional name for the returned operation. Returns: An Operation","by updating `var_list`. This method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`.","variables in `var_list`. This is the first part of `minimize()`. It returns a","overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors):","variables and gradients. \"\"\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param):","any saved state) from this configuration. Returns: Python dictionary. \"\"\" config = {\"name\":","is not a valid type. \"\"\" valid_dtypes = self._valid_dtypes() for t in tensors:","def apply_grad_to_update_var(var, grad): \"\"\"Apply gradient to variable.\"\"\" if isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying to","the gradient computed for `loss`. name: Optional name for the returned operation. Returns:","class and override the following methods: - resource_apply_dense (update variable given gradient tensor","sure that all of your ops have a \" \"gradient defined (i.e. are","six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util","the variable to be updated. indices: a `Tensor` of integral type representing the","and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an","Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has been overridden instead","`get_config`, capable of instantiating the same optimizer from the config dictionary. Arguments: config:","gradients across all replicas. To average gradients, you divide your loss by the","If none of the variables have gradients. \"\"\" grads_and_vars = _filter_grads(grads_and_vars) var_list =","be set to \" \"VariableSynchronization.ON_READ only for non-trainable variables. \" \"You have specified","[] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if","list of (gradient, variable) pairs where \"gradient\" is the gradient for \"variable\". Note","base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops","its eagerly created slot variable # unintentionally (specifically make_template would add a dependency","for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\"Gradients does not exist for","unique. Returns: An `Operation` which updates the value of the variable. \"\"\" raise","or any of the update ops are # symbolic then the step update","`loss`, or a callable returning the list or tuple of `Variable` objects. Use","dense gradients to the variable `handle`. Args: grad: a `Tensor` representing the gradient.","valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid types for loss, variables and gradients. Subclasses should override","with Keras models In Keras models, sometimes variables are created when the model","code: Example: ```python # Create an optimizer with the desired parameters. opt =","process the gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of","docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum non-unique","\" + str(pv.shape) + \" not compatible with \" \"provided weight shape \"","IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated","objects used to create this optimizer, such as a function used for a","with rank >= 1. indices: A one-dimensional integer `Tensor`, indexing into the first","with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For implementing the","slot variable # unintentionally (specifically make_template would add a dependency on # a","local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\"decay\", var_dtype) lr_t = lr_t / (1.","return self._get_hyper(name) raise e def __setattr__(self, name, value): \"\"\"Override setattr to support dynamic","context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util","callable will be called during `apply_gradients()` to get the value for the hyper","length \" + str(len(weights)) + \", but the optimizer was expecting \" +","graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with","saved without the non-slot # variable, or if the non-slot variable is saved","the step update should be carried out under a graph # context. (eager","optimizer object iself (e.g. through `apply_gradients`). \"\"\" # TODO(allenl): Make the restored optimizer","`values` slices associated with each unique index. \"\"\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values","dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \"\"\"Returns gradients of `loss` with","= kwargs.pop(\"clipvalue\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None): \"\"\"Minimize `loss`","var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \"\"\"Get the slot","License for the specific language governing permissions and # limitations under the License.","_compute_gradients(self, loss, var_list, grad_loss=None): \"\"\"Compute gradients of `loss` for the variables in `var_list`.","Slots have names and you can ask the optimizer for the names of","method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want to","= variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \"\"\"Get decayed learning rate as a Tensor","get_config(self): \"\"\"Returns the config of the optimimizer. An optimizer config is a Python","that takes no argument and returns the value # to minimize. loss =","creation if there is an active variable creator # scope. Generally we'd like","= trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are","isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper # pylint: disable=protected-access","objects. ValueError: If some arguments are invalid, or var_list is None. \"\"\" #","not implemented). \"\"\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for","to add Ops to train a model. You never use this class directly,","def _distributed_apply(self, distribution, grads_and_vars, name): \"\"\"`apply_gradients` using a `DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM,","user needs to perform synchronization if necessary. ### Slots Many optimizer subclasses, such","to use for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to","= tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g","any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or any_symbolic:","= backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape !=","`Tensor` of dtype `resource` which points to the variable to be updated. indices:","\"License\"); # you may not use this file except in compliance with the","--------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \"\"\"Restore a newly created slot variable's value.\"\"\"","catches these after normal creation and adds restore ops to the graph. This","eager mode the name is derived from the var unique id. If distribution","tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output)","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","if weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value","continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True","that we handle weight decay in a reasonable way. with backprop.GradientTape() as tape:","(\"Gradients does not exist for variables %s when minimizing the loss.\"), ([v.name for","`loss`. Returns: A list of (gradient, variable) pairs. Variable is always present, but","update should be carried out under a graph # context. (eager updates execute","eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1,","to a single graph, and so shouldn't keep Tensors as member variables. Generally","``` ### Write a customized optimizer. If you intend to create your own","shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight =","return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to avoid infinite recursion","trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable,","specific language governing permissions and # limitations under the License. # ============================================================================== \"\"\"Version","`global_step` was not None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars`","`variable` has just been added to a dependency graph (causing us to realize","backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape:","slot variable `Trackable` object to be restored. slot_name: The name of this `Optimizer`'s","to be restored. slot_name: The name of this `Optimizer`'s slot to restore into.","var_list, grad_loss) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads]","for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}.","that when using `tf.distribute.Strategy`, the first component of a tensor's shape is the","to the optimizer subclass constructor (the `__init__` method), and then passed to `self._set_hyper()`.","`var`.\"\"\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key,","def iterations(self): \"\"\"Variable. The number of training steps this Optimizer has run.\"\"\" if","\" + str(len(params)) + \" weights. Provided weights: \" + str(weights)[:50] + \"...\")","used to compute a single step. As a result, using `tf.math.reduce_mean` will give","them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the","for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]","is a Python dictionary (serializable) containing the configuration of an optimizer. The same","trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\") else: # Set trainable to be false when variable","self._weights = [] self._iterations = None # For implementing Trackable. Stores information about","the variable. \"\"\" # pylint: disable=protected-access # Get the distributed variable if it","v in grads_and_vars if g is not None and v.dtype != dtypes.resource ])","else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property","_assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors","and `apply_gradients()` explicitly instead of using this function. Args: loss: A callable taking","Loss tensor. params: List of variables. Returns: List of gradient tensors. Raises: ValueError:","(as in an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices`","`Tensor` of dtype `resource` which points to the variable to be updated. Returns:","to the variable to be updated. indices: a `Tensor` of integral type representing","no arguments which returns the value to minimize. var_list: list or tuple of","return param() if callable(param) else param def _resource_apply_dense(self, grad, handle): \"\"\"Add ops to","`global_step`. Raises: ValueError: If some of the variables are not `Variable` objects. \"\"\"","Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer","variables which have not yet been created # (trackable._CheckpointPosition objects). # {slot_name :","self.add_weight( \"iter\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable):","math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \"\"\"Overridden to support hyperparameter access.\"\"\"","model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before","[process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","the constraint will be applied to the variable after the gradient has been","the optimizer for the variable it created to hold the slot value. This","for this optimizer's slots.\"\"\" return self._slot_names def add_slot(self, var, slot_name, initializer=\"zeros\"): \"\"\"Add a","in writing, software # distributed under the License is distributed on an \"AS","\"learning_rate\" in config: if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config)","with base_layer. def set_weights(self, weights): params = self.weights if len(params) != len(weights): raise","callable when the variable list would otherwise be incomplete before `minimize` since the","minimize. var_list: list or tuple of `Variable` objects to update to minimize `loss`,","would otherwise be incomplete before `minimize` since the variables are created at the","v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \"\"\"set hyper `name`","\"\"\" allowed_kwargs = {\"clipnorm\", \"clipvalue\", \"lr\", \"decay\"} for k in kwargs: if k","TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights): params = self.weights","Default to the name passed to the `Optimizer` constructor. Returns: An `Operation` that","callables. If they are callable, the callable will be called during `apply_gradients()` to","operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none","For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \"\"\"Restore","grad equal to None.\"\"\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered","Optimizers which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid","using a `DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _,","slot to restore into. variable: The variable object this slot is being created","can be many times too big. ### Variable Constraint All Keras optimizers respect","= lambda: 3 * var1 + 2 * var2 # In eager mode,","but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage","tensor, variable constraint is not supported. ### Thread Compatibility The entire optimizer is","the variables to train. These are called <i>Slots</i>. Slots have names and you","config raise NotImplementedError( \"Restoring functional Optimzers from SavedModels is not currently \" \"supported.","gradients of `loss` for the variables in `var_list`. This is the first part","as callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential()","expecting \" + str(len(params)) + \" weights. Provided weights: \" + str(weights)[:50] +","`tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not using these","# variable, or if the non-slot variable is saved without the optimizer; #","dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer =","weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params,","TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( \"Restoring functional Optimzers from","unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices)","with the highest restore # UID in case slot variables have their own","hyper `name` to value. value can be callable, tensor, numeric.\"\"\" if isinstance(value, trackable.Trackable):","is not None: raise RuntimeError(\"Cannot set `iterations` to a new Variable after \"","# TODO(josh11b): Test that we handle weight decay in a reasonable way. with","clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return grads def apply_gradients(self, grads_and_vars,","value. This can be useful if you want to log debug a training","[self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \"\"\"set hyper `name` to value. value can be","this method to avoid the overhead of summing. Args: grad: a `Tensor` representing","restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to","created for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`,","created when the model is first called, instead of construction time. Examples include","the gradients for a list of variables. with tf.GradientTape() as tape: loss =","`indices`. Args: values: A `Tensor` with rank >= 1. indices: A one-dimensional integer","contains the sum of `values` slices associated with each unique index. \"\"\" unique_indices,","names and you can ask the optimizer for the names of the slots","params = self.weights if len(params) != len(weights): raise ValueError( \"You called `set_weights(weights)` on","training or evaluation loops. See the `reduction` argument of your loss which should","_serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a hyperparameter that can be a float, callable, or Tensor.\"\"\"","create/restore slot variables # when possible, but this may mean that scopes intended","behavior, to sum non-unique indices and their associated gradients, is enforced by first","variable)). So we don't _track_ slot variables anywhere, and # instead special-case this","passed to `self._set_hyper()`. They can be either regular Python values (like 1.0), tensors,","less than 0: {}\".format(decay)) self._initial_decay = decay if \"clipnorm\" in kwargs: self.clipnorm =","number of training steps this Optimizer has run.\"\"\" if self._iterations is None: self._iterations","self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID first to","variable if the optimizer is saved without the non-slot # variable, or if","self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return","weights): if pv.shape != w.shape: raise ValueError(\"Optimizer weight shape \" + str(pv.shape) +","for g in grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply gradients","types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any tensor","vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError(\"No gradients","ValueError(\"Expected {} >= 0, received: {}\".format(k, kwargs[k])) self._use_locking = True self._name = name","name + \"/\" + slot_name class _RestoredOptimizer(OptimizerV2): \"\"\"A non-functional Optimizer implementation for checkpoint","tensors: Tensors to check. Raises: ValueError: If any tensor is not a valid","valid_dtypes: raise ValueError(\"Invalid type %r for %s, expected: %s.\" % (dtype, t.name, [v","value can be callable, tensor, numeric.\"\"\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if","is graph mode or any of the update ops are # symbolic then","In Keras models, sometimes variables are created when the model is first called,","\"_hyper\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self):","to `params`. Arguments: loss: Loss tensor. params: List of variables. Returns: List of","if slot_variable is not None: # If we've either made this slot variable,","value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if","param is callable.\"\"\" return param() if callable(param) else param def _resource_apply_dense(self, grad, handle):","= self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in","else: # Set trainable to be false when variable is to be synced","the gradients and applying them to the variables. If you want to process","v in valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid types for loss, variables and gradients. Subclasses","if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation","and so shouldn't keep Tensors as member variables. Generally you should be able","slot_name): \"\"\"Get the slot key for the variable: var_name/slot_name.\"\"\" name = _var_key(var) return","divide by the global batch size. Note that when using `tf.distribute.Strategy`, the first","prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value,","variable list would otherwise be incomplete before `minimize` since the variables are created","# Backwards compatibility with Keras optimizers. if name == \"lr\": name = \"learning_rate\"","a `Tensor` of dtype `resource` which points to the variable to be updated.","= self._get_hyper(\"decay\", var_dtype) lr_t = lr_t / (1. + decay_t * local_step) return","replicas being used to compute a single step. As a result, using `tf.math.reduce_mean`","creating it. Called when a variable which has an associated slot variable is","self._slots = {} self._slot_names = [] self._weights = [] self._iterations = None #","both computing the gradients and applying them to the variables. If you want","### Slots Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage","variable with a restoring initializer. No new variables are created when graph building.","being used to compute a single step. As a result, using `tf.math.reduce_mean` will","which the gradient is nonzero. Indices may be repeated. Returns: An `Operation` which","new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta)","get_slot_names(self): \"\"\"A list of names for this optimizer's slots.\"\"\" return self._slot_names def add_slot(self,","not for this case). Deferring is mostly harmless # (aside from double initialization),","callable.\"\"\" return param() if callable(param) else param def _resource_apply_dense(self, grad, handle): \"\"\"Add ops","weight shape \" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None,","2 of class Optimizer.\"\"\" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__","not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value):","important when graph building for the case when a slot variable has already","Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be","= self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)):","Args: var: the variable. Returns: the unique name of the variable. \"\"\" #","as `Adam` and `Adagrad` allocate and manage additional variables associated with the variables","or callables. If they are callable, the callable will be called during `apply_gradients()`","lr_t @abc.abstractmethod def get_config(self): \"\"\"Returns the config of the optimimizer. An optimizer config","this overhead. Args: grad: a `Tensor` representing the gradient for the affected indices.","Generally we'd like to eagerly create/restore slot variables # when possible, but this","2.0 (the \"License\"); # you may not use this file except in compliance","the optimizer subclass constructor (the `__init__` method), and then passed to `self._set_hyper()`. They","objects to update to minimize `loss`, or a callable returning the list or","def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created = True def get_config(self): # TODO(allenl): Save and","are invalid, or var_list is None. \"\"\" # TODO(josh11b): Test that we handle","return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \"\"\"Overridden to support hyperparameter","created slot variable's value.\"\"\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, [])","restore # UID in case slot variables have their own dependencies, in which","a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars =","the first time `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient","compatibility with Keras optimizers. if name == \"lr\": name = \"learning_rate\" if hasattr(self,","optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This","variables. Generally you should be able to use the _set_hyper()/state.get_hyper() facility instead. This","+ str(k)) # checks that all keyword arguments are non-negative. if kwargs[k] <","return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \"\"\"Call the function if","Returns: An `Operation` which updates the value of the variable. \"\"\" summed_grad, unique_indices","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the","Called when a variable which has an associated slot variable is created or","save the slot variable if the optimizer is saved without the non-slot #","the var shared name. In eager mode the name is derived from the","function not implemented). \"\"\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params)","[v for v in valid_dtypes])) def _valid_dtypes(self): \"\"\"Valid types for loss, variables and","to a dependency graph (causing us to realize that the slot variable needs","True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization,","else: return update_op update_ops = [] with backend.name_scope(name or self._name): for grad, var","Optimizer based on the order created.\"\"\" return self._weights def get_weights(self): params = self.weights","# # Unless required by applicable law or agreed to in writing, software","Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of","if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \"\"\"Get the slot key","\" not compatible with \" \"provided weight shape \" + str(w.shape)) weight_value_tuples.append((p, w))","If we've either made this slot variable, or if we've pulled out an","synchronization if necessary. ### Slots Many optimizer subclasses, such as `Adam` and `Adagrad`","express or implied. # See the License for the specific language governing permissions","self).__init__(\"_RestoredOptimizer\") self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore the Optimizer's","# `loss` is a callable that takes no argument and returns the value","\"\"\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self,","ops to the graph. This method is nonetheless important when graph building for","and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an active variable","\"clipnorm\" in kwargs: self.clipnorm = kwargs.pop(\"clipnorm\") if \"clipvalue\" in kwargs: self.clipvalue = kwargs.pop(\"clipvalue\")","dependency hypergraph with edges of the form (optimizer, non-slot # variable, variable)). So","sum of `values` slices associated with each unique index. \"\"\" unique_indices, new_index_positions =","tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda:","handle: a `Tensor` of dtype `resource` which points to the variable to be","`tf.GradientTape` and calls `apply_gradients()`. If you want to process the gradient before applying","that all keyword arguments are non-negative. if kwargs[k] < 0: raise ValueError(\"Expected {}","either express or implied. # See the License for the specific language governing","built-in training or evaluation loops. See the `reduction` argument of your loss which","'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv","not compatible with \" \"provided weight shape \" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples)","should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer","the License. # ============================================================================== \"\"\"Version 2 of class Optimizer.\"\"\" # pylint: disable=g-bad-name from","var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list):","number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable)","following methods: - resource_apply_dense (update variable given gradient tensor is dense) - resource_apply_sparse","graph mode or any of the update ops are # symbolic then the","must be called by the constructors of subclasses. Note that Optimizer instances should","rate. `lr` is included for backward compatibility, recommended to use `learning_rate` instead. Raises:","checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \"\"\"Restore a slot variable's value, possibly","and `summed_values` contains the sum of `values` slices associated with each unique index.","of learning rate. `lr` is included for backward compatibility, recommended to use `learning_rate`","gradients, you should use `tf.math.reduce_sum` to add up your per-example losses and then","filtered = tuple(filtered) if not filtered: raise ValueError(\"No gradients provided for any variable:","optimimizer. An optimizer config is a Python dictionary (serializable) containing the configuration of","don't _track_ slot variables anywhere, and # instead special-case this dependency and otherwise","from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from","than `Variable` objects. ValueError: If some arguments are invalid, or var_list is None.","the variables in `var_list`. If `global_step` was not `None`, that operation also increments","else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError(\"No gradients provided","for `loss`. Returns: A list of (gradient, variable) pairs. Variable is always present,","`Optimizer`'s slot to restore into. variable: The variable object this slot is being","distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\"name\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \"\"\"`apply_gradients` using a","raise NotImplementedError( \"Restoring functional Optimzers from SavedModels is not currently \" \"supported. Please","dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors","slot value. This can be useful if you want to log debug a","shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \"\"\"Variable. The number","= tuple(filtered) if not filtered: raise ValueError(\"No gradients provided for any variable: %s.\"","\"Cannot use a constraint function on a sparse variable.\") return self._resource_apply_sparse_duplicate_indices( grad.values, var,","nonzero. Indices are unique. Returns: An `Operation` which updates the value of the","tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking","with the variables to train. These are called <i>Slots</i>. Slots have names and","the License. # You may obtain a copy of the License at #","first time when `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient","no gradient for the given variable. Args: loss: A callable taking no arguments","the number of replicas being used to compute a single step. As a","of variables. Returns: List of gradient tensors. Raises: ValueError: In case any gradient","factor equal to the number of replicas being used to compute a single","is None: self._iterations = self.add_weight( \"iter\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations","assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self,","object iself (e.g. through `apply_gradients`). \"\"\" # TODO(allenl): Make the restored optimizer functional","_set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible. Args: name: A non-empty","self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \"\"\"Variable.","of length \" + str(len(weights)) + \", but the optimizer was expecting \"","to catch # `variable` also catch its eagerly created slot variable # unintentionally","in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of tuples","dependency on # a slot variable if not for this case). Deferring is","non-empty string. The name to use for accumulators created for the optimizer. **kwargs:","the returned operation. Default to the name passed to the `Optimizer` constructor. Returns:","under the License. # ============================================================================== \"\"\"Version 2 of class Optimizer.\"\"\" # pylint: disable=g-bad-name","super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \"\"\"A list of names for this optimizer's slots.\"\"\"","grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply gradients to variables. This","applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional name for","create_slots (if your optimizer algorithm requires additional variables) - get_config (serialization of the","raise ValueError(\"Invalid type %r for %s, expected: %s.\" % (dtype, t.name, [v for","of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of `indices` and `summed_values`","and gradients. Subclasses should override to allow other float types. Returns: Valid types","grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list,","= weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name):","from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \"\"\"Sums `values` associated with any non-unique","tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model","for a hyperparameter. Returns: An optimizer instance. \"\"\" if \"lr\" in config: config[\"learning_rate\"]","tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created = True def","loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list,","non-slot # variable, or if the non-slot variable is saved without the optimizer;","or `tf.keras.losses.Reduction.SUM` for not. If you are not using these and you want","the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes","Variable is always present, but gradient can be `None`. Raises: TypeError: If `var_list`","created.\"\"\" return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe","by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\"_RestoredOptimizer\") self._hypers_created = True","out under a graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): #","Keras optimizers respect variable constraints. If constraint function is passed to any variable,","= slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer","the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm`","from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from","in config: if isinstance(config[\"learning_rate\"], dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config) def","restored from a SavedModel. These variables may be referenced in functions along with","in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \"\"\"A list of","to `self._set_hyper()`. They can be either regular Python values (like 1.0), tensors, or","= self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def","`Operation` which updates the value of the variable. \"\"\" summed_grad, unique_indices = _deduplicate_indexed_slices(","\"optimizer\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper","object to be restored. slot_name: The name of this `Optimizer`'s slot to restore","in gradients that can be many times too big. ### Variable Constraint All","its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer","_valid_dtypes(self): \"\"\"Valid types for loss, variables and gradients. Subclasses should override to allow","it uses. Once you have a slot name you can ask the optimizer","raise ValueError( \"You called `set_weights(weights)` on optimizer \" + self._name + \" with","it. Called when a variable which has an associated slot variable is created","\" \"gradient defined (i.e. are differentiable). \" \"Common ops without gradient: \" \"K.argmax,","= t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\"Invalid type %r for %s,","thread-compatible. Args: name: A non-empty string. The name to use for accumulators created","functional Optimzers from SavedModels is not currently \" \"supported. Please file a feature","you use `tf.keras` built-in training or evaluation loops. See the `reduction` argument of","grads_and_vars, name=None): \"\"\"Apply gradients to variables. This is the second part of `minimize()`.","- get_config (serialization of the optimizer, include all hyper parameters) \"\"\" def __init__(self,","]) return grads_and_vars def get_gradients(self, loss, params): \"\"\"Returns gradients of `loss` with respect","slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an active variable creator","# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","you should use `tf.math.reduce_sum` to add up your per-example losses and then divide","type. \"\"\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if","for the variables in `var_list`. This is the first part of `minimize()`. It","`Operation` which updates the value of the variable. \"\"\" raise NotImplementedError() def _resource_scatter_add(self,","ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads,","for this case). Deferring is mostly harmless # (aside from double initialization), and","Calling `minimize()` takes care of both computing the gradients and applying them to","minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning","if len(params) != len(weights): raise ValueError( \"You called `set_weights(weights)` on optimizer \" +","process the gradients before applying them you can instead use the optimizer in","name passed to the `Optimizer` constructor. Returns: An `Operation` that applies the specified","var_list as callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model =","specified trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\") else: # Set trainable to be false when","name=None): \"\"\"Apply gradients to variables. This is the second part of `minimize()`. It","for gv in grads_and_vars] # Ask the optimizer to apply the capped gradients.","when `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed for","def _restore_slot_variable(self, slot_name, variable, slot_variable): \"\"\"Restore a newly created slot variable's value.\"\"\" variable_key","grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars =","that the slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object","the slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating","logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util","slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot","raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return","print_function import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx","def variables(self): \"\"\"Returns variables of this Optimizer based on the order created.\"\"\" return","ValueError(\"Invalid type %r for %s, expected: %s.\" % (dtype, t.name, [v for v","for non-trainable variables. \" \"You have specified trainable=True and \" \"synchronization=VariableSynchronization.ON_READ.\") else: #","var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ###","looking up slots. In graph mode the name is derived from the var","of names for this optimizer's slots.\"\"\" return self._slot_names def add_slot(self, var, slot_name, initializer=\"zeros\"):","`clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by value, `decay`","Please file a feature request if this limitation bothers \" \"you.\") revived_types.register_revived_type( \"optimizer\",","create your own optimization algorithm, simply inherit from this class and override the","is derived from the var unique id. If distribution strategy exists, get the","# update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write","grad, handle, indices): \"\"\"Add ops to apply sparse gradients to `handle`, with repeated","be false when variable is to be synced on read. trainable = False","we don't _track_ slot variables anywhere, and # instead special-case this dependency and","<i>Slots</i>. Slots have names and you can ask the optimizer for the names","needs to perform synchronization if necessary. ### Slots Many optimizer subclasses, such as","not thread-safe. The user needs to perform synchronization if necessary. ### Slots Many","= gradients.gradients(loss, params) for grad, param in zip(grads, params): if grad is None:","handle): \"\"\"Add ops to apply dense gradients to the variable `handle`. Args: grad:","slot_variable): \"\"\"Restore a newly created slot variable's value.\"\"\" variable_key = _var_key(variable) deferred_restorations =","we create the slot variable with a restoring initializer. No new variables are","variable.\"\"\" if isinstance(var, ops.Tensor): raise NotImplementedError(\"Trying to update a Tensor \", var) if","Note that Optimizer instances should not bind to a single graph, and so","# it's a dependency hypergraph with edges of the form (optimizer, non-slot #","it exists. if getattr(var, \"_distributed_container\", None) is not None: var = var._distributed_container() if","received: {}\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper = {} #","self._get_hyper(\"learning_rate\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype)","the returned operation. Returns: An Operation that updates the variables in `var_list`. If","unique name of the variable. \"\"\" # pylint: disable=protected-access # Get the distributed","Reserved. # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","any one object (because we don't want to # save the slot variable","= [] self._weights = [] self._iterations = None # For implementing Trackable. Stores","grads def apply_gradients(self, grads_and_vars, name=None): \"\"\"Apply gradients to variables. This is the second","variable object this slot is being created for. \"\"\" variable_key = _var_key(variable) slot_dict","# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under","able to use the _set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible.","_set_hyper(self, name, value): \"\"\"set hyper `name` to value. value can be callable, tensor,","initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy =","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","instead of construction time. Examples include 1) sequential models without input shape pre-defined,","not `None`, that operation also increments `global_step`. Raises: ValueError: If some of the","dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value","function. Args: loss: A callable taking no arguments which returns the value to","iterations(self, variable): if self._iterations is not None: raise RuntimeError(\"Cannot set `iterations` to a","if g is not None and v.dtype != dtypes.resource ]) return grads_and_vars def","a new Variable after \" \"the Optimizer weights have been created\") self._iterations =","reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \"\"\"Restore","{} self._slot_names = [] self._weights = [] self._iterations = None # For implementing","K.eval.\".format(param)) if hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if","kwargs: if k not in allowed_kwargs: raise TypeError(\"Unexpected keyword argument \" \"passed to","train. These are called <i>Slots</i>. Slots have names and you can ask the","an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name:","indices. handle: a `Tensor` of dtype `resource` which points to the variable to","dict): config[\"learning_rate\"] = learning_rate_schedule.deserialize( config[\"learning_rate\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \"\"\"Serialize a","callable, or Tensor.\"\"\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value):","Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients","with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default the","if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not","variable is saved without the optimizer; # it's a dependency hypergraph with edges","optimizer is currently thread compatible, not thread-safe. The user needs to perform synchronization","variable is created or restored. When executing eagerly, we create the slot variable","`Adam` and `Adagrad` allocate and manage additional variables associated with the variables to","name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \"\"\"Compute gradients of `loss` for the variables","slot_variable is not None: # If we've either made this slot variable, or","Subclasses should override to allow other float types. Returns: Valid types for loss,","callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \"\"\"Returns variables","callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden,","the value of the variable. \"\"\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return","allocate and manage additional variables associated with the variables to train. These are","variable given gradient tensor is sparse) - create_slots (if your optimizer algorithm requires","also catch its eagerly created slot variable # unintentionally (specifically make_template would add","is the gradient for \"variable\". Note that \"gradient\" can be a `Tensor`, an","to realize that the slot variable needs to be restored). Args: slot_variable_position: A","updated. Returns: An `Operation` which updates the value of the variable. \"\"\" raise","as a function used for a hyperparameter. Returns: An optimizer instance. \"\"\" if","list or tuple of `Variable` objects to update to minimize `loss`, or a","from __future__ import print_function import abc import functools import six from tensorflow.python.distribute import","Returns: A list of (gradient, variable) pairs. Variable is always present, but gradient","bothers \" \"you.\") revived_types.register_revived_type( \"optimizer\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(),","hasattr(self, \"clipnorm\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \"clipvalue\"):","if we've pulled out an # existing slot variable, we should restore it.","\"\"\"Overridden to support hyperparameter access.\"\"\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e:","= self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \"\"\"Creates an optimizer from","points to the variable to be updated. indices: a `Tensor` of integral type","]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \"\"\"set hyper `name` to value. value","variable first. Args: var: the variable. Returns: the unique name of the variable.","is a de-duplicated version of `indices` and `summed_values` contains the sum of `values`","backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list","is an active variable creator # scope. Generally we'd like to eagerly create/restore","self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor,","= {} # dict: {variable name : {slot name : variable}} self._slots =","= \"learning_rate\" if hasattr(self, \"_hyper\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2,","\"the Optimizer weights have been created\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype):","is not currently \" \"supported. Please file a feature request if this limitation","import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as","graph building for the case when a slot variable has already been created","existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make","\"clipvalue\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return","and then passed to `self._set_hyper()`. They can be either regular Python values (like","callable that takes no argument and returns the value # to minimize. loss","\"\"\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \"\"\"Add ops to apply sparse","Returns: An optimizer instance. \"\"\" if \"lr\" in config: config[\"learning_rate\"] = config.pop(\"lr\") if","global batch size. Note that when using `tf.distribute.Strategy`, the first component of a","mode, returns op that minimizes the loss by updating the listed # variables.","kwargs: self.clipvalue = kwargs.pop(\"clipvalue\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None):","`var_list`. This is the first part of `minimize()`. It returns a list of","instantiating the same optimizer from the config dictionary. Arguments: config: A Python dictionary,","# TODO(allenl): Make the restored optimizer functional by tracing its apply # methods.","keep Tensors as member variables. Generally you should be able to use the","except in compliance with the License. # You may obtain a copy of","ValueError: If some arguments are invalid, or var_list is None. \"\"\" # TODO(josh11b):","which returns the value to minimize. var_list: list or tuple of `Variable` objects","= [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var,","If gradient is sparse tensor, variable constraint is not supported. ### Thread Compatibility","reinstantiated later (without any saved state) from this configuration. Returns: Python dictionary. \"\"\"","a `Tensor` representing the gradient for the affected indices. handle: a `Tensor` of","if necessary. ### Slots Many optimizer subclasses, such as `Adam` and `Adagrad` allocate","NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \"\"\"Add ops to apply sparse gradients to","`variable` also catch its eagerly created slot variable # unintentionally (specifically make_template would","would add a dependency on # a slot variable if not for this","self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not None: raise RuntimeError(\"Cannot set","(ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value)","same optimizer from the config dictionary. Arguments: config: A Python dictionary, typically the","unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\"keras.optimizers.Optimizer\") class OptimizerV2(trackable.Trackable): \"\"\"Updated base class for optimizers. This class defines","Args: loss: A callable taking no arguments which returns the value to minimize.","Arguments: loss: Loss tensor. params: List of variables. Returns: List of gradient tensors.","(aside from double initialization), and makes variable creator scopes # behave the same","may not use this file except in compliance with the License. # You","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","these and you want to average gradients, you should use `tf.math.reduce_sum` to add","... } self._deferred_slot_restorations = {} decay = kwargs.pop(\"decay\", 0.0) if decay < 0.:","a `DistributionStrategy`.\"\"\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v","pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\"Optimizer","restore into. variable: The variable object this slot is being created for. \"\"\"","If name is malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars.","if hasattr(self, \"clipnorm\"): config[\"clipnorm\"] = self.clipnorm if hasattr(self, \"clipvalue\"): config[\"clipvalue\"] = self.clipvalue return","loss, var_list, grad_loss=None, name=None): \"\"\"Minimize `loss` by updating `var_list`. This method simply computes","# UID in case slot variables have their own dependencies, in which case","are created when the model is first called, instead of construction time. Examples","when using `tf.distribute.Strategy`, the first component of a tensor's shape is the *replica-local*","models. Pass var_list as callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1)","you should be able to use the _set_hyper()/state.get_hyper() facility instead. This class in","(gradient, variable). Do whatever you # need to the 'gradient' part, for example","self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if","one with the highest restore # UID in case slot variables have their","\"\"\"Get decayed learning rate as a Tensor with dtype=var_dtype.\"\"\" lr_t = self._get_hyper(\"learning_rate\", var_dtype)","all hyper parameters) \"\"\" def __init__(self, name, **kwargs): \"\"\"Create a new Optimizer. This","[dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \"\"\"Call the function if param is","may be repeated. Returns: An `Operation` which updates the value of the variable.","with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that","not `Variable` objects. \"\"\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name)","(e.g. if gradient function not implemented). \"\"\" params = nest.flatten(params) with backend.get_graph().as_default(): grads","optimizer instance. \"\"\" if \"lr\" in config: config[\"learning_rate\"] = config.pop(\"lr\") if \"learning_rate\" in","`apply_gradients()` explicitly instead of using this function. Args: loss: A callable taking no","the slot value. This can be useful if you want to log debug","class in stateful and thread-compatible. Args: name: A non-empty string. The name to","name, value): \"\"\"Override setattr to support dynamic hyperparameter setting.\"\"\" # Backwards compatibility with","{\"name\": self._name} if hasattr(self, \"clipnorm\"): config[\"clipnorm\"] = self.clipnorm if hasattr(self, \"clipvalue\"): config[\"clipvalue\"] =","learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self):","any variable: %s.\" % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning(","has already been created but `variable` has just been added to a dependency","\"\"\"Variable. The number of training steps this Optimizer has run.\"\"\" if self._iterations is","i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i,","# --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \"\"\"Restore a newly created slot variable's","tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \"\"\"Sums `values` associated","of (gradient, variable) pairs. name: Optional name for the returned operation. Default to","didn't make the slot variable. Defer restoring until it gets created # normally.","None: raise ValueError(\"Variable {} has `None` for gradient. \" \"Please make sure that","the graph. This method is nonetheless important when graph building for the case","the restored optimizer functional by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer,","variable): \"\"\"Restore a slot variable's value, possibly creating it. Called when a variable","backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors are all valid types (see","override this method to avoid the overhead of summing. Args: grad: a `Tensor`","accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`,","gradient is nonzero. Indices are unique. Returns: An `Operation` which updates the value","access.\"\"\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to avoid","update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for","when graph building. Instead, _restore_slot_variable catches these after normal creation and adds restore","method has been de-duplicated. Optimizers which deal correctly with non-unique indices may instead","gradients, you divide your loss by the global batch size, which is done","# a slot variable if not for this case). Deferring is mostly harmless","grads] if hasattr(self, \"clipvalue\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in","overwritten through user code: Example: ```python # Create an optimizer with the desired","resource_apply_dense (update variable given gradient tensor is dense) - resource_apply_sparse (update variable given","\"\"\"Valid types for loss, variables and gradients. Subclasses should override to allow other","trainable to be false when variable is to be synced on read. trainable","Processing gradients before applying them. Calling `minimize()` takes care of both computing the","* var1 + 2 * var2 * var2 # In graph mode, returns","tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops","has run.\"\"\" if self._iterations is None: self._iterations = self.add_weight( \"iter\", shape=[], dtype=dtypes.int64, trainable=False,","], ... }, # ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\"decay\", 0.0)","step. As a result, using `tf.math.reduce_mean` will give the wrong answer, resulting in","when the variable list would otherwise be incomplete before `minimize` and the variables","tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads","steps this Optimizer has run.\"\"\" if self._iterations is None: self._iterations = self.add_weight( \"iter\",","a slot variable has already been created but `variable` has just been added","\" \"Common ops without gradient: \" \"K.argmax, K.round, K.eval.\".format(param)) if hasattr(self, \"clipnorm\"): grads","== \"lr\": name = \"learning_rate\" if hasattr(self, \"_hyper\") and name in self._hyper: self._set_hyper(name,","variables of this Optimizer based on the order created.\"\"\" return self._weights @property def","var_dtype) lr_t = lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod","used to create this optimizer, such as a function used for a hyperparameter.","= dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization ==","tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2","based on the order created.\"\"\" return self._weights def get_weights(self): params = self.weights return","yet been created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ...","var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \"\"\"Get","List of (gradient, variable) pairs. name: Optional name for the returned operation. Default","variable's value, possibly creating it. Called when a variable which has an associated","opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and","2) subclassed models. Pass var_list as callable in these cases. Example: ```python opt","variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation)","+ var.op.name) with backend.name_scope(\"update\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic","need to the 'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]),","aware, which means it automatically sums gradients across all replicas. To average gradients,","grads_and_vars if g is not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)]","A Python dictionary, typically the output of get_config. custom_objects: A Python dictionary mapping","a Tensor \", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise","grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars","from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from","i, v)]): return x.value() # --------------- # For implementing the trackable interface #","def _assert_valid_dtypes(self, tensors): \"\"\"Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors:","with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name or"],"string":"[\n \"If you want to process the gradient before applying then call `tf.GradientTape` and\",\n \"hold the slot value. This can be useful if you want to log\",\n \"without the optimizer; # it's a dependency hypergraph with edges of the form\",\n \"dimension of `values` (as in an IndexedSlices object). Returns: A tuple of (`summed_values`,\",\n \"_prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically.\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"Args: grad: a `Tensor` representing the gradient for the affected indices. handle: a\",\n \"called `set_weights(weights)` on optimizer \\\" + self._name + \\\" with a weight list\",\n \"optimizer, include all hyper parameters) \\\"\\\"\\\" def __init__(self, name, **kwargs): \\\"\\\"\\\"Create a new\",\n \"pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) #\",\n \"which points to the variable to be updated. Returns: An `Operation` which updates\",\n \"= var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the\",\n \"tensors. Raises: ValueError: In case any gradient cannot be computed (e.g. if gradient\",\n \"in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name,\",\n \"a primary variable, for looking up slots. In graph mode the name is\",\n \"if there is no gradient for the given variable. Args: loss: A callable\",\n \"should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you\",\n \"weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if\",\n \"reverse of `get_config`, capable of instantiating the same optimizer from the config dictionary.\",\n \"for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name is\",\n \"iself (e.g. through `apply_gradients`). \\\"\\\"\\\" # TODO(allenl): Make the restored optimizer functional by\",\n \"= self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value):\",\n \"list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables>\",\n \"created or restored. When executing eagerly, we create the slot variable with a\",\n \"graph mode, returns op that minimizes the loss by updating the listed #\",\n \"to the variable to be updated. Returns: An `Operation` which updates the value\",\n \"var in grads_and_vars: scope_name = (\\\"\\\" if ops.executing_eagerly_outside_functions() else \\\"_\\\" + var.op.name) with\",\n \"which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this\",\n \"filtered def _var_key(var): \\\"\\\"\\\"Key for representing a primary variable, for looking up slots.\",\n \"from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from\",\n \"the gradients as you wish. 3. Apply the processed gradients with `apply_gradients()`. Example:\",\n \"`loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`.\",\n \"applying them you can instead use the optimizer in three steps: 1. Compute\",\n \"pairs where \\\"gradient\\\" is the gradient for \\\"variable\\\". Note that \\\"gradient\\\" can be\",\n \"\\\", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \\\"Cannot\",\n \"that applies the specified gradients. If `global_step` was not None, that operation also\",\n \"and applying them to the variables. If you want to process the gradients\",\n \"rate as a Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule):\",\n \"created but `variable` has just been added to a dependency graph (causing us\",\n \"in an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is\",\n \"a model. You never use this class directly, but instead instantiate one of\",\n \"not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var\",\n \"API to add Ops to train a model. You never use this class\",\n \"compatibility. Holds slot variables and hyperparameters when an optimizer is restored from a\",\n \"would otherwise be incomplete before `minimize` and the variables are created at the\",\n \"value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \\\"\\\"\\\"Add\",\n \"typically the output of get_config. custom_objects: A Python dictionary mapping names to additional\",\n \"initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy()\",\n \"or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or any_symbolic: # If\",\n \"in functions along with ops created by the original optimizer, but currently we\",\n \"def from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates an optimizer from its config. This method is\",\n \"the name is derived from the var shared name. In eager mode the\",\n \"raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients\",\n \"we'd like to eagerly create/restore slot variables # when possible, but this may\",\n \"tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients\",\n \"graph, and so shouldn't keep Tensors as member variables. Generally you should be\",\n \"call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update\",\n \"var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if\",\n \"`apply_gradients()` to get the value for the hyper parameter. Hyper parameters can be\",\n \"from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from\",\n \"and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \\\"\\\"\\\"set hyper\",\n \"newly created slot variable's value.\\\"\\\"\\\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key,\",\n \"these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes,\",\n \"dependencies, in which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key,\",\n \"of construction time. Examples include 1) sequential models without input shape pre-defined, or\",\n \"passed to any variable, the constraint will be applied to the variable after\",\n \"gradient. \\\" \\\"Please make sure that all of your ops have a \\\"\",\n \"many times too big. ### Variable Constraint All Keras optimizers respect variable constraints.\",\n \"var2]) ``` ### Write a customized optimizer. If you intend to create your\",\n \"\\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for k in kwargs: if k not in allowed_kwargs: raise\",\n \"g, v in grads_and_vars if g is not None and v.dtype != dtypes.resource\",\n \"you # need to the 'gradient' part, for example cap them, etc. capped_grads_and_vars\",\n \"the gradient for the affected indices. handle: a `Tensor` of dtype `resource` which\",\n \"add a dependency on # a slot variable if not for this case).\",\n \"restores, highest restore UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda\",\n \"= self.clipnorm if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue return config @classmethod def from_config(cls,\",\n \"= [v for (_, v) in grads_and_vars] # Create iteration if necessary. with\",\n \"either made this slot variable, or if we've pulled out an # existing\",\n \"`_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any tensor is not\",\n \"later (without any saved state) from this configuration. Returns: Python dictionary. \\\"\\\"\\\" config\",\n \"`Tensor` representing the gradient. handle: a `Tensor` of dtype `resource` which points to\",\n \"tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops\",\n \"self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and\",\n \"(slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"_distributed_apply(self, distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars)\",\n \"slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and\",\n \"slot variables have their own dependencies, in which case # those could differ\",\n \"[ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads, var_list))\",\n \"# Compute the gradients for a list of variables. with tf.GradientTape() as tape:\",\n \"then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this function. Args: loss:\",\n \"integral type representing the indices for which the gradient is nonzero. Indices may\",\n \"are created when graph building. Instead, _restore_slot_variable catches these after normal creation and\",\n \"mostly harmless # (aside from double initialization), and makes variable creator scopes #\",\n \"`Tensor` with rank >= 1. indices: A one-dimensional integer `Tensor`, indexing into the\",\n \"before `minimize` and the variables are created at the first time when `loss`\",\n \"answer, resulting in gradients that can be many times too big. ### Variable\",\n \"!= w.shape: raise ValueError(\\\"Optimizer weight shape \\\" + str(pv.shape) + \\\" not compatible\",\n \"always present, but gradient can be `None`. Raises: TypeError: If `var_list` contains anything\",\n \"Optimizer weights have been created\\\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get\",\n \"isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay >\",\n \"in which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append(\",\n \"def __getattribute__(self, name): \\\"\\\"\\\"Overridden to support hyperparameter access.\\\"\\\"\\\" try: return super(OptimizerV2, self).__getattribute__(name) except\",\n \"this dependency and otherwise pretend it's a normal # graph. if slot_variable is\",\n \"sums gradients across all replicas. To average gradients, you divide your loss by\",\n \"which means it automatically sums gradients across all replicas. To average gradients, you\",\n \"pretend it's a normal # graph. if slot_variable is not None: # If\",\n \"derived from the var shared name. In eager mode the name is derived\",\n \"entire optimizer is currently thread compatible, not thread-safe. The user needs to perform\",\n \"instead special-case this dependency and otherwise pretend it's a normal # graph. if\",\n \"in vars_with_empty_grads])) return filtered def _var_key(var): \\\"\\\"\\\"Key for representing a primary variable, for\",\n \"the value # to minimize. loss = lambda: 3 * var1 * var1\",\n \"__setattr__. if name == \\\"_hyper\\\": raise e # Backwards compatibility with Keras optimizers.\",\n \"if trainable: raise ValueError( \\\"Synchronization value can be set to \\\" \\\"VariableSynchronization.ON_READ only\",\n \"= self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None):\",\n \"for the variable it created to hold the slot value. This can be\",\n \"\\\"\\\"\\\"Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check.\",\n \"@keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class for optimizers. This class defines the API\",\n \"opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize\",\n \"Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of the variables have\",\n \"return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def\",\n \"name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable\",\n \"tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name]\",\n \"summing. Args: grad: a `Tensor` representing the gradient for the affected indices. handle:\",\n \"`Tensor` of integral type representing the indices for which the gradient is nonzero.\",\n \"tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine\",\n \"the optimizer in three steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process\",\n \"A callable taking no arguments which returns the value to minimize. var_list: list\",\n \"input shape pre-defined, or 2) subclassed models. Pass var_list as callable in these\",\n \"variables) - get_config (serialization of the optimizer, include all hyper parameters) \\\"\\\"\\\" def\",\n \"apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying to update\",\n \"= _var_key(var) return name + \\\"/\\\" + slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer\",\n \"are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError:\",\n \"mean that scopes intended to catch # `variable` also catch its eagerly created\",\n \"variable after the gradient has been applied to the variable. Important: If gradient\",\n \"This must be called by the constructors of subclasses. Note that Optimizer instances\",\n \"decay of learning rate. `lr` is included for backward compatibility, recommended to use\",\n \"slot_name: The name of this `Optimizer`'s slot to restore into. variable: The variable\",\n \"is dense) - resource_apply_sparse (update variable given gradient tensor is sparse) - create_slots\",\n \"tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras\",\n \"var_list_fn) ``` ### Processing gradients before applying them. Calling `minimize()` takes care of\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"support using the optimizer object iself (e.g. through `apply_gradients`). \\\"\\\"\\\" # TODO(allenl): Make\",\n \"an `IndexedSlices`, or `None` if there is no gradient for the given variable.\",\n \"in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value): \\\"\\\"\\\"Override setattr to\",\n \"None: raise RuntimeError(\\\"Cannot set `iterations` to a new Variable after \\\" \\\"the Optimizer\",\n \"average gradients, you should use `tf.math.reduce_sum` to add up your per-example losses and\",\n \"the value for the hyper parameter. Hyper parameters can be overwritten through user\",\n \"indices): \\\"\\\"\\\"Add ops to apply sparse gradients to `handle`, with repeated indices. Optimizers\",\n \"tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars)\",\n \"average gradients, you divide your loss by the global batch size, which is\",\n \": {slot name : variable}} self._slots = {} self._slot_names = [] self._weights =\",\n \"isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name],\",\n \"by norm; `clipvalue` is clip gradients by value, `decay` is included for backward\",\n \"set to \\\" \\\"VariableSynchronization.ON_READ only for non-trainable variables. \\\" \\\"You have specified trainable=True\",\n \"variable. Args: loss: A callable taking no arguments which returns the value to\",\n \"loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in\",\n \"to process the gradients before applying them you can instead use the optimizer\",\n \"`params`. Arguments: loss: Loss tensor. params: List of variables. Returns: List of gradient\",\n \"output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them. Calling\",\n \"variable # unintentionally (specifically make_template would add a dependency on # a slot\",\n \"not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var,\",\n \"isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize\",\n \"= [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \\\"clipvalue\\\"): grads = [\",\n \"= self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID first\",\n \"returned operation. Default to the name passed to the `Optimizer` constructor. Returns: An\",\n \"variables are not owned by any one object (because we don't want to\",\n \"disable=protected-access # Get the distributed variable if it exists. if getattr(var, \\\"_distributed_container\\\", None)\",\n \"var2 # In graph mode, returns op that minimizes the loss by updating\",\n \"is done automatically if you use `tf.keras` built-in training or evaluation loops. See\",\n \"and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set trainable to be false when variable is\",\n \"for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore a\",\n \"isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying to update a Tensor \\\", var) if isinstance(grad, ops.IndexedSlices):\",\n \"any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly()\",\n \"import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from\",\n \"\\\"lr\\\": name = \\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\") and name in self._hyper: self._set_hyper(name, value)\",\n \"aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \\\"\\\"\\\"Variable. The number of training steps\",\n \"Save and restore the Optimizer's config raise NotImplementedError( \\\"Restoring functional Optimzers from SavedModels\",\n \"import division from __future__ import print_function import abc import functools import six from\",\n \"# pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name)\",\n \"they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer =\",\n \"first. Args: var: the variable. Returns: the unique name of the variable. \\\"\\\"\\\"\",\n \"exists, get the primary variable first. Args: var: the variable. Returns: the unique\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"customized optimizer. If you intend to create your own optimization algorithm, simply inherit\",\n \"config: A Python dictionary, typically the output of get_config. custom_objects: A Python dictionary\",\n \"grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. Returns: A list\",\n \"i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i,\",\n \"tuples (gradient, variable). Do whatever you # need to the 'gradient' part, for\",\n \"args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads\",\n \"building for the case when a slot variable has already been created but\",\n \"`unique_indices`) where `unique_indices` is a de-duplicated version of `indices` and `summed_values` contains the\",\n \"but this may mean that scopes intended to catch # `variable` also catch\",\n \"tuple of `Variable` objects to update to minimize `loss`, or a callable returning\",\n \"Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations,\",\n \"dtypes.float64]) def _call_if_callable(self, param): \\\"\\\"\\\"Call the function if param is callable.\\\"\\\"\\\" return param()\",\n \"if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {})\",\n \"Variable after \\\" \\\"the Optimizer weights have been created\\\") self._iterations = variable self._weights.append(self._iterations)\",\n \"representing the gradient for the affected indices. handle: a `Tensor` of dtype `resource`\",\n \"return self._slot_names def add_slot(self, var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new slot variable for\",\n \"= tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input),\",\n \"a single graph, and so shouldn't keep Tensors as member variables. Generally you\",\n \"an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a\",\n \"variables. Returns: List of gradient tensors. Raises: ValueError: In case any gradient cannot\",\n \"zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\\\"Optimizer weight shape \\\" +\",\n \"the update ops are # symbolic then the step update should be carried\",\n \"vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var)\",\n \"nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values` associated with any\",\n \"tensors, or callables. If they are callable, the callable will be called during\",\n \"with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list):\",\n \"_filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] # Create iteration if\",\n \"__setattr__(self, name, value): \\\"\\\"\\\"Override setattr to support dynamic hyperparameter setting.\\\"\\\"\\\" # Backwards compatibility\",\n \"distribution strategy exists, get the primary variable first. Args: var: the variable. Returns:\",\n \"if this limitation bothers \\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration(\",\n \"`minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient,\",\n \"eagerly, we create the slot variable with a restoring initializer. No new variables\",\n \"(gradient, variable) pairs. Variable is always present, but gradient can be `None`. Raises:\",\n \"from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from\",\n \"name: Optional name for the returned operation. Default to the name passed to\",\n \"\\\" \\\"supported. Please file a feature request if this limitation bothers \\\" \\\"you.\\\")\",\n \"anything else than `Variable` objects. ValueError: If some arguments are invalid, or var_list\",\n \"All Keras optimizers respect variable constraints. If constraint function is passed to any\",\n \"grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. name: Optional name\",\n \"etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer\",\n \"isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name] = value\",\n \"value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if\",\n \"values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue\",\n \"OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper # pylint: disable=protected-access )])\",\n \"An `Operation` that applies the specified gradients. If `global_step` was not None, that\",\n \"non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a\",\n \"same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access\",\n \"to \\\" \\\"VariableSynchronization.ON_READ only for non-trainable variables. \\\" \\\"You have specified trainable=True and\",\n \"correct behavior, to sum non-unique indices and their associated gradients, is enforced by\",\n \"is called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. name:\",\n \"indices: A one-dimensional integer `Tensor`, indexing into the first dimension of `values` (as\",\n \"time. Examples include 1) sequential models without input shape pre-defined, or 2) subclassed\",\n \"name = \\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\") and name in self._hyper: self._set_hyper(name, value) else:\",\n \"TODO(allenl): Make the restored optimizer functional by tracing its apply # methods. def\",\n \"slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name,\",\n \"If you are not using these and you want to average gradients, you\",\n \"`None`, that operation also increments `global_step`. Raises: ValueError: If some of the variables\",\n \"self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \\\"\\\"\\\"A list of names\",\n \"training steps this Optimizer has run.\\\"\\\"\\\" if self._iterations is None: self._iterations = self.add_weight(\",\n \"optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is\",\n \"scope. Generally we'd like to eagerly create/restore slot variables # when possible, but\",\n \"gv in grads_and_vars] # Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars)\",\n \"gradient cannot be computed (e.g. if gradient function not implemented). \\\"\\\"\\\" params =\",\n \"isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if\",\n \"List of gradient tensors. Raises: ValueError: In case any gradient cannot be computed\",\n \"to a new Variable after \\\" \\\"the Optimizer weights have been created\\\") self._iterations\",\n \"type representing the indices for which the gradient is nonzero. Indices may be\",\n \"Maybe share this logic with base_layer. def set_weights(self, weights): params = self.weights if\",\n \"import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import\",\n \"name : {slot name : variable}} self._slots = {} self._slot_names = [] self._weights\",\n \"_, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient\",\n \"= tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn =\",\n \"No new variables are created when graph building. Instead, _restore_slot_variable catches these after\",\n \"`Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional\",\n \"gets created # normally. We keep a list rather than the one with\",\n \"if var.constraint is not None: raise RuntimeError( \\\"Cannot use a constraint function on\",\n \"passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may\",\n \"value # to minimize. loss = lambda: 3 * var1 + 2 *\",\n \"= list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is\",\n \"In graph mode, returns op that minimizes the loss by updating the listed\",\n \"six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise\",\n \"updates the value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle,\",\n \"created for. \\\"\\\"\\\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name,\",\n \"and you can ask the optimizer for the names of the slots that\",\n \"override this method must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices`\",\n \"a normal # graph. if slot_variable is not None: # If we've either\",\n \"first pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which\",\n \"we've either made this slot variable, or if we've pulled out an #\",\n \"activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output\",\n \"x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value()\",\n \"returns the value # to minimize. loss = lambda: 3 * var1 +\",\n \"- resource_apply_dense (update variable given gradient tensor is dense) - resource_apply_sparse (update variable\",\n \"the gradient is nonzero. Indices may be repeated. Returns: An `Operation` which updates\",\n \"return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \\\"\\\"\\\"Returns variables of\",\n \"when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position)\",\n \"when a slot variable has already been created but `variable` has just been\",\n \"Returns: Valid types for loss, variables and gradients. \\\"\\\"\\\" return set( [dtypes.float16, dtypes.bfloat16,\",\n \"= [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights):\",\n \"self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\\\"decay\\\", var_dtype) lr_t =\",\n \"gradient for the given variable. Args: loss: A callable taking no arguments which\",\n \"if dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \\\"\\\"\\\"Overridden to\",\n \"restore # slot variables which have not yet been created # (trackable._CheckpointPosition objects).\",\n \"def add_weight(self, name, shape, dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None:\",\n \"list of length \\\" + str(len(weights)) + \\\", but the optimizer was expecting\",\n \"(eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op\",\n \"be incomplete before `minimize` and the variables are created at the first time\",\n \"also increments `global_step`. Raises: ValueError: If some of the variables are not `Variable`\",\n \"self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients`\",\n \"param def _resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add ops to apply dense gradients to the\",\n \"component of a tensor's shape is the *replica-local* batch size, which is off\",\n \"them you can instead use the optimizer in three steps: 1. Compute the\",\n \"with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad:\",\n \"raise RuntimeError( \\\"Cannot use a constraint function on a sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices(\",\n \"for representing a primary variable, for looking up slots. In graph mode the\",\n \"under a graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint:\",\n \"to allow other float types. Returns: Valid types for loss, variables and gradients.\",\n \"to `handle`, with repeated indices. Optimizers which override this method must deal with\",\n \"variables have their own dependencies, in which case # those could differ between\",\n \"p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\\\"Optimizer weight\",\n \"`IndexedSlices`, or `None` if there is no gradient for the given variable. Args:\",\n \"details. By default the correct behavior, to sum non-unique indices and their associated\",\n \"_restore_slot_variable catches these after normal creation and adds restore ops to the graph.\",\n \"decay in a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list)\",\n \"if callable(value): value = value() if dtype: return math_ops.cast(value, dtype) else: return value\",\n \"additional variables associated with the variables to train. These are called <i>Slots</i>. Slots\",\n \"object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version\",\n \"of tuples (gradient, variable). Do whatever you # need to the 'gradient' part,\",\n \"deal correctly with duplicate indices may instead override this method to avoid the\",\n \"not use this file except in compliance with the License. # You may\",\n \"g in grads] if hasattr(self, \\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for\",\n \"self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key]\",\n \"optimizer in three steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process the\",\n \"edges of the form (optimizer, non-slot # variable, variable)). So we don't _track_\",\n \"using this function. Args: loss: A callable taking no arguments which returns the\",\n \"> 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\\\"decay\\\", var_dtype) lr_t = lr_t\",\n \"of using this function. Args: loss: A callable taking no arguments which returns\",\n \"from double initialization), and makes variable creator scopes # behave the same way\",\n \"the optimizer; # it's a dependency hypergraph with edges of the form (optimizer,\",\n \"Compute the gradients for a list of variables. with tf.GradientTape() as tape: loss\",\n \"gradients of `loss` with respect to `params`. Arguments: loss: Loss tensor. params: List\",\n \"from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import\",\n \"of `minimize()`. It returns a list of (gradient, variable) pairs where \\\"gradient\\\" is\",\n \"Returns: An `Operation` that applies the specified gradients. If `global_step` was not None,\",\n \"the gradients before applying them you can instead use the optimizer in three\",\n \"explicitly instead of using this function. Args: loss: A callable taking no arguments\",\n \"\\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying to update a Tensor\",\n \"config @classmethod def from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates an optimizer from its config. This\",\n \"var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def\",\n \"`indices` argument to this method has been de-duplicated. Optimizers which deal correctly with\",\n \"be synced on read. trainable = False elif trainable is None: trainable =\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import\",\n \"value): \\\"\\\"\\\"set hyper `name` to value. value can be callable, tensor, numeric.\\\"\\\"\\\" if\",\n \"using `tf.GradientTape` and calls `apply_gradients()`. If you want to process the gradient before\",\n \"dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend\",\n \"`summed_values` contains the sum of `values` slices associated with each unique index. \\\"\\\"\\\"\",\n \"name): \\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v\",\n \"slot variable for `var`.\\\"\\\"\\\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var)\",\n \"deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else:\",\n \"For implementing Trackable. Stores information about how to restore # slot variables which\",\n \"time `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed for\",\n \"Optimizers which deal correctly with duplicate indices may instead override this method to\",\n \"and returns the value # to minimize. loss = lambda: 3 * var1\",\n \"of dtype `resource` which points to the variable to be updated. indices: a\",\n \"2 * var2 * var2 # In graph mode, returns op that minimizes\",\n \"be overwritten through user code: Example: ```python # Create an optimizer with the\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"you want to process the gradients before applying them you can instead use\",\n \"{_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations = {} decay\",\n \"============================================================================== \\\"\\\"\\\"Version 2 of class Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name from __future__ import absolute_import\",\n \"if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if\",\n \"new Variable after \\\" \\\"the Optimizer weights have been created\\\") self._iterations = variable\",\n \"without input shape pre-defined, or 2) subclassed models. Pass var_list as callable in\",\n \"the affected indices. handle: a `Tensor` of dtype `resource` which points to the\",\n \"or self._name): for grad, var in grads_and_vars: scope_name = (\\\"\\\" if ops.executing_eagerly_outside_functions() else\",\n \"\\\"...\\\") if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv,\",\n \"data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them. Calling `minimize()` takes\",\n \"shape \\\" + str(pv.shape) + \\\" not compatible with \\\" \\\"provided weight shape\",\n \"called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. Returns: A\",\n \"hyper parameter. Hyper parameters can be overwritten through user code: Example: ```python #\",\n \"(update variable given gradient tensor is dense) - resource_apply_sparse (update variable given gradient\",\n \"and calls `apply_gradients()`. If you want to process the gradient before applying then\",\n \"variable, slot_variable): \\\"\\\"\\\"Restore a newly created slot variable's value.\\\"\\\"\\\" variable_key = _var_key(variable) deferred_restorations\",\n \"`Variable` objects to update to minimize `loss`, or a callable returning the list\",\n \"else param def _resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add ops to apply dense gradients to\",\n \"batch size, which is off by a factor equal to the number of\",\n \"filtered: raise ValueError(\\\"No gradients provided for any variable: %s.\\\" % ([v.name for _,\",\n \"Optimzers from SavedModels is not currently \\\" \\\"supported. Please file a feature request\",\n \"for `var`.\\\"\\\"\\\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict =\",\n \"name for the returned operation. Default to the name passed to the `Optimizer`\",\n \"all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If\",\n \"respect variable constraints. If constraint function is passed to any variable, the constraint\",\n \"to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead override this\",\n \"restoring initializer. No new variables are created when graph building. Instead, _restore_slot_variable catches\",\n \"any tensor is not a valid type. \\\"\\\"\\\" valid_dtypes = self._valid_dtypes() for t\",\n \"tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \\\"\\\"\\\"Returns variables of this Optimizer based\",\n \"that updates the variables in `var_list`. If `global_step` was not `None`, that operation\",\n \"slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable with grad equal to\",\n \"Tensor \\\", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError(\",\n \"if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p,\",\n \"An Operation that updates the variables in `var_list`. If `global_step` was not `None`,\",\n \"added to a dependency graph (causing us to realize that the slot variable\",\n \"\\\"gradient\\\" is the gradient for \\\"variable\\\". Note that \\\"gradient\\\" can be a `Tensor`,\",\n \"to minimize. var_list: list or tuple of `Variable` objects to update to minimize\",\n \"the same optimizer from the config dictionary. Arguments: config: A Python dictionary, typically\",\n \"currently \\\" \\\"supported. Please file a feature request if this limitation bothers \\\"\",\n \"you can ask the optimizer for the names of the slots that it\",\n \"update_op = self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var))\",\n \"mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2])\",\n \"`tf.keras` built-in training or evaluation loops. See the `reduction` argument of your loss\",\n \"limitations under the License. # ============================================================================== \\\"\\\"\\\"Version 2 of class Optimizer.\\\"\\\"\\\" # pylint:\",\n \"optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable\",\n \"done automatically if you use `tf.keras` built-in training or evaluation loops. See the\",\n \"(the `__init__` method), and then passed to `self._set_hyper()`. They can be either regular\",\n \"{slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations\",\n \"be a `Tensor`, an `IndexedSlices`, or `None` if there is no gradient for\",\n \"function on a sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad,\",\n \"list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not\",\n \"or any_symbolic: # If the current context is graph mode or any of\",\n \"variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1,\",\n \"return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values,\",\n \"is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer,\",\n \"self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes:\",\n \"Defer restoring until it gets created # normally. We keep a list rather\",\n \"to apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices`\",\n \"\\\" \\\"VariableSynchronization.ON_READ only for non-trainable variables. \\\" \\\"You have specified trainable=True and \\\"\",\n \"for which the gradient is nonzero. Indices are unique. Returns: An `Operation` which\",\n \"to minimize. loss = lambda: 3 * var1 * var1 + 2 *\",\n \"be updated. Returns: An `Operation` which updates the value of the variable. \\\"\\\"\\\"\",\n \"return config @classmethod def from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates an optimizer from its config.\",\n \"takes care of both computing the gradients and applying them to the variables.\",\n \"associated with each unique index. \\\"\\\"\\\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum(\",\n \"= initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \\\"Synchronization value can\",\n \"for the given variable. Args: loss: A callable taking no arguments which returns\",\n \"isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype)\",\n \"`decay` is included for backward compatibility to allow time inverse decay of learning\",\n \"slot variable has already been created but `variable` has just been added to\",\n \"learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if dtype: return math_ops.cast(value, dtype)\",\n \"optimizers. This class defines the API to add Ops to train a model.\",\n \"makes variable creator scopes # behave the same way they do when graph\",\n \"(serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated\",\n \"self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse\",\n \"the variable to be updated. Returns: An `Operation` which updates the value of\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"highest restore UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position:\",\n \"self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def _distributed_apply(self, distribution,\",\n \"var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not None\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not\",\n \"gradients to the variable `handle`. Args: grad: a `Tensor` representing the gradient. handle:\",\n \"from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from\",\n \"be less than 0: {}\\\".format(decay)) self._initial_decay = decay if \\\"clipnorm\\\" in kwargs: self.clipnorm\",\n \"self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int,\",\n \"to be synced on read. trainable = False elif trainable is None: trainable\",\n \"grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if\",\n \"Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"backend.get_value(value) return value def variables(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the\",\n \"of the variables have gradients. \\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for\",\n \"of this Optimizer based on the order created.\\\"\\\"\\\" return self._weights @property def weights(self):\",\n \"# limitations under the License. # ============================================================================== \\\"\\\"\\\"Version 2 of class Optimizer.\\\"\\\"\\\" #\",\n \"present, but gradient can be `None`. Raises: TypeError: If `var_list` contains anything else\",\n \"for the variable: var_name/slot_name.\\\"\\\"\\\" name = _var_key(var) return name + \\\"/\\\" + slot_name\",\n \"your own optimization algorithm, simply inherit from this class and override the following\",\n \"callable returning the list or tuple of `Variable` objects. Use callable when the\",\n \"(update variable given gradient tensor is sparse) - create_slots (if your optimizer algorithm\",\n \"Args: name: A non-empty string. The name to use for accumulators created for\",\n \"list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not\",\n \"in zip(grads, params): if grad is None: raise ValueError(\\\"Variable {} has `None` for\",\n \"# symbolic then the step update should be carried out under a graph\",\n \"In graph mode the name is derived from the var shared name. In\",\n \"for %s, expected: %s.\\\" % (dtype, t.name, [v for v in valid_dtypes])) def\",\n \"in grads_and_vars: scope_name = (\\\"\\\" if ops.executing_eagerly_outside_functions() else \\\"_\\\" + var.op.name) with backend.name_scope(\\\"update\\\"\",\n \"`tf.math.reduce_mean` will give the wrong answer, resulting in gradients that can be many\",\n \"allow other float types. Returns: Valid types for loss, variables and gradients. \\\"\\\"\\\"\",\n \"and thread-compatible. Args: name: A non-empty string. The name to use for accumulators\",\n \"= self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self):\",\n \"ValueError: In case any gradient cannot be computed (e.g. if gradient function not\",\n \"eagerly created slot variable # unintentionally (specifically make_template would add a dependency on\",\n \"Raises: ValueError: In case any gradient cannot be computed (e.g. if gradient function\",\n \"processed gradients with `apply_gradients()`. Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1)\",\n \"included for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name\",\n \"``` ### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means\",\n \"g is not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self,\",\n \"slots.\\\"\\\"\\\" return self._slot_names def add_slot(self, var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new slot variable\",\n \"gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional name for the\",\n \"callable(value): value = value() if dtype: return math_ops.cast(value, dtype) else: return value def\",\n \"if \\\"clipvalue\\\" in kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False def minimize(self, loss,\",\n \"loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for\",\n \"self._iterations is None: self._iterations = self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return\",\n \"name, shape, dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype =\",\n \"optimizer is saved without the non-slot # variable, or if the non-slot variable\",\n \"or `None` if there is no gradient for the given variable. Args: loss:\",\n \"= {\\\"name\\\": self._name} if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"]\",\n \"and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g,\",\n \"_get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule):\",\n \"allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for k in kwargs: if k not\",\n \"list would otherwise be incomplete before `minimize` and the variables are created at\",\n \"a hyperparameter that can be a float, callable, or Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name]\",\n \"optimizer from the config dictionary. Arguments: config: A Python dictionary, typically the output\",\n \"that all of your ops have a \\\" \\\"gradient defined (i.e. are differentiable).\",\n \"applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this function. Args:\",\n \"# behave the same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack):\",\n \"grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\\\"Gradients does not exist for variables %s when minimizing\",\n \"\\\" + str(len(weights)) + \\\", but the optimizer was expecting \\\" + str(len(params))\",\n \"if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if dtype: return\",\n \"be carried out under a graph # context. (eager updates execute immediately) with\",\n \"it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer restoring until\",\n \"else than `Variable` objects. ValueError: If some arguments are invalid, or var_list is\",\n \"to avoid infinite recursion with __setattr__. if name == \\\"_hyper\\\": raise e #\",\n \"a restoring initializer. No new variables are created when graph building. Instead, _restore_slot_variable\",\n \"# to minimize. loss = lambda: 3 * var1 + 2 * var2\",\n \"`minimize()`. It returns a list of (gradient, variable) pairs where \\\"gradient\\\" is the\",\n \"before applying them you can instead use the optimizer in three steps: 1.\",\n \"var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new slot variable for `var`.\\\"\\\"\\\" if slot_name not\",\n \"Do whatever you # need to the 'gradient' part, for example cap them,\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"we've pulled out an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable)\",\n \"name is derived from the var shared name. In eager mode the name\",\n \"None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer,\",\n \"Example: ```python # Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1)\",\n \"model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights\",\n \"opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer. If\",\n \"= zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient, variable). Do\",\n \"from this class and override the following methods: - resource_apply_dense (update variable given\",\n \"if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None):\",\n \"indicating the slot variable `Trackable` object to be restored. slot_name: The name of\",\n \"return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the slot key for the\",\n \"gradients. \\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars]\",\n \"is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there\",\n \"name: A non-empty string. The name to use for accumulators created for the\",\n \"are differentiable). \\\" \\\"Common ops without gradient: \\\" \\\"K.argmax, K.round, K.eval.\\\".format(param)) if hasattr(self,\",\n \"{}).pop(variable_key, []) # Iterate over restores, highest restore UID first to minimize the\",\n \"tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops\",\n \"class for optimizers. This class defines the API to add Ops to train\",\n \"Indices are unique. Returns: An `Operation` which updates the value of the variable.\",\n \"capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer to\",\n \"containing the configuration of an optimizer. The same optimizer can be reinstantiated later\",\n \"deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position,\",\n \"object (because we don't want to # save the slot variable if the\",\n \"return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter that can be a float,\",\n \"not a valid type. \\\"\\\"\\\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype\",\n \"loss by the global batch size, which is done automatically if you use\",\n \"to any variable, the constraint will be applied to the variable after the\",\n \"may be referenced in functions along with ops created by the original optimizer,\",\n \"opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2])\",\n \"%s when minimizing the loss.\\\"), ([v.name for v in vars_with_empty_grads])) return filtered def\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu'))\",\n \"If some arguments are invalid, or var_list is None. \\\"\\\"\\\" # TODO(josh11b): Test\",\n \"exists. if getattr(var, \\\"_distributed_container\\\", None) is not None: var = var._distributed_container() if var._in_graph_mode:\",\n \"primary variable first. Args: var: the variable. Returns: the unique name of the\",\n \"you may not use this file except in compliance with the License. #\",\n \"variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types\",\n \"+ \\\"/\\\" + slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer implementation for checkpoint compatibility.\",\n \"learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops\",\n \"A `Tensor` with rank >= 1. indices: A one-dimensional integer `Tensor`, indexing into\",\n \"which updates the value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad,\",\n \"we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable.\",\n \"self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed learning rate as a\",\n \"the Optimizer's config raise NotImplementedError( \\\"Restoring functional Optimzers from SavedModels is not currently\",\n \"operation. Returns: An Operation that updates the variables in `var_list`. If `global_step` was\",\n \"be many times too big. ### Variable Constraint All Keras optimizers respect variable\",\n \"method must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details.\",\n \"in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them. Calling `minimize()`\",\n \"self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created:\",\n \"= True self._name = name self._hyper = {} # dict: {variable name :\",\n \"self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value\",\n \"### Write a customized optimizer. If you intend to create your own optimization\",\n \"in case slot variables have their own dependencies, in which case # those\",\n \"gradient function not implemented). \\\"\\\"\\\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss,\",\n \"the `Optimizer` constructor. Returns: An `Operation` that applies the specified gradients. If `global_step`\",\n \"and the variables are created at the first time when `loss` is called.\",\n \"does not exist for variables %s when minimizing the loss.\\\"), ([v.name for v\",\n \"of `loss` with respect to `params`. Arguments: loss: Loss tensor. params: List of\",\n \"ops are # symbolic then the step update should be carried out under\",\n \"`var_list` contains anything else than `Variable` objects. ValueError: If some arguments are invalid,\",\n \"zip(grads, params): if grad is None: raise ValueError(\\\"Variable {} has `None` for gradient.\",\n \"name}) def _distributed_apply(self, distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to(\",\n \"import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import\",\n \"expected: %s.\\\" % (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid\",\n \"subclasses, such as `Adam` and `Adagrad` allocate and manage additional variables associated with\",\n \"`global_step` was not `None`, that operation also increments `global_step`. Raises: ValueError: If some\",\n \"# pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params):\",\n \"variable list would otherwise be incomplete before `minimize` and the variables are created\",\n \"`Optimizer` constructor. Returns: An `Operation` that applies the specified gradients. If `global_step` was\",\n \"handle weight decay in a reasonable way. with backprop.GradientTape() as tape: if not\",\n \"+ decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns the config of\",\n \"catch its eagerly created slot variable # unintentionally (specifically make_template would add a\",\n \"variables of this Optimizer based on the order created.\\\"\\\"\\\" return self._weights def get_weights(self):\",\n \"lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn,\",\n \"your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"`Operation` that applies the specified gradients. If `global_step` was not None, that operation\",\n \"value) def get_slot_names(self): \\\"\\\"\\\"A list of names for this optimizer's slots.\\\"\\\"\\\" return self._slot_names\",\n \"If _create_slots has been overridden instead of _create_vars. \\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\",\",\n \"backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is\",\n \"slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the\",\n \"otherwise be incomplete before `minimize` and the variables are created at the first\",\n \"context is graph mode or any of the update ops are # symbolic\",\n \"out an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: #\",\n \"the non-slot # variable, or if the non-slot variable is saved without the\",\n \"been added to a dependency graph (causing us to realize that the slot\",\n \"Keras optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\" if name in self._hyper:\",\n \"Instead, _restore_slot_variable catches these after normal creation and adds restore ops to the\",\n \"been created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ],\",\n \"0, received: {}\\\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper = {}\",\n \"self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable with grad equal\",\n \"disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads =\",\n \"trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors are\",\n \"def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to `handle`,\",\n \"dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ:\",\n \"var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss` by updating `var_list`. This method simply computes gradient\",\n \"@property def weights(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the order created.\\\"\\\"\\\"\",\n \"they are callable, the callable will be called during `apply_gradients()` to get the\",\n \"is malformed. ValueError: If none of the variables have gradients. \\\"\\\"\\\" grads_and_vars =\",\n \"functional by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created =\",\n \"Returns: Python dictionary. \\\"\\\"\\\" config = {\\\"name\\\": self._name} if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] =\",\n \"{}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable with grad equal to None.\\\"\\\"\\\"\",\n \"grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\" if\",\n \"optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables.\",\n \"with `apply_gradients()`. Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute\",\n \"**kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip\",\n \"given variable. Args: loss: A callable taking no arguments which returns the value\",\n \"simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) #\",\n \"the order created.\\\"\\\"\\\" return self._weights @property def weights(self): \\\"\\\"\\\"Returns variables of this Optimizer\",\n \"\\\" \\\"passed to optimizer: \\\" + str(k)) # checks that all keyword arguments\",\n \"it's a normal # graph. if slot_variable is not None: # If we've\",\n \"i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For\",\n \"var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients of `loss` for the variables in `var_list`. This is\",\n \"handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients\",\n \"UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True)\",\n \"self._weights @property def weights(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the order\",\n \"first dimension of `values` (as in an IndexedSlices object). Returns: A tuple of\",\n \"of summing. Args: grad: a `Tensor` representing the gradient for the affected indices.\",\n \"optimizer from its config. This method is the reverse of `get_config`, capable of\",\n \"variable}} self._slots = {} self._slot_names = [] self._weights = [] self._iterations = None\",\n \"# ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\\\"decay\\\", 0.0) if decay <\",\n \"ValueError: If none of the variables have gradients. \\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars) var_list\",\n \"return x.value() # --------------- # For implementing the trackable interface # --------------- def\",\n \"_ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def\",\n \"minimize. loss = lambda: 3 * var1 * var1 + 2 * var2\",\n \"def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore a slot variable's value, possibly creating\",\n \"to be updated. indices: a `Tensor` of integral type representing the indices for\",\n \"@iterations.setter def iterations(self, variable): if self._iterations is not None: raise RuntimeError(\\\"Cannot set `iterations`\",\n \"is saved without the optimizer; # it's a dependency hypergraph with edges of\",\n \"keyword arguments are non-negative. if kwargs[k] < 0: raise ValueError(\\\"Expected {} >= 0,\",\n \"import print_function import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as\",\n \"are callable, the callable will be called during `apply_gradients()` to get the value\",\n \"base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values,\",\n \"if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad,\",\n \"var_name/slot_name.\\\"\\\"\\\" name = _var_key(var) return name + \\\"/\\\" + slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A\",\n \"tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras\",\n \"callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if\",\n \"__getattribute__(self, name): \\\"\\\"\\\"Overridden to support hyperparameter access.\\\"\\\"\\\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError\",\n \"\\\" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\\\"zeros\\\", trainable=None,\",\n \"make_template would add a dependency on # a slot variable if not for\",\n \"# Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor)\",\n \"train a model. You never use this class directly, but instead instantiate one\",\n \"instead. This class in stateful and thread-compatible. Args: name: A non-empty string. The\",\n \"be called by the constructors of subclasses. Note that Optimizer instances should not\",\n \"{variable name : {slot name : variable}} self._slots = {} self._slot_names = []\",\n \"if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\\\"decay\\\", var_dtype) lr_t\",\n \"\\\"\\\"\\\" if \\\"lr\\\" in config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in config: if\",\n \"def _valid_dtypes(self): \\\"\\\"\\\"Valid types for loss, variables and gradients. Subclasses should override to\",\n \"losses and then divide by the global batch size. Note that when using\",\n \"have not yet been created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train):\",\n \"self.clipnorm if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue return config @classmethod def from_config(cls, config,\",\n \"to apply sparse gradients to `handle`, with repeated indices. Optimizers which override this\",\n \"+ self._name + \\\" with a weight list of length \\\" + str(len(weights))\",\n \"the distributed variable if it exists. if getattr(var, \\\"_distributed_container\\\", None) is not None:\",\n \"of (gradient, variable) pairs where \\\"gradient\\\" is the gradient for \\\"variable\\\". Note that\",\n \"\\\" with a weight list of length \\\" + str(len(weights)) + \\\", but\",\n \"mode the name is derived from the var unique id. If distribution strategy\",\n \"variable) pairs where \\\"gradient\\\" is the gradient for \\\"variable\\\". Note that \\\"gradient\\\" can\",\n \"config = {\\\"name\\\": self._name} if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm if hasattr(self, \\\"clipvalue\\\"):\",\n \"indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor`\",\n \"var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight\",\n \"config[\\\"clipnorm\\\"] = self.clipnorm if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue return config @classmethod def\",\n \"of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum non-unique indices\",\n \"value = value() if dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self,\",\n \"vars_with_empty_grads: logging.warning( (\\\"Gradients does not exist for variables %s when minimizing the loss.\\\"),\",\n \"is sparse tensor, variable constraint is not supported. ### Thread Compatibility The entire\",\n \"Args: grad: a `Tensor` representing the gradient. handle: a `Tensor` of dtype `resource`\",\n \"for (_, v) in grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _\",\n \"ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or any_symbolic: #\",\n \"callable, tensor, numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in\",\n \"slot_name, variable): \\\"\\\"\\\"Restore a slot variable's value, possibly creating it. Called when a\",\n \"weight decay in a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list):\",\n \"batch size. Note that when using `tf.distribute.Strategy`, the first component of a tensor's\",\n \"import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as\",\n \"updated. indices: a `Tensor` of integral type representing the indices for which the\",\n \"the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this method has\",\n \"name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return\",\n \"the form (optimizer, non-slot # variable, variable)). So we don't _track_ slot variables\",\n \"gradients by value, `decay` is included for backward compatibility to allow time inverse\",\n \"if decay < 0.: raise ValueError(\\\"decay cannot be less than 0: {}\\\".format(decay)) self._initial_decay\",\n \"be called during `apply_gradients()` to get the value for the hyper parameter. Hyper\",\n \"have a slot name you can ask the optimizer for the variable it\",\n \"are created at the first time `loss` is called. grad_loss: Optional. A `Tensor`\",\n \"in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\"\",\n \"for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying\",\n \"variable): if self._iterations is not None: raise RuntimeError(\\\"Cannot set `iterations` to a new\",\n \"apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument\",\n \"with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in zip(grads, params): if\",\n \"a slot variable if not for this case). Deferring is mostly harmless #\",\n \"incomplete before `minimize` and the variables are created at the first time when\",\n \"to the variable after the gradient has been applied to the variable. Important:\",\n \"{}\\\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper = {} # dict:\",\n \"= loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value,\",\n \"`iterations` to a new Variable after \\\" \\\"the Optimizer weights have been created\\\")\",\n \"slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be restored.\",\n \"def __setattr__(self, name, value): \\\"\\\"\\\"Override setattr to support dynamic hyperparameter setting.\\\"\\\"\\\" # Backwards\",\n \"name == \\\"_hyper\\\": raise e # Backwards compatibility with Keras optimizers. if name\",\n \"grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return grads\",\n \"which the gradient is nonzero. Indices are unique. Returns: An `Operation` which updates\",\n \"of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss,\",\n \"= tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight)\",\n \"indices and their associated gradients, is enforced by first pre-processing `grad` and `indices`\",\n \"shape \\\" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\\\"zeros\\\",\",\n \"a `Tensor`, an `IndexedSlices`, or `None` if there is no gradient for the\",\n \"indices: a `Tensor` of integral type representing the indices for which the gradient\",\n \"an optimizer from its config. This method is the reverse of `get_config`, capable\",\n \"tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops\",\n \"A `Tensor` holding the gradient computed for `loss`. name: Optional name for the\",\n \"synced on read. trainable = False elif trainable is None: trainable = True\",\n \"restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable with grad\",\n \"is the first part of `minimize()`. It returns a list of (gradient, variable)\",\n \"`minimize` and the variables are created at the first time when `loss` is\",\n \"to # save the slot variable if the optimizer is saved without the\",\n \"slot variable if the optimizer is saved without the non-slot # variable, or\",\n \"is the *replica-local* batch size, which is off by a factor equal to\",\n \"the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle,\",\n \"slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper\",\n \"trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name] = value else:\",\n \"there is an active variable creator # scope. Generally we'd like to eagerly\",\n \"then passed to `self._set_hyper()`. They can be either regular Python values (like 1.0),\",\n \"instance. \\\"\\\"\\\" if \\\"lr\\\" in config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in config:\",\n \"class is `tf.distribute.Strategy` aware, which means it automatically sums gradients across all replicas.\",\n \"def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed learning rate as a Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t\",\n \"from __future__ import absolute_import from __future__ import division from __future__ import print_function import\",\n \"form (optimizer, non-slot # variable, variable)). So we don't _track_ slot variables anywhere,\",\n \"variable: %s.\\\" % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\\\"Gradients\",\n \"config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in config: if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] =\",\n \"implemented). \\\"\\\"\\\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad,\",\n \"{}) weight = slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types) or\",\n \"None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if\",\n \"Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value()\",\n \"along with ops created by the original optimizer, but currently we do not\",\n \"tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base\",\n \"as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list =\",\n \"contains anything else than `Variable` objects. ValueError: If some arguments are invalid, or\",\n \"== \\\"_hyper\\\": raise e # Backwards compatibility with Keras optimizers. if name ==\",\n \"for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list\",\n \"a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value =\",\n \"tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values` associated with any non-unique `indices`.\",\n \"should be able to use the _set_hyper()/state.get_hyper() facility instead. This class in stateful\",\n \"if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name] =\",\n \"grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in\",\n \"get_gradients(self, loss, params): \\\"\\\"\\\"Returns gradients of `loss` with respect to `params`. Arguments: loss:\",\n \"type representing the indices for which the gradient is nonzero. Indices are unique.\",\n \"non-negative. if kwargs[k] < 0: raise ValueError(\\\"Expected {} >= 0, received: {}\\\".format(k, kwargs[k]))\",\n \"dictionary. Arguments: config: A Python dictionary, typically the output of get_config. custom_objects: A\",\n \"< 0.: raise ValueError(\\\"decay cannot be less than 0: {}\\\".format(decay)) self._initial_decay = decay\",\n \"variables may be referenced in functions along with ops created by the original\",\n \"def _create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically. for name, value\",\n \"slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var,\",\n \"self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not None and\",\n \"can ask the optimizer for the variable it created to hold the slot\",\n \"backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops\",\n \"after \\\" \\\"the Optimizer weights have been created\\\") self._iterations = variable self._weights.append(self._iterations) def\",\n \"\\\"\\\"\\\"Get the slot key for the variable: var_name/slot_name.\\\"\\\"\\\" name = _var_key(var) return name\",\n \"var_dtype): \\\"\\\"\\\"Get decayed learning rate as a Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\",\",\n \"self._slot_names = [] self._weights = [] self._iterations = None # For implementing Trackable.\",\n \"gradient computed for `loss`. name: Optional name for the returned operation. Returns: An\",\n \"the variables. If you want to process the gradients before applying them you\",\n \"If `global_step` was not `None`, that operation also increments `global_step`. Raises: ValueError: If\",\n \"= distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars\",\n \"backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self,\",\n \"# to minimize. loss = lambda: 3 * var1 * var1 + 2\",\n \"dependency graph (causing us to realize that the slot variable needs to be\",\n \"\\\"/\\\" + slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer implementation for checkpoint compatibility. Holds\",\n \"self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def _distributed_apply(self, distribution, grads_and_vars, name):\",\n \"from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import\",\n \"getattr(var, \\\"_distributed_container\\\", None) is not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name\",\n \"implementation for checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer is\",\n \"to variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying to update a Tensor \\\", var)\",\n \"hyperparameter setting.\\\"\\\"\\\" # Backwards compatibility with Keras optimizers. if name == \\\"lr\\\": name\",\n \"_track_ slot variables anywhere, and # instead special-case this dependency and otherwise pretend\",\n \"variables are created when graph building. Instead, _restore_slot_variable catches these after normal creation\",\n \"grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss` by updating `var_list`. This method simply computes gradient using\",\n \"grad, var in grads_and_vars: scope_name = (\\\"\\\" if ops.executing_eagerly_outside_functions() else \\\"_\\\" + var.op.name)\",\n \"So we don't _track_ slot variables anywhere, and # instead special-case this dependency\",\n \"construction time. Examples include 1) sequential models without input shape pre-defined, or 2)\",\n \"first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for\",\n \"i in update_ops) if not context.executing_eagerly() or any_symbolic: # If the current context\",\n \"# scope. Generally we'd like to eagerly create/restore slot variables # when possible,\",\n \"slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by\",\n \"for the returned operation. Default to the name passed to the `Optimizer` constructor.\",\n \"by a factor equal to the number of replicas being used to compute\",\n \"== \\\"lr\\\": name = \\\"learning_rate\\\" if name in self._hyper: return self._get_hyper(name) raise e\",\n \"for grad, param in zip(grads, params): if grad is None: raise ValueError(\\\"Variable {}\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of\",\n \"minimize(self, loss, var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss` by updating `var_list`. This method simply\",\n \"opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with Keras models In Keras\",\n \"tensor's shape is the *replica-local* batch size, which is off by a factor\",\n \"to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training\",\n \"computing the gradients and applying them to the variables. If you want to\",\n \"`loss` with respect to `params`. Arguments: loss: Loss tensor. params: List of variables.\",\n \"variables %s when minimizing the loss.\\\"), ([v.name for v in vars_with_empty_grads])) return filtered\",\n \"An `Operation` which updates the value of the variable. \\\"\\\"\\\" summed_grad, unique_indices =\",\n \"this class and override the following methods: - resource_apply_dense (update variable given gradient\",\n \"whatever you # need to the 'gradient' part, for example cap them, etc.\",\n \"= [] self._iterations = None # For implementing Trackable. Stores information about how\",\n \"return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share\",\n \"if self._iterations is None: self._iterations = self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations)\",\n \"0.: raise ValueError(\\\"decay cannot be less than 0: {}\\\".format(decay)) self._initial_decay = decay if\",\n \"differentiable). \\\" \\\"Common ops without gradient: \\\" \\\"K.argmax, K.round, K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"):\",\n \"var: the variable. Returns: the unique name of the variable. \\\"\\\"\\\" # pylint:\",\n \"processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars\",\n \"evaluation loops. See the `reduction` argument of your loss which should be set\",\n \"is called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. Returns:\",\n \"output of get_config. custom_objects: A Python dictionary mapping names to additional Python objects\",\n \"return value def variables(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the order\",\n \"is to be synced on read. trainable = False elif trainable is None:\",\n \"isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable:\",\n \"restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer restoring\",\n \"# pylint: disable=protected-access # Get the distributed variable if it exists. if getattr(var,\",\n \"config is a Python dictionary (serializable) containing the configuration of an optimizer. The\",\n \"lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t\",\n \"self._get_hyper(\\\"decay\\\", var_dtype) lr_t = lr_t / (1. + decay_t * local_step) return lr_t\",\n \"Deferring is mostly harmless # (aside from double initialization), and makes variable creator\",\n \"# those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars):\",\n \"v in vars_with_empty_grads])) return filtered def _var_key(var): \\\"\\\"\\\"Key for representing a primary variable,\",\n \"Iterate over restores, highest restore UID first to minimize the number # of\",\n \"graph building. Instead, _restore_slot_variable catches these after normal creation and adds restore ops\",\n \"just been added to a dependency graph (causing us to realize that the\",\n \"equal to None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered =\",\n \"slot_name=slot_name) # Slot variables are not owned by any one object (because we\",\n \"of integral type representing the indices for which the gradient is nonzero. Indices\",\n \"raise ValueError(\\\"Expected {} >= 0, received: {}\\\".format(k, kwargs[k])) self._use_locking = True self._name =\",\n \"Use callable when the variable list would otherwise be incomplete before `minimize` since\",\n \"a constraint function on a sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op\",\n \"if name not in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if\",\n \"the gradient computed for `loss`. Returns: A list of (gradient, variable) pairs. Variable\",\n \"the variables in `var_list`. This is the first part of `minimize()`. It returns\",\n \"synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \\\"Synchronization value can be set to\",\n \"handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to `handle`, with repeated indices.\",\n \"variable: var_name/slot_name.\\\"\\\"\\\" name = _var_key(var) return name + \\\"/\\\" + slot_name class _RestoredOptimizer(OptimizerV2):\",\n \"models In Keras models, sometimes variables are created when the model is first\",\n \": variable}} self._slots = {} self._slot_names = [] self._weights = [] self._iterations =\",\n \"to restore into. variable: The variable object this slot is being created for.\",\n \"Process the gradients as you wish. 3. Apply the processed gradients with `apply_gradients()`.\",\n \"self._name = name self._hyper = {} # dict: {variable name : {slot name\",\n \"the variable list would otherwise be incomplete before `minimize` since the variables are\",\n \"str(weights)[:50] + \\\"...\\\") if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params)\",\n \"affected indices. handle: a `Tensor` of dtype `resource` which points to the variable\",\n \"# methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True def get_config(self): # TODO(allenl):\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \\\"\\\"\\\"Returns gradients of\",\n \"initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight\",\n \"not None: # If we've either made this slot variable, or if we've\",\n \"optimizer is restored from a SavedModel. These variables may be referenced in functions\",\n \"currently thread compatible, not thread-safe. The user needs to perform synchronization if necessary.\",\n \"is clip gradients by value, `decay` is included for backward compatibility to allow\",\n \"non-unique indices and their associated gradients, is enforced by first pre-processing `grad` and\",\n \"to compute a single step. As a result, using `tf.math.reduce_mean` will give the\",\n \"grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered\",\n \"of `indices` and `summed_values` contains the sum of `values` slices associated with each\",\n \"to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer\",\n \"self.clipnorm) for g in grads] if hasattr(self, \\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue,\",\n \"distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager\",\n \"var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the slot key for the variable:\",\n \"an active variable creator # scope. Generally we'd like to eagerly create/restore slot\",\n \"uses. Once you have a slot name you can ask the optimizer for\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"(without any saved state) from this configuration. Returns: Python dictionary. \\\"\\\"\\\" config =\",\n \"ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer,\",\n \"hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue return config\",\n \"governing permissions and # limitations under the License. # ============================================================================== \\\"\\\"\\\"Version 2 of\",\n \"shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations\",\n \"Set trainable to be false when variable is to be synced on read.\",\n \"new slot variable for `var`.\\\"\\\"\\\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key =\",\n \"= False def minimize(self, loss, var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss` by updating `var_list`.\",\n \"as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads\",\n \"= self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step),\",\n \"hyper parameters) \\\"\\\"\\\" def __init__(self, name, **kwargs): \\\"\\\"\\\"Create a new Optimizer. This must\",\n \"trainable = False elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter(\",\n \"= True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True,\",\n \"that can be a float, callable, or Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name] if isinstance(value,\",\n \"a list of (gradient, variable) pairs where \\\"gradient\\\" is the gradient for \\\"variable\\\".\",\n \"dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be\",\n \"opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with\",\n \"self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss` by updating\",\n \"+ \\\"...\\\") if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for\",\n \"str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE):\",\n \"the optimizer object iself (e.g. through `apply_gradients`). \\\"\\\"\\\" # TODO(allenl): Make the restored\",\n \"the callable will be called during `apply_gradients()` to get the value for the\",\n \"(trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, #\",\n \"get_config (serialization of the optimizer, include all hyper parameters) \\\"\\\"\\\" def __init__(self, name,\",\n \"+ 2 * var2 # In eager mode, simply call minimize to update\",\n \"none of the variables have gradients. \\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v\",\n \"config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in config: if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize(\",\n \"-self.clipvalue, self.clipvalue) for g in grads ] return grads def apply_gradients(self, grads_and_vars, name=None):\",\n \"simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want to process\",\n \"weights): params = self.weights if len(params) != len(weights): raise ValueError( \\\"You called `set_weights(weights)`\",\n \"been overridden instead of _create_vars. \\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for\",\n \"summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad,\",\n \"position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable):\",\n \"if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] =\",\n \"with any non-unique `indices`. Args: values: A `Tensor` with rank >= 1. indices:\",\n \"possibly creating it. Called when a variable which has an associated slot variable\",\n \"* var2 # In graph mode, returns op that minimizes the loss by\",\n \"return var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name or self._name): for\",\n \"checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore a slot\",\n \"the optimimizer. An optimizer config is a Python dictionary (serializable) containing the configuration\",\n \"restored. slot_name: The name of this `Optimizer`'s slot to restore into. variable: The\",\n \"_filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable with grad equal to None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars) if\",\n \"the specific language governing permissions and # limitations under the License. # ==============================================================================\",\n \"care of both computing the gradients and applying them to the variables. If\",\n \"hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value):\",\n \"v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]):\",\n \"tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of `indices` and\",\n \"constraint will be applied to the variable after the gradient has been applied\",\n \"tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises:\",\n \"can be useful if you want to log debug a training algorithm, report\",\n \"A Python dictionary mapping names to additional Python objects used to create this\",\n \"the sum of `values` slices associated with each unique index. \\\"\\\"\\\" unique_indices, new_index_positions\",\n \"learning rate as a Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t,\",\n \"# --------------- # For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name,\",\n \"to average gradients, you should use `tf.math.reduce_sum` to add up your per-example losses\",\n \"backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name = (\\\"\\\" if ops.executing_eagerly_outside_functions()\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"Arguments: config: A Python dictionary, typically the output of get_config. custom_objects: A Python\",\n \"resource_apply_sparse (update variable given gradient tensor is sparse) - create_slots (if your optimizer\",\n \"name): \\\"\\\"\\\"Overridden to support hyperparameter access.\\\"\\\"\\\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as\",\n \"malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars. \\\"\\\"\\\" allowed_kwargs =\",\n \"self._iterations = self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def\",\n \"active variable creator # scope. Generally we'd like to eagerly create/restore slot variables\",\n \"sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint\",\n \"obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper # pylint:\",\n \"indices. Optimizers which override this method must deal with repeated indices. See the\",\n \"g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of\",\n \"[] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is None:\",\n \"and then divide by the global batch size. Note that when using `tf.distribute.Strategy`,\",\n \"trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not\",\n \"tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables\",\n \"`loss` is a callable that takes no argument and returns the value #\",\n \"# {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations = {}\",\n \"is None. \\\"\\\"\\\" # TODO(josh11b): Test that we handle weight decay in a\",\n \"context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an active\",\n \"such as a function used for a hyperparameter. Returns: An optimizer instance. \\\"\\\"\\\"\",\n \"this class directly, but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`,\",\n \"facility instead. This class in stateful and thread-compatible. Args: name: A non-empty string.\",\n \"# dict: {variable name : {slot name : variable}} self._slots = {} self._slot_names\",\n \"def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter that can be a float, callable, or\",\n \"trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \\\"\\\"\\\"Restore a newly created\",\n \"= self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def _distributed_apply(self,\",\n \"building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable =\",\n \"dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \\\"\\\"\\\"Call the function if param is callable.\\\"\\\"\\\" return\",\n \"NotImplementedError( \\\"Restoring functional Optimzers from SavedModels is not currently \\\" \\\"supported. Please file\",\n \"second part of `minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars:\",\n \"gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this\",\n \"def get_gradients(self, loss, params): \\\"\\\"\\\"Returns gradients of `loss` with respect to `params`. Arguments:\",\n \"`tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with the desired parameters. opt\",\n \"to optimizer: \\\" + str(k)) # checks that all keyword arguments are non-negative.\",\n \"sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[],\",\n \"strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False,\",\n \"class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and\",\n \"# Slot variables are not owned by any one object (because we don't\",\n \"them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead\",\n \"of the variable. \\\"\\\"\\\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle,\",\n \"using `tf.math.reduce_mean` will give the wrong answer, resulting in gradients that can be\",\n \"e # Backwards compatibility with Keras optimizers. if name == \\\"lr\\\": name =\",\n \"params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer.\",\n \"activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for\",\n \"with duplicate indices may instead override this method to avoid the overhead of\",\n \"name of this `Optimizer`'s slot to restore into. variable: The variable object this\",\n \"for gradient. \\\" \\\"Please make sure that all of your ops have a\",\n \"simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ```\",\n \"`apply_gradients()`. If you want to process the gradient before applying then call `tf.GradientTape`\",\n \"Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with\",\n \"`Tensor` holding the gradient computed for `loss`. Returns: A list of (gradient, variable)\",\n \"### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means it\",\n \"clip gradients by value, `decay` is included for backward compatibility to allow time\",\n \"def _set_hyper(self, name, value): \\\"\\\"\\\"set hyper `name` to value. value can be callable,\",\n \"set_weights(self, weights): params = self.weights if len(params) != len(weights): raise ValueError( \\\"You called\",\n \"increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of the\",\n \"!= dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \\\"\\\"\\\"set hyper `name` to\",\n \"the variables are not `Variable` objects. \\\"\\\"\\\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss)\",\n \"of instantiating the same optimizer from the config dictionary. Arguments: config: A Python\",\n \"update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a\",\n \"catch # `variable` also catch its eagerly created slot variable # unintentionally (specifically\",\n \"self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize\",\n \"deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID\",\n \"None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError:\",\n \"cannot be computed (e.g. if gradient function not implemented). \\\"\\\"\\\" params = nest.flatten(params)\",\n \"self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return\",\n \"slices associated with each unique index. \\\"\\\"\\\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values =\",\n \"not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list =\",\n \"for backward compatibility to allow time inverse decay of learning rate. `lr` is\",\n \"\\\"K.argmax, K.round, K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in\",\n \"variables. This is the second part of `minimize()`. It returns an `Operation` that\",\n \"the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager\",\n \"reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework\",\n \"float types. Returns: Valid types for loss, variables and gradients. \\\"\\\"\\\" return set(\",\n \"var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered =\",\n \"ValueError: If any tensor is not a valid type. \\\"\\\"\\\" valid_dtypes = self._valid_dtypes()\",\n \"\\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1,\",\n \"import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import\",\n \"\\\"\\\"\\\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param\",\n \"cannot be less than 0: {}\\\".format(decay)) self._initial_decay = decay if \\\"clipnorm\\\" in kwargs:\",\n \"have been created\\\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed learning\",\n \"weight list of length \\\" + str(len(weights)) + \\\", but the optimizer was\",\n \"the variable. \\\"\\\"\\\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices)\",\n \"```python # Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) #\",\n \"loss.\\\"), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): \\\"\\\"\\\"Key for representing\",\n \"Holds slot variables and hyperparameters when an optimizer is restored from a SavedModel.\",\n \"``` ### Custom training loop with Keras models In Keras models, sometimes variables\",\n \"value() if dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \\\"\\\"\\\"Overridden\",\n \"to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable`\",\n \"self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name]\",\n \"possible, but this may mean that scopes intended to catch # `variable` also\",\n \"initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \\\"\\\"\\\"Variable. The number of training\",\n \"# of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def\",\n \"a result, using `tf.math.reduce_mean` will give the wrong answer, resulting in gradients that\",\n \"initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def\",\n \"the slot key for the variable: var_name/slot_name.\\\"\\\"\\\" name = _var_key(var) return name +\",\n \"Test that we handle weight decay in a reasonable way. with backprop.GradientTape() as\",\n \"of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads\",\n \"var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any one object\",\n \"as you wish. 3. Apply the processed gradients with `apply_gradients()`. Example: ```python #\",\n \"value # to minimize. loss = lambda: 3 * var1 * var1 +\",\n \"KIND, either express or implied. # See the License for the specific language\",\n \"self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value =\",\n \"that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional name\",\n \"the variable. Returns: the unique name of the variable. \\\"\\\"\\\" # pylint: disable=protected-access\",\n \"otherwise be incomplete before `minimize` since the variables are created at the first\",\n \"\\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name,\",\n \"to restore # slot variables which have not yet been created # (trackable._CheckpointPosition\",\n \"% (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight\",\n \"minimizing the loss.\\\"), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): \\\"\\\"\\\"Key\",\n \"eagerly create/restore slot variables # when possible, but this may mean that scopes\",\n \"own optimization algorithm, simply inherit from this class and override the following methods:\",\n \"import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import\",\n \"params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in\",\n \"in `var_list`. If `global_step` was not `None`, that operation also increments `global_step`. Raises:\",\n \"gradients as you wish. 3. Apply the processed gradients with `apply_gradients()`. Example: ```python\",\n \"0: raise ValueError(\\\"Expected {} >= 0, received: {}\\\".format(k, kwargs[k])) self._use_locking = True self._name\",\n \"variable to be updated. indices: a `Tensor` of integral type representing the indices\",\n \"Similar to `_apply_sparse`, the `indices` argument to this method has been de-duplicated. Optimizers\",\n \"iterations(self): \\\"\\\"\\\"Variable. The number of training steps this Optimizer has run.\\\"\\\"\\\" if self._iterations\",\n \"this optimizer's slots.\\\"\\\"\\\" return self._slot_names def add_slot(self, var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new\",\n \"any_symbolic: # If the current context is graph mode or any of the\",\n \"sometimes variables are created when the model is first called, instead of construction\",\n \"that it uses. Once you have a slot name you can ask the\",\n \"any gradient cannot be computed (e.g. if gradient function not implemented). \\\"\\\"\\\" params\",\n \"loss, params): \\\"\\\"\\\"Returns gradients of `loss` with respect to `params`. Arguments: loss: Loss\",\n \"shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def\",\n \"\\\" \\\"provided weight shape \\\" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name,\",\n \"graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable\",\n \"to update to minimize `loss`, or a callable returning the list or tuple\",\n \"by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() #\",\n \"= initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy\",\n \"argument to this method has been de-duplicated. Optimizers which deal correctly with non-unique\",\n \"callable taking no arguments which returns the value to minimize. var_list: list or\",\n \"TODO(josh11b): Test that we handle weight decay in a reasonable way. with backprop.GradientTape()\",\n \"custom_objects=None): \\\"\\\"\\\"Creates an optimizer from its config. This method is the reverse of\",\n \"the `indices` argument to this method has been de-duplicated. Optimizers which deal correctly\",\n \"its config. This method is the reverse of `get_config`, capable of instantiating the\",\n \"loss = lambda: 3 * var1 * var1 + 2 * var2 *\",\n \"simply inherit from this class and override the following methods: - resource_apply_dense (update\",\n \"to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position\",\n \"is derived from the var shared name. In eager mode the name is\",\n \"In eager mode the name is derived from the var unique id. If\",\n \"k not in allowed_kwargs: raise TypeError(\\\"Unexpected keyword argument \\\" \\\"passed to optimizer: \\\"\",\n \"keras_export def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values` associated with any non-unique `indices`. Args: values:\",\n \"numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name]\",\n \"this Optimizer based on the order created.\\\"\\\"\\\" return self._weights def get_weights(self): params =\",\n \"`clipvalue` is clip gradients by value, `decay` is included for backward compatibility to\",\n \"instead of using this function. Args: loss: A callable taking no arguments which\",\n \"with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\":\",\n \"If the current context is graph mode or any of the update ops\",\n \"if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape,\",\n \"\\\"\\\"\\\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]):\",\n \"raise e # Backwards compatibility with Keras optimizers. if name == \\\"lr\\\": name\",\n \"override the following methods: - resource_apply_dense (update variable given gradient tensor is dense)\",\n \"thread compatible, not thread-safe. The user needs to perform synchronization if necessary. ###\",\n \"minimize `loss`, or a callable returning the list or tuple of `Variable` objects.\",\n \"initializer. No new variables are created when graph building. Instead, _restore_slot_variable catches these\",\n \"until it gets created # normally. We keep a list rather than the\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"loops. See the `reduction` argument of your loss which should be set to\",\n \"or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None):\",\n \"parameters can be overwritten through user code: Example: ```python # Create an optimizer\",\n \"If some of the variables are not `Variable` objects. \\\"\\\"\\\" grads_and_vars = self._compute_gradients(\",\n \"at the first time `loss` is called. grad_loss: Optional. A `Tensor` holding the\",\n \"str(k)) # checks that all keyword arguments are non-negative. if kwargs[k] < 0:\",\n \"distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i\",\n \"(`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of `indices` and `summed_values` contains\",\n \"gradient is sparse tensor, variable constraint is not supported. ### Thread Compatibility The\",\n \"backward compatibility to allow time inverse decay of learning rate. `lr` is included\",\n \"= nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g,\",\n \"decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns the config of the\",\n \"three steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process the gradients as\",\n \"tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list()\",\n \"manage additional variables associated with the variables to train. These are called <i>Slots</i>.\",\n \"### Custom training loop with Keras models In Keras models, sometimes variables are\",\n \"report stats about the slots, etc. ### Hyper parameters These are arguments passed\",\n \"unique id. If distribution strategy exists, get the primary variable first. Args: var:\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"the API to add Ops to train a model. You never use this\",\n \"`handle`, with repeated indices. Optimizers which override this method must deal with repeated\",\n \"\\\"\\\"\\\" # TODO(josh11b): Test that we handle weight decay in a reasonable way.\",\n \"strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name, slot_name), #\",\n \"update a Tensor \\\", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None:\",\n \"of an optimizer. The same optimizer can be reinstantiated later (without any saved\",\n \"for the hyper parameter. Hyper parameters can be overwritten through user code: Example:\",\n \"`loss`. name: Optional name for the returned operation. Returns: An Operation that updates\",\n \"computed (e.g. if gradient function not implemented). \\\"\\\"\\\" params = nest.flatten(params) with backend.get_graph().as_default():\",\n \"x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x,\",\n \"The name of this `Optimizer`'s slot to restore into. variable: The variable object\",\n \"gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware,\",\n \"case any gradient cannot be computed (e.g. if gradient function not implemented). \\\"\\\"\\\"\",\n \"case when a slot variable has already been created but `variable` has just\",\n \"def set_weights(self, weights): params = self.weights if len(params) != len(weights): raise ValueError( \\\"You\",\n \"variable it created to hold the slot value. This can be useful if\",\n \"slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass\",\n \"param): \\\"\\\"\\\"Call the function if param is callable.\\\"\\\"\\\" return param() if callable(param) else\",\n \"gradients. If `global_step` was not None, that operation also increments `global_step`. Raises: TypeError:\",\n \"with backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name = (\\\"\\\" if\",\n \"logic with base_layer. def set_weights(self, weights): params = self.weights if len(params) != len(weights):\",\n \"or tuple of `Variable` objects to update to minimize `loss`, or a callable\",\n \"be useful if you want to log debug a training algorithm, report stats\",\n \"var._unique_id def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the slot key for the variable: var_name/slot_name.\\\"\\\"\\\" name\",\n \"a function used for a hyperparameter. Returns: An optimizer instance. \\\"\\\"\\\" if \\\"lr\\\"\",\n \"if not context.executing_eagerly() or any_symbolic: # If the current context is graph mode\",\n \"the gradient has been applied to the variable. Important: If gradient is sparse\",\n \"SavedModels is not currently \\\" \\\"supported. Please file a feature request if this\",\n \"__future__ import absolute_import from __future__ import division from __future__ import print_function import abc\",\n \"lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients\",\n \"where \\\"gradient\\\" is the gradient for \\\"variable\\\". Note that \\\"gradient\\\" can be a\",\n \"request if this limitation bothers \\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda obj: isinstance(obj, OptimizerV2),\",\n \"None) is not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id\",\n \"equal to the number of replicas being used to compute a single step.\",\n \"was not None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is\",\n \"created at the first time when `loss` is called. grad_loss: Optional. A `Tensor`\",\n \"tuple(filtered) if not filtered: raise ValueError(\\\"No gradients provided for any variable: %s.\\\" %\",\n \"such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with the\",\n \"\\\"\\\"\\\"Sums `values` associated with any non-unique `indices`. Args: values: A `Tensor` with rank\",\n \"raise ValueError(\\\"No gradients provided for any variable: %s.\\\" % ([v.name for _, v\",\n \"vars_with_empty_grads])) return filtered def _var_key(var): \\\"\\\"\\\"Key for representing a primary variable, for looking\",\n \"Thread Compatibility The entire optimizer is currently thread compatible, not thread-safe. The user\",\n \"Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can\",\n \"% (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid types for\",\n \"the case when a slot variable has already been created but `variable` has\",\n \"for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`,\",\n \"# variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply\",\n \"The number of training steps this Optimizer has run.\\\"\\\"\\\" if self._iterations is None:\",\n \"+ \\\" not compatible with \\\" \\\"provided weight shape \\\" + str(w.shape)) weight_value_tuples.append((p,\",\n \"this case). Deferring is mostly harmless # (aside from double initialization), and makes\",\n \"the global batch size. Note that when using `tf.distribute.Strategy`, the first component of\",\n \"returning the list or tuple of `Variable` objects. Use callable when the variable\",\n \"self._name} if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue\",\n \"If `var_list` contains anything else than `Variable` objects. ValueError: If some arguments are\",\n \"\\\"\\\"\\\"Create a new Optimizer. This must be called by the constructors of subclasses.\",\n \"number of replicas being used to compute a single step. As a result,\",\n \"don't want to # save the slot variable if the optimizer is saved\",\n \"\\\"decay\\\"} for k in kwargs: if k not in allowed_kwargs: raise TypeError(\\\"Unexpected keyword\",\n \"All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the\",\n \"= _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices):\",\n \"trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True,\",\n \"This is the first part of `minimize()`. It returns a list of (gradient,\",\n \"Usage ```python # Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1)\",\n \"\\\" weights. Provided weights: \\\" + str(weights)[:50] + \\\"...\\\") if not params: return\",\n \"time inverse decay of learning rate. `lr` is included for backward compatibility, recommended\",\n \"constructor. Returns: An `Operation` that applies the specified gradients. If `global_step` was not\",\n \"if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply,\",\n \"grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g\",\n \"] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if\",\n \"hasattr(self, \\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ]\",\n \"optimizer class is `tf.distribute.Strategy` aware, which means it automatically sums gradients across all\",\n \"in allowed_kwargs: raise TypeError(\\\"Unexpected keyword argument \\\" \\\"passed to optimizer: \\\" + str(k))\",\n \"None: self._iterations = self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter\",\n \"the *replica-local* batch size, which is off by a factor equal to the\",\n \"optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\") and name\",\n \"Ops to train a model. You never use this class directly, but instead\",\n \"is mostly harmless # (aside from double initialization), and makes variable creator scopes\",\n \"want to # save the slot variable if the optimizer is saved without\",\n \"slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer restoring until it\",\n \"function if param is callable.\\\"\\\"\\\" return param() if callable(param) else param def _resource_apply_dense(self,\",\n \"else: return value def __getattribute__(self, name): \\\"\\\"\\\"Overridden to support hyperparameter access.\\\"\\\"\\\" try: return\",\n \"vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in\",\n \"or if the non-slot variable is saved without the optimizer; # it's a\",\n \"[] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad,\",\n \"can ask the optimizer for the names of the slots that it uses.\",\n \"if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \\\"\\\"\\\"Returns\",\n \"Optimizer. This must be called by the constructors of subclasses. Note that Optimizer\",\n \"= {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for k in kwargs: if k not in\",\n \"if it exists. if getattr(var, \\\"_distributed_container\\\", None) is not None: var = var._distributed_container()\",\n \"dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \\\"\\\"\\\"set hyper `name` to value.\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"the slots that it uses. Once you have a slot name you can\",\n \"are not owned by any one object (because we don't want to #\",\n \"for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not using these and\",\n \"as e: # Needed to avoid infinite recursion with __setattr__. if name ==\",\n \"variable is to be synced on read. trainable = False elif trainable is\",\n \"opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer. If you intend to\",\n \"A `Tensor` holding the gradient computed for `loss`. Returns: A list of (gradient,\",\n \"respect to `params`. Arguments: loss: Loss tensor. params: List of variables. Returns: List\",\n \"import absolute_import from __future__ import division from __future__ import print_function import abc import\",\n \"been created\\\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed learning rate\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"def get_config(self): \\\"\\\"\\\"Returns the config of the optimimizer. An optimizer config is a\",\n \"saved state) from this configuration. Returns: Python dictionary. \\\"\\\"\\\" config = {\\\"name\\\": self._name}\",\n \"_resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to `handle`, with\",\n \"super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore the\",\n \"Indices may be repeated. Returns: An `Operation` which updates the value of the\",\n \"unintentionally (specifically make_template would add a dependency on # a slot variable if\",\n \"and # instead special-case this dependency and otherwise pretend it's a normal #\",\n \"means it automatically sums gradients across all replicas. To average gradients, you divide\",\n \"types for loss, variables and gradients. Subclasses should override to allow other float\",\n \"isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if dtype: return math_ops.cast(value,\",\n \"for looking up slots. In graph mode the name is derived from the\",\n \"config: if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self,\",\n \"value to minimize. var_list: list or tuple of `Variable` objects to update to\",\n \"instead override this method to avoid the overhead of summing. Args: grad: a\",\n \"Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm;\",\n \"tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or any_symbolic: # If the\",\n \"else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\\\"%s/%s\\\"\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"list or tuple of `Variable` objects. Use callable when the variable list would\",\n \"Optional name for the returned operation. Returns: An Operation that updates the variables\",\n \"isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \\\"Cannot use a constraint\",\n \"\\\"\\\"\\\" config = {\\\"name\\\": self._name} if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm if hasattr(self,\",\n \"decayed learning rate as a Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype) if\",\n \"variable constraint is not supported. ### Thread Compatibility The entire optimizer is currently\",\n \"list of (gradient, variable) pairs. Variable is always present, but gradient can be\",\n \"return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars\",\n \"for not. If you are not using these and you want to average\",\n \"key for the variable: var_name/slot_name.\\\"\\\"\\\" name = _var_key(var) return name + \\\"/\\\" +\",\n \"a de-duplicated version of `indices` and `summed_values` contains the sum of `values` slices\",\n \"optimization algorithm, simply inherit from this class and override the following methods: -\",\n \"from __future__ import division from __future__ import print_function import abc import functools import\",\n \"1.0), tensors, or callables. If they are callable, the callable will be called\",\n \"the indices for which the gradient is nonzero. Indices are unique. Returns: An\",\n \"[v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad):\",\n \"(specifically make_template would add a dependency on # a slot variable if not\",\n \"should use `tf.math.reduce_sum` to add up your per-example losses and then divide by\",\n \"Slots Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage additional\",\n \"<call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g\",\n \"distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars =\",\n \"var_list) # grads_and_vars is a list of tuples (gradient, variable). Do whatever you\",\n \"initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \\\"Synchronization value can be\",\n \"\\\"provided weight shape \\\" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape,\",\n \"incomplete before `minimize` since the variables are created at the first time `loss`\",\n \"grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars =\",\n \"opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda:\",\n \"if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops\",\n \"or Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return\",\n \"in grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients to variables.\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"params, weights): if pv.shape != w.shape: raise ValueError(\\\"Optimizer weight shape \\\" + str(pv.shape)\",\n \"a callable returning the list or tuple of `Variable` objects. Use callable when\",\n \"points to the variable to be updated. Returns: An `Operation` which updates the\",\n \"(summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class for optimizers. This class\",\n \"algorithm, report stats about the slots, etc. ### Hyper parameters These are arguments\",\n \"`var_list`. This method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you\",\n \"We keep a list rather than the one with the highest restore #\",\n \"grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to the variable `handle`.\",\n \"x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- #\",\n \"your loss by the global batch size, which is done automatically if you\",\n \"across all replicas. To average gradients, you divide your loss by the global\",\n \"this logic with base_layer. def set_weights(self, weights): params = self.weights if len(params) !=\",\n \"ValueError( \\\"You called `set_weights(weights)` on optimizer \\\" + self._name + \\\" with a\",\n \"position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name,\",\n \"for. \\\"\\\"\\\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None)\",\n \"math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base\",\n \"grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients of `loss`\",\n \"before applying them. Calling `minimize()` takes care of both computing the gradients and\",\n \"if name == \\\"lr\\\": name = \\\"learning_rate\\\" if name in self._hyper: return self._get_hyper(name)\",\n \"_call_if_callable(self, param): \\\"\\\"\\\"Call the function if param is callable.\\\"\\\"\\\" return param() if callable(param)\",\n \"or restored. When executing eagerly, we create the slot variable with a restoring\",\n \"if the non-slot variable is saved without the optimizer; # it's a dependency\",\n \"variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict\",\n \"value can be set to \\\" \\\"VariableSynchronization.ON_READ only for non-trainable variables. \\\" \\\"You\",\n \"optimizer. The same optimizer can be reinstantiated later (without any saved state) from\",\n \"in `var_list`. This is the first part of `minimize()`. It returns a list\",\n \"is not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def\",\n \"of this Optimizer based on the order created.\\\"\\\"\\\" return self._weights def get_weights(self): params\",\n \"this limitation bothers \\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"= initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name,\",\n \"hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter that can be a float, callable, or Tensor.\\\"\\\"\\\" value\",\n \"{} >= 0, received: {}\\\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper\",\n \"disable=g-bad-name from __future__ import absolute_import from __future__ import division from __future__ import print_function\",\n \"= name self._hyper = {} # dict: {variable name : {slot name :\",\n \"\\\"\\\"\\\"Minimize `loss` by updating `var_list`. This method simply computes gradient using `tf.GradientTape` and\",\n \"about how to restore # slot variables which have not yet been created\",\n \"= config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in config: if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"],\",\n \"apply sparse gradients to `handle`, with repeated indices. Optimizers which override this method\",\n \"a valid type. \\\"\\\"\\\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype =\",\n \"{}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable()\",\n \"else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \\\"\\\"\\\"A list of names for this optimizer's\",\n \"The user needs to perform synchronization if necessary. ### Slots Many optimizer subclasses,\",\n \"for any variable: %s.\\\" % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads:\",\n \"optimizer \\\" + self._name + \\\" with a weight list of length \\\"\",\n \"not in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value) or\",\n \"defined (i.e. are differentiable). \\\" \\\"Common ops without gradient: \\\" \\\"K.argmax, K.round, K.eval.\\\".format(param))\",\n \"self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def\",\n \"inherit from this class and override the following methods: - resource_apply_dense (update variable\",\n \"must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By\",\n \"to minimize `loss`, or a callable returning the list or tuple of `Variable`\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or\",\n \"nonzero. Indices may be repeated. Returns: An `Operation` which updates the value of\",\n \"decay < 0.: raise ValueError(\\\"decay cannot be less than 0: {}\\\".format(decay)) self._initial_decay =\",\n \"(gradient, variable) pairs. name: Optional name for the returned operation. Default to the\",\n \"not None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed.\",\n \"var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype)\",\n \"self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any one\",\n \"restored. When executing eagerly, we create the slot variable with a restoring initializer.\",\n \"a list of tuples (gradient, variable). Do whatever you # need to the\",\n \"`Operation` which updates the value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self,\",\n \"make sure that all of your ops have a \\\" \\\"gradient defined (i.e.\",\n \"_var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if\",\n \"de-duplicated version of `indices` and `summed_values` contains the sum of `values` slices associated\",\n \"up your per-example losses and then divide by the global batch size. Note\",\n \"return grads_and_vars def get_gradients(self, loss, params): \\\"\\\"\\\"Returns gradients of `loss` with respect to\",\n \"is restored from a SavedModel. These variables may be referenced in functions along\",\n \"ValueError( \\\"Synchronization value can be set to \\\" \\\"VariableSynchronization.ON_READ only for non-trainable variables.\",\n \"and manage additional variables associated with the variables to train. These are called\",\n \"self._name): for grad, var in grads_and_vars: scope_name = (\\\"\\\" if ops.executing_eagerly_outside_functions() else \\\"_\\\"\",\n \"params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not None\",\n \"self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates an optimizer from its\",\n \"through `apply_gradients`). \\\"\\\"\\\" # TODO(allenl): Make the restored optimizer functional by tracing its\",\n \"= None # For implementing Trackable. Stores information about how to restore #\",\n \"want to process the gradients before applying them you can instead use the\",\n \"variables are not `Variable` objects. \\\"\\\"\\\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return\",\n \"to log debug a training algorithm, report stats about the slots, etc. ###\",\n \"Keras optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\") and\",\n \"recursion with __setattr__. if name == \\\"_hyper\\\": raise e # Backwards compatibility with\",\n \"def weights(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the order created.\\\"\\\"\\\" return\",\n \"= self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any\",\n \"with edges of the form (optimizer, non-slot # variable, variable)). So we don't\",\n \"__init__(self, name, **kwargs): \\\"\\\"\\\"Create a new Optimizer. This must be called by the\",\n \"exist for variables %s when minimizing the loss.\\\"), ([v.name for v in vars_with_empty_grads]))\",\n \"check. Raises: ValueError: If any tensor is not a valid type. \\\"\\\"\\\" valid_dtypes\",\n \"repeated. Returns: An `Operation` which updates the value of the variable. \\\"\\\"\\\" summed_grad,\",\n \"import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import\",\n \"compatibility with Keras optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\" if name\",\n \"the name passed to the `Optimizer` constructor. Returns: An `Operation` that applies the\",\n \"the optimizer was expecting \\\" + str(len(params)) + \\\" weights. Provided weights: \\\"\",\n \"loss, variables and gradients. \\\"\\\"\\\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self,\",\n \"tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\\\"Invalid type %r\",\n \"value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule))\",\n \"\\\"_distributed_container\\\", None) is not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return\",\n \"valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype not\",\n \"arguments are non-negative. if kwargs[k] < 0: raise ValueError(\\\"Expected {} >= 0, received:\",\n \"This is the second part of `minimize()`. It returns an `Operation` that applies\",\n \"avoid this overhead. Args: grad: a `Tensor` representing the gradient for the affected\",\n \"return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using\",\n \"isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return\",\n \"An optimizer config is a Python dictionary (serializable) containing the configuration of an\",\n \"is first called, instead of construction time. Examples include 1) sequential models without\",\n \"scopes # behave the same way they do when graph building. and not\",\n \"automatically sums gradients across all replicas. To average gradients, you divide your loss\",\n \"subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with\",\n \"`minimize()` takes care of both computing the gradients and applying them to the\",\n \"grad, handle): \\\"\\\"\\\"Add ops to apply dense gradients to the variable `handle`. Args:\",\n \"the highest restore # UID in case slot variables have their own dependencies,\",\n \"called <i>Slots</i>. Slots have names and you can ask the optimizer for the\",\n \"optimizer for the variable it created to hold the slot value. This can\",\n \"of (gradient, variable) pairs. Variable is always present, but gradient can be `None`.\",\n \"if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name):\",\n \"for t in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise\",\n \"are called <i>Slots</i>. Slots have names and you can ask the optimizer for\",\n \"%s, expected: %s.\\\" % (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self):\",\n \"# In graph mode, returns op that minimizes the loss by updating the\",\n \"which updates the value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_scatter_add(self, x,\",\n \"= [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer to apply\",\n \"up slots. In graph mode the name is derived from the var shared\",\n \"Returns: An Operation that updates the variables in `var_list`. If `global_step` was not\",\n \"value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \\\"\\\"\\\"Returns variables of this\",\n \"indices): \\\"\\\"\\\"Add ops to apply sparse gradients to the variable `handle`. Similar to\",\n \"setattr to support dynamic hyperparameter setting.\\\"\\\"\\\" # Backwards compatibility with Keras optimizers. if\",\n \"is always present, but gradient can be `None`. Raises: TypeError: If `var_list` contains\",\n \"= _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] # Create iteration\",\n \"return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply\",\n \"the optimizer is saved without the non-slot # variable, or if the non-slot\",\n \"Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn\",\n \"tensor is sparse) - create_slots (if your optimizer algorithm requires additional variables) -\",\n \"Generally you should be able to use the _set_hyper()/state.get_hyper() facility instead. This class\",\n \"of `loss` for the variables in `var_list`. This is the first part of\",\n \"Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the\",\n \"You never use this class directly, but instead instantiate one of its subclasses\",\n \"so shouldn't keep Tensors as member variables. Generally you should be able to\",\n \"from its config. This method is the reverse of `get_config`, capable of instantiating\",\n \"grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g\",\n \"id. If distribution strategy exists, get the primary variable first. Args: var: the\",\n \"slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is None: if\",\n \"a list rather than the one with the highest restore # UID in\",\n \"slot variables # when possible, but this may mean that scopes intended to\",\n \"is nonetheless important when graph building for the case when a slot variable\",\n \"] return grads def apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients to variables. This is\",\n \"<list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars\",\n \"on a sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var)\",\n \"grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v\",\n \"elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable,\",\n \"return # Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value,\",\n \"\\\" + str(weights)[:50] + \\\"...\\\") if not params: return weight_value_tuples = [] param_values\",\n \"can be callable, tensor, numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name\",\n \"creator # scope. Generally we'd like to eagerly create/restore slot variables # when\",\n \"It returns an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable)\",\n \"config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter that can be\",\n \"`lr` is included for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError:\",\n \"intended to catch # `variable` also catch its eagerly created slot variable #\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior,\",\n \"\\\"\\\"\\\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if\",\n \"(var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable(\",\n \"None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name):\",\n \"minimize. loss = lambda: 3 * var1 + 2 * var2 # In\",\n \"_var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore\",\n \"shape is the *replica-local* batch size, which is off by a factor equal\",\n \"Operation that updates the variables in `var_list`. If `global_step` was not `None`, that\",\n \"self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars\",\n \"optimizer; # it's a dependency hypergraph with edges of the form (optimizer, non-slot\",\n \"that operation also increments `global_step`. Raises: ValueError: If some of the variables are\",\n \"there is no gradient for the given variable. Args: loss: A callable taking\",\n \"names to additional Python objects used to create this optimizer, such as a\",\n \"of both computing the gradients and applying them to the variables. If you\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"model. You never use this class directly, but instead instantiate one of its\",\n \"= tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = []\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"[resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle,\",\n \"\\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars\",\n \"trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight\",\n \"if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list\",\n \"variables # when possible, but this may mean that scopes intended to catch\",\n \"to allow time inverse decay of learning rate. `lr` is included for backward\",\n \"if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self,\",\n \"t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\\\"Invalid type %r for %s, expected:\",\n \"_get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the slot key for the variable: var_name/slot_name.\\\"\\\"\\\" name = _var_key(var)\",\n \"not currently \\\" \\\"supported. Please file a feature request if this limitation bothers\",\n \"part of `minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars: List\",\n \"give the wrong answer, resulting in gradients that can be many times too\",\n \"index. \\\"\\\"\\\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return\",\n \"from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from\",\n \"variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call\",\n \"is the second part of `minimize()`. It returns an `Operation` that applies gradients.\",\n \"shape pre-defined, or 2) subclassed models. Pass var_list as callable in these cases.\",\n \"name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] =\",\n \"directly, but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ###\",\n \"local_step) return lr_t @abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns the config of the optimimizer. An\",\n \"= self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer,\",\n \"= var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \\\"clipnorm\\\"):\",\n \"Needed to avoid infinite recursion with __setattr__. if name == \\\"_hyper\\\": raise e\",\n \"use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has\",\n \"gradient for the affected indices. handle: a `Tensor` of dtype `resource` which points\",\n \"= kwargs.pop(\\\"decay\\\", 0.0) if decay < 0.: raise ValueError(\\\"decay cannot be less than\",\n \"self._iterations = None # For implementing Trackable. Stores information about how to restore\",\n \"a variable which has an associated slot variable is created or restored. When\",\n \"created by the original optimizer, but currently we do not support using the\",\n \"representing the indices for which the gradient is nonzero. Indices are unique. Returns:\",\n \"if vars_with_empty_grads: logging.warning( (\\\"Gradients does not exist for variables %s when minimizing the\",\n \"from the var shared name. In eager mode the name is derived from\",\n \"configuration of an optimizer. The same optimizer can be reinstantiated later (without any\",\n \"first part of `minimize()`. It returns a list of (gradient, variable) pairs where\",\n \"_var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is None:\",\n \"for a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars\",\n \"after normal creation and adds restore ops to the graph. This method is\",\n \"the loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2])\",\n \"with `tf.GradientTape`. 2. Process the gradients as you wish. 3. Apply the processed\",\n \"(1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns the config\",\n \"slot variables anywhere, and # instead special-case this dependency and otherwise pretend it's\",\n \"initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\\\"%s/%s\\\" %\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"division from __future__ import print_function import abc import functools import six from tensorflow.python.distribute\",\n \"variable) pairs. Variable is always present, but gradient can be `None`. Raises: TypeError:\",\n \"set `iterations` to a new Variable after \\\" \\\"the Optimizer weights have been\",\n \"not using these and you want to average gradients, you should use `tf.math.reduce_sum`\",\n \"grads_and_vars if g is not None and v.dtype != dtypes.resource ]) return grads_and_vars\",\n \"\\\"\\\"\\\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype\",\n \"Apply the processed gradients with `apply_gradients()`. Example: ```python # Create an optimizer. opt\",\n \"model is first called, instead of construction time. Examples include 1) sequential models\",\n \"returns a list of (gradient, variable) pairs where \\\"gradient\\\" is the gradient for\",\n \"\\\"clipvalue\\\" in kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False def minimize(self, loss, var_list,\",\n \"array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops\",\n \"clip gradients by norm; `clipvalue` is clip gradients by value, `decay` is included\",\n \"= tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads,\",\n \"\\\"passed to optimizer: \\\" + str(k)) # checks that all keyword arguments are\",\n \"this may mean that scopes intended to catch # `variable` also catch its\",\n \"norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility\",\n \"override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing the gradient\",\n \"else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value =\",\n \"a float, callable, or Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value)\",\n \"Valid types for loss, variables and gradients. \\\"\\\"\\\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32,\",\n \"# Iterate over restores, highest restore UID first to minimize the number #\",\n \"= any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or\",\n \"slots, etc. ### Hyper parameters These are arguments passed to the optimizer subclass\",\n \"updates the variables in `var_list`. If `global_step` was not `None`, that operation also\",\n \"# TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights): params =\",\n \"trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types)\",\n \"([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): \\\"\\\"\\\"Key for representing a\",\n \"`_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing the gradient for\",\n \"be applied to the variable after the gradient has been applied to the\",\n \"holding the gradient computed for `loss`. Returns: A list of (gradient, variable) pairs.\",\n \"\\\"\\\"\\\"Creates an optimizer from its config. This method is the reverse of `get_config`,\",\n \"for the names of the slots that it uses. Once you have a\",\n \"array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class\",\n \"= [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return grads def\",\n \"from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from\",\n \"variable, the constraint will be applied to the variable after the gradient has\",\n \"all keyword arguments are non-negative. if kwargs[k] < 0: raise ValueError(\\\"Expected {} >=\",\n \"to process the gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead\",\n \"var_list=[var1, var2]) ``` ### Custom training loop with Keras models In Keras models,\",\n \"aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors are all valid types\",\n \"it created to hold the slot value. This can be useful if you\",\n \"is no gradient for the given variable. Args: loss: A callable taking no\",\n \"list of names for this optimizer's slots.\\\"\\\"\\\" return self._slot_names def add_slot(self, var, slot_name,\",\n \"tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads =\",\n \"implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \\\"\\\"\\\"Restore a\",\n \"of training steps this Optimizer has run.\\\"\\\"\\\" if self._iterations is None: self._iterations =\",\n \"\\\"gradient\\\" can be a `Tensor`, an `IndexedSlices`, or `None` if there is no\",\n \"create the slot variable with a restoring initializer. No new variables are created\",\n \"_create_vars. \\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for k in kwargs: if\",\n \"a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer\",\n \"instead use the optimizer in three steps: 1. Compute the gradients with `tf.GradientTape`.\",\n \"zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient, variable). Do whatever\",\n \"a factor equal to the number of replicas being used to compute a\",\n \"you want to log debug a training algorithm, report stats about the slots,\",\n \"over restores, highest restore UID first to minimize the number # of assignments.\",\n \"has an associated slot variable is created or restored. When executing eagerly, we\",\n \"function used for a hyperparameter. Returns: An optimizer instance. \\\"\\\"\\\" if \\\"lr\\\" in\",\n \"variables associated with the variables to train. These are called <i>Slots</i>. Slots have\",\n \"self._slot_names def add_slot(self, var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new slot variable for `var`.\\\"\\\"\\\"\",\n \"be restored. slot_name: The name of this `Optimizer`'s slot to restore into. variable:\",\n \"variable which has an associated slot variable is created or restored. When executing\",\n \"normal # graph. if slot_variable is not None: # If we've either made\",\n \"not context.executing_eagerly() or any_symbolic: # If the current context is graph mode or\",\n \"= <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for\",\n \"self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def _distributed_apply(self, distribution, grads_and_vars,\",\n \"models without input shape pre-defined, or 2) subclassed models. Pass var_list as callable\",\n \"an associated slot variable is created or restored. When executing eagerly, we create\",\n \"self._get_hyper(name) raise e def __setattr__(self, name, value): \\\"\\\"\\\"Override setattr to support dynamic hyperparameter\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"been created but `variable` has just been added to a dependency graph (causing\",\n \"variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i,\",\n \"which is off by a factor equal to the number of replicas being\",\n \"Python objects used to create this optimizer, such as a function used for\",\n \"specified gradients. If `global_step` was not None, that operation also increments `global_step`. Raises:\",\n \"only for non-trainable variables. \\\" \\\"You have specified trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else:\",\n \"in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered)\",\n \"initialization), and makes variable creator scopes # behave the same way they do\",\n \"as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices):\",\n \"{} has `None` for gradient. \\\" \\\"Please make sure that all of your\",\n \"import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as\",\n \"3 * var1 * var1 + 2 * var2 * var2 # In\",\n \"gradient to variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying to update a Tensor \\\",\",\n \"config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter\",\n \"interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \\\"\\\"\\\"Restore a newly created slot\",\n \"additional variables) - get_config (serialization of the optimizer, include all hyper parameters) \\\"\\\"\\\"\",\n \"value. value can be callable, tensor, numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True)\",\n \"dictionary, typically the output of get_config. custom_objects: A Python dictionary mapping names to\",\n \"value.\\\"\\\"\\\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over\",\n \"has just been added to a dependency graph (causing us to realize that\",\n \"in grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers()\",\n \"of replicas being used to compute a single step. As a result, using\",\n \"if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss)\",\n \"a sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if\",\n \"\\\"\\\"\\\"Returns the config of the optimimizer. An optimizer config is a Python dictionary\",\n \"include 1) sequential models without input shape pre-defined, or 2) subclassed models. Pass\",\n \"instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing the\",\n \"return weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return\",\n \"which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position)\",\n \"the second part of `minimize()`. It returns an `Operation` that applies gradients. Args:\",\n \"instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python\",\n \"A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be restored. slot_name:\",\n \"passed to the `Optimizer` constructor. Returns: An `Operation` that applies the specified gradients.\",\n \"value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \\\"\\\"\\\"A list of names for this\",\n \"repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct\",\n \"decay = kwargs.pop(\\\"decay\\\", 0.0) if decay < 0.: raise ValueError(\\\"decay cannot be less\",\n \"{slot name : variable}} self._slots = {} self._slot_names = [] self._weights = []\",\n \"is a list of tuples (gradient, variable). Do whatever you # need to\",\n \"`trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be restored. slot_name: The\",\n \"keyword argument \\\" \\\"passed to optimizer: \\\" + str(k)) # checks that all\",\n \"special-case this dependency and otherwise pretend it's a normal # graph. if slot_variable\",\n \"from SavedModels is not currently \\\" \\\"supported. Please file a feature request if\",\n \"but currently we do not support using the optimizer object iself (e.g. through\",\n \"{\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for k in kwargs: if k not in allowed_kwargs:\",\n \"the hyper parameter. Hyper parameters can be overwritten through user code: Example: ```python\",\n \"\\\"VariableSynchronization.ON_READ only for non-trainable variables. \\\" \\\"You have specified trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\")\",\n \"constraint is not supported. ### Thread Compatibility The entire optimizer is currently thread\",\n \"lambda: 3 * var1 * var1 + 2 * var2 * var2 #\",\n \"with a weight list of length \\\" + str(len(weights)) + \\\", but the\",\n \"in a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value\",\n \"`__init__` method), and then passed to `self._set_hyper()`. They can be either regular Python\",\n \"Returns: the unique name of the variable. \\\"\\\"\\\" # pylint: disable=protected-access # Get\",\n \"is off by a factor equal to the number of replicas being used\",\n \"the _set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible. Args: name: A\",\n \"the list or tuple of `Variable` objects. Use callable when the variable list\",\n \"the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum\",\n \"variables are created at the first time when `loss` is called. grad_loss: Optional.\",\n \"%s.\\\" % (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid types\",\n \"loss = lambda: 3 * var1 + 2 * var2 # In eager\",\n \"variable creation if there is an active variable creator # scope. Generally we'd\",\n \"to update a Tensor \\\", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not\",\n \"if there is an active variable creator # scope. Generally we'd like to\",\n \"when possible, but this may mean that scopes intended to catch # `variable`\",\n \"add up your per-example losses and then divide by the global batch size.\",\n \"base_layer. def set_weights(self, weights): params = self.weights if len(params) != len(weights): raise ValueError(\",\n \"= distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name, slot_name), # pylint:\",\n \"have a \\\" \\\"gradient defined (i.e. are differentiable). \\\" \\\"Common ops without gradient:\",\n \"ops to apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the\",\n \"batch size, which is done automatically if you use `tf.keras` built-in training or\",\n \"Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return\",\n \"the specified gradients. If `global_step` was not None, that operation also increments `global_step`.\",\n \"duplicate indices may instead override this method to avoid the overhead of summing.\",\n \"`handle`. Similar to `_apply_sparse`, the `indices` argument to this method has been de-duplicated.\",\n \"v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v):\",\n \"If any tensor is not a valid type. \\\"\\\"\\\" valid_dtypes = self._valid_dtypes() for\",\n \"kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created =\",\n \"not support using the optimizer object iself (e.g. through `apply_gradients`). \\\"\\\"\\\" # TODO(allenl):\",\n \"to create your own optimization algorithm, simply inherit from this class and override\",\n \"six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else:\",\n \"implementing Trackable. Stores information about how to restore # slot variables which have\",\n \"if the optimizer is saved without the non-slot # variable, or if the\",\n \"str(pv.shape) + \\\" not compatible with \\\" \\\"provided weight shape \\\" + str(w.shape))\",\n \"invalid, or var_list is None. \\\"\\\"\\\" # TODO(josh11b): Test that we handle weight\",\n \"when a variable which has an associated slot variable is created or restored.\",\n \"of class Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import\",\n \"arguments passed to the optimizer subclass constructor (the `__init__` method), and then passed\",\n \"+ \\\", but the optimizer was expecting \\\" + str(len(params)) + \\\" weights.\",\n \"takes no argument and returns the value # to minimize. loss = lambda:\",\n \"you are not using these and you want to average gradients, you should\",\n \"training loop with Keras models In Keras models, sometimes variables are created when\",\n \"ops without gradient: \\\" \\\"K.argmax, K.round, K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g,\",\n \"for i in update_ops) if not context.executing_eagerly() or any_symbolic: # If the current\",\n \"update ops are # symbolic then the step update should be carried out\",\n \"slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore a slot variable's value, possibly creating it. Called when\",\n \"has been applied to the variable. Important: If gradient is sparse tensor, variable\",\n \"learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter that can\",\n \"Optimizer instances should not bind to a single graph, and so shouldn't keep\",\n \"gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want to process the gradient\",\n \"case). Deferring is mostly harmless # (aside from double initialization), and makes variable\",\n \"to this method has been de-duplicated. Optimizers which deal correctly with non-unique indices\",\n \"self._use_locking = True self._name = name self._hyper = {} # dict: {variable name\",\n \"if k not in allowed_kwargs: raise TypeError(\\\"Unexpected keyword argument \\\" \\\"passed to optimizer:\",\n \"steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process the gradients as you\",\n \"use `tf.math.reduce_sum` to add up your per-example losses and then divide by the\",\n \"times too big. ### Variable Constraint All Keras optimizers respect variable constraints. If\",\n \"return value def __getattribute__(self, name): \\\"\\\"\\\"Overridden to support hyperparameter access.\\\"\\\"\\\" try: return super(OptimizerV2,\",\n \"pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads\",\n \"when variable is to be synced on read. trainable = False elif trainable\",\n \"update_ops) if not context.executing_eagerly() or any_symbolic: # If the current context is graph\",\n \"self._iterations is not None: raise RuntimeError(\\\"Cannot set `iterations` to a new Variable after\",\n \"_RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters\",\n \"own dependencies, in which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name,\",\n \"return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients of `loss` for\",\n \"compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError:\",\n \"Examples include 1) sequential models without input shape pre-defined, or 2) subclassed models.\",\n \"adds restore ops to the graph. This method is nonetheless important when graph\",\n \"the variables have gradients. \\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_,\",\n \"of gradient tensors. Raises: ValueError: In case any gradient cannot be computed (e.g.\",\n \"get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v\",\n \"@abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns the config of the optimimizer. An optimizer config is\",\n \"parameters) \\\"\\\"\\\" def __init__(self, name, **kwargs): \\\"\\\"\\\"Create a new Optimizer. This must be\",\n \"+ scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or\",\n \"grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list =\",\n \"be computed (e.g. if gradient function not implemented). \\\"\\\"\\\" params = nest.flatten(params) with\",\n \"argument and returns the value # to minimize. loss = lambda: 3 *\",\n \"If you intend to create your own optimization algorithm, simply inherit from this\",\n \"dictionary. \\\"\\\"\\\" config = {\\\"name\\\": self._name} if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm if\",\n \"# `variable` also catch its eagerly created slot variable # unintentionally (specifically make_template\",\n \"def __init__(self, name, **kwargs): \\\"\\\"\\\"Create a new Optimizer. This must be called by\",\n \"single graph, and so shouldn't keep Tensors as member variables. Generally you should\",\n \"of the update ops are # symbolic then the step update should be\",\n \"subclass constructor (the `__init__` method), and then passed to `self._set_hyper()`. They can be\",\n \"a new Optimizer. This must be called by the constructors of subclasses. Note\",\n \"var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self,\",\n \"normally. We keep a list rather than the one with the highest restore\",\n \"of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies(\",\n \"which override this method must deal with repeated indices. See the docstring of\",\n \"not None: raise RuntimeError(\\\"Cannot set `iterations` to a new Variable after \\\" \\\"the\",\n \"slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict =\",\n \"Python values (like 1.0), tensors, or callables. If they are callable, the callable\",\n \"where `unique_indices` is a de-duplicated version of `indices` and `summed_values` contains the sum\",\n \"Slot variables are not owned by any one object (because we don't want\",\n \"g is not None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self,\",\n \"with __setattr__. if name == \\\"_hyper\\\": raise e # Backwards compatibility with Keras\",\n \"the reverse of `get_config`, capable of instantiating the same optimizer from the config\",\n \"per-example losses and then divide by the global batch size. Note that when\",\n \"trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not\",\n \"`DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in\",\n \"TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version\",\n \"weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value =\",\n \"for the affected indices. handle: a `Tensor` of dtype `resource` which points to\",\n \"argument \\\" \\\"passed to optimizer: \\\" + str(k)) # checks that all keyword\",\n \"objects. \\\"\\\"\\\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self,\",\n \"loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients\",\n \"_restore_slot_variable(self, slot_name, variable, slot_variable): \\\"\\\"\\\"Restore a newly created slot variable's value.\\\"\\\"\\\" variable_key =\",\n \"graph (causing us to realize that the slot variable needs to be restored).\",\n \"have names and you can ask the optimizer for the names of the\",\n \"`Adagrad` allocate and manage additional variables associated with the variables to train. These\",\n \"for grad, var in grads_and_vars: scope_name = (\\\"\\\" if ops.executing_eagerly_outside_functions() else \\\"_\\\" +\",\n \"method is nonetheless important when graph building for the case when a slot\",\n \"1. indices: A one-dimensional integer `Tensor`, indexing into the first dimension of `values`\",\n \"thread-safe. The user needs to perform synchronization if necessary. ### Slots Many optimizer\",\n \"var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ```\",\n \"name you can ask the optimizer for the variable it created to hold\",\n \"created to hold the slot value. This can be useful if you want\",\n \"on # a slot variable if not for this case). Deferring is mostly\",\n \"If you want to process the gradients before applying them you can instead\",\n \"self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute\",\n \"gradients before applying them you can instead use the optimizer in three steps:\",\n \"self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None)\",\n \"created # normally. We keep a list rather than the one with the\",\n \"to variables. This is the second part of `minimize()`. It returns an `Operation`\",\n \"= decay if \\\"clipnorm\\\" in kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in kwargs:\",\n \"+ str(len(params)) + \\\" weights. Provided weights: \\\" + str(weights)[:50] + \\\"...\\\") if\",\n \"`var_list`. If `global_step` was not `None`, that operation also increments `global_step`. Raises: ValueError:\",\n \"= nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in zip(grads,\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\"\",\n \"of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \\\"\\\"\\\"Add ops\",\n \"None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name\",\n \"size, which is done automatically if you use `tf.keras` built-in training or evaluation\",\n \"_resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add ops to apply dense gradients to the variable `handle`.\",\n \"# need to the 'gradient' part, for example cap them, etc. capped_grads_and_vars =\",\n \"for g in grads] if hasattr(self, \\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue)\",\n \"tensor is dense) - resource_apply_sparse (update variable given gradient tensor is sparse) -\",\n \"but `variable` has just been added to a dependency graph (causing us to\",\n \"make the slot variable. Defer restoring until it gets created # normally. We\",\n \"t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid types for loss, variables\",\n \"name == \\\"lr\\\": name = \\\"learning_rate\\\" if name in self._hyper: return self._get_hyper(name) raise\",\n \"_deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \\\"\\\"\\\"Add\",\n \"# grads_and_vars is a list of tuples (gradient, variable). Do whatever you #\",\n \"def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value,\",\n \"0: {}\\\".format(decay)) self._initial_decay = decay if \\\"clipnorm\\\" in kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if\",\n \"`Tensor` holding the gradient computed for `loss`. name: Optional name for the returned\",\n \"Raises: ValueError: If any tensor is not a valid type. \\\"\\\"\\\" valid_dtypes =\",\n \"params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in\",\n \"optimizer, but currently we do not support using the optimizer object iself (e.g.\",\n \"the processed gradients with `apply_gradients()`. Example: ```python # Create an optimizer. opt =\",\n \"can be either regular Python values (like 1.0), tensors, or callables. If they\",\n \"de-duplicated. Optimizers which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to\",\n \"then divide by the global batch size. Note that when using `tf.distribute.Strategy`, the\",\n \"callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value =\",\n \"= self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return\",\n \"var2 # In eager mode, simply call minimize to update the list of\",\n \"`learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has been\",\n \"# In eager mode, simply call minimize to update the list of variables.\",\n \"!= len(weights): raise ValueError( \\\"You called `set_weights(weights)` on optimizer \\\" + self._name +\",\n \"(optimizer, non-slot # variable, variable)). So we don't _track_ slot variables anywhere, and\",\n \"strategy exists, get the primary variable first. Args: var: the variable. Returns: the\",\n \"# If we've either made this slot variable, or if we've pulled out\",\n \"\\\"\\\"\\\"Version 2 of class Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name from __future__ import absolute_import from\",\n \"if kwargs[k] < 0: raise ValueError(\\\"Expected {} >= 0, received: {}\\\".format(k, kwargs[k])) self._use_locking\",\n \"variables(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the order created.\\\"\\\"\\\" return self._weights\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\\\"decay\\\", var_dtype) lr_t = lr_t /\",\n \"callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \\\"Synchronization\",\n \"to avoid the overhead of summing. Args: grad: a `Tensor` representing the gradient\",\n \"or tuple of `Variable` objects. Use callable when the variable list would otherwise\",\n \"out iterable with grad equal to None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars:\",\n \"\\\"\\\"\\\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters when\",\n \"method is the reverse of `get_config`, capable of instantiating the same optimizer from\",\n \"slot variable, or if we've pulled out an # existing slot variable, we\",\n \"to additional Python objects used to create this optimizer, such as a function\",\n \"import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from\",\n \"= self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype not in\",\n \"be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object\",\n \"learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.:\",\n \"has `None` for gradient. \\\" \\\"Please make sure that all of your ops\",\n \"indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \\\"\\\"\\\"Add ops to\",\n \"which updates the value of the variable. \\\"\\\"\\\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad,\",\n \"in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore a slot variable's\",\n \"input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them.\",\n \"`None`. Raises: TypeError: If `var_list` contains anything else than `Variable` objects. ValueError: If\",\n \"variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this method has been\",\n \"building. Instead, _restore_slot_variable catches these after normal creation and adds restore ops to\",\n \"for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var))\",\n \"# save the slot variable if the optimizer is saved without the non-slot\",\n \"TypeError: If `var_list` contains anything else than `Variable` objects. ValueError: If some arguments\",\n \"By default the correct behavior, to sum non-unique indices and their associated gradients,\",\n \"gradients.gradients(loss, params) for grad, param in zip(grads, params): if grad is None: raise\",\n \"Get the distributed variable if it exists. if getattr(var, \\\"_distributed_container\\\", None) is not\",\n \"raise ValueError(\\\"decay cannot be less than 0: {}\\\".format(decay)) self._initial_decay = decay if \\\"clipnorm\\\"\",\n \"tensor is not a valid type. \\\"\\\"\\\" valid_dtypes = self._valid_dtypes() for t in\",\n \"to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this method\",\n \"if \\\"learning_rate\\\" in config: if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return\",\n \"computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want to process the\",\n \"An optimizer instance. \\\"\\\"\\\" if \\\"lr\\\" in config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if \\\"learning_rate\\\"\",\n \"slot variable's value, possibly creating it. Called when a variable which has an\",\n \"weights have been created\\\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed\",\n \"variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed learning rate as a Tensor with\",\n \"is the reverse of `get_config`, capable of instantiating the same optimizer from the\",\n \"variable if not for this case). Deferring is mostly harmless # (aside from\",\n \"new Optimizer. This must be called by the constructors of subclasses. Note that\",\n \"or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError(\",\n \"value def variables(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the order created.\\\"\\\"\\\"\",\n \"the variables are created at the first time `loss` is called. grad_loss: Optional.\",\n \"self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this\",\n \"var) if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op\",\n \"mapping names to additional Python objects used to create this optimizer, such as\",\n \"@classmethod def from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates an optimizer from its config. This method\",\n \"allowed_kwargs: raise TypeError(\\\"Unexpected keyword argument \\\" \\\"passed to optimizer: \\\" + str(k)) #\",\n \"from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values`\",\n \"= [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads,\",\n \"have specified trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set trainable to be false\",\n \"`tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means it automatically sums gradients\",\n \"execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1)\",\n \"of the variables are not `Variable` objects. \\\"\\\"\\\" grads_and_vars = self._compute_gradients( loss, var_list=var_list,\",\n \"if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer\",\n \"value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value\",\n \"nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm)\",\n \"and gradients. \\\"\\\"\\\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \\\"\\\"\\\"Call\",\n \"args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if\",\n \"from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import\",\n \"associated with the variables to train. These are called <i>Slots</i>. Slots have names\",\n \"valid type. \\\"\\\"\\\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype\",\n \"name, **kwargs): \\\"\\\"\\\"Create a new Optimizer. This must be called by the constructors\",\n \"slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key = _var_key(var)\",\n \"slot variable with a restoring initializer. No new variables are created when graph\",\n \"do not support using the optimizer object iself (e.g. through `apply_gradients`). \\\"\\\"\\\" #\",\n \"def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values` associated with any non-unique `indices`. Args: values: A\",\n \"`handle`. Args: grad: a `Tensor` representing the gradient. handle: a `Tensor` of dtype\",\n \"\\\"\\\"\\\"Compute gradients of `loss` for the variables in `var_list`. This is the first\",\n \"be `None`. Raises: TypeError: If `var_list` contains anything else than `Variable` objects. ValueError:\",\n \"`Variable` objects. \\\"\\\"\\\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"str(len(weights)) + \\\", but the optimizer was expecting \\\" + str(len(params)) + \\\"\",\n \"to support hyperparameter access.\\\"\\\"\\\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: #\",\n \"`tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with the desired parameters.\",\n \"through user code: Example: ```python # Create an optimizer with the desired parameters.\",\n \"if pv.shape != w.shape: raise ValueError(\\\"Optimizer weight shape \\\" + str(pv.shape) + \\\"\",\n \"`values` (as in an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where\",\n \"was expecting \\\" + str(len(params)) + \\\" weights. Provided weights: \\\" + str(weights)[:50]\",\n \"infinite recursion with __setattr__. if name == \\\"_hyper\\\": raise e # Backwards compatibility\",\n \"var2]) opt_op.run() # In eager mode, simply call minimize to update the list\",\n \"before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this function.\",\n \"to be false when variable is to be synced on read. trainable =\",\n \"the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \\\"\\\"\\\"Restore a newly\",\n \"variable. \\\"\\\"\\\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def\",\n \"%s.\\\" % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\\\"Gradients does\",\n \"= array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\")\",\n \"decay_t = self._get_hyper(\\\"decay\\\", var_dtype) lr_t = lr_t / (1. + decay_t * local_step)\",\n \"slot name you can ask the optimizer for the variable it created to\",\n \"of _create_vars. \\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for k in kwargs:\",\n \"default the correct behavior, to sum non-unique indices and their associated gradients, is\",\n \"in update_ops) if not context.executing_eagerly() or any_symbolic: # If the current context is\",\n \"# If the current context is graph mode or any of the update\",\n \"disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight)\",\n \"hyperparameter. Returns: An optimizer instance. \\\"\\\"\\\" if \\\"lr\\\" in config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\")\",\n \"param in zip(grads, params): if grad is None: raise ValueError(\\\"Variable {} has `None`\",\n \"in stateful and thread-compatible. Args: name: A non-empty string. The name to use\",\n \"resulting in gradients that can be many times too big. ### Variable Constraint\",\n \"than the one with the highest restore # UID in case slot variables\",\n \"optimizer was expecting \\\" + str(len(params)) + \\\" weights. Provided weights: \\\" +\",\n \"the variable list would otherwise be incomplete before `minimize` and the variables are\",\n \"None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError(\\\"No\",\n \"### Usage ```python # Create an optimizer with the desired parameters. opt =\",\n \"dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization\",\n \"var1 + 2 * var2 * var2 # In graph mode, returns op\",\n \"To average gradients, you divide your loss by the global batch size, which\",\n \"same optimizer can be reinstantiated later (without any saved state) from this configuration.\",\n \"of this `Optimizer`'s slot to restore into. variable: The variable object this slot\",\n \"return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads,\",\n \"etc. ### Hyper parameters These are arguments passed to the optimizer subclass constructor\",\n \"OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class for optimizers. This class defines the API to add\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"config. This method is the reverse of `get_config`, capable of instantiating the same\",\n \"necessary. ### Slots Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and\",\n \"if ops.executing_eagerly_outside_functions() else \\\"_\\\" + var.op.name) with backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend( distribution.extended.update( var,\",\n \"hyperparameter that can be a float, callable, or Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name] if\",\n \"if getattr(var, \\\"_distributed_container\\\", None) is not None: var = var._distributed_container() if var._in_graph_mode: return\",\n \"@six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class for optimizers. This class defines the\",\n \"(_, v) in grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _ =\",\n \"def apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients to variables. This is the second part\",\n \"!= dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \\\"\\\"\\\"Returns gradients of `loss`\",\n \"var unique id. If distribution strategy exists, get the primary variable first. Args:\",\n \"grads_and_vars is a list of tuples (gradient, variable). Do whatever you # need\",\n \"which points to the variable to be updated. indices: a `Tensor` of integral\",\n \"keep a list rather than the one with the highest restore # UID\",\n \"kwargs[k] < 0: raise ValueError(\\\"Expected {} >= 0, received: {}\\\".format(k, kwargs[k])) self._use_locking =\",\n \"overwrite=True) if name not in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name]\",\n \"if name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value): \\\"\\\"\\\"Override\",\n \"`clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients\",\n \"import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values` associated with\",\n \"Hyper parameters can be overwritten through user code: Example: ```python # Create an\",\n \"computed for `loss`. Returns: A list of (gradient, variable) pairs. Variable is always\",\n \"was not `None`, that operation also increments `global_step`. Raises: ValueError: If some of\",\n \"__future__ import division from __future__ import print_function import abc import functools import six\",\n \"class directly, but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`.\",\n \"created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ...\",\n \"type %r for %s, expected: %s.\\\" % (dtype, t.name, [v for v in\",\n \"increments `global_step`. Raises: ValueError: If some of the variables are not `Variable` objects.\",\n \"not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = [] with\",\n \"as a Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step\",\n \"rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer.\",\n \"They can be either regular Python values (like 1.0), tensors, or callables. If\",\n \"(\\\"\\\" if ops.executing_eagerly_outside_functions() else \\\"_\\\" + var.op.name) with backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend( distribution.extended.update(\",\n \"aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not None:\",\n \"the `reduction` argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for\",\n \"than 0: {}\\\".format(decay)) self._initial_decay = decay if \\\"clipnorm\\\" in kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\")\",\n \"grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\" if isinstance(var,\",\n \"this slot is being created for. \\\"\\\"\\\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key,\",\n \"of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM`\",\n \"w.shape: raise ValueError(\\\"Optimizer weight shape \\\" + str(pv.shape) + \\\" not compatible with\",\n \"have gradients. \\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in\",\n \"def _compute_gradients(self, loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients of `loss` for the variables in\",\n \"\\\"\\\"\\\"Returns variables of this Optimizer based on the order created.\\\"\\\"\\\" return self._weights @property\",\n \"sparse gradients to `handle`, with repeated indices. Optimizers which override this method must\",\n \"1) sequential models without input shape pre-defined, or 2) subclassed models. Pass var_list\",\n \"scopes intended to catch # `variable` also catch its eagerly created slot variable\",\n \"apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True def get_config(self): #\",\n \"gradient tensors. Raises: ValueError: In case any gradient cannot be computed (e.g. if\",\n \"created.\\\"\\\"\\\" return self._weights @property def weights(self): \\\"\\\"\\\"Returns variables of this Optimizer based on\",\n \"A non-empty string. The name to use for accumulators created for the optimizer.\",\n \"value for the hyper parameter. Hyper parameters can be overwritten through user code:\",\n \"be either regular Python values (like 1.0), tensors, or callables. If they are\",\n \"first time `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed\",\n \"if name == \\\"_hyper\\\": raise e # Backwards compatibility with Keras optimizers. if\",\n \"with \\\" \\\"provided weight shape \\\" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self,\",\n \"The entire optimizer is currently thread compatible, not thread-safe. The user needs to\",\n \"you can instead use the optimizer in three steps: 1. Compute the gradients\",\n \"ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss,\",\n \"us to realize that the slot variable needs to be restored). Args: slot_variable_position:\",\n \"loss: Loss tensor. params: List of variables. Returns: List of gradient tensors. Raises:\",\n \"lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns\",\n \"loop with Keras models In Keras models, sometimes variables are created when the\",\n \"variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is\",\n \"dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if\",\n \"slot variable is created or restored. When executing eagerly, we create the slot\",\n \"to create this optimizer, such as a function used for a hyperparameter. Returns:\",\n \"name : variable}} self._slots = {} self._slot_names = [] self._weights = [] self._iterations\",\n \"is passed to any variable, the constraint will be applied to the variable\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"of dtype `resource` which points to the variable to be updated. Returns: An\",\n \"new variables are created when graph building. Instead, _restore_slot_variable catches these after normal\",\n \"trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \\\"\\\"\\\"Variable. The number of\",\n \"variable. Returns: the unique name of the variable. \\\"\\\"\\\" # pylint: disable=protected-access #\",\n \"This optimizer class is `tf.distribute.Strategy` aware, which means it automatically sums gradients across\",\n \"automatically if you use `tf.keras` built-in training or evaluation loops. See the `reduction`\",\n \"v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient to\",\n \"= list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is\",\n \"# pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var,\",\n \"= math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t =\",\n \"a customized optimizer. If you intend to create your own optimization algorithm, simply\",\n \"and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \\\"\\\"\\\"A\",\n \"one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create\",\n \"integer `Tensor`, indexing into the first dimension of `values` (as in an IndexedSlices\",\n \"* local_step) return lr_t @abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns the config of the optimimizer.\",\n \"highest restore # UID in case slot variables have their own dependencies, in\",\n \"when the variable list would otherwise be incomplete before `minimize` since the variables\",\n \"apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients to variables. This is the second part of\",\n \"(dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid types for loss,\",\n \"non-unique `indices`. Args: values: A `Tensor` with rank >= 1. indices: A one-dimensional\",\n \"parameters These are arguments passed to the optimizer subclass constructor (the `__init__` method),\",\n \"NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value()\",\n \"Optional. A `Tensor` holding the gradient computed for `loss`. name: Optional name for\",\n \"None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \\\"\\\"\\\"Returns\",\n \"gradients. Subclasses should override to allow other float types. Returns: Valid types for\",\n \"is saved without the non-slot # variable, or if the non-slot variable is\",\n \"= [] with backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name =\",\n \"`Trackable` object to be restored. slot_name: The name of this `Optimizer`'s slot to\",\n \"want to average gradients, you should use `tf.math.reduce_sum` to add up your per-example\",\n \"name, overwrite=True) if name not in self._hyper: self._hyper[name] = value else: prev_value =\",\n \"(if your optimizer algorithm requires additional variables) - get_config (serialization of the optimizer,\",\n \"grad_loss=None): \\\"\\\"\\\"Compute gradients of `loss` for the variables in `var_list`. This is the\",\n \"if not for this case). Deferring is mostly harmless # (aside from double\",\n \"([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\\\"Gradients does not exist\",\n \"the gradients with `tf.GradientTape`. 2. Process the gradients as you wish. 3. Apply\",\n \"var.constraint is not None: raise RuntimeError( \\\"Cannot use a constraint function on a\",\n \"minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in\",\n \"`None` if there is no gradient for the given variable. Args: loss: A\",\n \"variables and hyperparameters when an optimizer is restored from a SavedModel. These variables\",\n \"# Defer slot variable creation if there is an active variable creator #\",\n \"params): if grad is None: raise ValueError(\\\"Variable {} has `None` for gradient. \\\"\",\n \"tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list)\",\n \"and override the following methods: - resource_apply_dense (update variable given gradient tensor is\",\n \"optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\" if name in self._hyper: return\",\n \"global batch size, which is done automatically if you use `tf.keras` built-in training\",\n \"not exist for variables %s when minimizing the loss.\\\"), ([v.name for v in\",\n \"RuntimeError( \\\"Cannot use a constraint function on a sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values,\",\n \"params) for grad, param in zip(grads, params): if grad is None: raise ValueError(\\\"Variable\",\n \"backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in zip(grads, params): if grad\",\n \"\\\"Please make sure that all of your ops have a \\\" \\\"gradient defined\",\n \"return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return\",\n \"self, slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore a slot variable's value, possibly creating it. Called\",\n \"shared name. In eager mode the name is derived from the var unique\",\n \"deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead.\",\n \"slot variables and hyperparameters when an optimizer is restored from a SavedModel. These\",\n \"when graph building for the case when a slot variable has already been\",\n \"of `Variable` objects. Use callable when the variable list would otherwise be incomplete\",\n \"lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper #\",\n \"name of the variable. \\\"\\\"\\\" # pylint: disable=protected-access # Get the distributed variable\",\n \"variable's value.\\\"\\\"\\\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate\",\n \"This class in stateful and thread-compatible. Args: name: A non-empty string. The name\",\n \"initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any one object (because\",\n \"def minimize(self, loss, var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss` by updating `var_list`. This method\",\n \"= tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with tf.GradientTape()\",\n \"`name` to value. value can be callable, tensor, numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable): self._track_trackable(value,\",\n \"arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by\",\n \"+ 2 * var2 * var2 # In graph mode, returns op that\",\n \"are non-negative. if kwargs[k] < 0: raise ValueError(\\\"Expected {} >= 0, received: {}\\\".format(k,\",\n \"array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class for optimizers.\",\n \"the optimizer for the names of the slots that it uses. Once you\",\n \"a `Tensor` representing the gradient. handle: a `Tensor` of dtype `resource` which points\",\n \"name. In eager mode the name is derived from the var unique id.\",\n \"made this slot variable, or if we've pulled out an # existing slot\",\n \"the first time when `loss` is called. grad_loss: Optional. A `Tensor` holding the\",\n \"argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or\",\n \"= zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor):\",\n \"loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients of `loss` for the variables in `var_list`. This\",\n \"# Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list)\",\n \"\\\"_\\\" + var.op.name) with backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False))\",\n \"_decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed learning rate as a Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t =\",\n \"dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\\\"Invalid type %r for\",\n \"hypergraph with edges of the form (optimizer, non-slot # variable, variable)). So we\",\n \"the one with the highest restore # UID in case slot variables have\",\n \"`tf.keras.losses.Reduction.SUM` for not. If you are not using these and you want to\",\n \"names of the slots that it uses. Once you have a slot name\",\n \"not yet been created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position,\",\n \"self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params))\",\n \"None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \\\"\\\"\\\"set\",\n \"we don't want to # save the slot variable if the optimizer is\",\n \"* var2 * var2 # In graph mode, returns op that minimizes the\",\n \"the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate =\",\n \"the gradient for \\\"variable\\\". Note that \\\"gradient\\\" can be a `Tensor`, an `IndexedSlices`,\",\n \"grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] # Create\",\n \"as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import\",\n \"for pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise\",\n \"gradients, is enforced by first pre-processing `grad` and `indices` and passing them on\",\n \"which deal correctly with duplicate indices may instead override this method to avoid\",\n \"slot variable if not for this case). Deferring is mostly harmless # (aside\",\n \"optimizer can be reinstantiated later (without any saved state) from this configuration. Returns:\",\n \"`tf.distribute.Strategy` aware, which means it automatically sums gradients across all replicas. To average\",\n \"= {} decay = kwargs.pop(\\\"decay\\\", 0.0) if decay < 0.: raise ValueError(\\\"decay cannot\",\n \"def add_slot(self, var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new slot variable for `var`.\\\"\\\"\\\" if\",\n \"< 0: raise ValueError(\\\"Expected {} >= 0, received: {}\\\".format(k, kwargs[k])) self._use_locking = True\",\n \"this configuration. Returns: Python dictionary. \\\"\\\"\\\" config = {\\\"name\\\": self._name} if hasattr(self, \\\"clipnorm\\\"):\",\n \"Raises: ValueError: If some of the variables are not `Variable` objects. \\\"\\\"\\\" grads_and_vars\",\n \"self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return\",\n \"get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with\",\n \"(e.g. through `apply_gradients`). \\\"\\\"\\\" # TODO(allenl): Make the restored optimizer functional by tracing\",\n \"you have a slot name you can ask the optimizer for the variable\",\n \"if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \\\"Synchronization value can be set\",\n \"requires additional variables) - get_config (serialization of the optimizer, include all hyper parameters)\",\n \"grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list)\",\n \"valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any\",\n \"by the global batch size, which is done automatically if you use `tf.keras`\",\n \"var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in\",\n \"optimizers respect variable constraints. If constraint function is passed to any variable, the\",\n \"tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework\",\n \"if you want to log debug a training algorithm, report stats about the\",\n \"about the slots, etc. ### Hyper parameters These are arguments passed to the\",\n \"False def minimize(self, loss, var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss` by updating `var_list`. This\",\n \"\\\"\\\"\\\"Returns variables of this Optimizer based on the order created.\\\"\\\"\\\" return self._weights def\",\n \"grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if\",\n \"not in valid_dtypes: raise ValueError(\\\"Invalid type %r for %s, expected: %s.\\\" % (dtype,\",\n \"the gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using\",\n \"ops to apply sparse gradients to `handle`, with repeated indices. Optimizers which override\",\n \"with backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i,\",\n \"group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not\",\n \"v) in grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _ = self.iterations\",\n \"rather than the one with the highest restore # UID in case slot\",\n \"pv.shape != w.shape: raise ValueError(\\\"Optimizer weight shape \\\" + str(pv.shape) + \\\" not\",\n \"tensor. params: List of variables. Returns: List of gradient tensors. Raises: ValueError: In\",\n \"None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype,\",\n \"as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from\",\n \"kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False def minimize(self,\",\n \"slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the\",\n \"\\\"\\\"\\\"set hyper `name` to value. value can be callable, tensor, numeric.\\\"\\\"\\\" if isinstance(value,\",\n \"given gradient tensor is dense) - resource_apply_sparse (update variable given gradient tensor is\",\n \"without gradient: \\\" \\\"K.argmax, K.round, K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm)\",\n \"len(params) != len(weights): raise ValueError( \\\"You called `set_weights(weights)` on optimizer \\\" + self._name\",\n \"e: # Needed to avoid infinite recursion with __setattr__. if name == \\\"_hyper\\\":\",\n \"# For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable):\",\n \"of subclasses. Note that Optimizer instances should not bind to a single graph,\",\n \"+ slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot\",\n \"trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums\",\n \"the first part of `minimize()`. It returns a list of (gradient, variable) pairs\",\n \"capable of instantiating the same optimizer from the config dictionary. Arguments: config: A\",\n \"kwargs={\\\"name\\\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads =\",\n \"the model is first called, instead of construction time. Examples include 1) sequential\",\n \"limitation bothers \\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto:\",\n \"to perform synchronization if necessary. ### Slots Many optimizer subclasses, such as `Adam`\",\n \"initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables\",\n \"between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable with\",\n \"wrong answer, resulting in gradients that can be many times too big. ###\",\n \"called by the constructors of subclasses. Note that Optimizer instances should not bind\",\n \"compatible, not thread-safe. The user needs to perform synchronization if necessary. ### Slots\",\n \"a dependency graph (causing us to realize that the slot variable needs to\",\n \"and you want to average gradients, you should use `tf.math.reduce_sum` to add up\",\n \"When executing eagerly, we create the slot variable with a restoring initializer. No\",\n \"and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot(\",\n \"= kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False def\",\n \"a new slot variable for `var`.\\\"\\\"\\\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key\",\n \"grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for\",\n \"variable, variable)). So we don't _track_ slot variables anywhere, and # instead special-case\",\n \"to the graph. This method is nonetheless important when graph building for the\",\n \"like to eagerly create/restore slot variables # when possible, but this may mean\",\n \"= [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is\",\n \"base class for optimizers. This class defines the API to add Ops to\",\n \"to sum non-unique indices and their associated gradients, is enforced by first pre-processing\",\n \"value def __getattribute__(self, name): \\\"\\\"\\\"Overridden to support hyperparameter access.\\\"\\\"\\\" try: return super(OptimizerV2, self).__getattribute__(name)\",\n \"Args: values: A `Tensor` with rank >= 1. indices: A one-dimensional integer `Tensor`,\",\n \"step update should be carried out under a graph # context. (eager updates\",\n \"\\\"You called `set_weights(weights)` on optimizer \\\" + self._name + \\\" with a weight\",\n \"\\\"\\\"\\\"Add ops to apply dense gradients to the variable `handle`. Args: grad: a\",\n \"tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager\",\n \"is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer)\",\n \"name=None): \\\"\\\"\\\"Minimize `loss` by updating `var_list`. This method simply computes gradient using `tf.GradientTape`\",\n \"* var1 + 2 * var2 # In eager mode, simply call minimize\",\n \"var_list is None. \\\"\\\"\\\" # TODO(josh11b): Test that we handle weight decay in\",\n \"kwargs[k])) self._use_locking = True self._name = name self._hyper = {} # dict: {variable\",\n \"normal creation and adds restore ops to the graph. This method is nonetheless\",\n \"ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \\\"Cannot use a constraint function\",\n \"size, which is off by a factor equal to the number of replicas\",\n \"absolute_import from __future__ import division from __future__ import print_function import abc import functools\",\n \"if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay\",\n \"the slot variable with a restoring initializer. No new variables are created when\",\n \"reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars]\",\n \"True def get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError(\",\n \"The variable object this slot is being created for. \\\"\\\"\\\" variable_key = _var_key(variable)\",\n \"an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a\",\n \"this slot variable, or if we've pulled out an # existing slot variable,\",\n \"use for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to be\",\n \"= learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter that\",\n \"custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter that can be a\",\n \"override to allow other float types. Returns: Valid types for loss, variables and\",\n \"you can ask the optimizer for the variable it created to hold the\",\n \"None # For implementing Trackable. Stores information about how to restore # slot\",\n \"gradient tensor is dense) - resource_apply_sparse (update variable given gradient tensor is sparse)\",\n \"`loss` by updating `var_list`. This method simply computes gradient using `tf.GradientTape` and calls\",\n \"apply dense gradients to the variable `handle`. Args: grad: a `Tensor` representing the\",\n \"initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer,\",\n \"variable `handle`. Args: grad: a `Tensor` representing the gradient. handle: a `Tensor` of\",\n \"grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient, variable).\",\n \"param() if callable(param) else param def _resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add ops to apply\",\n \"to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate\",\n \"self).__setattr__(name, value) def get_slot_names(self): \\\"\\\"\\\"A list of names for this optimizer's slots.\\\"\\\"\\\" return\",\n \"`_apply_sparse`, the `indices` argument to this method has been de-duplicated. Optimizers which deal\",\n \"weight = tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value)\",\n \"created slot variable # unintentionally (specifically make_template would add a dependency on #\",\n \"grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient,\",\n \"this Optimizer has run.\\\"\\\"\\\" if self._iterations is None: self._iterations = self.add_weight( \\\"iter\\\", shape=[],\",\n \"can be a float, callable, or Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule):\",\n \"associated slot variable is created or restored. When executing eagerly, we create the\",\n \"you intend to create your own optimization algorithm, simply inherit from this class\",\n \"checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer is restored from\",\n \"create this optimizer, such as a function used for a hyperparameter. Returns: An\",\n \"This method is nonetheless important when graph building for the case when a\",\n \"of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with Keras models\",\n \"minimizes the loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1,\",\n \"optimizer's slots.\\\"\\\"\\\" return self._slot_names def add_slot(self, var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new slot\",\n \"name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \\\"\\\"\\\"Variable. The\",\n \"var_list: list or tuple of `Variable` objects to update to minimize `loss`, or\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights): params\",\n \"sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to\",\n \"\\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \\\"clipvalue\\\"): grads\",\n \"averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not using these and you\",\n \"saved without the optimizer; # it's a dependency hypergraph with edges of the\",\n \"compatibility to allow time inverse decay of learning rate. `lr` is included for\",\n \"is a callable that takes no argument and returns the value # to\",\n \"from the config dictionary. Arguments: config: A Python dictionary, typically the output of\",\n \"sparse) - create_slots (if your optimizer algorithm requires additional variables) - get_config (serialization\",\n \"Use callable when the variable list would otherwise be incomplete before `minimize` and\",\n \"sequential models without input shape pre-defined, or 2) subclassed models. Pass var_list as\",\n \"this method has been de-duplicated. Optimizers which deal correctly with non-unique indices may\",\n \"= \\\"learning_rate\\\" if name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name,\",\n \"`indices` and `summed_values` contains the sum of `values` slices associated with each unique\",\n \"use this class directly, but instead instantiate one of its subclasses such as\",\n \"of the slots that it uses. Once you have a slot name you\",\n \"that can be many times too big. ### Variable Constraint All Keras optimizers\",\n \"\\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if\",\n \"_create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically. for name, value in\",\n \"if not filtered: raise ValueError(\\\"No gradients provided for any variable: %s.\\\" % ([v.name\",\n \"# pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division from __future__\",\n \"not bind to a single graph, and so shouldn't keep Tensors as member\",\n \"for details. By default the correct behavior, to sum non-unique indices and their\",\n \"variable, or if we've pulled out an # existing slot variable, we should\",\n \"if hasattr(self, \\\"_hyper\\\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value)\",\n \"from the var unique id. If distribution strategy exists, get the primary variable\",\n \"no argument and returns the value # to minimize. loss = lambda: 3\",\n \"pairs. Variable is always present, but gradient can be `None`. Raises: TypeError: If\",\n \"var1 + 2 * var2 # In eager mode, simply call minimize to\",\n \"var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\\\"decay\\\", var_dtype)\",\n \"capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy`\",\n \"math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\\\"decay\\\",\",\n \"currently we do not support using the optimizer object iself (e.g. through `apply_gradients`).\",\n \"abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute\",\n \"this optimizer, such as a function used for a hyperparameter. Returns: An optimizer\",\n \"+ str(pv.shape) + \\\" not compatible with \\\" \\\"provided weight shape \\\" +\",\n \"that scopes intended to catch # `variable` also catch its eagerly created slot\",\n \"{} decay = kwargs.pop(\\\"decay\\\", 0.0) if decay < 0.: raise ValueError(\\\"decay cannot be\",\n \"\\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set trainable to be false when variable is to\",\n \"in config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in config: if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"]\",\n \"Python dictionary, typically the output of get_config. custom_objects: A Python dictionary mapping names\",\n \"which is done automatically if you use `tf.keras` built-in training or evaluation loops.\",\n \"slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() #\",\n \"functions along with ops created by the original optimizer, but currently we do\",\n \"\\\"Restoring functional Optimzers from SavedModels is not currently \\\" \\\"supported. Please file a\",\n \"\\\"\\\"\\\"Add a new slot variable for `var`.\\\"\\\"\\\" if slot_name not in self._slot_names: self._slot_names.append(slot_name)\",\n \"the variable `handle`. Args: grad: a `Tensor` representing the gradient. handle: a `Tensor`\",\n \"filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError(\\\"No gradients provided for\",\n \"in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\\\"Invalid type\",\n \"example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] #\",\n \"backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name]\",\n \"name = \\\"learning_rate\\\" if name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self,\",\n \"def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic\",\n \"you want to average gradients, you should use `tf.math.reduce_sum` to add up your\",\n \"= self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self,\",\n \"set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \\\"\\\"\\\"Call the function if param\",\n \"and restore the Optimizer's config raise NotImplementedError( \\\"Restoring functional Optimzers from SavedModels is\",\n \"3 * var1 + 2 * var2 # In eager mode, simply call\",\n \"want to process the gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly\",\n \"returned operation. Returns: An Operation that updates the variables in `var_list`. If `global_step`\",\n \"arguments which returns the value to minimize. var_list: list or tuple of `Variable`\",\n \"\\\"variable\\\". Note that \\\"gradient\\\" can be a `Tensor`, an `IndexedSlices`, or `None` if\",\n \"return lr_t @abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns the config of the optimimizer. An optimizer\",\n \"**kwargs): \\\"\\\"\\\"Create a new Optimizer. This must be called by the constructors of\",\n \"not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value):\",\n \"# checks that all keyword arguments are non-negative. if kwargs[k] < 0: raise\",\n \"the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with\",\n \"parameter. Hyper parameters can be overwritten through user code: Example: ```python # Create\",\n \"is sparse) - create_slots (if your optimizer algorithm requires additional variables) - get_config\",\n \"(gradient, variable) pairs where \\\"gradient\\\" is the gradient for \\\"variable\\\". Note that \\\"gradient\\\"\",\n \"if isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying to update a Tensor \\\", var) if isinstance(grad,\",\n \"slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer =\",\n \"be repeated. Returns: An `Operation` which updates the value of the variable. \\\"\\\"\\\"\",\n \"or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created\",\n \"a training algorithm, report stats about the slots, etc. ### Hyper parameters These\",\n \"be able to use the _set_hyper()/state.get_hyper() facility instead. This class in stateful and\",\n \"learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value\",\n \"var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]):\",\n \"= value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers()\",\n \"weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key\",\n \"callable when the variable list would otherwise be incomplete before `minimize` and the\",\n \"handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to the variable `handle`. Similar\",\n \"correctly with duplicate indices may instead override this method to avoid the overhead\",\n \"import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import\",\n \"# existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't\",\n \"kwargs.pop(\\\"decay\\\", 0.0) if decay < 0.: raise ValueError(\\\"decay cannot be less than 0:\",\n \"case slot variables have their own dependencies, in which case # those could\",\n \"set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not\",\n \"self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value): \\\"\\\"\\\"Override setattr to support\",\n \"NotImplementedError(\\\"Trying to update a Tensor \\\", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is\",\n \"can be overwritten through user code: Example: ```python # Create an optimizer with\",\n \"import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context from\",\n \"constraints. If constraint function is passed to any variable, the constraint will be\",\n \"slot variable creation if there is an active variable creator # scope. Generally\",\n \"are not using these and you want to average gradients, you should use\",\n \"= functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with\",\n \"update_op update_ops = [] with backend.name_scope(name or self._name): for grad, var in grads_and_vars:\",\n \"be reinstantiated later (without any saved state) from this configuration. Returns: Python dictionary.\",\n \"from this configuration. Returns: Python dictionary. \\\"\\\"\\\" config = {\\\"name\\\": self._name} if hasattr(self,\",\n \"name self._hyper = {} # dict: {variable name : {slot name : variable}}\",\n \"but the optimizer was expecting \\\" + str(len(params)) + \\\" weights. Provided weights:\",\n \"__init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore\",\n \"} self._deferred_slot_restorations = {} decay = kwargs.pop(\\\"decay\\\", 0.0) if decay < 0.: raise\",\n \"slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable with grad equal to None.\\\"\\\"\\\" grads_and_vars =\",\n \"Custom training loop with Keras models In Keras models, sometimes variables are created\",\n \"applied to the variable after the gradient has been applied to the variable.\",\n \"checks that all keyword arguments are non-negative. if kwargs[k] < 0: raise ValueError(\\\"Expected\",\n \"dict: {variable name : {slot name : variable}} self._slots = {} self._slot_names =\",\n \"gradient has been applied to the variable. Important: If gradient is sparse tensor,\",\n \"sparse tensor, variable constraint is not supported. ### Thread Compatibility The entire optimizer\",\n \"some arguments are invalid, or var_list is None. \\\"\\\"\\\" # TODO(josh11b): Test that\",\n \"deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore a slot variable's value,\",\n \"without the non-slot # variable, or if the non-slot variable is saved without\",\n \"first component of a tensor's shape is the *replica-local* batch size, which is\",\n \"ValueError: If some of the variables are not `Variable` objects. \\\"\\\"\\\" grads_and_vars =\",\n \"dense) - resource_apply_sparse (update variable given gradient tensor is sparse) - create_slots (if\",\n \"var.op.name) with backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic =\",\n \"graph mode the name is derived from the var shared name. In eager\",\n \"not supported. ### Thread Compatibility The entire optimizer is currently thread compatible, not\",\n \"gradient tensor is sparse) - create_slots (if your optimizer algorithm requires additional variables)\",\n \"the optimizer, include all hyper parameters) \\\"\\\"\\\" def __init__(self, name, **kwargs): \\\"\\\"\\\"Create a\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise\",\n \"the slot variable `Trackable` object to be restored. slot_name: The name of this\",\n \"functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var):\",\n \"tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops\",\n \"gradient: \\\" \\\"K.argmax, K.round, K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for\",\n \"This class defines the API to add Ops to train a model. You\",\n \"types. Returns: Valid types for loss, variables and gradients. \\\"\\\"\\\" return set( [dtypes.float16,\",\n \"representing the gradient. handle: a `Tensor` of dtype `resource` which points to the\",\n \"may mean that scopes intended to catch # `variable` also catch its eagerly\",\n \"rank >= 1. indices: A one-dimensional integer `Tensor`, indexing into the first dimension\",\n \"(causing us to realize that the slot variable needs to be restored). Args:\",\n \"not None: raise RuntimeError( \\\"Cannot use a constraint function on a sparse variable.\\\")\",\n \"by the original optimizer, but currently we do not support using the optimizer\",\n \"divide your loss by the global batch size, which is done automatically if\",\n \"synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or\",\n \"optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer.\",\n \"variable for `var`.\\\"\\\"\\\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict\",\n \"can be reinstantiated later (without any saved state) from this configuration. Returns: Python\",\n \"slot variable's value.\\\"\\\"\\\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) #\",\n \"will be called during `apply_gradients()` to get the value for the hyper parameter.\",\n \"[] self._iterations = None # For implementing Trackable. Stores information about how to\",\n \"dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \\\"\\\"\\\"Overridden to support\",\n \"% ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\\\"Gradients does not\",\n \"method), and then passed to `self._set_hyper()`. They can be either regular Python values\",\n \"filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"In eager mode, simply call minimize to update the list of variables. opt.minimize(loss,\",\n \"(like 1.0), tensors, or callables. If they are callable, the callable will be\",\n \"(i.e. are differentiable). \\\" \\\"Common ops without gradient: \\\" \\\"K.argmax, K.round, K.eval.\\\".format(param)) if\",\n \"initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils\",\n \"of `values` slices associated with each unique index. \\\"\\\"\\\" unique_indices, new_index_positions = array_ops.unique(indices)\",\n \"These are called <i>Slots</i>. Slots have names and you can ask the optimizer\",\n \"tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for\",\n \"associated with any non-unique `indices`. Args: values: A `Tensor` with rank >= 1.\",\n \"dictionary mapping names to additional Python objects used to create this optimizer, such\",\n \"slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created:\",\n \"variable to be updated. Returns: An `Operation` which updates the value of the\",\n \"= self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly()\",\n \"License. # You may obtain a copy of the License at # #\",\n \"at the first time when `loss` is called. grad_loss: Optional. A `Tensor` holding\",\n \"loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not.\",\n \"sum non-unique indices and their associated gradients, is enforced by first pre-processing `grad`\",\n \"with Keras optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\")\",\n \"for the returned operation. Returns: An Operation that updates the variables in `var_list`.\",\n \"if \\\"lr\\\" in config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in config: if isinstance(config[\\\"learning_rate\\\"],\",\n \"keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients\",\n \"float, callable, or Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if\",\n \"tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework\",\n \"from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from\",\n \"variable). Do whatever you # need to the 'gradient' part, for example cap\",\n \"part of `minimize()`. It returns a list of (gradient, variable) pairs where \\\"gradient\\\"\",\n \"grad, param in zip(grads, params): if grad is None: raise ValueError(\\\"Variable {} has\",\n \"self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients of `loss` for the\",\n \"\\\"\\\"\\\"Add ops to apply sparse gradients to `handle`, with repeated indices. Optimizers which\",\n \"the constructors of subclasses. Note that Optimizer instances should not bind to a\",\n \"hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates\",\n \"* var2 # In eager mode, simply call minimize to update the list\",\n \"initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer\",\n \"trainable: raise ValueError( \\\"Synchronization value can be set to \\\" \\\"VariableSynchronization.ON_READ only for\",\n \"perform synchronization if necessary. ### Slots Many optimizer subclasses, such as `Adam` and\",\n \"in three steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process the gradients\",\n \"(serialization of the optimizer, include all hyper parameters) \\\"\\\"\\\" def __init__(self, name, **kwargs):\",\n \"for g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g,\",\n \"Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division from\",\n \"[ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return grads def apply_gradients(self,\",\n \"self._hypers_created = True @property def iterations(self): \\\"\\\"\\\"Variable. The number of training steps this\",\n \"\\\"learning_rate\\\" if name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value):\",\n \"be referenced in functions along with ops created by the original optimizer, but\",\n \"with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype,\",\n \"= opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to\",\n \"to the variables. If you want to process the gradients before applying them\",\n \"\\\"\\\"\\\"Override setattr to support dynamic hyperparameter setting.\\\"\\\"\\\" # Backwards compatibility with Keras optimizers.\",\n \"used for a hyperparameter. Returns: An optimizer instance. \\\"\\\"\\\" if \\\"lr\\\" in config:\",\n \"returns the value to minimize. var_list: list or tuple of `Variable` objects to\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"Optimizers which override this method must deal with repeated indices. See the docstring\",\n \"As a result, using `tf.math.reduce_mean` will give the wrong answer, resulting in gradients\",\n \"may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing\",\n \"object this slot is being created for. \\\"\\\"\\\" variable_key = _var_key(variable) slot_dict =\",\n \"None: # If we've either made this slot variable, or if we've pulled\",\n \"the value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices):\",\n \"never use this class directly, but instead instantiate one of its subclasses such\",\n \"we handle weight decay in a reasonable way. with backprop.GradientTape() as tape: if\",\n \"self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name] = value else: prev_value\",\n \"`Variable` objects. Use callable when the variable list would otherwise be incomplete before\",\n \"is not supported. ### Thread Compatibility The entire optimizer is currently thread compatible,\",\n \"be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is\",\n \"raise ValueError( \\\"Synchronization value can be set to \\\" \\\"VariableSynchronization.ON_READ only for non-trainable\",\n \"a Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step =\",\n \"disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot\",\n \"variables anywhere, and # instead special-case this dependency and otherwise pretend it's a\",\n \"graph. if slot_variable is not None: # If we've either made this slot\",\n \"each unique index. \\\"\\\"\\\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions,\",\n \"= value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float,\",\n \"except AttributeError as e: # Needed to avoid infinite recursion with __setattr__. if\",\n \"grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not None: with\",\n \"weights(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the order created.\\\"\\\"\\\" return self._weights\",\n \"during `apply_gradients()` to get the value for the hyper parameter. Hyper parameters can\",\n \"if dtype not in valid_dtypes: raise ValueError(\\\"Invalid type %r for %s, expected: %s.\\\"\",\n \"we do not support using the optimizer object iself (e.g. through `apply_gradients`). \\\"\\\"\\\"\",\n \"a tensor's shape is the *replica-local* batch size, which is off by a\",\n \"by the global batch size. Note that when using `tf.distribute.Strategy`, the first component\",\n \"(callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value\",\n \"# (aside from double initialization), and makes variable creator scopes # behave the\",\n \"def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the slot key for the variable: var_name/slot_name.\\\"\\\"\\\" name =\",\n \"This method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want\",\n \"# Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use\",\n \"variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply\",\n \"them to the variables. If you want to process the gradients before applying\",\n \"\\\"Common ops without gradient: \\\" \\\"K.argmax, K.round, K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"): grads =\",\n \"types for loss, variables and gradients. \\\"\\\"\\\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64])\",\n \"`Tensor`, indexing into the first dimension of `values` (as in an IndexedSlices object).\",\n \"minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom\",\n \"how to restore # slot variables which have not yet been created #\",\n \"for loss, variables and gradients. Subclasses should override to allow other float types.\",\n \"called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. name: Optional\",\n \"class Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division\",\n \"loss: A callable taking no arguments which returns the value to minimize. var_list:\",\n \"distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name, slot_name), # pylint: disable=protected-access\",\n \"be a float, callable, or Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return\",\n \"the primary variable first. Args: var: the variable. Returns: the unique name of\",\n \"import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import\",\n \"to None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = []\",\n \"If constraint function is passed to any variable, the constraint will be applied\",\n \"but gradient can be `None`. Raises: TypeError: If `var_list` contains anything else than\",\n \"gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables\",\n \"use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors are all\",\n \"... }, # ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\\\"decay\\\", 0.0) if\",\n \"either regular Python values (like 1.0), tensors, or callables. If they are callable,\",\n \"revived_types.register_revived_type( \\\"optimizer\\\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1,\",\n \"you wish. 3. Apply the processed gradients with `apply_gradients()`. Example: ```python # Create\",\n \"def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"__future__ import print_function import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context\",\n \"realize that the slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition`\",\n \"variables have gradients. \\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v)\",\n \"provided for any variable: %s.\\\" % ([v.name for _, v in grads_and_vars],)) if\",\n \"desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no\",\n \"ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For implementing the trackable\",\n \"slot variables which have not yet been created # (trackable._CheckpointPosition objects). # {slot_name\",\n \"mode the name is derived from the var shared name. In eager mode\",\n \"2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache\",\n \"the names of the slots that it uses. Once you have a slot\",\n \"pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division from __future__ import\",\n \"tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \\\"Synchronization value can be set to \\\" \\\"VariableSynchronization.ON_READ\",\n \"variables and gradients. Subclasses should override to allow other float types. Returns: Valid\",\n \"read. trainable = False elif trainable is None: trainable = True variable =\",\n \"= slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer\",\n \"= self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else:\",\n \"ops.executing_eagerly_outside_functions() else \\\"_\\\" + var.op.name) with backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var,\",\n \"created when graph building. Instead, _restore_slot_variable catches these after normal creation and adds\",\n \"return var._unique_id def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the slot key for the variable: var_name/slot_name.\\\"\\\"\\\"\",\n \"other float types. Returns: Valid types for loss, variables and gradients. \\\"\\\"\\\" return\",\n \"# when possible, but this may mean that scopes intended to catch #\",\n \"gradient computed for `loss`. Returns: A list of (gradient, variable) pairs. Variable is\",\n \"algorithm requires additional variables) - get_config (serialization of the optimizer, include all hyper\",\n \"the configuration of an optimizer. The same optimizer can be reinstantiated later (without\",\n \"import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import\",\n \"values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \\\"\\\"\\\"Add ops\",\n \"raise ValueError(\\\"Optimizer weight shape \\\" + str(pv.shape) + \\\" not compatible with \\\"\",\n \"of the optimimizer. An optimizer config is a Python dictionary (serializable) containing the\",\n \"a SavedModel. These variables may be referenced in functions along with ops created\",\n \"by first pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers\",\n \"\\\"\\\"\\\"Serialize a hyperparameter that can be a float, callable, or Tensor.\\\"\\\"\\\" value =\",\n \"def _var_key(var): \\\"\\\"\\\"Key for representing a primary variable, for looking up slots. In\",\n \"overhead of summing. Args: grad: a `Tensor` representing the gradient for the affected\",\n \"loss, variables and gradients. Subclasses should override to allow other float types. Returns:\",\n \"# {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... }\",\n \"If `global_step` was not None, that operation also increments `global_step`. Raises: TypeError: If\",\n \"immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def\",\n \"gradient for \\\"variable\\\". Note that \\\"gradient\\\" can be a `Tensor`, an `IndexedSlices`, or\",\n \"\\\"\\\"\\\"Updated base class for optimizers. This class defines the API to add Ops\",\n \"use the optimizer in three steps: 1. Compute the gradients with `tf.GradientTape`. 2.\",\n \"the following methods: - resource_apply_dense (update variable given gradient tensor is dense) -\",\n \"def _call_if_callable(self, param): \\\"\\\"\\\"Call the function if param is callable.\\\"\\\"\\\" return param() if\",\n \"one object (because we don't want to # save the slot variable if\",\n \"return variable def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors are all valid types (see `_valid_dtypes`).\",\n \"it's a dependency hypergraph with edges of the form (optimizer, non-slot # variable,\",\n \"### Hyper parameters These are arguments passed to the optimizer subclass constructor (the\",\n \"dynamic hyperparameter setting.\\\"\\\"\\\" # Backwards compatibility with Keras optimizers. if name == \\\"lr\\\":\",\n \"Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or\",\n \"\\\"\\\"\\\" # pylint: disable=protected-access # Get the distributed variable if it exists. if\",\n \">= 1. indices: A one-dimensional integer `Tensor`, indexing into the first dimension of\",\n \"optimizer, such as a function used for a hyperparameter. Returns: An optimizer instance.\",\n \"{} # dict: {variable name : {slot name : variable}} self._slots = {}\",\n \"overhead. Args: grad: a `Tensor` representing the gradient for the affected indices. handle:\",\n \"as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from\",\n \"supported. ### Thread Compatibility The entire optimizer is currently thread compatible, not thread-safe.\",\n \"referenced in functions along with ops created by the original optimizer, but currently\",\n \"name is malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars. \\\"\\\"\\\"\",\n \"an optimizer. The same optimizer can be reinstantiated later (without any saved state)\",\n \"else \\\"_\\\" + var.op.name) with backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,),\",\n \"the order created.\\\"\\\"\\\" return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) #\",\n \"params: List of variables. Returns: List of gradient tensors. Raises: ValueError: In case\",\n \"ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers\",\n \"is `tf.distribute.Strategy` aware, which means it automatically sums gradients across all replicas. To\",\n \"zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor): raise\",\n \"this `Optimizer`'s slot to restore into. variable: The variable object this slot is\",\n \"RuntimeError: If _create_slots has been overridden instead of _create_vars. \\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\",\",\n \"tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn)\",\n \"instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has been overridden\",\n \"= math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step =\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"an optimizer is restored from a SavedModel. These variables may be referenced in\",\n \"parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument\",\n \"gradients before applying them. Calling `minimize()` takes care of both computing the gradients\",\n \"called during `apply_gradients()` to get the value for the hyper parameter. Hyper parameters\",\n \"to the variable `handle`. Args: grad: a `Tensor` representing the gradient. handle: a\",\n \"updates the value of the variable. \\\"\\\"\\\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices)\",\n \"param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape\",\n \"for v in valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid types for loss, variables and gradients.\",\n \"callable, the callable will be called during `apply_gradients()` to get the value for\",\n \"str(len(params)) + \\\" weights. Provided weights: \\\" + str(weights)[:50] + \\\"...\\\") if not\",\n \"the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class is\",\n \"list rather than the one with the highest restore # UID in case\",\n \"variable. \\\"\\\"\\\" # pylint: disable=protected-access # Get the distributed variable if it exists.\",\n \"self._name + \\\" with a weight list of length \\\" + str(len(weights)) +\",\n \"math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform\",\n \"variable. Important: If gradient is sparse tensor, variable constraint is not supported. ###\",\n \": # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations =\",\n \"the gradient. handle: a `Tensor` of dtype `resource` which points to the variable\",\n \"holding the gradient computed for `loss`. name: Optional name for the returned operation.\",\n \"`None` for gradient. \\\" \\\"Please make sure that all of your ops have\",\n \"`tf.math.reduce_sum` to add up your per-example losses and then divide by the global\",\n \"import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import\",\n \"created\\\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed learning rate as\",\n \"an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We\",\n \"of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable(\",\n \"dtype `resource` which points to the variable to be updated. Returns: An `Operation`\",\n \"update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop\",\n \"subclassed models. Pass var_list as callable in these cases. Example: ```python opt =\",\n \"inverse decay of learning rate. `lr` is included for backward compatibility, recommended to\",\n \"some of the variables are not `Variable` objects. \\\"\\\"\\\" grads_and_vars = self._compute_gradients( loss,\",\n \"if hasattr(self, \\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads\",\n \"variable if it exists. if getattr(var, \\\"_distributed_container\\\", None) is not None: var =\",\n \"of the optimizer, include all hyper parameters) \\\"\\\"\\\" def __init__(self, name, **kwargs): \\\"\\\"\\\"Create\",\n \"Returns: List of gradient tensors. Raises: ValueError: In case any gradient cannot be\",\n \"the correct behavior, to sum non-unique indices and their associated gradients, is enforced\",\n \"a single step. As a result, using `tf.math.reduce_mean` will give the wrong answer,\",\n \"dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable(\",\n \"False elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape,\",\n \"= lambda: 3 * var1 * var1 + 2 * var2 * var2\",\n \"get the primary variable first. Args: var: the variable. Returns: the unique name\",\n \"`tf.distribute.Strategy`, the first component of a tensor's shape is the *replica-local* batch size,\",\n \"the indices for which the gradient is nonzero. Indices may be repeated. Returns:\",\n \"\\\"lr\\\", \\\"decay\\\"} for k in kwargs: if k not in allowed_kwargs: raise TypeError(\\\"Unexpected\",\n \"grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \\\"clipvalue\\\"): grads =\",\n \"See the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to\",\n \"gradients with `apply_gradients()`. Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) #\",\n \"/ (1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns the\",\n \"\\\" + self._name + \\\" with a weight list of length \\\" +\",\n \"to the 'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1])\",\n \"self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self,\",\n \"if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if\",\n \"their own dependencies, in which case # those could differ between restores. self._deferred_slot_restorations.setdefault(\",\n \"support hyperparameter access.\\\"\\\"\\\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed\",\n \"tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils\",\n \"methods: - resource_apply_dense (update variable given gradient tensor is dense) - resource_apply_sparse (update\",\n \"not None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params):\",\n \"Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list\",\n \"to train a model. You never use this class directly, but instead instantiate\",\n \"`global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of the variables\",\n \"slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID first to minimize\",\n \"to use the _set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible. Args:\",\n \"is not None: raise RuntimeError( \\\"Cannot use a constraint function on a sparse\",\n \"additional Python objects used to create this optimizer, such as a function used\",\n \"its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True def get_config(self):\",\n \"opt_op.run() # In eager mode, simply call minimize to update the list of\",\n \"Note that \\\"gradient\\\" can be a `Tensor`, an `IndexedSlices`, or `None` if there\",\n \"time when `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed\",\n \"g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v\",\n \"the value to minimize. var_list: list or tuple of `Variable` objects to update\",\n \"and # limitations under the License. # ============================================================================== \\\"\\\"\\\"Version 2 of class Optimizer.\\\"\\\"\\\"\",\n \"to value. value can be callable, tensor, numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name,\",\n \"since the variables are created at the first time `loss` is called. grad_loss:\",\n \"recommended to use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If\",\n \"Optional. A `Tensor` holding the gradient computed for `loss`. Returns: A list of\",\n \"in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in\",\n \"grads_and_vars: scope_name = (\\\"\\\" if ops.executing_eagerly_outside_functions() else \\\"_\\\" + var.op.name) with backend.name_scope(\\\"update\\\" +\",\n \"of your ops have a \\\" \\\"gradient defined (i.e. are differentiable). \\\" \\\"Common\",\n \"Provided weights: \\\" + str(weights)[:50] + \\\"...\\\") if not params: return weight_value_tuples =\",\n \"(see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any tensor is\",\n \"of `Variable` objects to update to minimize `loss`, or a callable returning the\",\n \"`apply_gradients`). \\\"\\\"\\\" # TODO(allenl): Make the restored optimizer functional by tracing its apply\",\n \"a dependency on # a slot variable if not for this case). Deferring\",\n \"slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight\",\n \"owned by any one object (because we don't want to # save the\",\n \"return grads def apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients to variables. This is the\",\n \"RuntimeError(\\\"Cannot set `iterations` to a new Variable after \\\" \\\"the Optimizer weights have\",\n \"of `get_config`, capable of instantiating the same optimizer from the config dictionary. Arguments:\",\n \"run.\\\"\\\"\\\" if self._iterations is None: self._iterations = self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA)\",\n \"# Set trainable to be false when variable is to be synced on\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"Optional name for the returned operation. Default to the name passed to the\",\n \"_resource_apply_sparse(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to the variable\",\n \"the slot variable. Defer restoring until it gets created # normally. We keep\",\n \"ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA)\",\n \"+ \\\" with a weight list of length \\\" + str(len(weights)) + \\\",\",\n \"by the constructors of subclasses. Note that Optimizer instances should not bind to\",\n \"grads_and_vars] # Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ###\",\n \"ops to apply dense gradients to the variable `handle`. Args: grad: a `Tensor`\",\n \"var_list=[var1, var2]) ``` ### Write a customized optimizer. If you intend to create\",\n \"the function if param is callable.\\\"\\\"\\\" return param() if callable(param) else param def\",\n \"using `tf.distribute.Strategy`, the first component of a tensor's shape is the *replica-local* batch\",\n \"get the value for the hyper parameter. Hyper parameters can be overwritten through\",\n \"indices for which the gradient is nonzero. Indices are unique. Returns: An `Operation`\",\n \"the same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint:\",\n \"reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss()\",\n \"applying them to the variables. If you want to process the gradients before\",\n \"In case any gradient cannot be computed (e.g. if gradient function not implemented).\",\n \"result, using `tf.math.reduce_mean` will give the wrong answer, resulting in gradients that can\",\n \"Defer slot variable creation if there is an active variable creator # scope.\",\n \"if name == \\\"lr\\\": name = \\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\") and name in\",\n \"not owned by any one object (because we don't want to # save\",\n \"in grads] if hasattr(self, \\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g\",\n \"add Ops to train a model. You never use this class directly, but\",\n \"ValueError(\\\"No gradients provided for any variable: %s.\\\" % ([v.name for _, v in\",\n \"\\\"\\\"\\\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \\\"\\\"\\\"Call the function\",\n \"list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with Keras\",\n \">= 0, received: {}\\\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper =\",\n \"updates the value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_scatter_add(self, x, i,\",\n \"applying them. Calling `minimize()` takes care of both computing the gradients and applying\",\n \"slot_name, variable, slot_variable): \\\"\\\"\\\"Restore a newly created slot variable's value.\\\"\\\"\\\" variable_key = _var_key(variable)\",\n \"restore UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid,\",\n \"if you use `tf.keras` built-in training or evaluation loops. See the `reduction` argument\",\n \"scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i)\",\n \"dependency and otherwise pretend it's a normal # graph. if slot_variable is not\",\n \"if callable(param) else param def _resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add ops to apply dense\",\n \"derived from the var unique id. If distribution strategy exists, get the primary\",\n \"variable `Trackable` object to be restored. slot_name: The name of this `Optimizer`'s slot\",\n \"not in allowed_kwargs: raise TypeError(\\\"Unexpected keyword argument \\\" \\\"passed to optimizer: \\\" +\",\n \"import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import\",\n \"return backend.get_value(value) return value def variables(self): \\\"\\\"\\\"Returns variables of this Optimizer based on\",\n \"with grad equal to None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars\",\n \"include all hyper parameters) \\\"\\\"\\\" def __init__(self, name, **kwargs): \\\"\\\"\\\"Create a new Optimizer.\",\n \"\\\"lr\\\" in config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in config: if isinstance(config[\\\"learning_rate\\\"], dict):\",\n \"ops have a \\\" \\\"gradient defined (i.e. are differentiable). \\\" \\\"Common ops without\",\n \"self.weights if len(params) != len(weights): raise ValueError( \\\"You called `set_weights(weights)` on optimizer \\\"\",\n \"variable) pairs. name: Optional name for the returned operation. Default to the name\",\n \"_, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\\\"Gradients does not exist for variables\",\n \"a feature request if this limitation bothers \\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda obj:\",\n \"math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations,\",\n \"self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False\",\n \"original optimizer, but currently we do not support using the optimizer object iself\",\n \"optimizer subclass constructor (the `__init__` method), and then passed to `self._set_hyper()`. They can\",\n \"pulled out an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else:\",\n \"to minimize. loss = lambda: 3 * var1 + 2 * var2 #\",\n \"for loss, variables and gradients. \\\"\\\"\\\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def\",\n \"variables. If you want to process the gradients before applying them you can\",\n \"class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class for optimizers. This class defines the API to\",\n \"your per-example losses and then divide by the global batch size. Note that\",\n \"len(weights): raise ValueError( \\\"You called `set_weights(weights)` on optimizer \\\" + self._name + \\\"\",\n \"and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices\",\n \"learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name,\",\n \"[] self._weights = [] self._iterations = None # For implementing Trackable. Stores information\",\n \"double initialization), and makes variable creator scopes # behave the same way they\",\n \"by any one object (because we don't want to # save the slot\",\n \"should be carried out under a graph # context. (eager updates execute immediately)\",\n \"indices): \\\"\\\"\\\"Sums `values` associated with any non-unique `indices`. Args: values: A `Tensor` with\",\n \"update_ops = [] with backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name\",\n \"tuple of `Variable` objects. Use callable when the variable list would otherwise be\",\n \"initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name=\\\"%s/%s\\\" % (var._shared_name, slot_name),\",\n \"True @property def iterations(self): \\\"\\\"\\\"Variable. The number of training steps this Optimizer has\",\n \"useful if you want to log debug a training algorithm, report stats about\",\n \"or if we've pulled out an # existing slot variable, we should restore\",\n \"variable: The variable object this slot is being created for. \\\"\\\"\\\" variable_key =\",\n \"pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically. for name,\",\n \"var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically. for\",\n \"the given variable. Args: loss: A callable taking no arguments which returns the\",\n \"name == \\\"lr\\\": name = \\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\") and name in self._hyper:\",\n \"instances should not bind to a single graph, and so shouldn't keep Tensors\",\n \"return self._weights @property def weights(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the\",\n \"Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional name for the returned\",\n \"can be a `Tensor`, an `IndexedSlices`, or `None` if there is no gradient\",\n \"tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in\",\n \"the unique name of the variable. \\\"\\\"\\\" # pylint: disable=protected-access # Get the\",\n \"been de-duplicated. Optimizers which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices`\",\n \"first called, instead of construction time. Examples include 1) sequential models without input\",\n \"_var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None\",\n \"name to use for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed\",\n \"this method must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for\",\n \"# context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops):\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call(\",\n \"applied to the variable. Important: If gradient is sparse tensor, variable constraint is\",\n \"var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients of\",\n \"stateful and thread-compatible. Args: name: A non-empty string. The name to use for\",\n \"if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False,\",\n \"learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized\",\n \"Hyper parameters These are arguments passed to the optimizer subclass constructor (the `__init__`\",\n \"ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies(\",\n \"decay if \\\"clipnorm\\\" in kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in kwargs: self.clipvalue\",\n \"loss_value = loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads =\",\n \"A one-dimensional integer `Tensor`, indexing into the first dimension of `values` (as in\",\n \"TypeError(\\\"Unexpected keyword argument \\\" \\\"passed to optimizer: \\\" + str(k)) # checks that\",\n \"def get_slot_names(self): \\\"\\\"\\\"A list of names for this optimizer's slots.\\\"\\\"\\\" return self._slot_names def\",\n \"raise RuntimeError(\\\"Cannot set `iterations` to a new Variable after \\\" \\\"the Optimizer weights\",\n \"to check. Raises: ValueError: If any tensor is not a valid type. \\\"\\\"\\\"\",\n \"`Variable` objects. ValueError: If some arguments are invalid, or var_list is None. \\\"\\\"\\\"\",\n \"def _resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add ops to apply dense gradients to the variable\",\n \"is not None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss,\",\n \"and `Adagrad` allocate and manage additional variables associated with the variables to train.\",\n \"Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0\",\n \"weights: \\\" + str(weights)[:50] + \\\"...\\\") if not params: return weight_value_tuples = []\",\n \"version of `indices` and `summed_values` contains the sum of `values` slices associated with\",\n \"name, value): \\\"\\\"\\\"set hyper `name` to value. value can be callable, tensor, numeric.\\\"\\\"\\\"\",\n \"in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name,\",\n \"config of the optimimizer. An optimizer config is a Python dictionary (serializable) containing\",\n \"`_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead override this method\",\n \"returns the value # to minimize. loss = lambda: 3 * var1 *\",\n \"gradients that can be many times too big. ### Variable Constraint All Keras\",\n \"as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with the desired\",\n \"call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this function. Args: loss: A\",\n \"`grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly\",\n \"variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot\",\n \"that minimizes the loss by updating the listed # variables. opt_op = opt.minimize(loss,\",\n \"\\\" \\\"the Optimizer weights have been created\\\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self,\",\n \"do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue(\",\n \"Variable Constraint All Keras optimizers respect variable constraints. If constraint function is passed\",\n \"ops.Tensor): raise NotImplementedError(\\\"Trying to update a Tensor \\\", var) if isinstance(grad, ops.IndexedSlices): if\",\n \"return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not None: raise RuntimeError(\\\"Cannot\",\n \"primary variable, for looking up slots. In graph mode the name is derived\",\n \"\\\"\\\"\\\"Returns gradients of `loss` with respect to `params`. Arguments: loss: Loss tensor. params:\",\n \"name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value): \\\"\\\"\\\"Override setattr\",\n \"the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations:\",\n \"lambda: 3 * var1 + 2 * var2 # In eager mode, simply\",\n \"with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means it automatically sums\",\n \"function is passed to any variable, the constraint will be applied to the\",\n \"for \\\"variable\\\". Note that \\\"gradient\\\" can be a `Tensor`, an `IndexedSlices`, or `None`\",\n \"to be updated. Returns: An `Operation` which updates the value of the variable.\",\n \"_var_key(var) return name + \\\"/\\\" + slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer implementation\",\n \"tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context\",\n \"can be `None`. Raises: TypeError: If `var_list` contains anything else than `Variable` objects.\",\n \"using these and you want to average gradients, you should use `tf.math.reduce_sum` to\",\n \"2 * var2 # In eager mode, simply call minimize to update the\",\n \"var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \\\"clipnorm\\\"): grads =\",\n \"\\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set trainable to be false when variable is to be\",\n \"gradients to variables. This is the second part of `minimize()`. It returns an\",\n \"for the specific language governing permissions and # limitations under the License. #\",\n \"Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means it automatically\",\n \"We didn't make the slot variable. Defer restoring until it gets created #\",\n \"add_weight(self, name, shape, dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype\",\n \"distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop\",\n \"be incomplete before `minimize` since the variables are created at the first time\",\n \"off by a factor equal to the number of replicas being used to\",\n \"Python dictionary mapping names to additional Python objects used to create this optimizer,\",\n \"to train. These are called <i>Slots</i>. Slots have names and you can ask\",\n \"weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name]\",\n \"= _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self):\",\n \"returns an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs.\",\n \"Note that when using `tf.distribute.Strategy`, the first component of a tensor's shape is\",\n \"non-slot variable is saved without the optimizer; # it's a dependency hypergraph with\",\n \"have their own dependencies, in which case # those could differ between restores.\",\n \"{`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip\",\n \"var_dtype) decay_t = self._get_hyper(\\\"decay\\\", var_dtype) lr_t = lr_t / (1. + decay_t *\",\n \"Returns: An `Operation` which updates the value of the variable. \\\"\\\"\\\" raise NotImplementedError()\",\n \"value of the variable. \\\"\\\"\\\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad,\",\n \"call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ###\",\n \"grads_and_vars: List of (gradient, variable) pairs. name: Optional name for the returned operation.\",\n \"\\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] #\",\n \"debug a training algorithm, report stats about the slots, etc. ### Hyper parameters\",\n \"slots. In graph mode the name is derived from the var shared name.\",\n \"or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value\",\n \"The same optimizer can be reinstantiated later (without any saved state) from this\",\n \"constructor (the `__init__` method), and then passed to `self._set_hyper()`. They can be either\",\n \"This can be useful if you want to log debug a training algorithm,\",\n \"restore ops to the graph. This method is nonetheless important when graph building\",\n \"of the form (optimizer, non-slot # variable, variable)). So we don't _track_ slot\",\n \"list would otherwise be incomplete before `minimize` since the variables are created at\",\n \"unique index. \\\"\\\"\\\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0])\",\n \"If they are callable, the callable will be called during `apply_gradients()` to get\",\n \"\\\"\\\"\\\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values,\",\n \"loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g)\",\n \"w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype\",\n \"as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from\",\n \"vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) #\",\n \"tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest\",\n \"a weight list of length \\\" + str(len(weights)) + \\\", but the optimizer\",\n \"which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If\",\n \"compatible with \\\" \\\"provided weight shape \\\" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"the config of the optimimizer. An optimizer config is a Python dictionary (serializable)\",\n \"slot key for the variable: var_name/slot_name.\\\"\\\"\\\" name = _var_key(var) return name + \\\"/\\\"\",\n \"this Optimizer based on the order created.\\\"\\\"\\\" return self._weights @property def weights(self): \\\"\\\"\\\"Returns\",\n \"names for this optimizer's slots.\\\"\\\"\\\" return self._slot_names def add_slot(self, var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add\",\n \"ValueError: If name is malformed. RuntimeError: If _create_slots has been overridden instead of\",\n \"Python dictionary. \\\"\\\"\\\" config = {\\\"name\\\": self._name} if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm\",\n \"the value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v):\",\n \"constraint function is passed to any variable, the constraint will be applied to\",\n \"value): \\\"\\\"\\\"Override setattr to support dynamic hyperparameter setting.\\\"\\\"\\\" # Backwards compatibility with Keras\",\n \"\\\"Synchronization value can be set to \\\" \\\"VariableSynchronization.ON_READ only for non-trainable variables. \\\"\",\n \"to apply dense gradients to the variable `handle`. Args: grad: a `Tensor` representing\",\n \"information about how to restore # slot variables which have not yet been\",\n \"= tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns\",\n \"callable(param) else param def _resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add ops to apply dense gradients\",\n \"= [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is\",\n \"= _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is\",\n \"distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list\",\n \"custom_objects: A Python dictionary mapping names to additional Python objects used to create\",\n \"* var1 * var1 + 2 * var2 * var2 # In graph\",\n \"on optimizer \\\" + self._name + \\\" with a weight list of length\",\n \"a slot name you can ask the optimizer for the variable it created\",\n \"var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \\\"clipnorm\\\"): grads\",\n \"should not bind to a single graph, and so shouldn't keep Tensors as\",\n \"`minimize` since the variables are created at the first time `loss` is called.\",\n \"def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def\",\n \"restored optimizer functional by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\")\",\n \"+ \\\" weights. Provided weights: \\\" + str(weights)[:50] + \\\"...\\\") if not params:\",\n \"clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops\",\n \"scope_name = (\\\"\\\" if ops.executing_eagerly_outside_functions() else \\\"_\\\" + var.op.name) with backend.name_scope(\\\"update\\\" + scope_name):\",\n \"self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key =\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"in grads_and_vars] # Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ```\",\n \"context.executing_eagerly() or any_symbolic: # If the current context is graph mode or any\",\n \"config[\\\"clipvalue\\\"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates an optimizer\",\n \"\\\"\\\"\\\"Restore a newly created slot variable's value.\\\"\\\"\\\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get(\",\n \"return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars:\",\n \"= lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data:\",\n \"of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an\",\n \"gradients and applying them to the variables. If you want to process the\",\n \"value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with\",\n \"config dictionary. Arguments: config: A Python dictionary, typically the output of get_config. custom_objects:\",\n \"updating `var_list`. This method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If\",\n \"distributed variable if it exists. if getattr(var, \\\"_distributed_container\\\", None) is not None: var\",\n \"by value, `decay` is included for backward compatibility to allow time inverse decay\",\n \"such as `Adam` and `Adagrad` allocate and manage additional variables associated with the\",\n \"behave the same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): #\",\n \"operation also increments `global_step`. Raises: ValueError: If some of the variables are not\",\n \"with dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype)\",\n \"\\\" + str(k)) # checks that all keyword arguments are non-negative. if kwargs[k]\",\n \"ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params)\",\n \"# unintentionally (specifically make_template would add a dependency on # a slot variable\",\n \"True self._name = name self._hyper = {} # dict: {variable name : {slot\",\n \"member variables. Generally you should be able to use the _set_hyper()/state.get_hyper() facility instead.\",\n \"raise NotImplementedError(\\\"Trying to update a Tensor \\\", var) if isinstance(grad, ops.IndexedSlices): if var.constraint\",\n \"optimizer for the names of the slots that it uses. Once you have\",\n \"value, `decay` is included for backward compatibility to allow time inverse decay of\",\n \"the variable after the gradient has been applied to the variable. Important: If\",\n \"_deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values` associated with any non-unique `indices`. Args: values: A `Tensor`\",\n \"value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value()\",\n \"not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w\",\n \"is nonzero. Indices are unique. Returns: An `Operation` which updates the value of\",\n \"a newly created slot variable's value.\\\"\\\"\\\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name,\",\n \"AttributeError as e: # Needed to avoid infinite recursion with __setattr__. if name\",\n \"dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is\",\n \"an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of\",\n \"# variable, variable)). So we don't _track_ slot variables anywhere, and # instead\",\n \"any variable, the constraint will be applied to the variable after the gradient\",\n \"if grad is None: raise ValueError(\\\"Variable {} has `None` for gradient. \\\" \\\"Please\",\n \"# instead special-case this dependency and otherwise pretend it's a normal # graph.\",\n \"\\\"\\\"\\\"Call the function if param is callable.\\\"\\\"\\\" return param() if callable(param) else param\",\n \"models, sometimes variables are created when the model is first called, instead of\",\n \"is enforced by first pre-processing `grad` and `indices` and passing them on to\",\n \"Write a customized optimizer. If you intend to create your own optimization algorithm,\",\n \"import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import\",\n \"ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes\",\n \"calls `apply_gradients()`. If you want to process the gradient before applying then call\",\n \"for `loss`. name: Optional name for the returned operation. Returns: An Operation that\",\n \"output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ```\",\n \"tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with tf.GradientTape() as\",\n \"new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class for\",\n \"been applied to the variable. Important: If gradient is sparse tensor, variable constraint\",\n \"with the License. # You may obtain a copy of the License at\",\n \"or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else:\",\n \"unique_indices) def _resource_apply_sparse(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to\",\n \"in kwargs: if k not in allowed_kwargs: raise TypeError(\\\"Unexpected keyword argument \\\" \\\"passed\",\n \"gradients by norm; `clipvalue` is clip gradients by value, `decay` is included for\",\n \"indices may instead override this method to avoid the overhead of summing. Args:\",\n \"optimizer functional by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created\",\n \"var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the slot key for\",\n \"loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run()\",\n \"Tensors as member variables. Generally you should be able to use the _set_hyper()/state.get_hyper()\",\n \"to `_apply_sparse`, the `indices` argument to this method has been de-duplicated. Optimizers which\",\n \"is malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars. \\\"\\\"\\\" allowed_kwargs\",\n \"0.0) if decay < 0.: raise ValueError(\\\"decay cannot be less than 0: {}\\\".format(decay))\",\n \"constraint function on a sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op =\",\n \"= self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if\",\n \"given gradient tensor is sparse) - create_slots (if your optimizer algorithm requires additional\",\n \"gradients to `handle`, with repeated indices. Optimizers which override this method must deal\",\n \"var)) filtered = tuple(filtered) if not filtered: raise ValueError(\\\"No gradients provided for any\",\n \"Trackable. Stores information about how to restore # slot variables which have not\",\n \"the variable. Important: If gradient is sparse tensor, variable constraint is not supported.\",\n \"initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new slot variable for `var`.\\\"\\\"\\\" if slot_name not in self._slot_names:\",\n \"_resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # ---------------\",\n \"%r for %s, expected: %s.\\\" % (dtype, t.name, [v for v in valid_dtypes]))\",\n \"be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are\",\n \"dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t\",\n \"Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is\",\n \"permissions and # limitations under the License. # ============================================================================== \\\"\\\"\\\"Version 2 of class\",\n \"grad_loss) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if\",\n \"name is derived from the var unique id. If distribution strategy exists, get\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights):\",\n \"optimizer algorithm requires additional variables) - get_config (serialization of the optimizer, include all\",\n \"import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from\",\n \"object indicating the slot variable `Trackable` object to be restored. slot_name: The name\",\n \"- create_slots (if your optimizer algorithm requires additional variables) - get_config (serialization of\",\n \"passed to the optimizer subclass constructor (the `__init__` method), and then passed to\",\n \"`_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum non-unique indices and\",\n \"creator scopes # behave the same way they do when graph building. and\",\n \"functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util\",\n \"from a SavedModel. These variables may be referenced in functions along with ops\",\n \"objects. Use callable when the variable list would otherwise be incomplete before `minimize`\",\n \"enforced by first pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`.\",\n \"callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created =\",\n \"values (like 1.0), tensors, or callables. If they are callable, the callable will\",\n \"1. Compute the gradients with `tf.GradientTape`. 2. Process the gradients as you wish.\",\n \"= {} self._slot_names = [] self._weights = [] self._iterations = None # For\",\n \"Compatibility The entire optimizer is currently thread compatible, not thread-safe. The user needs\",\n \"name = _var_key(var) return name + \\\"/\\\" + slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional\",\n \"Raises: TypeError: If `var_list` contains anything else than `Variable` objects. ValueError: If some\",\n \"them. Calling `minimize()` takes care of both computing the gradients and applying them\",\n \"var2]) ``` ### Custom training loop with Keras models In Keras models, sometimes\",\n \"objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ...\",\n \"in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\\\"Gradients does not exist for variables %s when\",\n \"``` ### Processing gradients before applying them. Calling `minimize()` takes care of both\",\n \"self).__getattribute__(name) except AttributeError as e: # Needed to avoid infinite recursion with __setattr__.\",\n \"+ str(weights)[:50] + \\\"...\\\") if not params: return weight_value_tuples = [] param_values =\",\n \"mode or any of the update ops are # symbolic then the step\",\n \"\\\"lr\\\": name = \\\"learning_rate\\\" if name in self._hyper: return self._get_hyper(name) raise e def\",\n \"is not None: # If we've either made this slot variable, or if\",\n \"variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores,\",\n \"may instead override this method to avoid the overhead of summing. Args: grad:\",\n \"from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as\",\n \"k in kwargs: if k not in allowed_kwargs: raise TypeError(\\\"Unexpected keyword argument \\\"\",\n \"for optimizers. This class defines the API to add Ops to train a\",\n \"to support dynamic hyperparameter setting.\\\"\\\"\\\" # Backwards compatibility with Keras optimizers. if name\",\n \"returns op that minimizes the loss by updating the listed # variables. opt_op\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"ValueError(\\\"Variable {} has `None` for gradient. \\\" \\\"Please make sure that all of\",\n \"are # symbolic then the step update should be carried out under a\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"for v in vars_with_empty_grads])) return filtered def _var_key(var): \\\"\\\"\\\"Key for representing a primary\",\n \"stats about the slots, etc. ### Hyper parameters These are arguments passed to\",\n \"`Tensor` representing the gradient for the affected indices. handle: a `Tensor` of dtype\",\n \"opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to update\",\n \"Optimizer's config raise NotImplementedError( \\\"Restoring functional Optimzers from SavedModels is not currently \\\"\",\n \"math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\\\"decay\\\", var_dtype) lr_t = lr_t / (1. + decay_t\",\n \"import keras_export def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values` associated with any non-unique `indices`. Args:\",\n \"gradients for a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function>\",\n \"tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging\",\n \"the variables are created at the first time when `loss` is called. grad_loss:\",\n \"return filtered def _var_key(var): \\\"\\\"\\\"Key for representing a primary variable, for looking up\",\n \"case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def\",\n \"The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License,\",\n \"`tf.GradientTape` and `apply_gradients()` explicitly instead of using this function. Args: loss: A callable\",\n \"your optimizer algorithm requires additional variables) - get_config (serialization of the optimizer, include\",\n \"float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self,\",\n \"get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self,\",\n \"*replica-local* batch size, which is off by a factor equal to the number\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"weight = slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types) or callable(initializer):\",\n \"for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name]\",\n \"from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from\",\n \"order created.\\\"\\\"\\\" return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny):\",\n \"gradient can be `None`. Raises: TypeError: If `var_list` contains anything else than `Variable`\",\n \"return name + \\\"/\\\" + slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer implementation for\",\n \"### Processing gradients before applying them. Calling `minimize()` takes care of both computing\",\n \"method to avoid the overhead of summing. Args: grad: a `Tensor` representing the\",\n \"optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage additional variables associated\",\n \"self.clipvalue) for g in grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \\\"Cannot use\",\n \"being created for. \\\"\\\"\\\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable =\",\n \"# ============================================================================== \\\"\\\"\\\"Version 2 of class Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name from __future__ import\",\n \"in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\\\"Optimizer weight shape \\\"\",\n \"# slot variables which have not yet been created # (trackable._CheckpointPosition objects). #\",\n \"repeated indices. Optimizers which override this method must deal with repeated indices. See\",\n \"_var_key(var): \\\"\\\"\\\"Key for representing a primary variable, for looking up slots. In graph\",\n \"unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle,\",\n \"the global batch size, which is done automatically if you use `tf.keras` built-in\",\n \"callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list)\",\n \"getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self,\",\n \"t in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\\\"Invalid\",\n \"should override to allow other float types. Returns: Valid types for loss, variables\",\n \"\\\"gradient defined (i.e. are differentiable). \\\" \\\"Common ops without gradient: \\\" \\\"K.argmax, K.round,\",\n \"See the `reduction` argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE`\",\n \"value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight(\",\n \"are unique. Returns: An `Operation` which updates the value of the variable. \\\"\\\"\\\"\",\n \"with ops created by the original optimizer, but currently we do not support\",\n \"variable. Defer restoring until it gets created # normally. We keep a list\",\n \"= (\\\"\\\" if ops.executing_eagerly_outside_functions() else \\\"_\\\" + var.op.name) with backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend(\",\n \"[]).append( slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable with grad equal to None.\\\"\\\"\\\" grads_and_vars\",\n \"slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables\",\n \"it automatically sums gradients across all replicas. To average gradients, you divide your\",\n \"# We didn't make the slot variable. Defer restoring until it gets created\",\n \"\\\"supported. Please file a feature request if this limitation bothers \\\" \\\"you.\\\") revived_types.register_revived_type(\",\n \"Keras models In Keras models, sometimes variables are created when the model is\",\n \"= math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\\\"decay\\\", var_dtype) lr_t = lr_t / (1. +\",\n \"summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable):\",\n \"learning rate. `lr` is included for backward compatibility, recommended to use `learning_rate` instead.\",\n \"on the order created.\\\"\\\"\\\" return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params)\",\n \"that Optimizer instances should not bind to a single graph, and so shouldn't\",\n \"dtype not in valid_dtypes: raise ValueError(\\\"Invalid type %r for %s, expected: %s.\\\" %\",\n \"_create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore a slot variable's value, possibly creating it.\",\n \"local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step\",\n \"None) if weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer)\",\n \"config, custom_objects=None): \\\"\\\"\\\"Creates an optimizer from its config. This method is the reverse\",\n \"overridden instead of _create_vars. \\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for k\",\n \"the wrong answer, resulting in gradients that can be many times too big.\",\n \"use the _set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible. Args: name:\",\n \"value if callable(value): value = value() if dtype: return math_ops.cast(value, dtype) else: return\",\n \"= self.weights if len(params) != len(weights): raise ValueError( \\\"You called `set_weights(weights)` on optimizer\",\n \"variable creator # scope. Generally we'd like to eagerly create/restore slot variables #\",\n \"the non-slot variable is saved without the optimizer; # it's a dependency hypergraph\",\n \"model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn\",\n \"after the gradient has been applied to the variable. Important: If gradient is\",\n \"ask the optimizer for the names of the slots that it uses. Once\",\n \"this function. Args: loss: A callable taking no arguments which returns the value\",\n \"super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to avoid infinite recursion with\",\n \"weight shape \\\" + str(pv.shape) + \\\" not compatible with \\\" \\\"provided weight\",\n \"gradients. \\\"\\\"\\\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \\\"\\\"\\\"Call the\",\n \"deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default\",\n \"\\\"\\\"\\\" # TODO(allenl): Make the restored optimizer functional by tracing its apply #\",\n \"# Get the distributed variable if it exists. if getattr(var, \\\"_distributed_container\\\", None) is\",\n \"\\\" \\\"Please make sure that all of your ops have a \\\" \\\"gradient\",\n \"learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not\",\n \"It returns a list of (gradient, variable) pairs where \\\"gradient\\\" is the gradient\",\n \"that \\\"gradient\\\" can be a `Tensor`, an `IndexedSlices`, or `None` if there is\",\n \"slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new slot variable for `var`.\\\"\\\"\\\" if slot_name not in\",\n \"malformed. ValueError: If none of the variables have gradients. \\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars)\",\n \"their associated gradients, is enforced by first pre-processing `grad` and `indices` and passing\",\n \"into. variable: The variable object this slot is being created for. \\\"\\\"\\\" variable_key\",\n \"`values` associated with any non-unique `indices`. Args: values: A `Tensor` with rank >=\",\n \"to add up your per-example losses and then divide by the global batch\",\n \"is nonzero. Indices may be repeated. Returns: An `Operation` which updates the value\",\n \"v for g, v in grads_and_vars if g is not None and v.dtype\",\n \"context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return\",\n \"if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value)\",\n \"grad): \\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying to update a\",\n \"pre-defined, or 2) subclassed models. Pass var_list as callable in these cases. Example:\",\n \"in grads_and_vars if g is not None and v.dtype != dtypes.resource ]) return\",\n \"pairs. name: Optional name for the returned operation. Default to the name passed\",\n \"to the number of replicas being used to compute a single step. As\",\n \"a `Tensor` of integral type representing the indices for which the gradient is\",\n \"on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead override\",\n \"variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot\",\n \"name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \\\"\\\"\\\"A list\",\n \"operation. Default to the name passed to the `Optimizer` constructor. Returns: An `Operation`\",\n \"the config dictionary. Arguments: config: A Python dictionary, typically the output of get_config.\",\n \"# normally. We keep a list rather than the one with the highest\",\n \"if g is not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def\",\n \"var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name or self._name): for grad,\",\n \"v)]): return x.value() # --------------- # For implementing the trackable interface # ---------------\",\n \"g in grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients to\",\n \"which have not yet been created # (trackable._CheckpointPosition objects). # {slot_name : #\",\n \"[resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For implementing the trackable interface\",\n \"you want to process the gradient before applying then call `tf.GradientTape` and `apply_gradients()`\",\n \"is currently thread compatible, not thread-safe. The user needs to perform synchronization if\",\n \"ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name or self._name):\",\n \"= math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated\",\n \"### Thread Compatibility The entire optimizer is currently thread compatible, not thread-safe. The\",\n \"way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if\",\n \"and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \\\"\\\"\\\"Returns gradients\",\n \"Pass var_list as callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model\",\n \"cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask\",\n \"on the order created.\\\"\\\"\\\" return self._weights @property def weights(self): \\\"\\\"\\\"Returns variables of this\",\n \"cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter that can be a float, callable,\",\n \"share this logic with base_layer. def set_weights(self, weights): params = self.weights if len(params)\",\n \"can instead use the optimizer in three steps: 1. Compute the gradients with\",\n \"kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss` by\",\n \"# graph. if slot_variable is not None: # If we've either made this\",\n \"apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops)\",\n \"= True def get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise\",\n \"from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from\",\n \"The name to use for accumulators created for the optimizer. **kwargs: keyword arguments.\",\n \"not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight =\",\n \"3. Apply the processed gradients with `apply_gradients()`. Example: ```python # Create an optimizer.\",\n \"var_list) def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying\",\n \"iterable with grad equal to None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return\",\n \"revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export\",\n \"These are arguments passed to the optimizer subclass constructor (the `__init__` method), and\",\n \"\\\"_hyper\\\": raise e # Backwards compatibility with Keras optimizers. if name == \\\"lr\\\":\",\n \"w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\\\"Optimizer weight shape\",\n \"in valid_dtypes: raise ValueError(\\\"Invalid type %r for %s, expected: %s.\\\" % (dtype, t.name,\",\n \"are created at the first time when `loss` is called. grad_loss: Optional. A\",\n \"backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation)\",\n \"return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \\\"\\\"\\\"set hyper `name` to value. value can\",\n \"var shared name. In eager mode the name is derived from the var\",\n \"updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In\",\n \"regular Python values (like 1.0), tensors, or callables. If they are callable, the\",\n \"necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,),\",\n \"variable constraints. If constraint function is passed to any variable, the constraint will\",\n \"A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of `indices`\",\n \"{}\\\".format(decay)) self._initial_decay = decay if \\\"clipnorm\\\" in kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\"\",\n \"grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]): return\",\n \"tensors): \\\"\\\"\\\"Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to\",\n \"slot is being created for. \\\"\\\"\\\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {})\",\n \"the slots, etc. ### Hyper parameters These are arguments passed to the optimizer\",\n \"gv[1]) for gv in grads_and_vars] # Ask the optimizer to apply the capped\",\n \"}, # ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\\\"decay\\\", 0.0) if decay\",\n \"be updated. indices: a `Tensor` of integral type representing the indices for which\",\n \"these after normal creation and adds restore ops to the graph. This method\",\n \"avoid infinite recursion with __setattr__. if name == \\\"_hyper\\\": raise e # Backwards\",\n \"training algorithm, report stats about the slots, etc. ### Hyper parameters These are\",\n \"import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import\",\n \"optimizer. If you intend to create your own optimization algorithm, simply inherit from\",\n \"nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in zip(grads, params):\",\n \"var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops =\",\n \"def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def\",\n \"carried out under a graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default():\",\n \"TypeError: If `grads_and_vars` is malformed. ValueError: If none of the variables have gradients.\",\n \"those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter\",\n \"None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads\",\n \"This method is the reverse of `get_config`, capable of instantiating the same optimizer\",\n \"needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable\",\n \"for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply\",\n \"== tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \\\"Synchronization value can be set to \\\"\",\n \"values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class\",\n \"name: Optional name for the returned operation. Returns: An Operation that updates the\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def\",\n \"grad is None: raise ValueError(\\\"Variable {} has `None` for gradient. \\\" \\\"Please make\",\n \"order created.\\\"\\\"\\\" return self._weights @property def weights(self): \\\"\\\"\\\"Returns variables of this Optimizer based\",\n \"which has an associated slot variable is created or restored. When executing eagerly,\",\n \"compute a single step. As a result, using `tf.math.reduce_mean` will give the wrong\",\n \"def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper values\",\n \"tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the\",\n \"included for backward compatibility to allow time inverse decay of learning rate. `lr`\",\n \"# Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss`\",\n \"`resource` which points to the variable to be updated. Returns: An `Operation` which\",\n \"the current context is graph mode or any of the update ops are\",\n \"will give the wrong answer, resulting in gradients that can be many times\",\n \"for checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer is restored\",\n \"self._hyper = {} # dict: {variable name : {slot name : variable}} self._slots\",\n \"... ], ... }, # ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\\\"decay\\\",\",\n \"self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars =\",\n \"variable creator scopes # behave the same way they do when graph building.\",\n \"variable def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors are all valid types (see `_valid_dtypes`). Args:\",\n \"pylint: disable=protected-access # Get the distributed variable if it exists. if getattr(var, \\\"_distributed_container\\\",\",\n \"indexing into the first dimension of `values` (as in an IndexedSlices object). Returns:\",\n \"when an optimizer is restored from a SavedModel. These variables may be referenced\",\n \"is not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name,\",\n \"with each unique index. \\\"\\\"\\\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values,\",\n \"name=\\\"%s/%s\\\" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] =\",\n \"variable given gradient tensor is dense) - resource_apply_sparse (update variable given gradient tensor\",\n \"constructors of subclasses. Note that Optimizer instances should not bind to a single\",\n \"or a callable returning the list or tuple of `Variable` objects. Use callable\",\n \"ValueError(\\\"Optimizer weight shape \\\" + str(pv.shape) + \\\" not compatible with \\\" \\\"provided\",\n \"not filtered: raise ValueError(\\\"No gradients provided for any variable: %s.\\\" % ([v.name for\",\n \"subclasses. Note that Optimizer instances should not bind to a single graph, and\",\n \"raise e def __setattr__(self, name, value): \\\"\\\"\\\"Override setattr to support dynamic hyperparameter setting.\\\"\\\"\\\"\",\n \"variables to train. These are called <i>Slots</i>. Slots have names and you can\",\n \"+ str(len(weights)) + \\\", but the optimizer was expecting \\\" + str(len(params)) +\",\n \"to the name passed to the `Optimizer` constructor. Returns: An `Operation` that applies\",\n \"already been created but `variable` has just been added to a dependency graph\",\n \"(because we don't want to # save the slot variable if the optimizer\",\n \"a callable that takes no argument and returns the value # to minimize.\",\n \"\\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue return config @classmethod\",\n \"for the case when a slot variable has already been created but `variable`\",\n \"SavedModel. These variables may be referenced in functions along with ops created by\",\n \"= _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest\",\n \"creation and adds restore ops to the graph. This method is nonetheless important\",\n \"= value() if dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name):\",\n \"resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging\",\n \"clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([\",\n \"self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if dtype:\",\n \"opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which\",\n \"then the step update should be carried out under a graph # context.\",\n \"= <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads]\",\n \"UID in case slot variables have their own dependencies, in which case #\",\n \"when the model is first called, instead of construction time. Examples include 1)\",\n \"and adds restore ops to the graph. This method is nonetheless important when\",\n \"a \\\" \\\"gradient defined (i.e. are differentiable). \\\" \\\"Common ops without gradient: \\\"\",\n \"log debug a training algorithm, report stats about the slots, etc. ### Hyper\",\n \"tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export\",\n \"int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def\",\n \"`resource` which points to the variable to be updated. indices: a `Tensor` of\",\n \"0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer. If you intend\",\n \"with a restoring initializer. No new variables are created when graph building. Instead,\",\n \"Important: If gradient is sparse tensor, variable constraint is not supported. ### Thread\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"hyperparameter access.\\\"\\\"\\\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to\",\n \"if param is callable.\\\"\\\"\\\" return param() if callable(param) else param def _resource_apply_dense(self, grad,\",\n \"from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from\",\n \"the gradient is nonzero. Indices are unique. Returns: An `Operation` which updates the\",\n \"applies the specified gradients. If `global_step` was not None, that operation also increments\",\n \"Once you have a slot name you can ask the optimizer for the\",\n \"@property def iterations(self): \\\"\\\"\\\"Variable. The number of training steps this Optimizer has run.\\\"\\\"\\\"\",\n \"too big. ### Variable Constraint All Keras optimizers respect variable constraints. If constraint\",\n \"is clip gradients by norm; `clipvalue` is clip gradients by value, `decay` is\",\n \"has been overridden instead of _create_vars. \\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"}\",\n \"of get_config. custom_objects: A Python dictionary mapping names to additional Python objects used\",\n \"# TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( \\\"Restoring functional Optimzers\",\n \"op that minimizes the loss by updating the listed # variables. opt_op =\",\n \"algorithm, simply inherit from this class and override the following methods: - resource_apply_dense\",\n \"if self._hypers_created: return # Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()):\",\n \"variable has already been created but `variable` has just been added to a\",\n \"current context is graph mode or any of the update ops are #\",\n \"synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors are all valid\",\n \"var1 * var1 + 2 * var2 * var2 # In graph mode,\",\n \"None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is\",\n \"that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If\",\n \"from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import\",\n \"\\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates an\",\n \"else: # We didn't make the slot variable. Defer restoring until it gets\",\n \"Make the restored optimizer functional by tracing its apply # methods. def __init__(self):\",\n \"with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss,\",\n \"\\\"\\\"\\\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss,\",\n \"\\\"\\\"\\\"A list of names for this optimizer's slots.\\\"\\\"\\\" return self._slot_names def add_slot(self, var,\",\n \"- resource_apply_sparse (update variable given gradient tensor is sparse) - create_slots (if your\",\n \"with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with\",\n \"If `grads_and_vars` is malformed. ValueError: If none of the variables have gradients. \\\"\\\"\\\"\",\n \"representing a primary variable, for looking up slots. In graph mode the name\",\n \"the 'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for\",\n \"self._initial_decay = decay if \\\"clipnorm\\\" in kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in\",\n \"weights. Provided weights: \\\" + str(weights)[:50] + \\\"...\\\") if not params: return weight_value_tuples\",\n \"\\\" \\\"K.argmax, K.round, K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g\",\n \"License. # ============================================================================== \\\"\\\"\\\"Version 2 of class Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name from __future__\",\n \"Args: tensors: Tensors to check. Raises: ValueError: If any tensor is not a\",\n \"grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered:\",\n \"and makes variable creator scopes # behave the same way they do when\",\n \"of the variable. \\\"\\\"\\\" # pylint: disable=protected-access # Get the distributed variable if\",\n \"list of tuples (gradient, variable). Do whatever you # need to the 'gradient'\",\n \"def _resource_apply_sparse(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to the\",\n \"gradients with `tf.GradientTape`. 2. Process the gradients as you wish. 3. Apply the\",\n \"part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in\",\n \"`Tensor`, an `IndexedSlices`, or `None` if there is no gradient for the given\",\n \"initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts\",\n \"for which the gradient is nonzero. Indices may be repeated. Returns: An `Operation`\",\n \"string. The name to use for accumulators created for the optimizer. **kwargs: keyword\",\n \"grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list)\",\n \"the slot variable if the optimizer is saved without the non-slot # variable,\",\n \"Compute the gradients with `tf.GradientTape`. 2. Process the gradients as you wish. 3.\",\n \"# (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... },\",\n \"the first dimension of `values` (as in an IndexedSlices object). Returns: A tuple\",\n \"feature request if this limitation bothers \\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda obj: isinstance(obj,\",\n \"[checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations = {} decay =\",\n \"Stores information about how to restore # slot variables which have not yet\",\n \"config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in config: if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects)\",\n \"configuration. Returns: Python dictionary. \\\"\\\"\\\" config = {\\\"name\\\": self._name} if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"]\",\n \"raise TypeError(\\\"Unexpected keyword argument \\\" \\\"passed to optimizer: \\\" + str(k)) # checks\",\n \"A list of (gradient, variable) pairs. Variable is always present, but gradient can\",\n \"Backwards compatibility with Keras optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\" if\",\n \"def iterations(self, variable): if self._iterations is not None: raise RuntimeError(\\\"Cannot set `iterations` to\",\n \"file a feature request if this limitation bothers \\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda\",\n \"nonetheless important when graph building for the case when a slot variable has\",\n \"a graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access\",\n \"restoring until it gets created # normally. We keep a list rather than\",\n \"tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils\",\n \"return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class for optimizers. This\",\n \"ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name})\",\n \"def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable with grad equal to None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars)\",\n \"self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for\",\n \"= True @property def iterations(self): \\\"\\\"\\\"Variable. The number of training steps this Optimizer\",\n \"variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable\",\n \"list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05\",\n \"return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate\",\n \"variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with Keras models In\",\n \"e def __setattr__(self, name, value): \\\"\\\"\\\"Override setattr to support dynamic hyperparameter setting.\\\"\\\"\\\" #\",\n \"associated gradients, is enforced by first pre-processing `grad` and `indices` and passing them\",\n \"if gradient function not implemented). \\\"\\\"\\\" params = nest.flatten(params) with backend.get_graph().as_default(): grads =\",\n \"= self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable)\",\n \"indices for which the gradient is nonzero. Indices may be repeated. Returns: An\",\n \"non-slot # variable, variable)). So we don't _track_ slot variables anywhere, and #\",\n \"to the variable. Important: If gradient is sparse tensor, variable constraint is not\",\n \"the name is derived from the var unique id. If distribution strategy exists,\",\n \"var2 * var2 # In graph mode, returns op that minimizes the loss\",\n \"the overhead of summing. Args: grad: a `Tensor` representing the gradient for the\",\n \"def get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( \\\"Restoring\",\n \"\\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for\",\n \"None: raise RuntimeError( \\\"Cannot use a constraint function on a sparse variable.\\\") return\",\n \"with repeated indices. Optimizers which override this method must deal with repeated indices.\",\n \"An `Operation` which updates the value of the variable. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"any non-unique `indices`. Args: values: A `Tensor` with rank >= 1. indices: A\",\n \"if \\\"clipnorm\\\" in kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in kwargs: self.clipvalue =\",\n \"Optimizer based on the order created.\\\"\\\"\\\" return self._weights @property def weights(self): \\\"\\\"\\\"Returns variables\",\n \"hyperparameters when an optimizer is restored from a SavedModel. These variables may be\",\n \"model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input,\",\n \"= lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing\",\n \"if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( \\\"Cannot use a\",\n \"is callable.\\\"\\\"\\\" return param() if callable(param) else param def _resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add\",\n \"value, possibly creating it. Called when a variable which has an associated slot\",\n \"logging.warning( (\\\"Gradients does not exist for variables %s when minimizing the loss.\\\"), ([v.name\",\n \"self._deferred_slot_restorations = {} decay = kwargs.pop(\\\"decay\\\", 0.0) if decay < 0.: raise ValueError(\\\"decay\",\n \"a hyperparameter. Returns: An optimizer instance. \\\"\\\"\\\" if \\\"lr\\\" in config: config[\\\"learning_rate\\\"] =\",\n \"`set_weights(weights)` on optimizer \\\" + self._name + \\\" with a weight list of\",\n \"def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() #\",\n \"big. ### Variable Constraint All Keras optimizers respect variable constraints. If constraint function\",\n \"defines the API to add Ops to train a model. You never use\",\n \"instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python #\",\n \"and hyperparameters when an optimizer is restored from a SavedModel. These variables may\",\n \"the value # to minimize. loss = lambda: 3 * var1 + 2\",\n \"the variable it created to hold the slot value. This can be useful\",\n \"lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t =\",\n \"gradient. handle: a `Tensor` of dtype `resource` which points to the variable to\",\n \"want to log debug a training algorithm, report stats about the slots, etc.\",\n \"pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal\",\n \"checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned\",\n \"self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return #\",\n \"with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self,\",\n \"tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable\",\n \"updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return\",\n \"as member variables. Generally you should be able to use the _set_hyper()/state.get_hyper() facility\",\n \"variable, for looking up slots. In graph mode the name is derived from\",\n \"is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial(\",\n \"try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to avoid infinite\",\n \"use a constraint function on a sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices)\",\n \"not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable,\",\n \"[]) # Iterate over restores, highest restore UID first to minimize the number\",\n \"correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args:\",\n \"all replicas. To average gradients, you divide your loss by the global batch\",\n \"the output of get_config. custom_objects: A Python dictionary mapping names to additional Python\",\n \"to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not using\",\n \"[] with backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name = (\\\"\\\"\",\n \"+ str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO,\",\n \"None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable\",\n \"intend to create your own optimization algorithm, simply inherit from this class and\",\n \"size. Note that when using `tf.distribute.Strategy`, the first component of a tensor's shape\",\n \"`lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"class defines the API to add Ops to train a model. You never\",\n \"import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import\",\n \"`apply_gradients()`. Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the\",\n \"before `minimize` since the variables are created at the first time `loss` is\",\n \"_resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self,\",\n \"self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed learning rate as a Tensor with dtype=var_dtype.\\\"\\\"\\\"\",\n \"grads = gradients.gradients(loss, params) for grad, param in zip(grads, params): if grad is\",\n \"to hold the slot value. This can be useful if you want to\",\n \"for variables %s when minimizing the loss.\\\"), ([v.name for v in vars_with_empty_grads])) return\",\n \"grads_and_vars def get_gradients(self, loss, params): \\\"\\\"\\\"Returns gradients of `loss` with respect to `params`.\",\n \"user code: Example: ```python # Create an optimizer with the desired parameters. opt\",\n \"self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \\\"\\\"\\\"A list of names for\",\n \"Constraint All Keras optimizers respect variable constraints. If constraint function is passed to\",\n \"gradient is nonzero. Indices may be repeated. Returns: An `Operation` which updates the\",\n \"self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not None: raise\",\n \"into the first dimension of `values` (as in an IndexedSlices object). Returns: A\",\n \"arguments are invalid, or var_list is None. \\\"\\\"\\\" # TODO(josh11b): Test that we\",\n \"var_list = [v for (_, v) in grads_and_vars] # Create iteration if necessary.\",\n \"dtype `resource` which points to the variable to be updated. indices: a `Tensor`\",\n \"with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss,\",\n \"replicas. To average gradients, you divide your loss by the global batch size,\",\n \"any of the update ops are # symbolic then the step update should\",\n \"gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this\",\n \"dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \\\"\\\"\\\"Call the function if param is callable.\\\"\\\"\\\"\",\n \"initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( \\\"Synchronization value\",\n \"for g, v in grads_and_vars if g is not None and v.dtype !=\",\n \"has been de-duplicated. Optimizers which deal correctly with non-unique indices may instead override\",\n \"from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import\",\n \"gradients provided for any variable: %s.\\\" % ([v.name for _, v in grads_and_vars],))\",\n \"variables are created at the first time `loss` is called. grad_loss: Optional. A\",\n \"otherwise pretend it's a normal # graph. if slot_variable is not None: #\",\n \"language governing permissions and # limitations under the License. # ============================================================================== \\\"\\\"\\\"Version 2\",\n \"params): \\\"\\\"\\\"Returns gradients of `loss` with respect to `params`. Arguments: loss: Loss tensor.\",\n \"`loss` for the variables in `var_list`. This is the first part of `minimize()`.\",\n \"isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value,\",\n \"return value if callable(value): value = value() if dtype: return math_ops.cast(value, dtype) else:\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"_create_slots has been overridden instead of _create_vars. \\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\",\",\n \"setting.\\\"\\\"\\\" # Backwards compatibility with Keras optimizers. if name == \\\"lr\\\": name =\",\n \"v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For implementing\",\n \"all of your ops have a \\\" \\\"gradient defined (i.e. are differentiable). \\\"\",\n \"listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode,\",\n \"self._hypers_created: return # Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if\",\n \"using the optimizer object iself (e.g. through `apply_gradients`). \\\"\\\"\\\" # TODO(allenl): Make the\",\n \"get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( \\\"Restoring functional\",\n \"return update_op update_ops = [] with backend.name_scope(name or self._name): for grad, var in\",\n \"ValueError(\\\"decay cannot be less than 0: {}\\\".format(decay)) self._initial_decay = decay if \\\"clipnorm\\\" in\",\n \"on read. trainable = False elif trainable is None: trainable = True variable\",\n \"graph. This method is nonetheless important when graph building for the case when\",\n \"methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True def get_config(self): # TODO(allenl): Save\",\n \"avoid the overhead of summing. Args: grad: a `Tensor` representing the gradient for\",\n \"ops created by the original optimizer, but currently we do not support using\",\n \"name for the returned operation. Returns: An Operation that updates the variables in\",\n \"variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads =\",\n \"dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return\",\n \"from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from\",\n \"x.value() # --------------- # For implementing the trackable interface # --------------- def _restore_slot_variable(self,\",\n \"```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn =\",\n \"slots that it uses. Once you have a slot name you can ask\",\n \"= [] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else:\",\n \"slot variable. Defer restoring until it gets created # normally. We keep a\",\n \"a slot variable's value, possibly creating it. Called when a variable which has\",\n \"is being created for. \\\"\\\"\\\" variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable\",\n \"of `minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars: List of\",\n \"self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore the Optimizer's config\",\n \"cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid')\",\n \"harmless # (aside from double initialization), and makes variable creator scopes # behave\",\n \"to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue`\",\n \"or var_list is None. \\\"\\\"\\\" # TODO(josh11b): Test that we handle weight decay\",\n \"grad: a `Tensor` representing the gradient for the affected indices. handle: a `Tensor`\",\n \"\\\"\\\"\\\"Add ops to apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`,\",\n \"the loss.\\\"), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): \\\"\\\"\\\"Key for\",\n \"self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not None:\",\n \"in kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created\",\n \"representing the indices for which the gradient is nonzero. Indices may be repeated.\",\n \"import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import\",\n \"the var unique id. If distribution strategy exists, get the primary variable first.\",\n \"[v for (_, v) in grads_and_vars] # Create iteration if necessary. with ops.init_scope():\",\n \"update to minimize `loss`, or a callable returning the list or tuple of\",\n \"non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters when an\",\n \"are arguments passed to the optimizer subclass constructor (the `__init__` method), and then\",\n \"to the `Optimizer` constructor. Returns: An `Operation` that applies the specified gradients. If\",\n \"[clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g,\",\n \"from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from\",\n \"false when variable is to be synced on read. trainable = False elif\",\n \"tensor, numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper:\",\n \"is None: raise ValueError(\\\"Variable {} has `None` for gradient. \\\" \\\"Please make sure\",\n \"a dependency hypergraph with edges of the form (optimizer, non-slot # variable, variable)).\",\n \"\\\"\\\"\\\"Filter out iterable with grad equal to None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars) if not\",\n \"add_slot(self, var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a new slot variable for `var`.\\\"\\\"\\\" if slot_name\",\n \"-self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v\",\n \"is included for backward compatibility to allow time inverse decay of learning rate.\",\n \"way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"to use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots\",\n \"it gets created # normally. We keep a list rather than the one\",\n \"differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out iterable\",\n \"\\\" \\\"You have specified trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set trainable to\",\n \"\\\"You have specified trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set trainable to be\",\n \"not. If you are not using these and you want to average gradients,\",\n \"backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name is malformed.\",\n \"the original optimizer, but currently we do not support using the optimizer object\",\n \"Keras models, sometimes variables are created when the model is first called, instead\",\n \"If distribution strategy exists, get the primary variable first. Args: var: the variable.\",\n \"grad: a `Tensor` representing the gradient. handle: a `Tensor` of dtype `resource` which\",\n \"based on the order created.\\\"\\\"\\\" return self._weights @property def weights(self): \\\"\\\"\\\"Returns variables of\",\n \"= lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self):\",\n \"shouldn't keep Tensors as member variables. Generally you should be able to use\",\n \"symbolic then the step update should be carried out under a graph #\",\n \"# Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a\",\n \"single step. As a result, using `tf.math.reduce_mean` will give the wrong answer, resulting\",\n \"created at the first time `loss` is called. grad_loss: Optional. A `Tensor` holding\",\n \"var_list = [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def\",\n \"shape, dtype=None, initializer=\\\"zeros\\\", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32\",\n \"\\\"\\\"\\\" def __init__(self, name, **kwargs): \\\"\\\"\\\"Create a new Optimizer. This must be called\",\n \"# For implementing Trackable. Stores information about how to restore # slot variables\",\n \"in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value,\",\n \"for k in kwargs: if k not in allowed_kwargs: raise TypeError(\\\"Unexpected keyword argument\",\n \"could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): \\\"\\\"\\\"Filter out\",\n \"anywhere, and # instead special-case this dependency and otherwise pretend it's a normal\",\n \"be callable, tensor, numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not\",\n \"ask the optimizer for the variable it created to hold the slot value.\",\n \"values: A `Tensor` with rank >= 1. indices: A one-dimensional integer `Tensor`, indexing\",\n \"self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types)\",\n \"in kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None,\",\n \"--------------- # For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable,\",\n \"\\\"\\\"\\\"Restore a slot variable's value, possibly creating it. Called when a variable which\",\n \"restore the Optimizer's config raise NotImplementedError( \\\"Restoring functional Optimzers from SavedModels is not\",\n \"\\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1,\",\n \"and their associated gradients, is enforced by first pre-processing `grad` and `indices` and\",\n \"var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ###\",\n \"your ops have a \\\" \\\"gradient defined (i.e. are differentiable). \\\" \\\"Common ops\",\n \"`grads_and_vars` is malformed. ValueError: If none of the variables have gradients. \\\"\\\"\\\" grads_and_vars\",\n \"variables in `var_list`. If `global_step` was not `None`, that operation also increments `global_step`.\",\n \"variable, or if the non-slot variable is saved without the optimizer; # it's\",\n \"var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to update the\",\n \"These variables may be referenced in functions along with ops created by the\",\n \"you divide your loss by the global batch size, which is done automatically\",\n \"or evaluation loops. See the `reduction` argument of your loss which should be\",\n \"or 2) subclassed models. Pass var_list as callable in these cases. Example: ```python\",\n \"can be set to \\\" \\\"VariableSynchronization.ON_READ only for non-trainable variables. \\\" \\\"You have\",\n \"### Variable Constraint All Keras optimizers respect variable constraints. If constraint function is\",\n \"called, instead of construction time. Examples include 1) sequential models without input shape\",\n \"None. \\\"\\\"\\\" # TODO(josh11b): Test that we handle weight decay in a reasonable\",\n \"\\\"\\\"\\\"Apply gradients to variables. This is the second part of `minimize()`. It returns\",\n \"`reduction` argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging\",\n \"\\\"\\\"\\\"Key for representing a primary variable, for looking up slots. In graph mode\",\n \"and otherwise pretend it's a normal # graph. if slot_variable is not None:\",\n \"of a tensor's shape is the *replica-local* batch size, which is off by\",\n \"[(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer to apply the\",\n \"the variable: var_name/slot_name.\\\"\\\"\\\" name = _var_key(var) return name + \\\"/\\\" + slot_name class\",\n \"state) from this configuration. Returns: Python dictionary. \\\"\\\"\\\" config = {\\\"name\\\": self._name} if\",\n \"when minimizing the loss.\\\"), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var):\",\n \"wish. 3. Apply the processed gradients with `apply_gradients()`. Example: ```python # Create an\",\n \"= 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer. If you\",\n \"import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import\",\n \"v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\\\"Gradients does not exist for variables %s\",\n \"`decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by value,\",\n \"integral type representing the indices for which the gradient is nonzero. Indices are\",\n \"the first component of a tensor's shape is the *replica-local* batch size, which\",\n \"List of variables. Returns: List of gradient tensors. Raises: ValueError: In case any\",\n \"from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates an optimizer from its config. This method is the\",\n \"variables. \\\" \\\"You have specified trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set trainable\",\n \"with Keras optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\" if name in\",\n \"Optimizer has run.\\\"\\\"\\\" if self._iterations is None: self._iterations = self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64,\",\n \"instead of _create_vars. \\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for k in\",\n \"`self._set_hyper()`. They can be either regular Python values (like 1.0), tensors, or callables.\",\n \"bind to a single graph, and so shouldn't keep Tensors as member variables.\",\n \"# Needed to avoid infinite recursion with __setattr__. if name == \\\"_hyper\\\": raise\",\n \"to avoid this overhead. Args: grad: a `Tensor` representing the gradient for the\",\n \"support dynamic hyperparameter setting.\\\"\\\"\\\" # Backwards compatibility with Keras optimizers. if name ==\",\n \"update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate\",\n \"if self._iterations is not None: raise RuntimeError(\\\"Cannot set `iterations` to a new Variable\",\n \"aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer):\",\n \"apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class\",\n \"will be applied to the variable after the gradient has been applied to\",\n \"backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule\",\n \"tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops\",\n \"the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`.\",\n \"optimizer: \\\" + str(k)) # checks that all keyword arguments are non-negative. if\",\n \"taking no arguments which returns the value to minimize. var_list: list or tuple\",\n \"opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them. Calling `minimize()` takes care\",\n \"is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = []\",\n \"name not in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value)\",\n \"executing eagerly, we create the slot variable with a restoring initializer. No new\",\n \"`tf.GradientTape`. 2. Process the gradients as you wish. 3. Apply the processed gradients\",\n \"grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads =\",\n \"to eagerly create/restore slot variables # when possible, but this may mean that\",\n \"the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \\\"\\\"\\\"Add ops to\",\n \"one-dimensional integer `Tensor`, indexing into the first dimension of `values` (as in an\",\n \"get_config. custom_objects: A Python dictionary mapping names to additional Python objects used to\",\n \"Tensors to check. Raises: ValueError: If any tensor is not a valid type.\",\n \"# you may not use this file except in compliance with the License.\",\n \"= False elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name,\",\n \"`indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate\",\n \"raise ValueError(\\\"Variable {} has `None` for gradient. \\\" \\\"Please make sure that all\",\n \"to get the value for the hyper parameter. Hyper parameters can be overwritten\",\n \"use `tf.keras` built-in training or evaluation loops. See the `reduction` argument of your\",\n \"if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \\\"\\\"\\\"Returns variables of this Optimizer\",\n \"`unique_indices` is a de-duplicated version of `indices` and `summed_values` contains the sum of\",\n \"lr_t = lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod def\",\n \"in valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid types for loss, variables and gradients. Subclasses should\",\n \"allow time inverse decay of learning rate. `lr` is included for backward compatibility,\",\n \"= _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is\",\n \"2. Process the gradients as you wish. 3. Apply the processed gradients with\",\n \"dtype) else: return value def __getattribute__(self, name): \\\"\\\"\\\"Overridden to support hyperparameter access.\\\"\\\"\\\" try:\",\n \"is created or restored. When executing eagerly, we create the slot variable with\",\n \"params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if\",\n \"with respect to `params`. Arguments: loss: Loss tensor. params: List of variables. Returns:\",\n \"variables are created when the model is first called, instead of construction time.\",\n \"pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight)\",\n \"\\\", but the optimizer was expecting \\\" + str(len(params)) + \\\" weights. Provided\",\n \"hasattr(self, \\\"_hyper\\\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def\",\n \"non-trainable variables. \\\" \\\"You have specified trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set\",\n \"of `values` (as in an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`)\",\n \"K.round, K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads]\",\n \"are not `Variable` objects. \\\"\\\"\\\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars,\",\n \"is included for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If\",\n \"```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for\",\n \"Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage additional variables\",\n \"computed for `loss`. name: Optional name for the returned operation. Returns: An Operation\",\n \"by updating `var_list`. This method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`.\",\n \"variables in `var_list`. This is the first part of `minimize()`. It returns a\",\n \"overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors):\",\n \"variables and gradients. \\\"\\\"\\\" return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param):\",\n \"any saved state) from this configuration. Returns: Python dictionary. \\\"\\\"\\\" config = {\\\"name\\\":\",\n \"is not a valid type. \\\"\\\"\\\" valid_dtypes = self._valid_dtypes() for t in tensors:\",\n \"def apply_grad_to_update_var(var, grad): \\\"\\\"\\\"Apply gradient to variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying to\",\n \"the gradient computed for `loss`. name: Optional name for the returned operation. Returns:\",\n \"class and override the following methods: - resource_apply_dense (update variable given gradient tensor\",\n \"sure that all of your ops have a \\\" \\\"gradient defined (i.e. are\",\n \"six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util\",\n \"the variable to be updated. indices: a `Tensor` of integral type representing the\",\n \"and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an\",\n \"Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has been overridden instead\",\n \"`get_config`, capable of instantiating the same optimizer from the config dictionary. Arguments: config:\",\n \"gradients across all replicas. To average gradients, you divide your loss by the\",\n \"If none of the variables have gradients. \\\"\\\"\\\" grads_and_vars = _filter_grads(grads_and_vars) var_list =\",\n \"be set to \\\" \\\"VariableSynchronization.ON_READ only for non-trainable variables. \\\" \\\"You have specified\",\n \"[] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if\",\n \"list of (gradient, variable) pairs where \\\"gradient\\\" is the gradient for \\\"variable\\\". Note\",\n \"base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops\",\n \"its eagerly created slot variable # unintentionally (specifically make_template would add a dependency\",\n \"for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( (\\\"Gradients does not exist for\",\n \"unique. Returns: An `Operation` which updates the value of the variable. \\\"\\\"\\\" raise\",\n \"or any of the update ops are # symbolic then the step update\",\n \"`loss`, or a callable returning the list or tuple of `Variable` objects. Use\",\n \"dense gradients to the variable `handle`. Args: grad: a `Tensor` representing the gradient.\",\n \"valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid types for loss, variables and gradients. Subclasses should override\",\n \"with Keras models In Keras models, sometimes variables are created when the model\",\n \"code: Example: ```python # Create an optimizer with the desired parameters. opt =\",\n \"process the gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of\",\n \"docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum non-unique\",\n \"\\\" + str(pv.shape) + \\\" not compatible with \\\" \\\"provided weight shape \\\"\",\n \"IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated\",\n \"objects used to create this optimizer, such as a function used for a\",\n \"with rank >= 1. indices: A one-dimensional integer `Tensor`, indexing into the first\",\n \"with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For implementing the\",\n \"slot variable # unintentionally (specifically make_template would add a dependency on # a\",\n \"local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper(\\\"decay\\\", var_dtype) lr_t = lr_t / (1.\",\n \"return self._get_hyper(name) raise e def __setattr__(self, name, value): \\\"\\\"\\\"Override setattr to support dynamic\",\n \"context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util\",\n \"callable will be called during `apply_gradients()` to get the value for the hyper\",\n \"length \\\" + str(len(weights)) + \\\", but the optimizer was expecting \\\" +\",\n \"graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with\",\n \"saved without the non-slot # variable, or if the non-slot variable is saved\",\n \"the step update should be carried out under a graph # context. (eager\",\n \"optimizer object iself (e.g. through `apply_gradients`). \\\"\\\"\\\" # TODO(allenl): Make the restored optimizer\",\n \"`values` slices associated with each unique index. \\\"\\\"\\\" unique_indices, new_index_positions = array_ops.unique(indices) summed_values\",\n \"dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): \\\"\\\"\\\"Returns gradients of `loss` with\",\n \"= kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss`\",\n \"var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the slot\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"_compute_gradients(self, loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients of `loss` for the variables in `var_list`.\",\n \"Slots have names and you can ask the optimizer for the names of\",\n \"method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want to\",\n \"= variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): \\\"\\\"\\\"Get decayed learning rate as a Tensor\",\n \"get_config(self): \\\"\\\"\\\"Returns the config of the optimimizer. An optimizer config is a Python\",\n \"that takes no argument and returns the value # to minimize. loss =\",\n \"creation if there is an active variable creator # scope. Generally we'd like\",\n \"= trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are\",\n \"isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper # pylint: disable=protected-access\",\n \"objects. ValueError: If some arguments are invalid, or var_list is None. \\\"\\\"\\\" #\",\n \"not implemented). \\\"\\\"\\\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for\",\n \"to add Ops to train a model. You never use this class directly,\",\n \"def _distributed_apply(self, distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM,\",\n \"user needs to perform synchronization if necessary. ### Slots Many optimizer subclasses, such\",\n \"to use for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to\",\n \"= tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g\",\n \"any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or any_symbolic:\",\n \"= backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape !=\",\n \"`Tensor` of dtype `resource` which points to the variable to be updated. indices:\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"--------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \\\"\\\"\\\"Restore a newly created slot variable's value.\\\"\\\"\\\"\",\n \"catches these after normal creation and adds restore ops to the graph. This\",\n \"eager mode the name is derived from the var unique id. If distribution\",\n \"tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output)\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"if weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value\",\n \"continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True\",\n \"that we handle weight decay in a reasonable way. with backprop.GradientTape() as tape:\",\n \"(\\\"Gradients does not exist for variables %s when minimizing the loss.\\\"), ([v.name for\",\n \"`loss`. Returns: A list of (gradient, variable) pairs. Variable is always present, but\",\n \"update should be carried out under a graph # context. (eager updates execute\",\n \"eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1,\",\n \"to a single graph, and so shouldn't keep Tensors as member variables. Generally\",\n \"``` ### Write a customized optimizer. If you intend to create your own\",\n \"shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight =\",\n \"return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to avoid infinite recursion\",\n \"trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable,\",\n \"specific language governing permissions and # limitations under the License. # ============================================================================== \\\"\\\"\\\"Version\",\n \"`global_step` was not None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars`\",\n \"`variable` has just been added to a dependency graph (causing us to realize\",\n \"backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape:\",\n \"slot variable `Trackable` object to be restored. slot_name: The name of this `Optimizer`'s\",\n \"to be restored. slot_name: The name of this `Optimizer`'s slot to restore into.\",\n \"var_list, grad_loss) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads]\",\n \"for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}.\",\n \"that when using `tf.distribute.Strategy`, the first component of a tensor's shape is the\",\n \"to the optimizer subclass constructor (the `__init__` method), and then passed to `self._set_hyper()`.\",\n \"`var`.\\\"\\\"\\\" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key,\",\n \"def iterations(self): \\\"\\\"\\\"Variable. The number of training steps this Optimizer has run.\\\"\\\"\\\" if\",\n \"\\\" + str(len(params)) + \\\" weights. Provided weights: \\\" + str(weights)[:50] + \\\"...\\\")\",\n \"used to compute a single step. As a result, using `tf.math.reduce_mean` will give\",\n \"them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the\",\n \"for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]\",\n \"is a Python dictionary (serializable) containing the configuration of an optimizer. The same\",\n \"trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set trainable to be false when variable\",\n \"self._weights = [] self._iterations = None # For implementing Trackable. Stores information about\",\n \"the variable. \\\"\\\"\\\" # pylint: disable=protected-access # Get the distributed variable if it\",\n \"v in grads_and_vars if g is not None and v.dtype != dtypes.resource ])\",\n \"else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property\",\n \"_assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors\",\n \"and `apply_gradients()` explicitly instead of using this function. Args: loss: A callable taking\",\n \"Loss tensor. params: List of variables. Returns: List of gradient tensors. Raises: ValueError:\",\n \"(as in an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices`\",\n \"`Tensor` of dtype `resource` which points to the variable to be updated. Returns:\",\n \"to the variable to be updated. indices: a `Tensor` of integral type representing\",\n \"no arguments which returns the value to minimize. var_list: list or tuple of\",\n \"return param() if callable(param) else param def _resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add ops to\",\n \"`global_step`. Raises: ValueError: If some of the variables are not `Variable` objects. \\\"\\\"\\\"\",\n \"Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer\",\n \"variables which have not yet been created # (trackable._CheckpointPosition objects). # {slot_name :\",\n \"self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable):\",\n \"math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \\\"\\\"\\\"Overridden to support hyperparameter access.\\\"\\\"\\\"\",\n \"model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before\",\n \"[process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"the constraint will be applied to the variable after the gradient has been\",\n \"the optimizer for the variable it created to hold the slot value. This\",\n \"for this optimizer's slots.\\\"\\\"\\\" return self._slot_names def add_slot(self, var, slot_name, initializer=\\\"zeros\\\"): \\\"\\\"\\\"Add a\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"\\\"learning_rate\\\" in config: if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config)\",\n \"with base_layer. def set_weights(self, weights): params = self.weights if len(params) != len(weights): raise\",\n \"callable when the variable list would otherwise be incomplete before `minimize` since the\",\n \"minimize. var_list: list or tuple of `Variable` objects to update to minimize `loss`,\",\n \"would otherwise be incomplete before `minimize` since the variables are created at the\",\n \"v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \\\"\\\"\\\"set hyper `name`\",\n \"\\\"\\\"\\\" allowed_kwargs = {\\\"clipnorm\\\", \\\"clipvalue\\\", \\\"lr\\\", \\\"decay\\\"} for k in kwargs: if k\",\n \"TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights): params = self.weights\",\n \"Default to the name passed to the `Optimizer` constructor. Returns: An `Operation` that\",\n \"callables. If they are callable, the callable will be called during `apply_gradients()` to\",\n \"operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none\",\n \"For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \\\"\\\"\\\"Restore\",\n \"grad equal to None.\\\"\\\"\\\" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered\",\n \"Optimizers which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid\",\n \"using a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _,\",\n \"slot to restore into. variable: The variable object this slot is being created\",\n \"can be many times too big. ### Variable Constraint All Keras optimizers respect\",\n \"= lambda: 3 * var1 + 2 * var2 # In eager mode,\",\n \"but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage\",\n \"tensor, variable constraint is not supported. ### Thread Compatibility The entire optimizer is\",\n \"the variables to train. These are called <i>Slots</i>. Slots have names and you\",\n \"config raise NotImplementedError( \\\"Restoring functional Optimzers from SavedModels is not currently \\\" \\\"supported.\",\n \"gradients of `loss` for the variables in `var_list`. This is the first part\",\n \"as callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential()\",\n \"expecting \\\" + str(len(params)) + \\\" weights. Provided weights: \\\" + str(weights)[:50] +\",\n \"`tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not using these\",\n \"# variable, or if the non-slot variable is saved without the optimizer; #\",\n \"dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer =\",\n \"weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params,\",\n \"TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( \\\"Restoring functional Optimzers from\",\n \"unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices)\",\n \"with the highest restore # UID in case slot variables have their own\",\n \"hyper `name` to value. value can be callable, tensor, numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable):\",\n \"is not None: raise RuntimeError(\\\"Cannot set `iterations` to a new Variable after \\\"\",\n \"# TODO(josh11b): Test that we handle weight decay in a reasonable way. with\",\n \"clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return grads def apply_gradients(self, grads_and_vars,\",\n \"value. This can be useful if you want to log debug a training\",\n \"[self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \\\"\\\"\\\"set hyper `name` to value. value can be\",\n \"this method to avoid the overhead of summing. Args: grad: a `Tensor` representing\",\n \"restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to\",\n \"created for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`,\",\n \"created when the model is first called, instead of construction time. Examples include\",\n \"the gradients for a list of variables. with tf.GradientTape() as tape: loss =\",\n \"`indices`. Args: values: A `Tensor` with rank >= 1. indices: A one-dimensional integer\",\n \"contains the sum of `values` slices associated with each unique index. \\\"\\\"\\\" unique_indices,\",\n \"names and you can ask the optimizer for the names of the slots\",\n \"params = self.weights if len(params) != len(weights): raise ValueError( \\\"You called `set_weights(weights)` on\",\n \"training or evaluation loops. See the `reduction` argument of your loss which should\",\n \"_serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a hyperparameter that can be a float, callable, or Tensor.\\\"\\\"\\\"\",\n \"create/restore slot variables # when possible, but this may mean that scopes intended\",\n \"behavior, to sum non-unique indices and their associated gradients, is enforced by first\",\n \"variable)). So we don't _track_ slot variables anywhere, and # instead special-case this\",\n \"passed to `self._set_hyper()`. They can be either regular Python values (like 1.0), tensors,\",\n \"less than 0: {}\\\".format(decay)) self._initial_decay = decay if \\\"clipnorm\\\" in kwargs: self.clipnorm =\",\n \"number of training steps this Optimizer has run.\\\"\\\"\\\" if self._iterations is None: self._iterations\",\n \"self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID first to\",\n \"variable if the optimizer is saved without the non-slot # variable, or if\",\n \"self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return\",\n \"weights): if pv.shape != w.shape: raise ValueError(\\\"Optimizer weight shape \\\" + str(pv.shape) +\",\n \"for g in grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients\",\n \"types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any tensor\",\n \"vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError(\\\"No gradients\",\n \"ValueError(\\\"Expected {} >= 0, received: {}\\\".format(k, kwargs[k])) self._use_locking = True self._name = name\",\n \"name + \\\"/\\\" + slot_name class _RestoredOptimizer(OptimizerV2): \\\"\\\"\\\"A non-functional Optimizer implementation for checkpoint\",\n \"tensors: Tensors to check. Raises: ValueError: If any tensor is not a valid\",\n \"valid_dtypes: raise ValueError(\\\"Invalid type %r for %s, expected: %s.\\\" % (dtype, t.name, [v\",\n \"value can be callable, tensor, numeric.\\\"\\\"\\\" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if\",\n \"is graph mode or any of the update ops are # symbolic then\",\n \"In Keras models, sometimes variables are created when the model is first called,\",\n \"\\\"_hyper\\\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self):\",\n \"to `params`. Arguments: loss: Loss tensor. params: List of variables. Returns: List of\",\n \"if slot_variable is not None: # If we've either made this slot variable,\",\n \"value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if\",\n \"param is callable.\\\"\\\"\\\" return param() if callable(param) else param def _resource_apply_dense(self, grad, handle):\",\n \"= self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in\",\n \"else: # Set trainable to be false when variable is to be synced\",\n \"the gradients and applying them to the variables. If you want to process\",\n \"v in valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid types for loss, variables and gradients. Subclasses\",\n \"if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation\",\n \"and so shouldn't keep Tensors as member variables. Generally you should be able\",\n \"slot_name): \\\"\\\"\\\"Get the slot key for the variable: var_name/slot_name.\\\"\\\"\\\" name = _var_key(var) return\",\n \"divide by the global batch size. Note that when using `tf.distribute.Strategy`, the first\",\n \"prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value,\",\n \"variable list would otherwise be incomplete before `minimize` since the variables are created\",\n \"# Backwards compatibility with Keras optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\"\",\n \"a `Tensor` of dtype `resource` which points to the variable to be updated.\",\n \"= self._get_hyper(\\\"decay\\\", var_dtype) lr_t = lr_t / (1. + decay_t * local_step) return\",\n \"replicas being used to compute a single step. As a result, using `tf.math.reduce_mean`\",\n \"creating it. Called when a variable which has an associated slot variable is\",\n \"self._slots = {} self._slot_names = [] self._weights = [] self._iterations = None #\",\n \"both computing the gradients and applying them to the variables. If you want\",\n \"### Slots Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage\",\n \"variable with a restoring initializer. No new variables are created when graph building.\",\n \"being used to compute a single step. As a result, using `tf.math.reduce_mean` will\",\n \"which the gradient is nonzero. Indices may be repeated. Returns: An `Operation` which\",\n \"new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta)\",\n \"get_slot_names(self): \\\"\\\"\\\"A list of names for this optimizer's slots.\\\"\\\"\\\" return self._slot_names def add_slot(self,\",\n \"not for this case). Deferring is mostly harmless # (aside from double initialization),\",\n \"callable.\\\"\\\"\\\" return param() if callable(param) else param def _resource_apply_dense(self, grad, handle): \\\"\\\"\\\"Add ops\",\n \"weight shape \\\" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None,\",\n \"2 of class Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__\",\n \"not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value):\",\n \"important when graph building for the case when a slot variable has already\",\n \"Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be\",\n \"= self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)):\",\n \"Args: var: the variable. Returns: the unique name of the variable. \\\"\\\"\\\" #\",\n \"as `Adam` and `Adagrad` allocate and manage additional variables associated with the variables\",\n \"or callables. If they are callable, the callable will be called during `apply_gradients()`\",\n \"lr_t @abc.abstractmethod def get_config(self): \\\"\\\"\\\"Returns the config of the optimimizer. An optimizer config\",\n \"this overhead. Args: grad: a `Tensor` representing the gradient for the affected indices.\",\n \"Generally we'd like to eagerly create/restore slot variables # when possible, but this\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"the optimizer subclass constructor (the `__init__` method), and then passed to `self._set_hyper()`. They\",\n \"objects to update to minimize `loss`, or a callable returning the list or\",\n \"def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True def get_config(self): # TODO(allenl): Save and\",\n \"are invalid, or var_list is None. \\\"\\\"\\\" # TODO(josh11b): Test that we handle\",\n \"return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): \\\"\\\"\\\"Overridden to support hyperparameter\",\n \"created slot variable's value.\\\"\\\"\\\" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, [])\",\n \"restore # UID in case slot variables have their own dependencies, in which\",\n \"a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars =\",\n \"the first time `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient\",\n \"compatibility with Keras optimizers. if name == \\\"lr\\\": name = \\\"learning_rate\\\" if hasattr(self,\",\n \"optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This\",\n \"variables. Generally you should be able to use the _set_hyper()/state.get_hyper() facility instead. This\",\n \"+ str(k)) # checks that all keyword arguments are non-negative. if kwargs[k] <\",\n \"return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \\\"\\\"\\\"Call the function if\",\n \"Returns: An `Operation` which updates the value of the variable. \\\"\\\"\\\" summed_grad, unique_indices\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"Called when a variable which has an associated slot variable is created or\",\n \"save the slot variable if the optimizer is saved without the non-slot #\",\n \"the var shared name. In eager mode the name is derived from the\",\n \"function not implemented). \\\"\\\"\\\" params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params)\",\n \"[v for v in valid_dtypes])) def _valid_dtypes(self): \\\"\\\"\\\"Valid types for loss, variables and\",\n \"to a dependency graph (causing us to realize that the slot variable needs\",\n \"True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization,\",\n \"else: return update_op update_ops = [] with backend.name_scope(name or self._name): for grad, var\",\n \"Optimizer based on the order created.\\\"\\\"\\\" return self._weights def get_weights(self): params = self.weights\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of\",\n \"if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get the slot key\",\n \"\\\" not compatible with \\\" \\\"provided weight shape \\\" + str(w.shape)) weight_value_tuples.append((p, w))\",\n \"If we've either made this slot variable, or if we've pulled out an\",\n \"synchronization if necessary. ### Slots Many optimizer subclasses, such as `Adam` and `Adagrad`\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore the Optimizer's\",\n \"# `loss` is a callable that takes no argument and returns the value\",\n \"\\\"\\\"\\\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self,\",\n \"ops to the graph. This method is nonetheless important when graph building for\",\n \"and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an active variable\",\n \"\\\"clipnorm\\\" in kwargs: self.clipnorm = kwargs.pop(\\\"clipnorm\\\") if \\\"clipvalue\\\" in kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\")\",\n \"dependency hypergraph with edges of the form (optimizer, non-slot # variable, variable)). So\",\n \"sum of `values` slices associated with each unique index. \\\"\\\"\\\" unique_indices, new_index_positions =\",\n \"tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda:\",\n \"handle: a `Tensor` of dtype `resource` which points to the variable to be\",\n \"`tf.GradientTape` and calls `apply_gradients()`. If you want to process the gradient before applying\",\n \"that all keyword arguments are non-negative. if kwargs[k] < 0: raise ValueError(\\\"Expected {}\",\n \"either express or implied. # See the License for the specific language governing\",\n \"built-in training or evaluation loops. See the `reduction` argument of your loss which\",\n \"'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv\",\n \"not compatible with \\\" \\\"provided weight shape \\\" + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples)\",\n \"should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer\",\n \"the License. # ============================================================================== \\\"\\\"\\\"Version 2 of class Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name from\",\n \"var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list):\",\n \"number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable)\",\n \"following methods: - resource_apply_dense (update variable given gradient tensor is dense) - resource_apply_sparse\",\n \"graph mode or any of the update ops are # symbolic then the\",\n \"must be called by the constructors of subclasses. Note that Optimizer instances should\",\n \"rate. `lr` is included for backward compatibility, recommended to use `learning_rate` instead. Raises:\",\n \"checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore a slot variable's value, possibly\",\n \"and `summed_values` contains the sum of `values` slices associated with each unique index.\",\n \"of learning rate. `lr` is included for backward compatibility, recommended to use `learning_rate`\",\n \"gradients, you should use `tf.math.reduce_sum` to add up your per-example losses and then\",\n \"filtered = tuple(filtered) if not filtered: raise ValueError(\\\"No gradients provided for any variable:\",\n \"optimimizer. An optimizer config is a Python dictionary (serializable) containing the configuration of\",\n \"don't _track_ slot variables anywhere, and # instead special-case this dependency and otherwise\",\n \"from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from\",\n \"than `Variable` objects. ValueError: If some arguments are invalid, or var_list is None.\",\n \"the variables in `var_list`. If `global_step` was not `None`, that operation also increments\",\n \"else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError(\\\"No gradients provided\",\n \"for `loss`. Returns: A list of (gradient, variable) pairs. Variable is always present,\",\n \"`Optimizer`'s slot to restore into. variable: The variable object this slot is being\",\n \"distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={\\\"name\\\": name}) def _distributed_apply(self, distribution, grads_and_vars, name): \\\"\\\"\\\"`apply_gradients` using a\",\n \"raise NotImplementedError( \\\"Restoring functional Optimzers from SavedModels is not currently \\\" \\\"supported. Please\",\n \"dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors\",\n \"slot value. This can be useful if you want to log debug a\",\n \"shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \\\"\\\"\\\"Variable. The number\",\n \"= tuple(filtered) if not filtered: raise ValueError(\\\"No gradients provided for any variable: %s.\\\"\",\n \"\\\"Cannot use a constraint function on a sparse variable.\\\") return self._resource_apply_sparse_duplicate_indices( grad.values, var,\",\n \"nonzero. Indices are unique. Returns: An `Operation` which updates the value of the\",\n \"tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking\",\n \"with the variables to train. These are called <i>Slots</i>. Slots have names and\",\n \"the License. # You may obtain a copy of the License at #\",\n \"first time when `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient\",\n \"no gradient for the given variable. Args: loss: A callable taking no arguments\",\n \"the number of replicas being used to compute a single step. As a\",\n \"of variables. Returns: List of gradient tensors. Raises: ValueError: In case any gradient\",\n \"factor equal to the number of replicas being used to compute a single\",\n \"is None: self._iterations = self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations\",\n \"assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self,\",\n \"object iself (e.g. through `apply_gradients`). \\\"\\\"\\\" # TODO(allenl): Make the restored optimizer functional\",\n \"_set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible. Args: name: A non-empty\",\n \"self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): \\\"\\\"\\\"Variable.\",\n \"of length \\\" + str(len(weights)) + \\\", but the optimizer was expecting \\\"\",\n \"to catch # `variable` also catch its eagerly created slot variable # unintentionally\",\n \"in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of tuples\",\n \"dependency on # a slot variable if not for this case). Deferring is\",\n \"non-empty string. The name to use for accumulators created for the optimizer. **kwargs:\",\n \"the returned operation. Default to the name passed to the `Optimizer` constructor. Returns:\",\n \"under the License. # ============================================================================== \\\"\\\"\\\"Version 2 of class Optimizer.\\\"\\\"\\\" # pylint: disable=g-bad-name\",\n \"super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \\\"\\\"\\\"A list of names for this optimizer's slots.\\\"\\\"\\\"\",\n \"grads ] return grads def apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients to variables. This\",\n \"applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional name for\",\n \"create_slots (if your optimizer algorithm requires additional variables) - get_config (serialization of the\",\n \"raise ValueError(\\\"Invalid type %r for %s, expected: %s.\\\" % (dtype, t.name, [v for\",\n \"of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of `indices` and `summed_values`\",\n \"and gradients. Subclasses should override to allow other float types. Returns: Valid types\",\n \"grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list,\",\n \"= weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name):\",\n \"from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values` associated with any non-unique\",\n \"tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model\",\n \"for a hyperparameter. Returns: An optimizer instance. \\\"\\\"\\\" if \\\"lr\\\" in config: config[\\\"learning_rate\\\"]\",\n \"tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True def\",\n \"loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list,\",\n \"non-slot # variable, or if the non-slot variable is saved without the optimizer;\",\n \"or `tf.keras.losses.Reduction.SUM` for not. If you are not using these and you want\",\n \"the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes\",\n \"Variable is always present, but gradient can be `None`. Raises: TypeError: If `var_list`\",\n \"created.\\\"\\\"\\\" return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe\",\n \"by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__(\\\"_RestoredOptimizer\\\") self._hypers_created = True\",\n \"out under a graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): #\",\n \"Keras optimizers respect variable constraints. If constraint function is passed to any variable,\",\n \"= slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer\",\n \"the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm`\",\n \"from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from\",\n \"in config: if isinstance(config[\\\"learning_rate\\\"], dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config) def\",\n \"restored from a SavedModel. These variables may be referenced in functions along with\",\n \"in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): \\\"\\\"\\\"A list of\",\n \"to `self._set_hyper()`. They can be either regular Python values (like 1.0), tensors, or\",\n \"= self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def\",\n \"`Operation` which updates the value of the variable. \\\"\\\"\\\" summed_grad, unique_indices = _deduplicate_indexed_slices(\",\n \"\\\"optimizer\\\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper\",\n \"object to be restored. slot_name: The name of this `Optimizer`'s slot to restore\",\n \"in gradients that can be many times too big. ### Variable Constraint All\",\n \"its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer\",\n \"_valid_dtypes(self): \\\"\\\"\\\"Valid types for loss, variables and gradients. Subclasses should override to allow\",\n \"it uses. Once you have a slot name you can ask the optimizer\",\n \"raise ValueError( \\\"You called `set_weights(weights)` on optimizer \\\" + self._name + \\\" with\",\n \"it. Called when a variable which has an associated slot variable is created\",\n \"\\\" \\\"gradient defined (i.e. are differentiable). \\\" \\\"Common ops without gradient: \\\" \\\"K.argmax,\",\n \"= t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError(\\\"Invalid type %r for %s,\",\n \"thread-compatible. Args: name: A non-empty string. The name to use for accumulators created\",\n \"functional Optimzers from SavedModels is not currently \\\" \\\"supported. Please file a feature\",\n \"you use `tf.keras` built-in training or evaluation loops. See the `reduction` argument of\",\n \"grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients to variables. This is the second part of `minimize()`.\",\n \"- get_config (serialization of the optimizer, include all hyper parameters) \\\"\\\"\\\" def __init__(self,\",\n \"]) return grads_and_vars def get_gradients(self, loss, params): \\\"\\\"\\\"Returns gradients of `loss` with respect\",\n \"slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an active variable creator\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"you should use `tf.math.reduce_sum` to add up your per-example losses and then divide\",\n \"type. \\\"\\\"\\\" valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if\",\n \"for the variables in `var_list`. This is the first part of `minimize()`. It\",\n \"`Operation` which updates the value of the variable. \\\"\\\"\\\" raise NotImplementedError() def _resource_scatter_add(self,\",\n \"ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads,\",\n \"for this case). Deferring is mostly harmless # (aside from double initialization), and\",\n \"Calling `minimize()` takes care of both computing the gradients and applying them to\",\n \"minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning\",\n \"if len(params) != len(weights): raise ValueError( \\\"You called `set_weights(weights)` on optimizer \\\" +\",\n \"process the gradients before applying them you can instead use the optimizer in\",\n \"name passed to the `Optimizer` constructor. Returns: An `Operation` that applies the specified\",\n \"var_list as callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model =\",\n \"specified trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: # Set trainable to be false when\",\n \"name=None): \\\"\\\"\\\"Apply gradients to variables. This is the second part of `minimize()`. It\",\n \"for gv in grads_and_vars] # Ask the optimizer to apply the capped gradients.\",\n \"when `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed for\",\n \"def _restore_slot_variable(self, slot_name, variable, slot_variable): \\\"\\\"\\\"Restore a newly created slot variable's value.\\\"\\\"\\\" variable_key\",\n \"grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars =\",\n \"that the slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object\",\n \"the slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating\",\n \"logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util\",\n \"slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot\",\n \"raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return\",\n \"print_function import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx\",\n \"def variables(self): \\\"\\\"\\\"Returns variables of this Optimizer based on the order created.\\\"\\\"\\\" return\",\n \"ValueError(\\\"Invalid type %r for %s, expected: %s.\\\" % (dtype, t.name, [v for v\",\n \"for non-trainable variables. \\\" \\\"You have specified trainable=True and \\\" \\\"synchronization=VariableSynchronization.ON_READ.\\\") else: #\",\n \"var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ###\",\n \"looking up slots. In graph mode the name is derived from the var\",\n \"of names for this optimizer's slots.\\\"\\\"\\\" return self._slot_names def add_slot(self, var, slot_name, initializer=\\\"zeros\\\"):\",\n \"`clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by value, `decay`\",\n \"Please file a feature request if this limitation bothers \\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\",\",\n \"create your own optimization algorithm, simply inherit from this class and override the\",\n \"is derived from the var unique id. If distribution strategy exists, get the\",\n \"# update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write\",\n \"grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to `handle`, with repeated\",\n \"be false when variable is to be synced on read. trainable = False\",\n \"we don't _track_ slot variables anywhere, and # instead special-case this dependency and\",\n \"<i>Slots</i>. Slots have names and you can ask the optimizer for the names\",\n \"needs to perform synchronization if necessary. ### Slots Many optimizer subclasses, such as\",\n \"not thread-safe. The user needs to perform synchronization if necessary. ### Slots Many\",\n \"= gradients.gradients(loss, params) for grad, param in zip(grads, params): if grad is None:\",\n \"handle): \\\"\\\"\\\"Add ops to apply dense gradients to the variable `handle`. Args: grad:\",\n \"slot_variable): \\\"\\\"\\\"Restore a newly created slot variable's value.\\\"\\\"\\\" variable_key = _var_key(variable) deferred_restorations =\",\n \"we create the slot variable with a restoring initializer. No new variables are\",\n \"variable.\\\"\\\"\\\" if isinstance(var, ops.Tensor): raise NotImplementedError(\\\"Trying to update a Tensor \\\", var) if\",\n \"Note that Optimizer instances should not bind to a single graph, and so\",\n \"# it's a dependency hypergraph with edges of the form (optimizer, non-slot #\",\n \"it exists. if getattr(var, \\\"_distributed_container\\\", None) is not None: var = var._distributed_container() if\",\n \"received: {}\\\".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper = {} #\",\n \"self._get_hyper(\\\"learning_rate\\\", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype)\",\n \"the returned operation. Returns: An Operation that updates the variables in `var_list`. If\",\n \"unique name of the variable. \\\"\\\"\\\" # pylint: disable=protected-access # Get the distributed\",\n \"Reserved. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"any one object (because we don't want to # save the slot variable\",\n \"= [] self._weights = [] self._iterations = None # For implementing Trackable. Stores\",\n \"grads def apply_gradients(self, grads_and_vars, name=None): \\\"\\\"\\\"Apply gradients to variables. This is the second\",\n \"variable object this slot is being created for. \\\"\\\"\\\" variable_key = _var_key(variable) slot_dict\",\n \"# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under\",\n \"able to use the _set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible.\",\n \"_set_hyper(self, name, value): \\\"\\\"\\\"set hyper `name` to value. value can be callable, tensor,\",\n \"initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy =\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"instead of construction time. Examples include 1) sequential models without input shape pre-defined,\",\n \"not `None`, that operation also increments `global_step`. Raises: ValueError: If some of the\",\n \"dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value\",\n \"function. Args: loss: A callable taking no arguments which returns the value to\",\n \"iterations(self, variable): if self._iterations is not None: raise RuntimeError(\\\"Cannot set `iterations` to a\",\n \"if g is not None and v.dtype != dtypes.resource ]) return grads_and_vars def\",\n \"a new Variable after \\\" \\\"the Optimizer weights have been created\\\") self._iterations =\",\n \"reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): \\\"\\\"\\\"Restore\",\n \"{} self._slot_names = [] self._weights = [] self._iterations = None # For implementing\",\n \"K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if\",\n \"kwargs: if k not in allowed_kwargs: raise TypeError(\\\"Unexpected keyword argument \\\" \\\"passed to\",\n \"train. These are called <i>Slots</i>. Slots have names and you can ask the\",\n \"an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name:\",\n \"indices. handle: a `Tensor` of dtype `resource` which points to the variable to\",\n \"dict): config[\\\"learning_rate\\\"] = learning_rate_schedule.deserialize( config[\\\"learning_rate\\\"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): \\\"\\\"\\\"Serialize a\",\n \"callable, or Tensor.\\\"\\\"\\\" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value):\",\n \"Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients\",\n \"with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default the\",\n \"if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not\",\n \"variable is saved without the optimizer; # it's a dependency hypergraph with edges\",\n \"optimizer is currently thread compatible, not thread-safe. The user needs to perform synchronization\",\n \"variable is created or restored. When executing eagerly, we create the slot variable\",\n \"`Adam` and `Adagrad` allocate and manage additional variables associated with the variables to\",\n \"name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): \\\"\\\"\\\"Compute gradients of `loss` for the variables\",\n \"slot_variable is not None: # If we've either made this slot variable, or\",\n \"Subclasses should override to allow other float types. Returns: Valid types for loss,\",\n \"callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): \\\"\\\"\\\"Returns variables\",\n \"callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden,\",\n \"the value of the variable. \\\"\\\"\\\" summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return\",\n \"allocate and manage additional variables associated with the variables to train. These are\",\n \"variable given gradient tensor is sparse) - create_slots (if your optimizer algorithm requires\",\n \"also catch its eagerly created slot variable # unintentionally (specifically make_template would add\",\n \"is the gradient for \\\"variable\\\". Note that \\\"gradient\\\" can be a `Tensor`, an\",\n \"to realize that the slot variable needs to be restored). Args: slot_variable_position: A\",\n \"updated. Returns: An `Operation` which updates the value of the variable. \\\"\\\"\\\" raise\",\n \"as a function used for a hyperparameter. Returns: An optimizer instance. \\\"\\\"\\\" if\",\n \"list or tuple of `Variable` objects to update to minimize `loss`, or a\",\n \"from __future__ import print_function import abc import functools import six from tensorflow.python.distribute import\",\n \"Returns: A list of (gradient, variable) pairs. Variable is always present, but gradient\",\n \"bothers \\\" \\\"you.\\\") revived_types.register_revived_type( \\\"optimizer\\\", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(),\",\n \"hasattr(self, \\\"clipnorm\\\"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, \\\"clipvalue\\\"):\",\n \"if we've pulled out an # existing slot variable, we should restore it.\",\n \"\\\"\\\"\\\"Overridden to support hyperparameter access.\\\"\\\"\\\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e:\",\n \"= self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): \\\"\\\"\\\"Creates an optimizer from\",\n \"points to the variable to be updated. indices: a `Tensor` of integral type\",\n \"]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): \\\"\\\"\\\"set hyper `name` to value. value\",\n \"variable first. Args: var: the variable. Returns: the unique name of the variable.\",\n \"is a de-duplicated version of `indices` and `summed_values` contains the sum of `values`\",\n \"backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list\",\n \"is an active variable creator # scope. Generally we'd like to eagerly create/restore\",\n \"self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor,\",\n \"= {} # dict: {variable name : {slot name : variable}} self._slots =\",\n \"= \\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2,\",\n \"\\\"the Optimizer weights have been created\\\") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype):\",\n \"is not currently \\\" \\\"supported. Please file a feature request if this limitation\",\n \"import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as\",\n \"graph building for the case when a slot variable has already been created\",\n \"existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make\",\n \"\\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return\",\n \"and then passed to `self._set_hyper()`. They can be either regular Python values (like\",\n \"callable that takes no argument and returns the value # to minimize. loss\",\n \"\\\"\\\"\\\" raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse\",\n \"Returns: An optimizer instance. \\\"\\\"\\\" if \\\"lr\\\" in config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if\",\n \"global batch size. Note that when using `tf.distribute.Strategy`, the first component of a\",\n \"mode, returns op that minimizes the loss by updating the listed # variables.\",\n \"kwargs: self.clipvalue = kwargs.pop(\\\"clipvalue\\\") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None):\",\n \"`var_list`. This is the first part of `minimize()`. It returns a list of\",\n \"instantiating the same optimizer from the config dictionary. Arguments: config: A Python dictionary,\",\n \"# TODO(allenl): Make the restored optimizer functional by tracing its apply # methods.\",\n \"keep Tensors as member variables. Generally you should be able to use the\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"ValueError: If some arguments are invalid, or var_list is None. \\\"\\\"\\\" # TODO(josh11b):\",\n \"which returns the value to minimize. var_list: list or tuple of `Variable` objects\",\n \"= [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var,\",\n \"If gradient is sparse tensor, variable constraint is not supported. ### Thread Compatibility\",\n \"reinstantiated later (without any saved state) from this configuration. Returns: Python dictionary. \\\"\\\"\\\"\",\n \"a `Tensor` representing the gradient for the affected indices. handle: a `Tensor` of\",\n \"if necessary. ### Slots Many optimizer subclasses, such as `Adam` and `Adagrad` allocate\",\n \"NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): \\\"\\\"\\\"Add ops to apply sparse gradients to\",\n \"`variable` also catch its eagerly created slot variable # unintentionally (specifically make_template would\",\n \"would add a dependency on # a slot variable if not for this\",\n \"self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not None: raise RuntimeError(\\\"Cannot set\",\n \"(ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value)\",\n \"same optimizer from the config dictionary. Arguments: config: A Python dictionary, typically the\",\n \"unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export(\\\"keras.optimizers.Optimizer\\\") class OptimizerV2(trackable.Trackable): \\\"\\\"\\\"Updated base class for optimizers. This class defines\",\n \"Args: loss: A callable taking no arguments which returns the value to minimize.\",\n \"Arguments: loss: Loss tensor. params: List of variables. Returns: List of gradient tensors.\",\n \"(aside from double initialization), and makes variable creator scopes # behave the same\",\n \"may not use this file except in compliance with the License. # You\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"these and you want to average gradients, you should use `tf.math.reduce_sum` to add\",\n \"... } self._deferred_slot_restorations = {} decay = kwargs.pop(\\\"decay\\\", 0.0) if decay < 0.:\",\n \"a `DistributionStrategy`.\\\"\\\"\\\" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v\",\n \"pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError(\\\"Optimizer\",\n \"restore into. variable: The variable object this slot is being created for. \\\"\\\"\\\"\",\n \"If name is malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars.\",\n \"if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] = self.clipvalue return\",\n \"loss, var_list, grad_loss=None, name=None): \\\"\\\"\\\"Minimize `loss` by updating `var_list`. This method simply computes\",\n \"# UID in case slot variables have their own dependencies, in which case\",\n \"are created when the model is first called, instead of construction time. Examples\",\n \"when using `tf.distribute.Strategy`, the first component of a tensor's shape is the *replica-local*\",\n \"models. Pass var_list as callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1)\",\n \"you should be able to use the _set_hyper()/state.get_hyper() facility instead. This class in\",\n \"(gradient, variable). Do whatever you # need to the 'gradient' part, for example\",\n \"self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if\",\n \"one with the highest restore # UID in case slot variables have their\",\n \"\\\"\\\"\\\"Get decayed learning rate as a Tensor with dtype=var_dtype.\\\"\\\"\\\" lr_t = self._get_hyper(\\\"learning_rate\\\", var_dtype)\",\n \"all hyper parameters) \\\"\\\"\\\" def __init__(self, name, **kwargs): \\\"\\\"\\\"Create a new Optimizer. This\",\n \"[dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): \\\"\\\"\\\"Call the function if param is\",\n \"may be repeated. Returns: An `Operation` which updates the value of the variable.\",\n \"with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that\",\n \"not `Variable` objects. \\\"\\\"\\\" grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name)\",\n \"(e.g. if gradient function not implemented). \\\"\\\"\\\" params = nest.flatten(params) with backend.get_graph().as_default(): grads\",\n \"optimizer instance. \\\"\\\"\\\" if \\\"lr\\\" in config: config[\\\"learning_rate\\\"] = config.pop(\\\"lr\\\") if \\\"learning_rate\\\" in\",\n \"`apply_gradients()` explicitly instead of using this function. Args: loss: A callable taking no\",\n \"the slot value. This can be useful if you want to log debug\",\n \"class in stateful and thread-compatible. Args: name: A non-empty string. The name to\",\n \"name, value): \\\"\\\"\\\"Override setattr to support dynamic hyperparameter setting.\\\"\\\"\\\" # Backwards compatibility with\",\n \"{\\\"name\\\": self._name} if hasattr(self, \\\"clipnorm\\\"): config[\\\"clipnorm\\\"] = self.clipnorm if hasattr(self, \\\"clipvalue\\\"): config[\\\"clipvalue\\\"] =\",\n \"learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self):\",\n \"any variable: %s.\\\" % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning(\",\n \"has already been created but `variable` has just been added to a dependency\",\n \"\\\"\\\"\\\"Variable. The number of training steps this Optimizer has run.\\\"\\\"\\\" if self._iterations is\",\n \"i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i,\",\n \"# --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): \\\"\\\"\\\"Restore a newly created slot variable's\",\n \"tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): \\\"\\\"\\\"Sums `values` associated\",\n \"of (gradient, variable) pairs. name: Optional name for the returned operation. Default to\",\n \"didn't make the slot variable. Defer restoring until it gets created # normally.\",\n \"None: raise ValueError(\\\"Variable {} has `None` for gradient. \\\" \\\"Please make sure that\",\n \"the graph. This method is nonetheless important when graph building for the case\",\n \"the restored optimizer functional by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer,\",\n \"variable): \\\"\\\"\\\"Restore a slot variable's value, possibly creating it. Called when a variable\",\n \"backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors are all valid types (see\",\n \"override this method to avoid the overhead of summing. Args: grad: a `Tensor`\",\n \"accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`,\",\n \"gradient is nonzero. Indices are unique. Returns: An `Operation` which updates the value\",\n \"access.\\\"\\\"\\\" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to avoid\",\n \"update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for\",\n \"when graph building. Instead, _restore_slot_variable catches these after normal creation and adds restore\",\n \"method has been de-duplicated. Optimizers which deal correctly with non-unique indices may instead\",\n \"gradients, you divide your loss by the global batch size, which is done\",\n \"# a slot variable if not for this case). Deferring is mostly harmless\",\n \"grads] if hasattr(self, \\\"clipvalue\\\"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in\",\n \"overwritten through user code: Example: ```python # Create an optimizer with the desired\",\n \"resource_apply_dense (update variable given gradient tensor is dense) - resource_apply_sparse (update variable given\",\n \"\\\"\\\"\\\"Valid types for loss, variables and gradients. Subclasses should override to allow other\",\n \"trainable to be false when variable is to be synced on read. trainable\",\n \"Processing gradients before applying them. Calling `minimize()` takes care of both computing the\",\n \"* var1 + 2 * var2 * var2 # In graph mode, returns\",\n \"tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops\",\n \"has run.\\\"\\\"\\\" if self._iterations is None: self._iterations = self.add_weight( \\\"iter\\\", shape=[], dtype=dtypes.int64, trainable=False,\",\n \"], ... }, # ... } self._deferred_slot_restorations = {} decay = kwargs.pop(\\\"decay\\\", 0.0)\",\n \"step. As a result, using `tf.math.reduce_mean` will give the wrong answer, resulting in\",\n \"when the variable list would otherwise be incomplete before `minimize` and the variables\",\n \"tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads\",\n \"steps this Optimizer has run.\\\"\\\"\\\" if self._iterations is None: self._iterations = self.add_weight( \\\"iter\\\",\",\n \"a slot variable has already been created but `variable` has just been added\",\n \"\\\" \\\"Common ops without gradient: \\\" \\\"K.argmax, K.round, K.eval.\\\".format(param)) if hasattr(self, \\\"clipnorm\\\"): grads\",\n \"== \\\"lr\\\": name = \\\"learning_rate\\\" if hasattr(self, \\\"_hyper\\\") and name in self._hyper: self._set_hyper(name,\",\n \"variables of this Optimizer based on the order created.\\\"\\\"\\\" return self._weights @property def\",\n \"var_dtype) lr_t = lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod\",\n \"used to create this optimizer, such as a function used for a hyperparameter.\",\n \"= dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization ==\",\n \"tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2\",\n \"based on the order created.\\\"\\\"\\\" return self._weights def get_weights(self): params = self.weights return\",\n \"yet been created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ...\",\n \"var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): \\\"\\\"\\\"Get\",\n \"List of (gradient, variable) pairs. name: Optional name for the returned operation. Default\",\n \"variable's value, possibly creating it. Called when a variable which has an associated\",\n \"opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and\",\n \"2) subclassed models. Pass var_list as callable in these cases. Example: ```python opt\",\n \"variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation)\",\n \"+ var.op.name) with backend.name_scope(\\\"update\\\" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic\",\n \"need to the 'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]),\",\n \"aware, which means it automatically sums gradients across all replicas. To average gradients,\",\n \"grads_and_vars if g is not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)]\",\n \"A Python dictionary, typically the output of get_config. custom_objects: A Python dictionary mapping\",\n \"a Tensor \\\", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise\",\n \"grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars\",\n \"from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from\",\n \"i, v)]): return x.value() # --------------- # For implementing the trackable interface #\",\n \"def _assert_valid_dtypes(self, tensors): \\\"\\\"\\\"Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors:\",\n \"with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name or\"\n]","isConversational":false}}},{"rowIdx":1179,"cells":{"ngram":{"kind":"list like","value":["<PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert","url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert resultado.status_code ==","= { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes =","'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado =","requests headers = { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos'","<filename>escola/teste_get.py import requests headers = { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos","'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado","headers = { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes","'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers)","= 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert resultado.status_code == 200","'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos,","} url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert resultado.status_code","{ 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes'","import requests headers = { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos ="],"string":"[\n \"<PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert\",\n \"url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert resultado.status_code ==\",\n \"= { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes =\",\n \"'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado =\",\n \"requests headers = { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos'\",\n \"<filename>escola/teste_get.py import requests headers = { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos\",\n \"'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado\",\n \"headers = { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes\",\n \"'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers)\",\n \"= 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert resultado.status_code == 200\",\n \"'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos,\",\n \"} url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert resultado.status_code\",\n \"{ 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes'\",\n \"import requests headers = { 'content-type': 'application/json', 'Authorization': 'Token <PASSWORD>' } url_base_cursos =\"\n]","isConversational":false}}},{"rowIdx":1180,"cells":{"ngram":{"kind":"list like","value":["a rule to parse body as xml for requests with /broker/xml uri xml_rule","some specific rules for crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group) if group_id","\"description\": \"WAF will buffer the whole request body first and then release to","\"allowlist-start-with-ice\", \"description\": \"WAF will buffer the whole request body first and then release","Copyright 2021 VMware, Inc. import argparse import json import re import logging import","pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"] = True return if pre_crs[\"index\"] > index: index =","'--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller ip') args = parser.parse_args() if args.password:","WAF policy successfully') else: logger.error('Error : %s' % resp.text) if __name__ == '__main__':","groupid = int(match.group(\"group_id\")) assert groupid == 0 or 100 <= groupid <= 999,","== \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"] = True return if pre_crs[\"index\"] > index:","get_crs(api): tested_crs = \"CRS-2021-1\" resp = api.get(\"wafcrs?name=\" + tested_crs) if resp.status_code not in","\"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": { \"path\": { \"match_case\": \"SENSITIVE\", \"match_str\": [ \"/ice/\"","if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\", help='controller user', default='admin')","] pre_crs_group = { \"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule } index =","argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\"store\", help='controller user password',","as xml for requests with /broker/xml uri xml_rule = [ { \"index\": 0,","ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken,","parsed as XML in WAF\", \"rule\": \"SecRule REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule","VDI, client wants to stream data between client and server for some URLs","= api.get(\"wafcrs?name=\" + tested_crs) if resp.status_code not in range(200, 300): if resp.status_code ==","0, \"name\": \"enforce XML parsing for /broker/xml\", \"description\": \"Clients often send the wrong","assert match, \"can not extract group id from group '{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\"))","'--password', action=\"store\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\", help='tenant name', default='admin') parser.add_argument('-a',","add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF","POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group = {","parse body as xml for requests with /broker/xml uri xml_rule = [ {","\"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule } index = 0 if \"pre_crs_groups\" not","not in range(200, 300): if resp.status_code == 404: logger.error(\"Controller does not have CRS","rules and some specific rules for crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group)","\"description\": \"Clients often send the wrong Content-Type header. We ignore the header and","specific rules for crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group) if group_id >=","request body first and then release to backend. With VDI, client wants to","rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index = rule[\"index\"] allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule)","None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\"results\"] return waf_crs[0] def create_vdi_waf_policy(api,","VMware, Inc. import argparse import json import re import logging import os import","to allow request with uri beginning with /ice/ allowlist_rule={ \"index\": 0, \"name\": \"allowlist-start-with-ice\",","300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) if __name__","data between client and server for some URLs like /ice/..., we should allow","return if pre_crs[\"index\"] > index: index = pre_crs[\"index\"] pre_crs_group[\"index\"] = index + 1","help='controller user', default='admin') parser.add_argument('-p', '--password', action=\"store\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\",","else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj)","\"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": { \"path\": { \"match_case\": \"SENSITIVE\", \"match_str\": [ \"/ice/\" ], \"match_criteria\":","action=\"store\", help='controller ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password,","tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\"results\"] return waf_crs[0]","json.loads(resp.text)[\"results\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\",","successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj)","help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication","[]): if rule[\"rule_id\"] == \"920330\" or rule[\"rule_id\"] == \"932105\": rule[\"enable\"] = False def","buffer the whole request body first and then release to backend. With VDI,","return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules for","add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with uri beginning with /ice/","== \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index = rule[\"index\"] allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def","/broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group = { \"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule","some URLs like /ice/..., we should allow these URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ],","0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule)","pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"] = True return if pre_crs[\"index\"] >","\"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs = get_crs(api) if waf_crs is None: return","'__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\"store\",","xml for requests with /broker/xml uri xml_rule = [ { \"index\": 0, \"name\":","if resp.status_code not in range(200, 300): if resp.status_code == 404: logger.error(\"Controller does not","disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF","help='controller ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant,","False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml for requests","group '{}' not in expected range\".format(group[\"name\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side","group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\"enable\"] = False for rule in","sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args) else: update_waf_policy(api, args,","We ignore the header and enforce the request body to be parsed as","import argparse import json import re import logging import os import sys from","\"path\": { \"match_case\": \"SENSITIVE\", \"match_str\": [ \"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\" } } }","id from group '{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\")) assert groupid == 0 or 100","\"920330\" or rule[\"rule_id\"] == \"932105\": rule[\"enable\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule","index = pre_crs[\"index\"] pre_crs_group[\"index\"] = index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs =","body to be parsed as XML in WAF\", \"rule\": \"SecRule REQUEST_METHOD \\\"@streq POST\\\"","password or authtokentoken must be provided.\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not","allowlist rule to allow request with uri beginning with /ice/ allowlist_rule={ \"index\": 0,","== 0 or 100 <= groupid <= 999, \"group id for group '{}'","+ 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs = \"CRS-2021-1\" resp = api.get(\"wafcrs?name=\" + tested_crs)","uri xml_rule = [ { \"index\": 0, \"name\": \"enforce XML parsing for /broker/xml\",","[]): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\"enable\"] = False for rule","waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index = rule[\"index\"] allowlist_rule[\"index\"] =","= pattern.match(group[\"name\"]) assert match, \"can not extract group id from group '{}'\".format(group[\"name\"]) groupid","is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list() for group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj)","= 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] == \"allowlist-start-with-ice\":","or rule[\"rule_id\"] == \"932105\": rule[\"enable\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to","not in expected range\".format(group[\"name\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and","rule[\"rule_id\"] == \"932105\": rule[\"enable\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to parse","/ice/..., we should allow these URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": { \"path\":","if \"pre_crs_groups\" not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list() for pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if","resp = api.get(\"wafcrs?name=\" + tested_crs) if resp.status_code not in range(200, 300): if resp.status_code","os import sys from avi.sdk.avi_api import ApiSession API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger =","API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule","URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": { \"path\": { \"match_case\": \"SENSITIVE\", \"match_str\": [","[ \"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\" } } } index = 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\",","requests with /broker/xml uri xml_rule = [ { \"index\": 0, \"name\": \"enforce XML","1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs = \"CRS-2021-1\" resp = api.get(\"wafcrs?name=\" + tested_crs) if","300): if resp.status_code == 404: logger.error(\"Controller does not have CRS %s, please install","if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api =","parser.add_argument('-t', '--tenant', action=\"store\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\"store\",","SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request","\"SENSITIVE\", \"match_str\": [ \"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\" } } } index = 0","pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"] = True","{ \"path\": { \"match_case\": \"SENSITIVE\", \"match_str\": [ \"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\" } }","range(200, 300): if resp.status_code == 404: logger.error(\"Controller does not have CRS %s, please","parsing for /broker/xml\", \"description\": \"Clients often send the wrong Content-Type header. We ignore","\"CRS-2021-1\" resp = api.get(\"wafcrs?name=\" + tested_crs) if resp.status_code not in range(200, 300): if","sys from avi.sdk.avi_api import ApiSession API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def","group '{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\")) assert groupid == 0 or 100 <= groupid","argparse import json import re import logging import os import sys from avi.sdk.avi_api","300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api,","these URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": { \"path\": { \"match_case\": \"SENSITIVE\", \"match_str\":","} } } index = 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]:","args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip,","waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list() for","api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else:","waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300):","args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION)","[ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": { \"path\": { \"match_case\": \"SENSITIVE\", \"match_str\": [ \"/ice/\" ],","with /ice/ allowlist_rule={ \"index\": 0, \"name\": \"allowlist-start-with-ice\", \"description\": \"WAF will buffer the whole","rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index = rule[\"index\"]","#add a rule to parse body as xml for requests with /broker/xml uri","{ \"index\": 0, \"name\": \"enforce XML parsing for /broker/xml\", \"description\": \"Clients often send","waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list() for group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp","json import re import logging import os import sys from avi.sdk.avi_api import ApiSession","return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\":","import re import logging import os import sys from avi.sdk.avi_api import ApiSession API_VERSION","\"name\": \"enforce XML parsing for /broker/xml\", \"description\": \"Clients often send the wrong Content-Type","return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list() for group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj)","api_version=API_VERSION) else: logging.error(\"Either password or authtokentoken must be provided.\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy',","rule[\"index\"]>index: index = rule[\"index\"] allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\")","= int(match.group(\"group_id\")) assert groupid == 0 or 100 <= groupid <= 999, \"group","\"group id for group '{}' not in expected range\".format(group[\"name\"]) return groupid def disable_crs_response_rules(waf_policy_obj):","add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300):","REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group = { \"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\",","def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with uri beginning with","logging.error(\"Either password or authtokentoken must be provided.\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if","%waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error","], \"match_criteria\": \"BEGINS_WITH\" } } } index = 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for","side rules and some specific rules for crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id =","<= groupid <= 999, \"group id for group '{}' not in expected range\".format(group[\"name\"])","help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller ip') args","the wrong Content-Type header. We ignore the header and enforce the request body","in crs_group.get(\"rules\", []): if rule[\"rule_id\"] == \"920330\" or rule[\"rule_id\"] == \"932105\": rule[\"enable\"] =","logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\"results\"] return waf_crs[0] def create_vdi_waf_policy(api, args):","for group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if","and server for some URLs like /ice/..., we should allow these URLs\", \"actions\":","#disable response side rules and some specific rules for crs_group in waf_policy_obj.get(\"crs_groups\", []):","rule[\"index\"] allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"])","100 <= groupid <= 999, \"group id for group '{}' not in expected","beginning with /ice/ allowlist_rule={ \"index\": 0, \"name\": \"allowlist-start-with-ice\", \"description\": \"WAF will buffer the","pre_crs[\"enable\"] = True return if pre_crs[\"index\"] > index: index = pre_crs[\"index\"] pre_crs_group[\"index\"] =","if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s'","return None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\"results\"] return waf_crs[0] def","\"932105\": rule[\"enable\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as","resp.status_code == 404: logger.error(\"Controller does not have CRS %s, please install first.\" %","exit(0) waf_crs = json.loads(resp.text)[\"results\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \"name\":",": %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp =","} index = 0 if \"pre_crs_groups\" not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list() for","\"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow","= index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"]) assert match,","wrong Content-Type header. We ignore the header and enforce the request body to","then release to backend. With VDI, client wants to stream data between client","} index = 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"]","or 100 <= groupid <= 999, \"group id for group '{}' not in","{ \"match_case\": \"SENSITIVE\", \"match_str\": [ \"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\" } } } index","range\".format(group[\"name\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules","for requests with /broker/xml uri xml_rule = [ { \"index\": 0, \"name\": \"enforce","logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp","/broker/xml uri xml_rule = [ { \"index\": 0, \"name\": \"enforce XML parsing for","api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user,","= re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"]) assert match, \"can not extract group id from","re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"]) assert match, \"can not extract group id from group","api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either password or authtokentoken must","crs_group.get(\"rules\", []): if rule[\"rule_id\"] == \"920330\" or rule[\"rule_id\"] == \"932105\": rule[\"enable\"] = False","in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list() for pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\":","we should allow these URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": { \"path\": {","rule[\"rule_id\"] == \"920330\" or rule[\"rule_id\"] == \"932105\": rule[\"enable\"] = False def add_pre_crs_group(waf_policy_obj): #add","+ tested_crs) if resp.status_code not in range(200, 300): if resp.status_code == 404: logger.error(\"Controller","= index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs = \"CRS-2021-1\" resp = api.get(\"wafcrs?name=\"","else: logger.error('Error : %s' % resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser()","= get_crs(api) if waf_crs is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list() for group","= get_id_from_group(crs_group) if group_id >= 950: crs_group[\"enable\"] = False for rule in crs_group.get(\"rules\",","\"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index = rule[\"index\"] allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group):","Inc. import argparse import json import re import logging import os import sys","WAF policy successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj):","args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif","for rule in crs_group.get(\"rules\", []): if rule[\"rule_id\"] == \"920330\" or rule[\"rule_id\"] == \"932105\":","rule in crs_group.get(\"rules\", []): if rule[\"rule_id\"] == \"920330\" or rule[\"rule_id\"] == \"932105\": rule[\"enable\"]","authtokentoken must be provided.\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api,","\"match\": { \"path\": { \"match_case\": \"SENSITIVE\", \"match_str\": [ \"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\" }","\"BEGINS_WITH\" } } } index = 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for rule in","add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml for requests with /broker/xml","950: crs_group[\"enable\"] = False for rule in crs_group.get(\"rules\", []): if rule[\"rule_id\"] == \"920330\"","} waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list()","resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' %","id for group '{}' not in expected range\".format(group[\"name\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable","rules for crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group) if group_id >= 950:","pre_crs_group = { \"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule } index = 0","logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args,","waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs = \"CRS-2021-1\" resp = api.get(\"wafcrs?name=\" + tested_crs) if resp.status_code","waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"]) assert match, \"can not","get_crs(api) if waf_crs is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list() for group in","'--tenant', action=\"store\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller","% resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\", help='controller","waf_crs = json.loads(resp.text)[\"results\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \"name\": SYSTEM_WAF_POLICY_VDI,","uri beginning with /ice/ allowlist_rule={ \"index\": 0, \"name\": \"allowlist-start-with-ice\", \"description\": \"WAF will buffer","password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c',","install first.\" % tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs =","== \"932105\": rule[\"enable\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body","% tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\"results\"] return","in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index = rule[\"index\"] allowlist_rule[\"index\"]","{}).setdefault(\"rules\", []) for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index:","to backend. With VDI, client wants to stream data between client and server","REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group","waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list() for pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"]","\"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"]","args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in","if rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index = rule[\"index\"] allowlist_rule[\"index\"] = index+1","== 404: logger.error(\"Controller does not have CRS %s, please install first.\" % tested_crs)","= rule[\"index\"] allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match =","range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) def","to parse body as xml for requests with /broker/xml uri xml_rule = [","resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj)","pre_crs_group[\"index\"] = index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs = \"CRS-2021-1\" resp =","must be provided.\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args)","action=\"store\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller ip')","ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either password or authtokentoken must be provided.\")","waf_policy_obj[\"pre_crs_groups\"] = list() for pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] =","CRS %s, please install first.\" % tested_crs) return None logger.error('Error : %s', resp.text)","import ApiSession API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a","tested_crs) if resp.status_code not in range(200, 300): if resp.status_code == 404: logger.error(\"Controller does","URLs like /ice/..., we should allow these URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\":","0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule } index = 0 if \"pre_crs_groups\" not in","token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either password or authtokentoken must be provided.\") sys.exit(1) waf_policy_obj =","/broker/xml\", \"description\": \"Clients often send the wrong Content-Type header. We ignore the header","group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code","%s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s'","parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller ip') args = parser.parse_args() if","if pre_crs[\"index\"] > index: index = pre_crs[\"index\"] pre_crs_group[\"index\"] = index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group)","True return if pre_crs[\"index\"] > index: index = pre_crs[\"index\"] pre_crs_group[\"index\"] = index +","xml_rule } index = 0 if \"pre_crs_groups\" not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list()","first and then release to backend. With VDI, client wants to stream data","import sys from avi.sdk.avi_api import ApiSession API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__)","import logging import os import sys from avi.sdk.avi_api import ApiSession API_VERSION = \"18.2.13\"","like /ice/..., we should allow these URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": {","{ \"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule } index = 0 if \"pre_crs_groups\"","not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list() for pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] ==","add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create","tested_crs = \"CRS-2021-1\" resp = api.get(\"wafcrs?name=\" + tested_crs) if resp.status_code not in range(200,","from group '{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\")) assert groupid == 0 or 100 <=","be provided.\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args) else:","get_id_from_group(crs_group) if group_id >= 950: crs_group[\"enable\"] = False for rule in crs_group.get(\"rules\", []):","index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs = \"CRS-2021-1\" resp = api.get(\"wafcrs?name=\" +","xml_rule pre_crs[\"enable\"] = True return if pre_crs[\"index\"] > index: index = pre_crs[\"index\"] pre_crs_group[\"index\"]","often send the wrong Content-Type header. We ignore the header and enforce the","for group '{}' not in expected range\".format(group[\"name\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response","in range(200, 300): if resp.status_code == 404: logger.error(\"Controller does not have CRS %s,","= list() for group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy',","for some URLs like /ice/..., we should allow these URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\"","parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api",">= 950: crs_group[\"enable\"] = False for rule in crs_group.get(\"rules\", []): if rule[\"rule_id\"] ==","= logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with uri","0, \"name\": \"allowlist-start-with-ice\", \"description\": \"WAF will buffer the whole request body first and","xml_rule = [ { \"index\": 0, \"name\": \"enforce XML parsing for /broker/xml\", \"description\":","pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"]) assert match, \"can not extract group id","int(match.group(\"group_id\")) assert groupid == 0 or 100 <= groupid <= 999, \"group id","False for rule in crs_group.get(\"rules\", []): if rule[\"rule_id\"] == \"920330\" or rule[\"rule_id\"] ==","does not have CRS %s, please install first.\" % tested_crs) return None logger.error('Error","header. We ignore the header and enforce the request body to be parsed","have CRS %s, please install first.\" % tested_crs) return None logger.error('Error : %s',","2021 VMware, Inc. import argparse import json import re import logging import os","header and enforce the request body to be parsed as XML in WAF\",","allow these URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": { \"path\": { \"match_case\": \"SENSITIVE\",","successfully') else: logger.error('Error : %s' % resp.text) if __name__ == '__main__': parser =","def get_crs(api): tested_crs = \"CRS-2021-1\" resp = api.get(\"wafcrs?name=\" + tested_crs) if resp.status_code not","if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"] = True return if pre_crs[\"index\"]","index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"]) assert match, \"can","= xml_rule pre_crs[\"enable\"] = True return if pre_crs[\"index\"] > index: index = pre_crs[\"index\"]","def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml for requests with","and enforce the request body to be parsed as XML in WAF\", \"rule\":","waf_policy_obj = { \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs = get_crs(api)","= { \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs = get_crs(api) if","or authtokentoken must be provided.\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj:","logger.error('Error : %s' % resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u',","= False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml for","crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\"enable\"] =","provided.\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args) else: update_waf_policy(api,","} } index = 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if","group_id >= 950: crs_group[\"enable\"] = False for rule in crs_group.get(\"rules\", []): if rule[\"rule_id\"]","XML in WAF\", \"rule\": \"SecRule REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI \\\"@streq","else: logging.error(\"Either password or authtokentoken must be provided.\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI)","api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error","logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with","match, \"can not extract group id from group '{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\")) assert","parser.add_argument('-u', '--user', action=\"store\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\"store\", help='controller user password', default='<PASSWORD>')","help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller ip') args = parser.parse_args() if args.password: api","body as xml for requests with /broker/xml uri xml_rule = [ { \"index\":","assert groupid == 0 or 100 <= groupid <= 999, \"group id for","999, \"group id for group '{}' not in expected range\".format(group[\"name\"]) return groupid def","args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either password or authtokentoken must be provided.\") sys.exit(1)","to be parsed as XML in WAF\", \"rule\": \"SecRule REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\"","waf_crs is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list() for group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group)","def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if","client wants to stream data between client and server for some URLs like","whole request body first and then release to backend. With VDI, client wants","groupid <= 999, \"group id for group '{}' not in expected range\".format(group[\"name\"]) return","= parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken:","[]) for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index","\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group = { \"index\":","disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules for crs_group in waf_policy_obj.get(\"crs_groups\",","\"name\": \"allowlist-start-with-ice\", \"description\": \"WAF will buffer the whole request body first and then","action=\"store\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken',","index = rule[\"index\"] allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match","parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip,","be parsed as XML in WAF\", \"rule\": \"SecRule REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n","#add a allowlist rule to allow request with uri beginning with /ice/ allowlist_rule={","default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller ip') args = parser.parse_args()","waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200,","% resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'],","def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules for crs_group in","in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in","'--controller_ip', action=\"store\", help='controller ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user,","logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with uri beginning","enforce the request body to be parsed as XML in WAF\", \"rule\": \"SecRule","groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules for crs_group","from avi.sdk.avi_api import ApiSession API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj):","pattern.match(group[\"name\"]) assert match, \"can not extract group id from group '{}'\".format(group[\"name\"]) groupid =","\"Clients often send the wrong Content-Type header. We ignore the header and enforce","if waf_crs is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list() for group in waf_crs[\"groups\"]:","= api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else:","0 or 100 <= groupid <= 999, \"group id for group '{}' not","groupid == 0 or 100 <= groupid <= 999, \"group id for group","\"index\": 0, \"name\": \"enforce XML parsing for /broker/xml\", \"description\": \"Clients often send the","= \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to","None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] = list() for group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj)","disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create","import os import sys from avi.sdk.avi_api import ApiSession API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger","resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy","wants to stream data between client and server for some URLs like /ice/...,","first.\" % tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\"results\"]","request body to be parsed as XML in WAF\", \"rule\": \"SecRule REQUEST_METHOD \\\"@streq","resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\", help='controller user',","if resp.status_code == 404: logger.error(\"Controller does not have CRS %s, please install first.\"","= pre_crs[\"index\"] pre_crs_group[\"index\"] = index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs = \"CRS-2021-1\"","%s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\"results\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj =","response side rules and some specific rules for crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id","list() for group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj))","avi.sdk.avi_api import ApiSession API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add","update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code","{ \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs = get_crs(api) if waf_crs","data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error :","list() for pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"]","tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either","elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either password or","} ] pre_crs_group = { \"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule } index","create_vdi_waf_policy(api, args): waf_policy_obj = { \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs","allowlist_rule={ \"index\": 0, \"name\": \"allowlist-start-with-ice\", \"description\": \"WAF will buffer the whole request body","\"rules\": xml_rule } index = 0 if \"pre_crs_groups\" not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] =","allow request with uri beginning with /ice/ allowlist_rule={ \"index\": 0, \"name\": \"allowlist-start-with-ice\", \"description\":","= [ { \"index\": 0, \"name\": \"enforce XML parsing for /broker/xml\", \"description\": \"Clients","between client and server for some URLs like /ice/..., we should allow these","if group_id >= 950: crs_group[\"enable\"] = False for rule in crs_group.get(\"rules\", []): if","\\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group = { \"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule }","index = 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] ==","in expected range\".format(group[\"name\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some","api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either password","> index: index = pre_crs[\"index\"] pre_crs_group[\"index\"] = index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api):","= { \"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule } index = 0 if","waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if","for crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\"enable\"]","stream data between client and server for some URLs like /ice/..., we should","pre_crs[\"index\"] > index: index = pre_crs[\"index\"] pre_crs_group[\"index\"] = index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def","the whole request body first and then release to backend. With VDI, client","import json import re import logging import os import sys from avi.sdk.avi_api import","\"pre_crs_groups\" not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list() for pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"]","group id from group '{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\")) assert groupid == 0 or","resp.status_code not in range(200, 300): if resp.status_code == 404: logger.error(\"Controller does not have","token') parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller ip') args = parser.parse_args() if args.password: api =","404: logger.error(\"Controller does not have CRS %s, please install first.\" % tested_crs) return","\"match_criteria\": \"BEGINS_WITH\" } } } index = 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", []) for rule","extract group id from group '{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\")) assert groupid == 0","%s, please install first.\" % tested_crs) return None logger.error('Error : %s', resp.text) exit(0)","and some specific rules for crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group) if",": %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\"results\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj","= ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant,","# Copyright 2021 VMware, Inc. import argparse import json import re import logging","if rule[\"index\"]>index: index = rule[\"index\"] allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern =","XML parsing for /broker/xml\", \"description\": \"Clients often send the wrong Content-Type header. We","should allow these URLs\", \"actions\": [ \"WAF_POLICY_WHITELIST_ACTION_ALLOW\" ], \"match\": { \"path\": { \"match_case\":","parser.add_argument('-p', '--password', action=\"store\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\", help='tenant name', default='admin')","user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token')","backend. With VDI, client wants to stream data between client and server for","With VDI, client wants to stream data between client and server for some","a allowlist rule to allow request with uri beginning with /ice/ allowlist_rule={ \"index\":","waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"] = True return if","body first and then release to backend. With VDI, client wants to stream","the request body to be parsed as XML in WAF\", \"rule\": \"SecRule REQUEST_METHOD","\"SecRule REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ]","def get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"]) assert match, \"can not extract","logger.error(\"Controller does not have CRS %s, please install first.\" % tested_crs) return None","if rule[\"rule_id\"] == \"920330\" or rule[\"rule_id\"] == \"932105\": rule[\"enable\"] = False def add_pre_crs_group(waf_policy_obj):","waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\"","\"index\": 0, \"name\": \"allowlist-start-with-ice\", \"description\": \"WAF will buffer the whole request body first","waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index = rule[\"index\"] allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern","\"can not extract group id from group '{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\")) assert groupid","[ { \"index\": 0, \"name\": \"enforce XML parsing for /broker/xml\", \"description\": \"Clients often","data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error :","\"rule\": \"SecRule REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" }","'--user', action=\"store\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\"store\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t',","def create_vdi_waf_policy(api, args): waf_policy_obj = { \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" }","<= 999, \"group id for group '{}' not in expected range\".format(group[\"name\"]) return groupid","<gh_stars>0 # Copyright 2021 VMware, Inc. import argparse import json import re import","in WAF\", \"rule\": \"SecRule REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI \\\"@streq /broker/xml\\\"","SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs = get_crs(api) if waf_crs is None:","pre_crs[\"index\"] pre_crs_group[\"index\"] = index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs = \"CRS-2021-1\" resp","range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) if","args): waf_policy_obj = { \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs =","and then release to backend. With VDI, client wants to stream data between","'{}' not in expected range\".format(group[\"name\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules","\"name\": \"VDI_409_ENFORCE_XML\", \"rules\": xml_rule } index = 0 if \"pre_crs_groups\" not in waf_policy_obj:","not have CRS %s, please install first.\" % tested_crs) return None logger.error('Error :","policy successfully') else: logger.error('Error : %s' % resp.text) if __name__ == '__main__': parser","request with uri beginning with /ice/ allowlist_rule={ \"index\": 0, \"name\": \"allowlist-start-with-ice\", \"description\": \"WAF","/ice/ allowlist_rule={ \"index\": 0, \"name\": \"allowlist-start-with-ice\", \"description\": \"WAF will buffer the whole request","\\n SecRule REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group = { \"index\": 0,","action=\"store\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\"store\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant',","crs_group[\"enable\"] = False for rule in crs_group.get(\"rules\", []): if rule[\"rule_id\"] == \"920330\" or","== \"920330\" or rule[\"rule_id\"] == \"932105\": rule[\"enable\"] = False def add_pre_crs_group(waf_policy_obj): #add a","default='admin') parser.add_argument('-p', '--password', action=\"store\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\", help='tenant name',","logging import os import sys from avi.sdk.avi_api import ApiSession API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI'","for /broker/xml\", \"description\": \"Clients often send the wrong Content-Type header. We ignore the","%s' % resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\",","\"WAF will buffer the whole request body first and then release to backend.","\"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"] = True return if pre_crs[\"index\"] > index: index","rule to allow request with uri beginning with /ice/ allowlist_rule={ \"index\": 0, \"name\":","get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"]) assert match, \"can not extract group","allowlist_rule[\"index\"] = index+1 waf_policy_obj[\"whitelist\"][\"rules\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\"[^\\d]*(?P<group_id>\\d\\d\\d)\") match = pattern.match(group[\"name\"]) assert","= list() for pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule","rule to parse body as xml for requests with /broker/xml uri xml_rule =","index: index = pre_crs[\"index\"] pre_crs_group[\"index\"] = index + 1 waf_policy_obj[\"pre_crs_groups\"].append(pre_crs_group) def get_crs(api): tested_crs","ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION)","\\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group =","= ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either password or authtokentoken must be","in waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\"enable\"] = False","= True return if pre_crs[\"index\"] > index: index = pre_crs[\"index\"] pre_crs_group[\"index\"] = index","__name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\", help='controller user', default='admin') parser.add_argument('-p',","name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\"store\", help='controller ip') args =","will buffer the whole request body first and then release to backend. With","client and server for some URLs like /ice/..., we should allow these URLs\",","== '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\", help='controller user', default='admin') parser.add_argument('-p', '--password',","waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args) else: update_waf_policy(api, args, waf_policy_obj)","add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy","in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"] = True return","waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200,","args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else:",": %s' % resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user',","= \"CRS-2021-1\" resp = api.get(\"wafcrs?name=\" + tested_crs) if resp.status_code not in range(200, 300):","ignore the header and enforce the request body to be parsed as XML","\\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group = { \"index\": 0, \"name\": \"VDI_409_ENFORCE_XML\", \"rules\":","WAF\", \"rule\": \"SecRule REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\"","resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully')","policy successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj)","args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either password or authtokentoken","tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\"Either password or authtokentoken must be provided.\") sys.exit(1) waf_policy_obj","user', default='admin') parser.add_argument('-p', '--password', action=\"store\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\", help='tenant","0 if \"pre_crs_groups\" not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list() for pre_crs in waf_policy_obj[\"pre_crs_groups\"]:","with uri beginning with /ice/ allowlist_rule={ \"index\": 0, \"name\": \"allowlist-start-with-ice\", \"description\": \"WAF will","index = 0 if \"pre_crs_groups\" not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list() for pre_crs","logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) if __name__ ==","for pre_crs in waf_policy_obj[\"pre_crs_groups\"]: if pre_crs[\"name\"] == \"VDI_409_ENFORCE_XML\": pre_crs[\"rules\"] = xml_rule pre_crs[\"enable\"] =","re import logging import os import sys from avi.sdk.avi_api import ApiSession API_VERSION =","\"enforce XML parsing for /broker/xml\", \"description\": \"Clients often send the wrong Content-Type header.","\"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\" } } } index = 0 waf_policy_obj.setdefault(\"whitelist\", {}).setdefault(\"rules\", [])","= api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully')","= False for rule in crs_group.get(\"rules\", []): if rule[\"rule_id\"] == \"920330\" or rule[\"rule_id\"]","as XML in WAF\", \"rule\": \"SecRule REQUEST_METHOD \\\"@streq POST\\\" \\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\" \\n SecRule REQUEST_URI","with /broker/xml uri xml_rule = [ { \"index\": 0, \"name\": \"enforce XML parsing","for rule in waf_policy_obj[\"whitelist\"][\"rules\"][:]: if rule[\"name\"] == \"allowlist-start-with-ice\": waf_policy_obj[\"whitelist\"][\"rules\"].remove(rule) if rule[\"index\"]>index: index =","\"/api/wafprofile?name=System-WAF-Profile\" } waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"] =","= json.loads(resp.text)[\"results\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \"name\": SYSTEM_WAF_POLICY_VDI, \"mode\":","\"name\": SYSTEM_WAF_POLICY_VDI, \"mode\": \"WAF_MODE_DETECTION_ONLY\", \"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs = get_crs(api) if waf_crs is","\"waf_profile_ref\": \"/api/wafprofile?name=System-WAF-Profile\" } waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\"waf_crs_ref\"]=\"/api/wafcrs?name=\"+waf_crs[\"name\"] waf_policy_obj[\"crs_groups\"]","match = pattern.match(group[\"name\"]) assert match, \"can not extract group id from group '{}'\".format(group[\"name\"])","'{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\")) assert groupid == 0 or 100 <= groupid <=","Content-Type header. We ignore the header and enforce the request body to be","send the wrong Content-Type header. We ignore the header and enforce the request","api.get(\"wafcrs?name=\" + tested_crs) if resp.status_code not in range(200, 300): if resp.status_code == 404:","release to backend. With VDI, client wants to stream data between client and","], \"match\": { \"path\": { \"match_case\": \"SENSITIVE\", \"match_str\": [ \"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\"","waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\"enable\"] = False for","ApiSession API_VERSION = \"18.2.13\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist","= argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\"store\", help='controller user","not extract group id from group '{}'\".format(group[\"name\"]) groupid = int(match.group(\"group_id\")) assert groupid ==","please install first.\" % tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs","parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\"store\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\"store\", help='controller","\"match_str\": [ \"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\" } } } index = 0 waf_policy_obj.setdefault(\"whitelist\",","the header and enforce the request body to be parsed as XML in","default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\"store\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip',","= 0 if \"pre_crs_groups\" not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"] = list() for pre_crs in","rule[\"enable\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml","\"VDI_409_ENFORCE_XML\", \"rules\": xml_rule } index = 0 if \"pre_crs_groups\" not in waf_policy_obj: waf_policy_obj[\"pre_crs_groups\"]","to stream data between client and server for some URLs like /ice/..., we","in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text)","\"match_case\": \"SENSITIVE\", \"match_str\": [ \"/ice/\" ], \"match_criteria\": \"BEGINS_WITH\" } } } index =","server for some URLs like /ice/..., we should allow these URLs\", \"actions\": [","resp.text) exit(0) waf_crs = json.loads(resp.text)[\"results\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = {","SecRule REQUEST_URI \\\"@streq /broker/xml\\\" \\\"t:none,ctl:requestBodyProcessor=XML\\\"\" } ] pre_crs_group = { \"index\": 0, \"name\":","expected range\".format(group[\"name\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific","waf_policy_obj[\"crs_groups\"] = list() for group in waf_crs[\"groups\"]: waf_policy_obj[\"crs_groups\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp ="],"string":"[\n \"a rule to parse body as xml for requests with /broker/xml uri xml_rule\",\n \"some specific rules for crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id = get_id_from_group(crs_group) if group_id\",\n \"\\\"description\\\": \\\"WAF will buffer the whole request body first and then release to\",\n \"\\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will buffer the whole request body first and then release\",\n \"Copyright 2021 VMware, Inc. import argparse import json import re import logging import\",\n \"pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"] = True return if pre_crs[\\\"index\\\"] > index: index =\",\n \"'--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller ip') args = parser.parse_args() if args.password:\",\n \"WAF policy successfully') else: logger.error('Error : %s' % resp.text) if __name__ == '__main__':\",\n \"groupid = int(match.group(\\\"group_id\\\")) assert groupid == 0 or 100 <= groupid <= 999,\",\n \"== \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"] = True return if pre_crs[\\\"index\\\"] > index:\",\n \"get_crs(api): tested_crs = \\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\" + tested_crs) if resp.status_code not in\",\n \"\\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": { \\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\"\",\n \"if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller user', default='admin')\",\n \"] pre_crs_group = { \\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule } index =\",\n \"argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller user password',\",\n \"as xml for requests with /broker/xml uri xml_rule = [ { \\\"index\\\": 0,\",\n \"ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken,\",\n \"parsed as XML in WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule\",\n \"VDI, client wants to stream data between client and server for some URLs\",\n \"= api.get(\\\"wafcrs?name=\\\" + tested_crs) if resp.status_code not in range(200, 300): if resp.status_code ==\",\n \"0, \\\"name\\\": \\\"enforce XML parsing for /broker/xml\\\", \\\"description\\\": \\\"Clients often send the wrong\",\n \"assert match, \\\"can not extract group id from group '{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\"))\",\n \"'--password', action=\\\"store\\\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant name', default='admin') parser.add_argument('-a',\",\n \"add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF\",\n \"POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group = {\",\n \"parse body as xml for requests with /broker/xml uri xml_rule = [ {\",\n \"\\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule } index = 0 if \\\"pre_crs_groups\\\" not\",\n \"not in range(200, 300): if resp.status_code == 404: logger.error(\\\"Controller does not have CRS\",\n \"rules and some specific rules for crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id = get_id_from_group(crs_group)\",\n \"\\\"description\\\": \\\"Clients often send the wrong Content-Type header. We ignore the header and\",\n \"specific rules for crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id = get_id_from_group(crs_group) if group_id >=\",\n \"request body first and then release to backend. With VDI, client wants to\",\n \"rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule)\",\n \"None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def create_vdi_waf_policy(api,\",\n \"VMware, Inc. import argparse import json import re import logging import os import\",\n \"to allow request with uri beginning with /ice/ allowlist_rule={ \\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\",\",\n \"300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) if __name__\",\n \"data between client and server for some URLs like /ice/..., we should allow\",\n \"return if pre_crs[\\\"index\\\"] > index: index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index + 1\",\n \"help='controller user', default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\",\",\n \"else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj)\",\n \"\\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": { \\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\" ], \\\"match_criteria\\\":\",\n \"action=\\\"store\\\", help='controller ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password,\",\n \"tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"] return waf_crs[0]\",\n \"json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\",\",\n \"successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj)\",\n \"help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication\",\n \"[]): if rule[\\\"rule_id\\\"] == \\\"920330\\\" or rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"] = False def\",\n \"buffer the whole request body first and then release to backend. With VDI,\",\n \"return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules for\",\n \"add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with uri beginning with /ice/\",\n \"== \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def\",\n \"/broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group = { \\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule\",\n \"some URLs like /ice/..., we should allow these URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ],\",\n \"0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule)\",\n \"pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"] = True return if pre_crs[\\\"index\\\"] >\",\n \"\\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs = get_crs(api) if waf_crs is None: return\",\n \"'__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\",\",\n \"xml for requests with /broker/xml uri xml_rule = [ { \\\"index\\\": 0, \\\"name\\\":\",\n \"if resp.status_code not in range(200, 300): if resp.status_code == 404: logger.error(\\\"Controller does not\",\n \"disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF\",\n \"help='controller ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant,\",\n \"False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml for requests\",\n \"group '{}' not in expected range\\\".format(group[\\\"name\\\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side\",\n \"group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\\\"enable\\\"] = False for rule in\",\n \"sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args) else: update_waf_policy(api, args,\",\n \"We ignore the header and enforce the request body to be parsed as\",\n \"import argparse import json import re import logging import os import sys from\",\n \"\\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } } }\",\n \"id from group '{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\")) assert groupid == 0 or 100\",\n \"\\\"920330\\\" or rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule\",\n \"index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs =\",\n \"body to be parsed as XML in WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\"\",\n \"password or authtokentoken must be provided.\\\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not\",\n \"allowlist rule to allow request with uri beginning with /ice/ allowlist_rule={ \\\"index\\\": 0,\",\n \"== 0 or 100 <= groupid <= 999, \\\"group id for group '{}'\",\n \"+ 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs = \\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\" + tested_crs)\",\n \"uri xml_rule = [ { \\\"index\\\": 0, \\\"name\\\": \\\"enforce XML parsing for /broker/xml\\\",\",\n \"[]): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\\\"enable\\\"] = False for rule\",\n \"waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] =\",\n \"= pattern.match(group[\\\"name\\\"]) assert match, \\\"can not extract group id from group '{}'\\\".format(group[\\\"name\\\"]) groupid\",\n \"is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list() for group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj)\",\n \"= 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\":\",\n \"or rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to\",\n \"not in expected range\\\".format(group[\\\"name\\\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and\",\n \"rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to parse\",\n \"/ice/..., we should allow these URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": { \\\"path\\\":\",\n \"if \\\"pre_crs_groups\\\" not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if\",\n \"resp = api.get(\\\"wafcrs?name=\\\" + tested_crs) if resp.status_code not in range(200, 300): if resp.status_code\",\n \"os import sys from avi.sdk.avi_api import ApiSession API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger =\",\n \"API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule\",\n \"URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": { \\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [\",\n \"[ \\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } } } index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\",\",\n \"requests with /broker/xml uri xml_rule = [ { \\\"index\\\": 0, \\\"name\\\": \\\"enforce XML\",\n \"1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs = \\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\" + tested_crs) if\",\n \"300): if resp.status_code == 404: logger.error(\\\"Controller does not have CRS %s, please install\",\n \"if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api =\",\n \"parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\",\",\n \"SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request\",\n \"\\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } } } index = 0\",\n \"pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"] = True\",\n \"{ \\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } }\",\n \"range(200, 300): if resp.status_code == 404: logger.error(\\\"Controller does not have CRS %s, please\",\n \"parsing for /broker/xml\\\", \\\"description\\\": \\\"Clients often send the wrong Content-Type header. We ignore\",\n \"\\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\" + tested_crs) if resp.status_code not in range(200, 300): if\",\n \"sys from avi.sdk.avi_api import ApiSession API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def\",\n \"group '{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\")) assert groupid == 0 or 100 <= groupid\",\n \"argparse import json import re import logging import os import sys from avi.sdk.avi_api\",\n \"300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api,\",\n \"these URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": { \\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\":\",\n \"} } } index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]:\",\n \"args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip,\",\n \"waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list() for\",\n \"api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else:\",\n \"waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300):\",\n \"args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION)\",\n \"[ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": { \\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\" ],\",\n \"with /ice/ allowlist_rule={ \\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will buffer the whole\",\n \"rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"]\",\n \"#add a rule to parse body as xml for requests with /broker/xml uri\",\n \"{ \\\"index\\\": 0, \\\"name\\\": \\\"enforce XML parsing for /broker/xml\\\", \\\"description\\\": \\\"Clients often send\",\n \"waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list() for group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp\",\n \"json import re import logging import os import sys from avi.sdk.avi_api import ApiSession\",\n \"return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\":\",\n \"import re import logging import os import sys from avi.sdk.avi_api import ApiSession API_VERSION\",\n \"\\\"name\\\": \\\"enforce XML parsing for /broker/xml\\\", \\\"description\\\": \\\"Clients often send the wrong Content-Type\",\n \"return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list() for group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj)\",\n \"api_version=API_VERSION) else: logging.error(\\\"Either password or authtokentoken must be provided.\\\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy',\",\n \"rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\")\",\n \"= int(match.group(\\\"group_id\\\")) assert groupid == 0 or 100 <= groupid <= 999, \\\"group\",\n \"\\\"group id for group '{}' not in expected range\\\".format(group[\\\"name\\\"]) return groupid def disable_crs_response_rules(waf_policy_obj):\",\n \"add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300):\",\n \"REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group = { \\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\",\",\n \"def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with uri beginning with\",\n \"logging.error(\\\"Either password or authtokentoken must be provided.\\\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if\",\n \"%waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error\",\n \"], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } } } index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for\",\n \"side rules and some specific rules for crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id =\",\n \"<= groupid <= 999, \\\"group id for group '{}' not in expected range\\\".format(group[\\\"name\\\"])\",\n \"help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller ip') args\",\n \"the wrong Content-Type header. We ignore the header and enforce the request body\",\n \"in crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"] == \\\"920330\\\" or rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"] =\",\n \"logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def create_vdi_waf_policy(api, args):\",\n \"for group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if\",\n \"and server for some URLs like /ice/..., we should allow these URLs\\\", \\\"actions\\\":\",\n \"#disable response side rules and some specific rules for crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []):\",\n \"rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"])\",\n \"100 <= groupid <= 999, \\\"group id for group '{}' not in expected\",\n \"beginning with /ice/ allowlist_rule={ \\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will buffer the\",\n \"pre_crs[\\\"enable\\\"] = True return if pre_crs[\\\"index\\\"] > index: index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] =\",\n \"if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s'\",\n \"return None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def\",\n \"\\\"932105\\\": rule[\\\"enable\\\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as\",\n \"resp.status_code == 404: logger.error(\\\"Controller does not have CRS %s, please install first.\\\" %\",\n \"exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \\\"name\\\":\",\n \": %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp =\",\n \"} index = 0 if \\\"pre_crs_groups\\\" not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for\",\n \"\\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow\",\n \"= index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"]) assert match,\",\n \"wrong Content-Type header. We ignore the header and enforce the request body to\",\n \"then release to backend. With VDI, client wants to stream data between client\",\n \"} index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"]\",\n \"or 100 <= groupid <= 999, \\\"group id for group '{}' not in\",\n \"{ \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } } } index\",\n \"range\\\".format(group[\\\"name\\\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules\",\n \"for requests with /broker/xml uri xml_rule = [ { \\\"index\\\": 0, \\\"name\\\": \\\"enforce\",\n \"logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp\",\n \"/broker/xml uri xml_rule = [ { \\\"index\\\": 0, \\\"name\\\": \\\"enforce XML parsing for\",\n \"api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user,\",\n \"= re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"]) assert match, \\\"can not extract group id from\",\n \"re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"]) assert match, \\\"can not extract group id from group\",\n \"api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either password or authtokentoken must\",\n \"crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"] == \\\"920330\\\" or rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"] = False\",\n \"in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\":\",\n \"we should allow these URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": { \\\"path\\\": {\",\n \"rule[\\\"rule_id\\\"] == \\\"920330\\\" or rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"] = False def add_pre_crs_group(waf_policy_obj): #add\",\n \"+ tested_crs) if resp.status_code not in range(200, 300): if resp.status_code == 404: logger.error(\\\"Controller\",\n \"= index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs = \\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\"\",\n \"else: logger.error('Error : %s' % resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser()\",\n \"= get_crs(api) if waf_crs is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list() for group\",\n \"= get_id_from_group(crs_group) if group_id >= 950: crs_group[\\\"enable\\\"] = False for rule in crs_group.get(\\\"rules\\\",\",\n \"\\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group):\",\n \"Inc. import argparse import json import re import logging import os import sys\",\n \"WAF policy successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj):\",\n \"args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif\",\n \"for rule in crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"] == \\\"920330\\\" or rule[\\\"rule_id\\\"] == \\\"932105\\\":\",\n \"rule in crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"] == \\\"920330\\\" or rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"]\",\n \"authtokentoken must be provided.\\\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api,\",\n \"\\\"match\\\": { \\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" }\",\n \"\\\"BEGINS_WITH\\\" } } } index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for rule in\",\n \"add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml for requests with /broker/xml\",\n \"950: crs_group[\\\"enable\\\"] = False for rule in crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"] == \\\"920330\\\"\",\n \"} waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list()\",\n \"resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' %\",\n \"id for group '{}' not in expected range\\\".format(group[\\\"name\\\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable\",\n \"rules for crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id = get_id_from_group(crs_group) if group_id >= 950:\",\n \"pre_crs_group = { \\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule } index = 0\",\n \"logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args,\",\n \"waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs = \\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\" + tested_crs) if resp.status_code\",\n \"waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"]) assert match, \\\"can not\",\n \"get_crs(api) if waf_crs is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list() for group in\",\n \"'--tenant', action=\\\"store\\\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller\",\n \"% resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller\",\n \"waf_crs = json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \\\"name\\\": SYSTEM_WAF_POLICY_VDI,\",\n \"uri beginning with /ice/ allowlist_rule={ \\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will buffer\",\n \"password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c',\",\n \"install first.\\\" % tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs =\",\n \"== \\\"932105\\\": rule[\\\"enable\\\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body\",\n \"% tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"] return\",\n \"in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"]\",\n \"{}).setdefault(\\\"rules\\\", []) for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index:\",\n \"to backend. With VDI, client wants to stream data between client and server\",\n \"REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group\",\n \"waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"]\",\n \"\\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"]\",\n \"args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in\",\n \"if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1\",\n \"== 404: logger.error(\\\"Controller does not have CRS %s, please install first.\\\" % tested_crs)\",\n \"= rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match =\",\n \"range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) def\",\n \"to parse body as xml for requests with /broker/xml uri xml_rule = [\",\n \"resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj)\",\n \"pre_crs_group[\\\"index\\\"] = index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs = \\\"CRS-2021-1\\\" resp =\",\n \"must be provided.\\\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args)\",\n \"action=\\\"store\\\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller ip')\",\n \"ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either password or authtokentoken must be provided.\\\")\",\n \"waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] =\",\n \"CRS %s, please install first.\\\" % tested_crs) return None logger.error('Error : %s', resp.text)\",\n \"import ApiSession API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a\",\n \"tested_crs) if resp.status_code not in range(200, 300): if resp.status_code == 404: logger.error(\\\"Controller does\",\n \"URLs like /ice/..., we should allow these URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\":\",\n \"0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule } index = 0 if \\\"pre_crs_groups\\\" not in\",\n \"token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either password or authtokentoken must be provided.\\\") sys.exit(1) waf_policy_obj =\",\n \"/broker/xml\\\", \\\"description\\\": \\\"Clients often send the wrong Content-Type header. We ignore the header\",\n \"group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code\",\n \"%s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s'\",\n \"parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller ip') args = parser.parse_args() if\",\n \"if pre_crs[\\\"index\\\"] > index: index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group)\",\n \"True return if pre_crs[\\\"index\\\"] > index: index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index +\",\n \"xml_rule } index = 0 if \\\"pre_crs_groups\\\" not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list()\",\n \"first and then release to backend. With VDI, client wants to stream data\",\n \"import sys from avi.sdk.avi_api import ApiSession API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__)\",\n \"import logging import os import sys from avi.sdk.avi_api import ApiSession API_VERSION = \\\"18.2.13\\\"\",\n \"like /ice/..., we should allow these URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": {\",\n \"{ \\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule } index = 0 if \\\"pre_crs_groups\\\"\",\n \"not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] ==\",\n \"add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create\",\n \"tested_crs = \\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\" + tested_crs) if resp.status_code not in range(200,\",\n \"from group '{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\")) assert groupid == 0 or 100 <=\",\n \"be provided.\\\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args) else:\",\n \"get_id_from_group(crs_group) if group_id >= 950: crs_group[\\\"enable\\\"] = False for rule in crs_group.get(\\\"rules\\\", []):\",\n \"index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs = \\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\" +\",\n \"xml_rule pre_crs[\\\"enable\\\"] = True return if pre_crs[\\\"index\\\"] > index: index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"]\",\n \"often send the wrong Content-Type header. We ignore the header and enforce the\",\n \"for group '{}' not in expected range\\\".format(group[\\\"name\\\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response\",\n \"in range(200, 300): if resp.status_code == 404: logger.error(\\\"Controller does not have CRS %s,\",\n \"= list() for group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy',\",\n \"for some URLs like /ice/..., we should allow these URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\"\",\n \"parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api\",\n \">= 950: crs_group[\\\"enable\\\"] = False for rule in crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"] ==\",\n \"= logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with uri\",\n \"0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will buffer the whole request body first and\",\n \"xml_rule = [ { \\\"index\\\": 0, \\\"name\\\": \\\"enforce XML parsing for /broker/xml\\\", \\\"description\\\":\",\n \"pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"]) assert match, \\\"can not extract group id\",\n \"int(match.group(\\\"group_id\\\")) assert groupid == 0 or 100 <= groupid <= 999, \\\"group id\",\n \"False for rule in crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"] == \\\"920330\\\" or rule[\\\"rule_id\\\"] ==\",\n \"does not have CRS %s, please install first.\\\" % tested_crs) return None logger.error('Error\",\n \"header. We ignore the header and enforce the request body to be parsed\",\n \"have CRS %s, please install first.\\\" % tested_crs) return None logger.error('Error : %s',\",\n \"2021 VMware, Inc. import argparse import json import re import logging import os\",\n \"header and enforce the request body to be parsed as XML in WAF\\\",\",\n \"allow these URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": { \\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\",\",\n \"successfully') else: logger.error('Error : %s' % resp.text) if __name__ == '__main__': parser =\",\n \"def get_crs(api): tested_crs = \\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\" + tested_crs) if resp.status_code not\",\n \"if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"] = True return if pre_crs[\\\"index\\\"]\",\n \"index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"]) assert match, \\\"can\",\n \"= xml_rule pre_crs[\\\"enable\\\"] = True return if pre_crs[\\\"index\\\"] > index: index = pre_crs[\\\"index\\\"]\",\n \"def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml for requests with\",\n \"and enforce the request body to be parsed as XML in WAF\\\", \\\"rule\\\":\",\n \"waf_policy_obj = { \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs = get_crs(api)\",\n \"= { \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs = get_crs(api) if\",\n \"or authtokentoken must be provided.\\\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj:\",\n \"logger.error('Error : %s' % resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u',\",\n \"= False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml for\",\n \"crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\\\"enable\\\"] =\",\n \"provided.\\\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args) else: update_waf_policy(api,\",\n \"} } index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if\",\n \"group_id >= 950: crs_group[\\\"enable\\\"] = False for rule in crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"]\",\n \"XML in WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI \\\\\\\"@streq\",\n \"else: logging.error(\\\"Either password or authtokentoken must be provided.\\\") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI)\",\n \"api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error\",\n \"logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with\",\n \"match, \\\"can not extract group id from group '{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\")) assert\",\n \"parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller user password', default='<PASSWORD>')\",\n \"help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller ip') args = parser.parse_args() if args.password: api\",\n \"body as xml for requests with /broker/xml uri xml_rule = [ { \\\"index\\\":\",\n \"assert groupid == 0 or 100 <= groupid <= 999, \\\"group id for\",\n \"999, \\\"group id for group '{}' not in expected range\\\".format(group[\\\"name\\\"]) return groupid def\",\n \"args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either password or authtokentoken must be provided.\\\") sys.exit(1)\",\n \"to be parsed as XML in WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\"\",\n \"waf_crs is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list() for group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group)\",\n \"def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if\",\n \"client wants to stream data between client and server for some URLs like\",\n \"whole request body first and then release to backend. With VDI, client wants\",\n \"groupid <= 999, \\\"group id for group '{}' not in expected range\\\".format(group[\\\"name\\\"]) return\",\n \"= parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken:\",\n \"[]) for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index\",\n \"\\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group = { \\\"index\\\":\",\n \"disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules for crs_group in waf_policy_obj.get(\\\"crs_groups\\\",\",\n \"\\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will buffer the whole request body first and then\",\n \"action=\\\"store\\\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken',\",\n \"index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match\",\n \"parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip,\",\n \"be parsed as XML in WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n\",\n \"#add a allowlist rule to allow request with uri beginning with /ice/ allowlist_rule={\",\n \"default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller ip') args = parser.parse_args()\",\n \"waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200,\",\n \"% resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'],\",\n \"def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules for crs_group in\",\n \"in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in\",\n \"'--controller_ip', action=\\\"store\\\", help='controller ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user,\",\n \"logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with uri beginning\",\n \"enforce the request body to be parsed as XML in WAF\\\", \\\"rule\\\": \\\"SecRule\",\n \"groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules for crs_group\",\n \"from avi.sdk.avi_api import ApiSession API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj):\",\n \"pattern.match(group[\\\"name\\\"]) assert match, \\\"can not extract group id from group '{}'\\\".format(group[\\\"name\\\"]) groupid =\",\n \"\\\"Clients often send the wrong Content-Type header. We ignore the header and enforce\",\n \"if waf_crs is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list() for group in waf_crs[\\\"groups\\\"]:\",\n \"= api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else:\",\n \"0 or 100 <= groupid <= 999, \\\"group id for group '{}' not\",\n \"groupid == 0 or 100 <= groupid <= 999, \\\"group id for group\",\n \"\\\"index\\\": 0, \\\"name\\\": \\\"enforce XML parsing for /broker/xml\\\", \\\"description\\\": \\\"Clients often send the\",\n \"= \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to\",\n \"None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] = list() for group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj)\",\n \"disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create\",\n \"import os import sys from avi.sdk.avi_api import ApiSession API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger\",\n \"resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy\",\n \"wants to stream data between client and server for some URLs like /ice/...,\",\n \"first.\\\" % tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"]\",\n \"request body to be parsed as XML in WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq\",\n \"resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller user',\",\n \"if resp.status_code == 404: logger.error(\\\"Controller does not have CRS %s, please install first.\\\"\",\n \"= pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs = \\\"CRS-2021-1\\\"\",\n \"%s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj =\",\n \"response side rules and some specific rules for crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id\",\n \"list() for group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj))\",\n \"avi.sdk.avi_api import ApiSession API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add\",\n \"update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code\",\n \"{ \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs = get_crs(api) if waf_crs\",\n \"data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error :\",\n \"list() for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"]\",\n \"tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either\",\n \"elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either password or\",\n \"} ] pre_crs_group = { \\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule } index\",\n \"create_vdi_waf_policy(api, args): waf_policy_obj = { \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs\",\n \"allowlist_rule={ \\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will buffer the whole request body\",\n \"\\\"rules\\\": xml_rule } index = 0 if \\\"pre_crs_groups\\\" not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] =\",\n \"allow request with uri beginning with /ice/ allowlist_rule={ \\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\":\",\n \"= [ { \\\"index\\\": 0, \\\"name\\\": \\\"enforce XML parsing for /broker/xml\\\", \\\"description\\\": \\\"Clients\",\n \"between client and server for some URLs like /ice/..., we should allow these\",\n \"if group_id >= 950: crs_group[\\\"enable\\\"] = False for rule in crs_group.get(\\\"rules\\\", []): if\",\n \"\\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group = { \\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule }\",\n \"index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] ==\",\n \"in expected range\\\".format(group[\\\"name\\\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some\",\n \"api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either password\",\n \"> index: index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api):\",\n \"= { \\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule } index = 0 if\",\n \"waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if\",\n \"for crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\\\"enable\\\"]\",\n \"stream data between client and server for some URLs like /ice/..., we should\",\n \"pre_crs[\\\"index\\\"] > index: index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def\",\n \"the whole request body first and then release to backend. With VDI, client\",\n \"import json import re import logging import os import sys from avi.sdk.avi_api import\",\n \"\\\"pre_crs_groups\\\" not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"]\",\n \"group id from group '{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\")) assert groupid == 0 or\",\n \"resp.status_code not in range(200, 300): if resp.status_code == 404: logger.error(\\\"Controller does not have\",\n \"token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller ip') args = parser.parse_args() if args.password: api =\",\n \"404: logger.error(\\\"Controller does not have CRS %s, please install first.\\\" % tested_crs) return\",\n \"\\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } } } index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", []) for rule\",\n \"extract group id from group '{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\")) assert groupid == 0\",\n \"%s, please install first.\\\" % tested_crs) return None logger.error('Error : %s', resp.text) exit(0)\",\n \"and some specific rules for crs_group in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id = get_id_from_group(crs_group) if\",\n \": %s', resp.text) exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj\",\n \"= ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant,\",\n \"# Copyright 2021 VMware, Inc. import argparse import json import re import logging\",\n \"if rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern =\",\n \"XML parsing for /broker/xml\\\", \\\"description\\\": \\\"Clients often send the wrong Content-Type header. We\",\n \"should allow these URLs\\\", \\\"actions\\\": [ \\\"WAF_POLICY_WHITELIST_ACTION_ALLOW\\\" ], \\\"match\\\": { \\\"path\\\": { \\\"match_case\\\":\",\n \"parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant name', default='admin')\",\n \"user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token')\",\n \"backend. With VDI, client wants to stream data between client and server for\",\n \"With VDI, client wants to stream data between client and server for some\",\n \"a allowlist rule to allow request with uri beginning with /ice/ allowlist_rule={ \\\"index\\\":\",\n \"waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"] = True return if\",\n \"body first and then release to backend. With VDI, client wants to stream\",\n \"the request body to be parsed as XML in WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD\",\n \"\\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ]\",\n \"def get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"]) assert match, \\\"can not extract\",\n \"logger.error(\\\"Controller does not have CRS %s, please install first.\\\" % tested_crs) return None\",\n \"if rule[\\\"rule_id\\\"] == \\\"920330\\\" or rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"] = False def add_pre_crs_group(waf_policy_obj):\",\n \"waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\"\",\n \"\\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will buffer the whole request body first\",\n \"waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index = rule[\\\"index\\\"] allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern\",\n \"\\\"can not extract group id from group '{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\")) assert groupid\",\n \"[ { \\\"index\\\": 0, \\\"name\\\": \\\"enforce XML parsing for /broker/xml\\\", \\\"description\\\": \\\"Clients often\",\n \"data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error :\",\n \"\\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" }\",\n \"'--user', action=\\\"store\\\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t',\",\n \"def create_vdi_waf_policy(api, args): waf_policy_obj = { \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" }\",\n \"<= 999, \\\"group id for group '{}' not in expected range\\\".format(group[\\\"name\\\"]) return groupid\",\n \"<gh_stars>0 # Copyright 2021 VMware, Inc. import argparse import json import re import\",\n \"in WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\"\",\n \"SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs = get_crs(api) if waf_crs is None:\",\n \"pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs = \\\"CRS-2021-1\\\" resp\",\n \"range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) if\",\n \"args): waf_policy_obj = { \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs =\",\n \"and then release to backend. With VDI, client wants to stream data between\",\n \"'{}' not in expected range\\\".format(group[\\\"name\\\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules\",\n \"\\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule } index = 0 if \\\"pre_crs_groups\\\" not in waf_policy_obj:\",\n \"not have CRS %s, please install first.\\\" % tested_crs) return None logger.error('Error :\",\n \"policy successfully') else: logger.error('Error : %s' % resp.text) if __name__ == '__main__': parser\",\n \"request with uri beginning with /ice/ allowlist_rule={ \\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF\",\n \"/ice/ allowlist_rule={ \\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will buffer the whole request\",\n \"\\\\n SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group = { \\\"index\\\": 0,\",\n \"action=\\\"store\\\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant',\",\n \"crs_group[\\\"enable\\\"] = False for rule in crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"] == \\\"920330\\\" or\",\n \"== \\\"920330\\\" or rule[\\\"rule_id\\\"] == \\\"932105\\\": rule[\\\"enable\\\"] = False def add_pre_crs_group(waf_policy_obj): #add a\",\n \"default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant name',\",\n \"logging import os import sys from avi.sdk.avi_api import ApiSession API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI'\",\n \"for /broker/xml\\\", \\\"description\\\": \\\"Clients often send the wrong Content-Type header. We ignore the\",\n \"%s' % resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\",\",\n \"\\\"WAF will buffer the whole request body first and then release to backend.\",\n \"\\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"] = True return if pre_crs[\\\"index\\\"] > index: index\",\n \"rule to allow request with uri beginning with /ice/ allowlist_rule={ \\\"index\\\": 0, \\\"name\\\":\",\n \"get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"]) assert match, \\\"can not extract group\",\n \"allowlist_rule[\\\"index\\\"] = index+1 waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile(\\\"[^\\\\d]*(?P<group_id>\\\\d\\\\d\\\\d)\\\") match = pattern.match(group[\\\"name\\\"]) assert\",\n \"= list() for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule\",\n \"rule to parse body as xml for requests with /broker/xml uri xml_rule =\",\n \"index: index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index + 1 waf_policy_obj[\\\"pre_crs_groups\\\"].append(pre_crs_group) def get_crs(api): tested_crs\",\n \"ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION)\",\n \"\\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group =\",\n \"= ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either password or authtokentoken must be\",\n \"in waf_policy_obj.get(\\\"crs_groups\\\", []): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\\\"enable\\\"] = False\",\n \"= True return if pre_crs[\\\"index\\\"] > index: index = pre_crs[\\\"index\\\"] pre_crs_group[\\\"index\\\"] = index\",\n \"__name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller user', default='admin') parser.add_argument('-p',\",\n \"name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action=\\\"store\\\", help='controller ip') args =\",\n \"will buffer the whole request body first and then release to backend. With\",\n \"client and server for some URLs like /ice/..., we should allow these URLs\\\",\",\n \"== '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller user', default='admin') parser.add_argument('-p', '--password',\",\n \"waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args) else: update_waf_policy(api, args, waf_policy_obj)\",\n \"add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy\",\n \"in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"] = True return\",\n \"waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200,\",\n \"args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else:\",\n \": %s' % resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user',\",\n \"= \\\"CRS-2021-1\\\" resp = api.get(\\\"wafcrs?name=\\\" + tested_crs) if resp.status_code not in range(200, 300):\",\n \"ignore the header and enforce the request body to be parsed as XML\",\n \"\\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group = { \\\"index\\\": 0, \\\"name\\\": \\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\":\",\n \"WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\"\",\n \"resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully')\",\n \"policy successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj)\",\n \"args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either password or authtokentoken\",\n \"tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error(\\\"Either password or authtokentoken must be provided.\\\") sys.exit(1) waf_policy_obj\",\n \"user', default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller user password', default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant\",\n \"0 if \\\"pre_crs_groups\\\" not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]:\",\n \"with uri beginning with /ice/ allowlist_rule={ \\\"index\\\": 0, \\\"name\\\": \\\"allowlist-start-with-ice\\\", \\\"description\\\": \\\"WAF will\",\n \"index = 0 if \\\"pre_crs_groups\\\" not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for pre_crs\",\n \"logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) if __name__ ==\",\n \"for pre_crs in waf_policy_obj[\\\"pre_crs_groups\\\"]: if pre_crs[\\\"name\\\"] == \\\"VDI_409_ENFORCE_XML\\\": pre_crs[\\\"rules\\\"] = xml_rule pre_crs[\\\"enable\\\"] =\",\n \"re import logging import os import sys from avi.sdk.avi_api import ApiSession API_VERSION =\",\n \"\\\"enforce XML parsing for /broker/xml\\\", \\\"description\\\": \\\"Clients often send the wrong Content-Type header.\",\n \"\\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } } } index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\", {}).setdefault(\\\"rules\\\", [])\",\n \"= api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully')\",\n \"= False for rule in crs_group.get(\\\"rules\\\", []): if rule[\\\"rule_id\\\"] == \\\"920330\\\" or rule[\\\"rule_id\\\"]\",\n \"as XML in WAF\\\", \\\"rule\\\": \\\"SecRule REQUEST_METHOD \\\\\\\"@streq POST\\\\\\\" \\\\\\\"phase:1,id:4099822,t:none,nolog,pass,chain\\\\\\\" \\\\n SecRule REQUEST_URI\",\n \"with /broker/xml uri xml_rule = [ { \\\"index\\\": 0, \\\"name\\\": \\\"enforce XML parsing\",\n \"for rule in waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"][:]: if rule[\\\"name\\\"] == \\\"allowlist-start-with-ice\\\": waf_policy_obj[\\\"whitelist\\\"][\\\"rules\\\"].remove(rule) if rule[\\\"index\\\"]>index: index =\",\n \"\\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"] =\",\n \"= json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { \\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\":\",\n \"\\\"name\\\": SYSTEM_WAF_POLICY_VDI, \\\"mode\\\": \\\"WAF_MODE_DETECTION_ONLY\\\", \\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs = get_crs(api) if waf_crs is\",\n \"\\\"waf_profile_ref\\\": \\\"/api/wafprofile?name=System-WAF-Profile\\\" } waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj[\\\"waf_crs_ref\\\"]=\\\"/api/wafcrs?name=\\\"+waf_crs[\\\"name\\\"] waf_policy_obj[\\\"crs_groups\\\"]\",\n \"match = pattern.match(group[\\\"name\\\"]) assert match, \\\"can not extract group id from group '{}'\\\".format(group[\\\"name\\\"])\",\n \"'{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\")) assert groupid == 0 or 100 <= groupid <=\",\n \"Content-Type header. We ignore the header and enforce the request body to be\",\n \"send the wrong Content-Type header. We ignore the header and enforce the request\",\n \"api.get(\\\"wafcrs?name=\\\" + tested_crs) if resp.status_code not in range(200, 300): if resp.status_code == 404:\",\n \"release to backend. With VDI, client wants to stream data between client and\",\n \"], \\\"match\\\": { \\\"path\\\": { \\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\"\",\n \"waf_policy_obj.get(\\\"crs_groups\\\", []): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group[\\\"enable\\\"] = False for\",\n \"ApiSession API_VERSION = \\\"18.2.13\\\" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist\",\n \"= argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller user\",\n \"not extract group id from group '{}'\\\".format(group[\\\"name\\\"]) groupid = int(match.group(\\\"group_id\\\")) assert groupid ==\",\n \"please install first.\\\" % tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs\",\n \"parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action=\\\"store\\\", help='controller user', default='admin') parser.add_argument('-p', '--password', action=\\\"store\\\", help='controller\",\n \"\\\"match_str\\\": [ \\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } } } index = 0 waf_policy_obj.setdefault(\\\"whitelist\\\",\",\n \"the header and enforce the request body to be parsed as XML in\",\n \"default='<PASSWORD>') parser.add_argument('-t', '--tenant', action=\\\"store\\\", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip',\",\n \"= 0 if \\\"pre_crs_groups\\\" not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"] = list() for pre_crs in\",\n \"rule[\\\"enable\\\"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml\",\n \"\\\"VDI_409_ENFORCE_XML\\\", \\\"rules\\\": xml_rule } index = 0 if \\\"pre_crs_groups\\\" not in waf_policy_obj: waf_policy_obj[\\\"pre_crs_groups\\\"]\",\n \"to stream data between client and server for some URLs like /ice/..., we\",\n \"in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text)\",\n \"\\\"match_case\\\": \\\"SENSITIVE\\\", \\\"match_str\\\": [ \\\"/ice/\\\" ], \\\"match_criteria\\\": \\\"BEGINS_WITH\\\" } } } index =\",\n \"server for some URLs like /ice/..., we should allow these URLs\\\", \\\"actions\\\": [\",\n \"resp.text) exit(0) waf_crs = json.loads(resp.text)[\\\"results\\\"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = {\",\n \"SecRule REQUEST_URI \\\\\\\"@streq /broker/xml\\\\\\\" \\\\\\\"t:none,ctl:requestBodyProcessor=XML\\\\\\\"\\\" } ] pre_crs_group = { \\\"index\\\": 0, \\\"name\\\":\",\n \"expected range\\\".format(group[\\\"name\\\"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific\",\n \"waf_policy_obj[\\\"crs_groups\\\"] = list() for group in waf_crs[\\\"groups\\\"]: waf_policy_obj[\\\"crs_groups\\\"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp =\"\n]","isConversational":false}}},{"rowIdx":1181,"cells":{"ngram":{"kind":"list like","value":["(False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\", \"category_id\", 5), (False, \"/category,5/\", \"category_id\", 5), (True,","attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping","5), (False, \"/category,5/\", \"category_id\", 5), (True, \"/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/department,3440/department,3443/\", \"id\", 3443),","path, f, v\", [ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True,","parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"]) for","test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id = target_parent","target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt","parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\"id\": d},","[1000, 1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ], )","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the","x in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc)","by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company.","== expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping ==","ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\"type\"], x[\"name\"]) for x in nodes] == [(\"category\",","Licensed under the MIT License (the \"License\"); you may not use this file","not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent)","License. \"\"\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory","make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"])","(True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\", \"category_id\", 5), (False,","\"is_leaf, path, f, v\", [ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\", \"id\", 3443),","the License for the specific language governing permissions and limitations under the License.","target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping","== {} @pytest.mark.parametrize( \"dep_chain,expected\", [ ( [\"a\", \"b\", \"c\"], [ (\"category\", \"默认目录\"), (\"department\",","f, v = path_method(data) assert f == f assert v == v @pytest.mark.parametrize(","= path_method(data) assert f == f assert v == v @pytest.mark.parametrize( \"dep_chain, expected\",","and limitations under the License. \"\"\" import pytest from bkuser_core.bkiam.constants import ResourceType from","License for the specific language governing permissions and limitations under the License. \"\"\"","d in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent =","Unless required by applicable law or agreed to in writing, software distributed under","\"b\"], [(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain,","( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent =","nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\"type\"],","v\", [ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\", \"category_id\",","the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS","= make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def","\"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\", \"category_id\", 5), (False, \"/category,5/\", \"category_id\", 5), (True, \"/department,3440/department,3443/\",","expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {}","\"non-leaf\" f, v = path_method(data) assert f == f assert v == v","License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing,","specific language governing permissions and limitations under the License. \"\"\" import pytest from","= target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id)","http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed","for the specific language governing permissions and limitations under the License. \"\"\" import","copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed","if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping =","f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"] data = {\"value\": path} if","community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent","\"category_id\", 5), (True, \"/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/department,3440/department,3443/\", \"id\", 3443), ], ) def","governing permissions and limitations under the License. \"\"\" import pytest from bkuser_core.bkiam.constants import","for x in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes =","), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in","\"a\"), (\"department\", \"b\"), (\"department\", \"c\"), ], ), ( [\"a\", \"b\"], [(\"category\", \"默认目录\"), (\"department\",","parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) #","(\"department\", \"a\"), (\"department\", \"b\"), (\"department\", \"c\"), ], ), ( [\"a\", \"b\"], [(\"category\", \"默认目录\"),","software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT","by applicable law or agreed to in writing, software distributed under the License","\"\"\" TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User)","{\"value\": path} if not is_leaf: data[\"node_type\"] = \"non-leaf\" f, v = path_method(data) assert","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License","assert v == v @pytest.mark.parametrize( \"dep_chain, expected\", [ ( [1000, 1001, 1002], {\"_bk_iam_path_\":","limitations under the License. \"\"\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models","parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"]) for x in nodes]","\"category_id\", 5), (False, \"/category,5/\", \"category_id\", 5), (True, \"/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/department,3440/department,3443/\", \"id\",","A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License","IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \"dep_chain,expected\", [ ( [\"a\",","the MIT License (the \"License\"); you may not use this file except in","in compliance with the License. You may obtain a copy of the License","under the MIT License (the \"License\"); you may not use this file except","KIND, either express or implied. See the License for the specific language governing","key_mapping[\"department._bk_iam_path_\"] data = {\"value\": path} if not is_leaf: data[\"node_type\"] = \"non-leaf\" f, v","\"b\"), (\"department\", \"c\"), ], ), ( [\"a\", \"b\"], [(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\",","], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in dep_chain:","in writing, software distributed under the License is distributed on an \"AS IS\"","in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d,","writing, software distributed under the License is distributed on an \"AS IS\" BASIS,","\"/category,5/\", \"category_id\", 5), (True, \"/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/department,3440/department,3443/\", \"id\", 3443), ], )","Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the","or agreed to in writing, software distributed under the License is distributed on","Tencent company. All rights reserved. Licensed under the MIT License (the \"License\"); you","dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\"id\":","with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT","= ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"] data = {\"value\": path} if not is_leaf: data[\"node_type\"]","= ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\"type\"], x[\"name\"]) for x in nodes] ==","bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path, f, v\",","[ (\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\"), (\"department\", \"c\"), ], ), ( [\"a\",",") def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"]","Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed","= {\"value\": path} if not is_leaf: data[\"node_type\"] = \"non-leaf\" f, v = path_method(data)","Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"]) for x in nodes] == expected","import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path, f, v\", [","is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright","the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29","You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by","(True, \"/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/department,3440/department,3443/\", \"id\", 3443), ], ) def test_get_key_mapping(self, is_leaf,","License (the \"License\"); you may not use this file except in compliance with","OR CONDITIONS OF ANY KIND, either express or implied. See the License for","OF ANY KIND, either express or implied. See the License for the specific","], ), ( [\"a\", \"b\"], [(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\")], ), ],","pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path, f, v\", [ (True, \"/category,5/department,3440/department,3443/\",","may not use this file except in compliance with the License. You may","under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR","target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert","[(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected):","on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","data = {\"value\": path} if not is_leaf: data[\"node_type\"] = \"non-leaf\" f, v =","@pytest.mark.parametrize( \"dep_chain, expected\", [ ( [1000, 1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000],","language governing permissions and limitations under the License. \"\"\" import pytest from bkuser_core.bkiam.constants","), ( [\"a\", \"b\"], [(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\")], ), ], )","3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method","\"\"\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from","= key_mapping[\"department._bk_iam_path_\"] data = {\"value\": path} if not is_leaf: data[\"node_type\"] = \"non-leaf\" f,","= make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"],","def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id =","if not is_leaf: data[\"node_type\"] = \"non-leaf\" f, v = path_method(data) assert f ==","See the License for the specific language governing permissions and limitations under the","reserved. Licensed under the MIT License (the \"License\"); you may not use this","source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a","the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless","3443), (False, \"/department,3440/department,3443/\", \"id\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v):","path_method = key_mapping[\"department._bk_iam_path_\"] data = {\"value\": path} if not is_leaf: data[\"node_type\"] = \"non-leaf\"","def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"] data","key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"] data = {\"value\": path} if not is_leaf:","(False, \"/department,3440/department,3443/\", \"id\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping","ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db","obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law","in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert","if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建","3443), (False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\", \"category_id\", 5), (False, \"/category,5/\", \"category_id\", 5),","this file except in compliance with the License. You may obtain a copy","\"License\"); you may not use this file except in compliance with the License.","\"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected):","is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY","= \"non-leaf\" f, v = path_method(data) assert f == f assert v ==","you may not use this file except in compliance with the License. You","\"/category,1/department,1000/\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d","agreed to in writing, software distributed under the License is distributed on an","nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\"type\"], x[\"name\"]) for x in nodes] == [(\"category\", \"默认目录\")]","MIT License (the \"License\"); you may not use this file except in compliance","\"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\", \"category_id\", 5), (False, \"/category,5/\", \"category_id\",","\"parent_id\", 3443), (False, \"/department,3440/department,3443/\", \"id\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f,","), ( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent","distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES","target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent)","under the License. \"\"\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import","bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark","\"b\", \"c\"], [ (\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\"), (\"department\", \"c\"), ], ),","implied. See the License for the specific language governing permissions and limitations under","open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited,","), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in","树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"]) for x in nodes] ==","target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes","1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ], ) def test_get_attributes_mapping(self,","path_method(data) assert f == f assert v == v @pytest.mark.parametrize( \"dep_chain, expected\", [","dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id)","assert f == f assert v == v @pytest.mark.parametrize( \"dep_chain, expected\", [ (","[ ( [\"a\", \"b\", \"c\"], [ (\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\"), (\"department\",","force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc","ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"]) for x in nodes] == expected def test_get_resource_nodes_other(self): pc","in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d),","@pytest.mark.parametrize( \"is_leaf, path, f, v\", [ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\", \"id\",","use this file except in compliance with the License. You may obtain a","test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \"dep_chain,expected\",","pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\"type\"], x[\"name\"]) for x in nodes]","is_leaf: data[\"node_type\"] = \"non-leaf\" f, v = path_method(data) assert f == f assert","making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All","pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C)","import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils","只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"]) for x in","pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \"dep_chain,expected\", [","(False, \"/category,5/\", \"category_id\", 5), (True, \"/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/department,3440/department,3443/\", \"id\", 3443), ],","required by applicable law or agreed to in writing, software distributed under the","= pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path, f, v\", [ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\",","\"/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/department,3440/department,3443/\", \"id\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path,","= target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加","ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \"dep_chain,expected\", [ ( [\"a\", \"b\", \"c\"], [","== f assert v == v @pytest.mark.parametrize( \"dep_chain, expected\", [ ( [1000, 1001,","[\"a\", \"b\", \"c\"], [ (\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\"), (\"department\", \"c\"), ],","[\"a\", \"b\"], [(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\")], ), ], ) def test_get_resource_nodes_dep(self,","to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021","= None for d in dep_chain: parent_id = target_parent if not target_parent else","= ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"]) for x in nodes] == expected def test_get_resource_nodes_other(self):","expected\", [ ( [1000, 1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"},","蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights","ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path,","distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","(\"department\", \"c\"), ], ), ( [\"a\", \"b\"], [(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\")],","not use this file except in compliance with the License. You may obtain","the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in","[1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None","-*- coding: utf-8 -*- \"\"\" TencentBlueKing is pleased to support the open source","f, v\", [ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\",","\"/department,3440/department,3443/\", \"id\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping =","import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark =","License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required","ANY KIND, either express or implied. See the License for the specific language","may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable","file except in compliance with the License. You may obtain a copy of","target_parent = None for d in dep_chain: parent_id = target_parent if not target_parent","= ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \"dep_chain,expected\", [ (","# 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"]) for x","d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc =","All rights reserved. Licensed under the MIT License (the \"License\"); you may not","\"dep_chain,expected\", [ ( [\"a\", \"b\", \"c\"], [ (\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\"),","], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method =","\"/category,5/\", \"category_id\", 5), (False, \"/category,5/\", \"category_id\", 5), (True, \"/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/department,3440/department,3443/\",","THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT","(the \"License\"); you may not use this file except in compliance with the","support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL","[(x[\"type\"], x[\"name\"]) for x in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default()","== v @pytest.mark.parametrize( \"dep_chain, expected\", [ ( [1000, 1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ),","def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id =","Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf,","\"c\"), ], ), ( [\"a\", \"b\"], [(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\")], ),","parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default()","( [1000, 1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ],","company. All rights reserved. Licensed under the MIT License (the \"License\"); you may","\"dep_chain, expected\", [ ( [1000, 1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000], {\"_bk_iam_path_\":","None for d in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk","\"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent","available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.","of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to","(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\"), (\"department\", \"c\"), ], ), ( [\"a\", \"b\"],","ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"] data = {\"value\": path} if not is_leaf: data[\"node_type\"] =","utf-8 -*- \"\"\" TencentBlueKing is pleased to support the open source community by","License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF","(\"department\", \"b\"), (\"department\", \"c\"), ], ), ( [\"a\", \"b\"], [(\"category\", \"默认目录\"), (\"department\", \"a\"),","law or agreed to in writing, software distributed under the License is distributed","v == v @pytest.mark.parametrize( \"dep_chain, expected\", [ ( [1000, 1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"},","attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \"dep_chain,expected\", [ ( [\"a\", \"b\",","2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the","{\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain,","( [\"a\", \"b\", \"c\"], [ (\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\"), (\"department\", \"c\"),","v = path_method(data) assert f == f assert v == v @pytest.mark.parametrize( \"dep_chain,","[ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\", \"category_id\", 5),","target_parent = make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected","a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or","either express or implied. See the License for the specific language governing permissions","3443), (True, \"/category,5/\", \"category_id\", 5), (False, \"/category,5/\", \"category_id\", 5), (True, \"/department,3440/department,3443/\", \"parent_id\", 3443),","target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping ==","(\"department\", \"a\"), (\"department\", \"b\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent =","\"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\", \"category_id\", 5), (False, \"/category,5/\",","@pytest.mark.parametrize( \"dep_chain,expected\", [ ( [\"a\", \"b\", \"c\"], [ (\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\",","rights reserved. Licensed under the MIT License (the \"License\"); you may not use","or implied. See the License for the specific language governing permissions and limitations","test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id = target_parent","\"b\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d","coding: utf-8 -*- \"\"\" TencentBlueKing is pleased to support the open source community","(True, \"/category,5/\", \"category_id\", 5), (False, \"/category,5/\", \"category_id\", 5), (True, \"/department,3440/department,3443/\", \"parent_id\", 3443), (False,","not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild()","( [\"a\", \"b\"], [(\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\")], ), ], ) def","class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path, f, v\", [ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False,","(C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under","1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ], ) def","CONDITIONS OF ANY KIND, either express or implied. See the License for the","\"id\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT)","{} @pytest.mark.parametrize( \"dep_chain,expected\", [ ( [\"a\", \"b\", \"c\"], [ (\"category\", \"默认目录\"), (\"department\", \"a\"),","to in writing, software distributed under the License is distributed on an \"AS","f == f assert v == v @pytest.mark.parametrize( \"dep_chain, expected\", [ ( [1000,","from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path, f,","TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available.","except in compliance with the License. You may obtain a copy of the",") def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id","assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert","== expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\"type\"], x[\"name\"])","an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"] data =","pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path, f, v\", [ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443),","make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path, f, v\", [ (True,","f assert v == v @pytest.mark.parametrize( \"dep_chain, expected\", [ ( [1000, 1001, 1002],","\"a\"), (\"department\", \"b\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None","TestResourceTypeEnum: @pytest.mark.parametrize( \"is_leaf, path, f, v\", [ (True, \"/category,5/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/category,5/department,3440/department,3443/\",","expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\"type\"], x[\"name\"]) for","(\"department\", \"b\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for","at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software","{\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for","= ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping =","x[\"name\"]) for x in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes","\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"] data = {\"value\":","pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import","def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\"type\"], x[\"name\"]) for x","target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert","the License. \"\"\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department,","attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping","assert attributes_mapping == {} @pytest.mark.parametrize( \"dep_chain,expected\", [ ( [\"a\", \"b\", \"c\"], [ (\"category\",","make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self):","assert [(x[\"type\"], x[\"name\"]) for x in nodes] == expected def test_get_resource_nodes_other(self): pc =","compliance with the License. You may obtain a copy of the License at","def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize(","5), (True, \"/department,3440/department,3443/\", \"parent_id\", 3443), (False, \"/department,3440/department,3443/\", \"id\", 3443), ], ) def test_get_key_mapping(self,",") def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id","express or implied. See the License for the specific language governing permissions and","[ ( [1000, 1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), ( [1000], {\"_bk_iam_path_\": \"/category,1/department,1000/\"}, ),","# -*- coding: utf-8 -*- \"\"\" TencentBlueKing is pleased to support the open","], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in dep_chain:","else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes =","path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"] data = {\"value\": path}","\"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\"), (\"department\", \"c\"), ], ), ( [\"a\", \"b\"], [(\"category\",","applicable law or agreed to in writing, software distributed under the License is","v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\"department._bk_iam_path_\"] data = {\"value\": path} if not","else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\"id\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping","attributes_mapping == {} @pytest.mark.parametrize( \"dep_chain,expected\", [ ( [\"a\", \"b\", \"c\"], [ (\"category\", \"默认目录\"),","bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum:","a Tencent company. All rights reserved. Licensed under the MIT License (the \"License\");","not is_leaf: data[\"node_type\"] = \"non-leaf\" f, v = path_method(data) assert f == f","= ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \"dep_chain,expected\", [ ( [\"a\", \"b\", \"c\"],","from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department","the specific language governing permissions and limitations under the License. \"\"\" import pytest","BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See","\"c\"], [ (\"category\", \"默认目录\"), (\"department\", \"a\"), (\"department\", \"b\"), (\"department\", \"c\"), ], ), (","data[\"node_type\"] = \"non-leaf\" f, v = path_method(data) assert f == f assert v","ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc)","nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\"type\"], x[\"name\"]) for x in nodes] == expected def","\"id\", 3443), (True, \"/category,5/\", \"category_id\", 5), (False, \"/category,5/\", \"category_id\", 5), (True, \"/department,3440/department,3443/\", \"parent_id\",","path} if not is_leaf: data[\"node_type\"] = \"non-leaf\" f, v = path_method(data) assert f","import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize(","v @pytest.mark.parametrize( \"dep_chain, expected\", [ ( [1000, 1001, 1002], {\"_bk_iam_path_\": \"/category,1/department,1000/department,1001/department,1002/\"}, ), (","expected): target_parent = None for d in dep_chain: parent_id = target_parent if not","permissions and limitations under the License. \"\"\" import pytest from bkuser_core.bkiam.constants import ResourceType","-*- \"\"\" TencentBlueKing is pleased to support the open source community by making","dep_chain, expected): target_parent = None for d in dep_chain: parent_id = target_parent if","test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\"type\"], x[\"name\"]) for x in","<reponame>Chace-wang/bk-user # -*- coding: utf-8 -*- \"\"\" TencentBlueKing is pleased to support the","for d in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent","from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class"],"string":"[\n \"(False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True, \\\"/category,5/\\\", \\\"category_id\\\", 5), (False, \\\"/category,5/\\\", \\\"category_id\\\", 5), (True,\",\n \"attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping\",\n \"5), (False, \\\"/category,5/\\\", \\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443),\",\n \"path, f, v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True,\",\n \"parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for\",\n \"test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id = target_parent\",\n \"target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt\",\n \"parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\": d},\",\n \"[1000, 1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ], )\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\",\n \"x in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc)\",\n \"by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company.\",\n \"== expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping ==\",\n \"ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] == [(\\\"category\\\",\",\n \"Licensed under the MIT License (the \\\"License\\\"); you may not use this file\",\n \"not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent)\",\n \"License. \\\"\\\"\\\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory\",\n \"make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"])\",\n \"(True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True, \\\"/category,5/\\\", \\\"category_id\\\", 5), (False,\",\n \"\\\"is_leaf, path, f, v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443),\",\n \"the License for the specific language governing permissions and limitations under the License.\",\n \"target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping\",\n \"== {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ ( [\\\"a\\\", \\\"b\\\", \\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\",\",\n \"f, v = path_method(data) assert f == f assert v == v @pytest.mark.parametrize(\",\n \"= path_method(data) assert f == f assert v == v @pytest.mark.parametrize( \\\"dep_chain, expected\\\",\",\n \"and limitations under the License. \\\"\\\"\\\" import pytest from bkuser_core.bkiam.constants import ResourceType from\",\n \"License for the specific language governing permissions and limitations under the License. \\\"\\\"\\\"\",\n \"d in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent =\",\n \"Unless required by applicable law or agreed to in writing, software distributed under\",\n \"\\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain,\",\n \"( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent =\",\n \"nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\\\"type\\\"],\",\n \"v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True, \\\"/category,5/\\\", \\\"category_id\\\",\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS\",\n \"= make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def\",\n \"\\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True, \\\"/category,5/\\\", \\\"category_id\\\", 5), (False, \\\"/category,5/\\\", \\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\",\",\n \"expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {}\",\n \"\\\"non-leaf\\\" f, v = path_method(data) assert f == f assert v == v\",\n \"License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing,\",\n \"specific language governing permissions and limitations under the License. \\\"\\\"\\\" import pytest from\",\n \"= target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id)\",\n \"http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed\",\n \"for the specific language governing permissions and limitations under the License. \\\"\\\"\\\" import\",\n \"copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed\",\n \"if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping =\",\n \"f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\": path} if\",\n \"community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent\",\n \"\\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ], ) def\",\n \"governing permissions and limitations under the License. \\\"\\\"\\\" import pytest from bkuser_core.bkiam.constants import\",\n \"for x in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes =\",\n \"), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in\",\n \"\\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ], ), ( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\",\",\n \"parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) #\",\n \"(\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ], ), ( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"),\",\n \"software distributed under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT\",\n \"by applicable law or agreed to in writing, software distributed under the License\",\n \"\\\"\\\"\\\" TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User)\",\n \"{\\\"value\\\": path} if not is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\" f, v = path_method(data) assert\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License\",\n \"assert v == v @pytest.mark.parametrize( \\\"dep_chain, expected\\\", [ ( [1000, 1001, 1002], {\\\"_bk_iam_path_\\\":\",\n \"limitations under the License. \\\"\\\"\\\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models\",\n \"parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes]\",\n \"\\\"category_id\\\", 5), (False, \\\"/category,5/\\\", \\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\",\",\n \"A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License\",\n \"IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ ( [\\\"a\\\",\",\n \"the MIT License (the \\\"License\\\"); you may not use this file except in\",\n \"in compliance with the License. You may obtain a copy of the License\",\n \"under the MIT License (the \\\"License\\\"); you may not use this file except\",\n \"KIND, either express or implied. See the License for the specific language governing\",\n \"key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\": path} if not is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\" f, v\",\n \"\\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ], ), ( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\",\",\n \"], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in dep_chain:\",\n \"in writing, software distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d,\",\n \"writing, software distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"\\\"/category,5/\\\", \\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ], )\",\n \"Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the\",\n \"or agreed to in writing, software distributed under the License is distributed on\",\n \"Tencent company. All rights reserved. Licensed under the MIT License (the \\\"License\\\"); you\",\n \"dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\":\",\n \"with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT\",\n \"= ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\": path} if not is_leaf: data[\\\"node_type\\\"]\",\n \"= ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] ==\",\n \"bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path, f, v\\\",\",\n \"[ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ], ), ( [\\\"a\\\",\",\n \") def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"]\",\n \"Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed\",\n \"= {\\\"value\\\": path} if not is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\" f, v = path_method(data)\",\n \"Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] == expected\",\n \"import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path, f, v\\\", [\",\n \"is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright\",\n \"the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29\",\n \"You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by\",\n \"(True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ], ) def test_get_key_mapping(self, is_leaf,\",\n \"License (the \\\"License\\\"); you may not use this file except in compliance with\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. See the License for\",\n \"OF ANY KIND, either express or implied. See the License for the specific\",\n \"], ), ( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ), ],\",\n \"pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path, f, v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\",\",\n \"may not use this file except in compliance with the License. You may\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR\",\n \"target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert\",\n \"[(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected):\",\n \"on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"data = {\\\"value\\\": path} if not is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\" f, v =\",\n \"@pytest.mark.parametrize( \\\"dep_chain, expected\\\", [ ( [1000, 1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000],\",\n \"language governing permissions and limitations under the License. \\\"\\\"\\\" import pytest from bkuser_core.bkiam.constants\",\n \"), ( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ), ], )\",\n \"3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method\",\n \"\\\"\\\"\\\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from\",\n \"= key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\": path} if not is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\" f,\",\n \"= make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"],\",\n \"def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id =\",\n \"if not is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\" f, v = path_method(data) assert f ==\",\n \"See the License for the specific language governing permissions and limitations under the\",\n \"reserved. Licensed under the MIT License (the \\\"License\\\"); you may not use this\",\n \"source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a\",\n \"the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless\",\n \"3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v):\",\n \"path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\": path} if not is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\"\",\n \"def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data\",\n \"key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\": path} if not is_leaf:\",\n \"(False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping\",\n \"ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db\",\n \"obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law\",\n \"in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert\",\n \"if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建\",\n \"3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True, \\\"/category,5/\\\", \\\"category_id\\\", 5), (False, \\\"/category,5/\\\", \\\"category_id\\\", 5),\",\n \"this file except in compliance with the License. You may obtain a copy\",\n \"\\\"License\\\"); you may not use this file except in compliance with the License.\",\n \"\\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected):\",\n \"is distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"= \\\"non-leaf\\\" f, v = path_method(data) assert f == f assert v ==\",\n \"you may not use this file except in compliance with the License. You\",\n \"\\\"/category,1/department,1000/\\\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d\",\n \"agreed to in writing, software distributed under the License is distributed on an\",\n \"nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] == [(\\\"category\\\", \\\"默认目录\\\")]\",\n \"MIT License (the \\\"License\\\"); you may not use this file except in compliance\",\n \"\\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True, \\\"/category,5/\\\", \\\"category_id\\\", 5), (False, \\\"/category,5/\\\", \\\"category_id\\\",\",\n \"\\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f,\",\n \"), ( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES\",\n \"target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent)\",\n \"under the License. \\\"\\\"\\\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import\",\n \"bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark\",\n \"\\\"b\\\", \\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ], ),\",\n \"implied. See the License for the specific language governing permissions and limitations under\",\n \"open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited,\",\n \"), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in\",\n \"树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] ==\",\n \"target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes\",\n \"1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ], ) def test_get_attributes_mapping(self,\",\n \"path_method(data) assert f == f assert v == v @pytest.mark.parametrize( \\\"dep_chain, expected\\\", [\",\n \"dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id)\",\n \"assert f == f assert v == v @pytest.mark.parametrize( \\\"dep_chain, expected\\\", [ (\",\n \"[ ( [\\\"a\\\", \\\"b\\\", \\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\",\",\n \"force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc\",\n \"ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] == expected def test_get_resource_nodes_other(self): pc\",\n \"in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d),\",\n \"@pytest.mark.parametrize( \\\"is_leaf, path, f, v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\",\",\n \"use this file except in compliance with the License. You may obtain a\",\n \"test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\",\",\n \"pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes]\",\n \"is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\" f, v = path_method(data) assert f == f assert\",\n \"making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All\",\n \"pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C)\",\n \"import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils\",\n \"只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in\",\n \"pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [\",\n \"(False, \\\"/category,5/\\\", \\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ],\",\n \"required by applicable law or agreed to in writing, software distributed under the\",\n \"= pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path, f, v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\",\",\n \"\\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path,\",\n \"= target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加\",\n \"ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ ( [\\\"a\\\", \\\"b\\\", \\\"c\\\"], [\",\n \"== f assert v == v @pytest.mark.parametrize( \\\"dep_chain, expected\\\", [ ( [1000, 1001,\",\n \"[\\\"a\\\", \\\"b\\\", \\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ],\",\n \"[\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ), ], ) def test_get_resource_nodes_dep(self,\",\n \"to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021\",\n \"= None for d in dep_chain: parent_id = target_parent if not target_parent else\",\n \"= ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] == expected def test_get_resource_nodes_other(self):\",\n \"expected\\\", [ ( [1000, 1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"},\",\n \"蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights\",\n \"ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path,\",\n \"distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"(\\\"department\\\", \\\"c\\\"), ], ), ( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")],\",\n \"not use this file except in compliance with the License. You may obtain\",\n \"the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in\",\n \"[1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None\",\n \"-*- coding: utf-8 -*- \\\"\\\"\\\" TencentBlueKing is pleased to support the open source\",\n \"f, v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True, \\\"/category,5/\\\",\",\n \"\\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping =\",\n \"import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark =\",\n \"License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required\",\n \"ANY KIND, either express or implied. See the License for the specific language\",\n \"may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable\",\n \"file except in compliance with the License. You may obtain a copy of\",\n \"target_parent = None for d in dep_chain: parent_id = target_parent if not target_parent\",\n \"= ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ (\",\n \"# 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x\",\n \"d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc =\",\n \"All rights reserved. Licensed under the MIT License (the \\\"License\\\"); you may not\",\n \"\\\"dep_chain,expected\\\", [ ( [\\\"a\\\", \\\"b\\\", \\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"),\",\n \"], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method =\",\n \"\\\"/category,5/\\\", \\\"category_id\\\", 5), (False, \\\"/category,5/\\\", \\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\",\",\n \"THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT\",\n \"(the \\\"License\\\"); you may not use this file except in compliance with the\",\n \"support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL\",\n \"[(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default()\",\n \"== v @pytest.mark.parametrize( \\\"dep_chain, expected\\\", [ ( [1000, 1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ),\",\n \"def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id =\",\n \"Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf,\",\n \"\\\"c\\\"), ], ), ( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ),\",\n \"parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default()\",\n \"( [1000, 1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ],\",\n \"company. All rights reserved. Licensed under the MIT License (the \\\"License\\\"); you may\",\n \"\\\"dep_chain, expected\\\", [ ( [1000, 1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000], {\\\"_bk_iam_path_\\\":\",\n \"None for d in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk\",\n \"\\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent\",\n \"available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.\",\n \"of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to\",\n \"(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ], ), ( [\\\"a\\\", \\\"b\\\"],\",\n \"ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\": path} if not is_leaf: data[\\\"node_type\\\"] =\",\n \"utf-8 -*- \\\"\\\"\\\" TencentBlueKing is pleased to support the open source community by\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"(\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ], ), ( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"),\",\n \"law or agreed to in writing, software distributed under the License is distributed\",\n \"v == v @pytest.mark.parametrize( \\\"dep_chain, expected\\\", [ ( [1000, 1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"},\",\n \"attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ ( [\\\"a\\\", \\\"b\\\",\",\n \"2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the\",\n \"{\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain,\",\n \"( [\\\"a\\\", \\\"b\\\", \\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"),\",\n \"v = path_method(data) assert f == f assert v == v @pytest.mark.parametrize( \\\"dep_chain,\",\n \"[ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True, \\\"/category,5/\\\", \\\"category_id\\\", 5),\",\n \"target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected\",\n \"a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or\",\n \"either express or implied. See the License for the specific language governing permissions\",\n \"3443), (True, \\\"/category,5/\\\", \\\"category_id\\\", 5), (False, \\\"/category,5/\\\", \\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443),\",\n \"target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping ==\",\n \"(\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent =\",\n \"\\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\", \\\"id\\\", 3443), (True, \\\"/category,5/\\\", \\\"category_id\\\", 5), (False, \\\"/category,5/\\\",\",\n \"@pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ ( [\\\"a\\\", \\\"b\\\", \\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\",\",\n \"rights reserved. Licensed under the MIT License (the \\\"License\\\"); you may not use\",\n \"or implied. See the License for the specific language governing permissions and limitations\",\n \"test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id = target_parent\",\n \"\\\"b\\\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d\",\n \"coding: utf-8 -*- \\\"\\\"\\\" TencentBlueKing is pleased to support the open source community\",\n \"(True, \\\"/category,5/\\\", \\\"category_id\\\", 5), (False, \\\"/category,5/\\\", \\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False,\",\n \"not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild()\",\n \"( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ), ], ) def\",\n \"class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path, f, v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False,\",\n \"(C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under\",\n \"1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ], ) def\",\n \"CONDITIONS OF ANY KIND, either express or implied. See the License for the\",\n \"\\\"id\\\", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT)\",\n \"{} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ ( [\\\"a\\\", \\\"b\\\", \\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"),\",\n \"to in writing, software distributed under the License is distributed on an \\\"AS\",\n \"f == f assert v == v @pytest.mark.parametrize( \\\"dep_chain, expected\\\", [ ( [1000,\",\n \"from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path, f,\",\n \"TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available.\",\n \"except in compliance with the License. You may obtain a copy of the\",\n \") def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id\",\n \"assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert\",\n \"== expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\\\"type\\\"], x[\\\"name\\\"])\",\n \"an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data =\",\n \"pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path, f, v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443),\",\n \"make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path, f, v\\\", [ (True,\",\n \"f assert v == v @pytest.mark.parametrize( \\\"dep_chain, expected\\\", [ ( [1000, 1001, 1002],\",\n \"\\\"a\\\"), (\\\"department\\\", \\\"b\\\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None\",\n \"TestResourceTypeEnum: @pytest.mark.parametrize( \\\"is_leaf, path, f, v\\\", [ (True, \\\"/category,5/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/category,5/department,3440/department,3443/\\\",\",\n \"expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for\",\n \"(\\\"department\\\", \\\"b\\\")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for\",\n \"at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software\",\n \"{\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for\",\n \"= ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping =\",\n \"x[\\\"name\\\"]) for x in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes\",\n \"\\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\":\",\n \"pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import\",\n \"def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x\",\n \"target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert\",\n \"the License. \\\"\\\"\\\" import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department,\",\n \"attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping\",\n \"assert attributes_mapping == {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ ( [\\\"a\\\", \\\"b\\\", \\\"c\\\"], [ (\\\"category\\\",\",\n \"make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self):\",\n \"assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] == expected def test_get_resource_nodes_other(self): pc =\",\n \"compliance with the License. You may obtain a copy of the License at\",\n \"def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize(\",\n \"5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\", 3443), (False, \\\"/department,3440/department,3443/\\\", \\\"id\\\", 3443), ], ) def test_get_key_mapping(self,\",\n \") def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id\",\n \"express or implied. See the License for the specific language governing permissions and\",\n \"[ ( [1000, 1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), ( [1000], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/\\\"}, ),\",\n \"# -*- coding: utf-8 -*- \\\"\\\"\\\" TencentBlueKing is pleased to support the open\",\n \"], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in dep_chain:\",\n \"else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes =\",\n \"path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\": path}\",\n \"\\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ], ), ( [\\\"a\\\", \\\"b\\\"], [(\\\"category\\\",\",\n \"applicable law or agreed to in writing, software distributed under the License is\",\n \"v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping[\\\"department._bk_iam_path_\\\"] data = {\\\"value\\\": path} if not\",\n \"else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={\\\"id\\\": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping\",\n \"attributes_mapping == {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ ( [\\\"a\\\", \\\"b\\\", \\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"),\",\n \"bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum:\",\n \"a Tencent company. All rights reserved. Licensed under the MIT License (the \\\"License\\\");\",\n \"not is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\" f, v = path_method(data) assert f == f\",\n \"= ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( \\\"dep_chain,expected\\\", [ ( [\\\"a\\\", \\\"b\\\", \\\"c\\\"],\",\n \"from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department\",\n \"the specific language governing permissions and limitations under the License. \\\"\\\"\\\" import pytest\",\n \"BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See\",\n \"\\\"c\\\"], [ (\\\"category\\\", \\\"默认目录\\\"), (\\\"department\\\", \\\"a\\\"), (\\\"department\\\", \\\"b\\\"), (\\\"department\\\", \\\"c\\\"), ], ), (\",\n \"data[\\\"node_type\\\"] = \\\"non-leaf\\\" f, v = path_method(data) assert f == f assert v\",\n \"ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc)\",\n \"nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in nodes] == expected def\",\n \"\\\"id\\\", 3443), (True, \\\"/category,5/\\\", \\\"category_id\\\", 5), (False, \\\"/category,5/\\\", \\\"category_id\\\", 5), (True, \\\"/department,3440/department,3443/\\\", \\\"parent_id\\\",\",\n \"path} if not is_leaf: data[\\\"node_type\\\"] = \\\"non-leaf\\\" f, v = path_method(data) assert f\",\n \"import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize(\",\n \"v @pytest.mark.parametrize( \\\"dep_chain, expected\\\", [ ( [1000, 1001, 1002], {\\\"_bk_iam_path_\\\": \\\"/category,1/department,1000/department,1001/department,1002/\\\"}, ), (\",\n \"expected): target_parent = None for d in dep_chain: parent_id = target_parent if not\",\n \"permissions and limitations under the License. \\\"\\\"\\\" import pytest from bkuser_core.bkiam.constants import ResourceType\",\n \"-*- \\\"\\\"\\\" TencentBlueKing is pleased to support the open source community by making\",\n \"dep_chain, expected): target_parent = None for d in dep_chain: parent_id = target_parent if\",\n \"test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x[\\\"type\\\"], x[\\\"name\\\"]) for x in\",\n \"<reponame>Chace-wang/bk-user # -*- coding: utf-8 -*- \\\"\\\"\\\" TencentBlueKing is pleased to support the\",\n \"for d in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent\",\n \"from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class\"\n]","isConversational":false}}},{"rowIdx":1182,"cells":{"ngram":{"kind":"list like","value":["run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks import runtools,","# get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks import runtools, simple","# from votesim.benchmarks.benchrunner import ( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark,","from votesim.benchmarks.benchrunner import ( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, #","import ( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # ) from","<filename>votesim/benchmarks/__init__.py # from votesim.benchmarks.benchrunner import ( # run_benchmark, # get_benchmarks, # post_benchmark, #","# run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks import","( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks","votesim.benchmarks.benchrunner import ( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # )"],"string":"[\n \"run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks import runtools,\",\n \"# get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks import runtools, simple\",\n \"# from votesim.benchmarks.benchrunner import ( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark,\",\n \"from votesim.benchmarks.benchrunner import ( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, #\",\n \"import ( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # ) from\",\n \"<filename>votesim/benchmarks/__init__.py # from votesim.benchmarks.benchrunner import ( # run_benchmark, # get_benchmarks, # post_benchmark, #\",\n \"# run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks import\",\n \"( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks\",\n \"votesim.benchmarks.benchrunner import ( # run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # )\"\n]","isConversational":false}}},{"rowIdx":1183,"cells":{"ngram":{"kind":"list like","value":["= str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines,","total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines)","get object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key","the cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all","the lines of data into a list of lists # all_lines = [line","csv import psycopg2 as ps import os from dotenv import load_dotenv load_dotenv() dbname","import load_dotenv load_dotenv() dbname = os.environ[\"db\"] host = os.environ[\"host\"] port = os.environ[\"port\"] user","as app import csv import psycopg2 as ps import os from dotenv import","for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all the lines of data","csv.reader(data.splitlines()) # for row in csv_data: # datestr = row[0] #.replace('/', '-') #","'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line","# for row in csv_data: # datestr = row[0] #.replace('/', '-') # #","= str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return","event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from S3","# read CSV # csv_data = csv.reader(data.splitlines()) # for row in csv_data: #","for line in all_lines] print_all_lines return {\"message\": \"success!!! Check the cloud watch logs","s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] #","= str(row[0][-5:]) # location = str(row[1]) # order = str(row[3]) # total =","watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all the lines","context): cursor = connection.cursor() cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall()) # Get key and bucket","ps import os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\"db\"] host =","= str(row[1]) # order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location,","all_lines = [] # read CSV # csv_data = csv.reader(data.splitlines()) # for row","boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines","cursor = connection.cursor() cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall()) # Get key and bucket informaition","https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all the lines of data into a list of lists","port = os.environ[\"port\"] user = os.environ[\"user\"] password = os.environ[\"pass\"] connection = ps.connect(dbname=dbname, host=host,","1\", ()) print(cursor.fetchall()) # Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket","port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall())","data into a list of lists # all_lines = [line for line in","ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\"SELECT 1\",","= os.environ[\"port\"] user = os.environ[\"user\"] password = os.environ[\"pass\"] connection = ps.connect(dbname=dbname, host=host, port=port,","Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use","boto3 library to get object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket","cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all the","os.environ[\"port\"] user = os.environ[\"user\"] password = os.environ[\"pass\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user,","os.environ[\"pass\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor =","informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get","src.app as app import csv import psycopg2 as ps import os from dotenv","#.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # #","key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data =","# all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines","key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object","= os.environ[\"db\"] host = os.environ[\"host\"] port = os.environ[\"port\"] user = os.environ[\"user\"] password =","cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall()) # Get key and bucket informaition key = event['Records'][0]['s3']['object']['key']","# # time = str(row[0][-5:]) # location = str(row[1]) # order = str(row[3])","def handle(event, context): cursor = connection.cursor() cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall()) # Get key","as ps import os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\"db\"] host","in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all the lines of data into a list","# print(date_obj) # # time = str(row[0][-5:]) # location = str(row[1]) # order","# datestr = row[0] #.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr,","# return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in","bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to","import csv import psycopg2 as ps import os from dotenv import load_dotenv load_dotenv()","[print(line) for line in all_lines] print_all_lines return {\"message\": \"success!!! Check the cloud watch","line in all_lines] print_all_lines return {\"message\": \"success!!! Check the cloud watch logs for","this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all the lines of data into","in all_lines] print_all_lines return {\"message\": \"success!!! Check the cloud watch logs for this","into a list of lists # all_lines = [line for line in csv_data]","host=host, port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\"SELECT 1\", ())","datestr = row[0] #.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y","'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for","= [print(line) for line in all_lines] print_all_lines return {\"message\": \"success!!! Check the cloud","# total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list #","= [] # read CSV # csv_data = csv.reader(data.splitlines()) # for row in","csv_data: # datestr = row[0] #.replace('/', '-') # # print(datestr) # date_obj =","Check the cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form","= key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data","app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines] print_all_lines return {\"message\": \"success!!!","str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list","lists # all_lines = [line for line in csv_data] # print(data) # print(all_lines)","event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from S3 s3 = boto3.client('s3')","list of lists # all_lines = [line for line in csv_data] # print(data)","= os.environ[\"user\"] password = os.environ[\"pass\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def","print_all_lines = [print(line) for line in all_lines] print_all_lines return {\"message\": \"success!!! Check the","s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key) data =","Form all the lines of data into a list of lists # all_lines","print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines] print_all_lines return {\"message\":","print(cursor.fetchall()) # Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name']","()) print(cursor.fetchall()) # Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket =","S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key) data","library to get object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket =","= ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\"SELECT","logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all the lines of","load_dotenv() dbname = os.environ[\"db\"] host = os.environ[\"host\"] port = os.environ[\"port\"] user = os.environ[\"user\"]","str(row[0][-5:]) # location = str(row[1]) # order = str(row[3]) # total = str(row[4])","row in csv_data: # datestr = row[0] #.replace('/', '-') # # print(datestr) #","os.environ[\"host\"] port = os.environ[\"port\"] user = os.environ[\"user\"] password = os.environ[\"pass\"] connection = ps.connect(dbname=dbname,","# print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # #","{\"message\": \"success!!! Check the cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"}","user = os.environ[\"user\"] password = os.environ[\"pass\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password)","Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV #","%H:%M') # # print(date_obj) # # time = str(row[0][-5:]) # location = str(row[1])","= event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from","= event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from S3 s3 =","dotenv import load_dotenv load_dotenv() dbname = os.environ[\"db\"] host = os.environ[\"host\"] port = os.environ[\"port\"]","return {\"message\": \"success!!! Check the cloud watch logs for this lambda in cloudwatch","os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\"db\"] host = os.environ[\"host\"] port","= connection.cursor() cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall()) # Get key and bucket informaition key","= boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8')","str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data)","object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key =","# Form all the lines of data into a list of lists #","password = os.environ[\"pass\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context):","os.environ[\"db\"] host = os.environ[\"host\"] port = os.environ[\"port\"] user = os.environ[\"user\"] password = os.environ[\"pass\"]","all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines =","os.environ[\"user\"] password = os.environ[\"pass\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event,","print_all_lines return {\"message\": \"success!!! Check the cloud watch logs for this lambda in","str(row[1]) # order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order,","# order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total})","load_dotenv load_dotenv() dbname = os.environ[\"db\"] host = os.environ[\"host\"] port = os.environ[\"port\"] user =","[] # read CSV # csv_data = csv.reader(data.splitlines()) # for row in csv_data:","date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time = str(row[0][-5:])","order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) #","= os.environ[\"pass\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor","key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3","return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines]","= datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time = str(row[0][-5:]) #","time = str(row[0][-5:]) # location = str(row[1]) # order = str(row[3]) # total","connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor()","all_lines] print_all_lines return {\"message\": \"success!!! Check the cloud watch logs for this lambda","import psycopg2 as ps import os from dotenv import load_dotenv load_dotenv() dbname =","# # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) #","= s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = []","= os.environ[\"host\"] port = os.environ[\"port\"] user = os.environ[\"user\"] password = os.environ[\"pass\"] connection =","= csv.reader(data.splitlines()) # for row in csv_data: # datestr = row[0] #.replace('/', '-')","import os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\"db\"] host = os.environ[\"host\"]","\"success!!! Check the cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} #","cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all the lines of data into a list of","location = str(row[1]) # order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj,","# use boto3 library to get object from S3 s3 = boto3.client('s3') s3_object","for row in csv_data: # datestr = row[0] #.replace('/', '-') # # print(datestr)","print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time","from dotenv import load_dotenv load_dotenv() dbname = os.environ[\"db\"] host = os.environ[\"host\"] port =","'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line)","'-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj)","host = os.environ[\"host\"] port = os.environ[\"port\"] user = os.environ[\"user\"] password = os.environ[\"pass\"] connection","= row[0] #.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M')","datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time = str(row[0][-5:]) # location","# print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines] print_all_lines return","app import csv import psycopg2 as ps import os from dotenv import load_dotenv","psycopg2 as ps import os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\"db\"]","s3_object = s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines =","user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall()) #","# time = str(row[0][-5:]) # location = str(row[1]) # order = str(row[3]) #","bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV","from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key)","row[0] #.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') #","# Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] #","# # print(date_obj) # # time = str(row[0][-5:]) # location = str(row[1]) #","in csv_data: # datestr = row[0] #.replace('/', '-') # # print(datestr) # date_obj","data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data = csv.reader(data.splitlines())","csv_data = csv.reader(data.splitlines()) # for row in csv_data: # datestr = row[0] #.replace('/',","print(date_obj) # # time = str(row[0][-5:]) # location = str(row[1]) # order =","data) print_all_lines = [print(line) for line in all_lines] print_all_lines return {\"message\": \"success!!! Check","a list of lists # all_lines = [line for line in csv_data] #","boto3 import src.app as app import csv import psycopg2 as ps import os","and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library","of data into a list of lists # all_lines = [line for line","= bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read","CSV # csv_data = csv.reader(data.splitlines()) # for row in csv_data: # datestr =","lines of data into a list of lists # all_lines = [line for","# date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time =","'%d/%m/%Y %H:%M') # # print(date_obj) # # time = str(row[0][-5:]) # location =","import src.app as app import csv import psycopg2 as ps import os from","handle(event, context): cursor = connection.cursor() cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall()) # Get key and","read CSV # csv_data = csv.reader(data.splitlines()) # for row in csv_data: # datestr","import boto3 import src.app as app import csv import psycopg2 as ps import","cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines] print_all_lines","= s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data = csv.reader(data.splitlines()) #","s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data = csv.reader(data.splitlines()) # for","lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\"} # Form all the lines of data into a","use boto3 library to get object from S3 s3 = boto3.client('s3') s3_object =","password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall()) # Get","# csv_data = csv.reader(data.splitlines()) # for row in csv_data: # datestr = row[0]","bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from S3 s3","all the lines of data into a list of lists # all_lines =","dbname = os.environ[\"db\"] host = os.environ[\"host\"] port = os.environ[\"port\"] user = os.environ[\"user\"] password","connection.cursor() cursor.execute(\"SELECT 1\", ()) print(cursor.fetchall()) # Get key and bucket informaition key =","of lists # all_lines = [line for line in csv_data] # print(data) #","# location = str(row[1]) # order = str(row[3]) # total = str(row[4]) #","to get object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket,"],"string":"[\n \"= str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines,\",\n \"total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines)\",\n \"get object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key\",\n \"the cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all\",\n \"the lines of data into a list of lists # all_lines = [line\",\n \"csv import psycopg2 as ps import os from dotenv import load_dotenv load_dotenv() dbname\",\n \"import load_dotenv load_dotenv() dbname = os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"] user\",\n \"as app import csv import psycopg2 as ps import os from dotenv import\",\n \"for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all the lines of data\",\n \"csv.reader(data.splitlines()) # for row in csv_data: # datestr = row[0] #.replace('/', '-') #\",\n \"'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line\",\n \"# for row in csv_data: # datestr = row[0] #.replace('/', '-') # #\",\n \"= str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return\",\n \"event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from S3\",\n \"# read CSV # csv_data = csv.reader(data.splitlines()) # for row in csv_data: #\",\n \"for line in all_lines] print_all_lines return {\\\"message\\\": \\\"success!!! Check the cloud watch logs\",\n \"s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] #\",\n \"= str(row[0][-5:]) # location = str(row[1]) # order = str(row[3]) # total =\",\n \"watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all the lines\",\n \"context): cursor = connection.cursor() cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall()) # Get key and bucket\",\n \"ps import os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\\\"db\\\"] host =\",\n \"= str(row[1]) # order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location,\",\n \"all_lines = [] # read CSV # csv_data = csv.reader(data.splitlines()) # for row\",\n \"boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines\",\n \"cursor = connection.cursor() cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall()) # Get key and bucket informaition\",\n \"https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all the lines of data into a list of lists\",\n \"port = os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname, host=host,\",\n \"1\\\", ()) print(cursor.fetchall()) # Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket\",\n \"port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall())\",\n \"data into a list of lists # all_lines = [line for line in\",\n \"ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\\\"SELECT 1\\\",\",\n \"= os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname, host=host, port=port,\",\n \"Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use\",\n \"boto3 library to get object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket\",\n \"cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all the\",\n \"os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user,\",\n \"os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor =\",\n \"informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get\",\n \"src.app as app import csv import psycopg2 as ps import os from dotenv\",\n \"#.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # #\",\n \"key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data =\",\n \"# all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines\",\n \"key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object\",\n \"= os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"] password =\",\n \"cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall()) # Get key and bucket informaition key = event['Records'][0]['s3']['object']['key']\",\n \"# # time = str(row[0][-5:]) # location = str(row[1]) # order = str(row[3])\",\n \"def handle(event, context): cursor = connection.cursor() cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall()) # Get key\",\n \"as ps import os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\\\"db\\\"] host\",\n \"in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all the lines of data into a list\",\n \"# print(date_obj) # # time = str(row[0][-5:]) # location = str(row[1]) # order\",\n \"# datestr = row[0] #.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr,\",\n \"# return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in\",\n \"bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to\",\n \"import csv import psycopg2 as ps import os from dotenv import load_dotenv load_dotenv()\",\n \"[print(line) for line in all_lines] print_all_lines return {\\\"message\\\": \\\"success!!! Check the cloud watch\",\n \"line in all_lines] print_all_lines return {\\\"message\\\": \\\"success!!! Check the cloud watch logs for\",\n \"this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all the lines of data into\",\n \"in all_lines] print_all_lines return {\\\"message\\\": \\\"success!!! Check the cloud watch logs for this\",\n \"into a list of lists # all_lines = [line for line in csv_data]\",\n \"host=host, port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\\\"SELECT 1\\\", ())\",\n \"datestr = row[0] #.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y\",\n \"'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for\",\n \"= [print(line) for line in all_lines] print_all_lines return {\\\"message\\\": \\\"success!!! Check the cloud\",\n \"# total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list #\",\n \"= [] # read CSV # csv_data = csv.reader(data.splitlines()) # for row in\",\n \"csv_data: # datestr = row[0] #.replace('/', '-') # # print(datestr) # date_obj =\",\n \"Check the cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form\",\n \"= key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data\",\n \"app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines] print_all_lines return {\\\"message\\\": \\\"success!!!\",\n \"str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list\",\n \"lists # all_lines = [line for line in csv_data] # print(data) # print(all_lines)\",\n \"event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from S3 s3 = boto3.client('s3')\",\n \"list of lists # all_lines = [line for line in csv_data] # print(data)\",\n \"= os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def\",\n \"print_all_lines = [print(line) for line in all_lines] print_all_lines return {\\\"message\\\": \\\"success!!! Check the\",\n \"s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key) data =\",\n \"Form all the lines of data into a list of lists # all_lines\",\n \"print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines] print_all_lines return {\\\"message\\\":\",\n \"print(cursor.fetchall()) # Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name']\",\n \"()) print(cursor.fetchall()) # Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket =\",\n \"S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key) data\",\n \"library to get object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket =\",\n \"= ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\\\"SELECT\",\n \"logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all the lines of\",\n \"load_dotenv() dbname = os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"]\",\n \"str(row[0][-5:]) # location = str(row[1]) # order = str(row[3]) # total = str(row[4])\",\n \"row in csv_data: # datestr = row[0] #.replace('/', '-') # # print(datestr) #\",\n \"os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname,\",\n \"# print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # #\",\n \"{\\\"message\\\": \\\"success!!! Check the cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"}\",\n \"user = os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password)\",\n \"Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV #\",\n \"%H:%M') # # print(date_obj) # # time = str(row[0][-5:]) # location = str(row[1])\",\n \"= event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from\",\n \"= event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from S3 s3 =\",\n \"dotenv import load_dotenv load_dotenv() dbname = os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"]\",\n \"return {\\\"message\\\": \\\"success!!! Check the cloud watch logs for this lambda in cloudwatch\",\n \"os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"] port\",\n \"= connection.cursor() cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall()) # Get key and bucket informaition key\",\n \"= boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8')\",\n \"str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data)\",\n \"object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key =\",\n \"# Form all the lines of data into a list of lists #\",\n \"password = os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context):\",\n \"os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"]\",\n \"all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines =\",\n \"os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event,\",\n \"print_all_lines return {\\\"message\\\": \\\"success!!! Check the cloud watch logs for this lambda in\",\n \"str(row[1]) # order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order,\",\n \"# order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total})\",\n \"load_dotenv load_dotenv() dbname = os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"] user =\",\n \"[] # read CSV # csv_data = csv.reader(data.splitlines()) # for row in csv_data:\",\n \"date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time = str(row[0][-5:])\",\n \"order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) #\",\n \"= os.environ[\\\"pass\\\"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor\",\n \"key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3\",\n \"return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines]\",\n \"= datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time = str(row[0][-5:]) #\",\n \"time = str(row[0][-5:]) # location = str(row[1]) # order = str(row[3]) # total\",\n \"connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor()\",\n \"all_lines] print_all_lines return {\\\"message\\\": \\\"success!!! Check the cloud watch logs for this lambda\",\n \"import psycopg2 as ps import os from dotenv import load_dotenv load_dotenv() dbname =\",\n \"# # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) #\",\n \"= s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = []\",\n \"= os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"] connection =\",\n \"= csv.reader(data.splitlines()) # for row in csv_data: # datestr = row[0] #.replace('/', '-')\",\n \"import os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"]\",\n \"\\\"success!!! Check the cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} #\",\n \"cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all the lines of data into a list of\",\n \"location = str(row[1]) # order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj,\",\n \"# use boto3 library to get object from S3 s3 = boto3.client('s3') s3_object\",\n \"for row in csv_data: # datestr = row[0] #.replace('/', '-') # # print(datestr)\",\n \"print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time\",\n \"from dotenv import load_dotenv load_dotenv() dbname = os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"] port =\",\n \"'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line)\",\n \"'-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj)\",\n \"host = os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"] password = os.environ[\\\"pass\\\"] connection\",\n \"= row[0] #.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M')\",\n \"datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time = str(row[0][-5:]) # location\",\n \"# print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines] print_all_lines return\",\n \"app import csv import psycopg2 as ps import os from dotenv import load_dotenv\",\n \"psycopg2 as ps import os from dotenv import load_dotenv load_dotenv() dbname = os.environ[\\\"db\\\"]\",\n \"s3_object = s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines =\",\n \"user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall()) #\",\n \"# time = str(row[0][-5:]) # location = str(row[1]) # order = str(row[3]) #\",\n \"bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV\",\n \"from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key)\",\n \"row[0] #.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') #\",\n \"# Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] #\",\n \"# # print(date_obj) # # time = str(row[0][-5:]) # location = str(row[1]) #\",\n \"in csv_data: # datestr = row[0] #.replace('/', '-') # # print(datestr) # date_obj\",\n \"data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data = csv.reader(data.splitlines())\",\n \"csv_data = csv.reader(data.splitlines()) # for row in csv_data: # datestr = row[0] #.replace('/',\",\n \"print(date_obj) # # time = str(row[0][-5:]) # location = str(row[1]) # order =\",\n \"data) print_all_lines = [print(line) for line in all_lines] print_all_lines return {\\\"message\\\": \\\"success!!! Check\",\n \"a list of lists # all_lines = [line for line in csv_data] #\",\n \"boto3 import src.app as app import csv import psycopg2 as ps import os\",\n \"and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library\",\n \"of data into a list of lists # all_lines = [line for line\",\n \"= bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read\",\n \"CSV # csv_data = csv.reader(data.splitlines()) # for row in csv_data: # datestr =\",\n \"lines of data into a list of lists # all_lines = [line for\",\n \"# date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time =\",\n \"'%d/%m/%Y %H:%M') # # print(date_obj) # # time = str(row[0][-5:]) # location =\",\n \"import src.app as app import csv import psycopg2 as ps import os from\",\n \"handle(event, context): cursor = connection.cursor() cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall()) # Get key and\",\n \"read CSV # csv_data = csv.reader(data.splitlines()) # for row in csv_data: # datestr\",\n \"import boto3 import src.app as app import csv import psycopg2 as ps import\",\n \"cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines] print_all_lines\",\n \"= s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data = csv.reader(data.splitlines()) #\",\n \"s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data = csv.reader(data.splitlines()) # for\",\n \"lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups\\\"} # Form all the lines of data into a\",\n \"use boto3 library to get object from S3 s3 = boto3.client('s3') s3_object =\",\n \"password=password) def handle(event, context): cursor = connection.cursor() cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall()) # Get\",\n \"# csv_data = csv.reader(data.splitlines()) # for row in csv_data: # datestr = row[0]\",\n \"bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from S3 s3\",\n \"all the lines of data into a list of lists # all_lines =\",\n \"dbname = os.environ[\\\"db\\\"] host = os.environ[\\\"host\\\"] port = os.environ[\\\"port\\\"] user = os.environ[\\\"user\\\"] password\",\n \"connection.cursor() cursor.execute(\\\"SELECT 1\\\", ()) print(cursor.fetchall()) # Get key and bucket informaition key =\",\n \"of lists # all_lines = [line for line in csv_data] # print(data) #\",\n \"# location = str(row[1]) # order = str(row[3]) # total = str(row[4]) #\",\n \"to get object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket,\"\n]","isConversational":false}}},{"rowIdx":1184,"cells":{"ngram":{"kind":"list like","value":["Crown Copyright 2017-2021 Met Office. # All rights reserved. # # Redistribution and","and the following disclaimer in the documentation # and/or other materials provided with","EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES","has a multi valued threshold coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2) expected = \"Expected a","(C) British Crown Copyright 2017-2021 Met Office. # All rights reserved. # #","without # modification, are permitted provided that the following conditions are met: #","[2, 2] shape = [n_thresholds, *shape] if n_thresholds > 0 else shape data","in binary form must reproduce the above copyright notice, # this list of","All rights reserved. # # Redistribution and use in source and binary forms,","software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY","shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\",","# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE","PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS","# this software without specific prior written permission. # # THIS SOFTWARE IS","thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", ) return cube def test_basic(): \"\"\"Test that with a","cube is transformed into a shower condition cube.\"\"\" cube = set_up_test_cube() result =","raised if the proxy diagnostic cube has a multi valued threshold coordinate.\"\"\" cube","AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #","THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,","DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED","condition cube.\"\"\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\") assert result.name()","CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR #","set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\") assert result.name() == \"probability_of_shower_condition_above_threshold\" assert result.dtype","code must retain the above copyright notice, this # list of conditions and","a multi valued threshold coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2) expected = \"Expected a single","result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \"shower_condition\" assert threshold_coord.units","and the following disclaimer. # # * Redistributions in binary form must reproduce","condition conversion\"\"\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape]",") return cube def test_basic(): \"\"\"Test that with a valid input the cube","= \"Input has no threshold coordinate and cannot be used\" with pytest.raises(CoordinateNotFoundError, match=expected):","== FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \"shower_condition\" assert threshold_coord.units ==","from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from","# # * Redistributions in binary form must reproduce the above copyright notice,","BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF","threshold coordinate.\"\"\" cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input has no threshold coordinate","SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS #","an exception is raised if the proxy diagnostic cube has a multi valued","the proxy diagnostic cube does not have a threshold coordinate.\"\"\" cube = set_up_test_cube()","LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR","threshold coordinate and cannot be used\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test","does not have a threshold coordinate.\"\"\" cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input","np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape] if n_thresholds > 0","* Redistributions in binary form must reproduce the above copyright notice, # this","this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED","# * Neither the name of the copyright holder nor the names of","CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT","transformed into a shower condition cube.\"\"\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord","cube = set_up_test_cube(n_thresholds=2) expected = \"Expected a single valued threshold coordinate.*\" with pytest.raises(ValueError,","CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR","BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN","(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS","with a valid input the cube is transformed into a shower condition cube.\"\"\"","IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING","TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR","with or without # modification, are permitted provided that the following conditions are","== cube.data).all() assert threshold_coord.name() == \"shower_condition\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \"\"\"Test","EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF #","BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL","# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS","improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set up a cube testing shower condition conversion\"\"\"","# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN","endorse or promote products derived from # this software without specific prior written","and/or other materials provided with the distribution. # # * Neither the name","Redistributions of source code must retain the above copyright notice, this # list","import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import","OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO","OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF","NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A","valued threshold coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2) expected = \"Expected a single valued threshold","\"shower_condition\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \"\"\"Test an exception is raised if","proxy diagnostic cube does not have a threshold coordinate.\"\"\" cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\")","= result.coord(var_name=\"threshold\") assert result.name() == \"probability_of_shower_condition_above_threshold\" assert result.dtype == FLOAT_DTYPE assert (result.data ==","spatial_grid=\"equalarea\", ) return cube def test_basic(): \"\"\"Test that with a valid input the","\"\"\"Test an exception is raised if the proxy diagnostic cube has a multi","rights reserved. # # Redistribution and use in source and binary forms, with","has no threshold coordinate and cannot be used\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def","conditions and the following disclaimer. # # * Redistributions in binary form must","OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY","coordinate and cannot be used\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test an","pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test an exception is raised if the proxy","input the cube is transformed into a shower condition cube.\"\"\" cube = set_up_test_cube()","permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS","the following conditions are met: # # * Redistributions of source code must","copyright holder nor the names of its # contributors may be used to","if n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube(","data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", )","that the following conditions are met: # # * Redistributions of source code","shower condition cube.\"\"\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\") assert","reproduce the above copyright notice, # this list of conditions and the following","PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS","ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT","HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,","the documentation # and/or other materials provided with the distribution. # # *","cube = set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", ) return cube def test_basic():","TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE","result.name() == \"probability_of_shower_condition_above_threshold\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name()","1 def test_no_threshold_coord(): \"\"\"Test an exception is raised if the proxy diagnostic cube","DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY","of precipitation_type utilities\"\"\" import numpy as np import pytest from iris.exceptions import CoordinateNotFoundError","0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\",","IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY","or without # modification, are permitted provided that the following conditions are met:","as np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from","without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE","\"probability_of_shower_condition_above_threshold\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \"shower_condition\"","INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,","= np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", ) return","NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF","copyright notice, # this list of conditions and the following disclaimer in the","HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,","WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN","WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) #","no threshold coordinate and cannot be used\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord():","Tests of precipitation_type utilities\"\"\" import numpy as np import pytest from iris.exceptions import","assert (result.data == cube.data).all() assert threshold_coord.name() == \"shower_condition\" assert threshold_coord.units == 1 def","cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input has no threshold coordinate and cannot be used\" with","and cannot be used\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test an exception","LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING,","notice, this # list of conditions and the following disclaimer. # # *","documentation # and/or other materials provided with the distribution. # # * Neither","AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR","2] shape = [n_thresholds, *shape] if n_thresholds > 0 else shape data =","ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE #","PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;","make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set up a cube testing shower","this list of conditions and the following disclaimer in the documentation # and/or","of conditions and the following disclaimer in the documentation # and/or other materials","are permitted provided that the following conditions are met: # # * Redistributions","list of conditions and the following disclaimer. # # * Redistributions in binary","that with a valid input the cube is transformed into a shower condition","list of conditions and the following disclaimer in the documentation # and/or other","name of the copyright holder nor the names of its # contributors may","IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \"\"\" Tests of precipitation_type","= make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\") assert result.name() == \"probability_of_shower_condition_above_threshold\" assert result.dtype == FLOAT_DTYPE","and use in source and binary forms, with or without # modification, are","shape = [n_thresholds, *shape] if n_thresholds > 0 else shape data = np.ones(shape,","the distribution. # # * Neither the name of the copyright holder nor","# this list of conditions and the following disclaimer in the documentation #","provided that the following conditions are met: # # * Redistributions of source","the cube is transformed into a shower condition cube.\"\"\" cube = set_up_test_cube() result","ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT","# -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021","HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT","EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \"\"\" Tests of","of its # contributors may be used to endorse or promote products derived","its # contributors may be used to endorse or promote products derived from","EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT,","= [n_thresholds, *shape] if n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE)","def test_basic(): \"\"\"Test that with a valid input the cube is transformed into","MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT","utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office. #","LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT","else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1,","contributors may be used to endorse or promote products derived from # this","match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test an exception is raised if the proxy diagnostic","INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT,","OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.","exception is raised if the proxy diagnostic cube does not have a threshold","Redistribution and use in source and binary forms, with or without # modification,","----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office. # All rights reserved.","source and binary forms, with or without # modification, are permitted provided that","BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR","SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED","make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test an exception is raised if the proxy diagnostic cube","from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set up a cube testing shower condition","the name of the copyright holder nor the names of its # contributors","FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT","NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,","IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR","of conditions and the following disclaimer. # # * Redistributions in binary form","(INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE","PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,","above copyright notice, this # list of conditions and the following disclaimer. #","the following disclaimer. # # * Redistributions in binary form must reproduce the","derived from # this software without specific prior written permission. # # THIS","diagnostic cube does not have a threshold coordinate.\"\"\" cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected","POSSIBILITY OF SUCH DAMAGE. \"\"\" Tests of precipitation_type utilities\"\"\" import numpy as np","set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", ) return cube def test_basic(): \"\"\"Test that","a valid input the cube is transformed into a shower condition cube.\"\"\" cube","a threshold coordinate.\"\"\" cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input has no threshold","set_up_test_cube(n_thresholds=1): \"\"\"Set up a cube testing shower condition conversion\"\"\" thresholds = np.arange(n_thresholds) shape","cube def test_basic(): \"\"\"Test that with a valid input the cube is transformed","iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes","# # * Neither the name of the copyright holder nor the names","# contributors may be used to endorse or promote products derived from #","permitted provided that the following conditions are met: # # * Redistributions of","import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import","Met Office. # All rights reserved. # # Redistribution and use in source","other materials provided with the distribution. # # * Neither the name of","a shower condition cube.\"\"\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\")","COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES,","np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", ) return cube","\"\"\" Tests of precipitation_type utilities\"\"\" import numpy as np import pytest from iris.exceptions","if the proxy diagnostic cube has a multi valued threshold coordinate.\"\"\" cube =","pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube","= set_up_test_cube(n_thresholds=2) expected = \"Expected a single valued threshold coordinate.*\" with pytest.raises(ValueError, match=expected):","forms, with or without # modification, are permitted provided that the following conditions","used\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test an exception is raised if","# # * Redistributions of source code must retain the above copyright notice,","with the distribution. # # * Neither the name of the copyright holder","above copyright notice, # this list of conditions and the following disclaimer in","threshold_units=1, spatial_grid=\"equalarea\", ) return cube def test_basic(): \"\"\"Test that with a valid input","data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", ) return cube def test_basic(): \"\"\"Test that with","in source and binary forms, with or without # modification, are permitted provided","precipitation_type utilities\"\"\" import numpy as np import pytest from iris.exceptions import CoordinateNotFoundError from","the following disclaimer in the documentation # and/or other materials provided with the","# # Redistribution and use in source and binary forms, with or without","ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF","import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set up a cube testing shower condition conversion\"\"\" thresholds","shower condition conversion\"\"\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds,","is transformed into a shower condition cube.\"\"\" cube = set_up_test_cube() result = make_shower_condition_cube(cube)","the above copyright notice, this # list of conditions and the following disclaimer.","\"\"\"Set up a cube testing shower condition conversion\"\"\" thresholds = np.arange(n_thresholds) shape =","used to endorse or promote products derived from # this software without specific","-*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met","Copyright 2017-2021 Met Office. # All rights reserved. # # Redistribution and use","COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,","> 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds,","SUCH DAMAGE. \"\"\" Tests of precipitation_type utilities\"\"\" import numpy as np import pytest","promote products derived from # this software without specific prior written permission. #","prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS","== \"shower_condition\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \"\"\"Test an exception is raised","British Crown Copyright 2017-2021 Met Office. # All rights reserved. # # Redistribution","IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED.","OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR","thresholds = np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape] if n_thresholds","result.coord(var_name=\"threshold\") assert result.name() == \"probability_of_shower_condition_above_threshold\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all()","[n_thresholds, *shape] if n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube","coding: utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office.","assert threshold_coord.units == 1 def test_no_threshold_coord(): \"\"\"Test an exception is raised if the","THE # POSSIBILITY OF SUCH DAMAGE. \"\"\" Tests of precipitation_type utilities\"\"\" import numpy","OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE","OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR","have a threshold coordinate.\"\"\" cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input has no","import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set","INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT","ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING","use in source and binary forms, with or without # modification, are permitted","with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test an exception is raised if the","from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def","THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" #","must reproduce the above copyright notice, # this list of conditions and the","this # list of conditions and the following disclaimer. # # * Redistributions","# All rights reserved. # # Redistribution and use in source and binary","following conditions are met: # # * Redistributions of source code must retain","cube does not have a threshold coordinate.\"\"\" cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected =","set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input has no threshold coordinate and cannot be used\"","disclaimer in the documentation # and/or other materials provided with the distribution. #","to endorse or promote products derived from # this software without specific prior","assert threshold_coord.name() == \"shower_condition\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \"\"\"Test an exception","up a cube testing shower condition conversion\"\"\" thresholds = np.arange(n_thresholds) shape = [2,","# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN","if the proxy diagnostic cube does not have a threshold coordinate.\"\"\" cube =","# (C) British Crown Copyright 2017-2021 Met Office. # All rights reserved. #","import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set up a cube testing","conversion\"\"\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape] if","# POSSIBILITY OF SUCH DAMAGE. \"\"\" Tests of precipitation_type utilities\"\"\" import numpy as","cube has a multi valued threshold coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2) expected = \"Expected","improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1):","distribution. # # * Neither the name of the copyright holder nor the","CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube","threshold_coord = result.coord(var_name=\"threshold\") assert result.name() == \"probability_of_shower_condition_above_threshold\" assert result.dtype == FLOAT_DTYPE assert (result.data","in the documentation # and/or other materials provided with the distribution. # #","SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF","GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION)","IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED","AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT","# and/or other materials provided with the distribution. # # * Neither the","products derived from # this software without specific prior written permission. # #","result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\") assert result.name() == \"probability_of_shower_condition_above_threshold\" assert result.dtype ==","# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN #","CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY","the above copyright notice, # this list of conditions and the following disclaimer","A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER","OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF","from # this software without specific prior written permission. # # THIS SOFTWARE","cube.data).all() assert threshold_coord.name() == \"shower_condition\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \"\"\"Test an","# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE","OF SUCH DAMAGE. \"\"\" Tests of precipitation_type utilities\"\"\" import numpy as np import","is raised if the proxy diagnostic cube does not have a threshold coordinate.\"\"\"","be used to endorse or promote products derived from # this software without","# ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office. # All rights","IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,","* Neither the name of the copyright holder nor the names of its","multi valued threshold coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2) expected = \"Expected a single valued","cube.\"\"\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\") assert result.name() ==","ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF","IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY","coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2) expected = \"Expected a single valued threshold coordinate.*\" with","materials provided with the distribution. # # * Neither the name of the","AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL","from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set up a","FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \"shower_condition\" assert threshold_coord.units == 1","def test_no_threshold_coord(): \"\"\"Test an exception is raised if the proxy diagnostic cube does","return cube def test_basic(): \"\"\"Test that with a valid input the cube is","and binary forms, with or without # modification, are permitted provided that the","conditions and the following disclaimer in the documentation # and/or other materials provided","names of its # contributors may be used to endorse or promote products","copyright notice, this # list of conditions and the following disclaimer. # #","the proxy diagnostic cube has a multi valued threshold coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2)","not have a threshold coordinate.\"\"\" cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input has","SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND","threshold_coord.units == 1 def test_no_threshold_coord(): \"\"\"Test an exception is raised if the proxy","DAMAGE. \"\"\" Tests of precipitation_type utilities\"\"\" import numpy as np import pytest from","SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT,","numpy as np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE","proxy diagnostic cube has a multi valued threshold coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2) expected","disclaimer. # # * Redistributions in binary form must reproduce the above copyright","ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED","WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND","provided with the distribution. # # * Neither the name of the copyright","\"\"\"Test that with a valid input the cube is transformed into a shower","modification, are permitted provided that the following conditions are met: # # *","an exception is raised if the proxy diagnostic cube does not have a","must retain the above copyright notice, this # list of conditions and the","are met: # # * Redistributions of source code must retain the above","DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE","OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR","OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,","LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)","# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES","OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON","= set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\") assert result.name() == \"probability_of_shower_condition_above_threshold\" assert","cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\") assert result.name() == \"probability_of_shower_condition_above_threshold\"","assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \"shower_condition\" assert","the copyright holder nor the names of its # contributors may be used","LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,","improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set up a cube","= np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape] if n_thresholds >","LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND","DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES;","be used\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test an exception is raised","THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE #","OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS","THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE","== 1 def test_no_threshold_coord(): \"\"\"Test an exception is raised if the proxy diagnostic","met: # # * Redistributions of source code must retain the above copyright","= set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input has no threshold coordinate and cannot be","coordinate.\"\"\" cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input has no threshold coordinate and","THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \"\"\"","Redistributions in binary form must reproduce the above copyright notice, # this list","np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities","THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF","# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS","def set_up_test_cube(n_thresholds=1): \"\"\"Set up a cube testing shower condition conversion\"\"\" thresholds = np.arange(n_thresholds)","is raised if the proxy diagnostic cube has a multi valued threshold coordinate.\"\"\"","of the copyright holder nor the names of its # contributors may be","OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER","# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"","of source code must retain the above copyright notice, this # list of","\"\"\"Test an exception is raised if the proxy diagnostic cube does not have","exception is raised if the proxy diagnostic cube has a multi valued threshold","into a shower condition cube.\"\"\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord =","# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE","threshold coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2) expected = \"Expected a single valued threshold coordinate.*\"","notice, # this list of conditions and the following disclaimer in the documentation","make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\"threshold\") assert result.name() == \"probability_of_shower_condition_above_threshold\" assert result.dtype == FLOAT_DTYPE assert","test_multi_valued_threshold_coord(): \"\"\"Test an exception is raised if the proxy diagnostic cube has a","n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data,","= [2, 2] shape = [n_thresholds, *shape] if n_thresholds > 0 else shape","THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR IMPLIED","the names of its # contributors may be used to endorse or promote","ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \"\"\" Tests of precipitation_type utilities\"\"\"","TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE","variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", ) return cube def test_basic(): \"\"\"Test that with a valid","USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY","STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY","a cube testing shower condition conversion\"\"\" thresholds = np.arange(n_thresholds) shape = [2, 2]","reserved. # # Redistribution and use in source and binary forms, with or","\"AS IS\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED","# Redistribution and use in source and binary forms, with or without #","INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS","WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE","nor the names of its # contributors may be used to endorse or","FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT","cannot be used\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \"\"\"Test an exception is","source code must retain the above copyright notice, this # list of conditions","conditions are met: # # * Redistributions of source code must retain the","retain the above copyright notice, this # list of conditions and the following","holder nor the names of its # contributors may be used to endorse","BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" # AND ANY EXPRESS OR","FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set up","import numpy as np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import","\"Input has no threshold coordinate and cannot be used\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube)","valid input the cube is transformed into a shower condition cube.\"\"\" cube =","OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER","binary forms, with or without # modification, are permitted provided that the following","*shape] if n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube =","OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #","Office. # All rights reserved. # # Redistribution and use in source and","testing shower condition conversion\"\"\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape =","NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY","may be used to endorse or promote products derived from # this software","threshold_coord.name() == \"shower_condition\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \"\"\"Test an exception is","(result.data == cube.data).all() assert threshold_coord.name() == \"shower_condition\" assert threshold_coord.units == 1 def test_no_threshold_coord():","expected = \"Input has no threshold coordinate and cannot be used\" with pytest.raises(CoordinateNotFoundError,","# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS","cube testing shower condition conversion\"\"\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape","== \"probability_of_shower_condition_above_threshold\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() ==","binary form must reproduce the above copyright notice, # this list of conditions","assert result.name() == \"probability_of_shower_condition_above_threshold\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert","following disclaimer in the documentation # and/or other materials provided with the distribution.","dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", ) return cube def","USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH","CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY,","SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \"\"\" Tests","utilities\"\"\" import numpy as np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants","2017-2021 Met Office. # All rights reserved. # # Redistribution and use in","set_up_test_cube(n_thresholds=2) expected = \"Expected a single valued threshold coordinate.*\" with pytest.raises(ValueError, match=expected): make_shower_condition_cube(cube)","FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE","-*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office. # All","form must reproduce the above copyright notice, # this list of conditions and","PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR","# list of conditions and the following disclaimer. # # * Redistributions in","Neither the name of the copyright holder nor the names of its #","cube = set_up_test_cube() cube.remove_coord(\"texture_of_cloud_area_fraction\") expected = \"Input has no threshold coordinate and cannot","= set_up_probability_cube( data, thresholds, variable_name=\"texture_of_cloud_area_fraction\", threshold_units=1, spatial_grid=\"equalarea\", ) return cube def test_basic(): \"\"\"Test","shape = [2, 2] shape = [n_thresholds, *shape] if n_thresholds > 0 else","def test_multi_valued_threshold_coord(): \"\"\"Test an exception is raised if the proxy diagnostic cube has","test_basic(): \"\"\"Test that with a valid input the cube is transformed into a","# * Redistributions of source code must retain the above copyright notice, this","# modification, are permitted provided that the following conditions are met: # #","written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND","# * Redistributions in binary form must reproduce the above copyright notice, #","OF THE # POSSIBILITY OF SUCH DAMAGE. \"\"\" Tests of precipitation_type utilities\"\"\" import","set_up_probability_cube def set_up_test_cube(n_thresholds=1): \"\"\"Set up a cube testing shower condition conversion\"\"\" thresholds =","test_no_threshold_coord(): \"\"\"Test an exception is raised if the proxy diagnostic cube does not","diagnostic cube has a multi valued threshold coordinate.\"\"\" cube = set_up_test_cube(n_thresholds=2) expected =","OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF","or promote products derived from # this software without specific prior written permission.","specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT","* Redistributions of source code must retain the above copyright notice, this #","following disclaimer. # # * Redistributions in binary form must reproduce the above","raised if the proxy diagnostic cube does not have a threshold coordinate.\"\"\" cube"],"string":"[\n \"Crown Copyright 2017-2021 Met Office. # All rights reserved. # # Redistribution and\",\n \"and the following disclaimer in the documentation # and/or other materials provided with\",\n \"EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES\",\n \"has a multi valued threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2) expected = \\\"Expected a\",\n \"(C) British Crown Copyright 2017-2021 Met Office. # All rights reserved. # #\",\n \"without # modification, are permitted provided that the following conditions are met: #\",\n \"[2, 2] shape = [n_thresholds, *shape] if n_thresholds > 0 else shape data\",\n \"in binary form must reproduce the above copyright notice, # this list of\",\n \"All rights reserved. # # Redistribution and use in source and binary forms,\",\n \"software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY\",\n \"shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\",\",\n \"# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\",\n \"PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS\",\n \"# this software without specific prior written permission. # # THIS SOFTWARE IS\",\n \"thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return cube def test_basic(): \\\"\\\"\\\"Test that with a\",\n \"cube is transformed into a shower condition cube.\\\"\\\"\\\" cube = set_up_test_cube() result =\",\n \"raised if the proxy diagnostic cube has a multi valued threshold coordinate.\\\"\\\"\\\" cube\",\n \"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #\",\n \"THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\",\n \"DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\",\n \"condition cube.\\\"\\\"\\\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\") assert result.name()\",\n \"CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR #\",\n \"set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\") assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype\",\n \"code must retain the above copyright notice, this # list of conditions and\",\n \"a multi valued threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2) expected = \\\"Expected a single\",\n \"result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \\\"shower_condition\\\" assert threshold_coord.units\",\n \"and the following disclaimer. # # * Redistributions in binary form must reproduce\",\n \"condition conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape]\",\n \") return cube def test_basic(): \\\"\\\"\\\"Test that with a valid input the cube\",\n \"= \\\"Input has no threshold coordinate and cannot be used\\\" with pytest.raises(CoordinateNotFoundError, match=expected):\",\n \"== FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \\\"shower_condition\\\" assert threshold_coord.units ==\",\n \"from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from\",\n \"# # * Redistributions in binary form must reproduce the above copyright notice,\",\n \"BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF\",\n \"threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input has no threshold coordinate\",\n \"SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS #\",\n \"an exception is raised if the proxy diagnostic cube has a multi valued\",\n \"the proxy diagnostic cube does not have a threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube()\",\n \"LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\",\n \"threshold coordinate and cannot be used\\\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test\",\n \"does not have a threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input\",\n \"np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape] if n_thresholds > 0\",\n \"* Redistributions in binary form must reproduce the above copyright notice, # this\",\n \"this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED\",\n \"# * Neither the name of the copyright holder nor the names of\",\n \"CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\",\n \"transformed into a shower condition cube.\\\"\\\"\\\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord\",\n \"cube = set_up_test_cube(n_thresholds=2) expected = \\\"Expected a single valued threshold coordinate.*\\\" with pytest.raises(ValueError,\",\n \"CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR\",\n \"BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\",\n \"(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS\",\n \"with a valid input the cube is transformed into a shower condition cube.\\\"\\\"\\\"\",\n \"IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING\",\n \"TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\",\n \"with or without # modification, are permitted provided that the following conditions are\",\n \"== cube.data).all() assert threshold_coord.name() == \\\"shower_condition\\\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \\\"\\\"\\\"Test\",\n \"EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF #\",\n \"BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL\",\n \"# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS\",\n \"improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a cube testing shower condition conversion\\\"\\\"\\\"\",\n \"# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN\",\n \"endorse or promote products derived from # this software without specific prior written\",\n \"and/or other materials provided with the distribution. # # * Neither the name\",\n \"Redistributions of source code must retain the above copyright notice, this # list\",\n \"import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import\",\n \"OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO\",\n \"OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\",\n \"NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\",\n \"valued threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2) expected = \\\"Expected a single valued threshold\",\n \"\\\"shower_condition\\\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if\",\n \"proxy diagnostic cube does not have a threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\")\",\n \"= result.coord(var_name=\\\"threshold\\\") assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype == FLOAT_DTYPE assert (result.data ==\",\n \"spatial_grid=\\\"equalarea\\\", ) return cube def test_basic(): \\\"\\\"\\\"Test that with a valid input the\",\n \"\\\"\\\"\\\"Test an exception is raised if the proxy diagnostic cube has a multi\",\n \"rights reserved. # # Redistribution and use in source and binary forms, with\",\n \"has no threshold coordinate and cannot be used\\\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def\",\n \"conditions and the following disclaimer. # # * Redistributions in binary form must\",\n \"OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY\",\n \"coordinate and cannot be used\\\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an\",\n \"pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy\",\n \"input the cube is transformed into a shower condition cube.\\\"\\\"\\\" cube = set_up_test_cube()\",\n \"permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\",\n \"the following conditions are met: # # * Redistributions of source code must\",\n \"copyright holder nor the names of its # contributors may be used to\",\n \"if n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube(\",\n \"data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", )\",\n \"that the following conditions are met: # # * Redistributions of source code\",\n \"shower condition cube.\\\"\\\"\\\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\") assert\",\n \"reproduce the above copyright notice, # this list of conditions and the following\",\n \"PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS\",\n \"ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT\",\n \"HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,\",\n \"the documentation # and/or other materials provided with the distribution. # # *\",\n \"cube = set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return cube def test_basic():\",\n \"TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\",\n \"result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name()\",\n \"1 def test_no_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy diagnostic cube\",\n \"DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\",\n \"of precipitation_type utilities\\\"\\\"\\\" import numpy as np import pytest from iris.exceptions import CoordinateNotFoundError\",\n \"0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\",\",\n \"IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY\",\n \"or without # modification, are permitted provided that the following conditions are met:\",\n \"as np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from\",\n \"without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE\",\n \"\\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \\\"shower_condition\\\"\",\n \"INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\",\n \"= np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return\",\n \"NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF\",\n \"copyright notice, # this list of conditions and the following disclaimer in the\",\n \"HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,\",\n \"WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN\",\n \"WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) #\",\n \"no threshold coordinate and cannot be used\\\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord():\",\n \"Tests of precipitation_type utilities\\\"\\\"\\\" import numpy as np import pytest from iris.exceptions import\",\n \"assert (result.data == cube.data).all() assert threshold_coord.name() == \\\"shower_condition\\\" assert threshold_coord.units == 1 def\",\n \"cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input has no threshold coordinate and cannot be used\\\" with\",\n \"and cannot be used\\\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception\",\n \"LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING,\",\n \"notice, this # list of conditions and the following disclaimer. # # *\",\n \"documentation # and/or other materials provided with the distribution. # # * Neither\",\n \"AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR\",\n \"2] shape = [n_thresholds, *shape] if n_thresholds > 0 else shape data =\",\n \"ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE #\",\n \"PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\",\n \"make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a cube testing shower\",\n \"this list of conditions and the following disclaimer in the documentation # and/or\",\n \"of conditions and the following disclaimer in the documentation # and/or other materials\",\n \"are permitted provided that the following conditions are met: # # * Redistributions\",\n \"list of conditions and the following disclaimer. # # * Redistributions in binary\",\n \"that with a valid input the cube is transformed into a shower condition\",\n \"list of conditions and the following disclaimer in the documentation # and/or other\",\n \"name of the copyright holder nor the names of its # contributors may\",\n \"IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \\\"\\\"\\\" Tests of precipitation_type\",\n \"= make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\") assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype == FLOAT_DTYPE\",\n \"and use in source and binary forms, with or without # modification, are\",\n \"shape = [n_thresholds, *shape] if n_thresholds > 0 else shape data = np.ones(shape,\",\n \"the distribution. # # * Neither the name of the copyright holder nor\",\n \"# this list of conditions and the following disclaimer in the documentation #\",\n \"provided that the following conditions are met: # # * Redistributions of source\",\n \"the cube is transformed into a shower condition cube.\\\"\\\"\\\" cube = set_up_test_cube() result\",\n \"ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\",\n \"# -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021\",\n \"HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT\",\n \"EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \\\"\\\"\\\" Tests of\",\n \"of its # contributors may be used to endorse or promote products derived\",\n \"its # contributors may be used to endorse or promote products derived from\",\n \"EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT,\",\n \"= [n_thresholds, *shape] if n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE)\",\n \"def test_basic(): \\\"\\\"\\\"Test that with a valid input the cube is transformed into\",\n \"MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT\",\n \"utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office. #\",\n \"LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT\",\n \"else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1,\",\n \"contributors may be used to endorse or promote products derived from # this\",\n \"match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy diagnostic\",\n \"INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT,\",\n \"OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE.\",\n \"exception is raised if the proxy diagnostic cube does not have a threshold\",\n \"Redistribution and use in source and binary forms, with or without # modification,\",\n \"----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office. # All rights reserved.\",\n \"source and binary forms, with or without # modification, are permitted provided that\",\n \"BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\",\n \"SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED\",\n \"make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy diagnostic cube\",\n \"from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a cube testing shower condition\",\n \"the name of the copyright holder nor the names of its # contributors\",\n \"FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\",\n \"NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\",\n \"IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR\",\n \"of conditions and the following disclaimer. # # * Redistributions in binary form\",\n \"(INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE\",\n \"PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\",\n \"above copyright notice, this # list of conditions and the following disclaimer. #\",\n \"the following disclaimer. # # * Redistributions in binary form must reproduce the\",\n \"derived from # this software without specific prior written permission. # # THIS\",\n \"diagnostic cube does not have a threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected\",\n \"POSSIBILITY OF SUCH DAMAGE. \\\"\\\"\\\" Tests of precipitation_type utilities\\\"\\\"\\\" import numpy as np\",\n \"set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return cube def test_basic(): \\\"\\\"\\\"Test that\",\n \"a valid input the cube is transformed into a shower condition cube.\\\"\\\"\\\" cube\",\n \"a threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input has no threshold\",\n \"set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a cube testing shower condition conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds) shape\",\n \"cube def test_basic(): \\\"\\\"\\\"Test that with a valid input the cube is transformed\",\n \"iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes\",\n \"# # * Neither the name of the copyright holder nor the names\",\n \"# contributors may be used to endorse or promote products derived from #\",\n \"permitted provided that the following conditions are met: # # * Redistributions of\",\n \"import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import\",\n \"Met Office. # All rights reserved. # # Redistribution and use in source\",\n \"other materials provided with the distribution. # # * Neither the name of\",\n \"a shower condition cube.\\\"\\\"\\\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\")\",\n \"COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES,\",\n \"np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return cube\",\n \"\\\"\\\"\\\" Tests of precipitation_type utilities\\\"\\\"\\\" import numpy as np import pytest from iris.exceptions\",\n \"if the proxy diagnostic cube has a multi valued threshold coordinate.\\\"\\\"\\\" cube =\",\n \"pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube\",\n \"= set_up_test_cube(n_thresholds=2) expected = \\\"Expected a single valued threshold coordinate.*\\\" with pytest.raises(ValueError, match=expected):\",\n \"forms, with or without # modification, are permitted provided that the following conditions\",\n \"used\\\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if\",\n \"# # * Redistributions of source code must retain the above copyright notice,\",\n \"with the distribution. # # * Neither the name of the copyright holder\",\n \"above copyright notice, # this list of conditions and the following disclaimer in\",\n \"threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return cube def test_basic(): \\\"\\\"\\\"Test that with a valid input\",\n \"data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return cube def test_basic(): \\\"\\\"\\\"Test that with\",\n \"in source and binary forms, with or without # modification, are permitted provided\",\n \"precipitation_type utilities\\\"\\\"\\\" import numpy as np import pytest from iris.exceptions import CoordinateNotFoundError from\",\n \"the following disclaimer in the documentation # and/or other materials provided with the\",\n \"# # Redistribution and use in source and binary forms, with or without\",\n \"ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\",\n \"import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a cube testing shower condition conversion\\\"\\\"\\\" thresholds\",\n \"shower condition conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds,\",\n \"is transformed into a shower condition cube.\\\"\\\"\\\" cube = set_up_test_cube() result = make_shower_condition_cube(cube)\",\n \"the above copyright notice, this # list of conditions and the following disclaimer.\",\n \"\\\"\\\"\\\"Set up a cube testing shower condition conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds) shape =\",\n \"used to endorse or promote products derived from # this software without specific\",\n \"-*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met\",\n \"Copyright 2017-2021 Met Office. # All rights reserved. # # Redistribution and use\",\n \"COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\",\n \"> 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds,\",\n \"SUCH DAMAGE. \\\"\\\"\\\" Tests of precipitation_type utilities\\\"\\\"\\\" import numpy as np import pytest\",\n \"promote products derived from # this software without specific prior written permission. #\",\n \"prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS\",\n \"== \\\"shower_condition\\\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \\\"\\\"\\\"Test an exception is raised\",\n \"British Crown Copyright 2017-2021 Met Office. # All rights reserved. # # Redistribution\",\n \"IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED.\",\n \"OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\",\n \"thresholds = np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape] if n_thresholds\",\n \"result.coord(var_name=\\\"threshold\\\") assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all()\",\n \"[n_thresholds, *shape] if n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube\",\n \"coding: utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office.\",\n \"assert threshold_coord.units == 1 def test_no_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the\",\n \"THE # POSSIBILITY OF SUCH DAMAGE. \\\"\\\"\\\" Tests of precipitation_type utilities\\\"\\\"\\\" import numpy\",\n \"OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE\",\n \"OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\",\n \"have a threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input has no\",\n \"import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set\",\n \"INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT\",\n \"ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\",\n \"use in source and binary forms, with or without # modification, are permitted\",\n \"with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the\",\n \"from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def\",\n \"THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" #\",\n \"must reproduce the above copyright notice, # this list of conditions and the\",\n \"this # list of conditions and the following disclaimer. # # * Redistributions\",\n \"# All rights reserved. # # Redistribution and use in source and binary\",\n \"following conditions are met: # # * Redistributions of source code must retain\",\n \"cube does not have a threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected =\",\n \"set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input has no threshold coordinate and cannot be used\\\"\",\n \"disclaimer in the documentation # and/or other materials provided with the distribution. #\",\n \"to endorse or promote products derived from # this software without specific prior\",\n \"assert threshold_coord.name() == \\\"shower_condition\\\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \\\"\\\"\\\"Test an exception\",\n \"up a cube testing shower condition conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds) shape = [2,\",\n \"# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN\",\n \"if the proxy diagnostic cube does not have a threshold coordinate.\\\"\\\"\\\" cube =\",\n \"# (C) British Crown Copyright 2017-2021 Met Office. # All rights reserved. #\",\n \"import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a cube testing\",\n \"conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape] if\",\n \"# POSSIBILITY OF SUCH DAMAGE. \\\"\\\"\\\" Tests of precipitation_type utilities\\\"\\\"\\\" import numpy as\",\n \"cube has a multi valued threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2) expected = \\\"Expected\",\n \"improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1):\",\n \"distribution. # # * Neither the name of the copyright holder nor the\",\n \"CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube\",\n \"threshold_coord = result.coord(var_name=\\\"threshold\\\") assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype == FLOAT_DTYPE assert (result.data\",\n \"in the documentation # and/or other materials provided with the distribution. # #\",\n \"SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\",\n \"GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION)\",\n \"IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED\",\n \"AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT\",\n \"# and/or other materials provided with the distribution. # # * Neither the\",\n \"products derived from # this software without specific prior written permission. # #\",\n \"result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\") assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype ==\",\n \"# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN #\",\n \"CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY\",\n \"the above copyright notice, # this list of conditions and the following disclaimer\",\n \"A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER\",\n \"OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF\",\n \"from # this software without specific prior written permission. # # THIS SOFTWARE\",\n \"cube.data).all() assert threshold_coord.name() == \\\"shower_condition\\\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \\\"\\\"\\\"Test an\",\n \"# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\",\n \"OF SUCH DAMAGE. \\\"\\\"\\\" Tests of precipitation_type utilities\\\"\\\"\\\" import numpy as np import\",\n \"is raised if the proxy diagnostic cube does not have a threshold coordinate.\\\"\\\"\\\"\",\n \"be used to endorse or promote products derived from # this software without\",\n \"# ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office. # All rights\",\n \"IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,\",\n \"* Neither the name of the copyright holder nor the names of its\",\n \"multi valued threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2) expected = \\\"Expected a single valued\",\n \"cube.\\\"\\\"\\\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\") assert result.name() ==\",\n \"ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF\",\n \"IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY\",\n \"coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2) expected = \\\"Expected a single valued threshold coordinate.*\\\" with\",\n \"materials provided with the distribution. # # * Neither the name of the\",\n \"AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL\",\n \"from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a\",\n \"FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \\\"shower_condition\\\" assert threshold_coord.units == 1\",\n \"def test_no_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy diagnostic cube does\",\n \"return cube def test_basic(): \\\"\\\"\\\"Test that with a valid input the cube is\",\n \"and binary forms, with or without # modification, are permitted provided that the\",\n \"conditions and the following disclaimer in the documentation # and/or other materials provided\",\n \"names of its # contributors may be used to endorse or promote products\",\n \"copyright notice, this # list of conditions and the following disclaimer. # #\",\n \"the proxy diagnostic cube has a multi valued threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2)\",\n \"not have a threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input has\",\n \"SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND\",\n \"threshold_coord.units == 1 def test_no_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy\",\n \"DAMAGE. \\\"\\\"\\\" Tests of precipitation_type utilities\\\"\\\"\\\" import numpy as np import pytest from\",\n \"SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT,\",\n \"numpy as np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE\",\n \"proxy diagnostic cube has a multi valued threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2) expected\",\n \"disclaimer. # # * Redistributions in binary form must reproduce the above copyright\",\n \"ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED\",\n \"WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND\",\n \"provided with the distribution. # # * Neither the name of the copyright\",\n \"\\\"\\\"\\\"Test that with a valid input the cube is transformed into a shower\",\n \"modification, are permitted provided that the following conditions are met: # # *\",\n \"an exception is raised if the proxy diagnostic cube does not have a\",\n \"must retain the above copyright notice, this # list of conditions and the\",\n \"are met: # # * Redistributions of source code must retain the above\",\n \"DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE\",\n \"OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR\",\n \"OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,\",\n \"LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\",\n \"# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES\",\n \"OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON\",\n \"= set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\") assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert\",\n \"cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\") assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\"\",\n \"assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == \\\"shower_condition\\\" assert\",\n \"the copyright holder nor the names of its # contributors may be used\",\n \"LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,\",\n \"improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a cube\",\n \"= np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape] if n_thresholds >\",\n \"LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND\",\n \"DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES;\",\n \"be used\\\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception is raised\",\n \"THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE #\",\n \"OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS\",\n \"THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE\",\n \"== 1 def test_no_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy diagnostic\",\n \"met: # # * Redistributions of source code must retain the above copyright\",\n \"= set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input has no threshold coordinate and cannot be\",\n \"coordinate.\\\"\\\"\\\" cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input has no threshold coordinate and\",\n \"THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \\\"\\\"\\\"\",\n \"Redistributions in binary form must reproduce the above copyright notice, # this list\",\n \"np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities\",\n \"THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF\",\n \"# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\",\n \"def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a cube testing shower condition conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds)\",\n \"is raised if the proxy diagnostic cube has a multi valued threshold coordinate.\\\"\\\"\\\"\",\n \"of the copyright holder nor the names of its # contributors may be\",\n \"OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER\",\n \"# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\"\",\n \"of source code must retain the above copyright notice, this # list of\",\n \"\\\"\\\"\\\"Test an exception is raised if the proxy diagnostic cube does not have\",\n \"exception is raised if the proxy diagnostic cube has a multi valued threshold\",\n \"into a shower condition cube.\\\"\\\"\\\" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord =\",\n \"# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE\",\n \"threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2) expected = \\\"Expected a single valued threshold coordinate.*\\\"\",\n \"notice, # this list of conditions and the following disclaimer in the documentation\",\n \"make_shower_condition_cube(cube) threshold_coord = result.coord(var_name=\\\"threshold\\\") assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype == FLOAT_DTYPE assert\",\n \"test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy diagnostic cube has a\",\n \"n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data,\",\n \"= [2, 2] shape = [n_thresholds, *shape] if n_thresholds > 0 else shape\",\n \"THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED\",\n \"the names of its # contributors may be used to endorse or promote\",\n \"ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \\\"\\\"\\\" Tests of precipitation_type utilities\\\"\\\"\\\"\",\n \"TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE\",\n \"variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return cube def test_basic(): \\\"\\\"\\\"Test that with a valid\",\n \"USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY\",\n \"STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY\",\n \"a cube testing shower condition conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds) shape = [2, 2]\",\n \"reserved. # # Redistribution and use in source and binary forms, with or\",\n \"\\\"AS IS\\\" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED\",\n \"# Redistribution and use in source and binary forms, with or without #\",\n \"INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\",\n \"WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\",\n \"nor the names of its # contributors may be used to endorse or\",\n \"FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT\",\n \"cannot be used\\\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception is\",\n \"source code must retain the above copyright notice, this # list of conditions\",\n \"conditions are met: # # * Redistributions of source code must retain the\",\n \"retain the above copyright notice, this # list of conditions and the following\",\n \"holder nor the names of its # contributors may be used to endorse\",\n \"BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" # AND ANY EXPRESS OR\",\n \"FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up\",\n \"import numpy as np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import\",\n \"\\\"Input has no threshold coordinate and cannot be used\\\" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube)\",\n \"valid input the cube is transformed into a shower condition cube.\\\"\\\"\\\" cube =\",\n \"OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER\",\n \"binary forms, with or without # modification, are permitted provided that the following\",\n \"*shape] if n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube =\",\n \"OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #\",\n \"Office. # All rights reserved. # # Redistribution and use in source and\",\n \"testing shower condition conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape =\",\n \"NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY\",\n \"may be used to endorse or promote products derived from # this software\",\n \"threshold_coord.name() == \\\"shower_condition\\\" assert threshold_coord.units == 1 def test_no_threshold_coord(): \\\"\\\"\\\"Test an exception is\",\n \"(result.data == cube.data).all() assert threshold_coord.name() == \\\"shower_condition\\\" assert threshold_coord.units == 1 def test_no_threshold_coord():\",\n \"expected = \\\"Input has no threshold coordinate and cannot be used\\\" with pytest.raises(CoordinateNotFoundError,\",\n \"# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS\",\n \"cube testing shower condition conversion\\\"\\\"\\\" thresholds = np.arange(n_thresholds) shape = [2, 2] shape\",\n \"== \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() ==\",\n \"binary form must reproduce the above copyright notice, # this list of conditions\",\n \"assert result.name() == \\\"probability_of_shower_condition_above_threshold\\\" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert\",\n \"following disclaimer in the documentation # and/or other materials provided with the distribution.\",\n \"dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return cube def\",\n \"USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH\",\n \"CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY,\",\n \"SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. \\\"\\\"\\\" Tests\",\n \"utilities\\\"\\\"\\\" import numpy as np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants\",\n \"2017-2021 Met Office. # All rights reserved. # # Redistribution and use in\",\n \"set_up_test_cube(n_thresholds=2) expected = \\\"Expected a single valued threshold coordinate.*\\\" with pytest.raises(ValueError, match=expected): make_shower_condition_cube(cube)\",\n \"FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE\",\n \"-*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office. # All\",\n \"form must reproduce the above copyright notice, # this list of conditions and\",\n \"PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\",\n \"# list of conditions and the following disclaimer. # # * Redistributions in\",\n \"Neither the name of the copyright holder nor the names of its #\",\n \"cube = set_up_test_cube() cube.remove_coord(\\\"texture_of_cloud_area_fraction\\\") expected = \\\"Input has no threshold coordinate and cannot\",\n \"= set_up_probability_cube( data, thresholds, variable_name=\\\"texture_of_cloud_area_fraction\\\", threshold_units=1, spatial_grid=\\\"equalarea\\\", ) return cube def test_basic(): \\\"\\\"\\\"Test\",\n \"shape = [2, 2] shape = [n_thresholds, *shape] if n_thresholds > 0 else\",\n \"def test_multi_valued_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy diagnostic cube has\",\n \"test_basic(): \\\"\\\"\\\"Test that with a valid input the cube is transformed into a\",\n \"# * Redistributions of source code must retain the above copyright notice, this\",\n \"# modification, are permitted provided that the following conditions are met: # #\",\n \"written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND\",\n \"# * Redistributions in binary form must reproduce the above copyright notice, #\",\n \"OF THE # POSSIBILITY OF SUCH DAMAGE. \\\"\\\"\\\" Tests of precipitation_type utilities\\\"\\\"\\\" import\",\n \"set_up_probability_cube def set_up_test_cube(n_thresholds=1): \\\"\\\"\\\"Set up a cube testing shower condition conversion\\\"\\\"\\\" thresholds =\",\n \"test_no_threshold_coord(): \\\"\\\"\\\"Test an exception is raised if the proxy diagnostic cube does not\",\n \"diagnostic cube has a multi valued threshold coordinate.\\\"\\\"\\\" cube = set_up_test_cube(n_thresholds=2) expected =\",\n \"OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF\",\n \"or promote products derived from # this software without specific prior written permission.\",\n \"specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT\",\n \"* Redistributions of source code must retain the above copyright notice, this #\",\n \"following disclaimer. # # * Redistributions in binary form must reproduce the above\",\n \"raised if the proxy diagnostic cube does not have a threshold coordinate.\\\"\\\"\\\" cube\"\n]","isConversational":false}}},{"rowIdx":1185,"cells":{"ngram":{"kind":"list like","value":["self.auth = auth def add_item( self, name, price, quantity, image, category_id, reorder_point, auth):","available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"UPDATE","self.conn = model.conn self.cur = model.cur query = \"\"\"INSERT INTO items(name, price, quantity,","quantity = %s WHERE item_id= %s \"\"\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm =","= name self.price = price self.quantity = quantity self.category_id = category_id self.reorder_point =","self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE item_id =","ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"DELETE FROM items WHERE","query = \"\"\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute(","'''retrieves items by finding them using their category. all items in the same","items WHERE item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item","section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables","combination''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT","%s, quantity = %s, image = %s, category = %s, reorder_point = %s,","%s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity):","AND price = %s;\"\"\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def","finding them using their unique unique combination''' model = ModelSetup() self.conn = model.conn","self.cur = model.cur query = \"\"\"UPDATE items SET quantity = %s WHERE item_id=","category_id, reorder_point, auth): '''Adds item given the above arguements. Then returns the created","self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds item given the above","= %s;\"\"\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def update_item( self,","auth def add_item( self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds item","* FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone()","category_id, reorder_point, auth)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE name =","\"\"\"SELECT * FROM items WHERE category LIKE %s;\"\"\" self.cur.execute(query, (category)) self.item = self.cur.fetchall()","self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def update_item( self, item_id, price,","quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items class''' self.name =","= model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE item_id","image, category_id, reorder_point, auth): '''Adds item given the above arguements. Then returns the","the db are changed to what is provided''' model = ModelSetup() self.conn =","AND price = %s;\"\"\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def","self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added","item_id= %s \"\"\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit()","item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT","FROM items WHERE name LIKE %s AND price = %s;\"\"\" self.cur.execute(query, (name, price))","them''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT","%s, category = %s, reorder_point = %s, created_by = %s WHERE item_id= %s","their unique item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query","price self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point self.auth = auth","price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all records of items","WHERE item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def","self.items def get_by_id(self, item_id): '''retrieves one item by finding them using their unique","item_id = %s;\"\"\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self,","= %s AND price = %s;\"\"\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return","= \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self,","created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm","items WHERE item_id = %s;\"\"\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item","= self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity","self.cur = model.cur query = \"\"\"UPDATE items SET price = %s, quantity =","them using their category. all items in the same category are retrieved''' model","'''retrieves one item by finding them using their unique unique combination''' model =","quantity): '''updates item's quantity.adds the quantity added to the quantity available''' model =",")) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item by","WHERE category LIKE %s;\"\"\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self,","= %s, image = %s, category = %s, reorder_point = %s, created_by =","query_confirm = \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id, ))","self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added to the","item_id, quantity): '''updates item's quantity.adds the quantity added to the quantity available''' model","changed to what is provided''' model = ModelSetup() self.conn = model.conn self.cur =","WHERE item_id= %s \"\"\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM","= model.cur query = \"\"\"SELECT * FROM items WHERE category LIKE %s;\"\"\" self.cur.execute(query,","of items in the databas and returns them''' model = ModelSetup() self.conn =","SET price = %s, quantity = %s, image = %s, category = %s,","= quantity self.category_id = category_id self.reorder_point = reorder_point self.auth = auth def add_item(","ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"UPDATE items SET quantity","self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query)","self.reorder_point = reorder_point self.auth = auth def add_item( self, name, price, quantity, image,","= %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id,","query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \"\"\"SELECT *","using their unique item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur","variables for the items class''' self.name = name self.price = price self.quantity =","query = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def","finding them using their category. all items in the same category are retrieved'''","self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE name = %s AND price","%s;\"\"\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves","details. the values in the db are changed to what is provided''' model","%s AND price = %s;\"\"\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item","the data logic of the items section''' def __init__( self, name=None, price=None, quantity=None,","name = %s AND price = %s;\"\"\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone()",")) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query_confirm) self.items = self.cur.fetchall() return","name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items class'''","values in the db are changed to what is provided''' model = ModelSetup()","FROM items WHERE category LIKE %s;\"\"\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item","query = \"\"\"UPDATE items SET price = %s, quantity = %s, image =","= \"\"\"UPDATE items SET price = %s, quantity = %s, image = %s,","%s WHERE item_id= %s \"\"\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth,","= model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE name","ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items","self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates","(quantity, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\"","auth): '''updates item's details. the values in the db are changed to what","model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE category LIKE","category are retrieved''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query","name, price): '''retrieves one item by finding them using their unique unique combination'''","items by finding them using their category. all items in the same category","= ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"UPDATE items SET","* FROM items WHERE category LIKE %s;\"\"\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return","an item by finding them using the item_id''' model = ModelSetup() self.conn =","by finding them using the item_id''' model = ModelSetup() self.conn = model.conn self.cur","model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query) self.items =","model.cur query = \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query, (item_id,","model.cur query = \"\"\"UPDATE items SET price = %s, quantity = %s, image","in the same category are retrieved''' model = ModelSetup() self.conn = model.conn self.cur","self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id,","item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self,","self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all records","\"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id):","reorder_point = %s, created_by = %s WHERE item_id= %s \"\"\" self.cur.execute( query, (price,","their category. all items in the same category are retrieved''' model = ModelSetup()","image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE name","'''updates item's quantity.adds the quantity added to the quantity available''' model = ModelSetup()","(item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item","return self.item def delete_item(self, item_id): '''deletes an item by finding them using the","item's quantity.adds the quantity added to the quantity available''' model = ModelSetup() self.conn","FROM items;\"\"\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one","WHERE item_id= %s \"\"\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id))","model.cur query = \"\"\"SELECT * FROM items WHERE name LIKE %s AND price","model.conn self.cur = model.cur query = \"\"\"DELETE FROM items WHERE item_id = %s\"\"\"","= \"\"\"SELECT * FROM items WHERE name LIKE %s AND price = %s;\"\"\"","same category are retrieved''' model = ModelSetup() self.conn = model.conn self.cur = model.cur","quantity.adds the quantity added to the quantity available''' model = ModelSetup() self.conn =","add_item( self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds item given the","items class''' self.name = name self.price = price self.quantity = quantity self.category_id =","self.item def get_by_category(self, category): '''retrieves items by finding them using their category. all","the created item''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query","self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \"\"\"SELECT","self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE","self.item def get_by_name_and_price(self, name, price): '''retrieves one item by finding them using their","image = %s, category = %s, reorder_point = %s, created_by = %s WHERE","quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items","(name, price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all records of","unique unique combination''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query","(name, price)) self.item = self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity,","def add_item( self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds item given","* FROM items WHERE name LIKE %s AND price = %s;\"\"\" self.cur.execute(query, (name,","all records of items in the databas and returns them''' model = ModelSetup()","db are changed to what is provided''' model = ModelSetup() self.conn = model.conn","self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's details. the values","\"\"\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name,","model.cur query = \"\"\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\"","def get_by_category(self, category): '''retrieves items by finding them using their category. all items","= ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT * FROM","quantity available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =","image, category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point,","the item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =","FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return","category. all items in the same category are retrieved''' model = ModelSetup() self.conn","delete_item(self, item_id): '''deletes an item by finding them using the item_id''' model =","self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by finding them using their","item_id): '''retrieves one item by finding them using their unique item_id''' model =","= model.cur query = \"\"\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\","'''deletes an item by finding them using the item_id''' model = ModelSetup() self.conn","%s \"\"\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE","auth): '''Adds item given the above arguements. Then returns the created item''' model","reorder_point, auth): '''Adds item given the above arguements. Then returns the created item'''","return self.items def get_by_id(self, item_id): '''retrieves one item by finding them using their","self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth):","ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items;\"\"\"","items;\"\"\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item","self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by finding them","= category_id self.reorder_point = reorder_point self.auth = auth def add_item( self, name, price,","items WHERE item_id = %s\"\"\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \"\"\"SELECT *","LIKE %s AND price = %s;\"\"\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return","self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item by finding","what is provided''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query","quantity added to the quantity available''' model = ModelSetup() self.conn = model.conn self.cur","%s, created_by = %s WHERE item_id= %s \"\"\" self.cur.execute( query, (price, quantity, image,","(category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one item","model.conn self.cur = model.cur query = \"\"\"UPDATE items SET price = %s, quantity","price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items class''' self.name","query_confirm = \"\"\"SELECT * FROM items WHERE name = %s AND price =","'''gets all records of items in the databas and returns them''' model =","VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm =","model.cur query = \"\"\"SELECT * FROM items WHERE category LIKE %s;\"\"\" self.cur.execute(query, (category))","quantity, image, category_id, reorder_point, auth): '''updates item's details. the values in the db","return self.item def update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates",")) self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by finding","quantity self.category_id = category_id self.reorder_point = reorder_point self.auth = auth def add_item( self,","model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT *","= model.cur query = \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query,","added to the quantity available''' model = ModelSetup() self.conn = model.conn self.cur =","model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"INSERT INTO","them using the item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur","= \"\"\"SELECT * FROM items WHERE name = %s AND price = %s;\"\"\"","self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item by finding","items WHERE name LIKE %s AND price = %s;\"\"\" self.cur.execute(query, (name, price)) self.item","their unique unique combination''' model = ModelSetup() self.conn = model.conn self.cur = model.cur","the quantity available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query","data logic of the items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None,","\"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query, (item_id, )) self.item =","\"\"\"SELECT * FROM items WHERE name LIKE %s AND price = %s;\"\"\" self.cur.execute(query,","= model.conn self.cur = model.cur query = \"\"\"UPDATE items SET quantity = %s","item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's details. the values in","%s, reorder_point = %s, created_by = %s WHERE item_id= %s \"\"\" self.cur.execute( query,","are changed to what is provided''' model = ModelSetup() self.conn = model.conn self.cur","def get_by_name_and_price(self, name, price): '''retrieves one item by finding them using their unique","by finding them using their category. all items in the same category are","category_id self.reorder_point = reorder_point self.auth = auth def add_item( self, name, price, quantity,","ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"UPDATE items SET price","price)) self.item = self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity, image,","WHERE name = %s AND price = %s;\"\"\" self.cur.execute(query_confirm, (name, price)) self.item =","self.conn = model.conn self.cur = model.cur query = \"\"\"UPDATE items SET price =","(price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM","update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's details. the","%s WHERE item_id= %s \"\"\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT *","auth)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE name = %s AND","class ItemsModel(ModelSetup): '''Handles the data logic of the items section''' def __init__( self,","self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point self.auth = auth def","self.price = price self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point self.auth","* FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone()","self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items","price, quantity, image, category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name, price, quantity, image,","%s \"\"\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm","return self.item def get_all(self): '''gets all records of items in the databas and","finding them using the item_id''' model = ModelSetup() self.conn = model.conn self.cur =","\"\"\"UPDATE items SET quantity = %s WHERE item_id= %s \"\"\" self.cur.execute(query, (quantity, item_id))","self.item def delete_item(self, item_id): '''deletes an item by finding them using the item_id'''","query = \"\"\"DELETE FROM items WHERE item_id = %s\"\"\" self.cur.execute(query, (item_id, )) self.conn.commit()","= \"\"\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query,","return self.item def get_by_name_and_price(self, name, price): '''retrieves one item by finding them using","item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"DELETE","WHERE item_id = %s\"\"\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \"\"\"SELECT * FROM","self.item = self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity, image, category_id,","self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one","ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"INSERT INTO items(name, price,","the above arguements. Then returns the created item''' model = ModelSetup() self.conn =","item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self,","= %s, created_by = %s WHERE item_id= %s \"\"\" self.cur.execute( query, (price, quantity,","= %s, category = %s, reorder_point = %s, created_by = %s WHERE item_id=","\"\"\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm =","= self.cur.fetchone() return self.item def get_all(self): '''gets all records of items in the","category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE item_id","category LIKE %s;\"\"\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name,","= model.conn self.cur = model.cur query = \"\"\"INSERT INTO items(name, price, quantity, image,","= ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"DELETE FROM items","auth=None): '''Initializes the variables for the items class''' self.name = name self.price =","'''updates item's details. the values in the db are changed to what is","FROM items WHERE name = %s AND price = %s;\"\"\" self.cur.execute(query_confirm, (name, price))","self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the","\"\"\"UPDATE items SET price = %s, quantity = %s, image = %s, category","finding them using their unique item_id''' model = ModelSetup() self.conn = model.conn self.cur","reorder_point, auth)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE name = %s","price = %s;\"\"\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def update_item(","returns the created item''' model = ModelSetup() self.conn = model.conn self.cur = model.cur","%s;\"\"\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def update_item( self, item_id,","ItemsModel(ModelSetup): '''Handles the data logic of the items section''' def __init__( self, name=None,","category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth))","def get_all(self): '''gets all records of items in the databas and returns them'''","quantity = %s, image = %s, category = %s, reorder_point = %s, created_by","self.item def get_all(self): '''gets all records of items in the databas and returns","reorder_point=None, auth=None): '''Initializes the variables for the items class''' self.name = name self.price","= \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query, (item_id, )) self.item","price): '''retrieves one item by finding them using their unique unique combination''' model","unique combination''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =","quantity, image, category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name, price, quantity, image, category_id,","and returns them''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query","= model.conn self.cur = model.cur query = \"\"\"UPDATE items SET price = %s,","update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added to the quantity available'''","= self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item by finding them","def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for","model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE name LIKE","image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE","self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE category LIKE %s;\"\"\"","* FROM items;\"\"\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves","logic of the items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None,","returns them''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =","= model.cur query = \"\"\"DELETE FROM items WHERE item_id = %s\"\"\" self.cur.execute(query, (item_id,","self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items","= %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id):","= model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE category","'''Adds item given the above arguements. Then returns the created item''' model =","%s AND price = %s;\"\"\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item","self.cur = model.cur query = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query) self.items = self.cur.fetchall()","query = \"\"\"SELECT * FROM items WHERE category LIKE %s;\"\"\" self.cur.execute(query, (category)) self.item","from .db_conn import ModelSetup class ItemsModel(ModelSetup): '''Handles the data logic of the items","= self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one item by finding","to what is provided''' model = ModelSetup() self.conn = model.conn self.cur = model.cur","= %s\"\"\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query_confirm)","query = \"\"\"SELECT * FROM items WHERE name LIKE %s AND price =","self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query_confirm) self.items =","name self.price = price self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point","model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE item_id =","self.cur = model.cur query = \"\"\"DELETE FROM items WHERE item_id = %s\"\"\" self.cur.execute(query,","them using their unique item_id''' model = ModelSetup() self.conn = model.conn self.cur =","the quantity added to the quantity available''' model = ModelSetup() self.conn = model.conn","return self.item def get_by_category(self, category): '''retrieves items by finding them using their category.","one item by finding them using their unique unique combination''' model = ModelSetup()","all items in the same category are retrieved''' model = ModelSetup() self.conn =","\"\"\"DELETE FROM items WHERE item_id = %s\"\"\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm =","above arguements. Then returns the created item''' model = ModelSetup() self.conn = model.conn","using their unique unique combination''' model = ModelSetup() self.conn = model.conn self.cur =","self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item by","model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"DELETE FROM","quantity, image, category_id, reorder_point, auth): '''Adds item given the above arguements. Then returns","= model.cur query = \"\"\"SELECT * FROM items WHERE name LIKE %s AND","category_id, reorder_point, auth): '''updates item's details. the values in the db are changed","category): '''retrieves items by finding them using their category. all items in the","\"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item =","item by finding them using their unique item_id''' model = ModelSetup() self.conn =","WHERE name LIKE %s AND price = %s;\"\"\" self.cur.execute(query, (name, price)) self.item =","quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE","self.conn = model.conn self.cur = model.cur query = \"\"\"DELETE FROM items WHERE item_id","model.cur query = \"\"\"DELETE FROM items WHERE item_id = %s\"\"\" self.cur.execute(query, (item_id, ))","= price self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point self.auth =","the databas and returns them''' model = ModelSetup() self.conn = model.conn self.cur =","items SET price = %s, quantity = %s, image = %s, category =","reorder_point self.auth = auth def add_item( self, name, price, quantity, image, category_id, reorder_point,","category = %s, reorder_point = %s, created_by = %s WHERE item_id= %s \"\"\"",")) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds","LIKE %s;\"\"\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price):","= auth def add_item( self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds","reorder_point, auth): '''updates item's details. the values in the db are changed to","price, quantity, image, category_id, reorder_point, auth): '''Adds item given the above arguements. Then","by finding them using their unique unique combination''' model = ModelSetup() self.conn =","get_by_category(self, category): '''retrieves items by finding them using their category. all items in","category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items class''' self.name = name","get_by_id(self, item_id): '''retrieves one item by finding them using their unique item_id''' model","items(name, price, quantity, image, category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name, price, quantity,","= self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by finding them using","class''' self.name = name self.price = price self.quantity = quantity self.category_id = category_id","price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items","the variables for the items class''' self.name = name self.price = price self.quantity","(item_id, )) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query_confirm) self.items = self.cur.fetchall()","self.conn = model.conn self.cur = model.cur query = \"\"\"UPDATE items SET quantity =","by finding them using their unique item_id''' model = ModelSetup() self.conn = model.conn","is provided''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =","SET quantity = %s WHERE item_id= %s \"\"\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm","= model.cur query = \"\"\"UPDATE items SET quantity = %s WHERE item_id= %s","Then returns the created item''' model = ModelSetup() self.conn = model.conn self.cur =","in the db are changed to what is provided''' model = ModelSetup() self.conn","FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return","item given the above arguements. Then returns the created item''' model = ModelSetup()","price = %s;\"\"\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def get_all(self):","%s;\"\"\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves","provided''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"UPDATE","item_id = %s\"\"\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items;\"\"\"","= model.cur query = \"\"\"UPDATE items SET price = %s, quantity = %s,","= model.conn self.cur = model.cur query = \"\"\"DELETE FROM items WHERE item_id =","self.name = name self.price = price self.quantity = quantity self.category_id = category_id self.reorder_point","return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added to","\"\"\"SELECT * FROM items WHERE name = %s AND price = %s;\"\"\" self.cur.execute(query_confirm,","model.cur query = \"\"\"UPDATE items SET quantity = %s WHERE item_id= %s \"\"\"","price, quantity, image, category_id, reorder_point, auth): '''updates item's details. the values in the","item_id= %s \"\"\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items","are retrieved''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =","= self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity, image, category_id, reorder_point,","self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an","reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit()","query = \"\"\"UPDATE items SET quantity = %s WHERE item_id= %s \"\"\" self.cur.execute(query,","self.cur.fetchone() return self.item def get_all(self): '''gets all records of items in the databas","item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query_confirm,","= ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"INSERT INTO items(name,","'''Handles the data logic of the items section''' def __init__( self, name=None, price=None,","self.category_id = category_id self.reorder_point = reorder_point self.auth = auth def add_item( self, name,","to the quantity available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur","= model.cur query = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query) self.items = self.cur.fetchall() return","query = \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query, (item_id, ))","self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item by finding them using","items WHERE name = %s AND price = %s;\"\"\" self.cur.execute(query_confirm, (name, price)) self.item","= \"\"\"UPDATE items SET quantity = %s WHERE item_id= %s \"\"\" self.cur.execute(query, (quantity,","%s, image = %s, category = %s, reorder_point = %s, created_by = %s","them using their unique unique combination''' model = ModelSetup() self.conn = model.conn self.cur","self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item by finding them using","self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one item by","= self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item by finding them","self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE name LIKE %s","'''Initializes the variables for the items class''' self.name = name self.price = price","items in the databas and returns them''' model = ModelSetup() self.conn = model.conn","arguements. Then returns the created item''' model = ModelSetup() self.conn = model.conn self.cur","name LIKE %s AND price = %s;\"\"\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone()","given the above arguements. Then returns the created item''' model = ModelSetup() self.conn","item''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"INSERT","= %s WHERE item_id= %s \"\"\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT","items SET quantity = %s WHERE item_id= %s \"\"\" self.cur.execute(query, (quantity, item_id)) self.conn.commit()","item's details. the values in the db are changed to what is provided'''","= %s, reorder_point = %s, created_by = %s WHERE item_id= %s \"\"\" self.cur.execute(","self.conn.commit() query_confirm = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query_confirm) self.items = self.cur.fetchall() return self.items","self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT",".db_conn import ModelSetup class ItemsModel(ModelSetup): '''Handles the data logic of the items section'''","the items class''' self.name = name self.price = price self.quantity = quantity self.category_id","item by finding them using the item_id''' model = ModelSetup() self.conn = model.conn","self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all records of items in","= %s;\"\"\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets","def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added to the quantity","records of items in the databas and returns them''' model = ModelSetup() self.conn","%s\"\"\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query_confirm) self.items","unique item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =","= reorder_point self.auth = auth def add_item( self, name, price, quantity, image, category_id,","* FROM items WHERE name = %s AND price = %s;\"\"\" self.cur.execute(query_confirm, (name,","created_by = %s WHERE item_id= %s \"\"\" self.cur.execute( query, (price, quantity, image, category_id,","databas and returns them''' model = ModelSetup() self.conn = model.conn self.cur = model.cur","FROM items WHERE item_id = %s\"\"\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \"\"\"SELECT","retrieved''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"SELECT","query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT *","image, category_id, reorder_point, auth): '''updates item's details. the values in the db are","(item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's","get_all(self): '''gets all records of items in the databas and returns them''' model","in the databas and returns them''' model = ModelSetup() self.conn = model.conn self.cur","items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the","items in the same category are retrieved''' model = ModelSetup() self.conn = model.conn","def delete_item(self, item_id): '''deletes an item by finding them using the item_id''' model","reorder_point, auth, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE item_id =","using the item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query","INTO items(name, price, quantity, image, category, reorder_point, created_by)\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\"\"\" self.cur.execute( query, (name, price,","= %s;\"\"\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self, category):","\"\"\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE item_id","model.cur query = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items","= %s, quantity = %s, image = %s, category = %s, reorder_point =","'''retrieves one item by finding them using their unique item_id''' model = ModelSetup()","item_id): '''deletes an item by finding them using the item_id''' model = ModelSetup()","%s;\"\"\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all","import ModelSetup class ItemsModel(ModelSetup): '''Handles the data logic of the items section''' def","= model.conn self.cur = model.cur query = \"\"\"SELECT * FROM items;\"\"\" self.cur.execute(query) self.items","created item''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =","(name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM","= \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item","= %s WHERE item_id= %s \"\"\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point,","get_by_name_and_price(self, name, price): '''retrieves one item by finding them using their unique unique","= \"\"\"SELECT * FROM items WHERE category LIKE %s;\"\"\" self.cur.execute(query, (category)) self.item =","def update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's details.","self.cur = model.cur query = \"\"\"INSERT INTO items(name, price, quantity, image, category, reorder_point,","the same category are retrieved''' model = ModelSetup() self.conn = model.conn self.cur =","%s;\"\"\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes","the items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes","item by finding them using their unique unique combination''' model = ModelSetup() self.conn","(item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by","one item by finding them using their unique item_id''' model = ModelSetup() self.conn","self.cur = model.cur query = \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\"","self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one item by finding them","model.conn self.cur = model.cur query = \"\"\"UPDATE items SET quantity = %s WHERE","the values in the db are changed to what is provided''' model =","name, price, quantity, image, category_id, reorder_point, auth): '''Adds item given the above arguements.","= \"\"\"DELETE FROM items WHERE item_id = %s\"\"\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm","for the items class''' self.name = name self.price = price self.quantity = quantity","def get_by_id(self, item_id): '''retrieves one item by finding them using their unique item_id'''","ModelSetup class ItemsModel(ModelSetup): '''Handles the data logic of the items section''' def __init__(","__init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the","items WHERE category LIKE %s;\"\"\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def","model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"UPDATE items","auth, item_id)) self.conn.commit() query_confirm = \"\"\"SELECT * FROM items WHERE item_id = %s;\"\"\"","using their category. all items in the same category are retrieved''' model =","of the items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None):","WHERE item_id = %s;\"\"\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def","price = %s, quantity = %s, image = %s, category = %s, reorder_point","self.item def update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's","model.conn self.cur = model.cur query = \"\"\"INSERT INTO items(name, price, quantity, image, category,"],"string":"[\n \"self.auth = auth def add_item( self, name, price, quantity, image, category_id, reorder_point, auth):\",\n \"available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE\",\n \"self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"INSERT INTO items(name, price, quantity,\",\n \"quantity = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm =\",\n \"= name self.price = price self.quantity = quantity self.category_id = category_id self.reorder_point =\",\n \"self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE item_id =\",\n \"ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"DELETE FROM items WHERE\",\n \"query = \\\"\\\"\\\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute(\",\n \"'''retrieves items by finding them using their category. all items in the same\",\n \"items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item\",\n \"section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables\",\n \"combination''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT\",\n \"%s, quantity = %s, image = %s, category = %s, reorder_point = %s,\",\n \"%s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity):\",\n \"AND price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def\",\n \"finding them using their unique unique combination''' model = ModelSetup() self.conn = model.conn\",\n \"self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET quantity = %s WHERE item_id=\",\n \"category_id, reorder_point, auth): '''Adds item given the above arguements. Then returns the created\",\n \"self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds item given the above\",\n \"= %s;\\\"\\\"\\\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def update_item( self,\",\n \"auth def add_item( self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds item\",\n \"* FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone()\",\n \"category_id, reorder_point, auth)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE name =\",\n \"\\\"\\\"\\\"SELECT * FROM items WHERE category LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item = self.cur.fetchall()\",\n \"self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def update_item( self, item_id, price,\",\n \"quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items class''' self.name =\",\n \"= model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE item_id\",\n \"image, category_id, reorder_point, auth): '''Adds item given the above arguements. Then returns the\",\n \"the db are changed to what is provided''' model = ModelSetup() self.conn =\",\n \"AND price = %s;\\\"\\\"\\\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def\",\n \"self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added\",\n \"item_id= %s \\\"\\\"\\\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit()\",\n \"item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT\",\n \"FROM items WHERE name LIKE %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query, (name, price))\",\n \"them''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT\",\n \"%s, category = %s, reorder_point = %s, created_by = %s WHERE item_id= %s\",\n \"their unique item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query\",\n \"price self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point self.auth = auth\",\n \"price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all records of items\",\n \"WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def\",\n \"self.items def get_by_id(self, item_id): '''retrieves one item by finding them using their unique\",\n \"item_id = %s;\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self,\",\n \"= %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return\",\n \"= \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self,\",\n \"created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm\",\n \"items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item\",\n \"= self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity\",\n \"self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET price = %s, quantity =\",\n \"them using their category. all items in the same category are retrieved''' model\",\n \"'''retrieves one item by finding them using their unique unique combination''' model =\",\n \"quantity): '''updates item's quantity.adds the quantity added to the quantity available''' model =\",\n \")) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item by\",\n \"WHERE category LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self,\",\n \"= %s, image = %s, category = %s, reorder_point = %s, created_by =\",\n \"query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, ))\",\n \"self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added to the\",\n \"item_id, quantity): '''updates item's quantity.adds the quantity added to the quantity available''' model\",\n \"changed to what is provided''' model = ModelSetup() self.conn = model.conn self.cur =\",\n \"WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM\",\n \"= model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE category LIKE %s;\\\"\\\"\\\" self.cur.execute(query,\",\n \"of items in the databas and returns them''' model = ModelSetup() self.conn =\",\n \"SET price = %s, quantity = %s, image = %s, category = %s,\",\n \"= quantity self.category_id = category_id self.reorder_point = reorder_point self.auth = auth def add_item(\",\n \"ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET quantity\",\n \"self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query)\",\n \"self.reorder_point = reorder_point self.auth = auth def add_item( self, name, price, quantity, image,\",\n \"= %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id,\",\n \"query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT *\",\n \"using their unique item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur\",\n \"variables for the items class''' self.name = name self.price = price self.quantity =\",\n \"query = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def\",\n \"finding them using their category. all items in the same category are retrieved'''\",\n \"self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE name = %s AND price\",\n \"%s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves\",\n \"details. the values in the db are changed to what is provided''' model\",\n \"%s AND price = %s;\\\"\\\"\\\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item\",\n \"the data logic of the items section''' def __init__( self, name=None, price=None, quantity=None,\",\n \"name = %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone()\",\n \")) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query_confirm) self.items = self.cur.fetchall() return\",\n \"name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items class'''\",\n \"values in the db are changed to what is provided''' model = ModelSetup()\",\n \"FROM items WHERE category LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item\",\n \"query = \\\"\\\"\\\"UPDATE items SET price = %s, quantity = %s, image =\",\n \"= \\\"\\\"\\\"UPDATE items SET price = %s, quantity = %s, image = %s,\",\n \"%s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth,\",\n \"= model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE name\",\n \"ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items\",\n \"self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates\",\n \"(quantity, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\"\",\n \"auth): '''updates item's details. the values in the db are changed to what\",\n \"model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE category LIKE\",\n \"category are retrieved''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query\",\n \"name, price): '''retrieves one item by finding them using their unique unique combination'''\",\n \"items by finding them using their category. all items in the same category\",\n \"= ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET\",\n \"* FROM items WHERE category LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return\",\n \"an item by finding them using the item_id''' model = ModelSetup() self.conn =\",\n \"by finding them using the item_id''' model = ModelSetup() self.conn = model.conn self.cur\",\n \"model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items =\",\n \"model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query, (item_id,\",\n \"model.cur query = \\\"\\\"\\\"UPDATE items SET price = %s, quantity = %s, image\",\n \"in the same category are retrieved''' model = ModelSetup() self.conn = model.conn self.cur\",\n \"self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id,\",\n \"item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self,\",\n \"self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all records\",\n \"\\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id):\",\n \"reorder_point = %s, created_by = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute( query, (price,\",\n \"their category. all items in the same category are retrieved''' model = ModelSetup()\",\n \"image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE name\",\n \"'''updates item's quantity.adds the quantity added to the quantity available''' model = ModelSetup()\",\n \"(item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item\",\n \"return self.item def delete_item(self, item_id): '''deletes an item by finding them using the\",\n \"item's quantity.adds the quantity added to the quantity available''' model = ModelSetup() self.conn\",\n \"FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one\",\n \"WHERE item_id= %s \\\"\\\"\\\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id))\",\n \"model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE name LIKE %s AND price\",\n \"model.conn self.cur = model.cur query = \\\"\\\"\\\"DELETE FROM items WHERE item_id = %s\\\"\\\"\\\"\",\n \"= \\\"\\\"\\\"SELECT * FROM items WHERE name LIKE %s AND price = %s;\\\"\\\"\\\"\",\n \"same category are retrieved''' model = ModelSetup() self.conn = model.conn self.cur = model.cur\",\n \"quantity.adds the quantity added to the quantity available''' model = ModelSetup() self.conn =\",\n \"add_item( self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds item given the\",\n \"items class''' self.name = name self.price = price self.quantity = quantity self.category_id =\",\n \"self.item def get_by_category(self, category): '''retrieves items by finding them using their category. all\",\n \"the created item''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query\",\n \"self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT\",\n \"self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE\",\n \"self.item def get_by_name_and_price(self, name, price): '''retrieves one item by finding them using their\",\n \"image = %s, category = %s, reorder_point = %s, created_by = %s WHERE\",\n \"quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items\",\n \"(name, price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all records of\",\n \"unique unique combination''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query\",\n \"(name, price)) self.item = self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity,\",\n \"def add_item( self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds item given\",\n \"* FROM items WHERE name LIKE %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query, (name,\",\n \"all records of items in the databas and returns them''' model = ModelSetup()\",\n \"db are changed to what is provided''' model = ModelSetup() self.conn = model.conn\",\n \"self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's details. the values\",\n \"\\\"\\\"\\\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name,\",\n \"model.cur query = \\\"\\\"\\\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\"\",\n \"def get_by_category(self, category): '''retrieves items by finding them using their category. all items\",\n \"= ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM\",\n \"quantity available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =\",\n \"image, category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point,\",\n \"the item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =\",\n \"FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return\",\n \"category. all items in the same category are retrieved''' model = ModelSetup() self.conn\",\n \"delete_item(self, item_id): '''deletes an item by finding them using the item_id''' model =\",\n \"self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by finding them using their\",\n \"item_id): '''retrieves one item by finding them using their unique item_id''' model =\",\n \"= model.cur query = \\\"\\\"\\\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\\\\",\n \"'''deletes an item by finding them using the item_id''' model = ModelSetup() self.conn\",\n \"%s \\\"\\\"\\\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE\",\n \"auth): '''Adds item given the above arguements. Then returns the created item''' model\",\n \"reorder_point, auth): '''Adds item given the above arguements. Then returns the created item'''\",\n \"return self.items def get_by_id(self, item_id): '''retrieves one item by finding them using their\",\n \"self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth):\",\n \"ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\"\",\n \"items;\\\"\\\"\\\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item\",\n \"self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by finding them\",\n \"= category_id self.reorder_point = reorder_point self.auth = auth def add_item( self, name, price,\",\n \"items WHERE item_id = %s\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT *\",\n \"LIKE %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return\",\n \"self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item by finding\",\n \"what is provided''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query\",\n \"quantity added to the quantity available''' model = ModelSetup() self.conn = model.conn self.cur\",\n \"%s, created_by = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute( query, (price, quantity, image,\",\n \"(category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one item\",\n \"model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET price = %s, quantity\",\n \"price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items class''' self.name\",\n \"query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE name = %s AND price =\",\n \"'''gets all records of items in the databas and returns them''' model =\",\n \"VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm =\",\n \"model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE category LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category))\",\n \"quantity, image, category_id, reorder_point, auth): '''updates item's details. the values in the db\",\n \"return self.item def update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates\",\n \")) self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by finding\",\n \"quantity self.category_id = category_id self.reorder_point = reorder_point self.auth = auth def add_item( self,\",\n \"model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT *\",\n \"= model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query,\",\n \"added to the quantity available''' model = ModelSetup() self.conn = model.conn self.cur =\",\n \"model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"INSERT INTO\",\n \"them using the item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur\",\n \"= \\\"\\\"\\\"SELECT * FROM items WHERE name = %s AND price = %s;\\\"\\\"\\\"\",\n \"self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item by finding\",\n \"items WHERE name LIKE %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query, (name, price)) self.item\",\n \"their unique unique combination''' model = ModelSetup() self.conn = model.conn self.cur = model.cur\",\n \"the quantity available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query\",\n \"data logic of the items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None,\",\n \"\\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.item =\",\n \"\\\"\\\"\\\"SELECT * FROM items WHERE name LIKE %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query,\",\n \"= model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET quantity = %s\",\n \"item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's details. the values in\",\n \"%s, reorder_point = %s, created_by = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute( query,\",\n \"are changed to what is provided''' model = ModelSetup() self.conn = model.conn self.cur\",\n \"def get_by_name_and_price(self, name, price): '''retrieves one item by finding them using their unique\",\n \"by finding them using their category. all items in the same category are\",\n \"category_id self.reorder_point = reorder_point self.auth = auth def add_item( self, name, price, quantity,\",\n \"ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET price\",\n \"price)) self.item = self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity, image,\",\n \"WHERE name = %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price)) self.item =\",\n \"self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET price =\",\n \"(price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM\",\n \"update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's details. the\",\n \"%s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT *\",\n \"auth)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE name = %s AND\",\n \"class ItemsModel(ModelSetup): '''Handles the data logic of the items section''' def __init__( self,\",\n \"self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point self.auth = auth def\",\n \"self.price = price self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point self.auth\",\n \"* FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone()\",\n \"self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items\",\n \"price, quantity, image, category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name, price, quantity, image,\",\n \"%s \\\"\\\"\\\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm\",\n \"return self.item def get_all(self): '''gets all records of items in the databas and\",\n \"finding them using the item_id''' model = ModelSetup() self.conn = model.conn self.cur =\",\n \"\\\"\\\"\\\"UPDATE items SET quantity = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(query, (quantity, item_id))\",\n \"self.item def delete_item(self, item_id): '''deletes an item by finding them using the item_id'''\",\n \"query = \\\"\\\"\\\"DELETE FROM items WHERE item_id = %s\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.conn.commit()\",\n \"= \\\"\\\"\\\"INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query,\",\n \"return self.item def get_by_name_and_price(self, name, price): '''retrieves one item by finding them using\",\n \"item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"DELETE\",\n \"WHERE item_id = %s\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM\",\n \"self.item = self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity, image, category_id,\",\n \"self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one\",\n \"ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"INSERT INTO items(name, price,\",\n \"the above arguements. Then returns the created item''' model = ModelSetup() self.conn =\",\n \"item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self,\",\n \"= %s, created_by = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute( query, (price, quantity,\",\n \"= %s, category = %s, reorder_point = %s, created_by = %s WHERE item_id=\",\n \"\\\"\\\"\\\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm =\",\n \"= self.cur.fetchone() return self.item def get_all(self): '''gets all records of items in the\",\n \"category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE item_id\",\n \"category LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name,\",\n \"= model.conn self.cur = model.cur query = \\\"\\\"\\\"INSERT INTO items(name, price, quantity, image,\",\n \"= ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"DELETE FROM items\",\n \"auth=None): '''Initializes the variables for the items class''' self.name = name self.price =\",\n \"'''updates item's details. the values in the db are changed to what is\",\n \"FROM items WHERE name = %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price))\",\n \"self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the\",\n \"\\\"\\\"\\\"UPDATE items SET price = %s, quantity = %s, image = %s, category\",\n \"finding them using their unique item_id''' model = ModelSetup() self.conn = model.conn self.cur\",\n \"reorder_point, auth)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE name = %s\",\n \"price = %s;\\\"\\\"\\\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def update_item(\",\n \"returns the created item''' model = ModelSetup() self.conn = model.conn self.cur = model.cur\",\n \"%s;\\\"\\\"\\\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def update_item( self, item_id,\",\n \"ItemsModel(ModelSetup): '''Handles the data logic of the items section''' def __init__( self, name=None,\",\n \"category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth))\",\n \"def get_all(self): '''gets all records of items in the databas and returns them'''\",\n \"quantity = %s, image = %s, category = %s, reorder_point = %s, created_by\",\n \"self.item def get_all(self): '''gets all records of items in the databas and returns\",\n \"reorder_point=None, auth=None): '''Initializes the variables for the items class''' self.name = name self.price\",\n \"= \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.item\",\n \"price): '''retrieves one item by finding them using their unique unique combination''' model\",\n \"unique combination''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =\",\n \"quantity, image, category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name, price, quantity, image, category_id,\",\n \"and returns them''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query\",\n \"= model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET price = %s,\",\n \"update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added to the quantity available'''\",\n \"= self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item by finding them\",\n \"def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for\",\n \"model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE name LIKE\",\n \"image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE\",\n \"self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE category LIKE %s;\\\"\\\"\\\"\",\n \"* FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves\",\n \"logic of the items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None,\",\n \"returns them''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =\",\n \"= model.cur query = \\\"\\\"\\\"DELETE FROM items WHERE item_id = %s\\\"\\\"\\\" self.cur.execute(query, (item_id,\",\n \"self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items\",\n \"= %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id):\",\n \"= model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE category\",\n \"'''Adds item given the above arguements. Then returns the created item''' model =\",\n \"%s AND price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item\",\n \"self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items = self.cur.fetchall()\",\n \"query = \\\"\\\"\\\"SELECT * FROM items WHERE category LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item\",\n \"from .db_conn import ModelSetup class ItemsModel(ModelSetup): '''Handles the data logic of the items\",\n \"= self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one item by finding\",\n \"to what is provided''' model = ModelSetup() self.conn = model.conn self.cur = model.cur\",\n \"= %s\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query_confirm)\",\n \"query = \\\"\\\"\\\"SELECT * FROM items WHERE name LIKE %s AND price =\",\n \"self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query_confirm) self.items =\",\n \"name self.price = price self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point\",\n \"model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE item_id =\",\n \"self.cur = model.cur query = \\\"\\\"\\\"DELETE FROM items WHERE item_id = %s\\\"\\\"\\\" self.cur.execute(query,\",\n \"them using their unique item_id''' model = ModelSetup() self.conn = model.conn self.cur =\",\n \"the quantity added to the quantity available''' model = ModelSetup() self.conn = model.conn\",\n \"return self.item def get_by_category(self, category): '''retrieves items by finding them using their category.\",\n \"one item by finding them using their unique unique combination''' model = ModelSetup()\",\n \"all items in the same category are retrieved''' model = ModelSetup() self.conn =\",\n \"\\\"\\\"\\\"DELETE FROM items WHERE item_id = %s\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm =\",\n \"above arguements. Then returns the created item''' model = ModelSetup() self.conn = model.conn\",\n \"using their unique unique combination''' model = ModelSetup() self.conn = model.conn self.cur =\",\n \"self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item by\",\n \"model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"DELETE FROM\",\n \"quantity, image, category_id, reorder_point, auth): '''Adds item given the above arguements. Then returns\",\n \"= model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE name LIKE %s AND\",\n \"category_id, reorder_point, auth): '''updates item's details. the values in the db are changed\",\n \"category): '''retrieves items by finding them using their category. all items in the\",\n \"\\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item =\",\n \"item by finding them using their unique item_id''' model = ModelSetup() self.conn =\",\n \"WHERE name LIKE %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query, (name, price)) self.item =\",\n \"quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE\",\n \"self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"DELETE FROM items WHERE item_id\",\n \"model.cur query = \\\"\\\"\\\"DELETE FROM items WHERE item_id = %s\\\"\\\"\\\" self.cur.execute(query, (item_id, ))\",\n \"= price self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point self.auth =\",\n \"the databas and returns them''' model = ModelSetup() self.conn = model.conn self.cur =\",\n \"items SET price = %s, quantity = %s, image = %s, category =\",\n \"reorder_point self.auth = auth def add_item( self, name, price, quantity, image, category_id, reorder_point,\",\n \"category = %s, reorder_point = %s, created_by = %s WHERE item_id= %s \\\"\\\"\\\"\",\n \")) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds\",\n \"LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price):\",\n \"= auth def add_item( self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds\",\n \"reorder_point, auth): '''updates item's details. the values in the db are changed to\",\n \"price, quantity, image, category_id, reorder_point, auth): '''Adds item given the above arguements. Then\",\n \"by finding them using their unique unique combination''' model = ModelSetup() self.conn =\",\n \"get_by_category(self, category): '''retrieves items by finding them using their category. all items in\",\n \"category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items class''' self.name = name\",\n \"get_by_id(self, item_id): '''retrieves one item by finding them using their unique item_id''' model\",\n \"items(name, price, quantity, image, category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name, price, quantity,\",\n \"= self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by finding them using\",\n \"class''' self.name = name self.price = price self.quantity = quantity self.category_id = category_id\",\n \"price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items\",\n \"the variables for the items class''' self.name = name self.price = price self.quantity\",\n \"(item_id, )) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query_confirm) self.items = self.cur.fetchall()\",\n \"self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET quantity =\",\n \"by finding them using their unique item_id''' model = ModelSetup() self.conn = model.conn\",\n \"is provided''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =\",\n \"SET quantity = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm\",\n \"= model.cur query = \\\"\\\"\\\"UPDATE items SET quantity = %s WHERE item_id= %s\",\n \"Then returns the created item''' model = ModelSetup() self.conn = model.conn self.cur =\",\n \"in the db are changed to what is provided''' model = ModelSetup() self.conn\",\n \"FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return\",\n \"item given the above arguements. Then returns the created item''' model = ModelSetup()\",\n \"price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def get_all(self):\",\n \"%s;\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves\",\n \"provided''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE\",\n \"item_id = %s\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\"\",\n \"= model.cur query = \\\"\\\"\\\"UPDATE items SET price = %s, quantity = %s,\",\n \"= model.conn self.cur = model.cur query = \\\"\\\"\\\"DELETE FROM items WHERE item_id =\",\n \"self.name = name self.price = price self.quantity = quantity self.category_id = category_id self.reorder_point\",\n \"return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added to\",\n \"\\\"\\\"\\\"SELECT * FROM items WHERE name = %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm,\",\n \"model.cur query = \\\"\\\"\\\"UPDATE items SET quantity = %s WHERE item_id= %s \\\"\\\"\\\"\",\n \"price, quantity, image, category_id, reorder_point, auth): '''updates item's details. the values in the\",\n \"item_id= %s \\\"\\\"\\\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items\",\n \"are retrieved''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =\",\n \"= self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity, image, category_id, reorder_point,\",\n \"self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an\",\n \"reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit()\",\n \"query = \\\"\\\"\\\"UPDATE items SET quantity = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(query,\",\n \"self.cur.fetchone() return self.item def get_all(self): '''gets all records of items in the databas\",\n \"item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm,\",\n \"= ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"INSERT INTO items(name,\",\n \"'''Handles the data logic of the items section''' def __init__( self, name=None, price=None,\",\n \"self.category_id = category_id self.reorder_point = reorder_point self.auth = auth def add_item( self, name,\",\n \"to the quantity available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur\",\n \"= model.cur query = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items = self.cur.fetchall() return\",\n \"query = \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query, (item_id, ))\",\n \"self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item by finding them using\",\n \"items WHERE name = %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price)) self.item\",\n \"= \\\"\\\"\\\"UPDATE items SET quantity = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(query, (quantity,\",\n \"%s, image = %s, category = %s, reorder_point = %s, created_by = %s\",\n \"them using their unique unique combination''' model = ModelSetup() self.conn = model.conn self.cur\",\n \"self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item by finding them using\",\n \"self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one item by\",\n \"= self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item by finding them\",\n \"self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE name LIKE %s\",\n \"'''Initializes the variables for the items class''' self.name = name self.price = price\",\n \"items in the databas and returns them''' model = ModelSetup() self.conn = model.conn\",\n \"arguements. Then returns the created item''' model = ModelSetup() self.conn = model.conn self.cur\",\n \"name LIKE %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone()\",\n \"given the above arguements. Then returns the created item''' model = ModelSetup() self.conn\",\n \"item''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"INSERT\",\n \"= %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT\",\n \"items SET quantity = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(query, (quantity, item_id)) self.conn.commit()\",\n \"item's details. the values in the db are changed to what is provided'''\",\n \"= %s, reorder_point = %s, created_by = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute(\",\n \"self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query_confirm) self.items = self.cur.fetchall() return self.items\",\n \"self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT\",\n \".db_conn import ModelSetup class ItemsModel(ModelSetup): '''Handles the data logic of the items section'''\",\n \"the items class''' self.name = name self.price = price self.quantity = quantity self.category_id\",\n \"item by finding them using the item_id''' model = ModelSetup() self.conn = model.conn\",\n \"self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all records of items in\",\n \"= %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets\",\n \"def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added to the quantity\",\n \"records of items in the databas and returns them''' model = ModelSetup() self.conn\",\n \"%s\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query_confirm) self.items\",\n \"unique item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =\",\n \"= reorder_point self.auth = auth def add_item( self, name, price, quantity, image, category_id,\",\n \"* FROM items WHERE name = %s AND price = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name,\",\n \"created_by = %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute( query, (price, quantity, image, category_id,\",\n \"databas and returns them''' model = ModelSetup() self.conn = model.conn self.cur = model.cur\",\n \"FROM items WHERE item_id = %s\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT\",\n \"retrieved''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT\",\n \"query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT *\",\n \"image, category_id, reorder_point, auth): '''updates item's details. the values in the db are\",\n \"(item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's\",\n \"get_all(self): '''gets all records of items in the databas and returns them''' model\",\n \"in the databas and returns them''' model = ModelSetup() self.conn = model.conn self.cur\",\n \"items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the\",\n \"items in the same category are retrieved''' model = ModelSetup() self.conn = model.conn\",\n \"def delete_item(self, item_id): '''deletes an item by finding them using the item_id''' model\",\n \"reorder_point, auth, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE item_id =\",\n \"using the item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query\",\n \"INTO items(name, price, quantity, image, category, reorder_point, created_by)\\\\ VALUES(%s,%s,%s,%s,%s,%s,%s);\\\"\\\"\\\" self.cur.execute( query, (name, price,\",\n \"= %s;\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self, category):\",\n \"\\\"\\\"\\\" self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE item_id\",\n \"model.cur query = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items = self.cur.fetchall() return self.items\",\n \"= %s, quantity = %s, image = %s, category = %s, reorder_point =\",\n \"'''retrieves one item by finding them using their unique item_id''' model = ModelSetup()\",\n \"item_id): '''deletes an item by finding them using the item_id''' model = ModelSetup()\",\n \"%s;\\\"\\\"\\\" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all\",\n \"import ModelSetup class ItemsModel(ModelSetup): '''Handles the data logic of the items section''' def\",\n \"= model.conn self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items;\\\"\\\"\\\" self.cur.execute(query) self.items\",\n \"created item''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query =\",\n \"(name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM\",\n \"= \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item\",\n \"= %s WHERE item_id= %s \\\"\\\"\\\" self.cur.execute( query, (price, quantity, image, category_id, reorder_point,\",\n \"get_by_name_and_price(self, name, price): '''retrieves one item by finding them using their unique unique\",\n \"= \\\"\\\"\\\"SELECT * FROM items WHERE category LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item =\",\n \"def update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's details.\",\n \"self.cur = model.cur query = \\\"\\\"\\\"INSERT INTO items(name, price, quantity, image, category, reorder_point,\",\n \"the same category are retrieved''' model = ModelSetup() self.conn = model.conn self.cur =\",\n \"%s;\\\"\\\"\\\" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes\",\n \"the items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes\",\n \"item by finding them using their unique unique combination''' model = ModelSetup() self.conn\",\n \"(item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by\",\n \"one item by finding them using their unique item_id''' model = ModelSetup() self.conn\",\n \"self.cur = model.cur query = \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\"\",\n \"self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one item by finding them\",\n \"model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items SET quantity = %s WHERE\",\n \"the values in the db are changed to what is provided''' model =\",\n \"name, price, quantity, image, category_id, reorder_point, auth): '''Adds item given the above arguements.\",\n \"= \\\"\\\"\\\"DELETE FROM items WHERE item_id = %s\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm\",\n \"for the items class''' self.name = name self.price = price self.quantity = quantity\",\n \"def get_by_id(self, item_id): '''retrieves one item by finding them using their unique item_id'''\",\n \"ModelSetup class ItemsModel(ModelSetup): '''Handles the data logic of the items section''' def __init__(\",\n \"__init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the\",\n \"items WHERE category LIKE %s;\\\"\\\"\\\" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def\",\n \"model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \\\"\\\"\\\"UPDATE items\",\n \"auth, item_id)) self.conn.commit() query_confirm = \\\"\\\"\\\"SELECT * FROM items WHERE item_id = %s;\\\"\\\"\\\"\",\n \"using their category. all items in the same category are retrieved''' model =\",\n \"of the items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None):\",\n \"WHERE item_id = %s;\\\"\\\"\\\" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def\",\n \"price = %s, quantity = %s, image = %s, category = %s, reorder_point\",\n \"self.item def update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's\",\n \"model.conn self.cur = model.cur query = \\\"\\\"\\\"INSERT INTO items(name, price, quantity, image, category,\"\n]","isConversational":false}}},{"rowIdx":1186,"cells":{"ngram":{"kind":"list like","value":["else: # Simple class name; find from the pre-calculated mappings. if text not","javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name to","options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes","node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a","from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os import re def","import directives from docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import","rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner,","# Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?'","# Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$',","an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir)","'</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if","self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise","role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if","content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext,","to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {}","= env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:] is_annotation = True","import register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html","text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference'","starts with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] =","role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner,","api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno,","self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register","text[1:] is_annotation = True else: is_annotation = False if text.find('.') != -1: #","re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class files. We rely on","on the simple assumption that # a class name always starts with an","os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore","hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir):","if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir',","package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') + '/package-summary.html'","for dirname, subdirs, files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename)","node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node) env = self.builder.env","mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc:","content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes =","not basename.endswith('.html'): # Ignore the non-class files. We rely on the simple assumption","or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') +","basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:]","sphinx.errors import ExtensionError import os import re def api_role(role, rawtext, text, lineno, inliner,","in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): #","return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings","suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text +","in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class files.","inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True,","javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame index.html path. uri =","a class from Javadoc: ' + text) uri = javadoc_mappings[text] text = text.replace('$',","inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def","javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings","if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') + '/package-summary.html' else: #","raise ExtensionError('Cannot find a class from Javadoc: ' + text) uri = javadoc_mappings[text]","tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc',","a class name always starts with an upper-case English alphabet. continue simple_class_name =","Javadoc: ' + text) uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend","pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find a class from","node.astext() if text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation = False","if text.find('.') != -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): #","= {} for dirname, subdirs, files in os.walk(javadoc_dir): for basename in files: if","= '@' + text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal","basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep, '/') + '/' + basename","Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text):","def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes = ['code',","= ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes,","[node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self,","def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text,","'/index.html?' + uri # Prepend the '@' back again if necessary if is_annotation:","-1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri =","'/').replace('$', '.') + '.html' text = re.sub(r'^.*\\.', '', text).replace('$', '.') else: # Simple","the mappings from a simple class name to its Javadoc file. if not","the non-class files. We rely on the simple assumption that # a class","again if necessary if is_annotation: text = '@' + text # Emit the","frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri #","name; find from the pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot","literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') #","text = re.sub(r'^.*\\.', '', text).replace('$', '.') else: # Simple class name; find from","Package uri = text.replace('.', '/') + '/package-summary.html' else: # Class uri = text.replace('.',","from docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html import HTMLTranslator from","node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the","!= -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri","continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep, '/') +","index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend","Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference","Prepend the '@' back again if necessary if is_annotation: text = '@' +","role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno,","from the pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find a","import re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role,","mappings from a simple class name to its Javadoc file. if not hasattr(env,","'__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir): for","# a class name always starts with an upper-case English alphabet. continue simple_class_name","options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def","'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural)","role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering","suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix.","HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span',","node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>')","register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal","import HTMLTranslator from sphinx.errors import ExtensionError import os import re def api_role(role, rawtext,","+ text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node,","if text not in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: '","set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext,","os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api',","content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node =","classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not","= os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name to its","dirname, subdirs, files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or","node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node,","self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name","Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self,","= text[1:] is_annotation = True else: is_annotation = False if text.find('.') != -1:","set_classes from docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html import HTMLTranslator","Build the mappings from a simple class name to its Javadoc file. if","True else: is_annotation = False if text.find('.') != -1: # FQCN or package","in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build","Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname,","'$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep, '/') + '/' + basename else:","path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the","return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return","lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content):","that # a class name always starts with an upper-case English alphabet. continue","'/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text","{'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals.","javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:] is_annotation =","text = text[1:] is_annotation = True else: is_annotation = False if text.find('.') !=","text = node.astext() if text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation","the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node:","# Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role)","We rely on the simple assumption that # a class name always starts","options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node","javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append","a simple class name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__","def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options =","api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in","for basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the","Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' +","a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es')","'.html' text = re.sub(r'^.*\\.', '', text).replace('$', '.') else: # Simple class name; find","env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir): for basename","+ text) uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame","back again if necessary if is_annotation: text = '@' + text # Emit","class name; find from the pre-calculated mappings. if text not in javadoc_mappings: raise","os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name to its Javadoc","lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options:","self.body.append(text + '</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='',",".replace(os.sep, '/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext()","api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal","always starts with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name]","rawtext, text, lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes'","apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno,","# Ignore the non-class files. We rely on the simple assumption that #","env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:] is_annotation = True else:","CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>')","is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes:","Class uri = text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\.', '', text).replace('$',","files. We rely on the simple assumption that # a class name always","import ExtensionError import os import re def api_role(role, rawtext, text, lineno, inliner, options={},","# Simple class name; find from the pre-calculated mappings. if text not in","if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>')","+ '</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix'))","with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname,","text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options,","from sphinx.errors import ExtensionError import os import re def api_role(role, rawtext, text, lineno,","content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext,","'/') + '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.') + '.html'","False if text.find('.') != -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text):","'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural","CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app):","'apidocs'), 'html') # Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role)","register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals. old_visitor =","classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text,","name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings =","suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def","+ '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.') + '.html' text","= {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML","= javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame index.html path. uri","apiplural_role) # Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal =","javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: ' + text) uri =","javadoc_dir) \\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text","env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the '@' back again if","return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext,","+ '.html' text = re.sub(r'^.*\\.', '', text).replace('$', '.') else: # Simple class name;","+ uri # Prepend the '@' back again if necessary if is_annotation: text","self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a","re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir,","text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options,","not in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) #","text not in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: ' +","'/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.') + '.html' text =","simple class name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ =","its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for","'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text","docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors","basename.endswith('.html'): # Ignore the non-class files. We rely on the simple assumption that","uri # Prepend the '@' back again if necessary if is_annotation: text =","HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node: api_visit_literal(self, node, old_visitor)","the pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find a class","return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text,","'api_reference' not in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir'])","find from the pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find","find a class from Javadoc: ' + text) uri = javadoc_mappings[text] text =","directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals. old_visitor","or not basename.endswith('.html'): # Ignore the non-class files. We rely on the simple","text.find('.') != -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package","'', text).replace('$', '.') else: # Simple class name; find from the pre-calculated mappings.","text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]):","Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) #","= basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep, '/') + '/' +","inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def","os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the '@' back again","re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext,","'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode","text = '@' + text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils","not in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: ' + text)","text).replace('$', '.') else: # Simple class name; find from the pre-calculated mappings. if","English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep,","'@' back again if necessary if is_annotation: text = '@' + text #","options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role,","basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class","external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix. if node.attributes['is_plural']:","HTMLTranslator from sphinx.errors import ExtensionError import os import re def api_role(role, rawtext, text,","nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if","else: is_annotation = False if text.find('.') != -1: # FQCN or package name.","'html') # Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural',","Ignore the non-class files. We rely on the simple assumption that # a","the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external","'/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if","# Prepend the '@' back again if necessary if is_annotation: text = '@'","= node.astext() if text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation =","docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os","inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options)","if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class files. We rely","not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files in","Simple class name; find from the pre-calculated mappings. if text not in javadoc_mappings:","next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from","if necessary if is_annotation: text = '@' + text # Emit the tags.","inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes'])","is_annotation = True else: is_annotation = False if text.find('.') != -1: # FQCN","= False if text.find('.') != -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$',","= os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the '@' back","files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'):","name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') + '/package-summary.html' else:","nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os import re","env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple","javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir): for basename in files:","class name always starts with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.',","# FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.',","non-class files. We rely on the simple assumption that # a class name","else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:] is_annotation","CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix. if","app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options = {'class': directives.class_option}","the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept","import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os import","def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node) env","' + text) uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend the","import os import re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return","= self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class","rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner,","lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content)","from a simple class name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'):","literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node: api_visit_literal(self, node, old_visitor) pass","the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri","setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options = {'class':","api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node) env =","= nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor):","'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the","options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural,","necessary if is_annotation: text = '@' + text # Emit the tags. self.body.append(self.starttag(node,","nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options","api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text,","upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\","\\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text =","in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: ' + text) uri","'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node],","the simple assumption that # a class name always starts with an upper-case","= os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings =","rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={},","if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files","basename) or not basename.endswith('.html'): # Ignore the non-class files. We rely on the","= text.replace('$', '.') # Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep,","is_annotation = False if text.find('.') != -1: # FQCN or package name. if","text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options,","# Class uri = text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\.', '',","rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node: api_visit_literal(self,","subdirs, files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or not","name always starts with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$')","from Javadoc: ' + text) uri = javadoc_mappings[text] text = text.replace('$', '.') #","'.') else: # Simple class name; find from the pre-calculated mappings. if text","'@' + text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc'))","text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'),","files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class files. We","+ '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'):","directives from docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError","if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return","<reponame>linxGnu/armeria from docutils.parsers.rst.roles import register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import","os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__","raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role.","the '@' back again if necessary if is_annotation: text = '@' + text","next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir","class name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings","def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text,","re.sub(r'^.*\\.', '', text).replace('$', '.') else: # Simple class name; find from the pre-calculated","['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True,","text, lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in","rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner,","if text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation = False if","# Package uri = text.replace('.', '/') + '/package-summary.html' else: # Class uri =","from docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import","+ '/index.html?' + uri # Prepend the '@' back again if necessary if","plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else:","api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno,","is_annotation: text = '@' + text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='',","text.replace('.', '/') + '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.') +","self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the","docutils.parsers.rst.roles import register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import nodes from","uri = text.replace('.', '/') + '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$',","javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node,","lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content)","{} for dirname, subdirs, files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]',","class from Javadoc: ' + text) uri = javadoc_mappings[text] text = text.replace('$', '.')","text): # Package uri = text.replace('.', '/') + '/package-summary.html' else: # Class uri","self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri))","'.') # Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') +","else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') #","api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of","= text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\.', '', text).replace('$', '.') else:","assumption that # a class name always starts with an upper-case English alphabet.","self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc'","os import re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False,","uri = text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\.', '', text).replace('$', '.')","= text.replace('.', '/') + '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.')","options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role,","'.') + '.html' text = re.sub(r'^.*\\.', '', text).replace('$', '.') else: # Simple class","text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation = False if text.find('.')","= True else: is_annotation = False if text.find('.') != -1: # FQCN or","api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference']","of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node: api_visit_literal(self, node,","classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self,","text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a',","= javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir): for basename in","else: # Class uri = text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\.',","# Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda","uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the '@'","ExtensionError('Cannot find a class from Javadoc: ' + text) uri = javadoc_mappings[text] text","alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep, '/')","content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role,","rely on the simple assumption that # a class name always starts with","ExtensionError import os import re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]):","uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame index.html path.","text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\.', '', text).replace('$', '.') else: #","text = text.replace('$', '.') # Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir,","register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html import","FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/')","+ basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text =","if 'api_reference' not in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir =","simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\ .replace(os.sep, '/') + '/'","simple assumption that # a class name always starts with an upper-case English","if is_annotation: text = '@' + text # Emit the tags. self.body.append(self.starttag(node, 'code',","= re.sub(r'^.*\\.', '', text).replace('$', '.') else: # Simple class name; find from the","text) uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame index.html","file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs,","[] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node)","self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html')","suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s')","# Build the mappings from a simple class name to its Javadoc file.","'/') + '/index.html?' + uri # Prepend the '@' back again if necessary","in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], []","# Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='',","re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') + '/package-summary.html' else: # Class","sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os import re def api_role(role,","lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return","from docutils.parsers.rst.roles import register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import nodes","text.replace('$', '.') # Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/')"],"string":"[\n \"else: # Simple class name; find from the pre-calculated mappings. if text not\",\n \"javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name to\",\n \"options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes\",\n \"node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a\",\n \"from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os import re def\",\n \"import directives from docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import\",\n \"rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner,\",\n \"# Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?'\",\n \"# Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$',\",\n \"an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir)\",\n \"'</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if\",\n \"self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise\",\n \"role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if\",\n \"content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext,\",\n \"to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {}\",\n \"= env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:] is_annotation = True\",\n \"import register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html\",\n \"text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference'\",\n \"starts with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] =\",\n \"role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner,\",\n \"api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno,\",\n \"self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register\",\n \"text[1:] is_annotation = True else: is_annotation = False if text.find('.') != -1: #\",\n \"re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class files. We rely on\",\n \"on the simple assumption that # a class name always starts with an\",\n \"os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore\",\n \"hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir):\",\n \"if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir',\",\n \"package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') + '/package-summary.html'\",\n \"for dirname, subdirs, files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename)\",\n \"node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node) env = self.builder.env\",\n \"mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc:\",\n \"content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes =\",\n \"not basename.endswith('.html'): # Ignore the non-class files. We rely on the simple assumption\",\n \"or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') +\",\n \"basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:]\",\n \"sphinx.errors import ExtensionError import os import re def api_role(role, rawtext, text, lineno, inliner,\",\n \"in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): #\",\n \"return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings\",\n \"suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text +\",\n \"in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class files.\",\n \"inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True,\",\n \"javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame index.html path. uri =\",\n \"a class from Javadoc: ' + text) uri = javadoc_mappings[text] text = text.replace('$',\",\n \"inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def\",\n \"javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings\",\n \"if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') + '/package-summary.html' else: #\",\n \"raise ExtensionError('Cannot find a class from Javadoc: ' + text) uri = javadoc_mappings[text]\",\n \"tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc',\",\n \"a class name always starts with an upper-case English alphabet. continue simple_class_name =\",\n \"Javadoc: ' + text) uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend\",\n \"pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find a class from\",\n \"node.astext() if text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation = False\",\n \"if text.find('.') != -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): #\",\n \"= {} for dirname, subdirs, files in os.walk(javadoc_dir): for basename in files: if\",\n \"= '@' + text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal\",\n \"basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep, '/') + '/' + basename\",\n \"Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text):\",\n \"def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes = ['code',\",\n \"= ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes,\",\n \"[node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self,\",\n \"def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text,\",\n \"'/index.html?' + uri # Prepend the '@' back again if necessary if is_annotation:\",\n \"-1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri =\",\n \"'/').replace('$', '.') + '.html' text = re.sub(r'^.*\\\\.', '', text).replace('$', '.') else: # Simple\",\n \"the mappings from a simple class name to its Javadoc file. if not\",\n \"the non-class files. We rely on the simple assumption that # a class\",\n \"again if necessary if is_annotation: text = '@' + text # Emit the\",\n \"frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri #\",\n \"name; find from the pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot\",\n \"literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') #\",\n \"text = re.sub(r'^.*\\\\.', '', text).replace('$', '.') else: # Simple class name; find from\",\n \"Package uri = text.replace('.', '/') + '/package-summary.html' else: # Class uri = text.replace('.',\",\n \"from docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html import HTMLTranslator from\",\n \"node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the\",\n \"!= -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri\",\n \"continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep, '/') +\",\n \"index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend\",\n \"Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference\",\n \"Prepend the '@' back again if necessary if is_annotation: text = '@' +\",\n \"role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno,\",\n \"from the pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find a\",\n \"import re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role,\",\n \"mappings from a simple class name to its Javadoc file. if not hasattr(env,\",\n \"'__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir): for\",\n \"# a class name always starts with an upper-case English alphabet. continue simple_class_name\",\n \"options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def\",\n \"'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural)\",\n \"role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering\",\n \"suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix.\",\n \"HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span',\",\n \"node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>')\",\n \"register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal\",\n \"import HTMLTranslator from sphinx.errors import ExtensionError import os import re def api_role(role, rawtext,\",\n \"+ text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node,\",\n \"if text not in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: '\",\n \"set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext,\",\n \"os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api',\",\n \"content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node =\",\n \"classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not\",\n \"= os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name to its\",\n \"dirname, subdirs, files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or\",\n \"node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node,\",\n \"self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name\",\n \"Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self,\",\n \"= text[1:] is_annotation = True else: is_annotation = False if text.find('.') != -1:\",\n \"set_classes from docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html import HTMLTranslator\",\n \"Build the mappings from a simple class name to its Javadoc file. if\",\n \"True else: is_annotation = False if text.find('.') != -1: # FQCN or package\",\n \"in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build\",\n \"Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname,\",\n \"'$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep, '/') + '/' + basename else:\",\n \"path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the\",\n \"return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return\",\n \"lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content):\",\n \"that # a class name always starts with an upper-case English alphabet. continue\",\n \"'/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text\",\n \"{'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals.\",\n \"javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:] is_annotation =\",\n \"text = text[1:] is_annotation = True else: is_annotation = False if text.find('.') !=\",\n \"text = node.astext() if text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation\",\n \"the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node:\",\n \"# Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role)\",\n \"We rely on the simple assumption that # a class name always starts\",\n \"options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node\",\n \"javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append\",\n \"a simple class name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__\",\n \"def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options =\",\n \"api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in\",\n \"for basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the\",\n \"Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' +\",\n \"a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es')\",\n \"'.html' text = re.sub(r'^.*\\\\.', '', text).replace('$', '.') else: # Simple class name; find\",\n \"env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir): for basename\",\n \"+ text) uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame\",\n \"back again if necessary if is_annotation: text = '@' + text # Emit\",\n \"class name; find from the pre-calculated mappings. if text not in javadoc_mappings: raise\",\n \"os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name to its Javadoc\",\n \"lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options:\",\n \"self.body.append(text + '</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='',\",\n \".replace(os.sep, '/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext()\",\n \"api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal\",\n \"always starts with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name]\",\n \"rawtext, text, lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes'\",\n \"apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno,\",\n \"# Ignore the non-class files. We rely on the simple assumption that #\",\n \"env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:] is_annotation = True else:\",\n \"CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>')\",\n \"is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes:\",\n \"Class uri = text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\\\.', '', text).replace('$',\",\n \"files. We rely on the simple assumption that # a class name always\",\n \"import ExtensionError import os import re def api_role(role, rawtext, text, lineno, inliner, options={},\",\n \"# Simple class name; find from the pre-calculated mappings. if text not in\",\n \"if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>')\",\n \"+ '</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix'))\",\n \"with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname,\",\n \"text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options,\",\n \"from sphinx.errors import ExtensionError import os import re def api_role(role, rawtext, text, lineno,\",\n \"content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext,\",\n \"'/') + '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.') + '.html'\",\n \"False if text.find('.') != -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text):\",\n \"'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural\",\n \"CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app):\",\n \"'apidocs'), 'html') # Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role)\",\n \"register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals. old_visitor =\",\n \"classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text,\",\n \"name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings =\",\n \"suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def\",\n \"+ '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.') + '.html' text\",\n \"= {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML\",\n \"= javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame index.html path. uri\",\n \"apiplural_role) # Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal =\",\n \"javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: ' + text) uri =\",\n \"javadoc_dir) \\\\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text\",\n \"env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the '@' back again if\",\n \"return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext,\",\n \"+ '.html' text = re.sub(r'^.*\\\\.', '', text).replace('$', '.') else: # Simple class name;\",\n \"+ uri # Prepend the '@' back again if necessary if is_annotation: text\",\n \"self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a\",\n \"re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir,\",\n \"text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options,\",\n \"not in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) #\",\n \"text not in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: ' +\",\n \"'/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.') + '.html' text =\",\n \"simple class name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ =\",\n \"its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for\",\n \"'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text\",\n \"docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors\",\n \"basename.endswith('.html'): # Ignore the non-class files. We rely on the simple assumption that\",\n \"uri # Prepend the '@' back again if necessary if is_annotation: text =\",\n \"HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node: api_visit_literal(self, node, old_visitor)\",\n \"the pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find a class\",\n \"return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text,\",\n \"'api_reference' not in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir'])\",\n \"find from the pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find\",\n \"find a class from Javadoc: ' + text) uri = javadoc_mappings[text] text =\",\n \"directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals. old_visitor\",\n \"or not basename.endswith('.html'): # Ignore the non-class files. We rely on the simple\",\n \"text.find('.') != -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package\",\n \"'', text).replace('$', '.') else: # Simple class name; find from the pre-calculated mappings.\",\n \"text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]):\",\n \"Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) #\",\n \"= basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep, '/') + '/' +\",\n \"inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def\",\n \"os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the '@' back again\",\n \"re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext,\",\n \"'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode\",\n \"text = '@' + text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils\",\n \"not in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: ' + text)\",\n \"text).replace('$', '.') else: # Simple class name; find from the pre-calculated mappings. if\",\n \"English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep,\",\n \"'@' back again if necessary if is_annotation: text = '@' + text #\",\n \"options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role,\",\n \"basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class\",\n \"external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix. if node.attributes['is_plural']:\",\n \"HTMLTranslator from sphinx.errors import ExtensionError import os import re def api_role(role, rawtext, text,\",\n \"nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if\",\n \"else: is_annotation = False if text.find('.') != -1: # FQCN or package name.\",\n \"'html') # Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural',\",\n \"Ignore the non-class files. We rely on the simple assumption that # a\",\n \"the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external\",\n \"'/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if\",\n \"# Prepend the '@' back again if necessary if is_annotation: text = '@'\",\n \"= node.astext() if text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation =\",\n \"docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os\",\n \"inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options)\",\n \"if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class files. We rely\",\n \"not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files in\",\n \"Simple class name; find from the pre-calculated mappings. if text not in javadoc_mappings:\",\n \"next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from\",\n \"if necessary if is_annotation: text = '@' + text # Emit the tags.\",\n \"inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes'])\",\n \"is_annotation = True else: is_annotation = False if text.find('.') != -1: # FQCN\",\n \"= False if text.find('.') != -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$',\",\n \"= os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the '@' back\",\n \"files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'):\",\n \"name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') + '/package-summary.html' else:\",\n \"nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os import re\",\n \"env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple\",\n \"javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir): for basename in files:\",\n \"class name always starts with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.',\",\n \"# FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.',\",\n \"non-class files. We rely on the simple assumption that # a class name\",\n \"else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:] is_annotation\",\n \"CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix. if\",\n \"app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options = {'class': directives.class_option}\",\n \"the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept\",\n \"import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os import\",\n \"def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node) env\",\n \"' + text) uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend the\",\n \"import os import re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return\",\n \"= self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class\",\n \"rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner,\",\n \"lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content)\",\n \"from a simple class name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'):\",\n \"literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node: api_visit_literal(self, node, old_visitor) pass\",\n \"the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri\",\n \"setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options = {'class':\",\n \"api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node) env =\",\n \"= nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor):\",\n \"'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the\",\n \"options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural,\",\n \"necessary if is_annotation: text = '@' + text # Emit the tags. self.body.append(self.starttag(node,\",\n \"nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options\",\n \"api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text,\",\n \"upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\\\\",\n \"\\\\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text =\",\n \"in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: ' + text) uri\",\n \"'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node],\",\n \"the simple assumption that # a class name always starts with an upper-case\",\n \"= os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings =\",\n \"rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={},\",\n \"if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files\",\n \"basename) or not basename.endswith('.html'): # Ignore the non-class files. We rely on the\",\n \"= text.replace('$', '.') # Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep,\",\n \"is_annotation = False if text.find('.') != -1: # FQCN or package name. if\",\n \"text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options,\",\n \"# Class uri = text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\\\.', '',\",\n \"rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node: api_visit_literal(self,\",\n \"subdirs, files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or not\",\n \"name always starts with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$')\",\n \"from Javadoc: ' + text) uri = javadoc_mappings[text] text = text.replace('$', '.') #\",\n \"'.') else: # Simple class name; find from the pre-calculated mappings. if text\",\n \"'@' + text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc'))\",\n \"text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'),\",\n \"files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class files. We\",\n \"+ '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'):\",\n \"directives from docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError\",\n \"if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return\",\n \"<reponame>linxGnu/armeria from docutils.parsers.rst.roles import register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import\",\n \"os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__\",\n \"raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role.\",\n \"the '@' back again if necessary if is_annotation: text = '@' + text\",\n \"next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir\",\n \"class name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings\",\n \"def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text,\",\n \"re.sub(r'^.*\\\\.', '', text).replace('$', '.') else: # Simple class name; find from the pre-calculated\",\n \"['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True,\",\n \"text, lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in\",\n \"rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner,\",\n \"if text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation = False if\",\n \"# Package uri = text.replace('.', '/') + '/package-summary.html' else: # Class uri =\",\n \"from docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import\",\n \"+ '/index.html?' + uri # Prepend the '@' back again if necessary if\",\n \"plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else:\",\n \"api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno,\",\n \"is_annotation: text = '@' + text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='',\",\n \"text.replace('.', '/') + '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.') +\",\n \"self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the\",\n \"docutils.parsers.rst.roles import register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import nodes from\",\n \"uri = text.replace('.', '/') + '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$',\",\n \"javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node,\",\n \"lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content)\",\n \"{} for dirname, subdirs, files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]',\",\n \"class from Javadoc: ' + text) uri = javadoc_mappings[text] text = text.replace('$', '.')\",\n \"text): # Package uri = text.replace('.', '/') + '/package-summary.html' else: # Class uri\",\n \"self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri))\",\n \"'.') # Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') +\",\n \"else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') #\",\n \"api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of\",\n \"= text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\\\.', '', text).replace('$', '.') else:\",\n \"assumption that # a class name always starts with an upper-case English alphabet.\",\n \"self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc'\",\n \"os import re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False,\",\n \"uri = text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\\\.', '', text).replace('$', '.')\",\n \"= text.replace('.', '/') + '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.')\",\n \"options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role,\",\n \"'.') + '.html' text = re.sub(r'^.*\\\\.', '', text).replace('$', '.') else: # Simple class\",\n \"text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation = False if text.find('.')\",\n \"= True else: is_annotation = False if text.find('.') != -1: # FQCN or\",\n \"api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference']\",\n \"of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node: api_visit_literal(self, node,\",\n \"classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self,\",\n \"text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a',\",\n \"= javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir): for basename in\",\n \"else: # Class uri = text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\\\.',\",\n \"# Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda\",\n \"uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the '@'\",\n \"ExtensionError('Cannot find a class from Javadoc: ' + text) uri = javadoc_mappings[text] text\",\n \"alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep, '/')\",\n \"content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role,\",\n \"rely on the simple assumption that # a class name always starts with\",\n \"ExtensionError import os import re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]):\",\n \"uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame index.html path.\",\n \"text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\\\\.', '', text).replace('$', '.') else: #\",\n \"text = text.replace('$', '.') # Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir,\",\n \"register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html import\",\n \"FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/')\",\n \"+ basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text =\",\n \"if 'api_reference' not in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir =\",\n \"simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \\\\ .replace(os.sep, '/') + '/'\",\n \"simple assumption that # a class name always starts with an upper-case English\",\n \"if is_annotation: text = '@' + text # Emit the tags. self.body.append(self.starttag(node, 'code',\",\n \"= re.sub(r'^.*\\\\.', '', text).replace('$', '.') else: # Simple class name; find from the\",\n \"text) uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame index.html\",\n \"file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs,\",\n \"[] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node)\",\n \"self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html')\",\n \"suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s')\",\n \"# Build the mappings from a simple class name to its Javadoc file.\",\n \"'/') + '/index.html?' + uri # Prepend the '@' back again if necessary\",\n \"in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], []\",\n \"# Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='',\",\n \"re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') + '/package-summary.html' else: # Class\",\n \"sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os import re def api_role(role,\",\n \"lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return\",\n \"from docutils.parsers.rst.roles import register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import nodes\",\n \"text.replace('$', '.') # Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/')\"\n]","isConversational":false}}},{"rowIdx":1187,"cells":{"ngram":{"kind":"list like","value":["for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run()","__init__(self, manager, interval, *locks): self.manager = manager self.storage = manager.storage self.interval = interval","interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def","self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def stop(self): self.running","varnishapi authors. All rights reserved. # Use of this source code is governed","def __init__(self, manager, interval, *locks): self.manager = manager self.storage = manager.storage self.interval =","self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True","rights reserved. # Use of this source code is governed by a BSD-style","found in the LICENSE file. import time from feaas import storage class Base(object):","*locks): self.manager = manager self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names):","self.manager = manager self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker","the LICENSE file. import time from feaas import storage class Base(object): def __init__(self,","storage class Base(object): def __init__(self, manager, interval, *locks): self.manager = manager self.storage =","is governed by a BSD-style # license that can be found in the","license that can be found in the LICENSE file. import time from feaas","that can be found in the LICENSE file. import time from feaas import","BSD-style # license that can be found in the LICENSE file. import time","source code is governed by a BSD-style # license that can be found","self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for","in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def","def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self):","# Copyright 2014 varnishapi authors. All rights reserved. # Use of this source","= manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name","manager self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage)","manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in","2014 varnishapi authors. All rights reserved. # Use of this source code is","governed by a BSD-style # license that can be found in the LICENSE","def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def stop(self): self.running =","manager, interval, *locks): self.manager = manager self.storage = manager.storage self.interval = interval def","lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval)","from feaas import storage class Base(object): def __init__(self, manager, interval, *locks): self.manager =","storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running:","of this source code is governed by a BSD-style # license that can","authors. All rights reserved. # Use of this source code is governed by","# license that can be found in the LICENSE file. import time from","All rights reserved. # Use of this source code is governed by a","LICENSE file. import time from feaas import storage class Base(object): def __init__(self, manager,","import time from feaas import storage class Base(object): def __init__(self, manager, interval, *locks):","= manager self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker =","file. import time from feaas import storage class Base(object): def __init__(self, manager, interval,","Base(object): def __init__(self, manager, interval, *locks): self.manager = manager self.storage = manager.storage self.interval","self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names:","Use of this source code is governed by a BSD-style # license that","class Base(object): def __init__(self, manager, interval, *locks): self.manager = manager self.storage = manager.storage","= storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while","= interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name)","lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def stop(self):","be found in the LICENSE file. import time from feaas import storage class","in the LICENSE file. import time from feaas import storage class Base(object): def","reserved. # Use of this source code is governed by a BSD-style #","this source code is governed by a BSD-style # license that can be","# Use of this source code is governed by a BSD-style # license","feaas import storage class Base(object): def __init__(self, manager, interval, *locks): self.manager = manager","a BSD-style # license that can be found in the LICENSE file. import","import storage class Base(object): def __init__(self, manager, interval, *locks): self.manager = manager self.storage","*lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running =","init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running","interval, *locks): self.manager = manager self.storage = manager.storage self.interval = interval def init_locker(self,","by a BSD-style # license that can be found in the LICENSE file.","loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def stop(self): self.running = False","Copyright 2014 varnishapi authors. All rights reserved. # Use of this source code","code is governed by a BSD-style # license that can be found in","time from feaas import storage class Base(object): def __init__(self, manager, interval, *locks): self.manager","can be found in the LICENSE file. import time from feaas import storage"],"string":"[\n \"for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run()\",\n \"__init__(self, manager, interval, *locks): self.manager = manager self.storage = manager.storage self.interval = interval\",\n \"interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def\",\n \"self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def stop(self): self.running\",\n \"varnishapi authors. All rights reserved. # Use of this source code is governed\",\n \"def __init__(self, manager, interval, *locks): self.manager = manager self.storage = manager.storage self.interval =\",\n \"self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True\",\n \"rights reserved. # Use of this source code is governed by a BSD-style\",\n \"found in the LICENSE file. import time from feaas import storage class Base(object):\",\n \"*locks): self.manager = manager self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names):\",\n \"self.manager = manager self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker\",\n \"the LICENSE file. import time from feaas import storage class Base(object): def __init__(self,\",\n \"storage class Base(object): def __init__(self, manager, interval, *locks): self.manager = manager self.storage =\",\n \"is governed by a BSD-style # license that can be found in the\",\n \"license that can be found in the LICENSE file. import time from feaas\",\n \"that can be found in the LICENSE file. import time from feaas import\",\n \"BSD-style # license that can be found in the LICENSE file. import time\",\n \"source code is governed by a BSD-style # license that can be found\",\n \"self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for\",\n \"in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def\",\n \"def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self):\",\n \"# Copyright 2014 varnishapi authors. All rights reserved. # Use of this source\",\n \"= manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name\",\n \"manager self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage)\",\n \"manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in\",\n \"2014 varnishapi authors. All rights reserved. # Use of this source code is\",\n \"governed by a BSD-style # license that can be found in the LICENSE\",\n \"def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def stop(self): self.running =\",\n \"manager, interval, *locks): self.manager = manager self.storage = manager.storage self.interval = interval def\",\n \"lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval)\",\n \"from feaas import storage class Base(object): def __init__(self, manager, interval, *locks): self.manager =\",\n \"storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running:\",\n \"of this source code is governed by a BSD-style # license that can\",\n \"authors. All rights reserved. # Use of this source code is governed by\",\n \"# license that can be found in the LICENSE file. import time from\",\n \"All rights reserved. # Use of this source code is governed by a\",\n \"LICENSE file. import time from feaas import storage class Base(object): def __init__(self, manager,\",\n \"import time from feaas import storage class Base(object): def __init__(self, manager, interval, *locks):\",\n \"= manager self.storage = manager.storage self.interval = interval def init_locker(self, *lock_names): self.locker =\",\n \"file. import time from feaas import storage class Base(object): def __init__(self, manager, interval,\",\n \"Base(object): def __init__(self, manager, interval, *locks): self.manager = manager self.storage = manager.storage self.interval\",\n \"self.interval = interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names:\",\n \"Use of this source code is governed by a BSD-style # license that\",\n \"class Base(object): def __init__(self, manager, interval, *locks): self.manager = manager self.storage = manager.storage\",\n \"= storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while\",\n \"= interval def init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name)\",\n \"lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def stop(self):\",\n \"be found in the LICENSE file. import time from feaas import storage class\",\n \"in the LICENSE file. import time from feaas import storage class Base(object): def\",\n \"reserved. # Use of this source code is governed by a BSD-style #\",\n \"this source code is governed by a BSD-style # license that can be\",\n \"# Use of this source code is governed by a BSD-style # license\",\n \"feaas import storage class Base(object): def __init__(self, manager, interval, *locks): self.manager = manager\",\n \"a BSD-style # license that can be found in the LICENSE file. import\",\n \"import storage class Base(object): def __init__(self, manager, interval, *locks): self.manager = manager self.storage\",\n \"*lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running =\",\n \"init_locker(self, *lock_names): self.locker = storage.MultiLocker(self.storage) for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running\",\n \"interval, *locks): self.manager = manager self.storage = manager.storage self.interval = interval def init_locker(self,\",\n \"by a BSD-style # license that can be found in the LICENSE file.\",\n \"loop(self): self.running = True while self.running: self.run() time.sleep(self.interval) def stop(self): self.running = False\",\n \"Copyright 2014 varnishapi authors. All rights reserved. # Use of this source code\",\n \"code is governed by a BSD-style # license that can be found in\",\n \"time from feaas import storage class Base(object): def __init__(self, manager, interval, *locks): self.manager\",\n \"can be found in the LICENSE file. import time from feaas import storage\"\n]","isConversational":false}}},{"rowIdx":1188,"cells":{"ngram":{"kind":"list like","value":["# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","writing, software # distributed under the License is distributed on an \"AS IS\"","else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading","KIND, either express or implied. # See the License for the specific language","'rb') as f: blocks = f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines() for b in blocks","Unless required by applicable law or agreed to in writing, software # distributed","'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions =","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","# See the License for the specific language governing permissions and # limitations","'Version: ' versions = [l[len(prefix):] for l in lines if l.startswith(prefix)] version =","in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else None","License. # You may obtain a copy of the License at # #","b] # extract version number of every package package_versions = {} for lines","l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] = PlatformPackageDescriptor(version,","import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main')","from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists',","source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] = PlatformPackageDescriptor(version, source_name) return package_versions","'source': url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch,","# Copyright 2014 Open Source Robotics Foundation, Inc. # # Licensed under the","law or agreed to in writing, software # distributed under the License is","the License for the specific language governing permissions and # limitations under the","lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for l","compliance with the License. # You may obtain a copy of the License","= fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package blocks with","# split package blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\n\\n') blocks","logging import os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl,","== 1 else None prefix = 'Source: ' source_names = [l[len(prefix):] for l","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","= os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url,","this file except in compliance with the License. # You may obtain a","2014 Open Source Robotics Foundation, Inc. # # Licensed under the Apache License,","the Apache License, Version 2.0 (the \"License\"); # you may not use this","= os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename","with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines() for b","= [b.splitlines() for b in blocks if b] # extract version number of","you may not use this file except in compliance with the License. #","for the specific language governing permissions and # limitations under the License. import","file: %s' % cache_filename) # split package blocks with open(cache_filename, 'rb') as f:","blocks = f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines() for b in blocks if b] #","extract version number of every package package_versions = {} for lines in blocks:","target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url","if b] # extract version number of every package package_versions = {} for","source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name = source_names[0] if","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if","% cache_filename) # split package blocks with open(cache_filename, 'rb') as f: blocks =","os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir):","f: blocks = f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines() for b in blocks if b]","get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch ==","import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join(","ANY KIND, either express or implied. # See the License for the specific","specific language governing permissions and # limitations under the License. import logging import","Foundation, Inc. # # Licensed under the Apache License, Version 2.0 (the \"License\");","permissions and # limitations under the License. import logging import os from .common","in compliance with the License. # You may obtain a copy of the","def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename)","and # limitations under the License. import logging import os from .common import","package blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines()","for b in blocks if b] # extract version number of every package","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #","= 'Version: ' versions = [l[len(prefix):] for l in lines if l.startswith(prefix)] version","use this file except in compliance with the License. # You may obtain","version number of every package package_versions = {} for lines in blocks: prefix","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","not use this file except in compliance with the License. # You may","source_name = source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] = PlatformPackageDescriptor(version, source_name)","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See","Open Source Robotics Foundation, Inc. # # Licensed under the Apache License, Version","= 'Source: ' source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name","if len(versions) == 1 else None prefix = 'Source: ' source_names = [l[len(prefix):]","debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz')","the License. import logging import os from .common import PlatformPackageDescriptor from .http_cache import","url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz')","= lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for l in lines","%s' % cache_filename) # split package blocks with open(cache_filename, 'rb') as f: blocks","See the License for the specific language governing permissions and # limitations under","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","License, Version 2.0 (the \"License\"); # you may not use this file except","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix =","cache_filename) # split package blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\n\\n')","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):]","= os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s'","'main') if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url =","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","l in lines if l.startswith(prefix)] version = versions[0] if len(versions) == 1 else","= versions[0] if len(versions) == 1 else None prefix = 'Source: ' source_names","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file:","OF ANY KIND, either express or implied. # See the License for the","Source Robotics Foundation, Inc. # # Licensed under the Apache License, Version 2.0","assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for","2.0 (the \"License\"); # you may not use this file except in compliance","# you may not use this file except in compliance with the License.","' source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name = source_names[0]","for l in lines if l.startswith(prefix)] version = versions[0] if len(versions) == 1","= f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines() for b in blocks if b] # extract","= {} for lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name","agreed to in writing, software # distributed under the License is distributed on","{} for lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name =","import logging import os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def","for lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):]","in blocks if b] # extract version number of every package package_versions =","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the","url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url =","License. import logging import os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip","package_versions = {} for lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix)","(the \"License\"); # you may not use this file except in compliance with","# # Unless required by applicable law or agreed to in writing, software","% target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) #","logging.debug('Reading file: %s' % cache_filename) # split package blocks with open(cache_filename, 'rb') as","os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url, 'source',","express or implied. # See the License for the specific language governing permissions","target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split","Version 2.0 (the \"License\"); # you may not use this file except in","# Unless required by applicable law or agreed to in writing, software #","else None prefix = 'Source: ' source_names = [l[len(prefix):] for l in lines","except in compliance with the License. # You may obtain a copy of","[l[len(prefix):] for l in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) ==","by applicable law or agreed to in writing, software # distributed under the","= source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] = PlatformPackageDescriptor(version, source_name) return","from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url",".common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url =","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","every package package_versions = {} for lines in blocks: prefix = 'Package: '","under the License. import logging import os from .common import PlatformPackageDescriptor from .http_cache","limitations under the License. import logging import os from .common import PlatformPackageDescriptor from","either express or implied. # See the License for the specific language governing","number of every package package_versions = {} for lines in blocks: prefix =","prefix = 'Version: ' versions = [l[len(prefix):] for l in lines if l.startswith(prefix)]","software # distributed under the License is distributed on an \"AS IS\" BASIS,","None prefix = 'Source: ' source_names = [l[len(prefix):] for l in lines if","Inc. # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s'","governing permissions and # limitations under the License. import logging import os from","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","may not use this file except in compliance with the License. # You","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","= [l[len(prefix):] for l in lines if l.startswith(prefix)] version = versions[0] if len(versions)","b in blocks if b] # extract version number of every package package_versions","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines() for b in blocks if b] # extract version","blocks if b] # extract version number of every package package_versions = {}","lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix","open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines() for b in","version = versions[0] if len(versions) == 1 else None prefix = 'Source: '","file except in compliance with the License. # You may obtain a copy","# limitations under the License. import logging import os from .common import PlatformPackageDescriptor","blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines() for","in lines if l.startswith(prefix)] version = versions[0] if len(versions) == 1 else None","if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] =","package package_versions = {} for lines in blocks: prefix = 'Package: ' assert","if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url,","[b.splitlines() for b in blocks if b] # extract version number of every","cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","split package blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\n\\n') blocks =","= 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions","License for the specific language governing permissions and # limitations under the License.","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","language governing permissions and # limitations under the License. import logging import os","[l[len(prefix):] for l in lines if l.startswith(prefix)] version = versions[0] if len(versions) ==","cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package blocks","the License. # You may obtain a copy of the License at #","versions[0] if len(versions) == 1 else None prefix = 'Source: ' source_names =","== 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' %","to in writing, software # distributed under the License is distributed on an","lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for l in lines if","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","' versions = [l[len(prefix):] for l in lines if l.startswith(prefix)] version = versions[0]","# distributed under the License is distributed on an \"AS IS\" BASIS, #","implied. # See the License for the specific language governing permissions and #","versions = [l[len(prefix):] for l in lines if l.startswith(prefix)] version = versions[0] if","\"License\"); # you may not use this file except in compliance with the","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","required by applicable law or agreed to in writing, software # distributed under","l.startswith(prefix)] version = versions[0] if len(versions) == 1 else None prefix = 'Source:","applicable law or agreed to in writing, software # distributed under the License","cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package blocks with open(cache_filename, 'rb')","os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' %","lines if l.startswith(prefix)] version = versions[0] if len(versions) == 1 else None prefix","1 else None prefix = 'Source: ' source_names = [l[len(prefix):] for l in","for l in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1","'Source: ' source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name =","fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package blocks with open(cache_filename,","os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename =","prefix = 'Source: ' source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)]","'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url,","as f: blocks = f.read().decode('utf8').split('\\n\\n') blocks = [b.splitlines() for b in blocks if","or agreed to in writing, software # distributed under the License is distributed","'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir)","l in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else","or implied. # See the License for the specific language governing permissions and","blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version:","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","CONDITIONS OF ANY KIND, either express or implied. # See the License for","Apache License, Version 2.0 (the \"License\"); # you may not use this file","import os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target,","OR CONDITIONS OF ANY KIND, either express or implied. # See the License","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl,","target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source':","'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package",".http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name,","'dists', target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else:","# extract version number of every package package_versions = {} for lines in","len(versions) == 1 else None prefix = 'Source: ' source_names = [l[len(prefix):] for","lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name]","= [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names)","with the License. # You may obtain a copy of the License at","the specific language governing permissions and # limitations under the License. import logging","of every package package_versions = {} for lines in blocks: prefix = 'Package:","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: '","Robotics Foundation, Inc. # # Licensed under the Apache License, Version 2.0 (the","in writing, software # distributed under the License is distributed on an \"AS","debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for l in","Copyright 2014 Open Source Robotics Foundation, Inc. # # Licensed under the Apache","blocks = [b.splitlines() for b in blocks if b] # extract version number","if l.startswith(prefix)] version = versions[0] if len(versions) == 1 else None prefix =","under the Apache License, Version 2.0 (the \"License\"); # you may not use"],"string":"[\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading\",\n \"KIND, either express or implied. # See the License for the specific language\",\n \"'rb') as f: blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines() for b in blocks\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions =\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"'Version: ' versions = [l[len(prefix):] for l in lines if l.startswith(prefix)] version =\",\n \"in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else None\",\n \"License. # You may obtain a copy of the License at # #\",\n \"b] # extract version number of every package package_versions = {} for lines\",\n \"l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] = PlatformPackageDescriptor(version,\",\n \"import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main')\",\n \"from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists',\",\n \"source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] = PlatformPackageDescriptor(version, source_name) return package_versions\",\n \"'source': url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch,\",\n \"# Copyright 2014 Open Source Robotics Foundation, Inc. # # Licensed under the\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for l\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"= fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package blocks with\",\n \"# split package blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks\",\n \"logging import os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl,\",\n \"== 1 else None prefix = 'Source: ' source_names = [l[len(prefix):] for l\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"= os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url,\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"2014 Open Source Robotics Foundation, Inc. # # Licensed under the Apache License,\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"= os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename\",\n \"with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines() for b\",\n \"= [b.splitlines() for b in blocks if b] # extract version number of\",\n \"you may not use this file except in compliance with the License. #\",\n \"for the specific language governing permissions and # limitations under the License. import\",\n \"file: %s' % cache_filename) # split package blocks with open(cache_filename, 'rb') as f:\",\n \"blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines() for b in blocks if b] #\",\n \"extract version number of every package package_versions = {} for lines in blocks:\",\n \"target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url\",\n \"if b] # extract version number of every package package_versions = {} for\",\n \"source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name = source_names[0] if\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if\",\n \"% cache_filename) # split package blocks with open(cache_filename, 'rb') as f: blocks =\",\n \"os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir):\",\n \"f: blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines() for b in blocks if b]\",\n \"get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch ==\",\n \"import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join(\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"specific language governing permissions and # limitations under the License. import logging import\",\n \"Foundation, Inc. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"permissions and # limitations under the License. import logging import os from .common\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename)\",\n \"and # limitations under the License. import logging import os from .common import\",\n \"package blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines()\",\n \"for b in blocks if b] # extract version number of every package\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"= 'Version: ' versions = [l[len(prefix):] for l in lines if l.startswith(prefix)] version\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"version number of every package package_versions = {} for lines in blocks: prefix\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"not use this file except in compliance with the License. # You may\",\n \"source_name = source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] = PlatformPackageDescriptor(version, source_name)\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"Open Source Robotics Foundation, Inc. # # Licensed under the Apache License, Version\",\n \"= 'Source: ' source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name\",\n \"if len(versions) == 1 else None prefix = 'Source: ' source_names = [l[len(prefix):]\",\n \"debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz')\",\n \"the License. import logging import os from .common import PlatformPackageDescriptor from .http_cache import\",\n \"url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz')\",\n \"= lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for l in lines\",\n \"%s' % cache_filename) # split package blocks with open(cache_filename, 'rb') as f: blocks\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix =\",\n \"cache_filename) # split package blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\\\n\\\\n')\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):]\",\n \"= os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s'\",\n \"'main') if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url =\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"l in lines if l.startswith(prefix)] version = versions[0] if len(versions) == 1 else\",\n \"= versions[0] if len(versions) == 1 else None prefix = 'Source: ' source_names\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file:\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"Source Robotics Foundation, Inc. # # Licensed under the Apache License, Version 2.0\",\n \"assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"# you may not use this file except in compliance with the License.\",\n \"' source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name = source_names[0]\",\n \"for l in lines if l.startswith(prefix)] version = versions[0] if len(versions) == 1\",\n \"= f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines() for b in blocks if b] # extract\",\n \"= {} for lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"{} for lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name =\",\n \"import logging import os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def\",\n \"for lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):]\",\n \"in blocks if b] # extract version number of every package package_versions =\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url =\",\n \"License. import logging import os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip\",\n \"package_versions = {} for lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix)\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"% target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) #\",\n \"logging.debug('Reading file: %s' % cache_filename) # split package blocks with open(cache_filename, 'rb') as\",\n \"os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url, 'source',\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"else None prefix = 'Source: ' source_names = [l[len(prefix):] for l in lines\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"[l[len(prefix):] for l in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) ==\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"= source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] = PlatformPackageDescriptor(version, source_name) return\",\n \"from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url\",\n \".common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url =\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"every package package_versions = {} for lines in blocks: prefix = 'Package: '\",\n \"under the License. import logging import os from .common import PlatformPackageDescriptor from .http_cache\",\n \"limitations under the License. import logging import os from .common import PlatformPackageDescriptor from\",\n \"either express or implied. # See the License for the specific language governing\",\n \"number of every package package_versions = {} for lines in blocks: prefix =\",\n \"prefix = 'Version: ' versions = [l[len(prefix):] for l in lines if l.startswith(prefix)]\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"None prefix = 'Source: ' source_names = [l[len(prefix):] for l in lines if\",\n \"Inc. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s'\",\n \"governing permissions and # limitations under the License. import logging import os from\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"may not use this file except in compliance with the License. # You\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"= [l[len(prefix):] for l in lines if l.startswith(prefix)] version = versions[0] if len(versions)\",\n \"b in blocks if b] # extract version number of every package package_versions\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines() for b in blocks if b] # extract version\",\n \"blocks if b] # extract version number of every package package_versions = {}\",\n \"lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix\",\n \"open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines() for b in\",\n \"version = versions[0] if len(versions) == 1 else None prefix = 'Source: '\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"# limitations under the License. import logging import os from .common import PlatformPackageDescriptor\",\n \"blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines() for\",\n \"in lines if l.startswith(prefix)] version = versions[0] if len(versions) == 1 else None\",\n \"if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] =\",\n \"package package_versions = {} for lines in blocks: prefix = 'Package: ' assert\",\n \"if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url,\",\n \"[b.splitlines() for b in blocks if b] # extract version number of every\",\n \"cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"split package blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks =\",\n \"= 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"language governing permissions and # limitations under the License. import logging import os\",\n \"[l[len(prefix):] for l in lines if l.startswith(prefix)] version = versions[0] if len(versions) ==\",\n \"cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package blocks\",\n \"the License. # You may obtain a copy of the License at #\",\n \"versions[0] if len(versions) == 1 else None prefix = 'Source: ' source_names =\",\n \"== 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' %\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for l in lines if\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"' versions = [l[len(prefix):] for l in lines if l.startswith(prefix)] version = versions[0]\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"versions = [l[len(prefix):] for l in lines if l.startswith(prefix)] version = versions[0] if\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"l.startswith(prefix)] version = versions[0] if len(versions) == 1 else None prefix = 'Source:\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package blocks with open(cache_filename, 'rb')\",\n \"os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' %\",\n \"lines if l.startswith(prefix)] version = versions[0] if len(versions) == 1 else None prefix\",\n \"1 else None prefix = 'Source: ' source_names = [l[len(prefix):] for l in\",\n \"for l in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1\",\n \"'Source: ' source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name =\",\n \"fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package blocks with open(cache_filename,\",\n \"os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename =\",\n \"prefix = 'Source: ' source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)]\",\n \"'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url,\",\n \"as f: blocks = f.read().decode('utf8').split('\\\\n\\\\n') blocks = [b.splitlines() for b in blocks if\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir)\",\n \"l in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version:\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"import os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target,\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl,\",\n \"target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source':\",\n \"'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package\",\n \".http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name,\",\n \"'dists', target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else:\",\n \"# extract version number of every package package_versions = {} for lines in\",\n \"len(versions) == 1 else None prefix = 'Source: ' source_names = [l[len(prefix):] for\",\n \"lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name]\",\n \"= [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names)\",\n \"with the License. # You may obtain a copy of the License at\",\n \"the specific language governing permissions and # limitations under the License. import logging\",\n \"of every package package_versions = {} for lines in blocks: prefix = 'Package:\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: '\",\n \"Robotics Foundation, Inc. # # Licensed under the Apache License, Version 2.0 (the\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for l in\",\n \"Copyright 2014 Open Source Robotics Foundation, Inc. # # Licensed under the Apache\",\n \"blocks = [b.splitlines() for b in blocks if b] # extract version number\",\n \"if l.startswith(prefix)] version = versions[0] if len(versions) == 1 else None prefix =\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\"\n]","isConversational":false}}},{"rowIdx":1189,"cells":{"ngram":{"kind":"list like","value":[],"string":"[]","isConversational":false}}},{"rowIdx":1190,"cells":{"ngram":{"kind":"list like","value":["Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] =","in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i,","Unless required by applicable law or agreed to in writing, software # distributed","strides=(1,1)): n, c, h_in, w_in = x.shape d, c, k, j = weight.shape","list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz,","keepdim=True) xmu = z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m","delete_allpths(pth_dir=None) print('\\n=========================== Store network model Results Start =========================') y = model(x) print('=========================== Store","if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z","return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name: return","print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i],","= tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma","for backward in torch.autograd.Function Args: var: output Variable params: dict of (name, Variable)","elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif","Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask = featuremap[i]","layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features =","(pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('#","= last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif","return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d'","p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =","if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding #","'Conv' in name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward'","get_layers(last_connections, model): return_layers = [] tmp_layers = [] for layer in model.modules(): if","{id(v): k for k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12',","featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z","in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i","padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name","== 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW =","= (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout):","key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for","Variable) param_map = {id(v): k for k, v in params.items()} node_attr = dict(style='filled',","return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name","# return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name =","loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)):","{0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz =","z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)","return_dz[i] = dLoss_dz print('# Skip this layer because the layer has been calcualted!')","weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j,","# return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name']","model Start ===============================') for i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i,","W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape:","print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride = params[1] padding","= {'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name':","= z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB","conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C, H, W = z.shape","W = z.shape D, C, k1, k2 = K.shape N, D, H1, W1","'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1'","else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue","= ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling,","current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name:","tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding","in name: return 'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in","Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size']","parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] =","weight, strides=(1,1)): n, c, h_in, w_in = x.shape d, c, k, j =","batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('#","tensor_B.shape): print('Shape of two compard tensors is not equal.') return None error =","== len(connections): print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None})","break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break","of two compard tensors is not equal.') return None error = 0 error_tolerance","'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1","'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k]","tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {}","return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz,","'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z,","padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {}","g = make_dot(y[0]) return g else: g = make_dot(y) return g @torch.no_grad() def","i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========',","'Mean' in name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name:","z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape))","= dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps =","torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability -","= {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size,","-1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz,","'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp","that require grad (TODO: make optional) \"\"\" if params is not None: assert","padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else:","i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in","(output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else:","'.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None):","tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):","parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name']","print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ',","if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size #","'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View'","(padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0,","featuremap_dir == None: pth_dir = \"./tmp_file/\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir)","governing permissions and # limitations under the License. #!/usr/bin/python3 import torch import torch.nn","generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def","'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k]","'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz,","layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif","==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for j in range(len(layer)):","new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections =","ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape:","[] fc_conv_weights = [] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name =","= layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps","j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape)","= Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d' % v","j:strides[1] * j + pooling[1]] += next_dz[n, c, i, j] / (pooling[0] *","padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else:","list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz","except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1],","= (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if","ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz","fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params","stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding,","nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size","layer because the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward","= list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name:","= tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z)","-1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View'","== last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i)","layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features =","elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1]","padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else:","#print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ')","print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz =","= z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0),","= {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size,","bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0),","'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d'","connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index","layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name =","== 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding =","AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride')","num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None)","{} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features #","if 'None' in key1 or 'None' in value1: print('Not completed for '+key+' or","')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index = [] for i in range(len(connections)): item_key =","in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i","len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value","list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp,","def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape))","MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params","print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return","== {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\n') return last_connections","current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k]","(stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not","eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z,","Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict,","os if pth_dir == None: pth_dir = \"./tmp_file/\" for root, dirs, files in","range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad()","p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif","eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] =","isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k]","elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif","'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6'","dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('#","padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z =","else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif","return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad() def","0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding)","name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name:","parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] =","dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape:","i + flat_idx // pooling[1] w_idx = strides[1] * j + flat_idx %","view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz)","software # distributed under the License is distributed on an \"AS IS\" BASIS,","featuremap = [] if featuremap_dir == None: pth_dir = \"./tmp_file/\" else: pth_dir =","= _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1)","else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])): if","= featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape)","= tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated loss:","j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1,","'' node_name = '%s\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)),","in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel']","== 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0:","# kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size)","AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name","elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name #","= fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i]","return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz","model.modules(): if not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights = []","padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params","eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer,","graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\n===========================","_conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz,","delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors End","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","= {id(v): k for k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left',","= dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif","= stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] =","', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ',","ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c","list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif","= [] for layer in model.modules(): if not ':' in str(layer): layers.append(layer) parameters","been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End ==========================') continue p =","layer == 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer ==","layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name =","not been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000,","average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==","= fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop()","parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name']","tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif","elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name #","{} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):","padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] *","print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides) padding_next_dz","Variable) to add names to node that require grad (TODO: make optional) \"\"\"","tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size","list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N","if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because the","+= 1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add","return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name: return","dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):","current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name:","network model Results Start =========================') y = model(x) print('=========================== Store network model Results","return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i","last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list):","list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])):","weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool'","return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for k","index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): #","list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1","= padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU):","np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error","loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers =","'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z","'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1],","end=' ') return error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for","range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================')","== 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz,","for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0],","keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('#","= list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in","= z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 = xmu**2 var =","z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):","len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1]","padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if","= out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] =","'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k]","in range(N): for c in range(C): for h in range(H): for w in","= layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] =","autograd graph Blue nodes are the Variables that require grad, orange are Tensors","= num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i]","in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0]","output Variable params: dict of (name, Variable) to add names to node that","= xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis,","z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z =","w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A,","= layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features","import copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in","(dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape","as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz)","isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel","key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' +","last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz","# kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size)","xmu = z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar","', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = []","'.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor)","labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\","* pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape:","list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides)","= nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch =","elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k]","layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if","continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in","n in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for j","= MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] =","= 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat'","w_in = x.shape d, c, k, j = weight.shape x_pad = x x_pad","or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 == 0: pass print('error',","print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w],","def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir == None: pth_dir =","> error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate:","dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] ==","# return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif","= N*H*W shape = [N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1)","padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name =","pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz","== 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p)","torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ',","m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 = xmu**2","add names to node that require grad (TODO: make optional) \"\"\" if params","-1, -1): connections.remove(pop_index[i]) new_connections = [] for item in connections: key, value =","for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j, k,","'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View'","j:strides[1] * j + pooling[1]]) h_idx = strides[0] * i + flat_idx //","relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =","= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride","+ flat_idx // pooling[1] w_idx = strides[1] * j + flat_idx % pooling[1]","fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0, 0) stride = layer['stride']","(TODO: make optional) \"\"\" if params is not None: assert isinstance(params.values()[0], Variable) param_map","return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name: return","return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0]","for i in range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img,","isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k]","index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1]","in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if","new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections","# in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels')","weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0, 0) stride","in item_key: if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not","= 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params","= tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params","if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad()","if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!')","BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps","Args: var: output Variable params: dict of (name, Variable) to add names to","0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加","i in range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\","Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections","isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride","in range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value =","eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz","range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i","def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if","layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps =","dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\n=========================== Store network model Results Start =========================') y","torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz,","torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True))","parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name = 'View' parameters[i] =","layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name']","= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma","= [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer =","H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N,","layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif","/ (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]]","in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel']","np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 ==","not use this file except in compliance with the License. # You may","or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N))","name = param_map[id(u)] if params is not None else '' node_name = '%s\\n","tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] =","p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params","= cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for","z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop()","# p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params","Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name","print('\\n=========================== Restore network model Start ===============================') for i in range(len(connections)): print('# Restore network","torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1]","zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor","if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif","== 4: N, C, H, W = tensor_A.shape for n in range(N): for","pz = torch.zeros(N, D, H_last, W_last) for n in range(N): for d in","# in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features')","tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0]","tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated loss: ',","var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3","print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask","= eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear):","', error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) == 2: N,","dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i]","dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz","dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0","print('Not completed for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if","= (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif","Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\n') Loss =","i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img)","tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps =","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","W = z.shape m = N*H*W shape = [N,C,H,W] import numpy as np","error_tolerance: error += 1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else:","# padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def","'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat'","== 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end='","= Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors","len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1)","+ 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0","= {'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name':","padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling =","dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in","except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz,","add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var,","layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'],","= list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i","agreed to in writing, software # distributed under the License is distributed on","range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================')","None: pth_dir = \"./tmp_file/\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums =","k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d'","y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+'","elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride']","'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def","if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z","last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in","======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!')","isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True))","print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in","# out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif","u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var,","j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i +","for the specific language governing permissions and # limitations under the License. #!/usr/bin/python3","z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling =","or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C)","Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name =","', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape:","c, k, j = weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0])","g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors Start ====================================')","{}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key: if i ==","files = os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if '.pth' in","for u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if","Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding')","weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) ==","layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel =","in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else:","dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name']","= (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if","print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3))","relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling","padding): if padding[0] > 0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0],","torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True))","print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('#","# eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif","output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz):","', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4:","= [] for i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\n======================== {0:3}","== 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View':","keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true,","Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z","tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] =","parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i] =","parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name}","= _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return","else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple):","isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add':","= layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop()","AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params","= list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if","c, i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:,","or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None)","eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z,","# stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride)","tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] =","%s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var,","list(last_z.shape)) if params: pooling = params[0] stride = params[1] padding = params[2] output_size","= [] tmp_layers = [] for layer in model.modules(): if not ':' in","= dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ',","in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0],","to in writing, software # distributed under the License is distributed on an","在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape))","num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps']","out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name","stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling","featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in","{} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel #","Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride')","= K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('#","nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name","range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================')","implied. # See the License for the specific language governing permissions and #","featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)):","dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End","tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+'","= dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('#","1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1))","Store network model Results Start =========================') y = model(x) print('=========================== Store network model","- y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('===========================","= '%s\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var)","range(D): for h in range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]):","'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv'","layer_name} elif layer == 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif","list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape:","return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in range(C):","if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for i in","===========================\\n') if 'GoogLeNet' in str(model).split('\\n')[0]: g = make_dot(y[0]) return g else: g =","list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for j","'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name","= tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params","in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================')","= tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] =","'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding","next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) #","', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz,","dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('#","fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params","z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N =","'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name","= kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] =","in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable","(stride, stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not","gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =","str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad))","nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel","Apache License, Version 2.0 (the \"License\"); # you may not use this file","dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ',","= dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape))","', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def","-> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index =","# padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding)","layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d):","name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in name:","if g_view: g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors Start ====================================') result = model(img) print('===========================","parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================')","x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors Start ====================================') result = model(img)","for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x =","dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ',","dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z","if 'label' in graph[i] and graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label'","current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not","elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name #","k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k]","in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x =","padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout):","if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z =","else: print('Not completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if","', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad))","next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z,","fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride']","print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('#","'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because","', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ',","for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def","get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None:","item in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' +","= eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params","# See the License for the specific language governing permissions and # limitations","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights","isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy","return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] =","ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides,","layer[j][k] print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if","if params: pooling = params[0] stride = params[1] padding = params[2] output_size =","ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] =","backward in torch.autograd.Function Args: var: output Variable params: dict of (name, Variable) to","def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor =","= kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] =","padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params","= layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel","get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i","last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0:","k2 = K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape))","error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap","elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] =","dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self","torch.nn as nn import torch.nn.functional as F from graphviz import Digraph, render from","pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return","c in range(C): for h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w]","out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params =","dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z):","start = 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 ==","#delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape))","the Apache License, Version 2.0 (the \"License\"); # you may not use this","kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else:","'.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz","you may not use this file except in compliance with the License. #","mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name']","N*H*W shape = [N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1)","= params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1])","not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding","elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop","'GoogLeNet' in str(model).split('\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label) _,","layer = layers[i] if name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections,","0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU'","tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride","return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape))","in name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\n')","not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp =","nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size","z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z)","= 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape)","= torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz =","parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name']","'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add'","stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride']","# return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params =","print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz","return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0],","else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name","y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ',","np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif","@torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1. for i in range(len(x)):","or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif","'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if","print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape)","{0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def","> error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ',","= layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d):","for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name =","return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name","= featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding']","== 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except:","def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0","parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz,","zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling,","error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir == None:","from graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true):","w_idx = strides[1] * j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx]","completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz,","for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c],","file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers =","for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return","list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N","are Tensors saved for backward in torch.autograd.Function Args: var: output Variable params: dict","= kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] =","AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","= layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name","stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k]","not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding","dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward","kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size']","current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] +","stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else:","hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot","''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if not","tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1]","not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for layer","list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if not","Variable params: dict of (name, Variable) to add names to node that require","fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model,","list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C):","= num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight)","i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i])","in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item in connections: key,","layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel #","= relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz","else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif","return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if","if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\n')","isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding","if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding =","fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k]","flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j] dz","for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if","# padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding)","if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad()","dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape))","torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz,","return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two","0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :,","C, H, W = z.shape _, _, out_h, out_w = next_dz.shape padding_z =","parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir =","featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i,","layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name']","featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz,","elif 'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph","isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size","Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i]","0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights","i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] *","1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape)","z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C, H,","exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear'","elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip","index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1,","list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if","params is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k,","get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for","permissions and # limitations under the License. #!/usr/bin/python3 import torch import torch.nn as","= torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\","return 'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean' in name or","next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]]","'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph =","tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors =","stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding)","get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i])","network model Start ===============================') for i in range(len(connections)): print('# Restore network model: processing","layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d'","new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = [] for i in","= tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] =","'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward'","', padding) print('# strides: ', strides) N, C, H, W = z.shape _,","for layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0","add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\n=========================== Store network model Results Start","in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1]","== 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif","featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name']","p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z =","elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride']","tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {}","# in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels')","== 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name","print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift)","(stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not","None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add':","padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz","for root, dirs, files in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',):","for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def","stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz)","'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name: return 'View'","return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('#","> 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:,","y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)),","max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==","= make_dot(y[0]) return g else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name):","gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape)","=========================') y = model(x) print('=========================== Store network model Results End ===========================\\n') if 'GoogLeNet'","torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability","name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d'","', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x,","range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item in connections: key, value","if len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape for n in","else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None)","1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def","layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] =","for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\n===========================","elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name #","None: pth_dir = \"./tmp_file/\" for root, dirs, files in os.walk(pth_dir, topdown=False): for name","for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i","= layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel","(ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape))","else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding","# eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight","= layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] =","p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] =","in range(D): for h in range(0, H_last, strides[0]): for w in range(0, W_last,","', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1):","copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name =","in size])+')' def add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()),","0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) ==","layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding","i in range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value","MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params","for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i,","batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==","stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] =","dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def","label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors","= list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0,","{0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift =","return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g","size_to_str(size): return '('+(', ').join(['%d' % v for v in size])+')' def add_nodes(var): if","padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0]","isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add':","= layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps","last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start,","'None' in item_key: if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if","len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding","pooling = params[0] stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\","strides) N, C, H, W = z.shape _, _, out_h, out_w = next_dz.shape","layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size,","= len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index","Start =========================') y = model(x) print('=========================== Store network model Results End ===========================\\n') if","== 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for j in","= tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W","= [] fc_conv_weights = [] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name","===============================') for i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key","last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j","else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0]","strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz =","name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name:","-1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name:","gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop =","in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr,","tmp[1] + 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j])","'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params =","= (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d):","'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding']","== 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View':","in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] =","== list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1])","output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not been","if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name #","-1, -1): layer = parameters[i] print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if","= (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i]","range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance:","= layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] =","layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding","parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name","'(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1,","list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p)","@torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and padding[1] > 0: return","isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params)","= torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad()","* i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]])","i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer","print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z =","W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape","h in range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] =","the License. #!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional as F","dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ',","= [] for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for","next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride","layers.append(layer) parameters = [] fc_conv_weights = [] for layer in layers: if isinstance(layer,","layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params =","torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i +","in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor","list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ',","add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i]","graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in graph[i] and graph[i][-1] ==","', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0),","tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1]","name, start_i): for i in range(start_i, len(layers)): layer = layers[i] if name in","next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask,","### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss:","gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB","== 1: C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance","Store network model Results End ===========================\\n') if 'GoogLeNet' in str(model).split('\\n')[0]: g = make_dot(y[0])","= padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU):","value1: print('Not completed for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1})","p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params elif isinstance(layer,","and graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in graph[i] and graph[i][-1]","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] =","dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params","print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z =","AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j","axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c]","copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\n=========================== Restore network model Start","= stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] =","isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i]","exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1 or 'None' in value1:","graph[i] and graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in graph[i] and","name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name:","= z.shape m = N*H*W shape = [N,C,H,W] import numpy as np ax","loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv","isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size","print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('#","range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)):","= return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] =","= {'layer_name': layer_name} elif layer == 'View': layer_name = 'View' parameters[i] = {'layer_name':","'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features']","in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features']","z = featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding =","z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz","output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else:","AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params","padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout):","pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass #print('right',","= tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors","'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name']","gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap =","layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB =","pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1),","padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz","= 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors]","Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]):","BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps","'_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in","z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :]","dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] ==","elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name #","= padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d'","See the License for the specific language governing permissions and # limitations under","return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {}","'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if","fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i]","= 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size')","calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1,","range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add'","= (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if","assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v in params.items()} node_attr","AdaptiveAvgPool2d_params ### else: print('The layer has not been processed in get_structure_parameters!') return parameters,","print('# padding: ', padding) print('# strides: ', strides) N, C, H, W =","dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz)","elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0,","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz","* j:strides[1] * j + pooling[1]]) h_idx = strides[0] * i + flat_idx","else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i in","AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed","tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape for n","= layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz)","item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i])","nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features","### else: print('The layer has not been processed in get_structure_parameters!') return parameters, fc_conv_weights","if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] ==","else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name =","name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1. for i in","else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout':","isinstance(last_tensors[i], str): # Add or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0]","', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('#","j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i,","dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):","for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed","= 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add'","last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i],","padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding']","= dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==","'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name","if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif","= parameters[i] print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d':","End ======================================\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\n')[0]: loss_torch = Loss(result[0], label)","for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0]","def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed =","elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride']","padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding']","for i in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\n======================== {0:3}","> error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ',","get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz","z.shape D, C, k1, k2 = K.shape N, D, H1, W1 = next_dz.shape","find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1]","= _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db =","featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1): layer =","d, c, k, j = weight.shape x_pad = x x_pad = x_pad.unfold(2, k,","graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index = [] for i in range(len(connections)): item_key = list(connections[i].keys())[0]","completed for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not","# p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer,","return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name']","num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz)","else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1","Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return","# kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size)","shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen = set() def","+= next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0],","parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not","list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp =","0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start","z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz","return error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for n in","{} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p #","0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ')","{} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel #","return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C,","H, W = z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z,","value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0])","if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters","weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides:","in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+'","kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride,","new_connections = [] for item in connections: key, value = list(item.items())[0] key1 =","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","dLoss_dB @torch.no_grad() def make_dot(var, params=None): \"\"\" Produces Graphviz representation of PyTorch autograd graph","current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name:","tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch","for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis,","dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated","= xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 =","end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0","'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0),","dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad()","# return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name']","range(len(layer[j])): tmp_layer = layer[j][k] print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name']","dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\n=========================== Store","= F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for","= generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors Start ====================================') result","layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name","{} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple):","# out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight","+ pooling[0], strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n, c, i,","weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =","function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections not done!","W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last) for n","n, c, h_in, w_in = x.shape d, c, k, j = weight.shape x_pad","isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel","len(layers)): layer = layers[i] if name in str(layer): return layer, i @torch.no_grad() def","[] for item in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) +","= AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] =","average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',","flat_idx // pooling[1] w_idx = strides[1] * j + flat_idx % pooling[1] padding_dz[n,","if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding #","def _insert_zeros(dz, strides): N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) +","elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name #","Restore network model End =================================\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for","===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict,","= get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1],","return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('#","'Cat' in name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return","Graphviz representation of PyTorch autograd graph Blue nodes are the Variables that require","eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ',","', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K,","Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding']","Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in","set() def size_to_str(size): return '('+(', ').join(['%d' % v for v in size])+')' def","= tmp[1] + 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if","def generate_connections(g): graph = str(g).split('\\n') labels = {} connections = [] for i","if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p","ConvolutedDog (https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version 2.0 (the \"License\");","''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label,","completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in","print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz =","Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features","keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return","{} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features #","dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss:","probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor =","z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps:","print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] >","y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i","for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0]","return g else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu'",":, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else:","pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z,","= Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections","layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel =","tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size","range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or View if last_tensors[i] ==","= out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params","layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop =","in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] =","tmp_layer = layer[j][k] print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] ==","fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape:","[] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k]","= batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward","==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \"\"\" Produces Graphviz","tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward()","else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple):","return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not been processed in","[item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 =","elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else:","for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' ->","not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z =","= torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for","view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params:","', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask)","print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) == 2: N, C =","last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in","dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif","print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet'","= params[0] stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1))","= featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed","last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed == []: last_connections =","MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride')","else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple):","= index break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end =","1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape))","KIND, either express or implied. # See the License for the specific language","= layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] =","return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ',","np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for","if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride #","= (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if","dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not","for k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### #","= layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] =","strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j +","= _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0),","for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start","elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer,","print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return","= torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1,","= dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape","error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end='","this layer because the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+'","layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop","last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}:","in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if","else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j,","padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for i","def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C, H, W =","Tensors saved for backward in torch.autograd.Function Args: var: output Variable params: dict of","tensor_A.shape for n in range(N): for c in range(C): for h in range(H):","if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t","', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H,","np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H,","= {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name']","stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else:","j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\n=========================== {0:3}","K, z, padding=(0, 0), strides=(1, 1)): N, C, H, W = z.shape D,","rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) == 2:","\"./tmp_file/\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i","def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap","if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20","layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if","# return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name =","list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add')","padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else:","= torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1),","= list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if","m = N*H*W shape = [N,C,H,W] import numpy as np ax = list(np.arange(len(shape)))","MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding']","ANY KIND, either express or implied. # See the License for the specific","'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors","strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n, c, i, j] /","= copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if","@torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name:","list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in,","{'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name}","if not isinstance(layer, list): print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name']","'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name =","(kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not","start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed =","w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error +=","(padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] =","stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding,","featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU':","== 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name","')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index = [] for i in","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See","Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad()","= dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride","return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in","', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ',","0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :,","y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ',","elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name #","BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return","AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding']","= tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size,","padding=(0, 0), strides=(1, 1)): N, C, H, W = z.shape D, C, k1,","model): return_layers = [] tmp_layers = [] for layer in model.modules(): if not","len(connections): print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None':","Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in","conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z","in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1","pth_dir == None: pth_dir = \"./tmp_file/\" for root, dirs, files in os.walk(pth_dir, topdown=False):","current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i]","print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str):","isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in","print('=========================== Generate Tensors End ======================================\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\n')[0]: loss_torch","not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1,","_conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape d, c, k, j","name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\n') labels","layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma)","', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ',","= padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params =","for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1,","tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding","print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0]","layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz","== 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding =","list(gamma.shape)) N, C, H, W = z.shape m = N*H*W shape = [N,C,H,W]","0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass","= Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] =","= str(g).split('\\n') labels = {} connections = [] for i in range(len(graph)): if","len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps']","next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape:","#!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional as F from graphviz","len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] >","for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def","dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)):","k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot","dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\","layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps #","layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding","View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] ==","BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return","== 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat':","import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float()","layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer ==","in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in","fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer:","for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in","z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop","2: N, C = tensor_A.shape for n in range(N): for c in range(C):","padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d):","if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in","for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for","= 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features')","layer has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None):","model Results End ===========================\\n') if 'GoogLeNet' in str(model).split('\\n')[0]: g = make_dot(y[0]) return g","i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name =","np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 == 0: pass print('error', np_A[n,c,h,w],","for c in range(C): for h in range(H): for w in range(W): if","[] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params =","param_map[id(u)] if params is not None else '' node_name = '%s\\n %s' %","notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed')","====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\n') Loss = nn.CrossEntropyLoss() if","* j:strides[1] * j + pooling[1]] += next_dz[n, c, i, j] / (pooling[0]","layers = [] for layer in model.modules(): if not ':' in str(layer): layers.append(layer)","isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride","name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1. for","return g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward'","+ tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy())","i in range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label,","+ H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last)","i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if","= 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features')","# 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ',","value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z,","# kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size)","= tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params =","{} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):","== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z","topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x","= layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz)","= {} connections = [] for i in range(len(graph)): if 'label' in graph[i]","else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None':","list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']')","return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ',","graph = str(g).split('\\n') labels = {} connections = [] for i in range(len(graph)):","Tensors End ======================================\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\n')[0]: loss_torch = Loss(result[0],","last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i]","if 'GoogLeNet' in str(model).split('\\n')[0]: g = make_dot(y[0]) return g else: g = make_dot(y)","AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size']","if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z","return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name,","{'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name}","index_tensors += 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue","AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has","'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in","elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0,","layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding']","Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] =","= (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer","return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = [] for","layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel =","')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\","eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name","done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections","= {} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features","limitations under the License. #!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional","compard tensors is not equal.') return None error = 0 error_tolerance = 0.001","output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size']","'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None) return","np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate:","in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] =","'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model):","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","= tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1","range(N): for d in range(D): for h in range(0, H_last, strides[0]): for w","layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps =","z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding =","== 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1]","kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else:","print('The layer has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def","'None'}) print('=========================== Restore network model End =================================\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name,","c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('#","num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] =","== 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] =","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz","% pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j] dz =","len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape for n in range(N):","前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\n========================","fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] =","torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape:","3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0)","Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer,","layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features #","return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name: return","layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] =","c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c])","applicable law or agreed to in writing, software # distributed under the License","'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i]","@torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir = \"./tmp_file/\" for","keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated","labels = {} connections = [] for i in range(len(graph)): if 'label' in","', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1),","str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1): if not","name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size)","= torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D,","padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return","padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return","tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers","def mul_items(tensor_size): x = list(tensor_size) mul = 1. for i in range(len(x)): mul","'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i]","copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer =","None error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy()","[] for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num","AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name =","print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad()","print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('#","eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i] =","@torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if not ':'","= strides[1] * j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] +=","return '('+(', ').join(['%d' % v for v in size])+')' def add_nodes(var): if var","def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x)","CONDITIONS OF ANY KIND, either express or implied. # See the License for","Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch","= tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] =","for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in","to add names to node that require grad (TODO: make optional) \"\"\" if","layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size,","dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz,","'label' in graph[i] and graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in","db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('#","'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2'","fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] =","c, h_in, w_in = x.shape d, c, k, j = weight.shape x_pad =","layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride","return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)","print('Not completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG'","writing, software # distributed under the License is distributed on an \"AS IS\"","padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d):","Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU'","layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name =","= exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1 or 'None' in","Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel","padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding']","= (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if","p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params","layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name =","eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape,","not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p =","featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if not","parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label,","== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask","= output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in","= z.shape D, C, k1, k2 = K.shape N, D, H1, W1 =","params is not None else '' node_name = '%s\\n %s' % (name, size_to_str(u.size()))","generate_connections(g): graph = str(g).split('\\n') labels = {} connections = [] for i in","isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight)","== 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if","[] print('\\n=========================== Restore network model Start ===============================') for i in range(len(connections)): print('# Restore","padding = tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW,","dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW,","compliance with the License. # You may obtain a copy of the License","for c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx","D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape))","H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz","return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape)","== 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop()","conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z","= (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if","MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride']","== 0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value ==","dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ',","torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape:","= tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] >","print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss,","value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split)","return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers = []","isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif","= merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers)","in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network","in range(len(layer[j])): tmp_layer = layer[j][k] print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if","not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride","= layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params =","if not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for","for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name:","AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name =","parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name","labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' ->","current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for","model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in","= layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif","= z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar =","if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout':","= {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name':","# return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name =","layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding","for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0]","'GoogLeNet' in str(model).split('\\n')[0]: g = make_dot(y[0]) return g else: g = make_dot(y) return","= output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not","params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] =","padding: ', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K =","align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen = set() def size_to_str(size):","connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0]","= tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] =","= kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] =","range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if","layer = parameters[i] if not isinstance(layer, list): print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start","')[1]}) pop_index = [] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '('","'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g):","list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape:","stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop() if not","featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d':","'.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z =","return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d'","return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i]","file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap","return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0]","nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p","not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z =","current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i","parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name","padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop =","[] for layer in model.modules(): if not ':' in str(layer): layers.append(layer) parameters =","''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in",":, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z,","kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride,","not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z =","'.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not","index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index","AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size']","=================================\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)):","[N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax)","if params is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for","i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\","parameters = [] fc_conv_weights = [] for layer in layers: if isinstance(layer, nn.Conv2d):","== 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding =","AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride']","in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = []","dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut","if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name =","dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz,","MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size']","0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key:","last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] ==","= torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape:","= fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3}","= torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('#","print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0,","item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for","else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in name:","padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else:","Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name","generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors Start ====================================') result =","= BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] =","in range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1]","make_dot(var, params=None): \"\"\" Produces Graphviz representation of PyTorch autograd graph Blue nodes are","def get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if not ':' in","key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)):","list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding)","'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if","= torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape:","np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate:","@torch.no_grad() def make_dot(var, params=None): \"\"\" Produces Graphviz representation of PyTorch autograd graph Blue","Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name","(the \"License\"); # you may not use this file except in compliance with","= 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size')","'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz)","i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not","in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz =","torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] *","output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not","next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n","file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model):","params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\"))","def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape d, c, k,","1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3)","# Unless required by applicable law or agreed to in writing, software #","t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x):","by applicable law or agreed to in writing, software # distributed under the","elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return","= AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] =","Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try:","C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c]","= {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features","connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = [] for i","(padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i] =","= [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed')","1 break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for","', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp","featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors","range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error +=","= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z)","kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size']","featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d':","list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride = params[1]","range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed == []: last_connections","dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z,","= layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop()","in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections,","= layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params",":, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1,","out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d):","file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad()","# padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding)","= 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View'","= in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k]","in name: return 'View' elif 'Mean' in name or 'Avg' in name: return","function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections):","current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1],","parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because the layer","next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape))","for v in size])+')' def add_nodes(var): if var not in seen: if torch.is_tensor(var):","file except in compliance with the License. # You may obtain a copy","print('The layer has not been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model,","not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import","def generate_g(model, x): delete_allpths(pth_dir=None) print('\\n=========================== Store network model Results Start =========================') y =","dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv =","elif layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer,","tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i],","saved for backward in torch.autograd.Function Args: var: output Variable params: dict of (name,","param_map = {id(v): k for k, v in params.items()} node_attr = dict(style='filled', shape='box',","\"\"\" Produces Graphviz representation of PyTorch autograd graph Blue nodes are the Variables","weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W = dz.shape","elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, [])","been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if","return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0]","i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key:","Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel","str(g).split('\\n') labels = {} connections = [] for i in range(len(graph)): if 'label'","x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1])","return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \"\"\" Produces Graphviz representation of PyTorch","range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])):","* j + pooling[1]]) h_idx = strides[0] * i + flat_idx // pooling[1]","0), strides=(1, 1)): N, C, H, W = z.shape D, C, k1, k2","key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)): if key2","return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] =","print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params):","if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'):","in key1 or 'None' in value1: print('Not completed for '+key+' or '+value+'! Check","elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else:","files in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad()","for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or View","pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx = strides[0] * i","= get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i,","[] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or","last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i,","tensor_A.shape for n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance","kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed ==","len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0]","p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params elif","fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else:","= layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name =","padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1]","'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features']","= torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def","end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if","= dLoss_dz print('# Skip this layer because the layer has been calcualted!') print('========================","index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1,","(stride, stride) else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not","else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple):","== 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz,","layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop =","tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1]","Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return","', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C","img, label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x) if g_view:","', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape m","stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding)","(H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last,","= featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if","= torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred =","num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params)","last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i","OR CONDITIONS OF ANY KIND, either express or implied. # See the License","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers):","isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features","{'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\n') return last_connections @torch.no_grad()","for n in range(N): for d in range(D): for h in range(0, H_last,","graph_attr=dict(size=\"12,12\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d' % v for v","', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0]","i in range(start_i, len(layers)): layer = layers[i] if name in str(layer): return layer,","tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item)","return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] =","', list(last_z.shape)) if params: pooling = params[0] stride = params[1] padding = params[2]","h_idx, w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:,","W pz = torch.zeros(N, D, H_last, W_last) for n in range(N): for d","@torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',","out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size,","not len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0,","== 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers)","eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name","range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================')","else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple):","range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not","is not None else '' node_name = '%s\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)),","next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape:","loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def","= index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections =","tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] =","z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz","layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding']","# return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {}","'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding'])","if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1,","mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz =","not isinstance(layer, list): print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] ==","(graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for","Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z","name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad()","tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma =","strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape ==","z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):","last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j])","j + pooling[1]]) h_idx = strides[0] * i + flat_idx // pooling[1] w_idx","stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride']","layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride,","BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name","= Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] =","weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z)","relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z","@torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i","tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name':","= {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel","= featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d':","', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os","print('# padding: ', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K","j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start):","else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in","tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif","= dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import","y = model(x) print('=========================== Store network model Results End ===========================\\n') if 'GoogLeNet' in","K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:","else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been","AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The","g else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in","parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] =","else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1):","featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz,","elif 'Mean' in name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in","layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name =","w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :,","N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last =","', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('#","end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp]","if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride #","elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer","= fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3}","dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape))","@torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers = [] for layer in","@torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape))","= featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] =","== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer:","pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i]","v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot =","= tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features","dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap)","= layers[i] if name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model):","loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW,","fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch:","= p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d'","= eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear):","elif layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer",":, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif","% (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'):","return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return","(parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i]","# output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size)","add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\n=========================== Store network model","print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('#","= 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add'","= [] for i in range(len(connections)): item = connections[i] if len(tmp_split) == 0:","{0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >=","elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else:","'.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for","= torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape))","range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward","get_split_connections(connections): return_connections = [] tmp_split = [] for i in range(len(connections)): item =","range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in","单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in","in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1):","#print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) ==","padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1),","range(C): for h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance","== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z","out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name","padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop() if","@torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C, H, W","list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy","layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif","Generate Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\n') Loss","stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else:","label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0,","End ==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask,","(0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding,","current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in","#print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None):","network model Results End ===========================\\n') if 'GoogLeNet' in str(model).split('\\n')[0]: g = make_dot(y[0]) return","'variable'): u = var.variable name = param_map[id(u)] if params is not None else","stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else:","require grad, orange are Tensors saved for backward in torch.autograd.Function Args: var: output","print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors","* i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]]","if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size #","{0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1]","last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling","print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1,","not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def","key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 =","Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ',","dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and padding[1] > 0:","tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for i","(padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name","C, k1, k2 = K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape:","AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding']","z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape))","= tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB","dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d' %","torch import torch.nn as nn import torch.nn.functional as F from graphviz import Digraph,","', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride =","range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy","dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] ==","for k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2')","x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def","C, H, W = tensor_A.shape for n in range(N): for c in range(C):","isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size","tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] =","out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not","(stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not","'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = []","= layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps,","shape = [N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis","= layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name","> error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate:","i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums:","'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d'","not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os","= torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return","loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections = generate_connections(g)","find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk","= layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i]","import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape:","print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\n=========================== Store network model Results","return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 =","= featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] =","len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask =","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]:","None else '' node_name = '%s\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue')","Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight","'+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate","import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\n===========================","= featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d':","last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else:","elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB","= layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] =","for h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or","']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split('","= [] print('\\n=========================== Restore network model Start ===============================') for i in range(len(connections)): print('#","tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer ==","elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride']","elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif","-1): connections.remove(pop_index[i]) new_connections = [] for item in connections: key, value = list(item.items())[0]","= featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif","dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View':","weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0, 0) stride","torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c,","elif isinstance(layer, list): for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer =","pooling[1] w_idx = strides[1] * j + flat_idx % pooling[1] padding_dz[n, c, h_idx,","'\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split('","BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer,","conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop","bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides) padding_next_dz =","padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)","return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('#","end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c","= layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] =","= 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features')","dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape))","range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return","error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 ==","'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool'","g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return","in graph[i] and graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in graph[i]","= tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params =","[] tmp_split = [] for i in range(len(connections)): item = connections[i] if len(tmp_split)","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear):","in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error","padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])):","mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK =","# 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add'","padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop =","torch.autograd.Function Args: var: output Variable params: dict of (name, Variable) to add names","out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k]","name: return 'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean' in name","'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz","= m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 =","0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask,","dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('#","= tensor_A.shape for n in range(N): for c in range(C): for h in","'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat' in name: return 'Cat'","print('\\n=========================== Generate Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\n')","= 0 for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): #","y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp,","dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t)","AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride')","elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i]","tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params)","pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('#","= 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params","last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\n=========================== Restore network","params: dict of (name, Variable) to add names to node that require grad","torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] *","', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz,","= (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params)","stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding,","0: return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K,","'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if","dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride =","padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1]","', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz,","= tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] =","= (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ###","= average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear':","Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name","') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in","= dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride","for i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+'","= (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z,","parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not been processed in get_structure_parameters!')","optional) \"\"\" if params is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v):","'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp","print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] +","torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('#","mul = 1. for i in range(len(x)): mul *= x[i] return mul @torch.no_grad()","i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j","= dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz","index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None)","tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0]","in name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in","get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)):","i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params","padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ',","kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size']","new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split =","print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+'","model Results Start =========================') y = model(x) print('=========================== Store network model Results End","i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n, c,","W = tensor_A.shape for n in range(N): for c in range(C): for h","MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params","End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ',","the License for the specific language governing permissions and # limitations under the","return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in name: return","h_idx = strides[0] * i + flat_idx // pooling[1] w_idx = strides[1] *","stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride']","= out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d):","def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape))","padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {}","img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None)","p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop()","# out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight","padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name})","= F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z,","print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N,","padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params","') return error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for n","not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add'","= fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==","'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1","k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历","'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean'","= padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params =","grad (TODO: make optional) \"\"\" if params is not None: assert isinstance(params.values()[0], Variable)","model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key: if","{'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name}","flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] *","return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif","z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C,","= num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k]","padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0],","out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer,","return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \"\"\" Produces Graphviz representation of","value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1 or 'None'","= fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0, 0) stride =","isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #","'View' elif 'Mean' in name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm'","@torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start =","= dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z)","= x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad()","elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif","Results Start =========================') y = model(x) print('=========================== Store network model Results End ===========================\\n')","= 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]:","isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i]","= relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i]","in name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in","(padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU):","[]: last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1]","list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i]","dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape))","AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding')","Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var,","if '->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\","range(N): for c in range(C): for h in range(H): for w in range(W):","0 import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if","return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU'","or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections","under the License. #!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional as","strides=(1, 1)): N, C, H, W = z.shape D, C, k1, k2 =","MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not","next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z,","for n in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for","error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N)","elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] =","n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c]","== ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]:","# p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params","= weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3,","for n in range(N): for c in range(C): for h in range(H): for","'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding']","layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name","c, h_idx, w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) #","tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding =","-> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index = [] for i","parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g =","(output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has","(kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not","for c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n,","{'layer_name': layer_name} elif layer == 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name}","> 0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0]","loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1,","dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz,","delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in model.modules():","z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k]","= \"./tmp_file/\" for root, dirs, files in os.walk(pth_dir, topdown=False): for name in files:","= 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size')","= 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p')","(W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last) for n in range(N):","# return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in get_structure_parameters_v1!') return","= tensor_A.shape for n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] >","parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights","list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:","relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz)","not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0] ==","+ pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx = strides[0] *","in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW,","continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else:","Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors Start","@torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor","featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop","stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else:","print('# gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape m = N*H*W","dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape:","= layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params)","last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i]","Version 2.0 (the \"License\"); # you may not use this file except in","Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] =","padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params","z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]]","padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in","is not equal.') return None error = 0 error_tolerance = 0.001 np_A =","params: pooling = params[0] stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1),","not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z =","========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding","(name, Variable) to add names to node that require grad (TODO: make optional)","_, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0) padding_dz","= dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma","'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers","== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz","def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer = layers[i] if","elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer ==","return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list):","else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N,","= layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling,","Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif","i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item in connections:","parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i] =","dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not","z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N, C,","(stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not","key1 or 'None' in value1: print('Not completed for '+key+' or '+value+'! Check exchange_name","import Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+'","name: return 'View' elif 'Mean' in name or 'Avg' in name: return 'AvgPool2d'","list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z","= layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel","= copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name","nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params","in name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name: return","k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i]","if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])),","x.shape d, c, k, j = weight.shape x_pad = x x_pad = x_pad.unfold(2,","img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated","isinstance(layer, list): import copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for","== None: pth_dir = \"./tmp_file/\" for root, dirs, files in os.walk(pth_dir, topdown=False): for","isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p","in name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in","or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i]","isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size","stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz =","graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split('","tmp_split = [] for i in range(len(connections)): item = connections[i] if len(tmp_split) ==","len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break","'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features']","for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i,","in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape)","in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n,","featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear':","that require grad, orange are Tensors saved for backward in torch.autograd.Function Args: var:","= conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU':","in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True))","#print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W)","@torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('#","layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name =","return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape:","in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None) return return_dz,","= get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for","tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is not equal.') return None","BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear'","not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for","get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i","= tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] =","', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ',","tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] =","layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size","-1): layer = parameters[i] print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name']","MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name","Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for j","_conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant',","Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride')","labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index = [] for i in range(len(connections)):","mask, p): print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape:","dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma =","featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d':","print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and","tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif","j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end =","MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding']","print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1","= max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape)","mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C):","int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape:","= relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps']","tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers","def _remove_padding(z, padding): if padding[0] > 0 and padding[1] > 0: return z[:,","kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else:","@torch.no_grad() def generate_connections(g): graph = str(g).split('\\n') labels = {} connections = [] for","layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name","= {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size,","x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out","padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps,","calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad()","'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View'","fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params","featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0, 0)","{} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple):","tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride =","= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz","in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for","= (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer,","def get_layers(last_connections, model): return_layers = [] tmp_layers = [] for layer in model.modules():","== tensor_B.shape): print('Shape of two compard tensors is not equal.') return None error","padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad()","Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride']","Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift","dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('#","not 'None' in item_key: if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0]","z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z =","= layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps","pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'})","def get_split_connections(connections): return_connections = [] tmp_split = [] for i in range(len(connections)): item","the Variables that require grad, orange are Tensors saved for backward in torch.autograd.Function","print('=========================== Restore network model End =================================\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i):","= output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in","height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d'","for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name","else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout'","# return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params =","= layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name']","out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if","tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1","z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z =",":, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding):","if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad() def","'next_functions'): for u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0])","layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz","c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True)","range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if","x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W =","(int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape)","str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap =","isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features","xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 =","eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z,","in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else:","featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz,","break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for i","equal.') return None error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B","mu = z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 = xmu**2 var","j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz,","= {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features","return None error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B =","else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple):","- mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5)","dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] ==","layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB =","tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride","'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward'","tensors is not equal.') return None error = 0 error_tolerance = 0.001 np_A","import os if pth_dir == None: pth_dir = \"./tmp_file/\" for root, dirs, files","= featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] ==","parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name}","parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name']","output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list):","@torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('#","return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers)","MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size']","not equal.') return None error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy()","dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability:","= 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors]","range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance:","graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in","in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features']","= featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0,","return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name']","return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in","= 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1,","featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif","= add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout':","BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name","i in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\n======================== {0:3} Layer:","dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape)","list): import copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k","= layer[j][k] print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d':","tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma)","elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name #","z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if","isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v in params.items()} node_attr =","layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d'","os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1. for i","i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore","in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params =","if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None","= fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0, 0) stride =","pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard","not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0,","= kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] =","OF ANY KIND, either express or implied. # See the License for the","x = Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate","nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size","for h in range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w]","fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop()","gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('========================","gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('===========================","tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W =","z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w)","p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d):","(kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not","= layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] =","elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors +=","if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z","1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w %","var.variable name = param_map[id(u)] if params is not None else '' node_name =","print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print('","weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0, 0) stride","isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params)","Backward End ==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz,","W_last) for n in range(N): for d in range(D): for h in range(0,","parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name']","@torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir == None: pth_dir","item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key","current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers,","== 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] =","= featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if","output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)):","', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz","c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx =","= 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz =","Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] =","in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None)","if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j","isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers","pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i]","in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1):","'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name']","z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz)","tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride","', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N,","layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name =","range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list)","for i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key =","layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop","i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========',","dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true","= in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i]","ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z =","stride) else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding,","@torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors","kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride,","in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers","{list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = []","tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer ==","= stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] =","dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape)","var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u","= 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p')","Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+'","end = index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections","'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel']","current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name:","0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride)","elif 'ViewBackward' in name: return 'View' elif 'Mean' in name or 'Avg' in","print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else:","params[0] stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz","return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0,","in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i],","k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0,","print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z =","tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance:","= F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss)","error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end='","generate_g(model, x): delete_allpths(pth_dir=None) print('\\n=========================== Store network model Results Start =========================') y = model(x)","return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list):","= fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z =","torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)]","the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End ==========================')","= AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for k in range(len(layer[j])):","', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.),","H_last, W_last) for n in range(N): for d in range(D): for h in","return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================')","index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections)","language governing permissions and # limitations under the License. #!/usr/bin/python3 import torch import","=============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape))","-1, -1): if isinstance(last_tensors[i], str): # Add or View if last_tensors[i] == 'Add':","H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('#","padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)):",">= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try:","dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps']","in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for j in","db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape d,","not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections)","'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features']","@torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors =","Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try:","+ '_' + value.split('_')[1] if 'None' in key1 or 'None' in value1: print('Not","= {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size,","AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not","W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W","padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding']","return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {}","for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if","elif 'AddBackward' in name: return 'Add' elif 'Cat' in name: return 'Cat' elif","rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import","isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size","def exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name: return","copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer,","stride) else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding,","not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride","else: loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers","next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides:","in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for","= F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N):","fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0, 0) stride = layer['stride']","connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1","g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors","= average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape)","len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z =","in name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return 'None'","z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i]","or agreed to in writing, software # distributed under the License is distributed","== 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except:","error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4:","pooling[1]] += next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz,","C, H, W = z.shape m = N*H*W shape = [N,C,H,W] import numpy","print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return","layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {}","= (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif","dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size']","layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif","= connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key =","0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] >","layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size,","pooling[1]]) h_idx = strides[0] * i + flat_idx // pooling[1] w_idx = strides[1]","print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('#","'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name']","return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)","0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add'","4: N, C, H, W = tensor_A.shape for n in range(N): for c","exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections not done! Check","= 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features')","layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz","MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name =","isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k]","in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu","if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta","processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x","y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values)","list): print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z","under the Apache License, Version 2.0 (the \"License\"); # you may not use","j = weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad =","Produces Graphviz representation of PyTorch autograd graph Blue nodes are the Variables that","dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1]","torch.zeros(N, D, H_last, W_last) for n in range(N): for d in range(D): for","strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad()","return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1],","i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i])","layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size,","generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights","License. # You may obtain a copy of the License at # #","if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding #","not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed):","fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen = set() def size_to_str(size): return","last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)):","else: print('The layer has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad()","v for v in size])+')' def add_nodes(var): if var not in seen: if","dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\n========================","if not 'None' in item_key: if i == 0: pass else: last_item_key =","layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout':","End =================================\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i,","0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j","', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('#","range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1):","torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss","C = tensor_A.shape for n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c]","get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1,","for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========',","strides: ', strides) N, C, H, W = z.shape _, _, out_h, out_w","= stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] =","out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i]","= BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] =","') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir","print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for","= Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections,","# num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps')","in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0]","dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i])","= {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features","next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad()","None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v in params.items()}","-1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(),","'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding","= 1. for i in range(len(x)): mul *= x[i] return mul @torch.no_grad() def","if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps","list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in","= model(img) print('=========================== Generate Tensors End ======================================\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in","isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p","padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding']","AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name","featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz","= model(x) print('=========================== Store network model Results End ===========================\\n') if 'GoogLeNet' in str(model).split('\\n')[0]:","connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return","range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif","1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif","in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' ->","= torch.zeros(N, D, H_last, W_last) for n in range(N): for d in range(D):","z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] ==","{} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features #","= layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop","Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \"\"\"","':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for layer in","dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen = set()","'%s\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if","model End =================================\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in","[] for i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\n======================== {0:3} Layer:","y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i in","1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z","return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :, :,","Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer","i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0]","parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name']","= conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU':","20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ',","padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name =","out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z)","= {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size,","+ 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else:","License, Version 2.0 (the \"License\"); # you may not use this file except","fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d):","elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c]","Copyright 2022 ConvolutedDog (https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version 2.0","print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in","key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1 or","dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size']","# return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params =","ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z,","seen = set() def size_to_str(size): return '('+(', ').join(['%d' % v for v in","in name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat' in","'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\n') labels = {} connections = []","torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w):","in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1","in str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for layer in layers:","dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz =","Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer","z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis,","if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name =","gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or","= (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D,","= layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] =","-1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in","End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for j in","error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ',","name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name:","return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {}","Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride']","kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else:","= fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0, 0) stride =","output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def","@torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape d, c,","= tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] =","judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is not","else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\n') labels = {} connections","layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name']","= get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for i in","dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz =","None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var)))","print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('#","list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W","1)): N, C, H, W = z.shape D, C, k1, k2 = K.shape","axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape:","out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N,","return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size']","dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride =","elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name: return 'View' elif","i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] *","elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] ==","dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz =","== 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif","':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1):","error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in range(C): if","in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir ==","= (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if","= dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz,","tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] ==","in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] =","= featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop =","AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not","# return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not been processed","= conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1:","len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3}","layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p =","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1","padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz","# return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name']","fc_conv_weights = [] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d'","range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error +=","Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return","i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or View if","= layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] =","in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in","return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\n') labels = {} connections =","{'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] =","out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size,","'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params =","# return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params =","been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False):","np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error","layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name':","list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape:","if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ',","range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name","print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape))","j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer =","loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif","name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name:","eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif","else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer","last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride = params[1] padding =","Start ===============================') for i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1))","p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz","in name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in","pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i in range(len(files)):","{} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p #","stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding,","in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params)","layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB =","= stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] =","delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \"\"\" Produces Graphviz representation","'ViewBackward' in name: return 'View' elif 'Mean' in name or 'Avg' in name:","'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])):","BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] =","z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================')","two compard tensors is not equal.') return None error = 0 error_tolerance =","item_key = list(connections[i].keys())[0] if not 'None' in item_key: if i == 0: pass","print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)):","swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0),","stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride']","params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name']","Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d' % v for","Variables that require grad, orange are Tensors saved for backward in torch.autograd.Function Args:","== 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if","1: C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance or","in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error","layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for","z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding:","+= 1 break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass","file_nums = [] for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort()","+ flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j]","zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('#","= tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps","in str(model).split('\\n')[0]: g = make_dot(y[0]) return g else: g = make_dot(y) return g","'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections =","= layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features","stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding,","kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride,","not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride","Skip this layer because the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\","layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size,","exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if","= featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz =","not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return","import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not","= 'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name = 'View'","exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections","or implied. # See the License for the specific language governing permissions and","elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params =","error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C =","padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in","k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK","def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in","g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in","list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]:","import os featuremap = [] if featuremap_dir == None: pth_dir = \"./tmp_file/\" else:","stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =","= torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1,","z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('#","orange are Tensors saved for backward in torch.autograd.Function Args: var: output Variable params:","error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for n in range(N):","# return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name =","{'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name}","'None' in value1: print('Not completed for '+key+' or '+value+'! Check exchange_name function!') exit()","= 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N,","== list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import","print('=========================== Store network model Results End ===========================\\n') if 'GoogLeNet' in str(model).split('\\n')[0]: g =","= Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] =","in range(N): for d in range(D): for h in range(0, H_last, strides[0]): for","strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding:","'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors","= [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true","delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir = \"./tmp_file/\" for root, dirs,","+= 1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w","make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU' elif","'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors =","(output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer,","parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name']","= Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif","tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d):","gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x) if","in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW,","dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not","dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('# next_dz.shape:","{0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i]","str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\n=========================== Store network","= in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size","current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers,","print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0]","-1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1])","kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride,","get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if not ':' in str(layer):","files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul =","out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i] =","= param_map[id(u)] if params is not None else '' node_name = '%s\\n %s'","kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride,","len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1]","', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ',","p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer,","last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] =","MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride']","isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features","Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding']","for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward'","{0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1]","in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] =","Generate Tensors End ======================================\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\n')[0]: loss_torch =","gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps)","= featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] ==","current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in","num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] =","'('+(', ').join(['%d' % v for v in size])+')' def add_nodes(var): if var not","return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for","= var.variable name = param_map[id(u)] if params is not None else '' node_name","cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape:","for c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c],","======================================\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\n')[0]: loss_torch = Loss(result[0], label) else:","F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0,","H, W = z.shape D, C, k1, k2 = K.shape N, D, H1,","print('Shape of two compard tensors is not equal.') return None error = 0","d in range(D): for h in range(0, H_last, strides[0]): for w in range(0,","names to node that require grad (TODO: make optional) \"\"\" if params is","ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen = set() def size_to_str(size): return '('+(',","z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz","padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params","{0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape:","dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+'","dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape:","N, C, H, W = z.shape _, _, out_h, out_w = next_dz.shape padding_z","for item in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_'","because the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End","@torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model)","value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) #","% 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate:","0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K)","if isinstance(last_tensors[i], str): # Add or View if last_tensors[i] == 'Add': last_tensors[i] =","in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul","'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding']","= tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel","fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================')","fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if","# stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride)","== 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer","name: return 'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in name:","use this file except in compliance with the License. # You may obtain","if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride #","2022 ConvolutedDog (https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version 2.0 (the","parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {}","Start ====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\n') Loss = nn.CrossEntropyLoss()","= in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size","= params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0],","model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in","', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c,","dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in =","End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in","print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'})","= list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split","node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions:","connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\n=========================== Restore network model Start ===============================')","isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params)","= [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2","'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name']","dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz","in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] =","out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W = dz.shape H_last =","dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride =","print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in","model(img) print('=========================== Generate Tensors End ======================================\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\n')[0]:","np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape","AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params","= generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters,","F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i","for n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or","return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir ==","print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor)","layers[i] if name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers","isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k]","zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape))","make optional) \"\"\" if params is not None: assert isinstance(params.values()[0], Variable) param_map =","return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\n') labels =","W = z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\","mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights =","error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) == 2: N, C","= layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB","layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif","gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer,","dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0)","AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer","= layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i]","error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if","os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0]))","item_key: if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key]","1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz,","elif layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz,","torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz,","= (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if","torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape))","keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True)","F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop()","kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride,","tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] =","list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ',","= x.shape d, c, k, j = weight.shape x_pad = x x_pad =","= (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif","print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB","c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c,","else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name","== 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for","return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size']","copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i],","j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ',","tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps #","'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p']","list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('#","get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img)","'+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) ==","Results End ===========================\\n') if 'GoogLeNet' in str(model).split('\\n')[0]: g = make_dot(y[0]) return g else:","find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer = layers[i] if name","= \"./tmp_file/\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = [] for","pop_index = [] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in","_insert_zeros(dz, strides): N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H","N = z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz =","if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","= list(connections[i].keys())[0] if not 'None' in item_key: if i == 0: pass else:","last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer =","torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu =","= tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) ==","else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple):","return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer","stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding,","stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding,","return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz","= max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d':","''' for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i],","for i in range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue","notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1]","layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size =","1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or","# out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size')","and # limitations under the License. #!/usr/bin/python3 import torch import torch.nn as nn","1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k","layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma)","if not len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections function!') exit()","elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB","i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in","mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z","in name: return 'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean' in","fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if","dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW,","# return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params =","= 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels')","', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================')","= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride","-> ')[1]}) pop_index = [] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if","# distributed under the License is distributed on an \"AS IS\" BASIS, #","_remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz","params=None): \"\"\" Produces Graphviz representation of PyTorch autograd graph Blue nodes are the","'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])):","num_Throwed += 1 break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else:","connections.remove(pop_index[i]) new_connections = [] for item in connections: key, value = list(item.items())[0] key1","def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape))","x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad,","return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z,","if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i","Add or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if","= layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] =","xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis,","= 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size')","-1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif","var: output Variable params: dict of (name, Variable) to add names to node","##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if not isinstance(layer,","in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params","output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The","eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape m =","* i + flat_idx // pooling[1] w_idx = strides[1] * j + flat_idx","isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding","Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1]","list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape))","kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride,","not len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz","_, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0)","dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of","def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:","v in size])+')' def add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)),","'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel']","w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ',","average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z","with the License. # You may obtain a copy of the License at","dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name']","_insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz","parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i]","Blue nodes are the Variables that require grad, orange are Tensors saved for","dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2","copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name =","= tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape for","dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps","-1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i]","fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif","params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else:","= padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d'","# num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps')","tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is not equal.')","node that require grad (TODO: make optional) \"\"\" if params is not None:","# stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride)","[] for layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers =","specific language governing permissions and # limitations under the License. #!/usr/bin/python3 import torch","# stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride)","fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if params is","if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name:","if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return","Dropout_params['p'] = p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name =","else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name","value.split('_')[1] if 'None' in key1 or 'None' in value1: print('Not completed for '+key+'","if params is not None else '' node_name = '%s\\n %s' % (name,","k1, k2 = K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ',","if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1,","F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for","isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i]","= layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] =","mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0)","in value1: print('Not completed for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1:","0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('#","law or agreed to in writing, software # distributed under the License is","PyTorch autograd graph Blue nodes are the Variables that require grad, orange are","0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C,","layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size,","merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) '''","n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W))","# Add or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0]","has not been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label,","layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz","dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not","torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return","torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h):","_remove_padding(z, padding): if padding[0] > 0 and padding[1] > 0: return z[:, :,","AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params","last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break","range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i])","mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p))","= list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0,","MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name =","{0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask =","elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors +=","layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride,","', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape:","graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index = [] for","= kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] =","return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ###","h_in, w_in = x.shape d, c, k, j = weight.shape x_pad = x","in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)):","in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] =","list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections","= [N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis =","tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding =","{'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name}","kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else:","nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result,","= x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw',","'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers)","parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name","ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape:","in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'):","print('\\n=========================== Store network model Results Start =========================') y = model(x) print('=========================== Store network","fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==","in compliance with the License. # You may obtain a copy of the","print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)):","return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in get_structure_parameters_v1!') return parameters,","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","= list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return","#####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not","copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])):","= layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop()","featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if '.pth'","N, C, H, W = z.shape D, C, k1, k2 = K.shape N,","size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u","return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name,","= [] if featuremap_dir == None: pth_dir = \"./tmp_file/\" else: pth_dir = featuremap_dir","+ pooling[1]] += next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz =","layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding","= dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad()","as F from graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad() def","dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape))","strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return","return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1): layer = parameters[i]","layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif","j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1,","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding =","0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0,","strides[0] * i + flat_idx // pooling[1] w_idx = strides[1] * j +","print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N,","grad, orange are Tensors saved for backward in torch.autograd.Function Args: var: output Variable","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","= dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen =","for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name:","n in range(N): for c in range(C): for h in range(H): for w","not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return","= find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for","> error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 == 0:","layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name =","u = var.variable name = param_map[id(u)] if params is not None else ''","# 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list):","tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else:","= featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params","= tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] =","= dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End","MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride')","(kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not","last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] =","z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz","layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End ==========================') continue","====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else:","MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name","list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz,","dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1]","isinstance(layer, list): print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d':","start = index break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end","layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name","for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error","fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d):","(kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not","= MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] =","# Copyright 2022 ConvolutedDog (https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version","output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size']","= layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps,","', list(gamma.shape)) N, C, H, W = z.shape m = N*H*W shape =","elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if params is not","size])+')' def add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange')","dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] ==","x): delete_allpths(pth_dir=None) print('\\n=========================== Store network model Results Start =========================') y = model(x) print('===========================","batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End","#print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) ==","if featuremap_dir == None: pth_dir = \"./tmp_file/\" else: pth_dir = featuremap_dir files =","pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape):","and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0:","-1): if isinstance(last_tensors[i], str): # Add or View if last_tensors[i] == 'Add': last_tensors[i]","'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name =","range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+'","= torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for i in","pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1)","def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is","range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' ->","{'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] =","end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start =","= [] tmp_split = [] for i in range(len(connections)): item = connections[i] if","= copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name","num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] =","g_view=False): x = Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\n===========================","print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in","= featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz =","hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if params is not None","loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1): layer","has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End ==========================') continue p","featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps","strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K","in name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in","np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return","print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz","eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape","= {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size,","# output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size)","z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ',","= layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] =","+ key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1","torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ',","kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride,","in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if","value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def","= 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j in","(padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif isinstance(layer,","weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z =","padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name","isinstance(layer, list): for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k]","> 0: return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz,","').join(['%d' % v for v in size])+')' def add_nodes(var): if var not in","= next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ',","layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size","dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape:","in range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000):","dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) ==","pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item","= layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling,","'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W","torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer","if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000","torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] *","# kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size)","= (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if","strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides):","0 for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支","y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors '''","range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz","tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k]","= return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0)","torch.nn.functional as F from graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad()","return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz)","layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this","layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif","= 'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat'","network model End =================================\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i","in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\n=========================== {0:3} Branch:","= dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']:","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] *","return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer,","* j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c,","def cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('#","= padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name':","(0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding,","return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {}","list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split =","in range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]]","= get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss,","import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer","= last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) ==","index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1","= get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for","% v for v in size])+')' def add_nodes(var): if var not in seen:","layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if","is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v","', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N, C, H,","layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif","keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz,","padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding)","next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape:","mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2","= layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] =","in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] =","== 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because the layer has","layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers = [] for","parameters[i] if not isinstance(layer, list): print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if","featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0, 0)","z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB =","= (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif","# padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding)","'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View'","', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('#","+= 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for","layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name']","strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K,","AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride']","= make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU'","== 2: N, C = tensor_A.shape for n in range(N): for c in",":, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0),","# return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params =","= layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] =","are the Variables that require grad, orange are Tensors saved for backward in","End ===========================\\n') if 'GoogLeNet' in str(model).split('\\n')[0]: g = make_dot(y[0]) return g else: g","# eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight","', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ',","layer has not been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img,","j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1","if name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers =","j in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer,","layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size","k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k]","output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else:","this file except in compliance with the License. # You may obtain a","= padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout'","calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model):","range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor =","len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p']","not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u =","add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return","(2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant',","{} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):","stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding,","list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp = []","return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear'","# out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size')","return_layers = [] tmp_layers = [] for layer in model.modules(): if not ':'","fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1]","D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1)","list(connections[i].keys())[0] if not 'None' in item_key: if i == 0: pass else: last_item_key","in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] >","_, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors =","\"./tmp_file/\" for root, dirs, files in os.walk(pth_dir, topdown=False): for name in files: if","def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:","# return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {}","key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad() def merge_connections(connections):","== 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors","dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not","k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight)","Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred","else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in range(i+1,","continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in","len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW","error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 == 0: pass","current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif","featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params =","list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides)","axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('#","to node that require grad (TODO: make optional) \"\"\" if params is not","for i in range(start_i, len(layers)): layer = layers[i] if name in str(layer): return","find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1","out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] =","params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling =","tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel #","dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def","= tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d):","featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if","= (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z,","padding: ', padding) print('# strides: ', strides) N, C, H, W = z.shape","else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple):","fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float()","get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true)","= {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name']","-1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU'","= cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = []","for d in range(D): for h in range(0, H_last, strides[0]): for w in","z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================')","xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu","tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1]","Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed","= exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1]","node_name = '%s\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__))","=========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)):","in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1,","in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else:","if not len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):","elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad()","if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1.","'Throwed') num_Throwed += 1 break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]]","@torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape))","dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss =","{} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):","elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for n in range(N): for","mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop()","'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if","print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true =","list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return","layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name","[]) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k","= next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z -","print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)):","layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d'","+= 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C elif","kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride,","elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer","if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if","tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z =","tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif","padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z,","featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==","import torch.nn.functional as F from graphviz import Digraph, render from torch.autograd import Variable","tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward","processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir","range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape)","= dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz","range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params =","featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0, 0)","= out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] =","list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1])","list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z,","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end","1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0,","= (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz =","= z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz,","MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding')","if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] =","==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p)","num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps']","in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start","i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape:","for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item in","str(model).split('\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections =","range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model","in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None)","\"\"\" if params is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k","0 notchoosed = [] print('\\n=========================== Restore network model Start ===============================') for i in","stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride']","= list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name:","tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0:","= (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k]","in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp =","= layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz","= 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size')","def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('#","axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape))","torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1]","processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key: if i","stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else:","strides[1] * j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n,","= (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ',","required by applicable law or agreed to in writing, software # distributed under","if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding =","= {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name':","layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size =","for i in range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split('","dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape:","#print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ')","print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap =","tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = []","(padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer,","== '\"': labels[(graph[i]+graph[i+1][1:]).split('\\t')[1].split(' ')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in graph[i] and graph[i][-1] == ']':","tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 =","swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0), 0,","not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('========================","weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z =","padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else: return","# return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name =","featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout':","index break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index","= torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu","else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers =","= [] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params","swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz","size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if params","label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x) if g_view: g.view()","'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean' in name or 'Avg'","dirs, files in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name))","batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End","= y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ',","= batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward","padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z,","range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0]","tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j):","Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d'","in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0]","featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d':","1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides,","in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split('","'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p']","* i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx =","layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride","License. #!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional as F from","N, C, H, W = z.shape m = N*H*W shape = [N,C,H,W] import","copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\n=========================== Restore","Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size","= {'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name':","max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',","seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is not None:","for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections)","End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \"\"\" Produces","= tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz =","parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name","return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat' in name: return","rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1:","len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]:","hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var)))","isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i]","layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] =","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","root, dirs, files in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root,","'_' + value.split('_')[1] if 'None' in key1 or 'None' in value1: print('Not completed","of (name, Variable) to add names to node that require grad (TODO: make","tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding =","', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ',","tmp_layers = [] for layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer)","'->' in graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split('","\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('#","error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ',","return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W = dz.shape H_last","calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p']","continue p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i]","else '' node_name = '%s\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else:","and graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->'","in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size =","index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not","and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz =","else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif","out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if","= [] for item in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0])","'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if","= AdaptiveAvgPool2d_params ### else: print('The layer has not been processed in get_structure_parameters!') return","+= next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding)","len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z =","len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1]","os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size):","', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and","not None else '' node_name = '%s\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name,","'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name = 'View' parameters[i]","= list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index","in model.modules(): if not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights =","j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1]","return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and padding[1] >","= set() def size_to_str(size): return '('+(', ').join(['%d' % v for v in size])+')'","= Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif","layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding","'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz,","in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================')","torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('#","network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key:","MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not","tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif","== []: last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if","AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size']","pooling[0], strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n, c, i, j]","[] if featuremap_dir == None: pth_dir = \"./tmp_file/\" else: pth_dir = featuremap_dir files","== list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)): if key2 ==","AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size']","tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed +=","return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1]","weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z)","nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features","dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)):","i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]] +=","in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or View if last_tensors[i]","= padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout'","else: if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate:","in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key:","str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for layer in layers: if","= find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers =","as nn import torch.nn.functional as F from graphviz import Digraph, render from torch.autograd","cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i","list): for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ###","copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss,","dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape))","elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name #","from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================')","print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W =","dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n,","list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N, C, H, W","Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer,","dLoss_dz print('# Skip this layer because the layer has been calcualted!') print('======================== {0:3}","np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w])","return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\n=========================== Store network model Results Start =========================')","parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name']","tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size","list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']')","or 'None' in value1: print('Not completed for '+key+' or '+value+'! Check exchange_name function!')","if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') '''","= 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {}","stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding,","return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {}","model(x) print('=========================== Store network model Results End ===========================\\n') if 'GoogLeNet' in str(model).split('\\n')[0]: g","exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections not","in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size =","z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant',","= featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop()","= featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0,","return 'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in name: return","= (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if","= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz,","y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item())","last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j])","= featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0,","range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' #####################","= tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] =","featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1]","graph Blue nodes are the Variables that require grad, orange are Tensors saved","# you may not use this file except in compliance with the License.","max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z","parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {}","layer in model.modules(): if not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights","flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz,","len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz =","len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =","tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] =","= (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if","list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight,","return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir","merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = []","error += 1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if","str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers =","print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz","'None' in key1 or 'None' in value1: print('Not completed for '+key+' or '+value+'!","padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params","BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer,","# padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding)","print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad()","for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('===========================","= tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] +","dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma =","0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride)","x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters,","')[0]]=\\ (graph[i]+graph[i+1][1:]).split('\\t')[1].split('\"')[1] if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0]","layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {}","y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability","next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z - mu","layer = parameters[i] print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] ==","last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item]","else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple):","'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name","output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in get_structure_parameters_v1!')","not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)),","else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name","value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for","* j + pooling[1]] += next_dz[n, c, i, j] / (pooling[0] * pooling[1])","torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View')","= torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape:","files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return","= 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size')","D, C, k1, k2 = K.shape N, D, H1, W1 = next_dz.shape print('#","zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ',","= list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) +","tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {}","return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0,","if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth')","+ last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list):","start, end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] =","last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End","'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward'","[] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true =","featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz,","stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z","z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1]","featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self","for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for","return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start","= parameters[i] if not isinstance(layer, list): print('\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================')","parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] =","dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name']","= [] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key","'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0])","try: padding = layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz,","License for the specific language governing permissions and # limitations under the License.","'AddBackward' in name: return 'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh'","np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return","= torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ',","np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error","tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features #","Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model)","if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size #","return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true","z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps,","not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections,","index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list):","Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] =","x = list(tensor_size) mul = 1. for i in range(len(x)): mul *= x[i]","'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because the layer has been","\"License\"); # you may not use this file except in compliance with the","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight","print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N","[] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed","if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1","parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz","parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name']","== 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz =","else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for","print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w):","kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride,","{} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features #","i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx","(kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not","Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections =","'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] ==","str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is not","in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import","error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C","dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db","= fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz)","+ '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None'","'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1,","dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] ==","N, C = tensor_A.shape for n in range(N): for c in range(C): if","= layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features","'['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz","list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad()","strides[1] * j:strides[1] * j + pooling[1]]) h_idx = strides[0] * i +","connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections)","for layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {}","list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_'","# in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features')","fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params","== 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding =","+= 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad()","or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections):","x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out =","= None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] ==","dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return","pth_dir = \"./tmp_file/\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = []","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for","= out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d):","in writing, software # distributed under the License is distributed on an \"AS","print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor),","params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z,","z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding =",":] elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else: return z","(padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif isinstance(layer,","H, W = tensor_A.shape for n in range(N): for c in range(C): for","len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling =","list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index in","None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split","y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred","stride) else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding,","get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir == None: pth_dir = \"./tmp_file/\"","len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+'","> 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:,","if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)): if","layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride","in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index in","eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i]","H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last) for","i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :,","label) else: loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections)","dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z","if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for","print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad()","return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] =","+ pooling[1]]) h_idx = strides[0] * i + flat_idx // pooling[1] w_idx =","= layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i]","= layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k]","num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img)","= {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel","', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability =","dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================')","-> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index = [] for i in range(len(connections)): item_key","in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed == []:","= layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params =","else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple):","numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut =","len(tensor_A.shape) == 2: N, C = tensor_A.shape for n in range(N): for c","'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding']","in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add'","padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2,","========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding","kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride,","if 'GoogLeNet' in str(model).split('\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label)","loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet'","else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple):","last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for","0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ',","if pth_dir == None: pth_dir = \"./tmp_file/\" for root, dirs, files in os.walk(pth_dir,","num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] =","isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors","= (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if","N, C, H, W = tensor_A.shape for n in range(N): for c in","torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H,","else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('#","parameters[i] print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z","if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] =","cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for","* i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n,","list(tensor_size) mul = 1. for i in range(len(x)): mul *= x[i] return mul","dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \"\"\" Produces Graphviz representation of PyTorch autograd","var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for","= 0 notchoosed = [] print('\\n=========================== Restore network model Start ===============================') for i","dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape))","Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('#","list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz,","if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] =","elif layer == 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer","= layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] =","Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else:","== None: pth_dir = \"./tmp_file/\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums","else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple):","not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return","def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections)","### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name","name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = []","F from graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict,","not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return","last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers,","copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0],","= {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p","the specific language governing permissions and # limitations under the License. #!/usr/bin/python3 import","= view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz =","list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers =","index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([])","if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is not equal.') return","i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers = [] for layer","len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling =","i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward","= tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] =","= (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last) for n in","out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not","= copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\n=========================== Restore network model","= p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d'","Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params","= list(tensor_size) mul = 1. for i in range(len(x)): mul *= x[i] return","padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name","def size_to_str(size): return '('+(', ').join(['%d' % v for v in size])+')' def add_nodes(var):","2.0 (the \"License\"); # you may not use this file except in compliance","Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] =","Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer,","tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1","pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z,","featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true)","print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape))","pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c","for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d'","= x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out","if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name #","Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size']","representation of PyTorch autograd graph Blue nodes are the Variables that require grad,","import torch.nn as nn import torch.nn.functional as F from graphviz import Digraph, render","= tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] =","not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the","torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz","= torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1]","= Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] =","i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx = strides[0]","of PyTorch autograd graph Blue nodes are the Variables that require grad, orange","'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0])","w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape))","padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding']","node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\")) seen","= p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params =","', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1)","var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\n===========================","# # Unless required by applicable law or agreed to in writing, software","dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def","value1}) if not len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections function!')","return_connections = [] tmp_split = [] for i in range(len(connections)): item = connections[i]","return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU'","+ value.split('_')[1] if 'None' in key1 or 'None' in value1: print('Not completed for","express or implied. # See the License for the specific language governing permissions","h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w]","seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name","= {} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p","params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1],","Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features","print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros","+ W pz = torch.zeros(N, D, H_last, W_last) for n in range(N): for","= 0 import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1):","either express or implied. # See the License for the specific language governing","not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps =","not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v in","len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key: if i == 0:","1. for i in range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model,","start_i): for i in range(start_i, len(layers)): layer = layers[i] if name in str(layer):","layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size =","shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c]","'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if","= layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif","kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else:","str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None) return return_dz, dLoss_dW,","fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] =","i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i,","dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy","strides): N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last","layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features =","in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) '''","pth_dir = \"./tmp_file/\" for root, dirs, files in os.walk(pth_dir, topdown=False): for name in","MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding')","== 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz =","elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer ==","k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================')","layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):","{} connections = [] for i in range(len(graph)): if 'label' in graph[i] and","graph[i]: connections.append({labels[graph[i].split('\\t')[1].split(' -> ')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]})","parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for k in","layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif","print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None'","torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def","AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not","the License. # You may obtain a copy of the License at #","padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def","featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in","nodes are the Variables that require grad, orange are Tensors saved for backward","layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size","= copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz =","in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in","tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB =","import torch import torch.nn as nn import torch.nn.functional as F from graphviz import","z, padding=(0, 0), strides=(1, 1)): N, C, H, W = z.shape D, C,","if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size #","list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and padding[1]","'['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides)","range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not","dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is","Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return","if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp","'.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z =","= [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if","lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name']","== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling","elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name =","for j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end","isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d'","list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k]","', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K =","> error_tolerance: error += 1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w])","last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1)","padding) print('# strides: ', strides) N, C, H, W = z.shape _, _,","', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for","elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif","@torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape))","elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0:","in name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in","= next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for","torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View')","layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding","n in range(N): for d in range(D): for h in range(0, H_last, strides[0]):","else: print('The layer has not been processed in get_structure_parameters!') return parameters, fc_conv_weights def","if padding[0] > 0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]]","layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features =","@torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer = layers[i]","list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View'","= in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight)","isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride","(name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for","= [] for layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers","'.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z","gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape m = N*H*W shape","elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name #","dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if","tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding","isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features","list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape))","padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z,","pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in","padding = layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW,","1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0:","# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer","AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding')","featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop()","range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {}","list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add')","result = model(img) print('=========================== Generate Tensors End ======================================\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet'","num_Throwed = 0 notchoosed = [] print('\\n=========================== Restore network model Start ===============================') for","g_view: g.view() delete_allpths(pth_dir=None) print('\\n=========================== Generate Tensors Start ====================================') result = model(img) print('=========================== Generate","z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not","@torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed","dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('#","graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\t')[1].split(' ')[0]]=\\ graph[i].split('\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if","range(start_i, len(layers)): layer = layers[i] if name in str(layer): return layer, i @torch.no_grad()","1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import","padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz,","Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer,","pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ',","dict of (name, Variable) to add names to node that require grad (TODO:","D, H_last, W_last) for n in range(N): for d in range(D): for h","for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list):","padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0,","stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz =","format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz =","last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1): if","# return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name =","last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights =","is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors:","tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start,","Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i]","z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] ==","in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1): if","next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p):","str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var)","j + pooling[1]] += next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz","# padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding)","np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape)","H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) +","elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name #","= tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps,","// pooling[1] w_idx = strides[1] * j + flat_idx % pooling[1] padding_dz[n, c,","dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if","return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name: return","def make_dot(var, params=None): \"\"\" Produces Graphviz representation of PyTorch autograd graph Blue nodes","kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size']","tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride =","-1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add'","= layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] =","str(model).split('\\n')[0]: g = make_dot(y[0]) return g else: g = make_dot(y) return g @torch.no_grad()","= tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i],","len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1]","isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap","= os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if '.pth' in files[i]:","return 'View' elif 'Mean' in name or 'Avg' in name: return 'AvgPool2d' elif","# limitations under the License. #!/usr/bin/python3 import torch import torch.nn as nn import","== 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'],","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","mul_items(tensor_size): x = list(tensor_size) mul = 1. for i in range(len(x)): mul *=","nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name","Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding')","in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers","# stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride)","= find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] +","u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in","break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'})","notchoosed = [] print('\\n=========================== Restore network model Start ===============================') for i in range(len(connections)):","= strides[0] * i + flat_idx // pooling[1] w_idx = strides[1] * j","print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)):","try: padding = tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1],","return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',","add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn)","[] for i in range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item)","def add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif","len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters =","featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz =","[] tmp_layers = [] for layer in model.modules(): if not ':' in str(layer):","== 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat':","stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride']","torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ',","fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir = \"./tmp_file/\"","return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors","tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name","= tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] =","in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model)","== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling","item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for","for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model,","== 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)","import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut","in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] >","parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name']","def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir = \"./tmp_file/\" for root,","make_dot(y[0]) return g else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if","tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str):","dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:","os featuremap = [] if featuremap_dir == None: pth_dir = \"./tmp_file/\" else: pth_dir","', end=' ') return error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape","elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name #","# stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride)","lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not","name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat' in name:","if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View':","= stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] =","in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B):","= layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] =","kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size']","Restore network model Start ===============================') for i in range(len(connections)): print('# Restore network model:","featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except:","return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed","c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j","== last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value,","parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name","= [] for i in range(len(graph)): if 'label' in graph[i] and graph[i][-1] ==","in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp =","parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] =","H, W = z.shape m = N*H*W shape = [N,C,H,W] import numpy as","nn import torch.nn.functional as F from graphviz import Digraph, render from torch.autograd import","require grad (TODO: make optional) \"\"\" if params is not None: assert isinstance(params.values()[0],","@torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = [] for i in range(len(connections)):","str): # Add or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] +","print('# Skip this layer because the layer has been calcualted!') print('======================== {0:3} Layer:","range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name","except in compliance with the License. # You may obtain a copy of","return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i]","range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if","mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz","return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if","not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding","padding[0] > 0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif","if not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0]","connections = [] for i in range(len(graph)): if 'label' in graph[i] and graph[i][-1]","'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z,","*= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = []","in range(C): for h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] >","dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('#","Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z =","MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer,","eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k]","tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride =","stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i]","layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask,","Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size","= layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size,","may not use this file except in compliance with the License. # You","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","k for k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1',","has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import","+ 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) ==","print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for","isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size","@torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('# next_dz.shape: ',","else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d'","(padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer,","in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def","')[0]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[0]:\\ labels[graph[i].split('\\t')[1].split(' -> ')[1]]+'_'+\\ graph[i].split('\\t')[1].split(' -> ')[1]}) pop_index = []","last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\n') return","kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size']","range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu =","C, H, W = z.shape D, C, k1, k2 = K.shape N, D,","== 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])):","eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params =","padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else:","# kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size)","weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop =","last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights =","return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy","render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start","p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {}","c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c])","= batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape)","p): print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ',","print('# strides: ', strides) N, C, H, W = z.shape _, _, out_h,","z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i]","AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer,","z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0,","= 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels')","if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif","N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',","BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] =","# return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for","Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = []","x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum(","= last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None':","in str(model).split('\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections","pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz,","p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name","'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\n') return last_connections @torch.no_grad() def","range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i,","AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not","layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] =","1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('#","'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\n') labels = {}","elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz))","z.shape m = N*H*W shape = [N,C,H,W] import numpy as np ax =","for layer in model.modules(): if not ':' in str(layer): layers.append(layer) parameters = []","print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('#","list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections):","(https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","= (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif","tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size","output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz","in torch.autograd.Function Args: var: output Variable params: dict of (name, Variable) to add","featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not","i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end =","dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1]","'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if","return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear'","[] for i in range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\"':","num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in","@torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1)","in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d):","padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] >","return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {}","', strides) N, C, H, W = z.shape _, _, out_h, out_w =","[tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not","torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\ k1-1-padding[0],k1-1-padding[0],0,0),","return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)):","fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):","dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z =","dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride =","= layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params","in range(start_i, len(layers)): layer = layers[i] if name in str(layer): return layer, i","j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0],","k, j = weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad","out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\ padding[0],0,0), mode='constant', value=0) padding_dz ="],"string":"[\n \"Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] =\",\n \"in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i,\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"strides=(1,1)): n, c, h_in, w_in = x.shape d, c, k, j = weight.shape\",\n \"list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz,\",\n \"keepdim=True) xmu = z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m\",\n \"delete_allpths(pth_dir=None) print('\\\\n=========================== Store network model Results Start =========================') y = model(x) print('=========================== Store\",\n \"if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z\",\n \"return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name: return\",\n \"print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i],\",\n \"= tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma\",\n \"for backward in torch.autograd.Function Args: var: output Variable params: dict of (name, Variable)\",\n \"elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif\",\n \"Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask = featuremap[i]\",\n \"layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features =\",\n \"(pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('#\",\n \"= last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif\",\n \"return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d'\",\n \"p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =\",\n \"if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding #\",\n \"'Conv' in name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward'\",\n \"get_layers(last_connections, model): return_layers = [] tmp_layers = [] for layer in model.modules(): if\",\n \"{id(v): k for k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12',\",\n \"featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z\",\n \"in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i\",\n \"padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name\",\n \"== 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW =\",\n \"= (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout):\",\n \"key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for\",\n \"Variable) param_map = {id(v): k for k, v in params.items()} node_attr = dict(style='filled',\",\n \"return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name\",\n \"# return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name =\",\n \"loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)):\",\n \"{0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz =\",\n \"z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)\",\n \"return_dz[i] = dLoss_dz print('# Skip this layer because the layer has been calcualted!')\",\n \"weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j,\",\n \"# return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name']\",\n \"model Start ===============================') for i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i,\",\n \"W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape:\",\n \"print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride = params[1] padding\",\n \"= {'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name':\",\n \"= z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB\",\n \"conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C, H, W = z.shape\",\n \"W = z.shape D, C, k1, k2 = K.shape N, D, H1, W1\",\n \"'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1'\",\n \"else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue\",\n \"= ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling,\",\n \"current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name:\",\n \"tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding\",\n \"in name: return 'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in\",\n \"Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size']\",\n \"parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] =\",\n \"weight, strides=(1,1)): n, c, h_in, w_in = x.shape d, c, k, j =\",\n \"batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('#\",\n \"tensor_B.shape): print('Shape of two compard tensors is not equal.') return None error =\",\n \"== len(connections): print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None})\",\n \"break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break\",\n \"of two compard tensors is not equal.') return None error = 0 error_tolerance\",\n \"'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1\",\n \"'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k]\",\n \"tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {}\",\n \"return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz,\",\n \"'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z,\",\n \"padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {}\",\n \"g = make_dot(y[0]) return g else: g = make_dot(y) return g @torch.no_grad() def\",\n \"i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========',\",\n \"'Mean' in name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name:\",\n \"z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape))\",\n \"= dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps =\",\n \"torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability -\",\n \"= {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size,\",\n \"-1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz,\",\n \"'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp\",\n \"that require grad (TODO: make optional) \\\"\\\"\\\" if params is not None: assert\",\n \"padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else:\",\n \"i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in\",\n \"(output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else:\",\n \"'.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None):\",\n \"tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):\",\n \"parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name']\",\n \"print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ',\",\n \"if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size #\",\n \"'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View'\",\n \"(padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0,\",\n \"featuremap_dir == None: pth_dir = \\\"./tmp_file/\\\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir)\",\n \"governing permissions and # limitations under the License. #!/usr/bin/python3 import torch import torch.nn\",\n \"generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def\",\n \"'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k]\",\n \"'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz,\",\n \"layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif\",\n \"==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for j in range(len(layer)):\",\n \"new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections =\",\n \"ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape:\",\n \"[] fc_conv_weights = [] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name =\",\n \"= layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps\",\n \"j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape)\",\n \"= Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d' % v\",\n \"j:strides[1] * j + pooling[1]] += next_dz[n, c, i, j] / (pooling[0] *\",\n \"padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else:\",\n \"list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz\",\n \"except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1],\",\n \"= (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if\",\n \"ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz\",\n \"fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params\",\n \"stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding,\",\n \"nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size\",\n \"layer because the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward\",\n \"= list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name:\",\n \"= tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z)\",\n \"-1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View'\",\n \"== last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i)\",\n \"layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features =\",\n \"elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1]\",\n \"padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else:\",\n \"#print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ')\",\n \"print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz =\",\n \"= z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0),\",\n \"= {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size,\",\n \"bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0),\",\n \"'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d'\",\n \"connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index\",\n \"layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name =\",\n \"== 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding =\",\n \"AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride')\",\n \"num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None)\",\n \"{} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features #\",\n \"if 'None' in key1 or 'None' in value1: print('Not completed for '+key+' or\",\n \"')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index = [] for i in range(len(connections)): item_key =\",\n \"in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i\",\n \"len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value\",\n \"list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp,\",\n \"def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape))\",\n \"MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params\",\n \"print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return\",\n \"== {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\\\n') return last_connections\",\n \"current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k]\",\n \"(stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not\",\n \"eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z,\",\n \"Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict,\",\n \"os if pth_dir == None: pth_dir = \\\"./tmp_file/\\\" for root, dirs, files in\",\n \"range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad()\",\n \"p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif\",\n \"eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] =\",\n \"isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k]\",\n \"elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif\",\n \"'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6'\",\n \"dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('#\",\n \"padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z =\",\n \"else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif\",\n \"return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad() def\",\n \"0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding)\",\n \"name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name:\",\n \"parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] =\",\n \"dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape:\",\n \"i + flat_idx // pooling[1] w_idx = strides[1] * j + flat_idx %\",\n \"view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz)\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"featuremap = [] if featuremap_dir == None: pth_dir = \\\"./tmp_file/\\\" else: pth_dir =\",\n \"= _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1)\",\n \"else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])): if\",\n \"= featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape)\",\n \"= tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated loss:\",\n \"j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1,\",\n \"'' node_name = '%s\\\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)),\",\n \"in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel']\",\n \"== 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0:\",\n \"# kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size)\",\n \"AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name\",\n \"elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name #\",\n \"= fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i]\",\n \"return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz\",\n \"model.modules(): if not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights = []\",\n \"padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params\",\n \"eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer,\",\n \"graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\\\n===========================\",\n \"_conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz,\",\n \"delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors End\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"= {id(v): k for k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left',\",\n \"= dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif\",\n \"= stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] =\",\n \"', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ',\",\n \"ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c\",\n \"list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif\",\n \"= [] for layer in model.modules(): if not ':' in str(layer): layers.append(layer) parameters\",\n \"been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End ==========================') continue p =\",\n \"layer == 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer ==\",\n \"layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name =\",\n \"not been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000,\",\n \"average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==\",\n \"= fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop()\",\n \"parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name']\",\n \"tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif\",\n \"elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name #\",\n \"{} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):\",\n \"padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] *\",\n \"print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides) padding_next_dz\",\n \"Variable) to add names to node that require grad (TODO: make optional) \\\"\\\"\\\"\",\n \"tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size\",\n \"list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N\",\n \"if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because the\",\n \"+= 1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add\",\n \"return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name: return\",\n \"dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):\",\n \"current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name:\",\n \"network model Results Start =========================') y = model(x) print('=========================== Store network model Results\",\n \"return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i\",\n \"last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list):\",\n \"list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])):\",\n \"weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool'\",\n \"return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for k\",\n \"index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): #\",\n \"list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1\",\n \"= padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU):\",\n \"np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error\",\n \"loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers =\",\n \"'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z\",\n \"'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1],\",\n \"end=' ') return error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for\",\n \"range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================')\",\n \"== 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz,\",\n \"for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0],\",\n \"keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('#\",\n \"= list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in\",\n \"= z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 = xmu**2 var =\",\n \"z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):\",\n \"len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1]\",\n \"padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if\",\n \"= out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] =\",\n \"'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k]\",\n \"in range(N): for c in range(C): for h in range(H): for w in\",\n \"= layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] =\",\n \"autograd graph Blue nodes are the Variables that require grad, orange are Tensors\",\n \"= num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i]\",\n \"in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0]\",\n \"output Variable params: dict of (name, Variable) to add names to node that\",\n \"= xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis,\",\n \"z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z =\",\n \"w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A,\",\n \"= layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features\",\n \"import copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in\",\n \"(dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape\",\n \"as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz)\",\n \"isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel\",\n \"key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' +\",\n \"last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz\",\n \"# kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size)\",\n \"xmu = z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar\",\n \"', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = []\",\n \"'.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor)\",\n \"labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\\",\n \"* pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape:\",\n \"list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides)\",\n \"= nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch =\",\n \"elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k]\",\n \"layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if\",\n \"continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in\",\n \"n in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for j\",\n \"= MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] =\",\n \"= 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat'\",\n \"w_in = x.shape d, c, k, j = weight.shape x_pad = x x_pad\",\n \"or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 == 0: pass print('error',\",\n \"print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w],\",\n \"def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir == None: pth_dir =\",\n \"> error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate:\",\n \"dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] ==\",\n \"# return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif\",\n \"= N*H*W shape = [N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1)\",\n \"padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name =\",\n \"pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz\",\n \"== 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p)\",\n \"torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ',\",\n \"m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 = xmu**2\",\n \"add names to node that require grad (TODO: make optional) \\\"\\\"\\\" if params\",\n \"-1, -1): connections.remove(pop_index[i]) new_connections = [] for item in connections: key, value =\",\n \"for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j, k,\",\n \"'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View'\",\n \"j:strides[1] * j + pooling[1]]) h_idx = strides[0] * i + flat_idx //\",\n \"relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =\",\n \"= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride\",\n \"+ flat_idx // pooling[1] w_idx = strides[1] * j + flat_idx % pooling[1]\",\n \"fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0, 0) stride = layer['stride']\",\n \"(TODO: make optional) \\\"\\\"\\\" if params is not None: assert isinstance(params.values()[0], Variable) param_map\",\n \"return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name: return\",\n \"return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0]\",\n \"for i in range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img,\",\n \"isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k]\",\n \"index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1]\",\n \"in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if\",\n \"new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections\",\n \"# in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels')\",\n \"weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0, 0) stride\",\n \"in item_key: if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not\",\n \"= 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params\",\n \"= tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params\",\n \"if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad()\",\n \"if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!')\",\n \"BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps\",\n \"Args: var: output Variable params: dict of (name, Variable) to add names to\",\n \"0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加\",\n \"i in range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\\",\n \"Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections\",\n \"isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride\",\n \"in range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value =\",\n \"eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz\",\n \"range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i\",\n \"def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if\",\n \"layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps =\",\n \"dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store network model Results Start =========================') y\",\n \"torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz,\",\n \"torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True))\",\n \"parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name = 'View' parameters[i] =\",\n \"layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name']\",\n \"= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma\",\n \"= [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer =\",\n \"H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N,\",\n \"layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif\",\n \"/ (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]]\",\n \"in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel']\",\n \"np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 ==\",\n \"not use this file except in compliance with the License. # You may\",\n \"or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N))\",\n \"name = param_map[id(u)] if params is not None else '' node_name = '%s\\\\n\",\n \"tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] =\",\n \"p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params\",\n \"= cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for\",\n \"z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop()\",\n \"# p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params\",\n \"Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name\",\n \"print('\\\\n=========================== Restore network model Start ===============================') for i in range(len(connections)): print('# Restore network\",\n \"torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1]\",\n \"zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor\",\n \"if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif\",\n \"== 4: N, C, H, W = tensor_A.shape for n in range(N): for\",\n \"pz = torch.zeros(N, D, H_last, W_last) for n in range(N): for d in\",\n \"# in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features')\",\n \"tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0]\",\n \"tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated loss: ',\",\n \"var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3\",\n \"print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask\",\n \"= eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear):\",\n \"', error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) == 2: N,\",\n \"dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i]\",\n \"dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz\",\n \"dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0\",\n \"print('Not completed for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if\",\n \"= (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif\",\n \"Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\\\n') Loss =\",\n \"i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img)\",\n \"tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps =\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"W = z.shape m = N*H*W shape = [N,C,H,W] import numpy as np\",\n \"error_tolerance: error += 1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else:\",\n \"# padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def\",\n \"'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat'\",\n \"== 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end='\",\n \"= Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors\",\n \"len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1)\",\n \"+ 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0\",\n \"= {'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name':\",\n \"padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling =\",\n \"dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in\",\n \"except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz,\",\n \"add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var,\",\n \"layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'],\",\n \"= list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================')\",\n \"None: pth_dir = \\\"./tmp_file/\\\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums =\",\n \"k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d'\",\n \"y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+'\",\n \"elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride']\",\n \"'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def\",\n \"if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z\",\n \"last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in\",\n \"======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!')\",\n \"isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True))\",\n \"print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in\",\n \"# out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif\",\n \"u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var,\",\n \"j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i +\",\n \"for the specific language governing permissions and # limitations under the License. #!/usr/bin/python3\",\n \"z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling =\",\n \"or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C)\",\n \"Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name =\",\n \"', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape:\",\n \"c, k, j = weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0])\",\n \"g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors Start ====================================')\",\n \"{}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key: if i ==\",\n \"files = os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if '.pth' in\",\n \"for u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if\",\n \"Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding')\",\n \"weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) ==\",\n \"layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel =\",\n \"in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else:\",\n \"dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name']\",\n \"= (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if\",\n \"print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3))\",\n \"relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling\",\n \"padding): if padding[0] > 0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0],\",\n \"torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True))\",\n \"print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('#\",\n \"# eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif\",\n \"output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz):\",\n \"', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4:\",\n \"= [] for i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\\\n======================== {0:3}\",\n \"== 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View':\",\n \"keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true,\",\n \"Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z\",\n \"tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] =\",\n \"parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i] =\",\n \"parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name}\",\n \"= _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return\",\n \"else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple):\",\n \"isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add':\",\n \"= layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop()\",\n \"AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params\",\n \"= list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if\",\n \"c, i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:,\",\n \"or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None)\",\n \"eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z,\",\n \"# stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride)\",\n \"tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] =\",\n \"%s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var,\",\n \"list(last_z.shape)) if params: pooling = params[0] stride = params[1] padding = params[2] output_size\",\n \"= [] tmp_layers = [] for layer in model.modules(): if not ':' in\",\n \"= dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ',\",\n \"in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0],\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape))\",\n \"num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps']\",\n \"out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name\",\n \"stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling\",\n \"featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in\",\n \"{} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel #\",\n \"Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride')\",\n \"= K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('#\",\n \"nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name\",\n \"range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================')\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)):\",\n \"dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End\",\n \"tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+'\",\n \"= dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('#\",\n \"1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1))\",\n \"Store network model Results Start =========================') y = model(x) print('=========================== Store network model\",\n \"- y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('===========================\",\n \"= '%s\\\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var)\",\n \"range(D): for h in range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]):\",\n \"'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv'\",\n \"layer_name} elif layer == 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif\",\n \"list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape:\",\n \"return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in range(C):\",\n \"if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for i in\",\n \"===========================\\\\n') if 'GoogLeNet' in str(model).split('\\\\n')[0]: g = make_dot(y[0]) return g else: g =\",\n \"list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for j\",\n \"'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name\",\n \"= tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params\",\n \"in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================')\",\n \"= tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] =\",\n \"'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding\",\n \"next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) #\",\n \"', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz,\",\n \"dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('#\",\n \"fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params\",\n \"z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N =\",\n \"'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name\",\n \"= kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] =\",\n \"in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable\",\n \"(stride, stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not\",\n \"gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =\",\n \"str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad))\",\n \"nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ',\",\n \"= dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape))\",\n \"', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def\",\n \"-> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index =\",\n \"# padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding)\",\n \"layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d):\",\n \"name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in name:\",\n \"if g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors Start ====================================') result = model(img) print('===========================\",\n \"parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================')\",\n \"x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors Start ====================================') result = model(img)\",\n \"for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x =\",\n \"dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ',\",\n \"dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z\",\n \"if 'label' in graph[i] and graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label'\",\n \"current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not\",\n \"elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name #\",\n \"k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k]\",\n \"in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x =\",\n \"padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout):\",\n \"if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z =\",\n \"else: print('Not completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if\",\n \"', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad))\",\n \"next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z,\",\n \"fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride']\",\n \"print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('#\",\n \"'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because\",\n \"', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ',\",\n \"for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def\",\n \"get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None:\",\n \"item in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' +\",\n \"= eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights\",\n \"isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy\",\n \"return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] =\",\n \"ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides,\",\n \"layer[j][k] print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if\",\n \"if params: pooling = params[0] stride = params[1] padding = params[2] output_size =\",\n \"ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] =\",\n \"backward in torch.autograd.Function Args: var: output Variable params: dict of (name, Variable) to\",\n \"def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor =\",\n \"= kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] =\",\n \"padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params\",\n \"= layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel\",\n \"get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i\",\n \"last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0:\",\n \"k2 = K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape))\",\n \"error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap\",\n \"elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] =\",\n \"dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self\",\n \"torch.nn as nn import torch.nn.functional as F from graphviz import Digraph, render from\",\n \"pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return\",\n \"c in range(C): for h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w]\",\n \"out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params =\",\n \"dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z):\",\n \"start = 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 ==\",\n \"#delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape))\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else:\",\n \"'.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz\",\n \"you may not use this file except in compliance with the License. #\",\n \"mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name']\",\n \"N*H*W shape = [N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1)\",\n \"= params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1])\",\n \"not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding\",\n \"elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop\",\n \"'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label) _,\",\n \"layer = layers[i] if name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections,\",\n \"0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU'\",\n \"tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride\",\n \"return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape))\",\n \"in name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\\\n')\",\n \"not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp =\",\n \"nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size\",\n \"z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z)\",\n \"= 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape)\",\n \"= torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz =\",\n \"parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name']\",\n \"'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add'\",\n \"stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride']\",\n \"# return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params =\",\n \"print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz\",\n \"return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0],\",\n \"else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name\",\n \"y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ',\",\n \"np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif\",\n \"@torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1. for i in range(len(x)):\",\n \"or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif\",\n \"'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if\",\n \"print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape)\",\n \"{0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def\",\n \"> error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ',\",\n \"= layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d):\",\n \"for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name =\",\n \"return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name\",\n \"= featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding']\",\n \"== 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except:\",\n \"def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0\",\n \"parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz,\",\n \"zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling,\",\n \"error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir == None:\",\n \"from graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true):\",\n \"w_idx = strides[1] * j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx]\",\n \"completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz,\",\n \"for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c],\",\n \"file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers =\",\n \"for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return\",\n \"list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N\",\n \"are Tensors saved for backward in torch.autograd.Function Args: var: output Variable params: dict\",\n \"= kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] =\",\n \"AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"= layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name\",\n \"stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k]\",\n \"not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding\",\n \"dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward\",\n \"kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size']\",\n \"current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] +\",\n \"stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else:\",\n \"hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot\",\n \"''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if not\",\n \"tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1]\",\n \"not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for layer\",\n \"list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if not\",\n \"Variable params: dict of (name, Variable) to add names to node that require\",\n \"fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model,\",\n \"list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C):\",\n \"= num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight)\",\n \"i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i])\",\n \"in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item in connections: key,\",\n \"layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel #\",\n \"= relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz\",\n \"else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif\",\n \"return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if\",\n \"if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\\\n')\",\n \"isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding\",\n \"if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding =\",\n \"fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k]\",\n \"flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j] dz\",\n \"for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if\",\n \"# padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding)\",\n \"if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad()\",\n \"dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape))\",\n \"torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz,\",\n \"return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two\",\n \"0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :,\",\n \"C, H, W = z.shape _, _, out_h, out_w = next_dz.shape padding_z =\",\n \"parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir =\",\n \"featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i,\",\n \"layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name']\",\n \"featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz,\",\n \"elif 'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph\",\n \"isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size\",\n \"Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i]\",\n \"0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights\",\n \"i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] *\",\n \"1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape)\",\n \"z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C, H,\",\n \"exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear'\",\n \"elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip\",\n \"index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1,\",\n \"list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if\",\n \"params is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k,\",\n \"get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for\",\n \"permissions and # limitations under the License. #!/usr/bin/python3 import torch import torch.nn as\",\n \"= torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\\",\n \"return 'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean' in name or\",\n \"next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]]\",\n \"'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph =\",\n \"tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors =\",\n \"stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding)\",\n \"get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i])\",\n \"network model Start ===============================') for i in range(len(connections)): print('# Restore network model: processing\",\n \"layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d'\",\n \"new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = [] for i in\",\n \"= tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] =\",\n \"'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward'\",\n \"', padding) print('# strides: ', strides) N, C, H, W = z.shape _,\",\n \"for layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0\",\n \"add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store network model Results Start\",\n \"in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1]\",\n \"== 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif\",\n \"featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name']\",\n \"p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z =\",\n \"elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride']\",\n \"tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {}\",\n \"# in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels')\",\n \"== 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name\",\n \"print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift)\",\n \"(stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not\",\n \"None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add':\",\n \"padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz\",\n \"for root, dirs, files in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',):\",\n \"for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def\",\n \"stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz)\",\n \"'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name: return 'View'\",\n \"return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('#\",\n \"> 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:,\",\n \"y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)),\",\n \"max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==\",\n \"= make_dot(y[0]) return g else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name):\",\n \"gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape)\",\n \"=========================') y = model(x) print('=========================== Store network model Results End ===========================\\\\n') if 'GoogLeNet'\",\n \"torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability\",\n \"name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d'\",\n \"', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x,\",\n \"range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item in connections: key, value\",\n \"if len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape for n in\",\n \"else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None)\",\n \"1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def\",\n \"layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] =\",\n \"for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\\\n===========================\",\n \"elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name #\",\n \"None: pth_dir = \\\"./tmp_file/\\\" for root, dirs, files in os.walk(pth_dir, topdown=False): for name\",\n \"for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i\",\n \"= layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel\",\n \"(ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape))\",\n \"else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding\",\n \"# eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight\",\n \"= layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] =\",\n \"p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] =\",\n \"in range(D): for h in range(0, H_last, strides[0]): for w in range(0, W_last,\",\n \"', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1):\",\n \"copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name =\",\n \"in size])+')' def add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()),\",\n \"0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) ==\",\n \"layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding\",\n \"i in range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value\",\n \"MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params\",\n \"for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i,\",\n \"batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==\",\n \"stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] =\",\n \"dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def\",\n \"label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors\",\n \"= list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0,\",\n \"{0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift =\",\n \"return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g\",\n \"size_to_str(size): return '('+(', ').join(['%d' % v for v in size])+')' def add_nodes(var): if\",\n \"padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0]\",\n \"isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add':\",\n \"= layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps\",\n \"last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start,\",\n \"'None' in item_key: if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if\",\n \"len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding\",\n \"pooling = params[0] stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\\",\n \"strides) N, C, H, W = z.shape _, _, out_h, out_w = next_dz.shape\",\n \"layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size,\",\n \"= len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index\",\n \"Start =========================') y = model(x) print('=========================== Store network model Results End ===========================\\\\n') if\",\n \"== 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for j in\",\n \"= tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W\",\n \"= [] fc_conv_weights = [] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name\",\n \"===============================') for i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key\",\n \"last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j\",\n \"else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0]\",\n \"strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz =\",\n \"name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name:\",\n \"-1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name:\",\n \"gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop =\",\n \"in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr,\",\n \"tmp[1] + 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j])\",\n \"'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params =\",\n \"= (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d):\",\n \"'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding']\",\n \"== 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View':\",\n \"in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] =\",\n \"== list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1])\",\n \"output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not been\",\n \"if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name #\",\n \"-1, -1): layer = parameters[i] print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if\",\n \"= (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i]\",\n \"range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance:\",\n \"= layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] =\",\n \"layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding\",\n \"parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name\",\n \"'(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1,\",\n \"list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p)\",\n \"@torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and padding[1] > 0: return\",\n \"isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params)\",\n \"= torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad()\",\n \"* i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]])\",\n \"i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer\",\n \"print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z =\",\n \"W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape\",\n \"h in range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] =\",\n \"the License. #!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional as F\",\n \"dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ',\",\n \"= [] for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for\",\n \"next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride\",\n \"layers.append(layer) parameters = [] fc_conv_weights = [] for layer in layers: if isinstance(layer,\",\n \"layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params =\",\n \"torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i +\",\n \"in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor\",\n \"list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ',\",\n \"add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i]\",\n \"graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in graph[i] and graph[i][-1] ==\",\n \"', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0),\",\n \"tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1]\",\n \"name, start_i): for i in range(start_i, len(layers)): layer = layers[i] if name in\",\n \"next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask,\",\n \"### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss:\",\n \"gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB\",\n \"== 1: C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance\",\n \"Store network model Results End ===========================\\\\n') if 'GoogLeNet' in str(model).split('\\\\n')[0]: g = make_dot(y[0])\",\n \"= padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU):\",\n \"value1: print('Not completed for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1})\",\n \"p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params elif isinstance(layer,\",\n \"and graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in graph[i] and graph[i][-1]\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] =\",\n \"dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params\",\n \"print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z =\",\n \"AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j\",\n \"axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c]\",\n \"copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\\\n=========================== Restore network model Start\",\n \"= stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] =\",\n \"isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i]\",\n \"exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1 or 'None' in value1:\",\n \"graph[i] and graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in graph[i] and\",\n \"name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name:\",\n \"= z.shape m = N*H*W shape = [N,C,H,W] import numpy as np ax\",\n \"loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv\",\n \"isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size\",\n \"print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('#\",\n \"range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)):\",\n \"= return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] =\",\n \"= {'layer_name': layer_name} elif layer == 'View': layer_name = 'View' parameters[i] = {'layer_name':\",\n \"'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features']\",\n \"in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features']\",\n \"z = featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding =\",\n \"z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz\",\n \"output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else:\",\n \"AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params\",\n \"padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout):\",\n \"pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass #print('right',\",\n \"= tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors\",\n \"'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name']\",\n \"gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap =\",\n \"layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB =\",\n \"pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1),\",\n \"padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz\",\n \"= 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors]\",\n \"Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]):\",\n \"BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps\",\n \"'_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in\",\n \"z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :]\",\n \"dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] ==\",\n \"elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name #\",\n \"= padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d'\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {}\",\n \"'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if\",\n \"fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i]\",\n \"= 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size')\",\n \"calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1,\",\n \"range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add'\",\n \"= (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if\",\n \"assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v in params.items()} node_attr\",\n \"AdaptiveAvgPool2d_params ### else: print('The layer has not been processed in get_structure_parameters!') return parameters,\",\n \"print('# padding: ', padding) print('# strides: ', strides) N, C, H, W =\",\n \"dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz)\",\n \"elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0,\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz\",\n \"* j:strides[1] * j + pooling[1]]) h_idx = strides[0] * i + flat_idx\",\n \"else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i in\",\n \"AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed\",\n \"tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape for n\",\n \"= layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz)\",\n \"item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i])\",\n \"nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features\",\n \"### else: print('The layer has not been processed in get_structure_parameters!') return parameters, fc_conv_weights\",\n \"if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] ==\",\n \"else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name =\",\n \"name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1. for i in\",\n \"else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout':\",\n \"isinstance(last_tensors[i], str): # Add or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0]\",\n \"', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('#\",\n \"j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i,\",\n \"dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):\",\n \"for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed\",\n \"= 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add'\",\n \"last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i],\",\n \"padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding']\",\n \"= dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==\",\n \"'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name\",\n \"if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif\",\n \"= parameters[i] print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d':\",\n \"End ======================================\\\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch = Loss(result[0], label)\",\n \"for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0]\",\n \"def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed =\",\n \"elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride']\",\n \"padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding']\",\n \"for i in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\\\n======================== {0:3}\",\n \"> error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ',\",\n \"get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz\",\n \"z.shape D, C, k1, k2 = K.shape N, D, H1, W1 = next_dz.shape\",\n \"find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1]\",\n \"= _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db =\",\n \"featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1): layer =\",\n \"d, c, k, j = weight.shape x_pad = x x_pad = x_pad.unfold(2, k,\",\n \"graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index = [] for i in range(len(connections)): item_key = list(connections[i].keys())[0]\",\n \"completed for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not\",\n \"# p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer,\",\n \"return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name']\",\n \"num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz)\",\n \"else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1\",\n \"Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return\",\n \"# kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size)\",\n \"shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen = set() def\",\n \"+= next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0],\",\n \"parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not\",\n \"list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp =\",\n \"0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start\",\n \"z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz\",\n \"return error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for n in\",\n \"{} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p #\",\n \"0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ')\",\n \"{} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel #\",\n \"return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C,\",\n \"H, W = z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z,\",\n \"value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0])\",\n \"if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters\",\n \"weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides:\",\n \"in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+'\",\n \"kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride,\",\n \"new_connections = [] for item in connections: key, value = list(item.items())[0] key1 =\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"dLoss_dB @torch.no_grad() def make_dot(var, params=None): \\\"\\\"\\\" Produces Graphviz representation of PyTorch autograd graph\",\n \"current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name:\",\n \"tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch\",\n \"for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis,\",\n \"dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated\",\n \"= xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 =\",\n \"end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0\",\n \"'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0),\",\n \"dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad()\",\n \"# return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name']\",\n \"range(len(layer[j])): tmp_layer = layer[j][k] print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name']\",\n \"dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store\",\n \"= F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for\",\n \"= generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors Start ====================================') result\",\n \"layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name\",\n \"{} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple):\",\n \"# out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight\",\n \"+ pooling[0], strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n, c, i,\",\n \"weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =\",\n \"function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections not done!\",\n \"W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last) for n\",\n \"n, c, h_in, w_in = x.shape d, c, k, j = weight.shape x_pad\",\n \"isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel\",\n \"len(layers)): layer = layers[i] if name in str(layer): return layer, i @torch.no_grad() def\",\n \"[] for item in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) +\",\n \"= AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] =\",\n \"average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',\",\n \"flat_idx // pooling[1] w_idx = strides[1] * j + flat_idx % pooling[1] padding_dz[n,\",\n \"if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding #\",\n \"def _insert_zeros(dz, strides): N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) +\",\n \"elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name #\",\n \"Restore network model End =================================\\\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for\",\n \"===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict,\",\n \"= get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1],\",\n \"return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('#\",\n \"'Cat' in name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return\",\n \"Graphviz representation of PyTorch autograd graph Blue nodes are the Variables that require\",\n \"eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ',\",\n \"', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K,\",\n \"Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding']\",\n \"Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in\",\n \"set() def size_to_str(size): return '('+(', ').join(['%d' % v for v in size])+')' def\",\n \"= tmp[1] + 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if\",\n \"def generate_connections(g): graph = str(g).split('\\\\n') labels = {} connections = [] for i\",\n \"if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p\",\n \"ConvolutedDog (https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label,\",\n \"completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in\",\n \"print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz =\",\n \"Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features\",\n \"keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return\",\n \"{} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features #\",\n \"dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss:\",\n \"probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor =\",\n \"z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps:\",\n \"print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] >\",\n \"y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i\",\n \"for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0]\",\n \"return g else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu'\",\n \":, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else:\",\n \"pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z,\",\n \"= Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections\",\n \"layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel =\",\n \"tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size\",\n \"range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or View if last_tensors[i] ==\",\n \"= out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params\",\n \"layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop =\",\n \"in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] =\",\n \"tmp_layer = layer[j][k] print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] ==\",\n \"fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape:\",\n \"[] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k]\",\n \"= batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward\",\n \"==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \\\"\\\"\\\" Produces Graphviz\",\n \"tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward()\",\n \"else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple):\",\n \"return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not been processed in\",\n \"[item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 =\",\n \"elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else:\",\n \"for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' ->\",\n \"not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z =\",\n \"= torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for\",\n \"view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params:\",\n \"', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask)\",\n \"print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) == 2: N, C =\",\n \"last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in\",\n \"dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif\",\n \"print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet'\",\n \"= params[0] stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1))\",\n \"= featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed\",\n \"last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed == []: last_connections =\",\n \"MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride')\",\n \"else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple):\",\n \"= index break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end =\",\n \"1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape))\",\n \"KIND, either express or implied. # See the License for the specific language\",\n \"= layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] =\",\n \"return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ',\",\n \"np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for\",\n \"if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride #\",\n \"= (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if\",\n \"dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not\",\n \"for k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### #\",\n \"= layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] =\",\n \"strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j +\",\n \"= _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0),\",\n \"for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start\",\n \"elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer,\",\n \"print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return\",\n \"= torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1,\",\n \"= dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape\",\n \"error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end='\",\n \"this layer because the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+'\",\n \"layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop\",\n \"last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}:\",\n \"in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if\",\n \"else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j,\",\n \"padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for i\",\n \"def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C, H, W =\",\n \"Tensors saved for backward in torch.autograd.Function Args: var: output Variable params: dict of\",\n \"tensor_A.shape for n in range(N): for c in range(C): for h in range(H):\",\n \"if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t\",\n \"', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H,\",\n \"np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H,\",\n \"= {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name']\",\n \"stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else:\",\n \"j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\\\n=========================== {0:3}\",\n \"K, z, padding=(0, 0), strides=(1, 1)): N, C, H, W = z.shape D,\",\n \"rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) == 2:\",\n \"\\\"./tmp_file/\\\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i\",\n \"def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap\",\n \"if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20\",\n \"layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if\",\n \"# return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name =\",\n \"list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add')\",\n \"padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else:\",\n \"= torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1),\",\n \"= list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if\",\n \"m = N*H*W shape = [N,C,H,W] import numpy as np ax = list(np.arange(len(shape)))\",\n \"MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding']\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors\",\n \"strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n, c, i, j] /\",\n \"= copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if\",\n \"@torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name:\",\n \"list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in,\",\n \"{'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name}\",\n \"if not isinstance(layer, list): print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name']\",\n \"'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name =\",\n \"(kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not\",\n \"start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed =\",\n \"w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error +=\",\n \"(padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] =\",\n \"stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding,\",\n \"featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU':\",\n \"== 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name\",\n \"')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index = [] for i in\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad()\",\n \"= dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride\",\n \"return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in\",\n \"', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ',\",\n \"0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :,\",\n \"y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ',\",\n \"elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name #\",\n \"BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return\",\n \"AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding']\",\n \"= tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size,\",\n \"padding=(0, 0), strides=(1, 1)): N, C, H, W = z.shape D, C, k1,\",\n \"model): return_layers = [] tmp_layers = [] for layer in model.modules(): if not\",\n \"len(connections): print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None':\",\n \"Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in\",\n \"conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z\",\n \"in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1\",\n \"pth_dir == None: pth_dir = \\\"./tmp_file/\\\" for root, dirs, files in os.walk(pth_dir, topdown=False):\",\n \"current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i]\",\n \"print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str):\",\n \"isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in\",\n \"print('=========================== Generate Tensors End ======================================\\\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch\",\n \"not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1,\",\n \"_conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape d, c, k, j\",\n \"name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\\\n') labels\",\n \"layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma)\",\n \"', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ',\",\n \"= padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params =\",\n \"for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1,\",\n \"tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding\",\n \"print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0]\",\n \"layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz\",\n \"== 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding =\",\n \"list(gamma.shape)) N, C, H, W = z.shape m = N*H*W shape = [N,C,H,W]\",\n \"0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass\",\n \"= Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] =\",\n \"= str(g).split('\\\\n') labels = {} connections = [] for i in range(len(graph)): if\",\n \"len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps']\",\n \"next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape:\",\n \"#!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional as F from graphviz\",\n \"len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] >\",\n \"for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def\",\n \"dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)):\",\n \"k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot\",\n \"dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\\",\n \"layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps #\",\n \"layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding\",\n \"View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] ==\",\n \"BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return\",\n \"== 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat':\",\n \"import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float()\",\n \"layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer ==\",\n \"in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in\",\n \"fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer:\",\n \"for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in\",\n \"z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop\",\n \"2: N, C = tensor_A.shape for n in range(N): for c in range(C):\",\n \"padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d):\",\n \"if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in\",\n \"for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for\",\n \"= 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features')\",\n \"layer has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None):\",\n \"model Results End ===========================\\\\n') if 'GoogLeNet' in str(model).split('\\\\n')[0]: g = make_dot(y[0]) return g\",\n \"i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name =\",\n \"np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 == 0: pass print('error', np_A[n,c,h,w],\",\n \"for c in range(C): for h in range(H): for w in range(W): if\",\n \"[] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params =\",\n \"param_map[id(u)] if params is not None else '' node_name = '%s\\\\n %s' %\",\n \"notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed')\",\n \"====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\\\n') Loss = nn.CrossEntropyLoss() if\",\n \"* j:strides[1] * j + pooling[1]] += next_dz[n, c, i, j] / (pooling[0]\",\n \"layers = [] for layer in model.modules(): if not ':' in str(layer): layers.append(layer)\",\n \"isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride\",\n \"name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1. for\",\n \"return g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward'\",\n \"+ tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy())\",\n \"i in range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label,\",\n \"+ H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last)\",\n \"i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if\",\n \"= 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features')\",\n \"# 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ',\",\n \"value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z,\",\n \"# kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size)\",\n \"= tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params =\",\n \"{} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):\",\n \"== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z\",\n \"topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x\",\n \"= layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz)\",\n \"= {} connections = [] for i in range(len(graph)): if 'label' in graph[i]\",\n \"else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None':\",\n \"list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']')\",\n \"return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ',\",\n \"graph = str(g).split('\\\\n') labels = {} connections = [] for i in range(len(graph)):\",\n \"Tensors End ======================================\\\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch = Loss(result[0],\",\n \"last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i]\",\n \"if 'GoogLeNet' in str(model).split('\\\\n')[0]: g = make_dot(y[0]) return g else: g = make_dot(y)\",\n \"AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size']\",\n \"if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z\",\n \"return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name,\",\n \"{'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name}\",\n \"index_tensors += 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue\",\n \"AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has\",\n \"'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in\",\n \"elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0,\",\n \"layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding']\",\n \"Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] =\",\n \"= (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer\",\n \"return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = [] for\",\n \"layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel =\",\n \"')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\\",\n \"eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name\",\n \"done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections\",\n \"= {} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features\",\n \"limitations under the License. #!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional\",\n \"compard tensors is not equal.') return None error = 0 error_tolerance = 0.001\",\n \"output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size']\",\n \"'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None) return\",\n \"np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate:\",\n \"in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] =\",\n \"'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model):\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"= tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1\",\n \"range(N): for d in range(D): for h in range(0, H_last, strides[0]): for w\",\n \"layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps =\",\n \"z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding =\",\n \"== 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1]\",\n \"kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else:\",\n \"print('The layer has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def\",\n \"'None'}) print('=========================== Restore network model End =================================\\\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name,\",\n \"c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('#\",\n \"num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] =\",\n \"== 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] =\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz\",\n \"% pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j] dz =\",\n \"len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape for n in range(N):\",\n \"前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\\\n========================\",\n \"fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] =\",\n \"torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape:\",\n \"3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0)\",\n \"Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer,\",\n \"layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features #\",\n \"return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name: return\",\n \"layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] =\",\n \"c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c])\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i]\",\n \"@torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir = \\\"./tmp_file/\\\" for\",\n \"keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated\",\n \"labels = {} connections = [] for i in range(len(graph)): if 'label' in\",\n \"', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1),\",\n \"str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1): if not\",\n \"name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size)\",\n \"= torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D,\",\n \"padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return\",\n \"padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return\",\n \"tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers\",\n \"def mul_items(tensor_size): x = list(tensor_size) mul = 1. for i in range(len(x)): mul\",\n \"'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i]\",\n \"copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer =\",\n \"None error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy()\",\n \"[] for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num\",\n \"AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name =\",\n \"print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad()\",\n \"print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('#\",\n \"eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i] =\",\n \"@torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if not ':'\",\n \"= strides[1] * j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] +=\",\n \"return '('+(', ').join(['%d' % v for v in size])+')' def add_nodes(var): if var\",\n \"def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x)\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch\",\n \"= tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] =\",\n \"for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in\",\n \"to add names to node that require grad (TODO: make optional) \\\"\\\"\\\" if\",\n \"layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size,\",\n \"dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz,\",\n \"'label' in graph[i] and graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in\",\n \"db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('#\",\n \"'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2'\",\n \"fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] =\",\n \"c, h_in, w_in = x.shape d, c, k, j = weight.shape x_pad =\",\n \"layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride\",\n \"return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)\",\n \"print('Not completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG'\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d):\",\n \"Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU'\",\n \"layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name =\",\n \"= exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1 or 'None' in\",\n \"Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel\",\n \"padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding']\",\n \"= (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if\",\n \"p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params\",\n \"layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name =\",\n \"eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape,\",\n \"not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p =\",\n \"featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if not\",\n \"parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label,\",\n \"== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask\",\n \"= output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in\",\n \"= z.shape D, C, k1, k2 = K.shape N, D, H1, W1 =\",\n \"params is not None else '' node_name = '%s\\\\n %s' % (name, size_to_str(u.size()))\",\n \"generate_connections(g): graph = str(g).split('\\\\n') labels = {} connections = [] for i in\",\n \"isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight)\",\n \"== 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if\",\n \"[] print('\\\\n=========================== Restore network model Start ===============================') for i in range(len(connections)): print('# Restore\",\n \"padding = tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW,\",\n \"dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW,\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"for c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx\",\n \"D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape))\",\n \"H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz\",\n \"return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape)\",\n \"== 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop()\",\n \"conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z\",\n \"= (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if\",\n \"MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride']\",\n \"== 0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value ==\",\n \"dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ',\",\n \"torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape:\",\n \"= tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] >\",\n \"print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss,\",\n \"value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split)\",\n \"return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers = []\",\n \"isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif\",\n \"= merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers)\",\n \"in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network\",\n \"in range(len(layer[j])): tmp_layer = layer[j][k] print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if\",\n \"not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride\",\n \"= layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params =\",\n \"if not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for\",\n \"for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name:\",\n \"AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name =\",\n \"parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name\",\n \"labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' ->\",\n \"current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for\",\n \"model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in\",\n \"= layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif\",\n \"= z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar =\",\n \"if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout':\",\n \"= {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name':\",\n \"# return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name =\",\n \"layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding\",\n \"for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0]\",\n \"'GoogLeNet' in str(model).split('\\\\n')[0]: g = make_dot(y[0]) return g else: g = make_dot(y) return\",\n \"= output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not\",\n \"params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] =\",\n \"padding: ', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K =\",\n \"align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen = set() def size_to_str(size):\",\n \"connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0]\",\n \"= tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] =\",\n \"= kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] =\",\n \"range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if\",\n \"layer = parameters[i] if not isinstance(layer, list): print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start\",\n \"')[1]}) pop_index = [] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '('\",\n \"'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g):\",\n \"list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape:\",\n \"stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop() if not\",\n \"featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d':\",\n \"'.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z =\",\n \"return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d'\",\n \"return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i]\",\n \"file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap\",\n \"return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0]\",\n \"nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p\",\n \"not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z =\",\n \"current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i\",\n \"parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name\",\n \"padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop =\",\n \"[] for layer in model.modules(): if not ':' in str(layer): layers.append(layer) parameters =\",\n \"''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in\",\n \":, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z,\",\n \"kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride,\",\n \"not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z =\",\n \"'.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not\",\n \"index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index\",\n \"AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size']\",\n \"=================================\\\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)):\",\n \"[N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax)\",\n \"if params is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for\",\n \"i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\\",\n \"parameters = [] fc_conv_weights = [] for layer in layers: if isinstance(layer, nn.Conv2d):\",\n \"== 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding =\",\n \"AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride']\",\n \"in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = []\",\n \"dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut\",\n \"if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name =\",\n \"dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz,\",\n \"MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size']\",\n \"0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key:\",\n \"last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] ==\",\n \"= torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape:\",\n \"= fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3}\",\n \"= torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('#\",\n \"print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0,\",\n \"item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for\",\n \"else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in name:\",\n \"padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else:\",\n \"Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name\",\n \"generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors Start ====================================') result =\",\n \"= BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] =\",\n \"in range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1]\",\n \"make_dot(var, params=None): \\\"\\\"\\\" Produces Graphviz representation of PyTorch autograd graph Blue nodes are\",\n \"def get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if not ':' in\",\n \"key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)):\",\n \"list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding)\",\n \"'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if\",\n \"= torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape:\",\n \"np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate:\",\n \"@torch.no_grad() def make_dot(var, params=None): \\\"\\\"\\\" Produces Graphviz representation of PyTorch autograd graph Blue\",\n \"Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"= 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size')\",\n \"'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz)\",\n \"i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not\",\n \"in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz =\",\n \"torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] *\",\n \"output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not\",\n \"next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n\",\n \"file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model):\",\n \"params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\"))\",\n \"def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape d, c, k,\",\n \"1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3)\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x):\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return\",\n \"= AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] =\",\n \"Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try:\",\n \"C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c]\",\n \"= {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features\",\n \"connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = [] for i\",\n \"(padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i] =\",\n \"= [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed')\",\n \"1 break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for\",\n \"', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp\",\n \"featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors\",\n \"range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error +=\",\n \"= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z)\",\n \"kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size']\",\n \"featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d':\",\n \"list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride = params[1]\",\n \"range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed == []: last_connections\",\n \"dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z,\",\n \"= layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop()\",\n \"in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections,\",\n \"= layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params\",\n \":, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1,\",\n \"out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d):\",\n \"file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad()\",\n \"# padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding)\",\n \"= 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View'\",\n \"= in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k]\",\n \"in name: return 'View' elif 'Mean' in name or 'Avg' in name: return\",\n \"function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections):\",\n \"current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1],\",\n \"parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because the layer\",\n \"next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape))\",\n \"for v in size])+')' def add_nodes(var): if var not in seen: if torch.is_tensor(var):\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"print('The layer has not been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model,\",\n \"not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import\",\n \"def generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store network model Results Start =========================') y =\",\n \"dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv =\",\n \"elif layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer,\",\n \"tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i],\",\n \"saved for backward in torch.autograd.Function Args: var: output Variable params: dict of (name,\",\n \"param_map = {id(v): k for k, v in params.items()} node_attr = dict(style='filled', shape='box',\",\n \"\\\"\\\"\\\" Produces Graphviz representation of PyTorch autograd graph Blue nodes are the Variables\",\n \"weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W = dz.shape\",\n \"elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, [])\",\n \"been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if\",\n \"return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0]\",\n \"i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key:\",\n \"Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel\",\n \"str(g).split('\\\\n') labels = {} connections = [] for i in range(len(graph)): if 'label'\",\n \"x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1])\",\n \"return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \\\"\\\"\\\" Produces Graphviz representation of PyTorch\",\n \"range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])):\",\n \"* j + pooling[1]]) h_idx = strides[0] * i + flat_idx // pooling[1]\",\n \"0), strides=(1, 1)): N, C, H, W = z.shape D, C, k1, k2\",\n \"key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)): if key2\",\n \"return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] =\",\n \"print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params):\",\n \"if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'):\",\n \"in key1 or 'None' in value1: print('Not completed for '+key+' or '+value+'! Check\",\n \"elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else:\",\n \"files in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad()\",\n \"for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or View\",\n \"pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx = strides[0] * i\",\n \"= get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i,\",\n \"[] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or\",\n \"last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i,\",\n \"tensor_A.shape for n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance\",\n \"kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed ==\",\n \"len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0]\",\n \"p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params elif\",\n \"fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else:\",\n \"= layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name =\",\n \"padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1]\",\n \"'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features']\",\n \"= torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def\",\n \"end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if\",\n \"= dLoss_dz print('# Skip this layer because the layer has been calcualted!') print('========================\",\n \"index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1,\",\n \"(stride, stride) else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not\",\n \"else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple):\",\n \"== 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz,\",\n \"layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop =\",\n \"tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1]\",\n \"Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return\",\n \"', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C\",\n \"img, label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x) if g_view:\",\n \"', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape m\",\n \"stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding)\",\n \"(H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last,\",\n \"= featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if\",\n \"= torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred =\",\n \"num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params)\",\n \"last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers):\",\n \"isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features\",\n \"{'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\\\n') return last_connections @torch.no_grad()\",\n \"for n in range(N): for d in range(D): for h in range(0, H_last,\",\n \"graph_attr=dict(size=\\\"12,12\\\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d' % v for v\",\n \"', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0]\",\n \"i in range(start_i, len(layers)): layer = layers[i] if name in str(layer): return layer,\",\n \"tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item)\",\n \"return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] =\",\n \"', list(last_z.shape)) if params: pooling = params[0] stride = params[1] padding = params[2]\",\n \"h_idx, w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:,\",\n \"W pz = torch.zeros(N, D, H_last, W_last) for n in range(N): for d\",\n \"@torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',\",\n \"out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size,\",\n \"not len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0,\",\n \"== 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers)\",\n \"eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name\",\n \"range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================')\",\n \"else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple):\",\n \"range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not\",\n \"is not None else '' node_name = '%s\\\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)),\",\n \"next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape:\",\n \"loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def\",\n \"= index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections =\",\n \"tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] =\",\n \"z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz\",\n \"layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding']\",\n \"# return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {}\",\n \"'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding'])\",\n \"if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1,\",\n \"mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz =\",\n \"not isinstance(layer, list): print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] ==\",\n \"(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for\",\n \"Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z\",\n \"name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad()\",\n \"tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma =\",\n \"strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape ==\",\n \"z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):\",\n \"last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j])\",\n \"j + pooling[1]]) h_idx = strides[0] * i + flat_idx // pooling[1] w_idx\",\n \"stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride']\",\n \"layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride,\",\n \"BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name\",\n \"= Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] =\",\n \"weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z)\",\n \"relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z\",\n \"@torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i\",\n \"tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name':\",\n \"= {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel\",\n \"= featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d':\",\n \"', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os\",\n \"print('# padding: ', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K\",\n \"j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start):\",\n \"else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in\",\n \"tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif\",\n \"= dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import\",\n \"y = model(x) print('=========================== Store network model Results End ===========================\\\\n') if 'GoogLeNet' in\",\n \"K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:\",\n \"else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been\",\n \"AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The\",\n \"g else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in\",\n \"parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] =\",\n \"else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1):\",\n \"featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz,\",\n \"elif 'Mean' in name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in\",\n \"layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name =\",\n \"w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :,\",\n \"N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last =\",\n \"', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('#\",\n \"end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp]\",\n \"if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride #\",\n \"elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer\",\n \"= fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3}\",\n \"dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape))\",\n \"@torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers = [] for layer in\",\n \"@torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape))\",\n \"= featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] =\",\n \"== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer:\",\n \"pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i]\",\n \"v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot =\",\n \"= tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features\",\n \"dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap)\",\n \"= layers[i] if name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model):\",\n \"loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW,\",\n \"fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch:\",\n \"= p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d'\",\n \"= eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear):\",\n \"elif layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer\",\n \":, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif\",\n \"% (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'):\",\n \"return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return\",\n \"(parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i]\",\n \"# output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size)\",\n \"add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store network model\",\n \"print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('#\",\n \"= 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add'\",\n \"= [] for i in range(len(connections)): item = connections[i] if len(tmp_split) == 0:\",\n \"{0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >=\",\n \"elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else:\",\n \"'.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for\",\n \"= torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape))\",\n \"range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward\",\n \"get_split_connections(connections): return_connections = [] tmp_split = [] for i in range(len(connections)): item =\",\n \"range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in\",\n \"单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in\",\n \"in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1):\",\n \"#print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) ==\",\n \"padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1),\",\n \"range(C): for h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance\",\n \"== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z\",\n \"out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name\",\n \"padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop() if\",\n \"@torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C, H, W\",\n \"list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy\",\n \"layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif\",\n \"Generate Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\\\n') Loss\",\n \"stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else:\",\n \"label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0,\",\n \"End ==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask,\",\n \"(0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding,\",\n \"current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in\",\n \"#print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None):\",\n \"network model Results End ===========================\\\\n') if 'GoogLeNet' in str(model).split('\\\\n')[0]: g = make_dot(y[0]) return\",\n \"'variable'): u = var.variable name = param_map[id(u)] if params is not None else\",\n \"stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else:\",\n \"require grad, orange are Tensors saved for backward in torch.autograd.Function Args: var: output\",\n \"print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors\",\n \"* i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]]\",\n \"if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size #\",\n \"{0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1]\",\n \"last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling\",\n \"print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1,\",\n \"not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def\",\n \"key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 =\",\n \"Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ',\",\n \"dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and padding[1] > 0:\",\n \"tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for i\",\n \"(padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name\",\n \"C, k1, k2 = K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape:\",\n \"AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding']\",\n \"z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape))\",\n \"= tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB\",\n \"dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d' %\",\n \"torch import torch.nn as nn import torch.nn.functional as F from graphviz import Digraph,\",\n \"', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride =\",\n \"range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy\",\n \"dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] ==\",\n \"for k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2')\",\n \"x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def\",\n \"C, H, W = tensor_A.shape for n in range(N): for c in range(C):\",\n \"isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size\",\n \"tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] =\",\n \"out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not\",\n \"(stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not\",\n \"'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = []\",\n \"= layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps,\",\n \"shape = [N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis\",\n \"= layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name\",\n \"> error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate:\",\n \"i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums:\",\n \"'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d'\",\n \"not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os\",\n \"= torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return\",\n \"loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections = generate_connections(g)\",\n \"find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk\",\n \"= layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i]\",\n \"import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape:\",\n \"print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store network model Results\",\n \"return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 =\",\n \"= featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] =\",\n \"len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask =\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]:\",\n \"None else '' node_name = '%s\\\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue')\",\n \"Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight\",\n \"'+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate\",\n \"import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\\\n===========================\",\n \"= featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d':\",\n \"last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else:\",\n \"elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB\",\n \"= layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] =\",\n \"for h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or\",\n \"']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split('\",\n \"= [] print('\\\\n=========================== Restore network model Start ===============================') for i in range(len(connections)): print('#\",\n \"tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer ==\",\n \"elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride']\",\n \"elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif\",\n \"-1): connections.remove(pop_index[i]) new_connections = [] for item in connections: key, value = list(item.items())[0]\",\n \"= featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif\",\n \"dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View':\",\n \"weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0, 0) stride\",\n \"torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c,\",\n \"elif isinstance(layer, list): for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer =\",\n \"pooling[1] w_idx = strides[1] * j + flat_idx % pooling[1] padding_dz[n, c, h_idx,\",\n \"'\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split('\",\n \"BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer,\",\n \"conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop\",\n \"bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides) padding_next_dz =\",\n \"padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)\",\n \"return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('#\",\n \"end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c\",\n \"= layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] =\",\n \"= 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features')\",\n \"dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape))\",\n \"range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return\",\n \"error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 ==\",\n \"'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool'\",\n \"g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return\",\n \"in graph[i] and graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in graph[i]\",\n \"= tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params =\",\n \"[] tmp_split = [] for i in range(len(connections)): item = connections[i] if len(tmp_split)\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear):\",\n \"in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error\",\n \"padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])):\",\n \"mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK =\",\n \"# 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add'\",\n \"padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop =\",\n \"torch.autograd.Function Args: var: output Variable params: dict of (name, Variable) to add names\",\n \"out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k]\",\n \"name: return 'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean' in name\",\n \"'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz\",\n \"= m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 =\",\n \"0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask,\",\n \"dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('#\",\n \"= tensor_A.shape for n in range(N): for c in range(C): for h in\",\n \"'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat' in name: return 'Cat'\",\n \"print('\\\\n=========================== Generate Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\\\n')\",\n \"= 0 for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): #\",\n \"y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp,\",\n \"dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t)\",\n \"AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride')\",\n \"elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i]\",\n \"tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params)\",\n \"pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('#\",\n \"= 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params\",\n \"last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\\\n=========================== Restore network\",\n \"params: dict of (name, Variable) to add names to node that require grad\",\n \"torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] *\",\n \"', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz,\",\n \"= (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params)\",\n \"stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding,\",\n \"0: return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K,\",\n \"'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if\",\n \"dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride =\",\n \"padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1]\",\n \"', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz,\",\n \"= tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] =\",\n \"= (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ###\",\n \"= average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear':\",\n \"Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name\",\n \"') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in\",\n \"= dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride\",\n \"for i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+'\",\n \"= (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z,\",\n \"parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not been processed in get_structure_parameters!')\",\n \"optional) \\\"\\\"\\\" if params is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v):\",\n \"'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp\",\n \"print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] +\",\n \"torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('#\",\n \"mul = 1. for i in range(len(x)): mul *= x[i] return mul @torch.no_grad()\",\n \"i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j\",\n \"= dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz\",\n \"index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None)\",\n \"tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0]\",\n \"in name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in\",\n \"get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)):\",\n \"i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params\",\n \"padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ',\",\n \"kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size']\",\n \"new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split =\",\n \"print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+'\",\n \"model Results Start =========================') y = model(x) print('=========================== Store network model Results End\",\n \"i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n, c,\",\n \"W = tensor_A.shape for n in range(N): for c in range(C): for h\",\n \"MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params\",\n \"End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ',\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in name: return\",\n \"h_idx = strides[0] * i + flat_idx // pooling[1] w_idx = strides[1] *\",\n \"stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride']\",\n \"= out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d):\",\n \"def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape))\",\n \"padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {}\",\n \"img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None)\",\n \"p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop()\",\n \"# out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight\",\n \"padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name})\",\n \"= F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z,\",\n \"print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N,\",\n \"padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params\",\n \"') return error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for n\",\n \"not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add'\",\n \"= fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==\",\n \"'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1\",\n \"k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历\",\n \"'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean'\",\n \"= padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params =\",\n \"grad (TODO: make optional) \\\"\\\"\\\" if params is not None: assert isinstance(params.values()[0], Variable)\",\n \"model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key: if\",\n \"{'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name}\",\n \"flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] *\",\n \"return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif\",\n \"z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C,\",\n \"= num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k]\",\n \"padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0],\",\n \"out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer,\",\n \"return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \\\"\\\"\\\" Produces Graphviz representation of\",\n \"value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1 or 'None'\",\n \"= fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0, 0) stride =\",\n \"isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"'View' elif 'Mean' in name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm'\",\n \"@torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start =\",\n \"= dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z)\",\n \"= x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad()\",\n \"elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif\",\n \"Results Start =========================') y = model(x) print('=========================== Store network model Results End ===========================\\\\n')\",\n \"= 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]:\",\n \"isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i]\",\n \"= relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i]\",\n \"in name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in\",\n \"(padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU):\",\n \"[]: last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1]\",\n \"list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i]\",\n \"dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape))\",\n \"AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding')\",\n \"Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var,\",\n \"if '->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\\",\n \"range(N): for c in range(C): for h in range(H): for w in range(W):\",\n \"0 import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if\",\n \"return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU'\",\n \"or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections\",\n \"under the License. #!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional as\",\n \"strides=(1, 1)): N, C, H, W = z.shape D, C, k1, k2 =\",\n \"MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not\",\n \"next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z,\",\n \"for n in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for\",\n \"error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N)\",\n \"elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] =\",\n \"n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c]\",\n \"== ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]:\",\n \"# p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params\",\n \"= weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3,\",\n \"for n in range(N): for c in range(C): for h in range(H): for\",\n \"'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding']\",\n \"layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name\",\n \"c, h_idx, w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) #\",\n \"tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding =\",\n \"-> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index = [] for i\",\n \"parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g =\",\n \"(output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has\",\n \"(kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not\",\n \"for c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n,\",\n \"{'layer_name': layer_name} elif layer == 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name}\",\n \"> 0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0]\",\n \"loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1,\",\n \"dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz,\",\n \"delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in model.modules():\",\n \"z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k]\",\n \"= \\\"./tmp_file/\\\" for root, dirs, files in os.walk(pth_dir, topdown=False): for name in files:\",\n \"= 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size')\",\n \"= 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p')\",\n \"(W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last) for n in range(N):\",\n \"# return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in get_structure_parameters_v1!') return\",\n \"= tensor_A.shape for n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] >\",\n \"parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights\",\n \"list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:\",\n \"relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz)\",\n \"not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0] ==\",\n \"+ pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx = strides[0] *\",\n \"in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW,\",\n \"continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else:\",\n \"Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors Start\",\n \"@torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor\",\n \"featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop\",\n \"stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else:\",\n \"print('# gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape m = N*H*W\",\n \"dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape:\",\n \"= layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params)\",\n \"last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i]\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] =\",\n \"padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params\",\n \"z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]]\",\n \"padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in\",\n \"is not equal.') return None error = 0 error_tolerance = 0.001 np_A =\",\n \"params: pooling = params[0] stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1),\",\n \"not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z =\",\n \"========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding\",\n \"(name, Variable) to add names to node that require grad (TODO: make optional)\",\n \"_, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0) padding_dz\",\n \"= dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma\",\n \"'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers\",\n \"== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz\",\n \"def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer = layers[i] if\",\n \"elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer ==\",\n \"return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list):\",\n \"else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N,\",\n \"= layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling,\",\n \"Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif\",\n \"i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item in connections:\",\n \"parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i] =\",\n \"dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not\",\n \"z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N, C,\",\n \"(stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not\",\n \"key1 or 'None' in value1: print('Not completed for '+key+' or '+value+'! Check exchange_name\",\n \"import Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+'\",\n \"name: return 'View' elif 'Mean' in name or 'Avg' in name: return 'AvgPool2d'\",\n \"list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z\",\n \"= layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel\",\n \"= copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name\",\n \"nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params\",\n \"in name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name: return\",\n \"k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i]\",\n \"if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])),\",\n \"x.shape d, c, k, j = weight.shape x_pad = x x_pad = x_pad.unfold(2,\",\n \"img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated\",\n \"isinstance(layer, list): import copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for\",\n \"== None: pth_dir = \\\"./tmp_file/\\\" for root, dirs, files in os.walk(pth_dir, topdown=False): for\",\n \"isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p\",\n \"in name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in\",\n \"or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i]\",\n \"isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size\",\n \"stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz =\",\n \"graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split('\",\n \"tmp_split = [] for i in range(len(connections)): item = connections[i] if len(tmp_split) ==\",\n \"len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break\",\n \"'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features']\",\n \"for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i,\",\n \"in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape)\",\n \"in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n,\",\n \"featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear':\",\n \"that require grad, orange are Tensors saved for backward in torch.autograd.Function Args: var:\",\n \"= conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU':\",\n \"in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True))\",\n \"#print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W)\",\n \"@torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('#\",\n \"layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name =\",\n \"return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape:\",\n \"in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None) return return_dz,\",\n \"= get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for\",\n \"tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is not equal.') return None\",\n \"BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear'\",\n \"not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for\",\n \"get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i\",\n \"= tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] =\",\n \"', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ',\",\n \"tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] =\",\n \"layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size\",\n \"-1): layer = parameters[i] print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name']\",\n \"MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name\",\n \"Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for j\",\n \"_conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant',\",\n \"Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride')\",\n \"labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index = [] for i in range(len(connections)):\",\n \"mask, p): print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape:\",\n \"dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma =\",\n \"featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d':\",\n \"print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and\",\n \"tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif\",\n \"j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end =\",\n \"MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding']\",\n \"print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1\",\n \"= max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape)\",\n \"mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C):\",\n \"int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape:\",\n \"= relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps']\",\n \"tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers\",\n \"def _remove_padding(z, padding): if padding[0] > 0 and padding[1] > 0: return z[:,\",\n \"kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else:\",\n \"@torch.no_grad() def generate_connections(g): graph = str(g).split('\\\\n') labels = {} connections = [] for\",\n \"layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name\",\n \"= {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size,\",\n \"x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out\",\n \"padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps,\",\n \"calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad()\",\n \"'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View'\",\n \"fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params\",\n \"featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0, 0)\",\n \"{} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple):\",\n \"tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride =\",\n \"= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz\",\n \"in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for\",\n \"= (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer,\",\n \"def get_layers(last_connections, model): return_layers = [] tmp_layers = [] for layer in model.modules():\",\n \"== tensor_B.shape): print('Shape of two compard tensors is not equal.') return None error\",\n \"padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad()\",\n \"Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride']\",\n \"Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift\",\n \"dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('#\",\n \"not 'None' in item_key: if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0]\",\n \"z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z =\",\n \"= layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps\",\n \"pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'})\",\n \"def get_split_connections(connections): return_connections = [] tmp_split = [] for i in range(len(connections)): item\",\n \"the Variables that require grad, orange are Tensors saved for backward in torch.autograd.Function\",\n \"print('=========================== Restore network model End =================================\\\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i):\",\n \"= output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in\",\n \"height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d'\",\n \"for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name\",\n \"else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout'\",\n \"# return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params =\",\n \"= layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name']\",\n \"out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if\",\n \"tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1\",\n \"z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z =\",\n \":, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding):\",\n \"if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad() def\",\n \"'next_functions'): for u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0])\",\n \"layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz\",\n \"c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True)\",\n \"range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if\",\n \"x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W =\",\n \"(int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape)\",\n \"str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap =\",\n \"isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features\",\n \"xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 =\",\n \"eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z,\",\n \"in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else:\",\n \"featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz,\",\n \"break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for i\",\n \"equal.') return None error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B\",\n \"mu = z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 = xmu**2 var\",\n \"j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz,\",\n \"= {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features\",\n \"return None error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B =\",\n \"else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple):\",\n \"- mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5)\",\n \"dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] ==\",\n \"layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB =\",\n \"tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride\",\n \"'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward'\",\n \"tensors is not equal.') return None error = 0 error_tolerance = 0.001 np_A\",\n \"import os if pth_dir == None: pth_dir = \\\"./tmp_file/\\\" for root, dirs, files\",\n \"= featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] ==\",\n \"parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name}\",\n \"parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name']\",\n \"output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list):\",\n \"@torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('#\",\n \"return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers)\",\n \"MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size']\",\n \"not equal.') return None error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy()\",\n \"dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability:\",\n \"= 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors]\",\n \"range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance:\",\n \"graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in\",\n \"in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features']\",\n \"= featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0,\",\n \"return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name']\",\n \"return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in\",\n \"= 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1,\",\n \"featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif\",\n \"= add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout':\",\n \"BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name\",\n \"i in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\\\\n======================== {0:3} Layer:\",\n \"dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape)\",\n \"list): import copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k\",\n \"= layer[j][k] print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d':\",\n \"tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma)\",\n \"elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name #\",\n \"z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if\",\n \"isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v in params.items()} node_attr =\",\n \"layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d'\",\n \"os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1. for i\",\n \"i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore\",\n \"in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params =\",\n \"if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None\",\n \"= fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0, 0) stride =\",\n \"pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard\",\n \"not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0,\",\n \"= kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] =\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"x = Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate\",\n \"nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size\",\n \"for h in range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w]\",\n \"fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop()\",\n \"gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('========================\",\n \"gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('===========================\",\n \"tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W =\",\n \"z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w)\",\n \"p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d):\",\n \"(kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not\",\n \"= layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] =\",\n \"elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors +=\",\n \"if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z\",\n \"1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w %\",\n \"var.variable name = param_map[id(u)] if params is not None else '' node_name =\",\n \"print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print('\",\n \"weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0, 0) stride\",\n \"isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params)\",\n \"Backward End ==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz,\",\n \"W_last) for n in range(N): for d in range(D): for h in range(0,\",\n \"parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name']\",\n \"@torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir == None: pth_dir\",\n \"item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key\",\n \"current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers,\",\n \"== 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] =\",\n \"= featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if\",\n \"output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)):\",\n \"', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz\",\n \"c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx =\",\n \"= 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz =\",\n \"Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] =\",\n \"in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None)\",\n \"if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j\",\n \"isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers\",\n \"pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i]\",\n \"in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1):\",\n \"'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name']\",\n \"z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz)\",\n \"tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride\",\n \"', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N,\",\n \"layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name =\",\n \"range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list)\",\n \"for i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key =\",\n \"layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop\",\n \"i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========',\",\n \"dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true\",\n \"= in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i]\",\n \"ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z =\",\n \"stride) else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding,\",\n \"@torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors\",\n \"kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride,\",\n \"in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers\",\n \"{list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = []\",\n \"tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer ==\",\n \"= stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] =\",\n \"dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape)\",\n \"var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u\",\n \"= 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p')\",\n \"Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+'\",\n \"end = index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections\",\n \"'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel']\",\n \"current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name:\",\n \"0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride)\",\n \"elif 'ViewBackward' in name: return 'View' elif 'Mean' in name or 'Avg' in\",\n \"print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else:\",\n \"params[0] stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz\",\n \"return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0,\",\n \"in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i],\",\n \"k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0,\",\n \"print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z =\",\n \"tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance:\",\n \"= F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss)\",\n \"error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end='\",\n \"generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store network model Results Start =========================') y = model(x)\",\n \"return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list):\",\n \"= fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z =\",\n \"torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)]\",\n \"the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End ==========================')\",\n \"= AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for k in range(len(layer[j])):\",\n \"', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.),\",\n \"H_last, W_last) for n in range(N): for d in range(D): for h in\",\n \"return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================')\",\n \"index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections)\",\n \"language governing permissions and # limitations under the License. #!/usr/bin/python3 import torch import\",\n \"=============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape))\",\n \"-1, -1): if isinstance(last_tensors[i], str): # Add or View if last_tensors[i] == 'Add':\",\n \"H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('#\",\n \"padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)):\",\n \">= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try:\",\n \"dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps']\",\n \"in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for j in\",\n \"db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape d,\",\n \"not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections)\",\n \"'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features']\",\n \"@torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors =\",\n \"Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try:\",\n \"+ '_' + value.split('_')[1] if 'None' in key1 or 'None' in value1: print('Not\",\n \"= {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size,\",\n \"AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not\",\n \"W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W\",\n \"padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding']\",\n \"return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {}\",\n \"for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if\",\n \"elif 'AddBackward' in name: return 'Add' elif 'Cat' in name: return 'Cat' elif\",\n \"rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import\",\n \"isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size\",\n \"def exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name: return\",\n \"copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer,\",\n \"stride) else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding,\",\n \"not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride\",\n \"else: loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers\",\n \"next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides:\",\n \"in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for\",\n \"= F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N):\",\n \"fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0, 0) stride = layer['stride']\",\n \"connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1\",\n \"g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors\",\n \"= average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape)\",\n \"len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z =\",\n \"in name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return 'None'\",\n \"z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i]\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"== 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except:\",\n \"error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4:\",\n \"pooling[1]] += next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz,\",\n \"C, H, W = z.shape m = N*H*W shape = [N,C,H,W] import numpy\",\n \"print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return\",\n \"layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {}\",\n \"= (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif\",\n \"dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size']\",\n \"layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif\",\n \"= connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key =\",\n \"0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] >\",\n \"layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size,\",\n \"pooling[1]]) h_idx = strides[0] * i + flat_idx // pooling[1] w_idx = strides[1]\",\n \"print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('#\",\n \"'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name']\",\n \"return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)\",\n \"0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add'\",\n \"4: N, C, H, W = tensor_A.shape for n in range(N): for c\",\n \"exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections not done! Check\",\n \"= 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features')\",\n \"layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz\",\n \"MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name =\",\n \"isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k]\",\n \"in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu\",\n \"if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta\",\n \"processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x\",\n \"y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values)\",\n \"list): print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"j = weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad =\",\n \"Produces Graphviz representation of PyTorch autograd graph Blue nodes are the Variables that\",\n \"dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1]\",\n \"torch.zeros(N, D, H_last, W_last) for n in range(N): for d in range(D): for\",\n \"strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad()\",\n \"return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1],\",\n \"i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i])\",\n \"layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size,\",\n \"generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights\",\n \"License. # You may obtain a copy of the License at # #\",\n \"if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding #\",\n \"not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed):\",\n \"fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen = set() def size_to_str(size): return\",\n \"last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)):\",\n \"else: print('The layer has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad()\",\n \"v for v in size])+')' def add_nodes(var): if var not in seen: if\",\n \"dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\\\n========================\",\n \"if not 'None' in item_key: if i == 0: pass else: last_item_key =\",\n \"layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout':\",\n \"End =================================\\\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i,\",\n \"0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j\",\n \"', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('#\",\n \"range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1):\",\n \"torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss\",\n \"C = tensor_A.shape for n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c]\",\n \"get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1,\",\n \"for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========',\",\n \"strides: ', strides) N, C, H, W = z.shape _, _, out_h, out_w\",\n \"= stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] =\",\n \"out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i]\",\n \"= BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] =\",\n \"') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir\",\n \"print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for\",\n \"= Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections,\",\n \"# num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps')\",\n \"in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0]\",\n \"dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i])\",\n \"= {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features\",\n \"next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad()\",\n \"None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v in params.items()}\",\n \"-1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(),\",\n \"'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding\",\n \"= 1. for i in range(len(x)): mul *= x[i] return mul @torch.no_grad() def\",\n \"if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps\",\n \"list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in\",\n \"= model(img) print('=========================== Generate Tensors End ======================================\\\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in\",\n \"isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p\",\n \"padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding']\",\n \"AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name\",\n \"featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz\",\n \"= model(x) print('=========================== Store network model Results End ===========================\\\\n') if 'GoogLeNet' in str(model).split('\\\\n')[0]:\",\n \"connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return\",\n \"range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif\",\n \"1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif\",\n \"in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' ->\",\n \"= torch.zeros(N, D, H_last, W_last) for n in range(N): for d in range(D):\",\n \"z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] ==\",\n \"{} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features #\",\n \"= layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop\",\n \"Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \\\"\\\"\\\"\",\n \"':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for layer in\",\n \"dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen = set()\",\n \"'%s\\\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if\",\n \"model End =================================\\\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in\",\n \"[] for i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\\\n======================== {0:3} Layer:\",\n \"y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i in\",\n \"1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z\",\n \"return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :, :,\",\n \"Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer\",\n \"i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0]\",\n \"parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name']\",\n \"= conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU':\",\n \"20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ',\",\n \"padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name =\",\n \"out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z)\",\n \"= {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size,\",\n \"+ 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else:\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d):\",\n \"elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c]\",\n \"Copyright 2022 ConvolutedDog (https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version 2.0\",\n \"print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in\",\n \"key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1 or\",\n \"dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size']\",\n \"# return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params =\",\n \"ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z,\",\n \"seen = set() def size_to_str(size): return '('+(', ').join(['%d' % v for v in\",\n \"in name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat' in\",\n \"'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\\\n') labels = {} connections = []\",\n \"torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w):\",\n \"in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1\",\n \"in str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for layer in layers:\",\n \"dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz =\",\n \"Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer\",\n \"z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis,\",\n \"if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name =\",\n \"gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or\",\n \"= (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D,\",\n \"= layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] =\",\n \"-1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in\",\n \"End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for j in\",\n \"error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ',\",\n \"name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name:\",\n \"return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {}\",\n \"Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride']\",\n \"kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else:\",\n \"= fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0, 0) stride =\",\n \"output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def\",\n \"@torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape d, c,\",\n \"= tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] =\",\n \"judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is not\",\n \"else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\\\n') labels = {} connections\",\n \"layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name']\",\n \"= get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for i in\",\n \"dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz =\",\n \"None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var)))\",\n \"print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('#\",\n \"list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W\",\n \"1)): N, C, H, W = z.shape D, C, k1, k2 = K.shape\",\n \"axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape:\",\n \"out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N,\",\n \"return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size']\",\n \"dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride =\",\n \"elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name: return 'View' elif\",\n \"i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] *\",\n \"elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] ==\",\n \"dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz =\",\n \"== 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif\",\n \"':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1):\",\n \"error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in range(C): if\",\n \"in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir ==\",\n \"= (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if\",\n \"= dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz,\",\n \"tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] ==\",\n \"in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] =\",\n \"= featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop =\",\n \"AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not\",\n \"# return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not been processed\",\n \"= conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1:\",\n \"len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3}\",\n \"layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p =\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1\",\n \"padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz\",\n \"# return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name']\",\n \"fc_conv_weights = [] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d'\",\n \"range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error +=\",\n \"Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return\",\n \"i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or View if\",\n \"= layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] =\",\n \"in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in\",\n \"return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\\\n') labels = {} connections =\",\n \"{'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] =\",\n \"out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size,\",\n \"'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params =\",\n \"# return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params =\",\n \"been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False):\",\n \"np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error\",\n \"layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name':\",\n \"list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape:\",\n \"if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ',\",\n \"range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name\",\n \"print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape))\",\n \"j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer =\",\n \"loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif\",\n \"name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name:\",\n \"eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif\",\n \"else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer\",\n \"last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride = params[1] padding =\",\n \"Start ===============================') for i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1))\",\n \"p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz\",\n \"in name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in\",\n \"pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i in range(len(files)):\",\n \"{} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p #\",\n \"stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding,\",\n \"in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params)\",\n \"layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB =\",\n \"= stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] =\",\n \"delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \\\"\\\"\\\" Produces Graphviz representation\",\n \"'ViewBackward' in name: return 'View' elif 'Mean' in name or 'Avg' in name:\",\n \"'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])):\",\n \"BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] =\",\n \"z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================')\",\n \"two compard tensors is not equal.') return None error = 0 error_tolerance =\",\n \"item_key = list(connections[i].keys())[0] if not 'None' in item_key: if i == 0: pass\",\n \"print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)):\",\n \"swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0),\",\n \"stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride']\",\n \"params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name']\",\n \"Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen = set() def size_to_str(size): return '('+(', ').join(['%d' % v for\",\n \"Variables that require grad, orange are Tensors saved for backward in torch.autograd.Function Args:\",\n \"== 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if\",\n \"1: C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance or\",\n \"in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error\",\n \"layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for\",\n \"z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding:\",\n \"+= 1 break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass\",\n \"file_nums = [] for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort()\",\n \"+ flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j]\",\n \"zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('#\",\n \"= tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps\",\n \"in str(model).split('\\\\n')[0]: g = make_dot(y[0]) return g else: g = make_dot(y) return g\",\n \"'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections =\",\n \"= layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features\",\n \"stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding,\",\n \"kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride,\",\n \"not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride\",\n \"Skip this layer because the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\\",\n \"layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size,\",\n \"exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if\",\n \"= featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz =\",\n \"not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return\",\n \"import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not\",\n \"= 'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name = 'View'\",\n \"exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params =\",\n \"error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C =\",\n \"padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in\",\n \"k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK\",\n \"def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in\",\n \"g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in\",\n \"list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]:\",\n \"import os featuremap = [] if featuremap_dir == None: pth_dir = \\\"./tmp_file/\\\" else:\",\n \"stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =\",\n \"= torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1,\",\n \"z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('#\",\n \"orange are Tensors saved for backward in torch.autograd.Function Args: var: output Variable params:\",\n \"error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for n in range(N):\",\n \"# return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name =\",\n \"{'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name}\",\n \"'None' in value1: print('Not completed for '+key+' or '+value+'! Check exchange_name function!') exit()\",\n \"= 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N,\",\n \"== list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import\",\n \"print('=========================== Store network model Results End ===========================\\\\n') if 'GoogLeNet' in str(model).split('\\\\n')[0]: g =\",\n \"= Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] =\",\n \"in range(N): for d in range(D): for h in range(0, H_last, strides[0]): for\",\n \"strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding:\",\n \"'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors\",\n \"= [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true\",\n \"delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir = \\\"./tmp_file/\\\" for root, dirs,\",\n \"+= 1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w\",\n \"make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU' elif\",\n \"'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors =\",\n \"(output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer,\",\n \"parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name']\",\n \"= Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif\",\n \"tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d):\",\n \"gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x) if\",\n \"in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW,\",\n \"dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not\",\n \"dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('# next_dz.shape:\",\n \"{0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i]\",\n \"str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store network\",\n \"= in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size\",\n \"current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers,\",\n \"print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0]\",\n \"-1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1])\",\n \"kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride,\",\n \"get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if not ':' in str(layer):\",\n \"files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul =\",\n \"out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i] =\",\n \"= param_map[id(u)] if params is not None else '' node_name = '%s\\\\n %s'\",\n \"kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride,\",\n \"len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1]\",\n \"', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ',\",\n \"p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer,\",\n \"last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] =\",\n \"MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride']\",\n \"isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features\",\n \"Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding']\",\n \"for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward'\",\n \"{0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1]\",\n \"in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] =\",\n \"Generate Tensors End ======================================\\\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch =\",\n \"gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps)\",\n \"= featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] ==\",\n \"current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in\",\n \"num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] =\",\n \"'('+(', ').join(['%d' % v for v in size])+')' def add_nodes(var): if var not\",\n \"return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for\",\n \"= var.variable name = param_map[id(u)] if params is not None else '' node_name\",\n \"cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape:\",\n \"for c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c],\",\n \"======================================\\\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch = Loss(result[0], label) else:\",\n \"F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0,\",\n \"H, W = z.shape D, C, k1, k2 = K.shape N, D, H1,\",\n \"print('Shape of two compard tensors is not equal.') return None error = 0\",\n \"d in range(D): for h in range(0, H_last, strides[0]): for w in range(0,\",\n \"names to node that require grad (TODO: make optional) \\\"\\\"\\\" if params is\",\n \"ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen = set() def size_to_str(size): return '('+(',\",\n \"z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz\",\n \"padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params\",\n \"{0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape:\",\n \"dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+'\",\n \"dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape:\",\n \"N, C, H, W = z.shape _, _, out_h, out_w = next_dz.shape padding_z\",\n \"for item in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_'\",\n \"because the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End\",\n \"@torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model)\",\n \"value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) #\",\n \"% 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate:\",\n \"0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K)\",\n \"if isinstance(last_tensors[i], str): # Add or View if last_tensors[i] == 'Add': last_tensors[i] =\",\n \"in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul\",\n \"'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding']\",\n \"= tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel\",\n \"fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================')\",\n \"fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if\",\n \"# stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride)\",\n \"== 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer\",\n \"name: return 'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in name:\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride #\",\n \"2022 ConvolutedDog (https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version 2.0 (the\",\n \"parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {}\",\n \"Start ====================================') result = model(img) print('=========================== Generate Tensors End ======================================\\\\n') Loss = nn.CrossEntropyLoss()\",\n \"= in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size\",\n \"= params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0],\",\n \"model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in\",\n \"', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c,\",\n \"dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in =\",\n \"End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in\",\n \"print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'})\",\n \"= list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split\",\n \"node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions:\",\n \"connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\\\n=========================== Restore network model Start ===============================')\",\n \"isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params)\",\n \"= [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2\",\n \"'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name']\",\n \"dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz\",\n \"in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] =\",\n \"out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W = dz.shape H_last =\",\n \"dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride =\",\n \"print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in\",\n \"model(img) print('=========================== Generate Tensors End ======================================\\\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\\\n')[0]:\",\n \"np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape\",\n \"AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params\",\n \"= generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters,\",\n \"F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i\",\n \"for n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or\",\n \"return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir ==\",\n \"print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor)\",\n \"layers[i] if name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers\",\n \"isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k]\",\n \"zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape))\",\n \"make optional) \\\"\\\"\\\" if params is not None: assert isinstance(params.values()[0], Variable) param_map =\",\n \"return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\\\n') labels =\",\n \"W = z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\\",\n \"mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights =\",\n \"error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) == 2: N, C\",\n \"= layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB\",\n \"layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif\",\n \"gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer,\",\n \"dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0)\",\n \"AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer\",\n \"= layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i]\",\n \"error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if\",\n \"os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0]))\",\n \"item_key: if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key]\",\n \"1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz,\",\n \"elif layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz,\",\n \"torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz,\",\n \"= (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if\",\n \"torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape))\",\n \"keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True)\",\n \"F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop()\",\n \"kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride,\",\n \"tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] =\",\n \"list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ',\",\n \"= x.shape d, c, k, j = weight.shape x_pad = x x_pad =\",\n \"= (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif\",\n \"print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB\",\n \"c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c,\",\n \"else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name\",\n \"== 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for\",\n \"return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size']\",\n \"copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i],\",\n \"j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ',\",\n \"tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps #\",\n \"'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p']\",\n \"list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('#\",\n \"get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img)\",\n \"'+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) ==\",\n \"Results End ===========================\\\\n') if 'GoogLeNet' in str(model).split('\\\\n')[0]: g = make_dot(y[0]) return g else:\",\n \"find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer = layers[i] if name\",\n \"= \\\"./tmp_file/\\\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = [] for\",\n \"pop_index = [] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in\",\n \"_insert_zeros(dz, strides): N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H\",\n \"N = z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz =\",\n \"if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"= list(connections[i].keys())[0] if not 'None' in item_key: if i == 0: pass else:\",\n \"last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer =\",\n \"torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu =\",\n \"= tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) ==\",\n \"else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple):\",\n \"return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer\",\n \"stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding,\",\n \"stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding,\",\n \"return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz\",\n \"= max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d':\",\n \"''' for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i],\",\n \"for i in range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue\",\n \"notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1]\",\n \"layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size =\",\n \"1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or\",\n \"# out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size')\",\n \"and # limitations under the License. #!/usr/bin/python3 import torch import torch.nn as nn\",\n \"1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k\",\n \"layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma)\",\n \"if not len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections function!') exit()\",\n \"elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB\",\n \"i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in\",\n \"mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z\",\n \"in name: return 'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean' in\",\n \"fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if\",\n \"dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW,\",\n \"# return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params =\",\n \"= 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels')\",\n \"', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================')\",\n \"= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride\",\n \"-> ')[1]}) pop_index = [] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"_remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz\",\n \"params=None): \\\"\\\"\\\" Produces Graphviz representation of PyTorch autograd graph Blue nodes are the\",\n \"'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])):\",\n \"num_Throwed += 1 break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else:\",\n \"connections.remove(pop_index[i]) new_connections = [] for item in connections: key, value = list(item.items())[0] key1\",\n \"def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape))\",\n \"x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad,\",\n \"return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z,\",\n \"if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i\",\n \"Add or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if\",\n \"= layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] =\",\n \"xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis,\",\n \"= 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size')\",\n \"-1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif\",\n \"var: output Variable params: dict of (name, Variable) to add names to node\",\n \"##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if not isinstance(layer,\",\n \"in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params\",\n \"output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The\",\n \"eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape m =\",\n \"* i + flat_idx // pooling[1] w_idx = strides[1] * j + flat_idx\",\n \"isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding\",\n \"Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1]\",\n \"list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape))\",\n \"kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride,\",\n \"not len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz\",\n \"_, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0)\",\n \"dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of\",\n \"def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:\",\n \"v in size])+')' def add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)),\",\n \"'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel']\",\n \"w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ',\",\n \"average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z\",\n \"with the License. # You may obtain a copy of the License at\",\n \"dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name']\",\n \"_insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz\",\n \"parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i]\",\n \"Blue nodes are the Variables that require grad, orange are Tensors saved for\",\n \"dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2\",\n \"copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name =\",\n \"= tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape for\",\n \"dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps\",\n \"-1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i]\",\n \"fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif\",\n \"params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else:\",\n \"= padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d'\",\n \"# num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps')\",\n \"tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is not equal.')\",\n \"node that require grad (TODO: make optional) \\\"\\\"\\\" if params is not None:\",\n \"# stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride)\",\n \"[] for layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers =\",\n \"specific language governing permissions and # limitations under the License. #!/usr/bin/python3 import torch\",\n \"# stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride)\",\n \"fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if params is\",\n \"if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name:\",\n \"if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return\",\n \"Dropout_params['p'] = p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name =\",\n \"else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name\",\n \"value.split('_')[1] if 'None' in key1 or 'None' in value1: print('Not completed for '+key+'\",\n \"if params is not None else '' node_name = '%s\\\\n %s' % (name,\",\n \"k1, k2 = K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ',\",\n \"if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1,\",\n \"F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for\",\n \"isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i]\",\n \"= layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] =\",\n \"mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0)\",\n \"in value1: print('Not completed for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1:\",\n \"0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('#\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"PyTorch autograd graph Blue nodes are the Variables that require grad, orange are\",\n \"0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C,\",\n \"layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size,\",\n \"merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) '''\",\n \"n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W))\",\n \"# Add or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0]\",\n \"has not been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label,\",\n \"layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz\",\n \"dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not\",\n \"torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return\",\n \"torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h):\",\n \"_remove_padding(z, padding): if padding[0] > 0 and padding[1] > 0: return z[:, :,\",\n \"AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params\",\n \"last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break\",\n \"range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i])\",\n \"mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p))\",\n \"= list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0,\",\n \"MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name =\",\n \"{0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask =\",\n \"elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors +=\",\n \"layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride,\",\n \"', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape:\",\n \"graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index = [] for\",\n \"= kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] =\",\n \"return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ###\",\n \"h_in, w_in = x.shape d, c, k, j = weight.shape x_pad = x\",\n \"in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)):\",\n \"in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] =\",\n \"list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections\",\n \"= [N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis =\",\n \"tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding =\",\n \"{'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name}\",\n \"kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else:\",\n \"nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result,\",\n \"= x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw',\",\n \"'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers)\",\n \"parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name\",\n \"ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape:\",\n \"in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'):\",\n \"print('\\\\n=========================== Store network model Results Start =========================') y = model(x) print('=========================== Store network\",\n \"fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)):\",\n \"return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in get_structure_parameters_v1!') return parameters,\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"= list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return\",\n \"#####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not\",\n \"copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])):\",\n \"= layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop()\",\n \"featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if '.pth'\",\n \"N, C, H, W = z.shape D, C, k1, k2 = K.shape N,\",\n \"size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u\",\n \"return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name,\",\n \"= [] if featuremap_dir == None: pth_dir = \\\"./tmp_file/\\\" else: pth_dir = featuremap_dir\",\n \"+ pooling[1]] += next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz =\",\n \"layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding\",\n \"= dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad()\",\n \"as F from graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad() def\",\n \"dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape))\",\n \"strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return\",\n \"return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1): layer = parameters[i]\",\n \"layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif\",\n \"j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1,\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding =\",\n \"0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0,\",\n \"strides[0] * i + flat_idx // pooling[1] w_idx = strides[1] * j +\",\n \"print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N,\",\n \"grad, orange are Tensors saved for backward in torch.autograd.Function Args: var: output Variable\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"= dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen =\",\n \"for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name:\",\n \"n in range(N): for c in range(C): for h in range(H): for w\",\n \"not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return\",\n \"= find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for\",\n \"> error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 == 0:\",\n \"layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name =\",\n \"u = var.variable name = param_map[id(u)] if params is not None else ''\",\n \"# 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list):\",\n \"tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else:\",\n \"= featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params\",\n \"= tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] =\",\n \"= dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End\",\n \"MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride')\",\n \"(kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not\",\n \"last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] =\",\n \"z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz\",\n \"layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End ==========================') continue\",\n \"====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else:\",\n \"MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name\",\n \"list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz,\",\n \"dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1]\",\n \"isinstance(layer, list): print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d':\",\n \"start = index break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end\",\n \"layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name\",\n \"for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error\",\n \"fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d):\",\n \"(kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not\",\n \"= MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] =\",\n \"# Copyright 2022 ConvolutedDog (https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version\",\n \"output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size']\",\n \"= layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps,\",\n \"', list(gamma.shape)) N, C, H, W = z.shape m = N*H*W shape =\",\n \"elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if params is not\",\n \"size])+')' def add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange')\",\n \"dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] ==\",\n \"x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store network model Results Start =========================') y = model(x) print('===========================\",\n \"batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End\",\n \"#print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) ==\",\n \"if featuremap_dir == None: pth_dir = \\\"./tmp_file/\\\" else: pth_dir = featuremap_dir files =\",\n \"pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape):\",\n \"and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0:\",\n \"-1): if isinstance(last_tensors[i], str): # Add or View if last_tensors[i] == 'Add': last_tensors[i]\",\n \"'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name =\",\n \"range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+'\",\n \"= torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for i in\",\n \"pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1)\",\n \"def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is\",\n \"range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' ->\",\n \"{'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] =\",\n \"end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start =\",\n \"= [] tmp_split = [] for i in range(len(connections)): item = connections[i] if\",\n \"= copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name\",\n \"num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] =\",\n \"g_view=False): x = Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n===========================\",\n \"print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in\",\n \"= featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz =\",\n \"hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if params is not None\",\n \"loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1): layer\",\n \"has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End ==========================') continue p\",\n \"featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps\",\n \"strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K\",\n \"in name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in\",\n \"np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return\",\n \"print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz\",\n \"eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape\",\n \"= {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size,\",\n \"# output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size)\",\n \"z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ',\",\n \"= layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] =\",\n \"+ key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1\",\n \"torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ',\",\n \"kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride,\",\n \"in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if\",\n \"value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def\",\n \"= 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j in\",\n \"(padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif isinstance(layer,\",\n \"weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z =\",\n \"padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name\",\n \"isinstance(layer, list): for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k]\",\n \"> 0: return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz,\",\n \"').join(['%d' % v for v in size])+')' def add_nodes(var): if var not in\",\n \"= next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ',\",\n \"layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size\",\n \"dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape:\",\n \"in range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000):\",\n \"dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) ==\",\n \"pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item\",\n \"= layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling,\",\n \"'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W\",\n \"torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer\",\n \"if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000\",\n \"torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] *\",\n \"# kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size)\",\n \"= (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if\",\n \"strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides):\",\n \"0 for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支\",\n \"y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors '''\",\n \"range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz\",\n \"tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k]\",\n \"= return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0)\",\n \"torch.nn.functional as F from graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad()\",\n \"return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz)\",\n \"layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this\",\n \"layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif\",\n \"= 'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat'\",\n \"network model End =================================\\\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i\",\n \"in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\\\n=========================== {0:3} Branch:\",\n \"= dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']:\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] *\",\n \"return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer,\",\n \"* j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c,\",\n \"def cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('#\",\n \"= padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name':\",\n \"(0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding,\",\n \"return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {}\",\n \"list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split =\",\n \"in range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]]\",\n \"= get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss,\",\n \"import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer\",\n \"= last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) ==\",\n \"index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1\",\n \"= get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for\",\n \"% v for v in size])+')' def add_nodes(var): if var not in seen:\",\n \"layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if\",\n \"is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v\",\n \"', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N, C, H,\",\n \"layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif\",\n \"keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz,\",\n \"padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding)\",\n \"next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape:\",\n \"mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2\",\n \"= layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] =\",\n \"in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] =\",\n \"== 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because the layer has\",\n \"layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers = [] for\",\n \"parameters[i] if not isinstance(layer, list): print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if\",\n \"featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0, 0)\",\n \"z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB =\",\n \"= (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif\",\n \"# padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding)\",\n \"'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View'\",\n \"', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('#\",\n \"+= 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for\",\n \"layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name']\",\n \"strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K,\",\n \"AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride']\",\n \"= make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU'\",\n \"== 2: N, C = tensor_A.shape for n in range(N): for c in\",\n \":, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0),\",\n \"# return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params =\",\n \"= layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] =\",\n \"are the Variables that require grad, orange are Tensors saved for backward in\",\n \"End ===========================\\\\n') if 'GoogLeNet' in str(model).split('\\\\n')[0]: g = make_dot(y[0]) return g else: g\",\n \"# eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight\",\n \"', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ',\",\n \"layer has not been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img,\",\n \"j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1\",\n \"if name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers =\",\n \"j in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer,\",\n \"layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size\",\n \"k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k]\",\n \"output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else:\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"= padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout'\",\n \"calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model):\",\n \"range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor =\",\n \"len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p']\",\n \"not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u =\",\n \"add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return\",\n \"(2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant',\",\n \"{} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):\",\n \"stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding,\",\n \"list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp = []\",\n \"return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear'\",\n \"# out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size')\",\n \"return_layers = [] tmp_layers = [] for layer in model.modules(): if not ':'\",\n \"fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1]\",\n \"D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1)\",\n \"list(connections[i].keys())[0] if not 'None' in item_key: if i == 0: pass else: last_item_key\",\n \"in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] >\",\n \"_, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors =\",\n \"\\\"./tmp_file/\\\" for root, dirs, files in os.walk(pth_dir, topdown=False): for name in files: if\",\n \"def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:\",\n \"# return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {}\",\n \"key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad() def merge_connections(connections):\",\n \"== 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors\",\n \"dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not\",\n \"k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight)\",\n \"Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred\",\n \"else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in range(i+1,\",\n \"continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in\",\n \"len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW\",\n \"error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 == 0: pass\",\n \"current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif\",\n \"featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params =\",\n \"list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides)\",\n \"axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('#\",\n \"to node that require grad (TODO: make optional) \\\"\\\"\\\" if params is not\",\n \"for i in range(start_i, len(layers)): layer = layers[i] if name in str(layer): return\",\n \"find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1\",\n \"out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] =\",\n \"params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling =\",\n \"tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel #\",\n \"dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def\",\n \"= tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d):\",\n \"featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if\",\n \"= (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z,\",\n \"padding: ', padding) print('# strides: ', strides) N, C, H, W = z.shape\",\n \"else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple):\",\n \"fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float()\",\n \"get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true)\",\n \"= {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name']\",\n \"-1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU'\",\n \"= cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = []\",\n \"for d in range(D): for h in range(0, H_last, strides[0]): for w in\",\n \"z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================')\",\n \"xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu\",\n \"tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1]\",\n \"Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed\",\n \"= exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1]\",\n \"node_name = '%s\\\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__))\",\n \"=========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)):\",\n \"in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1,\",\n \"in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else:\",\n \"if not len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):\",\n \"elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad()\",\n \"if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1.\",\n \"'Throwed') num_Throwed += 1 break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]]\",\n \"@torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape))\",\n \"dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss =\",\n \"{} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):\",\n \"elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for n in range(N): for\",\n \"mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop()\",\n \"'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if\",\n \"print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true =\",\n \"list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return\",\n \"layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name\",\n \"[]) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k\",\n \"= next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z -\",\n \"print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)):\",\n \"layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d'\",\n \"+= 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C elif\",\n \"kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride,\",\n \"elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer\",\n \"if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if\",\n \"tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z =\",\n \"tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif\",\n \"padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z,\",\n \"featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) ==\",\n \"import torch.nn.functional as F from graphviz import Digraph, render from torch.autograd import Variable\",\n \"tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward\",\n \"processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir\",\n \"range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape)\",\n \"= dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz\",\n \"range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params =\",\n \"featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0, 0)\",\n \"= out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] =\",\n \"list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1])\",\n \"list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z,\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end\",\n \"1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0,\",\n \"= (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz =\",\n \"= z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz,\",\n \"MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding')\",\n \"if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] =\",\n \"==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p)\",\n \"num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps']\",\n \"in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start\",\n \"i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape:\",\n \"for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item in\",\n \"str(model).split('\\\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections =\",\n \"range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model\",\n \"in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None)\",\n \"\\\"\\\"\\\" if params is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k\",\n \"0 notchoosed = [] print('\\\\n=========================== Restore network model Start ===============================') for i in\",\n \"stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride']\",\n \"= list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name:\",\n \"tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0:\",\n \"= (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k]\",\n \"in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp =\",\n \"= layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz\",\n \"= 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size')\",\n \"def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('#\",\n \"axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape))\",\n \"torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1]\",\n \"processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key: if i\",\n \"stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else:\",\n \"strides[1] * j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n,\",\n \"= (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ',\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding =\",\n \"= {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name':\",\n \"layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size =\",\n \"for i in range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\",\n \"dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape:\",\n \"#print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ')\",\n \"print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap =\",\n \"tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = []\",\n \"(padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer,\",\n \"== '\\\"': labels[(graph[i]+graph[i+1][1:]).split('\\\\t')[1].split(' ')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in graph[i] and graph[i][-1] == ']':\",\n \"tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 =\",\n \"swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0), 0,\",\n \"not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('========================\",\n \"weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z =\",\n \"padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else: return\",\n \"# return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name =\",\n \"featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout':\",\n \"index break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index\",\n \"= torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu\",\n \"else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers =\",\n \"= [] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params\",\n \"swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz\",\n \"size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if params\",\n \"label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x) if g_view: g.view()\",\n \"'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean' in name or 'Avg'\",\n \"dirs, files in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name))\",\n \"batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End\",\n \"= y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ',\",\n \"= batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward\",\n \"padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z,\",\n \"range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0]\",\n \"tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j):\",\n \"Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d'\",\n \"in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0]\",\n \"featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d':\",\n \"1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides,\",\n \"in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split('\",\n \"'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p']\",\n \"* i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx =\",\n \"layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride\",\n \"License. #!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional as F from\",\n \"N, C, H, W = z.shape m = N*H*W shape = [N,C,H,W] import\",\n \"copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\\\n=========================== Restore\",\n \"Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size\",\n \"= {'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name':\",\n \"max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',\",\n \"seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is not None:\",\n \"for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections)\",\n \"End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \\\"\\\"\\\" Produces\",\n \"= tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz =\",\n \"parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name\",\n \"return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat' in name: return\",\n \"rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1:\",\n \"len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]:\",\n \"hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var)))\",\n \"isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i]\",\n \"layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] =\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"root, dirs, files in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root,\",\n \"'_' + value.split('_')[1] if 'None' in key1 or 'None' in value1: print('Not completed\",\n \"of (name, Variable) to add names to node that require grad (TODO: make\",\n \"tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding =\",\n \"', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ',\",\n \"tmp_layers = [] for layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer)\",\n \"'->' in graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split('\",\n \"\\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('#\",\n \"error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ',\",\n \"return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W = dz.shape H_last\",\n \"calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\\\\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p']\",\n \"continue p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i]\",\n \"else '' node_name = '%s\\\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else:\",\n \"and graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->'\",\n \"in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size =\",\n \"index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not\",\n \"and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz =\",\n \"else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif\",\n \"out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if\",\n \"= [] for item in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0])\",\n \"'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if\",\n \"= AdaptiveAvgPool2d_params ### else: print('The layer has not been processed in get_structure_parameters!') return\",\n \"+= next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding)\",\n \"len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z =\",\n \"len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1]\",\n \"os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size):\",\n \"', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and\",\n \"not None else '' node_name = '%s\\\\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name,\",\n \"'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name = 'View' parameters[i]\",\n \"= list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index\",\n \"in model.modules(): if not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights =\",\n \"j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1]\",\n \"return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and padding[1] >\",\n \"= set() def size_to_str(size): return '('+(', ').join(['%d' % v for v in size])+')'\",\n \"= Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif\",\n \"layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding\",\n \"'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz,\",\n \"in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================')\",\n \"torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('#\",\n \"network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key:\",\n \"MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not\",\n \"tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif\",\n \"== []: last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if\",\n \"AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size']\",\n \"pooling[0], strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n, c, i, j]\",\n \"[] if featuremap_dir == None: pth_dir = \\\"./tmp_file/\\\" else: pth_dir = featuremap_dir files\",\n \"== list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)): if key2 ==\",\n \"AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size']\",\n \"tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed +=\",\n \"return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1]\",\n \"weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z)\",\n \"nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features\",\n \"dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)):\",\n \"i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]] +=\",\n \"in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or View if last_tensors[i]\",\n \"= padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout'\",\n \"else: if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate:\",\n \"in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key:\",\n \"str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for layer in layers: if\",\n \"= find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers =\",\n \"as nn import torch.nn.functional as F from graphviz import Digraph, render from torch.autograd\",\n \"cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i\",\n \"list): for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ###\",\n \"copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss,\",\n \"dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape))\",\n \"elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name #\",\n \"from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================')\",\n \"print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W =\",\n \"dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n,\",\n \"list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N, C, H, W\",\n \"Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer,\",\n \"dLoss_dz print('# Skip this layer because the layer has been calcualted!') print('======================== {0:3}\",\n \"np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w])\",\n \"return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n=========================== Store network model Results Start =========================')\",\n \"parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name']\",\n \"tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size\",\n \"list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']')\",\n \"or 'None' in value1: print('Not completed for '+key+' or '+value+'! Check exchange_name function!')\",\n \"if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') '''\",\n \"= 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {}\",\n \"stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding,\",\n \"return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {}\",\n \"model(x) print('=========================== Store network model Results End ===========================\\\\n') if 'GoogLeNet' in str(model).split('\\\\n')[0]: g\",\n \"exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections not\",\n \"in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size =\",\n \"z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant',\",\n \"= featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop()\",\n \"= featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0,\",\n \"return 'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in name: return\",\n \"= (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if\",\n \"= dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz,\",\n \"y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item())\",\n \"last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j])\",\n \"= featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0,\",\n \"range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' #####################\",\n \"= tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] =\",\n \"featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1]\",\n \"graph Blue nodes are the Variables that require grad, orange are Tensors saved\",\n \"# you may not use this file except in compliance with the License.\",\n \"max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z\",\n \"parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {}\",\n \"layer in model.modules(): if not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights\",\n \"flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz,\",\n \"len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz =\",\n \"len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz =\",\n \"tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] =\",\n \"= (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if\",\n \"list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight,\",\n \"return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir\",\n \"merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = []\",\n \"error += 1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if\",\n \"str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers =\",\n \"print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz\",\n \"'None' in key1 or 'None' in value1: print('Not completed for '+key+' or '+value+'!\",\n \"padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params\",\n \"BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer,\",\n \"# padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding)\",\n \"print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad()\",\n \"for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('===========================\",\n \"= tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] +\",\n \"dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma =\",\n \"0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride)\",\n \"x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters,\",\n \"')[0]]=\\\\ (graph[i]+graph[i+1][1:]).split('\\\\t')[1].split('\\\"')[1] if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0]\",\n \"layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {}\",\n \"y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability\",\n \"next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z - mu\",\n \"layer = parameters[i] print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] ==\",\n \"last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item]\",\n \"else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple):\",\n \"'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name\",\n \"output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in get_structure_parameters_v1!')\",\n \"not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)),\",\n \"else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name\",\n \"value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for\",\n \"* j + pooling[1]] += next_dz[n, c, i, j] / (pooling[0] * pooling[1])\",\n \"torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View')\",\n \"= torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape:\",\n \"files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return\",\n \"= 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size')\",\n \"D, C, k1, k2 = K.shape N, D, H1, W1 = next_dz.shape print('#\",\n \"zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ',\",\n \"= list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) +\",\n \"tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {}\",\n \"return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0,\",\n \"if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth')\",\n \"+ last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list):\",\n \"start, end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] =\",\n \"last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End\",\n \"'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward'\",\n \"[] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true =\",\n \"featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz,\",\n \"stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z\",\n \"z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1]\",\n \"featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self\",\n \"for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for\",\n \"return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start\",\n \"= parameters[i] if not isinstance(layer, list): print('\\\\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================')\",\n \"parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] =\",\n \"dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name']\",\n \"= [] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key\",\n \"'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0])\",\n \"try: padding = layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz,\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"'AddBackward' in name: return 'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh'\",\n \"np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return\",\n \"= torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ',\",\n \"np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error\",\n \"tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features #\",\n \"Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model)\",\n \"if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size #\",\n \"return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true\",\n \"z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps,\",\n \"not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections,\",\n \"index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list):\",\n \"Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] =\",\n \"x = list(tensor_size) mul = 1. for i in range(len(x)): mul *= x[i]\",\n \"'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because the layer has been\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight\",\n \"print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N\",\n \"[] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed\",\n \"if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1\",\n \"parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz\",\n \"parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name']\",\n \"== 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz =\",\n \"else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for\",\n \"print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w):\",\n \"kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride,\",\n \"{} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features #\",\n \"i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx\",\n \"(kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not\",\n \"Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections =\",\n \"'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] ==\",\n \"str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is not\",\n \"in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import\",\n \"error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C\",\n \"dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\\\\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db\",\n \"= fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz)\",\n \"+ '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None'\",\n \"'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1,\",\n \"dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] ==\",\n \"N, C = tensor_A.shape for n in range(N): for c in range(C): if\",\n \"= layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features\",\n \"'['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz\",\n \"list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad()\",\n \"strides[1] * j:strides[1] * j + pooling[1]]) h_idx = strides[0] * i +\",\n \"connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections)\",\n \"for layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {}\",\n \"list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_'\",\n \"# in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features')\",\n \"fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params\",\n \"== 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding =\",\n \"+= 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad()\",\n \"or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections):\",\n \"x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out =\",\n \"= None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] ==\",\n \"dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return\",\n \"pth_dir = \\\"./tmp_file/\\\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = []\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for\",\n \"= out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d):\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor),\",\n \"params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z,\",\n \"z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding =\",\n \":] elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else: return z\",\n \"(padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif isinstance(layer,\",\n \"H, W = tensor_A.shape for n in range(N): for c in range(C): for\",\n \"len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling =\",\n \"list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index in\",\n \"None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split\",\n \"y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred\",\n \"stride) else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding,\",\n \"get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir == None: pth_dir = \\\"./tmp_file/\\\"\",\n \"len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+'\",\n \"> 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:,\",\n \"if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)): if\",\n \"layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride\",\n \"in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index in\",\n \"eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i]\",\n \"H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last) for\",\n \"i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :,\",\n \"label) else: loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections)\",\n \"dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z\",\n \"if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for\",\n \"print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad()\",\n \"return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] =\",\n \"+ pooling[1]]) h_idx = strides[0] * i + flat_idx // pooling[1] w_idx =\",\n \"= layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i]\",\n \"= layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k]\",\n \"num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img)\",\n \"= {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel\",\n \"', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability =\",\n \"dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================')\",\n \"-> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index = [] for i in range(len(connections)): item_key\",\n \"in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed == []:\",\n \"= layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params =\",\n \"else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple):\",\n \"numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut =\",\n \"len(tensor_A.shape) == 2: N, C = tensor_A.shape for n in range(N): for c\",\n \"'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding']\",\n \"in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add'\",\n \"padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2,\",\n \"========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding\",\n \"kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride,\",\n \"if 'GoogLeNet' in str(model).split('\\\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label)\",\n \"loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet'\",\n \"else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple):\",\n \"last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for\",\n \"0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ',\",\n \"if pth_dir == None: pth_dir = \\\"./tmp_file/\\\" for root, dirs, files in os.walk(pth_dir,\",\n \"num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] =\",\n \"isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors\",\n \"= (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if\",\n \"N, C, H, W = tensor_A.shape for n in range(N): for c in\",\n \"torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H,\",\n \"else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('#\",\n \"parameters[i] print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z\",\n \"if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] =\",\n \"cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for\",\n \"* i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n,\",\n \"list(tensor_size) mul = 1. for i in range(len(x)): mul *= x[i] return mul\",\n \"dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): \\\"\\\"\\\" Produces Graphviz representation of PyTorch autograd\",\n \"var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for\",\n \"= 0 notchoosed = [] print('\\\\n=========================== Restore network model Start ===============================') for i\",\n \"dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape))\",\n \"Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('#\",\n \"list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz,\",\n \"if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] =\",\n \"elif layer == 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer\",\n \"= layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] =\",\n \"Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else:\",\n \"== None: pth_dir = \\\"./tmp_file/\\\" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums\",\n \"else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple):\",\n \"not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return\",\n \"def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections)\",\n \"### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name\",\n \"name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = []\",\n \"F from graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict,\",\n \"not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return\",\n \"last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\\\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers,\",\n \"copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0],\",\n \"= {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p\",\n \"the specific language governing permissions and # limitations under the License. #!/usr/bin/python3 import\",\n \"= view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz =\",\n \"list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers =\",\n \"index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([])\",\n \"if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is not equal.') return\",\n \"i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers = [] for layer\",\n \"len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling =\",\n \"i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\\\\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward\",\n \"= tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] =\",\n \"= (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last) for n in\",\n \"out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not\",\n \"= copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\\\\n=========================== Restore network model\",\n \"= p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d'\",\n \"Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params\",\n \"= list(tensor_size) mul = 1. for i in range(len(x)): mul *= x[i] return\",\n \"padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name\",\n \"def size_to_str(size): return '('+(', ').join(['%d' % v for v in size])+')' def add_nodes(var):\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] =\",\n \"Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer,\",\n \"tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1\",\n \"pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z,\",\n \"featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true)\",\n \"print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape))\",\n \"pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c\",\n \"for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d'\",\n \"= x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out\",\n \"if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name #\",\n \"Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size']\",\n \"representation of PyTorch autograd graph Blue nodes are the Variables that require grad,\",\n \"import torch.nn as nn import torch.nn.functional as F from graphviz import Digraph, render\",\n \"= tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] =\",\n \"not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz\",\n \"= torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1]\",\n \"= Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] =\",\n \"i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx = strides[0]\",\n \"of PyTorch autograd graph Blue nodes are the Variables that require grad, orange\",\n \"'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0])\",\n \"w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape))\",\n \"padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding']\",\n \"node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\\\"12,12\\\")) seen\",\n \"= p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params =\",\n \"', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1)\",\n \"var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\\\\n===========================\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def\",\n \"value1}) if not len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections function!')\",\n \"return_connections = [] tmp_split = [] for i in range(len(connections)): item = connections[i]\",\n \"return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU'\",\n \"+ value.split('_')[1] if 'None' in key1 or 'None' in value1: print('Not completed for\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w]\",\n \"seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name\",\n \"= {} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p\",\n \"params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1],\",\n \"Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features\",\n \"print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros\",\n \"+ W pz = torch.zeros(N, D, H_last, W_last) for n in range(N): for\",\n \"= 0 import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1):\",\n \"either express or implied. # See the License for the specific language governing\",\n \"not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps =\",\n \"not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v in\",\n \"len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key: if i == 0:\",\n \"1. for i in range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model,\",\n \"start_i): for i in range(start_i, len(layers)): layer = layers[i] if name in str(layer):\",\n \"layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size =\",\n \"shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c]\",\n \"'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if\",\n \"= layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif\",\n \"kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else:\",\n \"str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None) return return_dz, dLoss_dW,\",\n \"fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] =\",\n \"i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i,\",\n \"dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy\",\n \"strides): N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last\",\n \"layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features =\",\n \"in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) '''\",\n \"pth_dir = \\\"./tmp_file/\\\" for root, dirs, files in os.walk(pth_dir, topdown=False): for name in\",\n \"MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding')\",\n \"== 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz =\",\n \"elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer ==\",\n \"k in range(len(layer[j])): tmp_layer = layer[j][k] print('\\\\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================')\",\n \"layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple):\",\n \"{} connections = [] for i in range(len(graph)): if 'label' in graph[i] and\",\n \"graph[i]: connections.append({labels[graph[i].split('\\\\t')[1].split(' -> ')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]})\",\n \"parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for k in\",\n \"layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif\",\n \"print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None'\",\n \"torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def\",\n \"AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not\",\n \"the License. # You may obtain a copy of the License at #\",\n \"padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def\",\n \"featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in\",\n \"nodes are the Variables that require grad, orange are Tensors saved for backward\",\n \"layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size\",\n \"= copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz =\",\n \"in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in\",\n \"tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB =\",\n \"import torch import torch.nn as nn import torch.nn.functional as F from graphviz import\",\n \"z, padding=(0, 0), strides=(1, 1)): N, C, H, W = z.shape D, C,\",\n \"if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size #\",\n \"list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and padding[1]\",\n \"'['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides)\",\n \"range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not\",\n \"dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is\",\n \"Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return\",\n \"if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp\",\n \"'.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z =\",\n \"= [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if\",\n \"lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name']\",\n \"== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling\",\n \"elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name =\",\n \"for j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end\",\n \"isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d'\",\n \"list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k]\",\n \"', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K =\",\n \"> error_tolerance: error += 1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w])\",\n \"last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1)\",\n \"padding) print('# strides: ', strides) N, C, H, W = z.shape _, _,\",\n \"', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for\",\n \"elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif\",\n \"@torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape))\",\n \"elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0:\",\n \"in name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in\",\n \"= next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for\",\n \"torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View')\",\n \"layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding\",\n \"n in range(N): for d in range(D): for h in range(0, H_last, strides[0]):\",\n \"else: print('The layer has not been processed in get_structure_parameters!') return parameters, fc_conv_weights def\",\n \"if padding[0] > 0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]]\",\n \"layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features =\",\n \"@torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer = layers[i]\",\n \"list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View'\",\n \"= in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight)\",\n \"isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride\",\n \"(name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for\",\n \"= [] for layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers\",\n \"'.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z\",\n \"gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape m = N*H*W shape\",\n \"elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name #\",\n \"dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if\",\n \"tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding\",\n \"isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features\",\n \"list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape))\",\n \"padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z,\",\n \"pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in\",\n \"padding = layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW,\",\n \"1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0:\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer\",\n \"AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding')\",\n \"featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop()\",\n \"range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {}\",\n \"list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add')\",\n \"result = model(img) print('=========================== Generate Tensors End ======================================\\\\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet'\",\n \"num_Throwed = 0 notchoosed = [] print('\\\\n=========================== Restore network model Start ===============================') for\",\n \"g_view: g.view() delete_allpths(pth_dir=None) print('\\\\n=========================== Generate Tensors Start ====================================') result = model(img) print('=========================== Generate\",\n \"z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not\",\n \"@torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed\",\n \"dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('#\",\n \"graph[i] and graph[i][-1] == ']': labels[graph[i].split('\\\\t')[1].split(' ')[0]]=\\\\ graph[i].split('\\\\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if\",\n \"range(start_i, len(layers)): layer = layers[i] if name in str(layer): return layer, i @torch.no_grad()\",\n \"1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import\",\n \"padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz,\",\n \"Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer,\",\n \"pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ',\",\n \"dict of (name, Variable) to add names to node that require grad (TODO:\",\n \"D, H_last, W_last) for n in range(N): for d in range(D): for h\",\n \"for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list):\",\n \"padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0,\",\n \"stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz =\",\n \"format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz =\",\n \"last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1): if\",\n \"# return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name =\",\n \"last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights =\",\n \"is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors:\",\n \"tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start,\",\n \"Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i]\",\n \"z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] ==\",\n \"in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1): if\",\n \"next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p):\",\n \"str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var)\",\n \"j + pooling[1]] += next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz\",\n \"# padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding)\",\n \"np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape)\",\n \"H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) +\",\n \"elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name #\",\n \"= tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps,\",\n \"// pooling[1] w_idx = strides[1] * j + flat_idx % pooling[1] padding_dz[n, c,\",\n \"dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if\",\n \"return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name: return\",\n \"def make_dot(var, params=None): \\\"\\\"\\\" Produces Graphviz representation of PyTorch autograd graph Blue nodes\",\n \"kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size']\",\n \"tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride =\",\n \"-1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add'\",\n \"= layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] =\",\n \"str(model).split('\\\\n')[0]: g = make_dot(y[0]) return g else: g = make_dot(y) return g @torch.no_grad()\",\n \"= tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i],\",\n \"len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1]\",\n \"isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap\",\n \"= os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if '.pth' in files[i]:\",\n \"return 'View' elif 'Mean' in name or 'Avg' in name: return 'AvgPool2d' elif\",\n \"# limitations under the License. #!/usr/bin/python3 import torch import torch.nn as nn import\",\n \"== 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'],\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"mul_items(tensor_size): x = list(tensor_size) mul = 1. for i in range(len(x)): mul *=\",\n \"nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name\",\n \"Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding')\",\n \"in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers\",\n \"# stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride)\",\n \"= find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] +\",\n \"u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in\",\n \"break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'})\",\n \"notchoosed = [] print('\\\\n=========================== Restore network model Start ===============================') for i in range(len(connections)):\",\n \"= strides[0] * i + flat_idx // pooling[1] w_idx = strides[1] * j\",\n \"print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)):\",\n \"try: padding = tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1],\",\n \"return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',\",\n \"add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn)\",\n \"[] for i in range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item)\",\n \"def add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif\",\n \"len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters =\",\n \"featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz =\",\n \"[] tmp_layers = [] for layer in model.modules(): if not ':' in str(layer):\",\n \"== 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat':\",\n \"stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride']\",\n \"torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ',\",\n \"fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir = \\\"./tmp_file/\\\"\",\n \"return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors\",\n \"tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name\",\n \"= tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] =\",\n \"in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model)\",\n \"== len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling\",\n \"item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for\",\n \"for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model,\",\n \"== 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape)\",\n \"import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut\",\n \"in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] >\",\n \"parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name']\",\n \"def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir = \\\"./tmp_file/\\\" for root,\",\n \"make_dot(y[0]) return g else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if\",\n \"tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str):\",\n \"dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape:\",\n \"os featuremap = [] if featuremap_dir == None: pth_dir = \\\"./tmp_file/\\\" else: pth_dir\",\n \"', end=' ') return error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape\",\n \"elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name #\",\n \"# stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride)\",\n \"lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not\",\n \"name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat' in name:\",\n \"if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View':\",\n \"= stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] =\",\n \"in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B):\",\n \"= layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] =\",\n \"kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size']\",\n \"Restore network model Start ===============================') for i in range(len(connections)): print('# Restore network model:\",\n \"featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except:\",\n \"return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed\",\n \"c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j\",\n \"== last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value,\",\n \"parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name\",\n \"= [] for i in range(len(graph)): if 'label' in graph[i] and graph[i][-1] ==\",\n \"in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp =\",\n \"parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] =\",\n \"H, W = z.shape m = N*H*W shape = [N,C,H,W] import numpy as\",\n \"nn import torch.nn.functional as F from graphviz import Digraph, render from torch.autograd import\",\n \"require grad (TODO: make optional) \\\"\\\"\\\" if params is not None: assert isinstance(params.values()[0],\",\n \"@torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = [] for i in range(len(connections)):\",\n \"str): # Add or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] +\",\n \"print('# Skip this layer because the layer has been calcualted!') print('======================== {0:3} Layer:\",\n \"range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i]\",\n \"range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if\",\n \"mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz\",\n \"return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if\",\n \"not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding\",\n \"padding[0] > 0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif\",\n \"if not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0]\",\n \"connections = [] for i in range(len(graph)): if 'label' in graph[i] and graph[i][-1]\",\n \"'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z,\",\n \"*= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = []\",\n \"in range(C): for h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] >\",\n \"dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('#\",\n \"Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z =\",\n \"MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer,\",\n \"eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k]\",\n \"tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride =\",\n \"stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i]\",\n \"layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask,\",\n \"Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size\",\n \"= layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size,\",\n \"may not use this file except in compliance with the License. # You\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"k for k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1',\",\n \"has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import\",\n \"+ 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) ==\",\n \"print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for\",\n \"isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size\",\n \"@torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('# next_dz.shape: ',\",\n \"else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d'\",\n \"(padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer,\",\n \"in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def\",\n \"')[0]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[0]:\\\\ labels[graph[i].split('\\\\t')[1].split(' -> ')[1]]+'_'+\\\\ graph[i].split('\\\\t')[1].split(' -> ')[1]}) pop_index = []\",\n \"last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\\\n') return\",\n \"kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size']\",\n \"range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu =\",\n \"C, H, W = z.shape D, C, k1, k2 = K.shape N, D,\",\n \"== 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])):\",\n \"eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params =\",\n \"padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else:\",\n \"# kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size)\",\n \"weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop =\",\n \"last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights =\",\n \"return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy\",\n \"render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\\\\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start\",\n \"p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {}\",\n \"c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c])\",\n \"= batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape)\",\n \"p): print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ',\",\n \"print('# strides: ', strides) N, C, H, W = z.shape _, _, out_h,\",\n \"z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i]\",\n \"AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer,\",\n \"z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0,\",\n \"= 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels')\",\n \"if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif\",\n \"N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ',\",\n \"BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] =\",\n \"# return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for\",\n \"Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = []\",\n \"x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum(\",\n \"= last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None':\",\n \"in str(model).split('\\\\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections\",\n \"pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz,\",\n \"p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name\",\n \"'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\\\\n') return last_connections @torch.no_grad() def\",\n \"range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i,\",\n \"AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not\",\n \"layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] =\",\n \"1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('#\",\n \"'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\\\\n') labels = {}\",\n \"elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz))\",\n \"z.shape m = N*H*W shape = [N,C,H,W] import numpy as np ax =\",\n \"for layer in model.modules(): if not ':' in str(layer): layers.append(layer) parameters = []\",\n \"print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('#\",\n \"list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections):\",\n \"(https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"= (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif\",\n \"tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size\",\n \"output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \\\\ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz\",\n \"in torch.autograd.Function Args: var: output Variable params: dict of (name, Variable) to add\",\n \"featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not\",\n \"i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end =\",\n \"dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1]\",\n \"'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if\",\n \"return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear'\",\n \"[] for i in range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '\\\"':\",\n \"num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in\",\n \"@torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1)\",\n \"in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d):\",\n \"padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] >\",\n \"return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {}\",\n \"', strides) N, C, H, W = z.shape _, _, out_h, out_w =\",\n \"[tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not\",\n \"torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\\\\ k1-1-padding[0],k1-1-padding[0],0,0),\",\n \"return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)):\",\n \"fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape):\",\n \"dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z =\",\n \"dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride =\",\n \"= layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params\",\n \"in range(start_i, len(layers)): layer = layers[i] if name in str(layer): return layer, i\",\n \"j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0],\",\n \"k, j = weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad\",\n \"out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\\\\ padding[0],0,0), mode='constant', value=0) padding_dz =\"\n]","isConversational":false}}},{"rowIdx":1191,"cells":{"ngram":{"kind":"list like","value":["param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name,","self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm):",">>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step()","正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练","loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv =","norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = epsilon","param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.backup","\"\"\" def __init__(self, module): self.module = module self.backup = {} def attack( self,","loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练","正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>>","* r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name, param","batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>>","/ norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名","r_at = epsilon * param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ):","in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1:","{} def project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r)","param in self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] =","# 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> #","\"\"\" def __init__(self, module): self.module = module self.emb_backup = {} self.grad_backup = {}","# 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore()","param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): #","emb_name in name: assert name in self.backup param.data = self.backup[name] self.backup = {}",">>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad()","optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.emb_backup =","torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings'","= epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for","= epsilon * param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for","- self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon * r / torch.norm(r)","Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K = 3","torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name, param in self.module.named_parameters(): if",">>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度","and not torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at) def restore(","= PGD(module) >>> K = 3 >>> for batch_input, batch_label in data: >>>","self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in","fgm = FGM(module) >>> for batch_input, batch_label in data: >>> # 正常训练 >>>","in self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone()","batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>>","反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference:","loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>>","in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() #","param.requires_grad and emb_name in name: assert name in self.backup param.data = self.backup[name] self.backup","def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad","norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = alpha","+ r def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] =","https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.backup = {} def attack(",">>> if t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv","emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm","emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name:","self.backup = {} class PGD(object): \"\"\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>>","if norm != 0 and not torch.isnan(norm): r_at = epsilon * param.grad /",">>> # 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first","batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>>","= module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> #","反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference:","name: assert name in self.backup param.data = self.backup[name] self.backup = {} class PGD(object):","对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>>","= self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in","torch class FGM(object): \"\"\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化","return self.emb_backup[param_name] + r def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad:","import torch class FGM(object): \"\"\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> #","if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if","norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if","param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm","<gh_stars>1-10 import torch class FGM(object): \"\"\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>>","= torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = alpha *",">>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>>","Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.emb_backup = {}","epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon","): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert","if norm != 0 and not torch.isnan(norm): r_at = alpha * param.grad /","emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in","\"\"\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K","name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self,","PGD(module) >>> K = 3 >>> for batch_input, batch_label in data: >>> #","for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] =","# emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name:","loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack()","模型 Examples:: >>> pgd = PGD(module) >>> K = 3 >>> for batch_input,","for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack:","batch_label in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward()","module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K = 3 >>>","self.emb_backup = {} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False","{} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): #","恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def","and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if","data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad","if t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv =","self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon): r","param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon): r =","restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name","[1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.emb_backup = {} self.grad_backup",">>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> #","param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if",">>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.backup","): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in","在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() #","= module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() #","fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>>","{} class PGD(object): \"\"\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd =","in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in","in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm !=","and emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm !=","class PGD(object): \"\"\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module)",">>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv","self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in",">>> pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) #","batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t","module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数","param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param","# 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad()","对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() #","epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if","= self.backup[name] self.backup = {} class PGD(object): \"\"\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型","self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters():","range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>>","Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm = FGM(module) >>>","name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data,","attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and","param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r","pgd = PGD(module) >>> K = 3 >>> for batch_input, batch_label in data:","param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at =","self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name","* param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param","torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = epsilon * param.grad","in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.backup param.data",">>> # 初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label in data:","!= 0 and not torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at)","name in self.backup param.data = self.backup[name] self.backup = {} class PGD(object): \"\"\" 基于PGD算法的攻击机制",">>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module =","in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and","epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in","= module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name,","param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param","param.data = self.backup[name] self.backup = {} class PGD(object): \"\"\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`):","r_at = alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon)","norm != 0 and not torch.isnan(norm): r_at = alpha * param.grad / norm","): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name]","在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>> else: >>>","pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>>","else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>>","t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input,","not torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at) def restore( self,","optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.backup =","= {} class PGD(object): \"\"\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd","module self.emb_backup = {} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.',","is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name","# 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\"","torch.norm(r) > epsilon: r = epsilon * r / torch.norm(r) return self.emb_backup[param_name] +","in name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def","loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>>","in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.emb_backup param.data","backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self):","param.requires_grad and emb_name in name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup","name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0","module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数","K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>>","self.emb_backup = {} def project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name]","# 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input,",">>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self,","torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = alpha * param.grad","def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param","= torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = epsilon *","Examples:: >>> # 初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label in","https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.emb_backup = {} self.grad_backup =","r = epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self):","self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters():","= param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: param.grad =","in name: assert name in self.backup param.data = self.backup[name] self.backup = {} class","emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and","param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad)",">>> pgd = PGD(module) >>> K = 3 >>> for batch_input, batch_label in","= {} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ):","> epsilon: r = epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r",">>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1]","= param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at","self.emb_backup[param_name] + r def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name]","self.backup[name] self.backup = {} class PGD(object): \"\"\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples::","# 初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label in data: >>>","if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if","in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm =","if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm =","def project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) >","K = 3 >>> for batch_input, batch_label in data: >>> # 正常训练 >>>","module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for","def __init__(self, module): self.module = module self.backup = {} def attack( self, epsilon=1.,","self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.emb_backup param.data =","for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name","loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K):","(:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K = 3 >>> for","for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t","= FGM(module) >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss","[1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.backup = {} def",">>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t in","torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data,","模型 Examples:: >>> # 初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label",">>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>>","\"\"\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm =",">>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数","module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动","param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad:","param in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm","self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: param.grad","def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def","module): self.module = module self.emb_backup = {} self.grad_backup = {} def attack( self,","= alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def","/ norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters():","first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad()","self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名","if param.requires_grad and emb_name in name: assert name in self.backup param.data = self.backup[name]","= {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for","# 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data","= param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon * r",">>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>>","if param.requires_grad and emb_name in name: assert name in self.emb_backup param.data = self.emb_backup[name]","r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon *","!= 0 and not torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at)","norm != 0 and not torch.isnan(norm): r_at = epsilon * param.grad / norm","and emb_name in name: assert name in self.backup param.data = self.backup[name] self.backup =","optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() #","emb_name in name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {}","param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r =","epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad","# 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward()","optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module","and emb_name in name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup =","self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon * r / torch.norm(r) return","def __init__(self, module): self.module = module self.emb_backup = {} self.grad_backup = {} def","attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>>","0 and not torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at) param.data","Examples:: >>> pgd = PGD(module) >>> K = 3 >>> for batch_input, batch_label","batch_input, batch_label in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>>","fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728","module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm = FGM(module) >>> for","name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not","name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone()","FGM(module) >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss =","{} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if","self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm","epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name,","* param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'):","restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name","name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in","def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and","/ torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name, param in self.module.named_parameters():","for batch_input, batch_label in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label)","反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0))","param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param in","param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.emb_backup","emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0","for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for","self.module = module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for","反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label)","pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728","基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K =","# 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>> else:","PGD(object): \"\"\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>>","= self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon): r = param_data","# 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module):","loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>>","not torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name,","name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name]","emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: if","pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动,","self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm):","self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad)","# 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad()","= {} def project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if","and not torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at) param.data =","= {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters():","pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() #","!= K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label)","初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label in data: >>> #",">>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss = module(batch_input,","FGM(object): \"\"\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm","attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in","# 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad()","param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon * r /","module): self.module = module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ):","# 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K): >>>",">>> fgm = FGM(module) >>> for batch_input, batch_label in data: >>> # 正常训练",">>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度","alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad","= module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>>","in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon):","param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: param.grad = self.grad_backup[name]","3 >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss =","name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name,","loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>>","r def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone()","emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert","基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm = FGM(module)","= module self.emb_backup = {} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3,","= module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> #","is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not","alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self,","0 and not torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at) def","if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and","(:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm = FGM(module) >>> for batch_input,","= 3 >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss","project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon:",">>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step()","if torch.norm(r) > epsilon: r = epsilon * r / torch.norm(r) return self.emb_backup[param_name]","__init__(self, module): self.module = module self.emb_backup = {} self.grad_backup = {} def attack(","t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t !=",">>> K = 3 >>> for batch_input, batch_label in data: >>> # 正常训练","self.backup param.data = self.backup[name] self.backup = {} class PGD(object): \"\"\" 基于PGD算法的攻击机制 Args: module","self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters():","self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.backup param.data =","assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name,","class FGM(object): \"\"\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>>","norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for","r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name, param in",">>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.emb_backup","__init__(self, module): self.module = module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings'","self.module = module self.emb_backup = {} self.grad_backup = {} def attack( self, epsilon=1.,","self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon): r = param_data -","{} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name,","epsilon * param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name,","param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad","Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module = module self.backup = {}",">>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore()","assert name in self.backup param.data = self.backup[name] self.backup = {} class PGD(object): \"\"\"","in self.backup param.data = self.backup[name] self.backup = {} class PGD(object): \"\"\" 基于PGD算法的攻击机制 Args:","epsilon: r = epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r def","module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param",">>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1]","梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \"\"\" def __init__(self, module): self.module",">>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward()",">>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if",">>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数","def restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and"],"string":"[\n \"param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name,\",\n \"self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm):\",\n \">>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step()\",\n \"正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练\",\n \"loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv =\",\n \"norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = epsilon\",\n \"param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.backup\",\n \"\\\"\\\"\\\" def __init__(self, module): self.module = module self.backup = {} def attack( self,\",\n \"loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练\",\n \"正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>>\",\n \"* r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name, param\",\n \"batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>>\",\n \"/ norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名\",\n \"r_at = epsilon * param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ):\",\n \"in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1:\",\n \"{} def project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r)\",\n \"param in self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] =\",\n \"# 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> #\",\n \"\\\"\\\"\\\" def __init__(self, module): self.module = module self.emb_backup = {} self.grad_backup = {}\",\n \"# 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore()\",\n \"param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): #\",\n \"emb_name in name: assert name in self.backup param.data = self.backup[name] self.backup = {}\",\n \">>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad()\",\n \"optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.emb_backup =\",\n \"torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings'\",\n \"= epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for\",\n \"= epsilon * param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for\",\n \"- self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon * r / torch.norm(r)\",\n \"Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K = 3\",\n \"torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name, param in self.module.named_parameters(): if\",\n \">>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度\",\n \"and not torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at) def restore(\",\n \"= PGD(module) >>> K = 3 >>> for batch_input, batch_label in data: >>>\",\n \"self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in\",\n \"fgm = FGM(module) >>> for batch_input, batch_label in data: >>> # 正常训练 >>>\",\n \"in self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone()\",\n \"batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>>\",\n \"反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference:\",\n \"loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>>\",\n \"in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() #\",\n \"param.requires_grad and emb_name in name: assert name in self.backup param.data = self.backup[name] self.backup\",\n \"def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad\",\n \"norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = alpha\",\n \"+ r def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] =\",\n \"https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.backup = {} def attack(\",\n \">>> if t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv\",\n \"emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm\",\n \"emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name:\",\n \"self.backup = {} class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>>\",\n \"if norm != 0 and not torch.isnan(norm): r_at = epsilon * param.grad /\",\n \">>> # 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first\",\n \"batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>>\",\n \"= module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> #\",\n \"反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference:\",\n \"name: assert name in self.backup param.data = self.backup[name] self.backup = {} class PGD(object):\",\n \"对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>>\",\n \"= self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in\",\n \"torch class FGM(object): \\\"\\\"\\\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化\",\n \"return self.emb_backup[param_name] + r def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad:\",\n \"import torch class FGM(object): \\\"\\\"\\\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> #\",\n \"if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if\",\n \"norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if\",\n \"param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm\",\n \"<gh_stars>1-10 import torch class FGM(object): \\\"\\\"\\\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>>\",\n \"= torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = alpha *\",\n \">>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>>\",\n \"Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.emb_backup = {}\",\n \"epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon\",\n \"): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert\",\n \"if norm != 0 and not torch.isnan(norm): r_at = alpha * param.grad /\",\n \"emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in\",\n \"\\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K\",\n \"name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self,\",\n \"PGD(module) >>> K = 3 >>> for batch_input, batch_label in data: >>> #\",\n \"for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] =\",\n \"# emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name:\",\n \"loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack()\",\n \"模型 Examples:: >>> pgd = PGD(module) >>> K = 3 >>> for batch_input,\",\n \"for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack:\",\n \"batch_label in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward()\",\n \"module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K = 3 >>>\",\n \"self.emb_backup = {} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False\",\n \"{} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): #\",\n \"恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def\",\n \"and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if\",\n \"data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad\",\n \"if t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv =\",\n \"self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon): r\",\n \"param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon): r =\",\n \"restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name\",\n \"[1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.emb_backup = {} self.grad_backup\",\n \">>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> #\",\n \"param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if\",\n \">>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.backup\",\n \"): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in\",\n \"在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() #\",\n \"= module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() #\",\n \"fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>>\",\n \"{} class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd =\",\n \"in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in\",\n \"in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm !=\",\n \"and emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm !=\",\n \"class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module)\",\n \">>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv\",\n \"self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in\",\n \">>> pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) #\",\n \"batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t\",\n \"module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数\",\n \"param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param\",\n \"# 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad()\",\n \"对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() #\",\n \"epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if\",\n \"= self.backup[name] self.backup = {} class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型\",\n \"self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters():\",\n \"range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>>\",\n \"Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm = FGM(module) >>>\",\n \"name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data,\",\n \"attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and\",\n \"param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r\",\n \"pgd = PGD(module) >>> K = 3 >>> for batch_input, batch_label in data:\",\n \"param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at =\",\n \"self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name\",\n \"* param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param\",\n \"torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = epsilon * param.grad\",\n \"in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.backup param.data\",\n \">>> # 初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label in data:\",\n \"!= 0 and not torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at)\",\n \"name in self.backup param.data = self.backup[name] self.backup = {} class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制\",\n \">>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module =\",\n \"in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and\",\n \"epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in\",\n \"= module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name,\",\n \"param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param\",\n \"param.data = self.backup[name] self.backup = {} class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`):\",\n \"r_at = alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon)\",\n \"norm != 0 and not torch.isnan(norm): r_at = alpha * param.grad / norm\",\n \"): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name]\",\n \"在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>> else: >>>\",\n \"pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>>\",\n \"else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>>\",\n \"t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input,\",\n \"not torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at) def restore( self,\",\n \"optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.backup =\",\n \"= {} class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd\",\n \"module self.emb_backup = {} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.',\",\n \"is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name\",\n \"# 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\"\",\n \"torch.norm(r) > epsilon: r = epsilon * r / torch.norm(r) return self.emb_backup[param_name] +\",\n \"in name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def\",\n \"loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>>\",\n \"in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.emb_backup param.data\",\n \"backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self):\",\n \"param.requires_grad and emb_name in name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup\",\n \"name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0\",\n \"module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数\",\n \"K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>>\",\n \"self.emb_backup = {} def project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name]\",\n \"# 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input,\",\n \">>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self,\",\n \"torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = alpha * param.grad\",\n \"def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param\",\n \"= torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = epsilon *\",\n \"Examples:: >>> # 初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label in\",\n \"https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.emb_backup = {} self.grad_backup =\",\n \"r = epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self):\",\n \"self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters():\",\n \"= param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: param.grad =\",\n \"in name: assert name in self.backup param.data = self.backup[name] self.backup = {} class\",\n \"emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and\",\n \"param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad)\",\n \">>> pgd = PGD(module) >>> K = 3 >>> for batch_input, batch_label in\",\n \"= {} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ):\",\n \"> epsilon: r = epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r\",\n \">>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1]\",\n \"= param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at\",\n \"self.emb_backup[param_name] + r def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name]\",\n \"self.backup[name] self.backup = {} class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples::\",\n \"# 初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label in data: >>>\",\n \"if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if\",\n \"in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm =\",\n \"if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm =\",\n \"def project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) >\",\n \"K = 3 >>> for batch_input, batch_label in data: >>> # 正常训练 >>>\",\n \"module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for\",\n \"def __init__(self, module): self.module = module self.backup = {} def attack( self, epsilon=1.,\",\n \"self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.emb_backup param.data =\",\n \"for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name\",\n \"loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K):\",\n \"(:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K = 3 >>> for\",\n \"for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t\",\n \"= FGM(module) >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss\",\n \"[1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.backup = {} def\",\n \">>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t in\",\n \"torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data,\",\n \"模型 Examples:: >>> # 初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label\",\n \">>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>>\",\n \"\\\"\\\"\\\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm =\",\n \">>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数\",\n \"module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动\",\n \"param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad:\",\n \"param in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm\",\n \"self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: param.grad\",\n \"def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def\",\n \"module): self.module = module self.emb_backup = {} self.grad_backup = {} def attack( self,\",\n \"= alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def\",\n \"/ norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters():\",\n \"first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad()\",\n \"self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名\",\n \"if param.requires_grad and emb_name in name: assert name in self.backup param.data = self.backup[name]\",\n \"= {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for\",\n \"# 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data\",\n \"= param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon * r\",\n \">>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>>\",\n \"if param.requires_grad and emb_name in name: assert name in self.emb_backup param.data = self.emb_backup[name]\",\n \"r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon *\",\n \"!= 0 and not torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at)\",\n \"norm != 0 and not torch.isnan(norm): r_at = epsilon * param.grad / norm\",\n \"and emb_name in name: assert name in self.backup param.data = self.backup[name] self.backup =\",\n \"optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() #\",\n \"emb_name in name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {}\",\n \"param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r =\",\n \"epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad\",\n \"# 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward()\",\n \"optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module\",\n \"and emb_name in name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup =\",\n \"self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon * r / torch.norm(r) return\",\n \"def __init__(self, module): self.module = module self.emb_backup = {} self.grad_backup = {} def\",\n \"attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>>\",\n \"0 and not torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at) param.data\",\n \"Examples:: >>> pgd = PGD(module) >>> K = 3 >>> for batch_input, batch_label\",\n \"batch_input, batch_label in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>>\",\n \"fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728\",\n \"module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm = FGM(module) >>> for\",\n \"name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not\",\n \"name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone()\",\n \"FGM(module) >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss =\",\n \"{} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if\",\n \"self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm\",\n \"epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name,\",\n \"* param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'):\",\n \"restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name\",\n \"name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in\",\n \"def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and\",\n \"/ torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name, param in self.module.named_parameters():\",\n \"for batch_input, batch_label in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label)\",\n \"反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0))\",\n \"param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param in\",\n \"param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.emb_backup\",\n \"emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0\",\n \"for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for\",\n \"self.module = module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for\",\n \"反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label)\",\n \"pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728\",\n \"基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K =\",\n \"# 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>> else:\",\n \"PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>>\",\n \"= self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon): r = param_data\",\n \"# 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module):\",\n \"loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>>\",\n \"not torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name,\",\n \"name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name]\",\n \"emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: if\",\n \"pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动,\",\n \"self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm):\",\n \"self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad)\",\n \"# 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad()\",\n \"= {} def project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if\",\n \"and not torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at) param.data =\",\n \"= {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters():\",\n \"pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() #\",\n \"!= K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label)\",\n \"初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label in data: >>> #\",\n \">>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss = module(batch_input,\",\n \"FGM(object): \\\"\\\"\\\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm\",\n \"attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in\",\n \"# 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad()\",\n \"param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon * r /\",\n \"module): self.module = module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ):\",\n \"# 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K): >>>\",\n \">>> fgm = FGM(module) >>> for batch_input, batch_label in data: >>> # 正常训练\",\n \">>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度\",\n \"alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad\",\n \"= module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>>\",\n \"in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon):\",\n \"param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: param.grad = self.grad_backup[name]\",\n \"3 >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss =\",\n \"name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name,\",\n \"loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>>\",\n \"r def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone()\",\n \"emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert\",\n \"基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm = FGM(module)\",\n \"= module self.emb_backup = {} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3,\",\n \"= module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> #\",\n \"is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not\",\n \"alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self,\",\n \"0 and not torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at) def\",\n \"if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and\",\n \"(:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm = FGM(module) >>> for batch_input,\",\n \"= 3 >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss\",\n \"project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon:\",\n \">>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step()\",\n \"if torch.norm(r) > epsilon: r = epsilon * r / torch.norm(r) return self.emb_backup[param_name]\",\n \"__init__(self, module): self.module = module self.emb_backup = {} self.grad_backup = {} def attack(\",\n \"t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t !=\",\n \">>> K = 3 >>> for batch_input, batch_label in data: >>> # 正常训练\",\n \"self.backup param.data = self.backup[name] self.backup = {} class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args: module\",\n \"self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters():\",\n \"self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.backup param.data =\",\n \"assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name,\",\n \"class FGM(object): \\\"\\\"\\\" 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>>\",\n \"norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for\",\n \"r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name, param in\",\n \">>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.emb_backup\",\n \"__init__(self, module): self.module = module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings'\",\n \"self.module = module self.emb_backup = {} self.grad_backup = {} def attack( self, epsilon=1.,\",\n \"self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon): r = param_data -\",\n \"{} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name,\",\n \"epsilon * param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name,\",\n \"param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad\",\n \"Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module = module self.backup = {}\",\n \">>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore()\",\n \"assert name in self.backup param.data = self.backup[name] self.backup = {} class PGD(object): \\\"\\\"\\\"\",\n \"in self.backup param.data = self.backup[name] self.backup = {} class PGD(object): \\\"\\\"\\\" 基于PGD算法的攻击机制 Args:\",\n \"epsilon: r = epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r def\",\n \"module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param\",\n \">>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1]\",\n \"梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 \\\"\\\"\\\" def __init__(self, module): self.module\",\n \">>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward()\",\n \">>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if\",\n \">>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数\",\n \"def restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and\"\n]","isConversational":false}}},{"rowIdx":1192,"cells":{"ngram":{"kind":"list like","value":["= torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f:","OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu:","adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else:","checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params'])","KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else:","dest=\"relative\", action=\"store_true\", help=\"use relative or absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement","kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image,","kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return","256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame = camera.read() while True:","plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized)","# plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial:","if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def","2) if not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi',","True: ret, frame = camera.read() resized = resize(frame, (256, 256))[..., :3] if not","__name__ == \"__main__\": parser = ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path to config\") parser.add_argument(\"--source_image\", required=True,","if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)","skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector","# # x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized =","(256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source","'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', #","/ np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v for k, v","= DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def","source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale","(256, 256))[..., :3] if not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized))","not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path)","source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial =","if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial,","\"__main__\": parser = ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path to config\") parser.add_argument(\"--source_image\", required=True, help=\"path to","# y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB)","generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint =","import matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform import resize from","1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF == ord('q'): break camera.release() cv2.destroyAllWindows()","torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source = source.cuda() kp_source = kp_detector(source)","cv2.imshow(\"frame\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving","2, 0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if","parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement scale based on convex hull of keypoints\") parser.add_argument(\"--cpu\",","argparse import ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator","driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame) #x =","#from animate import normalize_kp # command = [ffmpeg, # '-y', # '-f', 'rawvideo',","image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use relative or absolute","not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu:","image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) # plt.imshow(np.array(image))","= load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3]","cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not","parser = ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path to config\") parser.add_argument(\"--source_image\", required=True, help=\"path to source","relative or absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement scale based on","= 1 kp_new = {k: v for k, v in kp_driving.items()} if use_relative_movement:","ret, frame = camera.read() resized = resize(frame, (256, 256))[..., :3] if not opt.cpu:","torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval()","kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \"__main__\": parser =","checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use relative or absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\",","{k: v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] -","np import matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform import resize","# '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source,","np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3,","1])[0] if __name__ == \"__main__\": parser = ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path to config\")","**config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint","= generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ ==","DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial,","= camera.read() resized = resize(frame, (256, 256))[..., :3] if not opt.cpu: resized =","= np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2,","adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative,","x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3,","# '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', #","256))[..., :3] if not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) #","y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) #","'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False):","= {k: v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value']","#permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF == ord('q'): break","kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source,","yaml import imageio import throttle import numpy as np import matplotlib.pyplot as plt","scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm","return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \"__main__\": parser = ArgumentParser()","3, 1, 2) if not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out","kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'],","= cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame = camera.read() resized","use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff +","'-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving,","return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f)","kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out =","# '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', # '-c:v', 'libx264', #","as np import matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform import","parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector =","as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda()","plt from argparse import ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull","kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale )","imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)","adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale =","'-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False,","ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path to config\") parser.add_argument(\"--source_image\", required=True, help=\"path to source image\") parser.add_argument(\"--checkpoint\",","use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2,","y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0)","generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving,","source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256))","kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new","action=\"store_true\", help=\"adapt movement scale based on convex hull of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\",","= yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'],","cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1,","*= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))","import DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg, # '-y', #","np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v for k, v in","generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval()","parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image,","kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None","in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value']","if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if","cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint","# plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) &","cv2 import torch import yaml import imageio import throttle import numpy as np","map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator)","parser.add_argument(\"--source_image\", required=True, help=\"path to source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\",","mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image","with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not","#x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape)","required=True, help=\"path to config\") parser.add_argument(\"--source_image\", required=True, help=\"path to source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path","throttle import numpy as np import matplotlib.pyplot as plt from argparse import ArgumentParser","open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu:","'-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', # '-c:v', 'libx264',","generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256,","config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector =","for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff","+ kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return","= y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1,","1 kp_new = {k: v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff","ret, frame = camera.read() while True: ret, frame = camera.read() resized = resize(frame,","camera.read() while True: ret, frame = camera.read() resized = resize(frame, (256, 256))[..., :3]","yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params'])","kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial,","scale based on convex hull of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False)","k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *=","KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg,","imageio import throttle import numpy as np import matplotlib.pyplot as plt from argparse","import imageio import throttle import numpy as np import matplotlib.pyplot as plt from","import ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator import","if not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x =","checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector =","3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF == ord('q'): break camera.release()","ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator","def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area","(256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame = camera.read() while","from skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from","dest=\"cpu\", action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config,","jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path,","= resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not","= None camera = cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame","kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator","#x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized","adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3, 1])[0]","# '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i',","# '-s', dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', #","kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path,","= kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera","or absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement scale based on convex","'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area","absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement scale based on convex hull","2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source,","kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0,","checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu:","1, 2) if not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out =","y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)","parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image =","'-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt',","frame = camera.read() resized = resize(frame, (256, 256))[..., :3] if not opt.cpu: resized","driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show()","opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() #","= [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', #","action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint,","x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1,","kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] =","= normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source,","use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path,","3, 1])[0] if __name__ == \"__main__\": parser = ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path to","help=\"path to source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use","ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k:","= torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector)","resize(frame, (256, 256))[..., :3] if not opt.cpu: resized = resized.cuda() # y =","fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving =","axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3, 1)","to source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use relative","help=\"adapt movement scale based on convex hull of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU","'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def","while True: ret, frame = camera.read() resized = resize(frame, (256, 256))[..., :3] if","normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area =","= torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False):","kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as","cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame = camera.read() resized =","import torch import yaml import imageio import throttle import numpy as np import","from argparse import ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull from","relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1,","'-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f',","cpu=False): with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if","0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1)",":3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source = source.cuda()","= forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\",","torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config","if __name__ == \"__main__\": parser = ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path to config\") parser.add_argument(\"--source_image\",","DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg, # '-y', # '-f',","help=\"use relative or absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement scale based","keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement scale based on convex hull of","resized = resize(frame, (256, 256))[..., :3] if not opt.cpu: resized = resized.cuda() #","if not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'),","kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp(","# image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) #","kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,))","kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[...,","None camera = cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame =","kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu","if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if","- kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff","from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from","not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator,","#out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0)","if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint =","= source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial","not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy()","dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast',","1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized,","cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized","kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame = camera.read() while True: ret,","# plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial =","parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image)","driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame","torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator =","ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback","def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator =","= torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # #","adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2,","cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0)","= KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))","v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale","import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command =","np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \"__main__\": parser = ArgumentParser() parser.add_argument(\"--config\",","resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector","kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x =","kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True):","= torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame =","generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if","kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \"__main__\": parser","driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new","use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area)","= DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source,","import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import","generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True,","== \"__main__\": parser = ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path to config\") parser.add_argument(\"--source_image\", required=True, help=\"path","import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import","= imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1,","= cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) #","# command = [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt',","kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame,","coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement scale based on convex hull of keypoints\")","based on convex hull of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False)","cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show()","# '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', # '-c:v',","to config\") parser.add_argument(\"--source_image\", required=True, help=\"path to source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\")","[ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s',","normalize_kp # command = [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', #","= np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v for","= kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff,","torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with","kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return","sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg, # '-y',","plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF","forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm","import numpy as np import matplotlib.pyplot as plt from argparse import ArgumentParser from","'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume","import normalize_kp # command = [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo',","cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame","import yaml import imageio import throttle import numpy as np import matplotlib.pyplot as","keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector","required=True, help=\"path to source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\",","torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path)","f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector","numpy as np import matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform","import cv2 import torch import yaml import imageio import throttle import numpy as","convex hull of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt =","import throttle import numpy as np import matplotlib.pyplot as plt from argparse import","forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame)","= ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale =","frame = camera.read() while True: ret, frame = camera.read() resized = resize(frame, (256,","kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(),","adapt_movement_scale = 1 kp_new = {k: v for k, v in kp_driving.items()} if","return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True,","= kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale,","to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use relative or absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\",","2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if","kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value']","256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source =","import resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import","'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial,","= driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF ==","= cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret,","'-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-',","if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator,","config\") parser.add_argument(\"--source_image\", required=True, help=\"path to source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\") parser.add_argument(\"--relative\",","kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector'])","plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial","use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3,","camera.read() resized = resize(frame, (256, 256))[..., :3] if not opt.cpu: resized = resized.cuda()","movement scale based on convex hull of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU mode\")","**config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda()","fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu )","3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source,","torch import yaml import imageio import throttle import numpy as np import matplotlib.pyplot","not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector","of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator,",":3] if not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x","kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale","from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from","= camera.read() while True: ret, frame = camera.read() resized = resize(frame, (256, 256))[...,","source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source = source.cuda() kp_source","DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image,","= ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path to config\") parser.add_argument(\"--source_image\", required=True, help=\"path to source image\")","'-s', dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset',","driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF == ord('q'):","kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume","default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use relative or absolute keypoint coordinates\")","generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x","= parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image =","if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff","torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x","kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector,","parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use relative or absolute keypoint","# '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if","dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement scale based on convex hull of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\",","= (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if","'-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, #","opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256,","kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] =","= y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2,","cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame =","# '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', #","generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \"__main__\":","cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale","30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame = camera.read()","resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu:","source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use relative or","def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame)","animate import normalize_kp # command = [ffmpeg, # '-y', # '-f', 'rawvideo', #","= resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image =","= resize(frame, (256, 256))[..., :3] if not opt.cpu: resized = resized.cuda() # y","@throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving","help=\"path to checkpoint\") parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use relative or absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\",","help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu)","'bgr24', # '-s', dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p',","relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative,","0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not","opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image","kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config =","cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0,","command = [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24',","resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x,","resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image","= driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x)","v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])","else: adapt_movement_scale = 1 kp_new = {k: v for k, v in kp_driving.items()}","[0, 2, 3, 1])[0] if __name__ == \"__main__\": parser = ArgumentParser() parser.add_argument(\"--config\", required=True,","= torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source = source.cuda() kp_source =","OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp","driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm =","kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera =","= ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new =","# x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x =","# 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area =","use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) /","np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v for k,","= OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not","help=\"path to config\") parser.add_argument(\"--source_image\", required=True, help=\"path to source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\", help=\"path to","adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v","# x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0,","2, 3, 1])[0] if __name__ == \"__main__\": parser = ArgumentParser() parser.add_argument(\"--config\", required=True, help=\"path","source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if","action=\"store_true\", help=\"use relative or absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt movement scale","ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1","checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source =","adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian']","from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg, #","kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff =","load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source","normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm)","# '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,","'-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv',","generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2)","2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving =","torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward(","load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'],","'-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale:","= torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator","kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame) #x = np.squeeze(driving_resized,","matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform import resize from scipy.spatial","kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])","else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector",") cv2.imshow(\"frame\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) #","modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command","generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator,","on convex hull of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt","camera = cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame = camera.read()","not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30,","from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp #","hull of keypoints\") parser.add_argument(\"--cpu\", dest=\"cpu\", action=\"store_true\", help=\"CPU mode\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args()","parser.add_argument(\"--relative\", dest=\"relative\", action=\"store_true\", help=\"use relative or absolute keypoint coordinates\") parser.add_argument(\"--adapt_scale\", dest=\"adapt_scale\", action=\"store_true\", help=\"adapt","modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate","(kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian:","source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\"frame\", fake_frame) #x","parser.add_argument(\"--config\", required=True, help=\"path to config\") parser.add_argument(\"--source_image\", required=True, help=\"path to source image\") parser.add_argument(\"--checkpoint\", default=\"vox-cpk.pth.tar\",","cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator'])","out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__","kp_new = {k: v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff =","kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative,",") out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\"prediction\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if","'-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream']","= kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out","source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3,","kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector,","as plt from argparse import ArgumentParser from skimage.transform import resize from scipy.spatial import","# '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension,"],"string":"[\n \"= torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f:\",\n \"OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu:\",\n \"adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else:\",\n \"checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params'])\",\n \"KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else:\",\n \"dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative or absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement\",\n \"kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image,\",\n \"kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return\",\n \"256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame = camera.read() while True:\",\n \"plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized)\",\n \"# plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial:\",\n \"if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def\",\n \"2) if not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi',\",\n \"True: ret, frame = camera.read() resized = resize(frame, (256, 256))[..., :3] if not\",\n \"__name__ == \\\"__main__\\\": parser = ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\", required=True,\",\n \"if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)\",\n \"skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector\",\n \"# # x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized =\",\n \"(256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source\",\n \"'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', #\",\n \"/ np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v for k, v\",\n \"= DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def\",\n \"source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale\",\n \"(256, 256))[..., :3] if not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized))\",\n \"not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path)\",\n \"source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial =\",\n \"if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial,\",\n \"\\\"__main__\\\": parser = ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to\",\n \"# y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB)\",\n \"generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint =\",\n \"import matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform import resize from\",\n \"1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF == ord('q'): break camera.release() cv2.destroyAllWindows()\",\n \"torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source = source.cuda() kp_source = kp_detector(source)\",\n \"cv2.imshow(\\\"frame\\\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving\",\n \"2, 0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if\",\n \"parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement scale based on convex hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\",\",\n \"argparse import ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator\",\n \"driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame) #x =\",\n \"#from animate import normalize_kp # command = [ffmpeg, # '-y', # '-f', 'rawvideo',\",\n \"image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative or absolute\",\n \"not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu:\",\n \"image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) # plt.imshow(np.array(image))\",\n \"= load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3]\",\n \"cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not\",\n \"parser = ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to source\",\n \"relative or absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement scale based on\",\n \"= 1 kp_new = {k: v for k, v in kp_driving.items()} if use_relative_movement:\",\n \"ret, frame = camera.read() resized = resize(frame, (256, 256))[..., :3] if not opt.cpu:\",\n \"torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval()\",\n \"kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \\\"__main__\\\": parser =\",\n \"checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative or absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\",\",\n \"{k: v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] -\",\n \"np import matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform import resize\",\n \"# '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source,\",\n \"np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3,\",\n \"1])[0] if __name__ == \\\"__main__\\\": parser = ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to config\\\")\",\n \"**config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint\",\n \"= generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ ==\",\n \"DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial,\",\n \"= camera.read() resized = resize(frame, (256, 256))[..., :3] if not opt.cpu: resized =\",\n \"= np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2,\",\n \"adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative,\",\n \"x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3,\",\n \"# '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', #\",\n \"256))[..., :3] if not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) #\",\n \"y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) #\",\n \"'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False):\",\n \"= {k: v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value']\",\n \"#permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF == ord('q'): break\",\n \"kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source,\",\n \"yaml import imageio import throttle import numpy as np import matplotlib.pyplot as plt\",\n \"scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm\",\n \"return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \\\"__main__\\\": parser = ArgumentParser()\",\n \"3, 1, 2) if not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out\",\n \"kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'],\",\n \"= cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame = camera.read() resized\",\n \"use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff +\",\n \"'-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving,\",\n \"return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f)\",\n \"kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out =\",\n \"# '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', # '-c:v', 'libx264', #\",\n \"as np import matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform import\",\n \"parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector =\",\n \"as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda()\",\n \"plt from argparse import ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull\",\n \"kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale )\",\n \"imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)\",\n \"adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale =\",\n \"'-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False,\",\n \"ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to source image\\\") parser.add_argument(\\\"--checkpoint\\\",\",\n \"use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2,\",\n \"y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0)\",\n \"generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving,\",\n \"source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256))\",\n \"kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new\",\n \"action=\\\"store_true\\\", help=\\\"adapt movement scale based on convex hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\",\",\n \"= yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'],\",\n \"cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1,\",\n \"*= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))\",\n \"import DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg, # '-y', #\",\n \"np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v for k, v in\",\n \"generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval()\",\n \"parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image,\",\n \"kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None\",\n \"in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value']\",\n \"if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if\",\n \"cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint\",\n \"# plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) &\",\n \"cv2 import torch import yaml import imageio import throttle import numpy as np\",\n \"map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator)\",\n \"parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\",\",\n \"mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image\",\n \"with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not\",\n \"#x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape)\",\n \"required=True, help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path\",\n \"throttle import numpy as np import matplotlib.pyplot as plt from argparse import ArgumentParser\",\n \"open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu:\",\n \"'-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', # '-c:v', 'libx264',\",\n \"generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256,\",\n \"config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector =\",\n \"for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff\",\n \"+ kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return\",\n \"= y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1,\",\n \"1 kp_new = {k: v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff\",\n \"ret, frame = camera.read() while True: ret, frame = camera.read() resized = resize(frame,\",\n \"camera.read() while True: ret, frame = camera.read() resized = resize(frame, (256, 256))[..., :3]\",\n \"yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params'])\",\n \"kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial,\",\n \"scale based on convex hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False)\",\n \"k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *=\",\n \"KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg,\",\n \"imageio import throttle import numpy as np import matplotlib.pyplot as plt from argparse\",\n \"import imageio import throttle import numpy as np import matplotlib.pyplot as plt from\",\n \"import ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator import\",\n \"if not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x =\",\n \"checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector =\",\n \"3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF == ord('q'): break camera.release()\",\n \"ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator\",\n \"def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area\",\n \"(256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame = camera.read() while\",\n \"from skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from\",\n \"dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config,\",\n \"jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path,\",\n \"= resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not\",\n \"= None camera = cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame\",\n \"kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator\",\n \"#x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized\",\n \"adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3, 1])[0]\",\n \"# '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i',\",\n \"# '-s', dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', #\",\n \"kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path,\",\n \"= kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera\",\n \"or absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement scale based on convex\",\n \"'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area\",\n \"absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement scale based on convex hull\",\n \"2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source,\",\n \"kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0,\",\n \"checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu:\",\n \"1, 2) if not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out =\",\n \"y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)\",\n \"parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image =\",\n \"'-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt',\",\n \"frame = camera.read() resized = resize(frame, (256, 256))[..., :3] if not opt.cpu: resized\",\n \"driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show()\",\n \"opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() #\",\n \"= [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', #\",\n \"action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint,\",\n \"x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1,\",\n \"kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] =\",\n \"= normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source,\",\n \"use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path,\",\n \"3, 1])[0] if __name__ == \\\"__main__\\\": parser = ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to\",\n \"help=\\\"path to source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use\",\n \"ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k:\",\n \"= torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector)\",\n \"resize(frame, (256, 256))[..., :3] if not opt.cpu: resized = resized.cuda() # y =\",\n \"fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving =\",\n \"axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3, 1)\",\n \"to source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative\",\n \"help=\\\"adapt movement scale based on convex hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU\",\n \"'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def\",\n \"while True: ret, frame = camera.read() resized = resize(frame, (256, 256))[..., :3] if\",\n \"normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area =\",\n \"= torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False):\",\n \"kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as\",\n \"cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame = camera.read() resized =\",\n \"import torch import yaml import imageio import throttle import numpy as np import\",\n \"from argparse import ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull from\",\n \"relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1,\",\n \"'-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f',\",\n \"cpu=False): with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if\",\n \"0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1)\",\n \":3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source = source.cuda()\",\n \"= forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\",\",\n \"torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config\",\n \"if __name__ == \\\"__main__\\\": parser = ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\",\",\n \"DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg, # '-y', # '-f',\",\n \"help=\\\"use relative or absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement scale based\",\n \"keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement scale based on convex hull of\",\n \"resized = resize(frame, (256, 256))[..., :3] if not opt.cpu: resized = resized.cuda() #\",\n \"if not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'),\",\n \"kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp(\",\n \"# image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) #\",\n \"kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,))\",\n \"kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[...,\",\n \"None camera = cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame =\",\n \"kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu\",\n \"if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if\",\n \"- kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff\",\n \"from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from\",\n \"not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator,\",\n \"#out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0)\",\n \"if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint =\",\n \"= source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial\",\n \"not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy()\",\n \"dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast',\",\n \"1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized,\",\n \"cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized\",\n \"kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame = camera.read() while True: ret,\",\n \"# plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial =\",\n \"parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image)\",\n \"driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame\",\n \"torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator =\",\n \"ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback\",\n \"def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator =\",\n \"= torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # #\",\n \"adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2,\",\n \"cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0)\",\n \"= KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))\",\n \"v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale\",\n \"import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command =\",\n \"np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \\\"__main__\\\": parser = ArgumentParser() parser.add_argument(\\\"--config\\\",\",\n \"resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector\",\n \"kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x =\",\n \"kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True):\",\n \"= torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame =\",\n \"generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if\",\n \"kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \\\"__main__\\\": parser\",\n \"driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new\",\n \"use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area)\",\n \"= DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source,\",\n \"import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import\",\n \"generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True,\",\n \"== \\\"__main__\\\": parser = ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path\",\n \"import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import\",\n \"= imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1,\",\n \"= cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) #\",\n \"# command = [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt',\",\n \"kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame,\",\n \"coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement scale based on convex hull of keypoints\\\")\",\n \"based on convex hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False)\",\n \"cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show()\",\n \"# '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', # '-c:v',\",\n \"to config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\")\",\n \"[ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s',\",\n \"normalize_kp # command = [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', #\",\n \"= np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v for\",\n \"= kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff,\",\n \"torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with\",\n \"kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return\",\n \"sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg, # '-y',\",\n \"plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF\",\n \"forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm\",\n \"import numpy as np import matplotlib.pyplot as plt from argparse import ArgumentParser from\",\n \"'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume\",\n \"import normalize_kp # command = [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo',\",\n \"cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame\",\n \"import yaml import imageio import throttle import numpy as np import matplotlib.pyplot as\",\n \"keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector\",\n \"required=True, help=\\\"path to source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\",\",\n \"torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path)\",\n \"f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector\",\n \"numpy as np import matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform\",\n \"import cv2 import torch import yaml import imageio import throttle import numpy as\",\n \"convex hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt =\",\n \"import throttle import numpy as np import matplotlib.pyplot as plt from argparse import\",\n \"forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame)\",\n \"= ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale =\",\n \"frame = camera.read() while True: ret, frame = camera.read() resized = resize(frame, (256,\",\n \"kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(),\",\n \"adapt_movement_scale = 1 kp_new = {k: v for k, v in kp_driving.items()} if\",\n \"return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True,\",\n \"= kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale,\",\n \"to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative or absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\",\",\n \"2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if\",\n \"kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value']\",\n \"256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source =\",\n \"import resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import\",\n \"'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial,\",\n \"= driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF ==\",\n \"= cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret,\",\n \"'-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-',\",\n \"if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator,\",\n \"config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\",\",\n \"kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector'])\",\n \"plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial\",\n \"use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3,\",\n \"camera.read() resized = resize(frame, (256, 256))[..., :3] if not opt.cpu: resized = resized.cuda()\",\n \"movement scale based on convex hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\")\",\n \"**config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda()\",\n \"fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu )\",\n \"3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source,\",\n \"torch import yaml import imageio import throttle import numpy as np import matplotlib.pyplot\",\n \"not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector\",\n \"of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator,\",\n \":3] if not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x\",\n \"kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale\",\n \"from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from\",\n \"= camera.read() while True: ret, frame = camera.read() resized = resize(frame, (256, 256))[...,\",\n \"source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source = source.cuda() kp_source\",\n \"DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image,\",\n \"= ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to source image\\\")\",\n \"'-s', dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset',\",\n \"driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF == ord('q'):\",\n \"kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume\",\n \"default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative or absolute keypoint coordinates\\\")\",\n \"generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x\",\n \"= parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image =\",\n \"if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff\",\n \"torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x\",\n \"kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector,\",\n \"parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative or absolute keypoint\",\n \"# '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if\",\n \"dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement scale based on convex hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\",\",\n \"= (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if\",\n \"'-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, #\",\n \"opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256,\",\n \"kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] =\",\n \"= y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2,\",\n \"cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame =\",\n \"# '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', #\",\n \"generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == \\\"__main__\\\":\",\n \"cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale\",\n \"30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame = camera.read()\",\n \"resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu:\",\n \"source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative or\",\n \"def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame)\",\n \"animate import normalize_kp # command = [ffmpeg, # '-y', # '-f', 'rawvideo', #\",\n \"= resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image =\",\n \"= resize(frame, (256, 256))[..., :3] if not opt.cpu: resized = resized.cuda() # y\",\n \"@throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving\",\n \"help=\\\"path to checkpoint\\\") parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative or absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\",\",\n \"help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu)\",\n \"'bgr24', # '-s', dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p',\",\n \"relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative,\",\n \"0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not\",\n \"opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image\",\n \"kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config =\",\n \"cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0,\",\n \"command = [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24',\",\n \"resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x,\",\n \"resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image\",\n \"= driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x)\",\n \"v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])\",\n \"else: adapt_movement_scale = 1 kp_new = {k: v for k, v in kp_driving.items()}\",\n \"[0, 2, 3, 1])[0] if __name__ == \\\"__main__\\\": parser = ArgumentParser() parser.add_argument(\\\"--config\\\", required=True,\",\n \"= torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source = source.cuda() kp_source =\",\n \"OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp\",\n \"driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm =\",\n \"kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera =\",\n \"= ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new =\",\n \"# x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x =\",\n \"# 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area =\",\n \"use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) /\",\n \"np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v for k,\",\n \"= OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not\",\n \"help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\", help=\\\"path to\",\n \"adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v\",\n \"# x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0,\",\n \"2, 3, 1])[0] if __name__ == \\\"__main__\\\": parser = ArgumentParser() parser.add_argument(\\\"--config\\\", required=True, help=\\\"path\",\n \"source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if\",\n \"action=\\\"store_true\\\", help=\\\"use relative or absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt movement scale\",\n \"ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1\",\n \"checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source =\",\n \"adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian']\",\n \"from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg, #\",\n \"kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff =\",\n \"load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source\",\n \"normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm)\",\n \"# '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,\",\n \"'-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv',\",\n \"generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2)\",\n \"2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving =\",\n \"torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward(\",\n \"load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'],\",\n \"'-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale:\",\n \"= torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator\",\n \"kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame) #x = np.squeeze(driving_resized,\",\n \"matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform import resize from scipy.spatial\",\n \"kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])\",\n \"else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector\",\n \") cv2.imshow(\\\"frame\\\", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) #\",\n \"modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command\",\n \"generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator,\",\n \"on convex hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt\",\n \"camera = cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame = camera.read()\",\n \"not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30,\",\n \"from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp #\",\n \"hull of keypoints\\\") parser.add_argument(\\\"--cpu\\\", dest=\\\"cpu\\\", action=\\\"store_true\\\", help=\\\"CPU mode\\\") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args()\",\n \"parser.add_argument(\\\"--relative\\\", dest=\\\"relative\\\", action=\\\"store_true\\\", help=\\\"use relative or absolute keypoint coordinates\\\") parser.add_argument(\\\"--adapt_scale\\\", dest=\\\"adapt_scale\\\", action=\\\"store_true\\\", help=\\\"adapt\",\n \"modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate\",\n \"(kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian:\",\n \"source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow(\\\"frame\\\", fake_frame) #x\",\n \"parser.add_argument(\\\"--config\\\", required=True, help=\\\"path to config\\\") parser.add_argument(\\\"--source_image\\\", required=True, help=\\\"path to source image\\\") parser.add_argument(\\\"--checkpoint\\\", default=\\\"vox-cpk.pth.tar\\\",\",\n \"cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator'])\",\n \"out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__\",\n \"kp_new = {k: v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff =\",\n \"kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative,\",\n \") out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out[\\\"prediction\\\"].data.cpu().numpy(), [0, 2, 3, 1])[0] if\",\n \"'-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream']\",\n \"= kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out\",\n \"source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3,\",\n \"kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector,\",\n \"as plt from argparse import ArgumentParser from skimage.transform import resize from scipy.spatial import\",\n \"# '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension,\"\n]","isConversational":false}}},{"rowIdx":1193,"cells":{"ngram":{"kind":"list like","value":["import ChanceConstraintConstant # to do list # 0. implement zero, Hessian term #","3. scaling with repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs","samples and states at different design # 2. table for variance reduction #","CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from","random samples and states at different design # 2. table for variance reduction","ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to","import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from",".chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant","to do list # 0. implement zero, Hessian term # 1. implement linear","ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE","and state, for both disk and submarine # 5. random sample and state","variance reduction # 3. plot trace estimation # 4. plot #bfgs iterations #","1. implement linear # 2. implement quadratic # 3. impelement SAA # to","quadratic + correction # what to show tomorrow # 1. variance reduction by","works # 2. quadratic approximation does not converge well, even without variance, does","from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from","from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do list","SAA does not run well in ccgo1, multiprocessor does not work, ### not","absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant","mean square error # 2. trace estimation by MC and randomized SVD #","disk and submarine # 5. random sample and state at different design #","with repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs # 4.","different design # 2. table for variance reduction # 3. plot trace estimation","# 2. implement quadratic # 3. impelement SAA # to do list #","results # 1. random samples and states at different design # 2. table","clear bug, simplifing adjoint solver works # 2. quadratic approximation does not converge","division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import","import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import","# from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant","implement zero, Hessian term # 1. implement linear # 2. implement quadratic #","well, even without variance, does not converge ### record eigenvector after m_tr[i].zero() #","# to do list # 0. implement zero, Hessian term # 1. implement","import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import","scaling with repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs #","multiprocessor does not work, ### not clear bug, simplifing adjoint solver works #","both disk and submarine # 5. random sample and state at different design","variance, does not converge ### record eigenvector after m_tr[i].zero() # 3. check gradient","CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear",".costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE","+ uncertainty), trace, variance reduction, #bfgs # 4. show the design and state,","show the design and state, for both disk and submarine # 5. random","# April 9, 2018, work on reporting results # 1. random samples and","sample and state at different design # April 9, 2018, work on reporting","plot trace estimation # 4. plot #bfgs iterations # obtain all results as","# 1. implement linear # 2. implement quadratic # 3. impelement SAA #",".controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE","repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs # 4. show","from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import","check gradient for quadratic + correction # what to show tomorrow # 1.","ccgo1, multiprocessor does not work, ### not clear bug, simplifing adjoint solver works","and state at different design # April 9, 2018, work on reporting results","term # 1. implement linear # 2. implement quadratic # 3. impelement SAA","# to do list # 1. SAA does not run well in ccgo1,","#bfgs # 4. show the design and state, for both disk and submarine","# 3. impelement SAA # to do list # 1. SAA does not",".chanceConstraintConstant import ChanceConstraintConstant # to do list # 0. implement zero, Hessian term","### record eigenvector after m_tr[i].zero() # 3. check gradient for quadratic + correction","import absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from","import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear #",".costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE","list # 0. implement zero, Hessian term # 1. implement linear # 2.","import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import","design # April 9, 2018, work on reporting results # 1. random samples","not work, ### not clear bug, simplifing adjoint solver works # 2. quadratic","quadratic approximation does not converge well, even without variance, does not converge ###","from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE #","not converge well, even without variance, does not converge ### record eigenvector after","2. quadratic approximation does not converge well, even without variance, does not converge","not clear bug, simplifing adjoint solver works # 2. quadratic approximation does not","# 2. trace estimation by MC and randomized SVD # 3. scaling with","and randomized SVD # 3. scaling with repsect to mesh (design + uncertainty),","for both disk and submarine # 5. random sample and state at different","do list # 1. SAA does not run well in ccgo1, multiprocessor does","what to show tomorrow # 1. variance reduction by mean square error #","MC and randomized SVD # 3. scaling with repsect to mesh (design +","variance reduction, #bfgs # 4. show the design and state, for both disk","SAA # to do list # 1. SAA does not run well in","list # 1. SAA does not run well in ccgo1, multiprocessor does not","trace, variance reduction, #bfgs # 4. show the design and state, for both","estimation by MC and randomized SVD # 3. scaling with repsect to mesh","1. random samples and states at different design # 2. table for variance","import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do list # 0.","record eigenvector after m_tr[i].zero() # 3. check gradient for quadratic + correction #","to show tomorrow # 1. variance reduction by mean square error # 2.","square error # 2. trace estimation by MC and randomized SVD # 3.","from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import","after m_tr[i].zero() # 3. check gradient for quadratic + correction # what to","ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do list # 0. implement","Hessian term # 1. implement linear # 2. implement quadratic # 3. impelement","the design and state, for both disk and submarine # 5. random sample","run well in ccgo1, multiprocessor does not work, ### not clear bug, simplifing","# 2. quadratic approximation does not converge well, even without variance, does not",".costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from","(design + uncertainty), trace, variance reduction, #bfgs # 4. show the design and","from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from","reduction # 3. plot trace estimation # 4. plot #bfgs iterations # obtain","work, ### not clear bug, simplifing adjoint solver works # 2. quadratic approximation",".costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic","at different design # April 9, 2018, work on reporting results # 1.","ChanceConstraintConstant # to do list # 0. implement zero, Hessian term # 1.","for quadratic + correction # what to show tomorrow # 1. variance reduction","from __future__ import absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import","CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import","April 9, 2018, work on reporting results # 1. random samples and states",".controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear",".costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic #","and submarine # 5. random sample and state at different design # April","from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from","# 1. random samples and states at different design # 2. table for","well in ccgo1, multiprocessor does not work, ### not clear bug, simplifing adjoint","converge well, even without variance, does not converge ### record eigenvector after m_tr[i].zero()","# 1. variance reduction by mean square error # 2. trace estimation by","by MC and randomized SVD # 3. scaling with repsect to mesh (design","adjoint solver works # 2. quadratic approximation does not converge well, even without","does not converge well, even without variance, does not converge ### record eigenvector","# from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do","# 5. random sample and state at different design # April 9, 2018,","state at different design # April 9, 2018, work on reporting results #","submarine # 5. random sample and state at different design # April 9,","print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant","simplifing adjoint solver works # 2. quadratic approximation does not converge well, even","ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear","zero, Hessian term # 1. implement linear # 2. implement quadratic # 3.","without variance, does not converge ### record eigenvector after m_tr[i].zero() # 3. check","implement linear # 2. implement quadratic # 3. impelement SAA # to do","SVD # 3. scaling with repsect to mesh (design + uncertainty), trace, variance","2. table for variance reduction # 3. plot trace estimation # 4. plot","# 3. plot trace estimation # 4. plot #bfgs iterations # obtain all","# 3. check gradient for quadratic + correction # what to show tomorrow","show tomorrow # 1. variance reduction by mean square error # 2. trace","design and state, for both disk and submarine # 5. random sample and","quadratic # 3. impelement SAA # to do list # 1. SAA does","trace estimation by MC and randomized SVD # 3. scaling with repsect to","import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic","implement quadratic # 3. impelement SAA # to do list # 1. SAA","does not work, ### not clear bug, simplifing adjoint solver works # 2.","# 3. scaling with repsect to mesh (design + uncertainty), trace, variance reduction,","for variance reduction # 3. plot trace estimation # 4. plot #bfgs iterations","1. SAA does not run well in ccgo1, multiprocessor does not work, ###","by mean square error # 2. trace estimation by MC and randomized SVD","from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic","<reponame>cpempire/soupy<gh_stars>1-10 from __future__ import absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE","+ correction # what to show tomorrow # 1. variance reduction by mean","correction # what to show tomorrow # 1. variance reduction by mean square","tomorrow # 1. variance reduction by mean square error # 2. trace estimation","state, for both disk and submarine # 5. random sample and state at","bug, simplifing adjoint solver works # 2. quadratic approximation does not converge well,",".chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do list #","1. variance reduction by mean square error # 2. trace estimation by MC","2. trace estimation by MC and randomized SVD # 3. scaling with repsect","CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE","3. impelement SAA # to do list # 1. SAA does not run","and states at different design # 2. table for variance reduction # 3.","import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import",".costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear","# 0. implement zero, Hessian term # 1. implement linear # 2. implement","9, 2018, work on reporting results # 1. random samples and states at","import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant #","# 1. SAA does not run well in ccgo1, multiprocessor does not work,","on reporting results # 1. random samples and states at different design #","m_tr[i].zero() # 3. check gradient for quadratic + correction # what to show","does not run well in ccgo1, multiprocessor does not work, ### not clear","__future__ import absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE","impelement SAA # to do list # 1. SAA does not run well","from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from","at different design # 2. table for variance reduction # 3. plot trace","linear # 2. implement quadratic # 3. impelement SAA # to do list","do list # 0. implement zero, Hessian term # 1. implement linear #","import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import","### not clear bug, simplifing adjoint solver works # 2. quadratic approximation does","not converge ### record eigenvector after m_tr[i].zero() # 3. check gradient for quadratic","random sample and state at different design # April 9, 2018, work on","4. show the design and state, for both disk and submarine # 5.","to do list # 1. SAA does not run well in ccgo1, multiprocessor","design # 2. table for variance reduction # 3. plot trace estimation #","CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic","CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE","# what to show tomorrow # 1. variance reduction by mean square error","0. implement zero, Hessian term # 1. implement linear # 2. implement quadratic","2018, work on reporting results # 1. random samples and states at different","reduction by mean square error # 2. trace estimation by MC and randomized","not run well in ccgo1, multiprocessor does not work, ### not clear bug,","gradient for quadratic + correction # what to show tomorrow # 1. variance","reporting results # 1. random samples and states at different design # 2.","in ccgo1, multiprocessor does not work, ### not clear bug, simplifing adjoint solver","error # 2. trace estimation by MC and randomized SVD # 3. scaling","trace estimation # 4. plot #bfgs iterations # obtain all results as planned","converge ### record eigenvector after m_tr[i].zero() # 3. check gradient for quadratic +","to mesh (design + uncertainty), trace, variance reduction, #bfgs # 4. show the","from .chanceConstraintConstant import ChanceConstraintConstant # to do list # 0. implement zero, Hessian","approximation does not converge well, even without variance, does not converge ### record","reduction, #bfgs # 4. show the design and state, for both disk and","# 4. show the design and state, for both disk and submarine #","randomized SVD # 3. scaling with repsect to mesh (design + uncertainty), trace,","table for variance reduction # 3. plot trace estimation # 4. plot #bfgs","5. random sample and state at different design # April 9, 2018, work","2. implement quadratic # 3. impelement SAA # to do list # 1.","3. plot trace estimation # 4. plot #bfgs iterations # obtain all results","solver works # 2. quadratic approximation does not converge well, even without variance,","states at different design # 2. table for variance reduction # 3. plot","even without variance, does not converge ### record eigenvector after m_tr[i].zero() # 3.","does not converge ### record eigenvector after m_tr[i].zero() # 3. check gradient for","eigenvector after m_tr[i].zero() # 3. check gradient for quadratic + correction # what","variance reduction by mean square error # 2. trace estimation by MC and","# from .chanceConstraintConstant import ChanceConstraintConstant # to do list # 0. implement zero,","# 2. table for variance reduction # 3. plot trace estimation # 4.","3. check gradient for quadratic + correction # what to show tomorrow #","work on reporting results # 1. random samples and states at different design","mesh (design + uncertainty), trace, variance reduction, #bfgs # 4. show the design","uncertainty), trace, variance reduction, #bfgs # 4. show the design and state, for","from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from","different design # April 9, 2018, work on reporting results # 1. random"],"string":"[\n \"import ChanceConstraintConstant # to do list # 0. implement zero, Hessian term #\",\n \"3. scaling with repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs\",\n \"samples and states at different design # 2. table for variance reduction #\",\n \"CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from\",\n \"random samples and states at different design # 2. table for variance reduction\",\n \"ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to\",\n \"import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from\",\n \".chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant\",\n \"to do list # 0. implement zero, Hessian term # 1. implement linear\",\n \"ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE\",\n \"and state, for both disk and submarine # 5. random sample and state\",\n \"variance reduction # 3. plot trace estimation # 4. plot #bfgs iterations #\",\n \"1. implement linear # 2. implement quadratic # 3. impelement SAA # to\",\n \"quadratic + correction # what to show tomorrow # 1. variance reduction by\",\n \"works # 2. quadratic approximation does not converge well, even without variance, does\",\n \"from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from\",\n \"from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do list\",\n \"SAA does not run well in ccgo1, multiprocessor does not work, ### not\",\n \"absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant\",\n \"mean square error # 2. trace estimation by MC and randomized SVD #\",\n \"disk and submarine # 5. random sample and state at different design #\",\n \"with repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs # 4.\",\n \"different design # 2. table for variance reduction # 3. plot trace estimation\",\n \"# 2. implement quadratic # 3. impelement SAA # to do list #\",\n \"results # 1. random samples and states at different design # 2. table\",\n \"clear bug, simplifing adjoint solver works # 2. quadratic approximation does not converge\",\n \"division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import\",\n \"import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import\",\n \"# from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant\",\n \"implement zero, Hessian term # 1. implement linear # 2. implement quadratic #\",\n \"well, even without variance, does not converge ### record eigenvector after m_tr[i].zero() #\",\n \"# to do list # 0. implement zero, Hessian term # 1. implement\",\n \"import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import\",\n \"scaling with repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs #\",\n \"multiprocessor does not work, ### not clear bug, simplifing adjoint solver works #\",\n \"both disk and submarine # 5. random sample and state at different design\",\n \"variance, does not converge ### record eigenvector after m_tr[i].zero() # 3. check gradient\",\n \"CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear\",\n \".costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE\",\n \"+ uncertainty), trace, variance reduction, #bfgs # 4. show the design and state,\",\n \"show the design and state, for both disk and submarine # 5. random\",\n \"# April 9, 2018, work on reporting results # 1. random samples and\",\n \"sample and state at different design # April 9, 2018, work on reporting\",\n \"plot trace estimation # 4. plot #bfgs iterations # obtain all results as\",\n \"# 1. implement linear # 2. implement quadratic # 3. impelement SAA #\",\n \".controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE\",\n \"repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs # 4. show\",\n \"from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import\",\n \"check gradient for quadratic + correction # what to show tomorrow # 1.\",\n \"ccgo1, multiprocessor does not work, ### not clear bug, simplifing adjoint solver works\",\n \"and state at different design # April 9, 2018, work on reporting results\",\n \"term # 1. implement linear # 2. implement quadratic # 3. impelement SAA\",\n \"# to do list # 1. SAA does not run well in ccgo1,\",\n \"#bfgs # 4. show the design and state, for both disk and submarine\",\n \"# 3. impelement SAA # to do list # 1. SAA does not\",\n \".chanceConstraintConstant import ChanceConstraintConstant # to do list # 0. implement zero, Hessian term\",\n \"### record eigenvector after m_tr[i].zero() # 3. check gradient for quadratic + correction\",\n \"import absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from\",\n \"import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear #\",\n \".costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE\",\n \"list # 0. implement zero, Hessian term # 1. implement linear # 2.\",\n \"import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import\",\n \"design # April 9, 2018, work on reporting results # 1. random samples\",\n \"not work, ### not clear bug, simplifing adjoint solver works # 2. quadratic\",\n \"quadratic approximation does not converge well, even without variance, does not converge ###\",\n \"from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE #\",\n \"not converge well, even without variance, does not converge ### record eigenvector after\",\n \"2. quadratic approximation does not converge well, even without variance, does not converge\",\n \"not clear bug, simplifing adjoint solver works # 2. quadratic approximation does not\",\n \"# 2. trace estimation by MC and randomized SVD # 3. scaling with\",\n \"and randomized SVD # 3. scaling with repsect to mesh (design + uncertainty),\",\n \"for both disk and submarine # 5. random sample and state at different\",\n \"do list # 1. SAA does not run well in ccgo1, multiprocessor does\",\n \"what to show tomorrow # 1. variance reduction by mean square error #\",\n \"MC and randomized SVD # 3. scaling with repsect to mesh (design +\",\n \"variance reduction, #bfgs # 4. show the design and state, for both disk\",\n \"SAA # to do list # 1. SAA does not run well in\",\n \"list # 1. SAA does not run well in ccgo1, multiprocessor does not\",\n \"trace, variance reduction, #bfgs # 4. show the design and state, for both\",\n \"estimation by MC and randomized SVD # 3. scaling with repsect to mesh\",\n \"1. random samples and states at different design # 2. table for variance\",\n \"import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do list # 0.\",\n \"record eigenvector after m_tr[i].zero() # 3. check gradient for quadratic + correction #\",\n \"to show tomorrow # 1. variance reduction by mean square error # 2.\",\n \"square error # 2. trace estimation by MC and randomized SVD # 3.\",\n \"from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import\",\n \"after m_tr[i].zero() # 3. check gradient for quadratic + correction # what to\",\n \"ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do list # 0. implement\",\n \"Hessian term # 1. implement linear # 2. implement quadratic # 3. impelement\",\n \"the design and state, for both disk and submarine # 5. random sample\",\n \"run well in ccgo1, multiprocessor does not work, ### not clear bug, simplifing\",\n \"# 2. quadratic approximation does not converge well, even without variance, does not\",\n \".costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from\",\n \"(design + uncertainty), trace, variance reduction, #bfgs # 4. show the design and\",\n \"from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from\",\n \"reduction # 3. plot trace estimation # 4. plot #bfgs iterations # obtain\",\n \"work, ### not clear bug, simplifing adjoint solver works # 2. quadratic approximation\",\n \".costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic\",\n \"at different design # April 9, 2018, work on reporting results # 1.\",\n \"ChanceConstraintConstant # to do list # 0. implement zero, Hessian term # 1.\",\n \"for quadratic + correction # what to show tomorrow # 1. variance reduction\",\n \"from __future__ import absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import\",\n \"CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import\",\n \"April 9, 2018, work on reporting results # 1. random samples and states\",\n \".controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear\",\n \".costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic #\",\n \"and submarine # 5. random sample and state at different design # April\",\n \"from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from\",\n \"# 1. random samples and states at different design # 2. table for\",\n \"well in ccgo1, multiprocessor does not work, ### not clear bug, simplifing adjoint\",\n \"converge well, even without variance, does not converge ### record eigenvector after m_tr[i].zero()\",\n \"# 1. variance reduction by mean square error # 2. trace estimation by\",\n \"by MC and randomized SVD # 3. scaling with repsect to mesh (design\",\n \"adjoint solver works # 2. quadratic approximation does not converge well, even without\",\n \"does not converge well, even without variance, does not converge ### record eigenvector\",\n \"# from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do\",\n \"# 5. random sample and state at different design # April 9, 2018,\",\n \"state at different design # April 9, 2018, work on reporting results #\",\n \"submarine # 5. random sample and state at different design # April 9,\",\n \"print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant\",\n \"simplifing adjoint solver works # 2. quadratic approximation does not converge well, even\",\n \"ControlPDEProblemMultiPDE from .costFunctionalConstant import CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear\",\n \"zero, Hessian term # 1. implement linear # 2. implement quadratic # 3.\",\n \"without variance, does not converge ### record eigenvector after m_tr[i].zero() # 3. check\",\n \"implement linear # 2. implement quadratic # 3. impelement SAA # to do\",\n \"SVD # 3. scaling with repsect to mesh (design + uncertainty), trace, variance\",\n \"2. table for variance reduction # 3. plot trace estimation # 4. plot\",\n \"# 3. plot trace estimation # 4. plot #bfgs iterations # obtain all\",\n \"# 3. check gradient for quadratic + correction # what to show tomorrow\",\n \"show tomorrow # 1. variance reduction by mean square error # 2. trace\",\n \"design and state, for both disk and submarine # 5. random sample and\",\n \"quadratic # 3. impelement SAA # to do list # 1. SAA does\",\n \"trace estimation by MC and randomized SVD # 3. scaling with repsect to\",\n \"import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic\",\n \"implement quadratic # 3. impelement SAA # to do list # 1. SAA\",\n \"does not work, ### not clear bug, simplifing adjoint solver works # 2.\",\n \"# 3. scaling with repsect to mesh (design + uncertainty), trace, variance reduction,\",\n \"for variance reduction # 3. plot trace estimation # 4. plot #bfgs iterations\",\n \"1. SAA does not run well in ccgo1, multiprocessor does not work, ###\",\n \"by mean square error # 2. trace estimation by MC and randomized SVD\",\n \"from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic\",\n \"<reponame>cpempire/soupy<gh_stars>1-10 from __future__ import absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE\",\n \"+ correction # what to show tomorrow # 1. variance reduction by mean\",\n \"correction # what to show tomorrow # 1. variance reduction by mean square\",\n \"tomorrow # 1. variance reduction by mean square error # 2. trace estimation\",\n \"state, for both disk and submarine # 5. random sample and state at\",\n \"bug, simplifing adjoint solver works # 2. quadratic approximation does not converge well,\",\n \".chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant # to do list #\",\n \"1. variance reduction by mean square error # 2. trace estimation by MC\",\n \"2. trace estimation by MC and randomized SVD # 3. scaling with repsect\",\n \"CostFunctionalConstant from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE\",\n \"3. impelement SAA # to do list # 1. SAA does not run\",\n \"and states at different design # 2. table for variance reduction # 3.\",\n \"import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import\",\n \".costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE # from .chanceConstraintQuadratic import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear\",\n \"# 0. implement zero, Hessian term # 1. implement linear # 2. implement\",\n \"9, 2018, work on reporting results # 1. random samples and states at\",\n \"import ChanceConstraintQuadratic # from .chanceConstraintLinear import ChanceConstraintLinear # from .chanceConstraintConstant import ChanceConstraintConstant #\",\n \"# 1. SAA does not run well in ccgo1, multiprocessor does not work,\",\n \"on reporting results # 1. random samples and states at different design #\",\n \"m_tr[i].zero() # 3. check gradient for quadratic + correction # what to show\",\n \"does not run well in ccgo1, multiprocessor does not work, ### not clear\",\n \"__future__ import absolute_import, division, print_function from .controlPDEProblem import ControlPDEProblem from .controlPDEProblemMultiPDE import ControlPDEProblemMultiPDE\",\n \"impelement SAA # to do list # 1. SAA does not run well\",\n \"from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from\",\n \"at different design # 2. table for variance reduction # 3. plot trace\",\n \"linear # 2. implement quadratic # 3. impelement SAA # to do list\",\n \"do list # 0. implement zero, Hessian term # 1. implement linear #\",\n \"import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import\",\n \"### not clear bug, simplifing adjoint solver works # 2. quadratic approximation does\",\n \"not converge ### record eigenvector after m_tr[i].zero() # 3. check gradient for quadratic\",\n \"random sample and state at different design # April 9, 2018, work on\",\n \"4. show the design and state, for both disk and submarine # 5.\",\n \"to do list # 1. SAA does not run well in ccgo1, multiprocessor\",\n \"design # 2. table for variance reduction # 3. plot trace estimation #\",\n \"CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic\",\n \"CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from .costFunctionalQuadratic import CostFunctionalQuadratic from .costFunctionalQuadraticMultiPDE import CostFunctionalQuadraticMultiPDE\",\n \"# what to show tomorrow # 1. variance reduction by mean square error\",\n \"0. implement zero, Hessian term # 1. implement linear # 2. implement quadratic\",\n \"2018, work on reporting results # 1. random samples and states at different\",\n \"reduction by mean square error # 2. trace estimation by MC and randomized\",\n \"not run well in ccgo1, multiprocessor does not work, ### not clear bug,\",\n \"gradient for quadratic + correction # what to show tomorrow # 1. variance\",\n \"reporting results # 1. random samples and states at different design # 2.\",\n \"in ccgo1, multiprocessor does not work, ### not clear bug, simplifing adjoint solver\",\n \"error # 2. trace estimation by MC and randomized SVD # 3. scaling\",\n \"trace estimation # 4. plot #bfgs iterations # obtain all results as planned\",\n \"converge ### record eigenvector after m_tr[i].zero() # 3. check gradient for quadratic +\",\n \"to mesh (design + uncertainty), trace, variance reduction, #bfgs # 4. show the\",\n \"from .chanceConstraintConstant import ChanceConstraintConstant # to do list # 0. implement zero, Hessian\",\n \"approximation does not converge well, even without variance, does not converge ### record\",\n \"reduction, #bfgs # 4. show the design and state, for both disk and\",\n \"# 4. show the design and state, for both disk and submarine #\",\n \"randomized SVD # 3. scaling with repsect to mesh (design + uncertainty), trace,\",\n \"table for variance reduction # 3. plot trace estimation # 4. plot #bfgs\",\n \"5. random sample and state at different design # April 9, 2018, work\",\n \"2. implement quadratic # 3. impelement SAA # to do list # 1.\",\n \"3. plot trace estimation # 4. plot #bfgs iterations # obtain all results\",\n \"solver works # 2. quadratic approximation does not converge well, even without variance,\",\n \"states at different design # 2. table for variance reduction # 3. plot\",\n \"even without variance, does not converge ### record eigenvector after m_tr[i].zero() # 3.\",\n \"does not converge ### record eigenvector after m_tr[i].zero() # 3. check gradient for\",\n \"eigenvector after m_tr[i].zero() # 3. check gradient for quadratic + correction # what\",\n \"variance reduction by mean square error # 2. trace estimation by MC and\",\n \"# from .chanceConstraintConstant import ChanceConstraintConstant # to do list # 0. implement zero,\",\n \"# 2. table for variance reduction # 3. plot trace estimation # 4.\",\n \"3. check gradient for quadratic + correction # what to show tomorrow #\",\n \"work on reporting results # 1. random samples and states at different design\",\n \"mesh (design + uncertainty), trace, variance reduction, #bfgs # 4. show the design\",\n \"uncertainty), trace, variance reduction, #bfgs # 4. show the design and state, for\",\n \"from .costFunctionalConstantMultiPDE import CostFunctionalConstantMultiPDE from .costFunctionalLinear import CostFunctionalLinear from .costFunctionalLinearMultiPDE import CostFunctionalLinearMultiPDE from\",\n \"different design # April 9, 2018, work on reporting results # 1. random\"\n]","isConversational":false}}},{"rowIdx":1194,"cells":{"ngram":{"kind":"list like","value":["from get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD","ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return std*z + mean","return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])),","OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config config","brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0,","val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1],","def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]","from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config config = get_config()","rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5,","ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from","CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as","import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast,","mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2,","OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import","RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion,","get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224,","STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5),","p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4,","fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train","Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness,","get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0,","import numpy as np from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate,","#Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16,","Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2","GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5),","std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return","RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5),","ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0],","RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp)","get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train():","MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)):","get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD =","GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor","transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0),","IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf,","]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1],","MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([","albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config config = get_config() MEAN","VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma,","STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return std*z","Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid","ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE,","Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def","ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color","import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229,","p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0,","numpy as np from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform,","get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] )","HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5),","= np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology","as ToTensor from get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456,","mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def","MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid =","def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1,","np from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop,","contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0,","RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16,","std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0],","CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray,","np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([","ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout,","p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]),","import ToTensorV2 as ToTensor from get_config import get_config config = get_config() MEAN =","RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast,","transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return","MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train","HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize,","ToTensor from get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406])","= Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5),","config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225])","GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4,","= get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def","p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1,","0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1),","MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1),","scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35,","Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0),","#Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5),","Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z,","np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0,","RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch","0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic","value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30,","Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config","sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0],","border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30,","as np from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion,","STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1],","GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import","ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config","min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return","RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from","min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(),","min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ])","albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop,","0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30),","NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config config =","p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid():","HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen,","RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle,","= np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train =","interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5),","#Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0,","(Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue,","from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop,","max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1],","max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])),","CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0],","= Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid","GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout,","RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise,","0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2),","transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(),","HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7),","ToTensorV2 as ToTensor from get_config import get_config config = get_config() MEAN = np.array([0.485,","STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return std*z +","STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]),"],"string":"[\n \"from get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD\",\n \"ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return std*z + mean\",\n \"return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])),\",\n \"OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config config\",\n \"brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0,\",\n \"val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1],\",\n \"def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]\",\n \"from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config config = get_config()\",\n \"rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5,\",\n \"ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from\",\n \"CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as\",\n \"import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast,\",\n \"mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2,\",\n \"OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import\",\n \"RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion,\",\n \"get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224,\",\n \"STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5),\",\n \"p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4,\",\n \"fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train\",\n \"Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness,\",\n \"get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0,\",\n \"import numpy as np from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate,\",\n \"#Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16,\",\n \"Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2\",\n \"GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5),\",\n \"std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return\",\n \"RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5),\",\n \"ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0],\",\n \"RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp)\",\n \"get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train():\",\n \"MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)):\",\n \"get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD =\",\n \"GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor\",\n \"transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0),\",\n \"IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf,\",\n \"]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1],\",\n \"MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([\",\n \"albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config config = get_config() MEAN\",\n \"VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma,\",\n \"STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return std*z\",\n \"Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid\",\n \"ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE,\",\n \"Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def\",\n \"ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color\",\n \"import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229,\",\n \"p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0,\",\n \"numpy as np from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform,\",\n \"get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] )\",\n \"HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5),\",\n \"= np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology\",\n \"as ToTensor from get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456,\",\n \"mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def\",\n \"MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid =\",\n \"def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1,\",\n \"np from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop,\",\n \"contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0,\",\n \"RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16,\",\n \"std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0],\",\n \"CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray,\",\n \"np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([\",\n \"ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout,\",\n \"p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]),\",\n \"import ToTensorV2 as ToTensor from get_config import get_config config = get_config() MEAN =\",\n \"RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast,\",\n \"transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return\",\n \"MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train\",\n \"HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize,\",\n \"ToTensor from get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406])\",\n \"= Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5),\",\n \"config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225])\",\n \"GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4,\",\n \"= get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def\",\n \"p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1,\",\n \"0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1),\",\n \"MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1),\",\n \"scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35,\",\n \"Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0),\",\n \"#Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5),\",\n \"Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z,\",\n \"np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0,\",\n \"RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch\",\n \"0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic\",\n \"value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30,\",\n \"Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config\",\n \"sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0],\",\n \"border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30,\",\n \"as np from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion,\",\n \"STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1],\",\n \"GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import\",\n \"ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config\",\n \"min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return\",\n \"RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from\",\n \"min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(),\",\n \"min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ])\",\n \"albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop,\",\n \"0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30),\",\n \"NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config config =\",\n \"p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid():\",\n \"HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen,\",\n \"RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle,\",\n \"= np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train =\",\n \"interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5),\",\n \"#Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0,\",\n \"(Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue,\",\n \"from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop,\",\n \"max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1],\",\n \"max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])),\",\n \"CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0],\",\n \"= Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid\",\n \"GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout,\",\n \"RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise,\",\n \"0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2),\",\n \"transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(),\",\n \"HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7),\",\n \"ToTensorV2 as ToTensor from get_config import get_config config = get_config() MEAN = np.array([0.485,\",\n \"STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return std*z +\",\n \"STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]),\"\n]","isConversational":false}}},{"rowIdx":1195,"cells":{"ngram":{"kind":"list like","value":["z def contains(self, cub): \"\"\"Tests if another cuboid is contained by this one","start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def","None if there is no intersection. Arguments: cub (Cuboid): The other cuboid. edges","the argument 'edges' is True the cuboid returned will have a volume of","length(self): return self.end.z - self.start.z class Cuboid(object): \"\"\"Basic cuboid primitive class. x, y,","\"\"\"Create an In-Depth segment given its bottom most end point and its length.","= other.width self.height = other.height self.depth = other.depth return True if not self.intersects(other,","cub (Cuboid): The other cuboid. edges (bool): If true, touching edges are considered","point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end): \"\"\"Arguments: start","+ length)) @property def length(self): return self.end.z - self.start.z class Cuboid(object): \"\"\"Basic cuboid","Arguments: other (Cuboid): Cuboid to join Returns: bool: True when successfully joined, False","x coordinate\"\"\" return self.x @property def right(self): \"\"\"Cuboid right edge x coordinate\"\"\" return","for sorting)\"\"\" return self.volume() < other.volume() def __eq__(self, other): \"\"\"Equal cuboids have same","False return (self.width == other.width and self.height == other.height and self.depth == other.depth","have a volume of 0. Returns None if there is no intersection. Arguments:","its length. Arguments: - start (Point): Starting Point - length (number): segment length","or cub.left == self.right and self.bottom == cub.top and cub.outeye == self.ineye or","0. Returns None if there is no intersection. Arguments: cub (Cuboid): The other","+ cub.width <= self.x + self.width and cub.z + cub.depth <= self.z +","the result is also a cuboid and the operation is successful then this","- point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end')","cuboid. edges (bool): If true, touching edges are considered an intersection, and a","(number): segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start,","outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def __init__(self, start, length): \"\"\"Create","or self.left == cub.right or self.right == cub.left or self.outeye == cub.ineye or","or self.outeye == cub.ineye or self.ineye == cub.outeye): return False # Discard corner","or self.right == cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye): return","width (int, float): height (int, float): depth (int, float): rid (identifier object): \"\"\"","There was no intersection. \"\"\" if not self.intersects(cub, edges=edges): return None bottom =","self.bottom, self.ineye) def __lt__(self, other): \"\"\"Compare cuboids by volume (used for sorting)\"\"\" return","bottom edge y coordinate\"\"\" return self.y @property def top(self): \"\"\"Cuboid top edge y","other.width self.height = other.height self.depth = other.depth return True if not self.intersects(other, edges=True):","'y', 'z') def __init__(self, x, y, z): self.x = x self.y = y","of 0 height or width or depth will be returned Returns: Cuboid: Intersection.","self.y = y self.z = z self.rid = rid @property def bottom(self): \"\"\"Cuboid","corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property","def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye)","isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property def length(self):","- y_min return True # Other cuboid is Right/Left from this if self.bottom","this if self.left == other.left and self.width == other.width and \\ self.outeye ==","if not isinstance(other, self.__class__): None return self.start == other.start and self.end == other.end","@property def top(self): \"\"\"Cuboid top edge y coordiante\"\"\" return self.y + self.height @property","return (self.x == other.x and self.y == other.y and self.z == other.z) def","other.width and self.height == other.height and self.depth == other.depth and self.x == other.x","False # Discard corner intersects if (self.left == cub.right and self.bottom == cub.top","cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye): return False # Discard","if not isinstance(other, self.__class__): return False return (self.width == other.width and self.height ==","argument 'edges' is True the cuboid returned will have a volume of 0.","depth will be returned Returns: Cuboid: Intersection. None: There was no intersection. \"\"\"","self.outeye == cub.ineye or self.ineye == cub.outeye): return False # Discard corner intersects","self.z, self.width, self.height, self.depth) def volume(self): \"\"\"Cuboid volume\"\"\" return self.width * self.height *","self).__init__( start, Point(start.x, start.y + length, start.z)) @property def length(self): return self.end.y -","self.width @property def outeye(self): \"\"\"Cuboid farther from eye edge z coordinate\"\"\" return self.z","self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property","{}, {}, {})\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \"\"\"Cuboid volume\"\"\"","cub.right and self.bottom == cub.top and cub.outeye == self.ineye or self.left == cub.right","== cub.top and cub.outeye == self.ineye or cub.left == self.right and cub.bottom ==","and cub.bottom == self.top and cub.outeye == self.ineye or cub.left == self.right and","= z def __eq__(self, other): return (self.x == other.x and self.y == other.y","+ cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False): \"\"\"Detect intersections between","min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth = z_max -","rid (identifier object): \"\"\" assert(height >= 0 and width >= 0 and depth","z coordinate\"\"\" return self.z @property def ineye(self): \"\"\"Cuboid nearer from eye edge z","@property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom,","(Cuboid): Cuboid to join Returns: bool: True when successfully joined, False otherwise \"\"\"","left edge x coordinate\"\"\" return self.x @property def right(self): \"\"\"Cuboid right edge x","or self.right < cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye): return","def __init__(self, x, y, z): self.x = x self.y = y self.z =","== cub.top and self.outeye == cub.ineye or cub.left == self.right and cub.bottom ==","self.height and cub.x + cub.width <= self.x + self.width and cub.z + cub.depth","will be returned Returns: Cuboid: Intersection. None: There was no intersection. \"\"\" if","self.y = y self.z = z def contains(self, cub): \"\"\"Tests if another cuboid","== cub.ineye or self.left == cub.right and cub.bottom == self.top and self.outeye ==","__init__(self, start, length): \"\"\"Create an In-Depth segment given its bottom most end point","y coordinate\"\"\" return self.y @property def top(self): \"\"\"Cuboid top edge y coordiante\"\"\" return","segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x,","(used for sorting)\"\"\" return self.volume() < other.volume() def __eq__(self, other): \"\"\"Equal cuboids have","not edges: if (self.bottom == cub.top or self.top == cub.bottom or self.left ==","start.y, start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\"","inside this one, False otherwise \"\"\" return (cub.y >= self.y and cub.x >=","and depth >= 0) self.width = width self.height = height self.depth = depth","point and its length. Arguments: - start (Point): Starting Point - length (number):","def __eq__(self, other): return (self.x == other.x and self.y == other.y and self.z","-*- from math import sqrt class Point(object): __slots__ = ('x', 'y', 'z') def","the cuboids are only touching by their edges, and the argument 'edges' is","(self.x == other.x and self.y == other.y and self.z == other.z) def __repr__(self):","other.y self.z = other.z self.width = other.width self.height = other.height self.depth = other.depth","edges=False): \"\"\"Returns the cuboid resulting of the intersection of this and cub If","edges (bool): Accept edge touching cuboids as intersects or not Returns: bool: True","== self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min","self.width = x_max - x_min return True # Other cuboid is Right/Left from","and cub. Args: cub (Cuboid): Cuboid to test for intersections. edges (bool): Accept","- \"\"\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self,","\"\"\"Tests if another cuboid is contained by this one Arguments: cub (Cuboid): The","no intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool): If true, touching","@property def length(self): return self.end.z - self.start.z class Cuboid(object): \"\"\"Basic cuboid primitive class.","self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return","self.height = y_max - y_min return True # Other cuboid is Right/Left from","return False # Discard edge intersects if not edges: if (self.bottom == cub.top","- length (number): segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment,","other (Cuboid): Cuboid to join Returns: bool: True when successfully joined, False otherwise","other.height and \\ self.outeye == other.outeye and self.depth == self.depth: x_min = min(self.left,","other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top)","\\ self.left == other.left and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max","\"\"\"Create an Horizontal segment given its left most end point and its length.","float): depth (int, float): rid (identifier object): \"\"\" assert(height >= 0 and width","== other.start and self.end == other.end def __repr__(self): return \"S({}, {})\".format(self.start, self.end) @property","def __repr__(self): return \"S({}, {})\".format(self.start, self.end) @property def length_squared(self): \"\"\"Faster than length and","\"\"\"Horizontal Segment\"\"\" def __init__(self, start, length): \"\"\"Create an Horizontal segment given its left","Arguments: - start (Point): Starting Point - length (number): segment length \"\"\" assert(isinstance(start,","coordinates width - height - depth - \"\"\" __slots__ = ('width', 'height', 'depth',","cub.top and cub.outeye == self.ineye or cub.left == self.right and cub.bottom == self.top","or cub.left == self.right and self.bottom == cub.top and self.outeye == cub.ineye or","def right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def","cub.outeye): return False # Discard edge intersects if not edges: if (self.bottom ==","be returned Returns: Cuboid: Intersection. None: There was no intersection. \"\"\" if not","class Cuboid(object): \"\"\"Basic cuboid primitive class. x, y, z-> Lower right corner coordinates","@property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top,","true, touching edges are considered an intersection, and a cuboid of 0 height","and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property","self.bottom == other.bottom and self.height == other.height and \\ self.left == other.left and","other): return (self.x == other.x and self.y == other.y and self.z == other.z)","True if other.contains(self): self.x = other.x self.y = other.y self.z = other.z self.width","Cuboid: Intersection. None: There was no intersection. \"\"\" if not self.intersects(cub, edges=edges): return","< other.volume() def __eq__(self, other): \"\"\"Equal cuboids have same properties.\"\"\" if not isinstance(other,","class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x, y, z): self.x","math import sqrt class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x,","self.depth = other.depth return True if not self.intersects(other, edges=True): return False # Other","( self.z - point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 + (self.y","then this cuboid is modified to the union. Arguments: other (Cuboid): Cuboid to","def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def","move(self, x, y, z): \"\"\"Move Cuboid to x,y,z coordinates Arguments: x (int, float):","\"S({}, {})\".format(self.start, self.end) @property def length_squared(self): \"\"\"Faster than length and useful for some","and cub.x >= self.x and cub.z >= self.z and cub.y + cub.height <=","and cub.bottom == self.top and self.outeye == cub.ineye or self.left == cub.right and","== other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min","return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return","its bottom most end point and its length. Arguments: - start (Point): Starting","other): \"\"\"Try to join a cuboid to this one. If the result is","x self.y = y self.z = z def __eq__(self, other): return (self.x ==","self.right and cub.bottom == self.top and cub.outeye == self.ineye): return False return True","Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self):","== cub.top and cub.outeye == self.ineye or self.left == cub.right and cub.bottom ==","other.volume() def __eq__(self, other): \"\"\"Equal cuboids have same properties.\"\"\" if not isinstance(other, self.__class__):","- outeye) def join(self, other): \"\"\"Try to join a cuboid to this one.","y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height =","coding: utf-8 -*- from math import sqrt class Point(object): __slots__ = ('x', 'y',","the operation is successful then this cuboid is modified to the union. Arguments:","Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self):","\"\"\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end = end def","def __iter__(self): \"\"\"Iterate through cuboid corners\"\"\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield","and cub.outeye == self.ineye or cub.left == self.right and cub.bottom == self.top and","Point(start.x, start.y + length, start.z)) @property def length(self): return self.end.y - self.start.y class","and the argument 'edges' is True the cuboid returned will have a volume","y_max - y_min return True # Other cuboid is Right/Left from this if","Right/Left from this if self.bottom == other.bottom and self.height == other.height and \\","other.bottom and self.height == other.height and \\ self.outeye == other.outeye and self.depth ==","self).__init__( start, Point(start.x, start.y, start.z + length)) @property def length(self): return self.end.z -","considered an intersection, and a cuboid of 0 height or width or depth","Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z))","self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z)","this cuboid and cub. Args: cub (Cuboid): Cuboid to test for intersections. edges","\"\"\"Iterate through cuboid corners\"\"\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield","-*- coding: utf-8 -*- from math import sqrt class Point(object): __slots__ = ('x',","join Returns: bool: True when successfully joined, False otherwise \"\"\" if self.contains(other): return","- bottom, ineye - outeye) def join(self, other): \"\"\"Try to join a cuboid","def __repr__(self): return \"P({}, {}, {})\".format(self.x, self.y, self.z) def distance(self, point): \"\"\"Calculate distance","self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return","width self.height = height self.depth = depth self.x = x self.y = y","self.depth)) def __iter__(self): \"\"\"Iterate through cuboid corners\"\"\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r","cub.z + cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False): \"\"\"Detect intersections","__init__(self, x, y, z): self.x = x self.y = y self.z = z","== self.top and self.outeye == cub.ineye or self.left == cub.right and self.bottom ==","result is also a cuboid and the operation is successful then this cuboid","cub.left == self.right and self.bottom == cub.top and cub.outeye == self.ineye or cub.left","return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return","self.height * self.depth def move(self, x, y, z): \"\"\"Move Cuboid to x,y,z coordinates","self.z and cub.y + cub.height <= self.y + self.height and cub.x + cub.width","corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property","super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property def length(self): return self.end.x","and self.bottom == cub.top and self.outeye == cub.ineye or cub.left == self.right and","other): if not isinstance(other, self.__class__): None return self.start == other.start and self.end ==","__repr__(self): return \"R({}, {}, {}, {}, {}, {})\".format( self.x, self.y, self.z, self.width, self.height,","def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye)","comparisons\"\"\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y,","cub.ineye) return Cuboid( left, bottom, outeye, right - left, top - bottom, ineye","height self.depth = depth self.x = x self.y = y self.z = z","self.outeye == other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max =","segment given its bottom most end point and its length. Arguments: - start","( self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self, start,","start.z)) @property def length(self): return self.end.y - self.start.y class DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def","== self.right and cub.bottom == self.top and cub.outeye == self.ineye): return False return","Segment\"\"\" def __init__(self, start, length): \"\"\"Create an In-Depth segment given its bottom most","return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def","class HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def __init__(self, start, length): \"\"\"Create an Horizontal segment given","'edges' is True the cuboid returned will have a volume of 0. Returns","this cuboid is modified to the union. Arguments: other (Cuboid): Cuboid to join","are considered an intersection, and a cuboid of 0 height or width or","to the union. Arguments: other (Cuboid): Cuboid to join Returns: bool: True when","depth self.x = x self.y = y self.z = z self.rid = rid","is modified to the union. Arguments: other (Cuboid): Cuboid to join Returns: bool:","an intersection, and a cuboid of 0 height or width or depth will","right edge x coordinate\"\"\" return self.x + self.width @property def outeye(self): \"\"\"Cuboid farther","and cub.y + cub.height <= self.y + self.height and cub.x + cub.width <=","edges are considered an intersection, and a cuboid of 0 height or width","self.outeye == cub.ineye or self.left == cub.right and cub.bottom == self.top and self.outeye","= min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right - left, top -","assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z +","def volume(self): \"\"\"Cuboid volume\"\"\" return self.width * self.height * self.depth def move(self, x,","= max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right) outeye =","'x', 'y', 'z', 'rid') def __init__(self, x, y, z, width, height, depth, rid=None):","intersects if not edges: if (self.bottom == cub.top or self.top == cub.bottom or","== other.width and \\ self.outeye == other.outeye and self.depth == self.depth: y_min =","right corner coordinates width - height - depth - \"\"\" __slots__ = ('width',","(int, float): width (int, float): height (int, float): depth (int, float): rid (identifier","Discard corner intersects if (self.left == cub.right and self.bottom == cub.top and self.outeye","self.width, self.height, self.depth)) def __iter__(self): \"\"\"Iterate through cuboid corners\"\"\" yield self.corner_top_l yield self.corner_top_r","= ('x', 'y', 'z') def __init__(self, x, y, z): self.x = x self.y","union. Arguments: other (Cuboid): Cuboid to join Returns: bool: True when successfully joined,","sorting)\"\"\" return self.volume() < other.volume() def __eq__(self, other): \"\"\"Equal cuboids have same properties.\"\"\"","\"\"\"Arguments: start (Point): Segment start point end (Point): Segment end point \"\"\" assert(isinstance(start,","self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left,","{}, {})\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \"\"\"Cuboid volume\"\"\" return","def ineye(self): \"\"\"Cuboid nearer from eye edge z coordinate\"\"\" return self.z + self.depth","z self.rid = rid @property def bottom(self): \"\"\"Cuboid bottom edge y coordinate\"\"\" return","z def __eq__(self, other): return (self.x == other.x and self.y == other.y and","def __repr__(self): return \"R({}, {}, {}, {}, {}, {})\".format( self.x, self.y, self.z, self.width,","== other.left and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye,","def __eq__(self, other): if not isinstance(other, self.__class__): None return self.start == other.start and","return self.width * self.height * self.depth def move(self, x, y, z): \"\"\"Move Cuboid","other.bottom and self.height == other.height and \\ self.left == other.left and self.width ==","= min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth = z_max","rid @property def bottom(self): \"\"\"Cuboid bottom edge y coordinate\"\"\" return self.y @property def","(int, float): rid (identifier object): \"\"\" assert(height >= 0 and width >= 0","self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self):","== cub.ineye or cub.left == self.right and self.bottom == cub.top and cub.outeye ==","self.outeye == cub.ineye or cub.left == self.right and cub.bottom == self.top and self.outeye","min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x,","or self.top < cub.bottom or self.left > cub.right or self.right < cub.left or","def top(self): \"\"\"Cuboid top edge y coordiante\"\"\" return self.y + self.height @property def","length_squared(self): \"\"\"Faster than length and useful for some comparisons\"\"\" return self.start.distance_squared(self.end) @property def","return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def","return Cuboid( left, bottom, outeye, right - left, top - bottom, ineye -","y, z-> Lower right corner coordinates width - height - depth - \"\"\"","length (number): segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__(","self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def __init__(self, start, length): \"\"\"Create a Vertical segment","if not edges: if (self.bottom == cub.top or self.top == cub.bottom or self.left","top - bottom, ineye - outeye) def join(self, other): \"\"\"Try to join a","intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool): If true, touching edges","x (int, float): X coordinate y (int, float): Y coordinate z (int, float):","a Vertical segment given its bottom most end point and its length. Arguments:","{}, {})\".format(self.x, self.y, self.z) def distance(self, point): \"\"\"Calculate distance to another point\"\"\" return","return False # Other cuboid is Up/Down from this if self.left == other.left","bottom most end point and its length. Arguments: - start (Point): Starting Point","+ self.width @property def outeye(self): \"\"\"Cuboid farther from eye edge z coordinate\"\"\" return","= depth self.x = x self.y = y self.z = z self.rid =","cub.x + cub.width <= self.x + self.width and cub.z + cub.depth <= self.z","y_min self.height = y_max - y_min return True # Other cuboid is Right/Left","self.z = z def __eq__(self, other): return (self.x == other.x and self.y ==","= x_max - x_min return True # Other cuboid is Right/Left from this","bool: True if the cuboids intersect, False otherwise \"\"\" # Not even touching","other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth = z_max - z_min","Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \"\"\"Compare cuboids by volume (used for sorting)\"\"\"","have same properties.\"\"\" if not isinstance(other, self.__class__): return False return (self.width == other.width","Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self):","Other cuboid is Up/Down from this if self.left == other.left and self.width ==","(Cuboid): Cuboid to test for intersections. edges (bool): Accept edge touching cuboids as","None: There was no intersection. \"\"\" if not self.intersects(cub, edges=edges): return None bottom","== other.y and self.z == other.z) def __hash__(self): return hash( (self.x, self.y, self.z,","== other.end def __repr__(self): return \"S({}, {})\".format(self.start, self.end) @property def length_squared(self): \"\"\"Faster than","(int, float): y (int, float): z (int, float): width (int, float): height (int,","other cuboiud Returns: bool: True if it is inside this one, False otherwise","(Point): Starting Point - length (number): segment length \"\"\" assert(isinstance(start, Point) and not","return None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top,","\"\"\" # Not even touching if (self.bottom > cub.top or self.top < cub.bottom","yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield","<= self.x + self.width and cub.z + cub.depth <= self.z + self.depth) def","def length_squared(self): \"\"\"Faster than length and useful for some comparisons\"\"\" return self.start.distance_squared(self.end) @property","< cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye): return False #","Args: x (int, float): y (int, float): z (int, float): width (int, float):","== self.top and self.outeye == cub.ineye or cub.left == self.right and self.bottom ==","and self.end == other.end def __repr__(self): return \"S({}, {})\".format(self.start, self.end) @property def length_squared(self):","other.left and self.width == other.width and \\ self.outeye == other.outeye and self.depth ==","x (int, float): y (int, float): z (int, float): width (int, float): height","x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min self.width =","self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left,","\"\"\"Initiating the Cuboid Args: x (int, float): y (int, float): z (int, float):","right - left, top - bottom, ineye - outeye) def join(self, other): \"\"\"Try","yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \"R({}, {},","\\ self.outeye == other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max","def right(self): \"\"\"Cuboid right edge x coordinate\"\"\" return self.x + self.width @property def","depth >= 0) self.width = width self.height = height self.depth = depth self.x","yield self.corner_bot_l_out def __repr__(self): return \"R({}, {}, {}, {}, {}, {})\".format( self.x, self.y,","def __init__(self, x, y, z, width, height, depth, rid=None): \"\"\"Initiating the Cuboid Args:","y self.z = z def __eq__(self, other): return (self.x == other.x and self.y","+ ( self.z - point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 +","def join(self, other): \"\"\"Try to join a cuboid to this one. If the","and the operation is successful then this cuboid is modified to the union.","other.y and self.z == other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width,","Arguments: cub (Cuboid): The other cuboiud Returns: bool: True if it is inside","and self.bottom == cub.top and self.outeye == cub.ineye or self.left == cub.right and","self.ineye) def __lt__(self, other): \"\"\"Compare cuboids by volume (used for sorting)\"\"\" return self.volume()","intersection of this and cub If the cuboids are only touching by their","cub.outeye == self.ineye or cub.left == self.right and self.bottom == cub.top and self.outeye","cub.right and cub.bottom == self.top and self.outeye == cub.ineye or self.left == cub.right","isinstance(other, self.__class__): return False return (self.width == other.width and self.height == other.height and","return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return","max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye,","\"\"\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x,","True the cuboid returned will have a volume of 0. Returns None if","and self.outeye == cub.ineye or self.left == cub.right and self.bottom == cub.top and","+ (self.y - point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point): return","point \"\"\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end = end","Args: cub (Cuboid): Cuboid to test for intersections. edges (bool): Accept edge touching","other.ineye) self.z = z_min self.depth = z_max - z_min return True return False","self.y == other.y and self.z == other.z) def __repr__(self): return \"P({}, {}, {})\".format(self.x,","is Up/Down from this if self.left == other.left and self.width == other.width and","== other.x and self.y == other.y and self.z == other.z) def __hash__(self): return","cuboid resulting of the intersection of this and cub If the cuboids are","the union. Arguments: other (Cuboid): Cuboid to join Returns: bool: True when successfully","intersection. \"\"\" if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left","top edge y coordiante\"\"\" return self.y + self.height @property def left(self): \"\"\"Cuboid left","@property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom,","def __eq__(self, other): \"\"\"Equal cuboids have same properties.\"\"\" if not isinstance(other, self.__class__): return","point end (Point): Segment end point \"\"\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start","def left(self): \"\"\"Cuboid left edge x coordinate\"\"\" return self.x @property def right(self): \"\"\"Cuboid","+ self.height and cub.x + cub.width <= self.x + self.width and cub.z +","\"\"\"Cuboid left edge x coordinate\"\"\" return self.x @property def right(self): \"\"\"Cuboid right edge","given its left most end point and its length. Arguments: - start (Point):","by this one Arguments: cub (Cuboid): The other cuboiud Returns: bool: True if","<= self.z + self.depth) def intersects(self, cub, edges=False): \"\"\"Detect intersections between this cuboid","and self.outeye == cub.ineye or cub.left == self.right and self.bottom == cub.top and","and \\ self.outeye == other.outeye and self.depth == self.depth: x_min = min(self.left, other.left)","corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property","- self.start.z class Cuboid(object): \"\"\"Basic cuboid primitive class. x, y, z-> Lower right","cub.z >= self.z and cub.y + cub.height <= self.y + self.height and cub.x","x self.y = y self.z = z self.rid = rid @property def bottom(self):","self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \"R({}, {}, {},","other.width and \\ self.outeye == other.outeye and self.depth == self.depth: y_min = min(self.bottom,","\"\"\"Cuboid volume\"\"\" return self.width * self.height * self.depth def move(self, x, y, z):","Cuboid to join Returns: bool: True when successfully joined, False otherwise \"\"\" if","* self.height * self.depth def move(self, x, y, z): \"\"\"Move Cuboid to x,y,z","sqrt class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x, y, z):","intersects(self, cub, edges=False): \"\"\"Detect intersections between this cuboid and cub. Args: cub (Cuboid):","cuboid of 0 height or width or depth will be returned Returns: Cuboid:","('x', 'y', 'z') def __init__(self, x, y, z): self.x = x self.y =","@property def bottom(self): \"\"\"Cuboid bottom edge y coordinate\"\"\" return self.y @property def top(self):","other.x and self.y == other.y and self.z == other.z) def __repr__(self): return \"P({},","depth, rid=None): \"\"\"Initiating the Cuboid Args: x (int, float): y (int, float): z","its left most end point and its length. Arguments: - start (Point): Starting","== other.bottom and self.height == other.height and \\ self.left == other.left and self.width","min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height = y_max -","return False # Discard corner intersects if (self.left == cub.right and self.bottom ==","if self.contains(other): return True if other.contains(self): self.x = other.x self.y = other.y self.z","self.end) @property def length_squared(self): \"\"\"Faster than length and useful for some comparisons\"\"\" return","contains(self, cub): \"\"\"Tests if another cuboid is contained by this one Arguments: cub","Not even touching if (self.bottom > cub.top or self.top < cub.bottom or self.left","= y self.z = z def __eq__(self, other): return (self.x == other.x and","cuboids have same properties.\"\"\" if not isinstance(other, self.__class__): return False return (self.width ==","Segment\"\"\" def __init__(self, start, length): \"\"\"Create a Vertical segment given its bottom most","__init__(self, start, length): \"\"\"Create a Vertical segment given its bottom most end point","self.end.z - self.start.z class Cuboid(object): \"\"\"Basic cuboid primitive class. x, y, z-> Lower","== other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top,","other.y and self.z == other.z) def __repr__(self): return \"P({}, {}, {})\".format(self.x, self.y, self.z)","height - depth - \"\"\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z',","return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def","width >= 0 and depth >= 0) self.width = width self.height = height","cub.ineye or self.ineye == cub.outeye): return False # Discard corner intersects if (self.left","== other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def","cub.outeye == self.ineye or cub.left == self.right and cub.bottom == self.top and cub.outeye","point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point):","cuboiud Returns: bool: True if it is inside this one, False otherwise \"\"\"","is True the cuboid returned will have a volume of 0. Returns None","cuboid and cub. Args: cub (Cuboid): Cuboid to test for intersections. edges (bool):","return self.x + self.width @property def outeye(self): \"\"\"Cuboid farther from eye edge z","import sqrt class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x, y,","DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def __init__(self, start, length): \"\"\"Create an In-Depth segment given its",">= 0) self.width = width self.height = height self.depth = depth self.x =","corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property","Intersection. None: There was no intersection. \"\"\" if not self.intersects(cub, edges=edges): return None","max(self.right, other.right) self.x = x_min self.width = x_max - x_min return True #","self.width * self.height * self.depth def move(self, x, y, z): \"\"\"Move Cuboid to","other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height = y_max - y_min","\"\"\"Calculate distance to another point\"\"\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2","x,y,z coordinates Arguments: x (int, float): X coordinate y (int, float): Y coordinate","super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property def length(self): return self.end.z","= y self.z = z self.rid = rid @property def bottom(self): \"\"\"Cuboid bottom","self.end == other.end def __repr__(self): return \"S({}, {})\".format(self.start, self.end) @property def length_squared(self): \"\"\"Faster","yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \"R({}, {}, {}, {}, {}, {})\".format(","ineye - outeye) def join(self, other): \"\"\"Try to join a cuboid to this","self.height @property def left(self): \"\"\"Cuboid left edge x coordinate\"\"\" return self.x @property def","self.y and cub.x >= self.x and cub.z >= self.z and cub.y + cub.height","self.y = y self.z = z def __eq__(self, other): return (self.x == other.x","and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property","X coordinate y (int, float): Y coordinate z (int, float): Z coordinate \"\"\"","return (self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2","self.z = z def contains(self, cub): \"\"\"Tests if another cuboid is contained by","<= self.y + self.height and cub.x + cub.width <= self.x + self.width and","def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye)","- point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2) def distance_squared(self,","self.bottom == other.bottom and self.height == other.height and \\ self.outeye == other.outeye and","cub.ineye or cub.left == self.right and cub.bottom == self.top and self.outeye == cub.ineye","return self.volume() < other.volume() def __eq__(self, other): \"\"\"Equal cuboids have same properties.\"\"\" if","@property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \"\"\"Compare cuboids by","height (int, float): depth (int, float): rid (identifier object): \"\"\" assert(height >= 0","cub.y + cub.height <= self.y + self.height and cub.x + cub.width <= self.x","for intersections. edges (bool): Accept edge touching cuboids as intersects or not Returns:","(bool): Accept edge touching cuboids as intersects or not Returns: bool: True if","return self.end.z - self.start.z class Cuboid(object): \"\"\"Basic cuboid primitive class. x, y, z->","other.contains(self): self.x = other.x self.y = other.y self.z = other.z self.width = other.width","\"\"\"Basic cuboid primitive class. x, y, z-> Lower right corner coordinates width -","self.height, self.depth)) def __iter__(self): \"\"\"Iterate through cuboid corners\"\"\" yield self.corner_top_l yield self.corner_top_r yield","one, False otherwise \"\"\" return (cub.y >= self.y and cub.x >= self.x and","self.width == other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z =","ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right - left, top","cub.bottom or self.left == cub.right or self.right == cub.left or self.outeye == cub.ineye","x, y, z, width, height, depth, rid=None): \"\"\"Initiating the Cuboid Args: x (int,","(self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \"\"\"Iterate through cuboid corners\"\"\" yield","another cuboid is contained by this one Arguments: cub (Cuboid): The other cuboiud","= ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y, z,","== self.right and self.bottom == cub.top and cub.outeye == self.ineye or cub.left ==","self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self):","other.end def __repr__(self): return \"S({}, {})\".format(self.start, self.end) @property def length_squared(self): \"\"\"Faster than length","True if it is inside this one, False otherwise \"\"\" return (cub.y >=","== self.right and cub.bottom == self.top and self.outeye == cub.ineye or cub.left ==","\"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y +","start, end): \"\"\"Arguments: start (Point): Segment start point end (Point): Segment end point","== self.ineye or cub.left == self.right and cub.bottom == self.top and cub.outeye ==","cub.bottom == self.top and self.outeye == cub.ineye or self.left == cub.right and self.bottom","segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x","self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \"R({},","and cub.bottom == self.top and cub.outeye == self.ineye): return False return True def","intersect, False otherwise \"\"\" # Not even touching if (self.bottom > cub.top or","True # Other cuboid is Right/Left from this if self.bottom == other.bottom and","- point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point): return (self.x -","from this if self.left == other.left and self.width == other.width and \\ self.outeye","other): \"\"\"Equal cuboids have same properties.\"\"\" if not isinstance(other, self.__class__): return False return","self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return","cub.top or self.top == cub.bottom or self.left == cub.right or self.right == cub.left","self.outeye == cub.ineye or self.left == cub.right and self.bottom == cub.top and cub.outeye","('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y, z, width,","coordinate \"\"\" self.x = x self.y = y self.z = z def contains(self,","length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return","other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth","bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def left(self):","a cuboid to this one. If the result is also a cuboid and","= end def __eq__(self, other): if not isinstance(other, self.__class__): None return self.start ==","by volume (used for sorting)\"\"\" return self.volume() < other.volume() def __eq__(self, other): \"\"\"Equal","or depth will be returned Returns: Cuboid: Intersection. None: There was no intersection.","and self.width == other.width and \\ self.outeye == other.outeye and self.depth == self.depth:","cub.top or self.top < cub.bottom or self.left > cub.right or self.right < cub.left","self.start = start self.end = end def __eq__(self, other): if not isinstance(other, self.__class__):","if self.bottom == other.bottom and self.height == other.height and \\ self.outeye == other.outeye","self.x + self.width and cub.z + cub.depth <= self.z + self.depth) def intersects(self,","= min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height = y_max","edge z coordinate\"\"\" return self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top,","and its length. Arguments: - start (Point): Starting Point - length (number): segment","depth - \"\"\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def","self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \"R({}, {}, {}, {}, {}, {})\".format( self.x,","z): \"\"\"Move Cuboid to x,y,z coordinates Arguments: x (int, float): X coordinate y","start (Point): Starting Point - length (number): segment length \"\"\" assert(isinstance(start, Point) and","= x_min self.width = x_max - x_min return True # Other cuboid is","self.start == other.start and self.end == other.end def __repr__(self): return \"S({}, {})\".format(self.start, self.end)","and self.y == other.y and self.z == other.z) def __hash__(self): return hash( (self.x,","== self.ineye or self.left == cub.right and cub.bottom == self.top and cub.outeye ==","@property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom,","float): rid (identifier object): \"\"\" assert(height >= 0 and width >= 0 and","end (Point): Segment end point \"\"\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start =","self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return","self.height = height self.depth = depth self.x = x self.y = y self.z","length)) @property def length(self): return self.end.z - self.start.z class Cuboid(object): \"\"\"Basic cuboid primitive","start.z + length)) @property def length(self): return self.end.z - self.start.z class Cuboid(object): \"\"\"Basic","float): z (int, float): width (int, float): height (int, float): depth (int, float):","The other cuboid. edges (bool): If true, touching edges are considered an intersection,","@property def outeye(self): \"\"\"Cuboid farther from eye edge z coordinate\"\"\" return self.z @property","# Other cuboid is Up/Down from this if self.left == other.left and self.width","if (self.left == cub.right and self.bottom == cub.top and self.outeye == cub.ineye or","isinstance(other, self.__class__): None return self.start == other.start and self.end == other.end def __repr__(self):","== self.top and cub.outeye == self.ineye): return False return True def intersection(self, cub,","start, length): \"\"\"Create an Horizontal segment given its left most end point and","isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property def length(self):","(Point): Segment end point \"\"\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start","@property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def __init__(self, start,","rid=None): \"\"\"Initiating the Cuboid Args: x (int, float): y (int, float): z (int,","width - height - depth - \"\"\" __slots__ = ('width', 'height', 'depth', 'x',","other cuboid. edges (bool): If true, touching edges are considered an intersection, and","False return True def intersection(self, cub, edges=False): \"\"\"Returns the cuboid resulting of the","'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y, z, width, height, depth,","cuboid returned will have a volume of 0. Returns None if there is","left, top - bottom, ineye - outeye) def join(self, other): \"\"\"Try to join","self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out","their edges, and the argument 'edges' is True the cuboid returned will have","cub.bottom == self.top and cub.outeye == self.ineye or cub.left == self.right and self.bottom","True if not self.intersects(other, edges=True): return False # Other cuboid is Up/Down from","'z', 'rid') def __init__(self, x, y, z, width, height, depth, rid=None): \"\"\"Initiating the","{})\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \"\"\"Cuboid volume\"\"\" return self.width","= other.x self.y = other.y self.z = other.z self.width = other.width self.height =","volume(self): \"\"\"Cuboid volume\"\"\" return self.width * self.height * self.depth def move(self, x, y,","y, z): self.x = x self.y = y self.z = z def __eq__(self,","return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y)","+ length, start.y, start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment):","segment given its left most end point and its length. Arguments: - start","self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \"\"\"Iterate through cuboid corners\"\"\" yield self.corner_top_l","self.left == cub.right and cub.bottom == self.top and cub.outeye == self.ineye or cub.left","distance_squared(self, point): return (self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z","Returns None if there is no intersection. Arguments: cub (Cuboid): The other cuboid.","self.left == other.left and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max =","If the result is also a cuboid and the operation is successful then","def __init__(self, start, length): \"\"\"Create a Vertical segment given its bottom most end","self.width = width self.height = height self.depth = depth self.x = x self.y","max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x,","> cub.ineye or self.ineye < cub.outeye): return False # Discard edge intersects if","volume (used for sorting)\"\"\" return self.volume() < other.volume() def __eq__(self, other): \"\"\"Equal cuboids","# Other cuboid is Right/Left from this if self.bottom == other.bottom and self.height","is inside this one, False otherwise \"\"\" return (cub.y >= self.y and cub.x","@property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class","a cuboid of 0 height or width or depth will be returned Returns:","join(self, other): \"\"\"Try to join a cuboid to this one. If the result","return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y,","self.top < cub.bottom or self.left > cub.right or self.right < cub.left or self.outeye","start, length): \"\"\"Create a Vertical segment given its bottom most end point and","length, start.y, start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment): \"\"\"Vertical","\"\"\"Returns the cuboid resulting of the intersection of this and cub If the","cub.bottom == self.top and self.outeye == cub.ineye or cub.left == self.right and self.bottom","self.left == cub.right or self.right == cub.left or self.outeye == cub.ineye or self.ineye","and \\ self.left == other.left and self.width == other.width: z_min = min(self.outeye, other.outeye)","object): \"\"\" assert(height >= 0 and width >= 0 and depth >= 0)","other.top) self.y = y_min self.height = y_max - y_min return True # Other","- point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end): \"\"\"Arguments:","return False return (self.width == other.width and self.height == other.height and self.depth ==","the cuboid resulting of the intersection of this and cub If the cuboids","The other cuboiud Returns: bool: True if it is inside this one, False","ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \"\"\"Horizontal","__eq__(self, other): \"\"\"Equal cuboids have same properties.\"\"\" if not isinstance(other, self.__class__): return False","class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def __init__(self, start, length): \"\"\"Create a Vertical segment given","= other.z self.width = other.width self.height = other.height self.depth = other.depth return True","self.z = z self.rid = rid @property def bottom(self): \"\"\"Cuboid bottom edge y","an In-Depth segment given its bottom most end point and its length. Arguments:","== other.width and self.height == other.height and self.depth == other.depth and self.x ==","self.z) def distance(self, point): \"\"\"Calculate distance to another point\"\"\" return sqrt((self.x - point.x)**2","most end point and its length. Arguments: - start (Point): Starting Point -","and a cuboid of 0 height or width or depth will be returned","self.depth) def volume(self): \"\"\"Cuboid volume\"\"\" return self.width * self.height * self.depth def move(self,",">= self.x and cub.z >= self.z and cub.y + cub.height <= self.y +","False otherwise \"\"\" if self.contains(other): return True if other.contains(self): self.x = other.x self.y","return min(self.start.z, self.end.z) class HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def __init__(self, start, length): \"\"\"Create an","width or depth will be returned Returns: Cuboid: Intersection. None: There was no","return self.start == other.start and self.end == other.end def __repr__(self): return \"S({}, {})\".format(self.start,","cub.outeye): return False # Discard corner intersects if (self.left == cub.right and self.bottom","eye edge z coordinate\"\"\" return self.z @property def ineye(self): \"\"\"Cuboid nearer from eye","== cub.top or self.top == cub.bottom or self.left == cub.right or self.right ==","self.x = other.x self.y = other.y self.z = other.z self.width = other.width self.height","> cub.right or self.right < cub.left or self.outeye > cub.ineye or self.ineye <","@property def right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property","self.width, self.height, self.depth) def volume(self): \"\"\"Cuboid volume\"\"\" return self.width * self.height * self.depth","and cub.x + cub.width <= self.x + self.width and cub.z + cub.depth <=","other.start and self.end == other.end def __repr__(self): return \"S({}, {})\".format(self.start, self.end) @property def","of 0. Returns None if there is no intersection. Arguments: cub (Cuboid): The","@property def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property","length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y,","self.ineye or cub.left == self.right and self.bottom == cub.top and self.outeye == cub.ineye","Returns: Cuboid: Intersection. None: There was no intersection. \"\"\" if not self.intersects(cub, edges=edges):","coordinate\"\"\" return self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property","def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def","top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def right(self):","Z coordinate \"\"\" self.x = x self.y = y self.z = z def","corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property","def __init__(self, start, length): \"\"\"Create an Horizontal segment given its left most end","start, Point(start.x + length, start.y, start.z)) @property def length(self): return self.end.x - self.start.x","== cub.outeye): return False # Discard corner intersects if (self.left == cub.right and","self.top == cub.bottom or self.left == cub.right or self.right == cub.left or self.outeye","other.height self.depth = other.depth return True if not self.intersects(other, edges=True): return False #","point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 + (self.y - point.y)**2 +","other.left) x_max = max(self.right, other.right) self.x = x_min self.width = x_max - x_min","touching edges are considered an intersection, and a cuboid of 0 height or","as intersects or not Returns: bool: True if the cuboids intersect, False otherwise","= min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid(","and cub If the cuboids are only touching by their edges, and the","self.y = other.y self.z = other.z self.width = other.width self.height = other.height self.depth","from this if self.bottom == other.bottom and self.height == other.height and \\ self.left","x, y, z-> Lower right corner coordinates width - height - depth -","cuboid primitive class. x, y, z-> Lower right corner coordinates width - height","= y self.z = z def contains(self, cub): \"\"\"Tests if another cuboid is","== self.right and self.bottom == cub.top and self.outeye == cub.ineye or cub.left ==","\"\"\"Detect intersections between this cuboid and cub. Args: cub (Cuboid): Cuboid to test","cuboids intersect, False otherwise \"\"\" # Not even touching if (self.bottom > cub.top","yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield","{}, {}, {}, {}, {})\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self):","self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out","cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right)","= start self.end = end def __eq__(self, other): if not isinstance(other, self.__class__): None","bool: True when successfully joined, False otherwise \"\"\" if self.contains(other): return True if","def contains(self, cub): \"\"\"Tests if another cuboid is contained by this one Arguments:","__iter__(self): \"\"\"Iterate through cuboid corners\"\"\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l","or cub.left == self.right and cub.bottom == self.top and cub.outeye == self.ineye): return","return True if other.contains(self): self.x = other.x self.y = other.y self.z = other.z","self.z + self.depth) def intersects(self, cub, edges=False): \"\"\"Detect intersections between this cuboid and","def __init__(self, start, end): \"\"\"Arguments: start (Point): Segment start point end (Point): Segment","self.depth = depth self.x = x self.y = y self.z = z self.rid","if there is no intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool):","class Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end): \"\"\"Arguments: start (Point):","__eq__(self, other): return (self.x == other.x and self.y == other.y and self.z ==","return self.end.y - self.start.y class DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def __init__(self, start, length): \"\"\"Create","class DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def __init__(self, start, length): \"\"\"Create an In-Depth segment given","self.depth == self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x =","def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \"\"\"Compare cuboids by volume","min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right - left, top - bottom,","edges, and the argument 'edges' is True the cuboid returned will have a","width, height, depth, rid=None): \"\"\"Initiating the Cuboid Args: x (int, float): y (int,","self.height == other.height and \\ self.outeye == other.outeye and self.depth == self.depth: x_min","yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \"R({}, {}, {}, {},","a volume of 0. Returns None if there is no intersection. Arguments: cub","cub.right and self.bottom == cub.top and self.outeye == cub.ineye or self.left == cub.right","== cub.top and self.outeye == cub.ineye or self.left == cub.right and cub.bottom ==","and self.y == other.y and self.z == other.z) def __repr__(self): return \"P({}, {},","this one. If the result is also a cuboid and the operation is","\"\"\" if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left =","intersects if (self.left == cub.right and self.bottom == cub.top and self.outeye == cub.ineye","return (self.width == other.width and self.height == other.height and self.depth == other.depth and","the intersection of this and cub If the cuboids are only touching by","when successfully joined, False otherwise \"\"\" if self.contains(other): return True if other.contains(self): self.x","cuboid and the operation is successful then this cuboid is modified to the","+ cub.height <= self.y + self.height and cub.x + cub.width <= self.x +","cub.left == self.right and cub.bottom == self.top and cub.outeye == self.ineye): return False","right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return","x coordinate\"\"\" return self.x + self.width @property def outeye(self): \"\"\"Cuboid farther from eye","properties.\"\"\" if not isinstance(other, self.__class__): return False return (self.width == other.width and self.height","== self.ineye): return False return True def intersection(self, cub, edges=False): \"\"\"Returns the cuboid","return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \"\"\"Iterate through cuboid","# Discard corner intersects if (self.left == cub.right and self.bottom == cub.top and","y self.z = z self.rid = rid @property def bottom(self): \"\"\"Cuboid bottom edge","self.y, self.z) def distance(self, point): \"\"\"Calculate distance to another point\"\"\" return sqrt((self.x -","False otherwise \"\"\" # Not even touching if (self.bottom > cub.top or self.top","nearer from eye edge z coordinate\"\"\" return self.z + self.depth @property def corner_top_l(self):","and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z","self.contains(other): return True if other.contains(self): self.x = other.x self.y = other.y self.z =","\"\"\" self.x = x self.y = y self.z = z def contains(self, cub):","Point(start.x, start.y, start.z + length)) @property def length(self): return self.end.z - self.start.z class","cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye)","self.width == other.width and \\ self.outeye == other.outeye and self.depth == self.depth: y_min","and self.outeye == cub.ineye or cub.left == self.right and cub.bottom == self.top and","self.y = y_min self.height = y_max - y_min return True # Other cuboid","cub.ineye or self.ineye < cub.outeye): return False # Discard edge intersects if not","def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment):","def length(self): return self.end.z - self.start.z class Cuboid(object): \"\"\"Basic cuboid primitive class. x,","\"\"\"Move Cuboid to x,y,z coordinates Arguments: x (int, float): X coordinate y (int,","x_max - x_min return True # Other cuboid is Right/Left from this if","and width >= 0 and depth >= 0) self.width = width self.height =","y (int, float): Y coordinate z (int, float): Z coordinate \"\"\" self.x =","self.width and cub.z + cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False):","Cuboid Args: x (int, float): y (int, float): z (int, float): width (int,","def __init__(self, start, length): \"\"\"Create an In-Depth segment given its bottom most end","# Discard edge intersects if not edges: if (self.bottom == cub.top or self.top","def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \"\"\"Iterate","\\ self.outeye == other.outeye and self.depth == self.depth: x_min = min(self.left, other.left) x_max","self.end.z) class HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def __init__(self, start, length): \"\"\"Create an Horizontal segment","max(self.ineye, other.ineye) self.z = z_min self.depth = z_max - z_min return True return","coordinate\"\"\" return self.z @property def ineye(self): \"\"\"Cuboid nearer from eye edge z coordinate\"\"\"","__hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \"\"\"Iterate through","left most end point and its length. Arguments: - start (Point): Starting Point","\"P({}, {}, {})\".format(self.x, self.y, self.z) def distance(self, point): \"\"\"Calculate distance to another point\"\"\"","and cub.outeye == self.ineye or self.left == cub.right and cub.bottom == self.top and","if other.contains(self): self.x = other.x self.y = other.y self.z = other.z self.width =","return \"S({}, {})\".format(self.start, self.end) @property def length_squared(self): \"\"\"Faster than length and useful for","+ self.height @property def left(self): \"\"\"Cuboid left edge x coordinate\"\"\" return self.x @property","def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye)","@property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top,","__slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y,","there is no intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool): If","was no intersection. \"\"\" if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom,","self.y @property def top(self): \"\"\"Cuboid top edge y coordiante\"\"\" return self.y + self.height","def intersects(self, cub, edges=False): \"\"\"Detect intersections between this cuboid and cub. Args: cub","\"\"\"Cuboid bottom edge y coordinate\"\"\" return self.y @property def top(self): \"\"\"Cuboid top edge","y self.z = z def contains(self, cub): \"\"\"Tests if another cuboid is contained","Up/Down from this if self.left == other.left and self.width == other.width and \\","even touching if (self.bottom > cub.top or self.top < cub.bottom or self.left >","edge y coordiante\"\"\" return self.y + self.height @property def left(self): \"\"\"Cuboid left edge","self.x + self.width @property def outeye(self): \"\"\"Cuboid farther from eye edge z coordinate\"\"\"","or self.top == cub.bottom or self.left == cub.right or self.right == cub.left or","touching cuboids as intersects or not Returns: bool: True if the cuboids intersect,","to another point\"\"\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + (","cuboid to this one. If the result is also a cuboid and the","y (int, float): z (int, float): width (int, float): height (int, float): depth","- start (Point): Starting Point - length (number): segment length \"\"\" assert(isinstance(start, Point)","self.volume() < other.volume() def __eq__(self, other): \"\"\"Equal cuboids have same properties.\"\"\" if not","self.top and cub.outeye == self.ineye or cub.left == self.right and self.bottom == cub.top","# Not even touching if (self.bottom > cub.top or self.top < cub.bottom or","end point \"\"\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end =","assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end = end def __eq__(self,","to join a cuboid to this one. If the result is also a","left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self):","+ self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return","self.ineye or cub.left == self.right and cub.bottom == self.top and cub.outeye == self.ineye):","sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2) def","assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y,","\"\"\"Cuboid nearer from eye edge z coordinate\"\"\" return self.z + self.depth @property def","distance to another point\"\"\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 +","self.x @property def right(self): \"\"\"Cuboid right edge x coordinate\"\"\" return self.x + self.width","cub.height <= self.y + self.height and cub.x + cub.width <= self.x + self.width","{}, {}, {}, {})\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \"\"\"Cuboid","'z') def __init__(self, x, y, z): self.x = x self.y = y self.z","self.outeye > cub.ineye or self.ineye < cub.outeye): return False # Discard edge intersects","returned will have a volume of 0. Returns None if there is no","cub): \"\"\"Tests if another cuboid is contained by this one Arguments: cub (Cuboid):","intersections. edges (bool): Accept edge touching cuboids as intersects or not Returns: bool:","== cub.right or self.right == cub.left or self.outeye == cub.ineye or self.ineye ==","self.ineye or self.left == cub.right and cub.bottom == self.top and cub.outeye == self.ineye","will have a volume of 0. Returns None if there is no intersection.","length(self): return self.end.y - self.start.y class DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def __init__(self, start, length):","returned Returns: Cuboid: Intersection. None: There was no intersection. \"\"\" if not self.intersects(cub,","left, bottom, outeye, right - left, top - bottom, ineye - outeye) def","== cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye): return False #","+ ( self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self,","return (cub.y >= self.y and cub.x >= self.x and cub.z >= self.z and","return self.z @property def ineye(self): \"\"\"Cuboid nearer from eye edge z coordinate\"\"\" return","None return self.start == other.start and self.end == other.end def __repr__(self): return \"S({},","self.__class__): return False return (self.width == other.width and self.height == other.height and self.depth","otherwise \"\"\" return (cub.y >= self.y and cub.x >= self.x and cub.z >=","other.depth and self.x == other.x and self.y == other.y and self.z == other.z)","return \"R({}, {}, {}, {}, {}, {})\".format( self.x, self.y, self.z, self.width, self.height, self.depth)","self.height == other.height and self.depth == other.depth and self.x == other.x and self.y","max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right,","__init__(self, start, length): \"\"\"Create an Horizontal segment given its left most end point","self.bottom == cub.top and self.outeye == cub.ineye or self.left == cub.right and cub.bottom","point\"\"\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z -","<gh_stars>10-100 # -*- coding: utf-8 -*- from math import sqrt class Point(object): __slots__","- point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2 class Segment(object):","= y_max - y_min return True # Other cuboid is Right/Left from this","outeye(self): \"\"\"Cuboid farther from eye edge z coordinate\"\"\" return self.z @property def ineye(self):","touching by their edges, and the argument 'edges' is True the cuboid returned","self.z - point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 + (self.y -","coordinate\"\"\" return self.y @property def top(self): \"\"\"Cuboid top edge y coordiante\"\"\" return self.y","cub, edges=False): \"\"\"Returns the cuboid resulting of the intersection of this and cub","corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \"\"\"Compare cuboids by volume (used","self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def __init__(self,","False # Other cuboid is Up/Down from this if self.left == other.left and","return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def","successfully joined, False otherwise \"\"\" if self.contains(other): return True if other.contains(self): self.x =","== other.depth and self.x == other.x and self.y == other.y and self.z ==","return self.y + self.height @property def left(self): \"\"\"Cuboid left edge x coordinate\"\"\" return","some comparisons\"\"\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self): return","self.bottom == cub.top and cub.outeye == self.ineye or self.left == cub.right and cub.bottom","max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def","cub.bottom == self.top and cub.outeye == self.ineye): return False return True def intersection(self,","self.y + self.height @property def left(self): \"\"\"Cuboid left edge x coordinate\"\"\" return self.x","self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top","\"\"\" assert(height >= 0 and width >= 0 and depth >= 0) self.width","bottom, ineye - outeye) def join(self, other): \"\"\"Try to join a cuboid to","\"\"\"Faster than length and useful for some comparisons\"\"\" return self.start.distance_squared(self.end) @property def length(self):","between this cuboid and cub. Args: cub (Cuboid): Cuboid to test for intersections.","cub.width <= self.x + self.width and cub.z + cub.depth <= self.z + self.depth)","return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \"\"\"Compare cuboids by volume (used for","== other.bottom and self.height == other.height and \\ self.outeye == other.outeye and self.depth","from math import sqrt class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self,","self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right,","# -*- coding: utf-8 -*- from math import sqrt class Point(object): __slots__ =","float): Z coordinate \"\"\" self.x = x self.y = y self.z = z","self.end.y - self.start.y class DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def __init__(self, start, length): \"\"\"Create an",">= self.y and cub.x >= self.x and cub.z >= self.z and cub.y +","edges=False): \"\"\"Detect intersections between this cuboid and cub. Args: cub (Cuboid): Cuboid to","other.right) self.x = x_min self.width = x_max - x_min return True # Other","length): \"\"\"Create a Vertical segment given its bottom most end point and its","(self.left == cub.right and self.bottom == cub.top and self.outeye == cub.ineye or self.left","not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property def","(int, float): Y coordinate z (int, float): Z coordinate \"\"\" self.x = x","Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end): \"\"\"Arguments: start (Point): Segment","isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property def length(self):","start, Point(start.x, start.y + length, start.z)) @property def length(self): return self.end.y - self.start.y","if self.bottom == other.bottom and self.height == other.height and \\ self.left == other.left","not self.intersects(other, edges=True): return False # Other cuboid is Up/Down from this if","False # Discard edge intersects if not edges: if (self.bottom == cub.top or","return False return True def intersection(self, cub, edges=False): \"\"\"Returns the cuboid resulting of","\"\"\"Vertical Segment\"\"\" def __init__(self, start, length): \"\"\"Create a Vertical segment given its bottom","other.outeye and self.depth == self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right)","> cub.top or self.top < cub.bottom or self.left > cub.right or self.right <","== self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min","\"\"\"Compare cuboids by volume (used for sorting)\"\"\" return self.volume() < other.volume() def __eq__(self,","are only touching by their edges, and the argument 'edges' is True the","min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye,",">= self.z and cub.y + cub.height <= self.y + self.height and cub.x +","def outeye(self): \"\"\"Cuboid farther from eye edge z coordinate\"\"\" return self.z @property def","Returns: bool: True if the cuboids intersect, False otherwise \"\"\" # Not even","Point) and isinstance(end, Point)) self.start = start self.end = end def __eq__(self, other):","to x,y,z coordinates Arguments: x (int, float): X coordinate y (int, float): Y","__repr__(self): return \"P({}, {}, {})\".format(self.x, self.y, self.z) def distance(self, point): \"\"\"Calculate distance to","and self.depth == other.depth and self.x == other.x and self.y == other.y and","height or width or depth will be returned Returns: Cuboid: Intersection. None: There","return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \"\"\"Horizontal Segment\"\"\"","self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \"R({}, {}, {}, {}, {},","farther from eye edge z coordinate\"\"\" return self.z @property def ineye(self): \"\"\"Cuboid nearer","self.y + self.height and cub.x + cub.width <= self.x + self.width and cub.z","cub.ineye or cub.left == self.right and self.bottom == cub.top and cub.outeye == self.ineye","self.x == other.x and self.y == other.y and self.z == other.z) def __hash__(self):","volume\"\"\" return self.width * self.height * self.depth def move(self, x, y, z): \"\"\"Move","min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left,","corners\"\"\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out","distance(self, point): \"\"\"Calculate distance to another point\"\"\" return sqrt((self.x - point.x)**2 + (self.y","return self.x @property def right(self): \"\"\"Cuboid right edge x coordinate\"\"\" return self.x +","start.y + length, start.z)) @property def length(self): return self.end.y - self.start.y class DSegment(Segment):","cub.top and self.outeye == cub.ineye or cub.left == self.right and cub.bottom == self.top","= max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top) right =","no intersection. \"\"\" if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom)","assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length,","return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def","self.x = x self.y = y self.z = z def contains(self, cub): \"\"\"Tests","\"\"\"In-Depth Segment\"\"\" def __init__(self, start, length): \"\"\"Create an In-Depth segment given its bottom","other.x self.y = other.y self.z = other.z self.width = other.width self.height = other.height","(identifier object): \"\"\" assert(height >= 0 and width >= 0 and depth >=","coordinate\"\"\" return self.x + self.width @property def outeye(self): \"\"\"Cuboid farther from eye edge","__eq__(self, other): if not isinstance(other, self.__class__): None return self.start == other.start and self.end","cub.ineye or self.left == cub.right and cub.bottom == self.top and self.outeye == cub.ineye","join a cuboid to this one. If the result is also a cuboid","self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x)","self.bottom == cub.top and self.outeye == cub.ineye or cub.left == self.right and cub.bottom","= other.y self.z = other.z self.width = other.width self.height = other.height self.depth =","== cub.ineye or self.ineye == cub.outeye): return False # Discard corner intersects if","\"\"\"Cuboid top edge y coordiante\"\"\" return self.y + self.height @property def left(self): \"\"\"Cuboid","\"R({}, {}, {}, {}, {}, {})\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def","self.x = x self.y = y self.z = z self.rid = rid @property","touching if (self.bottom > cub.top or self.top < cub.bottom or self.left > cub.right","for some comparisons\"\"\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self):","def length(self): return self.end.x - self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def __init__(self, start,","self.ineye < cub.outeye): return False # Discard edge intersects if not edges: if","@property def length(self): return self.end.x - self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def __init__(self,","= ('start', 'end') def __init__(self, start, end): \"\"\"Arguments: start (Point): Segment start point","= other.depth return True if not self.intersects(other, edges=True): return False # Other cuboid","length, start.z)) @property def length(self): return self.end.y - self.start.y class DSegment(Segment): \"\"\"In-Depth Segment\"\"\"","Cuboid( left, bottom, outeye, right - left, top - bottom, ineye - outeye)","= z def contains(self, cub): \"\"\"Tests if another cuboid is contained by this","self.right == cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye): return False","primitive class. x, y, z-> Lower right corner coordinates width - height -","of the intersection of this and cub If the cuboids are only touching","or self.left > cub.right or self.right < cub.left or self.outeye > cub.ineye or","{})\".format(self.start, self.end) @property def length_squared(self): \"\"\"Faster than length and useful for some comparisons\"\"\"","self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \"\"\"Compare cuboids","(int, float): X coordinate y (int, float): Y coordinate z (int, float): Z","'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y, z, width, height,","Other cuboid is Right/Left from this if self.bottom == other.bottom and self.height ==","\"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length,","outeye) def join(self, other): \"\"\"Try to join a cuboid to this one. If","cub.x >= self.x and cub.z >= self.z and cub.y + cub.height <= self.y","and self.height == other.height and \\ self.outeye == other.outeye and self.depth == self.depth:","\"\"\"Cuboid right edge x coordinate\"\"\" return self.x + self.width @property def outeye(self): \"\"\"Cuboid","edge intersects if not edges: if (self.bottom == cub.top or self.top == cub.bottom","self.width = other.width self.height = other.height self.depth = other.depth return True if not","self.bottom == cub.top and cub.outeye == self.ineye or cub.left == self.right and cub.bottom","length(self): return self.end.x - self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def __init__(self, start, length):","float): height (int, float): depth (int, float): rid (identifier object): \"\"\" assert(height >=","Accept edge touching cuboids as intersects or not Returns: bool: True if the","== cub.right and cub.bottom == self.top and self.outeye == cub.ineye or self.left ==","Cuboid to x,y,z coordinates Arguments: x (int, float): X coordinate y (int, float):","return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self,","self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y =","__init__(self, x, y, z, width, height, depth, rid=None): \"\"\"Initiating the Cuboid Args: x","- x_min return True # Other cuboid is Right/Left from this if self.bottom","self.z @property def ineye(self): \"\"\"Cuboid nearer from eye edge z coordinate\"\"\" return self.z","< cub.outeye): return False # Discard edge intersects if not edges: if (self.bottom","given its bottom most end point and its length. Arguments: - start (Point):","and self.bottom == cub.top and cub.outeye == self.ineye or self.left == cub.right and","start, Point(start.x, start.y, start.z + length)) @property def length(self): return self.end.z - self.start.z","cub.outeye == self.ineye or self.left == cub.right and cub.bottom == self.top and cub.outeye","= other.height self.depth = other.depth return True if not self.intersects(other, edges=True): return False","max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z,","self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \"\"\"Compare","intersections between this cuboid and cub. Args: cub (Cuboid): Cuboid to test for","start point end (Point): Segment end point \"\"\" assert(isinstance(start, Point) and isinstance(end, Point))","return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def","def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye)","the cuboids intersect, False otherwise \"\"\" # Not even touching if (self.bottom >","@property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom,","== self.top and cub.outeye == self.ineye or cub.left == self.right and self.bottom ==","z coordinate\"\"\" return self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye)","z-> Lower right corner coordinates width - height - depth - \"\"\" __slots__","'end') def __init__(self, start, end): \"\"\"Arguments: start (Point): Segment start point end (Point):","not isinstance(other, self.__class__): return False return (self.width == other.width and self.height == other.height","== cub.right and self.bottom == cub.top and self.outeye == cub.ineye or self.left ==","resulting of the intersection of this and cub If the cuboids are only","\"\"\" return (cub.y >= self.y and cub.x >= self.x and cub.z >= self.z","(self.y - point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point): return (self.x","self.right and cub.bottom == self.top and self.outeye == cub.ineye or cub.left == self.right","y_min return True # Other cuboid is Right/Left from this if self.bottom ==","Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x, y, z): self.x =","corner coordinates width - height - depth - \"\"\" __slots__ = ('width', 'height',","y, z): \"\"\"Move Cuboid to x,y,z coordinates Arguments: x (int, float): X coordinate","successful then this cuboid is modified to the union. Arguments: other (Cuboid): Cuboid","cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye)","return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def left(self): return","hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \"\"\"Iterate through cuboid corners\"\"\"","min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min self.width = x_max -","edge touching cuboids as intersects or not Returns: bool: True if the cuboids","@property def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def","and isinstance(end, Point)) self.start = start self.end = end def __eq__(self, other): if","cub.ineye or self.left == cub.right and self.bottom == cub.top and cub.outeye == self.ineye","@property def length(self): return self.end.y - self.start.y class DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def __init__(self,","and self.bottom == cub.top and cub.outeye == self.ineye or cub.left == self.right and","= height self.depth = depth self.x = x self.y = y self.z =","than length and useful for some comparisons\"\"\" return self.start.distance_squared(self.end) @property def length(self): return","if (self.bottom == cub.top or self.top == cub.bottom or self.left == cub.right or","cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye): return False # Discard","def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye)","is Right/Left from this if self.bottom == other.bottom and self.height == other.height and","self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \"\"\"Cuboid volume\"\"\" return self.width *","== cub.bottom or self.left == cub.right or self.right == cub.left or self.outeye ==","bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top) right","outeye, right - left, top - bottom, ineye - outeye) def join(self, other):","HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def __init__(self, start, length): \"\"\"Create an Horizontal segment given its","from eye edge z coordinate\"\"\" return self.z + self.depth @property def corner_top_l(self): return","\"\"\"Equal cuboids have same properties.\"\"\" if not isinstance(other, self.__class__): return False return (self.width","float): y (int, float): z (int, float): width (int, float): height (int, float):","* self.depth def move(self, x, y, z): \"\"\"Move Cuboid to x,y,z coordinates Arguments:","end point and its length. Arguments: - start (Point): Starting Point - length","Y coordinate z (int, float): Z coordinate \"\"\" self.x = x self.y =","self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left,","left(self): \"\"\"Cuboid left edge x coordinate\"\"\" return self.x @property def right(self): \"\"\"Cuboid right","z): self.x = x self.y = y self.z = z def __eq__(self, other):","length (number): segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__(","def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye)","cuboid is contained by this one Arguments: cub (Cuboid): The other cuboiud Returns:","'rid') def __init__(self, x, y, z, width, height, depth, rid=None): \"\"\"Initiating the Cuboid","VSegment(Segment): \"\"\"Vertical Segment\"\"\" def __init__(self, start, length): \"\"\"Create a Vertical segment given its","top(self): \"\"\"Cuboid top edge y coordiante\"\"\" return self.y + self.height @property def left(self):","other): \"\"\"Compare cuboids by volume (used for sorting)\"\"\" return self.volume() < other.volume() def","x self.y = y self.z = z def contains(self, cub): \"\"\"Tests if another","if another cuboid is contained by this one Arguments: cub (Cuboid): The other","__init__(self, start, end): \"\"\"Arguments: start (Point): Segment start point end (Point): Segment end","@property def left(self): \"\"\"Cuboid left edge x coordinate\"\"\" return self.x @property def right(self):","self.right < cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye): return False","@property def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property","through cuboid corners\"\"\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out","same properties.\"\"\" if not isinstance(other, self.__class__): return False return (self.width == other.width and","\"\"\"Create a Vertical segment given its bottom most end point and its length.","and useful for some comparisons\"\"\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property","\"\"\" if self.contains(other): return True if other.contains(self): self.x = other.x self.y = other.y","cub.right or self.right < cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye):","self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z)","Cuboid(object): \"\"\"Basic cuboid primitive class. x, y, z-> Lower right corner coordinates width","or self.ineye == cub.outeye): return False # Discard corner intersects if (self.left ==","coordinates Arguments: x (int, float): X coordinate y (int, float): Y coordinate z","length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x +","or width or depth will be returned Returns: Cuboid: Intersection. None: There was","(self.bottom > cub.top or self.top < cub.bottom or self.left > cub.right or self.right","cuboid corners\"\"\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield","start.y, start.z + length)) @property def length(self): return self.end.z - self.start.z class Cuboid(object):","self.x = x self.y = y self.z = z def __eq__(self, other): return","float): Y coordinate z (int, float): Z coordinate \"\"\" self.x = x self.y","of this and cub If the cuboids are only touching by their edges,","__lt__(self, other): \"\"\"Compare cuboids by volume (used for sorting)\"\"\" return self.volume() < other.volume()","intersects or not Returns: bool: True if the cuboids intersect, False otherwise \"\"\"","cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right - left,","@property def length_squared(self): \"\"\"Faster than length and useful for some comparisons\"\"\" return self.start.distance_squared(self.end)","def length(self): return self.end.y - self.start.y class DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def __init__(self, start,","+ self.width and cub.z + cub.depth <= self.z + self.depth) def intersects(self, cub,","coordinate y (int, float): Y coordinate z (int, float): Z coordinate \"\"\" self.x","this one, False otherwise \"\"\" return (cub.y >= self.y and cub.x >= self.x","other.height and \\ self.left == other.left and self.width == other.width: z_min = min(self.outeye,","{})\".format(self.x, self.y, self.z) def distance(self, point): \"\"\"Calculate distance to another point\"\"\" return sqrt((self.x","= x self.y = y self.z = z def contains(self, cub): \"\"\"Tests if","self.z, self.width, self.height, self.depth)) def __iter__(self): \"\"\"Iterate through cuboid corners\"\"\" yield self.corner_top_l yield","if not self.intersects(other, edges=True): return False # Other cuboid is Up/Down from this","= width self.height = height self.depth = depth self.x = x self.y =","to this one. If the result is also a cuboid and the operation","joined, False otherwise \"\"\" if self.contains(other): return True if other.contains(self): self.x = other.x","y, z, width, height, depth, rid=None): \"\"\"Initiating the Cuboid Args: x (int, float):","self.depth def move(self, x, y, z): \"\"\"Move Cuboid to x,y,z coordinates Arguments: x","- depth - \"\"\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid')","isinstance(end, Point)) self.start = start self.end = end def __eq__(self, other): if not","self.z == other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth))","\"\"\"Try to join a cuboid to this one. If the result is also","cuboid is Right/Left from this if self.bottom == other.bottom and self.height == other.height","If true, touching edges are considered an intersection, and a cuboid of 0","or cub.left == self.right and cub.bottom == self.top and self.outeye == cub.ineye or","coordinate\"\"\" return self.x @property def right(self): \"\"\"Cuboid right edge x coordinate\"\"\" return self.x","point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end') def","return \"P({}, {}, {})\".format(self.x, self.y, self.z) def distance(self, point): \"\"\"Calculate distance to another","(int, float): Z coordinate \"\"\" self.x = x self.y = y self.z =","Vertical segment given its bottom most end point and its length. Arguments: -","operation is successful then this cuboid is modified to the union. Arguments: other","length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y","z (int, float): Z coordinate \"\"\" self.x = x self.y = y self.z","edge y coordinate\"\"\" return self.y @property def top(self): \"\"\"Cuboid top edge y coordiante\"\"\"","and self.height == other.height and self.depth == other.depth and self.x == other.x and","other.depth return True if not self.intersects(other, edges=True): return False # Other cuboid is","cub.left == self.right and cub.bottom == self.top and self.outeye == cub.ineye or cub.left","self.left == cub.right and self.bottom == cub.top and cub.outeye == self.ineye or self.left","0 height or width or depth will be returned Returns: Cuboid: Intersection. None:","if the cuboids intersect, False otherwise \"\"\" # Not even touching if (self.bottom","cub If the cuboids are only touching by their edges, and the argument","return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2)","if it is inside this one, False otherwise \"\"\" return (cub.y >= self.y","other.z) def __repr__(self): return \"P({}, {}, {})\".format(self.x, self.y, self.z) def distance(self, point): \"\"\"Calculate","yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def","self).__init__( start, Point(start.x + length, start.y, start.z)) @property def length(self): return self.end.x -","self.__class__): None return self.start == other.start and self.end == other.end def __repr__(self): return","def bottom(self): \"\"\"Cuboid bottom edge y coordinate\"\"\" return self.y @property def top(self): \"\"\"Cuboid","def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def __init__(self, start, length):","def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def","utf-8 -*- from math import sqrt class Point(object): __slots__ = ('x', 'y', 'z')","Returns: bool: True when successfully joined, False otherwise \"\"\" if self.contains(other): return True","or self.ineye < cub.outeye): return False # Discard edge intersects if not edges:","other.x and self.y == other.y and self.z == other.z) def __hash__(self): return hash(","or self.outeye > cub.ineye or self.ineye < cub.outeye): return False # Discard edge","max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right -","edges=edges): return None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top =","(int, float): z (int, float): width (int, float): height (int, float): depth (int,","and self.z == other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height,","edges=True): return False # Other cuboid is Up/Down from this if self.left ==","self.height, self.depth) def volume(self): \"\"\"Cuboid volume\"\"\" return self.width * self.height * self.depth def","and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y","Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length))","coordinate z (int, float): Z coordinate \"\"\" self.x = x self.y = y","and cub.outeye == self.ineye): return False return True def intersection(self, cub, edges=False): \"\"\"Returns","this and cub If the cuboids are only touching by their edges, and","corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def","self.start.y class DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def __init__(self, start, length): \"\"\"Create an In-Depth segment","and self.height == other.height and \\ self.left == other.left and self.width == other.width:","+ length, start.z)) @property def length(self): return self.end.y - self.start.y class DSegment(Segment): \"\"\"In-Depth","== other.height and \\ self.outeye == other.outeye and self.depth == self.depth: x_min =","self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min self.width","- length (number): segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment,","(bool): If true, touching edges are considered an intersection, and a cuboid of","contained by this one Arguments: cub (Cuboid): The other cuboiud Returns: bool: True","cuboid is modified to the union. Arguments: other (Cuboid): Cuboid to join Returns:","@property def ineye(self): \"\"\"Cuboid nearer from eye edge z coordinate\"\"\" return self.z +","or self.left == cub.right and cub.bottom == self.top and self.outeye == cub.ineye or","cuboids by volume (used for sorting)\"\"\" return self.volume() < other.volume() def __eq__(self, other):","def distance(self, point): \"\"\"Calculate distance to another point\"\"\" return sqrt((self.x - point.x)**2 +","self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x)","Starting Point - length (number): segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length,","(int, float): depth (int, float): rid (identifier object): \"\"\" assert(height >= 0 and","cuboid is Up/Down from this if self.left == other.left and self.width == other.width","not isinstance(other, self.__class__): None return self.start == other.start and self.end == other.end def","cub (Cuboid): Cuboid to test for intersections. edges (bool): Accept edge touching cuboids","self.depth) def intersects(self, cub, edges=False): \"\"\"Detect intersections between this cuboid and cub. Args:","otherwise \"\"\" if self.contains(other): return True if other.contains(self): self.x = other.x self.y =","yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return","a cuboid and the operation is successful then this cuboid is modified to","bottom, outeye, right - left, top - bottom, ineye - outeye) def join(self,","point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__","Point)) self.start = start self.end = end def __eq__(self, other): if not isinstance(other,","self.left > cub.right or self.right < cub.left or self.outeye > cub.ineye or self.ineye","and cub.bottom == self.top and self.outeye == cub.ineye or cub.left == self.right and","Segment end point \"\"\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end","< cub.bottom or self.left > cub.right or self.right < cub.left or self.outeye >","point): \"\"\"Calculate distance to another point\"\"\" return sqrt((self.x - point.x)**2 + (self.y -","one. If the result is also a cuboid and the operation is successful","Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property def length(self): return","= max(self.right, other.right) self.x = x_min self.width = x_max - x_min return True","Segment\"\"\" def __init__(self, start, length): \"\"\"Create an Horizontal segment given its left most","Discard edge intersects if not edges: if (self.bottom == cub.top or self.top ==","height, depth, rid=None): \"\"\"Initiating the Cuboid Args: x (int, float): y (int, float):","length): \"\"\"Create an Horizontal segment given its left most end point and its","edge z coordinate\"\"\" return self.z @property def ineye(self): \"\"\"Cuboid nearer from eye edge","Point - length (number): segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point))","is successful then this cuboid is modified to the union. Arguments: other (Cuboid):","= max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right","= x self.y = y self.z = z def __eq__(self, other): return (self.x","== other.height and \\ self.left == other.left and self.width == other.width: z_min =","z, width, height, depth, rid=None): \"\"\"Initiating the Cuboid Args: x (int, float): y","intersection(self, cub, edges=False): \"\"\"Returns the cuboid resulting of the intersection of this and","== other.outeye and self.depth == self.depth: x_min = min(self.left, other.left) x_max = max(self.right,","self.height == other.height and \\ self.left == other.left and self.width == other.width: z_min","length): \"\"\"Create an In-Depth segment given its bottom most end point and its","def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def bottom(self):","- self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def __init__(self, start, length): \"\"\"Create a Vertical","self.top and self.outeye == cub.ineye or self.left == cub.right and self.bottom == cub.top","also a cuboid and the operation is successful then this cuboid is modified","self.x and cub.z >= self.z and cub.y + cub.height <= self.y + self.height","z (int, float): width (int, float): height (int, float): depth (int, float): rid","and self.x == other.x and self.y == other.y and self.z == other.z) def","Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other):","Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self):","min(self.start.z, self.end.z) class HSegment(Segment): \"\"\"Horizontal Segment\"\"\" def __init__(self, start, length): \"\"\"Create an Horizontal","True if the cuboids intersect, False otherwise \"\"\" # Not even touching if","(cub.y >= self.y and cub.x >= self.x and cub.z >= self.z and cub.y","Horizontal segment given its left most end point and its length. Arguments: -","self.y, self.z, self.width, self.height, self.depth) def volume(self): \"\"\"Cuboid volume\"\"\" return self.width * self.height","\"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z","cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom,","Returns: bool: True if it is inside this one, False otherwise \"\"\" return",">= 0 and width >= 0 and depth >= 0) self.width = width","by their edges, and the argument 'edges' is True the cuboid returned will","length. Arguments: - start (Point): Starting Point - length (number): segment length \"\"\"","cub.top and self.outeye == cub.ineye or self.left == cub.right and cub.bottom == self.top","if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left = max(self.left,","(Cuboid): The other cuboiud Returns: bool: True if it is inside this one,","(Point): Segment start point end (Point): Segment end point \"\"\" assert(isinstance(start, Point) and","False otherwise \"\"\" return (cub.y >= self.y and cub.x >= self.x and cub.z","If the cuboids are only touching by their edges, and the argument 'edges'","__slots__ = ('x', 'y', 'z') def __init__(self, x, y, z): self.x = x","cub (Cuboid): The other cuboiud Returns: bool: True if it is inside this","self.end = end def __eq__(self, other): if not isinstance(other, self.__class__): None return self.start","cuboids are only touching by their edges, and the argument 'edges' is True","and \\ self.outeye == other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom)","'y', 'z', 'rid') def __init__(self, x, y, z, width, height, depth, rid=None): \"\"\"Initiating","or self.left == cub.right and self.bottom == cub.top and cub.outeye == self.ineye or","other.left and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye)","= max(self.top, other.top) self.y = y_min self.height = y_max - y_min return True","the Cuboid Args: x (int, float): y (int, float): z (int, float): width","assert(height >= 0 and width >= 0 and depth >= 0) self.width =","right(self): \"\"\"Cuboid right edge x coordinate\"\"\" return self.x + self.width @property def outeye(self):","return self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def","Arguments: cub (Cuboid): The other cuboid. edges (bool): If true, touching edges are","y_max = max(self.top, other.top) self.y = y_min self.height = y_max - y_min return","= min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min self.width = x_max","cub. Args: cub (Cuboid): Cuboid to test for intersections. edges (bool): Accept edge","self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right,","(self.bottom == cub.top or self.top == cub.bottom or self.left == cub.right or self.right","Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property def length(self): return","(number): segment length \"\"\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start,","0 and depth >= 0) self.width = width self.height = height self.depth =","self.outeye == cub.ineye or cub.left == self.right and self.bottom == cub.top and cub.outeye","other.height and self.depth == other.depth and self.x == other.x and self.y == other.y","cuboids as intersects or not Returns: bool: True if the cuboids intersect, False","corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property","return True if not self.intersects(other, edges=True): return False # Other cuboid is Up/Down","is contained by this one Arguments: cub (Cuboid): The other cuboiud Returns: bool:","length and useful for some comparisons\"\"\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end)","self.y == other.y and self.z == other.z) def __hash__(self): return hash( (self.x, self.y,","class. x, y, z-> Lower right corner coordinates width - height - depth","or self.left == cub.right and cub.bottom == self.top and cub.outeye == self.ineye or","otherwise \"\"\" # Not even touching if (self.bottom > cub.top or self.top <","this if self.bottom == other.bottom and self.height == other.height and \\ self.left ==","or not Returns: bool: True if the cuboids intersect, False otherwise \"\"\" #","\"\"\"Cuboid farther from eye edge z coordinate\"\"\" return self.z @property def ineye(self): \"\"\"Cuboid","cub.outeye == self.ineye): return False return True def intersection(self, cub, edges=False): \"\"\"Returns the","to test for intersections. edges (bool): Accept edge touching cuboids as intersects or","(Cuboid): The other cuboid. edges (bool): If true, touching edges are considered an","bottom(self): \"\"\"Cuboid bottom edge y coordinate\"\"\" return self.y @property def top(self): \"\"\"Cuboid top","= y_min self.height = y_max - y_min return True # Other cuboid is","end): \"\"\"Arguments: start (Point): Segment start point end (Point): Segment end point \"\"\"","self.ineye): return False return True def intersection(self, cub, edges=False): \"\"\"Returns the cuboid resulting","Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self):","z_max = max(self.ineye, other.ineye) self.z = z_min self.depth = z_max - z_min return","x, y, z): self.x = x self.y = y self.z = z def","z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth =","Point(start.x + length, start.y, start.z)) @property def length(self): return self.end.x - self.start.x class","__slots__ = ('start', 'end') def __init__(self, start, end): \"\"\"Arguments: start (Point): Segment start","('start', 'end') def __init__(self, start, end): \"\"\"Arguments: start (Point): Segment start point end","self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y)","@property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top,","test for intersections. edges (bool): Accept edge touching cuboids as intersects or not","0) self.width = width self.height = height self.depth = depth self.x = x","- left, top - bottom, ineye - outeye) def join(self, other): \"\"\"Try to","if (self.bottom > cub.top or self.top < cub.bottom or self.left > cub.right or","and self.outeye == cub.ineye or self.left == cub.right and cub.bottom == self.top and","self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height","self.x = x_min self.width = x_max - x_min return True # Other cuboid","other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self):","+ (self.y - point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__ =","start (Point): Segment start point end (Point): Segment end point \"\"\" assert(isinstance(start, Point)","edges: if (self.bottom == cub.top or self.top == cub.bottom or self.left == cub.right","x, y, z): \"\"\"Move Cuboid to x,y,z coordinates Arguments: x (int, float): X","Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property def length(self): return","== cub.right and cub.bottom == self.top and cub.outeye == self.ineye or cub.left ==","self.height = other.height self.depth = other.depth return True if not self.intersects(other, edges=True): return","== other.left and self.width == other.width and \\ self.outeye == other.outeye and self.depth","self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return","max(self.top, other.top) self.y = y_min self.height = y_max - y_min return True #","cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False): \"\"\"Detect intersections between this","end def __eq__(self, other): if not isinstance(other, self.__class__): None return self.start == other.start","self.outeye == other.outeye and self.depth == self.depth: x_min = min(self.left, other.left) x_max =","x_min return True # Other cuboid is Right/Left from this if self.bottom ==","= min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye =","self.right and self.bottom == cub.top and self.outeye == cub.ineye or cub.left == self.right","bool: True if it is inside this one, False otherwise \"\"\" return (cub.y","the cuboid returned will have a volume of 0. Returns None if there",">= 0 and depth >= 0) self.width = width self.height = height self.depth","x_min self.width = x_max - x_min return True # Other cuboid is Right/Left","- self.start.y class DSegment(Segment): \"\"\"In-Depth Segment\"\"\" def __init__(self, start, length): \"\"\"Create an In-Depth","self.end.x - self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def __init__(self, start, length): \"\"\"Create a","== cub.right and self.bottom == cub.top and cub.outeye == self.ineye or self.left ==","- point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 + (self.y - point.y)**2","corner intersects if (self.left == cub.right and self.bottom == cub.top and self.outeye ==","True when successfully joined, False otherwise \"\"\" if self.contains(other): return True if other.contains(self):","left = max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right) outeye","(self.width == other.width and self.height == other.height and self.depth == other.depth and self.x","self.top and cub.outeye == self.ineye): return False return True def intersection(self, cub, edges=False):","float): X coordinate y (int, float): Y coordinate z (int, float): Z coordinate","Cuboid to test for intersections. edges (bool): Accept edge touching cuboids as intersects","start, length): \"\"\"Create an In-Depth segment given its bottom most end point and","intersection, and a cuboid of 0 height or width or depth will be","== self.ineye or cub.left == self.right and self.bottom == cub.top and self.outeye ==","True def intersection(self, cub, edges=False): \"\"\"Returns the cuboid resulting of the intersection of","edge x coordinate\"\"\" return self.x @property def right(self): \"\"\"Cuboid right edge x coordinate\"\"\"","this one Arguments: cub (Cuboid): The other cuboiud Returns: bool: True if it","= rid @property def bottom(self): \"\"\"Cuboid bottom edge y coordinate\"\"\" return self.y @property","cub.right and cub.bottom == self.top and cub.outeye == self.ineye or cub.left == self.right","self.z = other.z self.width = other.width self.height = other.height self.depth = other.depth return","cub.right or self.right == cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye):","y coordiante\"\"\" return self.y + self.height @property def left(self): \"\"\"Cuboid left edge x","return True def intersection(self, cub, edges=False): \"\"\"Returns the cuboid resulting of the intersection","volume of 0. Returns None if there is no intersection. Arguments: cub (Cuboid):","another point\"\"\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z","self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right,","float): width (int, float): height (int, float): depth (int, float): rid (identifier object):","edge x coordinate\"\"\" return self.x + self.width @property def outeye(self): \"\"\"Cuboid farther from","(self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2 class","and cub.outeye == self.ineye or cub.left == self.right and self.bottom == cub.top and","== other.y and self.z == other.z) def __repr__(self): return \"P({}, {}, {})\".format(self.x, self.y,","Arguments: x (int, float): X coordinate y (int, float): Y coordinate z (int,","== other.x and self.y == other.y and self.z == other.z) def __repr__(self): return","modified to the union. Arguments: other (Cuboid): Cuboid to join Returns: bool: True","min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z,","self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right,","self.z == other.z) def __repr__(self): return \"P({}, {}, {})\".format(self.x, self.y, self.z) def distance(self,","Segment start point end (Point): Segment end point \"\"\" assert(isinstance(start, Point) and isinstance(end,","it is inside this one, False otherwise \"\"\" return (cub.y >= self.y and","and cub.z + cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False): \"\"\"Detect","self.start.z class Cuboid(object): \"\"\"Basic cuboid primitive class. x, y, z-> Lower right corner","right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def ineye(self):","@property def right(self): \"\"\"Cuboid right edge x coordinate\"\"\" return self.x + self.width @property","Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self):","== other.height and self.depth == other.depth and self.x == other.x and self.y ==","self.depth == other.depth and self.x == other.x and self.y == other.y and self.z","cub, edges=False): \"\"\"Detect intersections between this cuboid and cub. Args: cub (Cuboid): Cuboid","not Returns: bool: True if the cuboids intersect, False otherwise \"\"\" # Not","x_max = max(self.right, other.right) self.x = x_min self.width = x_max - x_min return","one Arguments: cub (Cuboid): The other cuboiud Returns: bool: True if it is","and cub.z >= self.z and cub.y + cub.height <= self.y + self.height and","is no intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool): If true,","self.ineye == cub.outeye): return False # Discard corner intersects if (self.left == cub.right","from this if self.bottom == other.bottom and self.height == other.height and \\ self.outeye","and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property","self.left == cub.right and cub.bottom == self.top and self.outeye == cub.ineye or self.left","self.right and self.bottom == cub.top and cub.outeye == self.ineye or cub.left == self.right","and self.z == other.z) def __repr__(self): return \"P({}, {}, {})\".format(self.x, self.y, self.z) def","coordiante\"\"\" return self.y + self.height @property def left(self): \"\"\"Cuboid left edge x coordinate\"\"\"","useful for some comparisons\"\"\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def","(int, float): height (int, float): depth (int, float): rid (identifier object): \"\"\" assert(height","== cub.ineye or self.left == cub.right and self.bottom == cub.top and cub.outeye ==","return True # Other cuboid is Right/Left from this if self.bottom == other.bottom","== cub.ineye or cub.left == self.right and cub.bottom == self.top and self.outeye ==","only touching by their edges, and the argument 'edges' is True the cuboid","point): return (self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z -","if self.left == other.left and self.width == other.width and \\ self.outeye == other.outeye","cub.left == self.right and self.bottom == cub.top and self.outeye == cub.ineye or cub.left","self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end):","== other.z) def __repr__(self): return \"P({}, {}, {})\".format(self.x, self.y, self.z) def distance(self, point):","self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return","cub.top and cub.outeye == self.ineye or self.left == cub.right and cub.bottom == self.top","None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top)","start self.end = end def __eq__(self, other): if not isinstance(other, self.__class__): None return","Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z))","+ self.depth) def intersects(self, cub, edges=False): \"\"\"Detect intersections between this cuboid and cub.","self.top and self.outeye == cub.ineye or cub.left == self.right and self.bottom == cub.top","self.corner_bot_l_out def __repr__(self): return \"R({}, {}, {}, {}, {}, {})\".format( self.x, self.y, self.z,","In-Depth segment given its bottom most end point and its length. Arguments: -","(self.y - point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__ = ('start',","from eye edge z coordinate\"\"\" return self.z @property def ineye(self): \"\"\"Cuboid nearer from","edges (bool): If true, touching edges are considered an intersection, and a cuboid","point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2","an Horizontal segment given its left most end point and its length. Arguments:","ineye(self): \"\"\"Cuboid nearer from eye edge z coordinate\"\"\" return self.z + self.depth @property","- height - depth - \"\"\" __slots__ = ('width', 'height', 'depth', 'x', 'y',","to join Returns: bool: True when successfully joined, False otherwise \"\"\" if self.contains(other):","def move(self, x, y, z): \"\"\"Move Cuboid to x,y,z coordinates Arguments: x (int,","self.rid = rid @property def bottom(self): \"\"\"Cuboid bottom edge y coordinate\"\"\" return self.y","not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left)","return self.end.x - self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def __init__(self, start, length): \"\"\"Create","outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye,","self.left == other.left and self.width == other.width and \\ self.outeye == other.outeye and","= max(self.ineye, other.ineye) self.z = z_min self.depth = z_max - z_min return True","other.z self.width = other.width self.height = other.height self.depth = other.depth return True if","__repr__(self): return \"S({}, {})\".format(self.start, self.end) @property def length_squared(self): \"\"\"Faster than length and useful","super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property def length(self): return self.end.y","depth (int, float): rid (identifier object): \"\"\" assert(height >= 0 and width >=","def distance_squared(self, point): return (self.x - point.x)**2 + (self.y - point.y)**2 + (","not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property def","def __lt__(self, other): \"\"\"Compare cuboids by volume (used for sorting)\"\"\" return self.volume() <","Lower right corner coordinates width - height - depth - \"\"\" __slots__ =","0 and width >= 0 and depth >= 0) self.width = width self.height","self.intersects(other, edges=True): return False # Other cuboid is Up/Down from this if self.left","= x self.y = y self.z = z self.rid = rid @property def","eye edge z coordinate\"\"\" return self.z + self.depth @property def corner_top_l(self): return Point(self.left,","@property def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property","= z self.rid = rid @property def bottom(self): \"\"\"Cuboid bottom edge y coordinate\"\"\"","return self.y @property def top(self): \"\"\"Cuboid top edge y coordiante\"\"\" return self.y +","cub.bottom or self.left > cub.right or self.right < cub.left or self.outeye > cub.ineye","def intersection(self, cub, edges=False): \"\"\"Returns the cuboid resulting of the intersection of this","top = min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye","and self.depth == self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x","this if self.bottom == other.bottom and self.height == other.height and \\ self.outeye ==","is also a cuboid and the operation is successful then this cuboid is","not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property def"],"string":"[\n \"z def contains(self, cub): \\\"\\\"\\\"Tests if another cuboid is contained by this one\",\n \"start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def\",\n \"None if there is no intersection. Arguments: cub (Cuboid): The other cuboid. edges\",\n \"the argument 'edges' is True the cuboid returned will have a volume of\",\n \"length(self): return self.end.z - self.start.z class Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive class. x, y,\",\n \"\\\"\\\"\\\"Create an In-Depth segment given its bottom most end point and its length.\",\n \"= other.width self.height = other.height self.depth = other.depth return True if not self.intersects(other,\",\n \"cub (Cuboid): The other cuboid. edges (bool): If true, touching edges are considered\",\n \"point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end): \\\"\\\"\\\"Arguments: start\",\n \"+ length)) @property def length(self): return self.end.z - self.start.z class Cuboid(object): \\\"\\\"\\\"Basic cuboid\",\n \"Arguments: other (Cuboid): Cuboid to join Returns: bool: True when successfully joined, False\",\n \"x coordinate\\\"\\\"\\\" return self.x @property def right(self): \\\"\\\"\\\"Cuboid right edge x coordinate\\\"\\\"\\\" return\",\n \"for sorting)\\\"\\\"\\\" return self.volume() < other.volume() def __eq__(self, other): \\\"\\\"\\\"Equal cuboids have same\",\n \"False return (self.width == other.width and self.height == other.height and self.depth == other.depth\",\n \"have a volume of 0. Returns None if there is no intersection. Arguments:\",\n \"its length. Arguments: - start (Point): Starting Point - length (number): segment length\",\n \"or cub.left == self.right and self.bottom == cub.top and cub.outeye == self.ineye or\",\n \"0. Returns None if there is no intersection. Arguments: cub (Cuboid): The other\",\n \"+ cub.width <= self.x + self.width and cub.z + cub.depth <= self.z +\",\n \"the result is also a cuboid and the operation is successful then this\",\n \"- point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end')\",\n \"cuboid. edges (bool): If true, touching edges are considered an intersection, and a\",\n \"(number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start,\",\n \"outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create\",\n \"or self.left == cub.right or self.right == cub.left or self.outeye == cub.ineye or\",\n \"or self.outeye == cub.ineye or self.ineye == cub.outeye): return False # Discard corner\",\n \"or self.right == cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye): return\",\n \"width (int, float): height (int, float): depth (int, float): rid (identifier object): \\\"\\\"\\\"\",\n \"There was no intersection. \\\"\\\"\\\" if not self.intersects(cub, edges=edges): return None bottom =\",\n \"self.bottom, self.ineye) def __lt__(self, other): \\\"\\\"\\\"Compare cuboids by volume (used for sorting)\\\"\\\"\\\" return\",\n \"bottom edge y coordinate\\\"\\\"\\\" return self.y @property def top(self): \\\"\\\"\\\"Cuboid top edge y\",\n \"other.width self.height = other.height self.depth = other.depth return True if not self.intersects(other, edges=True):\",\n \"'y', 'z') def __init__(self, x, y, z): self.x = x self.y = y\",\n \"of 0 height or width or depth will be returned Returns: Cuboid: Intersection.\",\n \"self.y = y self.z = z self.rid = rid @property def bottom(self): \\\"\\\"\\\"Cuboid\",\n \"corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property\",\n \"def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye)\",\n \"isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property def length(self):\",\n \"- y_min return True # Other cuboid is Right/Left from this if self.bottom\",\n \"this if self.left == other.left and self.width == other.width and \\\\ self.outeye ==\",\n \"if not isinstance(other, self.__class__): None return self.start == other.start and self.end == other.end\",\n \"@property def top(self): \\\"\\\"\\\"Cuboid top edge y coordiante\\\"\\\"\\\" return self.y + self.height @property\",\n \"return (self.x == other.x and self.y == other.y and self.z == other.z) def\",\n \"other.width and self.height == other.height and self.depth == other.depth and self.x == other.x\",\n \"False # Discard corner intersects if (self.left == cub.right and self.bottom == cub.top\",\n \"cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye): return False # Discard\",\n \"if not isinstance(other, self.__class__): return False return (self.width == other.width and self.height ==\",\n \"argument 'edges' is True the cuboid returned will have a volume of 0.\",\n \"depth will be returned Returns: Cuboid: Intersection. None: There was no intersection. \\\"\\\"\\\"\",\n \"self.outeye == cub.ineye or self.ineye == cub.outeye): return False # Discard corner intersects\",\n \"self.z, self.width, self.height, self.depth) def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width * self.height *\",\n \"self).__init__( start, Point(start.x, start.y + length, start.z)) @property def length(self): return self.end.y -\",\n \"self.width @property def outeye(self): \\\"\\\"\\\"Cuboid farther from eye edge z coordinate\\\"\\\"\\\" return self.z\",\n \"self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property\",\n \"{}, {}, {})\\\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\"\",\n \"cub.right and self.bottom == cub.top and cub.outeye == self.ineye or self.left == cub.right\",\n \"== cub.top and cub.outeye == self.ineye or cub.left == self.right and cub.bottom ==\",\n \"and cub.bottom == self.top and cub.outeye == self.ineye or cub.left == self.right and\",\n \"= z def __eq__(self, other): return (self.x == other.x and self.y == other.y\",\n \"+ cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False): \\\"\\\"\\\"Detect intersections between\",\n \"min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth = z_max -\",\n \"rid (identifier object): \\\"\\\"\\\" assert(height >= 0 and width >= 0 and depth\",\n \"z coordinate\\\"\\\"\\\" return self.z @property def ineye(self): \\\"\\\"\\\"Cuboid nearer from eye edge z\",\n \"@property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom,\",\n \"(Cuboid): Cuboid to join Returns: bool: True when successfully joined, False otherwise \\\"\\\"\\\"\",\n \"left edge x coordinate\\\"\\\"\\\" return self.x @property def right(self): \\\"\\\"\\\"Cuboid right edge x\",\n \"or self.right < cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye): return\",\n \"def __init__(self, x, y, z): self.x = x self.y = y self.z =\",\n \"== cub.top and self.outeye == cub.ineye or cub.left == self.right and cub.bottom ==\",\n \"self.height and cub.x + cub.width <= self.x + self.width and cub.z + cub.depth\",\n \"will be returned Returns: Cuboid: Intersection. None: There was no intersection. \\\"\\\"\\\" if\",\n \"self.y = y self.z = z def contains(self, cub): \\\"\\\"\\\"Tests if another cuboid\",\n \"== cub.ineye or self.left == cub.right and cub.bottom == self.top and self.outeye ==\",\n \"__init__(self, start, length): \\\"\\\"\\\"Create an In-Depth segment given its bottom most end point\",\n \"y coordinate\\\"\\\"\\\" return self.y @property def top(self): \\\"\\\"\\\"Cuboid top edge y coordiante\\\"\\\"\\\" return\",\n \"segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x,\",\n \"(used for sorting)\\\"\\\"\\\" return self.volume() < other.volume() def __eq__(self, other): \\\"\\\"\\\"Equal cuboids have\",\n \"not edges: if (self.bottom == cub.top or self.top == cub.bottom or self.left ==\",\n \"start.y, start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\"\",\n \"inside this one, False otherwise \\\"\\\"\\\" return (cub.y >= self.y and cub.x >=\",\n \"and depth >= 0) self.width = width self.height = height self.depth = depth\",\n \"point and its length. Arguments: - start (Point): Starting Point - length (number):\",\n \"def __eq__(self, other): return (self.x == other.x and self.y == other.y and self.z\",\n \"-*- from math import sqrt class Point(object): __slots__ = ('x', 'y', 'z') def\",\n \"the cuboids are only touching by their edges, and the argument 'edges' is\",\n \"(self.x == other.x and self.y == other.y and self.z == other.z) def __repr__(self):\",\n \"other.y self.z = other.z self.width = other.width self.height = other.height self.depth = other.depth\",\n \"edges=False): \\\"\\\"\\\"Returns the cuboid resulting of the intersection of this and cub If\",\n \"edges (bool): Accept edge touching cuboids as intersects or not Returns: bool: True\",\n \"== self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min\",\n \"self.width = x_max - x_min return True # Other cuboid is Right/Left from\",\n \"and cub. Args: cub (Cuboid): Cuboid to test for intersections. edges (bool): Accept\",\n \"- \\\"\\\"\\\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self,\",\n \"\\\"\\\"\\\"Tests if another cuboid is contained by this one Arguments: cub (Cuboid): The\",\n \"no intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool): If true, touching\",\n \"@property def length(self): return self.end.z - self.start.z class Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive class.\",\n \"self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return\",\n \"self.height = y_max - y_min return True # Other cuboid is Right/Left from\",\n \"return False # Discard edge intersects if not edges: if (self.bottom == cub.top\",\n \"- length (number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment,\",\n \"other (Cuboid): Cuboid to join Returns: bool: True when successfully joined, False otherwise\",\n \"other.height and \\\\ self.outeye == other.outeye and self.depth == self.depth: x_min = min(self.left,\",\n \"other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top)\",\n \"\\\\ self.left == other.left and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max\",\n \"\\\"\\\"\\\"Create an Horizontal segment given its left most end point and its length.\",\n \"float): depth (int, float): rid (identifier object): \\\"\\\"\\\" assert(height >= 0 and width\",\n \"== other.start and self.end == other.end def __repr__(self): return \\\"S({}, {})\\\".format(self.start, self.end) @property\",\n \"def __repr__(self): return \\\"S({}, {})\\\".format(self.start, self.end) @property def length_squared(self): \\\"\\\"\\\"Faster than length and\",\n \"\\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an Horizontal segment given its left\",\n \"Arguments: - start (Point): Starting Point - length (number): segment length \\\"\\\"\\\" assert(isinstance(start,\",\n \"coordinates width - height - depth - \\\"\\\"\\\" __slots__ = ('width', 'height', 'depth',\",\n \"cub.top and cub.outeye == self.ineye or cub.left == self.right and cub.bottom == self.top\",\n \"or cub.left == self.right and self.bottom == cub.top and self.outeye == cub.ineye or\",\n \"def right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def\",\n \"cub.outeye): return False # Discard edge intersects if not edges: if (self.bottom ==\",\n \"be returned Returns: Cuboid: Intersection. None: There was no intersection. \\\"\\\"\\\" if not\",\n \"class Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive class. x, y, z-> Lower right corner coordinates\",\n \"@property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top,\",\n \"true, touching edges are considered an intersection, and a cuboid of 0 height\",\n \"and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property\",\n \"self.bottom == other.bottom and self.height == other.height and \\\\ self.left == other.left and\",\n \"other): return (self.x == other.x and self.y == other.y and self.z == other.z)\",\n \"True if other.contains(self): self.x = other.x self.y = other.y self.z = other.z self.width\",\n \"Cuboid: Intersection. None: There was no intersection. \\\"\\\"\\\" if not self.intersects(cub, edges=edges): return\",\n \"< other.volume() def __eq__(self, other): \\\"\\\"\\\"Equal cuboids have same properties.\\\"\\\"\\\" if not isinstance(other,\",\n \"class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x, y, z): self.x\",\n \"math import sqrt class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x,\",\n \"self.depth = other.depth return True if not self.intersects(other, edges=True): return False # Other\",\n \"( self.z - point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 + (self.y\",\n \"then this cuboid is modified to the union. Arguments: other (Cuboid): Cuboid to\",\n \"def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def\",\n \"move(self, x, y, z): \\\"\\\"\\\"Move Cuboid to x,y,z coordinates Arguments: x (int, float):\",\n \"\\\"S({}, {})\\\".format(self.start, self.end) @property def length_squared(self): \\\"\\\"\\\"Faster than length and useful for some\",\n \"and cub.x >= self.x and cub.z >= self.z and cub.y + cub.height <=\",\n \"and cub.bottom == self.top and self.outeye == cub.ineye or self.left == cub.right and\",\n \"== other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min\",\n \"return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return\",\n \"its bottom most end point and its length. Arguments: - start (Point): Starting\",\n \"other): \\\"\\\"\\\"Try to join a cuboid to this one. If the result is\",\n \"x self.y = y self.z = z def __eq__(self, other): return (self.x ==\",\n \"self.right and cub.bottom == self.top and cub.outeye == self.ineye): return False return True\",\n \"Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self):\",\n \"== cub.top and cub.outeye == self.ineye or self.left == cub.right and cub.bottom ==\",\n \"other.volume() def __eq__(self, other): \\\"\\\"\\\"Equal cuboids have same properties.\\\"\\\"\\\" if not isinstance(other, self.__class__):\",\n \"- outeye) def join(self, other): \\\"\\\"\\\"Try to join a cuboid to this one.\",\n \"y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height =\",\n \"coding: utf-8 -*- from math import sqrt class Point(object): __slots__ = ('x', 'y',\",\n \"the operation is successful then this cuboid is modified to the union. Arguments:\",\n \"Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self):\",\n \"\\\"\\\"\\\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end = end def\",\n \"def __iter__(self): \\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield\",\n \"and cub.outeye == self.ineye or cub.left == self.right and cub.bottom == self.top and\",\n \"Point(start.x, start.y + length, start.z)) @property def length(self): return self.end.y - self.start.y class\",\n \"and the argument 'edges' is True the cuboid returned will have a volume\",\n \"y_max - y_min return True # Other cuboid is Right/Left from this if\",\n \"Right/Left from this if self.bottom == other.bottom and self.height == other.height and \\\\\",\n \"other.bottom and self.height == other.height and \\\\ self.outeye == other.outeye and self.depth ==\",\n \"self).__init__( start, Point(start.x, start.y, start.z + length)) @property def length(self): return self.end.z -\",\n \"considered an intersection, and a cuboid of 0 height or width or depth\",\n \"Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z))\",\n \"self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z)\",\n \"this cuboid and cub. Args: cub (Cuboid): Cuboid to test for intersections. edges\",\n \"\\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield\",\n \"-*- coding: utf-8 -*- from math import sqrt class Point(object): __slots__ = ('x',\",\n \"join Returns: bool: True when successfully joined, False otherwise \\\"\\\"\\\" if self.contains(other): return\",\n \"- bottom, ineye - outeye) def join(self, other): \\\"\\\"\\\"Try to join a cuboid\",\n \"def __repr__(self): return \\\"P({}, {}, {})\\\".format(self.x, self.y, self.z) def distance(self, point): \\\"\\\"\\\"Calculate distance\",\n \"self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return\",\n \"width self.height = height self.depth = depth self.x = x self.y = y\",\n \"self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r\",\n \"cub.z + cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False): \\\"\\\"\\\"Detect intersections\",\n \"__init__(self, x, y, z): self.x = x self.y = y self.z = z\",\n \"== self.top and self.outeye == cub.ineye or self.left == cub.right and self.bottom ==\",\n \"result is also a cuboid and the operation is successful then this cuboid\",\n \"cub.left == self.right and self.bottom == cub.top and cub.outeye == self.ineye or cub.left\",\n \"return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return\",\n \"self.height * self.depth def move(self, x, y, z): \\\"\\\"\\\"Move Cuboid to x,y,z coordinates\",\n \"self.z and cub.y + cub.height <= self.y + self.height and cub.x + cub.width\",\n \"corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property\",\n \"super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property def length(self): return self.end.x\",\n \"and self.bottom == cub.top and self.outeye == cub.ineye or cub.left == self.right and\",\n \"other): if not isinstance(other, self.__class__): None return self.start == other.start and self.end ==\",\n \"__repr__(self): return \\\"R({}, {}, {}, {}, {}, {})\\\".format( self.x, self.y, self.z, self.width, self.height,\",\n \"def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye)\",\n \"comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y,\",\n \"cub.ineye) return Cuboid( left, bottom, outeye, right - left, top - bottom, ineye\",\n \"height self.depth = depth self.x = x self.y = y self.z = z\",\n \"self.outeye == other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max =\",\n \"segment given its bottom most end point and its length. Arguments: - start\",\n \"( self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self, start,\",\n \"start.z)) @property def length(self): return self.end.y - self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def\",\n \"== self.right and cub.bottom == self.top and cub.outeye == self.ineye): return False return\",\n \"Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an In-Depth segment given its bottom most\",\n \"return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def\",\n \"class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an Horizontal segment given\",\n \"'edges' is True the cuboid returned will have a volume of 0. Returns\",\n \"this cuboid is modified to the union. Arguments: other (Cuboid): Cuboid to join\",\n \"are considered an intersection, and a cuboid of 0 height or width or\",\n \"to the union. Arguments: other (Cuboid): Cuboid to join Returns: bool: True when\",\n \"depth self.x = x self.y = y self.z = z self.rid = rid\",\n \"is modified to the union. Arguments: other (Cuboid): Cuboid to join Returns: bool:\",\n \"an intersection, and a cuboid of 0 height or width or depth will\",\n \"right edge x coordinate\\\"\\\"\\\" return self.x + self.width @property def outeye(self): \\\"\\\"\\\"Cuboid farther\",\n \"and cub.y + cub.height <= self.y + self.height and cub.x + cub.width <=\",\n \"edges are considered an intersection, and a cuboid of 0 height or width\",\n \"self.outeye == cub.ineye or self.left == cub.right and cub.bottom == self.top and self.outeye\",\n \"= min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right - left, top -\",\n \"assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z +\",\n \"def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width * self.height * self.depth def move(self, x,\",\n \"= max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right) outeye =\",\n \"'x', 'y', 'z', 'rid') def __init__(self, x, y, z, width, height, depth, rid=None):\",\n \"intersects if not edges: if (self.bottom == cub.top or self.top == cub.bottom or\",\n \"== other.width and \\\\ self.outeye == other.outeye and self.depth == self.depth: y_min =\",\n \"right corner coordinates width - height - depth - \\\"\\\"\\\" __slots__ = ('width',\",\n \"(int, float): width (int, float): height (int, float): depth (int, float): rid (identifier\",\n \"Discard corner intersects if (self.left == cub.right and self.bottom == cub.top and self.outeye\",\n \"self.width, self.height, self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\" yield self.corner_top_l yield self.corner_top_r\",\n \"= ('x', 'y', 'z') def __init__(self, x, y, z): self.x = x self.y\",\n \"union. Arguments: other (Cuboid): Cuboid to join Returns: bool: True when successfully joined,\",\n \"sorting)\\\"\\\"\\\" return self.volume() < other.volume() def __eq__(self, other): \\\"\\\"\\\"Equal cuboids have same properties.\\\"\\\"\\\"\",\n \"\\\"\\\"\\\"Arguments: start (Point): Segment start point end (Point): Segment end point \\\"\\\"\\\" assert(isinstance(start,\",\n \"self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left,\",\n \"{}, {})\\\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return\",\n \"def ineye(self): \\\"\\\"\\\"Cuboid nearer from eye edge z coordinate\\\"\\\"\\\" return self.z + self.depth\",\n \"z self.rid = rid @property def bottom(self): \\\"\\\"\\\"Cuboid bottom edge y coordinate\\\"\\\"\\\" return\",\n \"z def __eq__(self, other): return (self.x == other.x and self.y == other.y and\",\n \"def __repr__(self): return \\\"R({}, {}, {}, {}, {}, {})\\\".format( self.x, self.y, self.z, self.width,\",\n \"== other.left and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye,\",\n \"def __eq__(self, other): if not isinstance(other, self.__class__): None return self.start == other.start and\",\n \"return self.width * self.height * self.depth def move(self, x, y, z): \\\"\\\"\\\"Move Cuboid\",\n \"other.bottom and self.height == other.height and \\\\ self.left == other.left and self.width ==\",\n \"= min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth = z_max\",\n \"rid @property def bottom(self): \\\"\\\"\\\"Cuboid bottom edge y coordinate\\\"\\\"\\\" return self.y @property def\",\n \"(int, float): rid (identifier object): \\\"\\\"\\\" assert(height >= 0 and width >= 0\",\n \"self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self):\",\n \"== cub.ineye or cub.left == self.right and self.bottom == cub.top and cub.outeye ==\",\n \"self.outeye == cub.ineye or cub.left == self.right and cub.bottom == self.top and self.outeye\",\n \"min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x,\",\n \"or self.top < cub.bottom or self.left > cub.right or self.right < cub.left or\",\n \"def top(self): \\\"\\\"\\\"Cuboid top edge y coordiante\\\"\\\"\\\" return self.y + self.height @property def\",\n \"length_squared(self): \\\"\\\"\\\"Faster than length and useful for some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property def\",\n \"return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def\",\n \"return Cuboid( left, bottom, outeye, right - left, top - bottom, ineye -\",\n \"y, z-> Lower right corner coordinates width - height - depth - \\\"\\\"\\\"\",\n \"length (number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__(\",\n \"self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create a Vertical segment\",\n \"if not edges: if (self.bottom == cub.top or self.top == cub.bottom or self.left\",\n \"top - bottom, ineye - outeye) def join(self, other): \\\"\\\"\\\"Try to join a\",\n \"intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool): If true, touching edges\",\n \"x (int, float): X coordinate y (int, float): Y coordinate z (int, float):\",\n \"a Vertical segment given its bottom most end point and its length. Arguments:\",\n \"{}, {})\\\".format(self.x, self.y, self.z) def distance(self, point): \\\"\\\"\\\"Calculate distance to another point\\\"\\\"\\\" return\",\n \"return False # Other cuboid is Up/Down from this if self.left == other.left\",\n \"bottom most end point and its length. Arguments: - start (Point): Starting Point\",\n \"+ self.width @property def outeye(self): \\\"\\\"\\\"Cuboid farther from eye edge z coordinate\\\"\\\"\\\" return\",\n \"= depth self.x = x self.y = y self.z = z self.rid =\",\n \"cub.x + cub.width <= self.x + self.width and cub.z + cub.depth <= self.z\",\n \"y_min self.height = y_max - y_min return True # Other cuboid is Right/Left\",\n \"self.z = z def __eq__(self, other): return (self.x == other.x and self.y ==\",\n \"= x_max - x_min return True # Other cuboid is Right/Left from this\",\n \"bool: True if the cuboids intersect, False otherwise \\\"\\\"\\\" # Not even touching\",\n \"other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth = z_max - z_min\",\n \"Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \\\"\\\"\\\"Compare cuboids by volume (used for sorting)\\\"\\\"\\\"\",\n \"have same properties.\\\"\\\"\\\" if not isinstance(other, self.__class__): return False return (self.width == other.width\",\n \"Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self):\",\n \"Other cuboid is Up/Down from this if self.left == other.left and self.width ==\",\n \"(Cuboid): Cuboid to test for intersections. edges (bool): Accept edge touching cuboids as\",\n \"None: There was no intersection. \\\"\\\"\\\" if not self.intersects(cub, edges=edges): return None bottom\",\n \"== other.y and self.z == other.z) def __hash__(self): return hash( (self.x, self.y, self.z,\",\n \"== other.end def __repr__(self): return \\\"S({}, {})\\\".format(self.start, self.end) @property def length_squared(self): \\\"\\\"\\\"Faster than\",\n \"(int, float): y (int, float): z (int, float): width (int, float): height (int,\",\n \"other cuboiud Returns: bool: True if it is inside this one, False otherwise\",\n \"(Point): Starting Point - length (number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not\",\n \"return None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top,\",\n \"\\\"\\\"\\\" # Not even touching if (self.bottom > cub.top or self.top < cub.bottom\",\n \"yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield\",\n \"<= self.x + self.width and cub.z + cub.depth <= self.z + self.depth) def\",\n \"def length_squared(self): \\\"\\\"\\\"Faster than length and useful for some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property\",\n \"< cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye): return False #\",\n \"Args: x (int, float): y (int, float): z (int, float): width (int, float):\",\n \"== self.top and self.outeye == cub.ineye or cub.left == self.right and self.bottom ==\",\n \"and self.end == other.end def __repr__(self): return \\\"S({}, {})\\\".format(self.start, self.end) @property def length_squared(self):\",\n \"other.left and self.width == other.width and \\\\ self.outeye == other.outeye and self.depth ==\",\n \"x (int, float): y (int, float): z (int, float): width (int, float): height\",\n \"x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min self.width =\",\n \"self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left,\",\n \"\\\"\\\"\\\"Initiating the Cuboid Args: x (int, float): y (int, float): z (int, float):\",\n \"right - left, top - bottom, ineye - outeye) def join(self, other): \\\"\\\"\\\"Try\",\n \"yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \\\"R({}, {},\",\n \"\\\\ self.outeye == other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max\",\n \"def right(self): \\\"\\\"\\\"Cuboid right edge x coordinate\\\"\\\"\\\" return self.x + self.width @property def\",\n \"depth >= 0) self.width = width self.height = height self.depth = depth self.x\",\n \"yield self.corner_bot_l_out def __repr__(self): return \\\"R({}, {}, {}, {}, {}, {})\\\".format( self.x, self.y,\",\n \"def __init__(self, x, y, z, width, height, depth, rid=None): \\\"\\\"\\\"Initiating the Cuboid Args:\",\n \"y self.z = z def __eq__(self, other): return (self.x == other.x and self.y\",\n \"+ ( self.z - point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 +\",\n \"def join(self, other): \\\"\\\"\\\"Try to join a cuboid to this one. If the\",\n \"and the operation is successful then this cuboid is modified to the union.\",\n \"other.y and self.z == other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width,\",\n \"Arguments: cub (Cuboid): The other cuboiud Returns: bool: True if it is inside\",\n \"and self.bottom == cub.top and self.outeye == cub.ineye or self.left == cub.right and\",\n \"self.ineye) def __lt__(self, other): \\\"\\\"\\\"Compare cuboids by volume (used for sorting)\\\"\\\"\\\" return self.volume()\",\n \"intersection of this and cub If the cuboids are only touching by their\",\n \"cub.outeye == self.ineye or cub.left == self.right and self.bottom == cub.top and self.outeye\",\n \"cub.right and cub.bottom == self.top and self.outeye == cub.ineye or self.left == cub.right\",\n \"isinstance(other, self.__class__): return False return (self.width == other.width and self.height == other.height and\",\n \"return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return\",\n \"max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye,\",\n \"\\\"\\\"\\\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x,\",\n \"True the cuboid returned will have a volume of 0. Returns None if\",\n \"and self.outeye == cub.ineye or self.left == cub.right and self.bottom == cub.top and\",\n \"+ (self.y - point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point): return\",\n \"point \\\"\\\"\\\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end = end\",\n \"Args: cub (Cuboid): Cuboid to test for intersections. edges (bool): Accept edge touching\",\n \"other.ineye) self.z = z_min self.depth = z_max - z_min return True return False\",\n \"self.y == other.y and self.z == other.z) def __repr__(self): return \\\"P({}, {}, {})\\\".format(self.x,\",\n \"is Up/Down from this if self.left == other.left and self.width == other.width and\",\n \"== other.x and self.y == other.y and self.z == other.z) def __hash__(self): return\",\n \"cuboid resulting of the intersection of this and cub If the cuboids are\",\n \"the union. Arguments: other (Cuboid): Cuboid to join Returns: bool: True when successfully\",\n \"intersection. \\\"\\\"\\\" if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left\",\n \"top edge y coordiante\\\"\\\"\\\" return self.y + self.height @property def left(self): \\\"\\\"\\\"Cuboid left\",\n \"@property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom,\",\n \"def __eq__(self, other): \\\"\\\"\\\"Equal cuboids have same properties.\\\"\\\"\\\" if not isinstance(other, self.__class__): return\",\n \"point end (Point): Segment end point \\\"\\\"\\\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start\",\n \"def left(self): \\\"\\\"\\\"Cuboid left edge x coordinate\\\"\\\"\\\" return self.x @property def right(self): \\\"\\\"\\\"Cuboid\",\n \"+ self.height and cub.x + cub.width <= self.x + self.width and cub.z +\",\n \"\\\"\\\"\\\"Cuboid left edge x coordinate\\\"\\\"\\\" return self.x @property def right(self): \\\"\\\"\\\"Cuboid right edge\",\n \"given its left most end point and its length. Arguments: - start (Point):\",\n \"by this one Arguments: cub (Cuboid): The other cuboiud Returns: bool: True if\",\n \"<= self.z + self.depth) def intersects(self, cub, edges=False): \\\"\\\"\\\"Detect intersections between this cuboid\",\n \"and self.outeye == cub.ineye or cub.left == self.right and self.bottom == cub.top and\",\n \"and \\\\ self.outeye == other.outeye and self.depth == self.depth: x_min = min(self.left, other.left)\",\n \"corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property\",\n \"- self.start.z class Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive class. x, y, z-> Lower right\",\n \"cub.z >= self.z and cub.y + cub.height <= self.y + self.height and cub.x\",\n \"x self.y = y self.z = z self.rid = rid @property def bottom(self):\",\n \"self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \\\"R({}, {}, {},\",\n \"other.width and \\\\ self.outeye == other.outeye and self.depth == self.depth: y_min = min(self.bottom,\",\n \"\\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width * self.height * self.depth def move(self, x, y, z):\",\n \"Cuboid to join Returns: bool: True when successfully joined, False otherwise \\\"\\\"\\\" if\",\n \"* self.height * self.depth def move(self, x, y, z): \\\"\\\"\\\"Move Cuboid to x,y,z\",\n \"sqrt class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x, y, z):\",\n \"intersects(self, cub, edges=False): \\\"\\\"\\\"Detect intersections between this cuboid and cub. Args: cub (Cuboid):\",\n \"cuboid of 0 height or width or depth will be returned Returns: Cuboid:\",\n \"('x', 'y', 'z') def __init__(self, x, y, z): self.x = x self.y =\",\n \"@property def bottom(self): \\\"\\\"\\\"Cuboid bottom edge y coordinate\\\"\\\"\\\" return self.y @property def top(self):\",\n \"other.x and self.y == other.y and self.z == other.z) def __repr__(self): return \\\"P({},\",\n \"depth, rid=None): \\\"\\\"\\\"Initiating the Cuboid Args: x (int, float): y (int, float): z\",\n \"its left most end point and its length. Arguments: - start (Point): Starting\",\n \"== other.bottom and self.height == other.height and \\\\ self.left == other.left and self.width\",\n \"min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height = y_max -\",\n \"return False # Discard corner intersects if (self.left == cub.right and self.bottom ==\",\n \"if self.contains(other): return True if other.contains(self): self.x = other.x self.y = other.y self.z\",\n \"self.end) @property def length_squared(self): \\\"\\\"\\\"Faster than length and useful for some comparisons\\\"\\\"\\\" return\",\n \"contains(self, cub): \\\"\\\"\\\"Tests if another cuboid is contained by this one Arguments: cub\",\n \"Not even touching if (self.bottom > cub.top or self.top < cub.bottom or self.left\",\n \"= y self.z = z def __eq__(self, other): return (self.x == other.x and\",\n \"cuboids have same properties.\\\"\\\"\\\" if not isinstance(other, self.__class__): return False return (self.width ==\",\n \"Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create a Vertical segment given its bottom most\",\n \"__init__(self, start, length): \\\"\\\"\\\"Create a Vertical segment given its bottom most end point\",\n \"self.end.z - self.start.z class Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive class. x, y, z-> Lower\",\n \"== other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top,\",\n \"other.y and self.z == other.z) def __repr__(self): return \\\"P({}, {}, {})\\\".format(self.x, self.y, self.z)\",\n \"height - depth - \\\"\\\"\\\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z',\",\n \"return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def\",\n \"width >= 0 and depth >= 0) self.width = width self.height = height\",\n \"cub.ineye or self.ineye == cub.outeye): return False # Discard corner intersects if (self.left\",\n \"== other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def\",\n \"cub.outeye == self.ineye or cub.left == self.right and cub.bottom == self.top and cub.outeye\",\n \"point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point):\",\n \"cuboiud Returns: bool: True if it is inside this one, False otherwise \\\"\\\"\\\"\",\n \"is True the cuboid returned will have a volume of 0. Returns None\",\n \"cuboid and cub. Args: cub (Cuboid): Cuboid to test for intersections. edges (bool):\",\n \"return self.x + self.width @property def outeye(self): \\\"\\\"\\\"Cuboid farther from eye edge z\",\n \"import sqrt class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x, y,\",\n \"DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an In-Depth segment given its\",\n \">= 0) self.width = width self.height = height self.depth = depth self.x =\",\n \"corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property\",\n \"Intersection. None: There was no intersection. \\\"\\\"\\\" if not self.intersects(cub, edges=edges): return None\",\n \"max(self.right, other.right) self.x = x_min self.width = x_max - x_min return True #\",\n \"self.width * self.height * self.depth def move(self, x, y, z): \\\"\\\"\\\"Move Cuboid to\",\n \"other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height = y_max - y_min\",\n \"\\\"\\\"\\\"Calculate distance to another point\\\"\\\"\\\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2\",\n \"x,y,z coordinates Arguments: x (int, float): X coordinate y (int, float): Y coordinate\",\n \"super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property def length(self): return self.end.z\",\n \"= y self.z = z self.rid = rid @property def bottom(self): \\\"\\\"\\\"Cuboid bottom\",\n \"self.end == other.end def __repr__(self): return \\\"S({}, {})\\\".format(self.start, self.end) @property def length_squared(self): \\\"\\\"\\\"Faster\",\n \"yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \\\"R({}, {}, {}, {}, {}, {})\\\".format(\",\n \"ineye - outeye) def join(self, other): \\\"\\\"\\\"Try to join a cuboid to this\",\n \"self.height @property def left(self): \\\"\\\"\\\"Cuboid left edge x coordinate\\\"\\\"\\\" return self.x @property def\",\n \"self.y and cub.x >= self.x and cub.z >= self.z and cub.y + cub.height\",\n \"self.y = y self.z = z def __eq__(self, other): return (self.x == other.x\",\n \"and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property\",\n \"X coordinate y (int, float): Y coordinate z (int, float): Z coordinate \\\"\\\"\\\"\",\n \"return (self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2\",\n \"self.z = z def contains(self, cub): \\\"\\\"\\\"Tests if another cuboid is contained by\",\n \"<= self.y + self.height and cub.x + cub.width <= self.x + self.width and\",\n \"def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye)\",\n \"- point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2) def distance_squared(self,\",\n \"self.bottom == other.bottom and self.height == other.height and \\\\ self.outeye == other.outeye and\",\n \"cub.ineye or cub.left == self.right and cub.bottom == self.top and self.outeye == cub.ineye\",\n \"return self.volume() < other.volume() def __eq__(self, other): \\\"\\\"\\\"Equal cuboids have same properties.\\\"\\\"\\\" if\",\n \"@property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \\\"\\\"\\\"Compare cuboids by\",\n \"height (int, float): depth (int, float): rid (identifier object): \\\"\\\"\\\" assert(height >= 0\",\n \"cub.y + cub.height <= self.y + self.height and cub.x + cub.width <= self.x\",\n \"for intersections. edges (bool): Accept edge touching cuboids as intersects or not Returns:\",\n \"(bool): Accept edge touching cuboids as intersects or not Returns: bool: True if\",\n \"return self.end.z - self.start.z class Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive class. x, y, z->\",\n \"other.contains(self): self.x = other.x self.y = other.y self.z = other.z self.width = other.width\",\n \"\\\"\\\"\\\"Basic cuboid primitive class. x, y, z-> Lower right corner coordinates width -\",\n \"self.height, self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\" yield self.corner_top_l yield self.corner_top_r yield\",\n \"one, False otherwise \\\"\\\"\\\" return (cub.y >= self.y and cub.x >= self.x and\",\n \"self.width == other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z =\",\n \"ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right - left, top\",\n \"cub.bottom or self.left == cub.right or self.right == cub.left or self.outeye == cub.ineye\",\n \"x, y, z, width, height, depth, rid=None): \\\"\\\"\\\"Initiating the Cuboid Args: x (int,\",\n \"(self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\" yield\",\n \"another cuboid is contained by this one Arguments: cub (Cuboid): The other cuboiud\",\n \"= ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y, z,\",\n \"== self.right and self.bottom == cub.top and cub.outeye == self.ineye or cub.left ==\",\n \"self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self):\",\n \"other.end def __repr__(self): return \\\"S({}, {})\\\".format(self.start, self.end) @property def length_squared(self): \\\"\\\"\\\"Faster than length\",\n \"True if it is inside this one, False otherwise \\\"\\\"\\\" return (cub.y >=\",\n \"== self.right and cub.bottom == self.top and self.outeye == cub.ineye or cub.left ==\",\n \"\\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y +\",\n \"start, end): \\\"\\\"\\\"Arguments: start (Point): Segment start point end (Point): Segment end point\",\n \"== self.ineye or cub.left == self.right and cub.bottom == self.top and cub.outeye ==\",\n \"cub.bottom == self.top and self.outeye == cub.ineye or self.left == cub.right and self.bottom\",\n \"segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x\",\n \"self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \\\"R({},\",\n \"and cub.bottom == self.top and cub.outeye == self.ineye): return False return True def\",\n \"intersect, False otherwise \\\"\\\"\\\" # Not even touching if (self.bottom > cub.top or\",\n \"True # Other cuboid is Right/Left from this if self.bottom == other.bottom and\",\n \"- point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point): return (self.x -\",\n \"from this if self.left == other.left and self.width == other.width and \\\\ self.outeye\",\n \"other): \\\"\\\"\\\"Equal cuboids have same properties.\\\"\\\"\\\" if not isinstance(other, self.__class__): return False return\",\n \"self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return\",\n \"cub.top or self.top == cub.bottom or self.left == cub.right or self.right == cub.left\",\n \"self.outeye == cub.ineye or self.left == cub.right and self.bottom == cub.top and cub.outeye\",\n \"('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y, z, width,\",\n \"coordinate \\\"\\\"\\\" self.x = x self.y = y self.z = z def contains(self,\",\n \"length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return\",\n \"other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth\",\n \"bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def left(self):\",\n \"a cuboid to this one. If the result is also a cuboid and\",\n \"= end def __eq__(self, other): if not isinstance(other, self.__class__): None return self.start ==\",\n \"by volume (used for sorting)\\\"\\\"\\\" return self.volume() < other.volume() def __eq__(self, other): \\\"\\\"\\\"Equal\",\n \"or depth will be returned Returns: Cuboid: Intersection. None: There was no intersection.\",\n \"and self.width == other.width and \\\\ self.outeye == other.outeye and self.depth == self.depth:\",\n \"cub.top or self.top < cub.bottom or self.left > cub.right or self.right < cub.left\",\n \"self.start = start self.end = end def __eq__(self, other): if not isinstance(other, self.__class__):\",\n \"if self.bottom == other.bottom and self.height == other.height and \\\\ self.outeye == other.outeye\",\n \"self.x + self.width and cub.z + cub.depth <= self.z + self.depth) def intersects(self,\",\n \"= min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height = y_max\",\n \"edge z coordinate\\\"\\\"\\\" return self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top,\",\n \"and its length. Arguments: - start (Point): Starting Point - length (number): segment\",\n \"depth - \\\"\\\"\\\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def\",\n \"self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \\\"R({}, {}, {}, {}, {}, {})\\\".format( self.x,\",\n \"z): \\\"\\\"\\\"Move Cuboid to x,y,z coordinates Arguments: x (int, float): X coordinate y\",\n \"start (Point): Starting Point - length (number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and\",\n \"= x_min self.width = x_max - x_min return True # Other cuboid is\",\n \"self.start == other.start and self.end == other.end def __repr__(self): return \\\"S({}, {})\\\".format(self.start, self.end)\",\n \"and self.y == other.y and self.z == other.z) def __hash__(self): return hash( (self.x,\",\n \"== self.ineye or self.left == cub.right and cub.bottom == self.top and cub.outeye ==\",\n \"@property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom,\",\n \"float): rid (identifier object): \\\"\\\"\\\" assert(height >= 0 and width >= 0 and\",\n \"end (Point): Segment end point \\\"\\\"\\\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start =\",\n \"self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return\",\n \"self.height = height self.depth = depth self.x = x self.y = y self.z\",\n \"length)) @property def length(self): return self.end.z - self.start.z class Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive\",\n \"start.z + length)) @property def length(self): return self.end.z - self.start.z class Cuboid(object): \\\"\\\"\\\"Basic\",\n \"float): z (int, float): width (int, float): height (int, float): depth (int, float):\",\n \"The other cuboid. edges (bool): If true, touching edges are considered an intersection,\",\n \"@property def outeye(self): \\\"\\\"\\\"Cuboid farther from eye edge z coordinate\\\"\\\"\\\" return self.z @property\",\n \"# Other cuboid is Up/Down from this if self.left == other.left and self.width\",\n \"if (self.left == cub.right and self.bottom == cub.top and self.outeye == cub.ineye or\",\n \"isinstance(other, self.__class__): None return self.start == other.start and self.end == other.end def __repr__(self):\",\n \"== self.top and cub.outeye == self.ineye): return False return True def intersection(self, cub,\",\n \"start, length): \\\"\\\"\\\"Create an Horizontal segment given its left most end point and\",\n \"isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property def length(self):\",\n \"(Point): Segment end point \\\"\\\"\\\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start\",\n \"@property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self, start,\",\n \"rid=None): \\\"\\\"\\\"Initiating the Cuboid Args: x (int, float): y (int, float): z (int,\",\n \"width - height - depth - \\\"\\\"\\\" __slots__ = ('width', 'height', 'depth', 'x',\",\n \"other cuboid. edges (bool): If true, touching edges are considered an intersection, and\",\n \"False return True def intersection(self, cub, edges=False): \\\"\\\"\\\"Returns the cuboid resulting of the\",\n \"'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y, z, width, height, depth,\",\n \"cuboid returned will have a volume of 0. Returns None if there is\",\n \"left, top - bottom, ineye - outeye) def join(self, other): \\\"\\\"\\\"Try to join\",\n \"self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out\",\n \"their edges, and the argument 'edges' is True the cuboid returned will have\",\n \"cub.bottom == self.top and cub.outeye == self.ineye or cub.left == self.right and self.bottom\",\n \"True if not self.intersects(other, edges=True): return False # Other cuboid is Up/Down from\",\n \"'z', 'rid') def __init__(self, x, y, z, width, height, depth, rid=None): \\\"\\\"\\\"Initiating the\",\n \"{})\\\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width\",\n \"= other.x self.y = other.y self.z = other.z self.width = other.width self.height =\",\n \"volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width * self.height * self.depth def move(self, x, y,\",\n \"y, z): self.x = x self.y = y self.z = z def __eq__(self,\",\n \"return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y)\",\n \"+ length, start.y, start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment):\",\n \"segment given its left most end point and its length. Arguments: - start\",\n \"self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\" yield self.corner_top_l\",\n \"self.left == cub.right and cub.bottom == self.top and cub.outeye == self.ineye or cub.left\",\n \"distance_squared(self, point): return (self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z\",\n \"Returns None if there is no intersection. Arguments: cub (Cuboid): The other cuboid.\",\n \"self.left == other.left and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max =\",\n \"If the result is also a cuboid and the operation is successful then\",\n \"def __init__(self, start, length): \\\"\\\"\\\"Create a Vertical segment given its bottom most end\",\n \"self.width = width self.height = height self.depth = depth self.x = x self.y\",\n \"max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x,\",\n \"> cub.ineye or self.ineye < cub.outeye): return False # Discard edge intersects if\",\n \"volume (used for sorting)\\\"\\\"\\\" return self.volume() < other.volume() def __eq__(self, other): \\\"\\\"\\\"Equal cuboids\",\n \"# Other cuboid is Right/Left from this if self.bottom == other.bottom and self.height\",\n \"is inside this one, False otherwise \\\"\\\"\\\" return (cub.y >= self.y and cub.x\",\n \"@property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class\",\n \"a cuboid of 0 height or width or depth will be returned Returns:\",\n \"join(self, other): \\\"\\\"\\\"Try to join a cuboid to this one. If the result\",\n \"return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y,\",\n \"self.top < cub.bottom or self.left > cub.right or self.right < cub.left or self.outeye\",\n \"start, length): \\\"\\\"\\\"Create a Vertical segment given its bottom most end point and\",\n \"length, start.y, start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical\",\n \"\\\"\\\"\\\"Returns the cuboid resulting of the intersection of this and cub If the\",\n \"cub.bottom == self.top and self.outeye == cub.ineye or cub.left == self.right and self.bottom\",\n \"self.left == cub.right or self.right == cub.left or self.outeye == cub.ineye or self.ineye\",\n \"and \\\\ self.left == other.left and self.width == other.width: z_min = min(self.outeye, other.outeye)\",\n \"object): \\\"\\\"\\\" assert(height >= 0 and width >= 0 and depth >= 0)\",\n \"other.top) self.y = y_min self.height = y_max - y_min return True # Other\",\n \"- point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end): \\\"\\\"\\\"Arguments:\",\n \"return False return (self.width == other.width and self.height == other.height and self.depth ==\",\n \"the cuboid resulting of the intersection of this and cub If the cuboids\",\n \"The other cuboiud Returns: bool: True if it is inside this one, False\",\n \"ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal\",\n \"__eq__(self, other): \\\"\\\"\\\"Equal cuboids have same properties.\\\"\\\"\\\" if not isinstance(other, self.__class__): return False\",\n \"class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create a Vertical segment given\",\n \"= other.z self.width = other.width self.height = other.height self.depth = other.depth return True\",\n \"self.z = z self.rid = rid @property def bottom(self): \\\"\\\"\\\"Cuboid bottom edge y\",\n \"an In-Depth segment given its bottom most end point and its length. Arguments:\",\n \"== other.width and self.height == other.height and self.depth == other.depth and self.x ==\",\n \"self.z) def distance(self, point): \\\"\\\"\\\"Calculate distance to another point\\\"\\\"\\\" return sqrt((self.x - point.x)**2\",\n \"most end point and its length. Arguments: - start (Point): Starting Point -\",\n \"and a cuboid of 0 height or width or depth will be returned\",\n \"self.depth) def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width * self.height * self.depth def move(self,\",\n \">= self.x and cub.z >= self.z and cub.y + cub.height <= self.y +\",\n \"False otherwise \\\"\\\"\\\" if self.contains(other): return True if other.contains(self): self.x = other.x self.y\",\n \"return min(self.start.z, self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an\",\n \"width or depth will be returned Returns: Cuboid: Intersection. None: There was no\",\n \"return self.start == other.start and self.end == other.end def __repr__(self): return \\\"S({}, {})\\\".format(self.start,\",\n \"cub.outeye): return False # Discard corner intersects if (self.left == cub.right and self.bottom\",\n \"eye edge z coordinate\\\"\\\"\\\" return self.z @property def ineye(self): \\\"\\\"\\\"Cuboid nearer from eye\",\n \"== cub.top or self.top == cub.bottom or self.left == cub.right or self.right ==\",\n \"self.x = other.x self.y = other.y self.z = other.z self.width = other.width self.height\",\n \"> cub.right or self.right < cub.left or self.outeye > cub.ineye or self.ineye <\",\n \"@property def right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property\",\n \"self.width, self.height, self.depth) def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width * self.height * self.depth\",\n \"and cub.x + cub.width <= self.x + self.width and cub.z + cub.depth <=\",\n \"other.start and self.end == other.end def __repr__(self): return \\\"S({}, {})\\\".format(self.start, self.end) @property def\",\n \"of 0. Returns None if there is no intersection. Arguments: cub (Cuboid): The\",\n \"@property def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property\",\n \"length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y,\",\n \"self.ineye or cub.left == self.right and self.bottom == cub.top and self.outeye == cub.ineye\",\n \"Returns: Cuboid: Intersection. None: There was no intersection. \\\"\\\"\\\" if not self.intersects(cub, edges=edges):\",\n \"coordinate\\\"\\\"\\\" return self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property\",\n \"def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def\",\n \"top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def right(self):\",\n \"Z coordinate \\\"\\\"\\\" self.x = x self.y = y self.z = z def\",\n \"corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property\",\n \"def __init__(self, start, length): \\\"\\\"\\\"Create an Horizontal segment given its left most end\",\n \"start, Point(start.x + length, start.y, start.z)) @property def length(self): return self.end.x - self.start.x\",\n \"== cub.outeye): return False # Discard corner intersects if (self.left == cub.right and\",\n \"self.top == cub.bottom or self.left == cub.right or self.right == cub.left or self.outeye\",\n \"other.height self.depth = other.depth return True if not self.intersects(other, edges=True): return False #\",\n \"point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 + (self.y - point.y)**2 +\",\n \"other.left) x_max = max(self.right, other.right) self.x = x_min self.width = x_max - x_min\",\n \"touching edges are considered an intersection, and a cuboid of 0 height or\",\n \"as intersects or not Returns: bool: True if the cuboids intersect, False otherwise\",\n \"= min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid(\",\n \"and cub If the cuboids are only touching by their edges, and the\",\n \"self.y = other.y self.z = other.z self.width = other.width self.height = other.height self.depth\",\n \"from this if self.bottom == other.bottom and self.height == other.height and \\\\ self.left\",\n \"x, y, z-> Lower right corner coordinates width - height - depth -\",\n \"cuboid primitive class. x, y, z-> Lower right corner coordinates width - height\",\n \"= y self.z = z def contains(self, cub): \\\"\\\"\\\"Tests if another cuboid is\",\n \"== self.right and self.bottom == cub.top and self.outeye == cub.ineye or cub.left ==\",\n \"\\\"\\\"\\\"Detect intersections between this cuboid and cub. Args: cub (Cuboid): Cuboid to test\",\n \"cuboids intersect, False otherwise \\\"\\\"\\\" # Not even touching if (self.bottom > cub.top\",\n \"yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield\",\n \"{}, {}, {}, {}, {})\\\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self):\",\n \"self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out\",\n \"cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right)\",\n \"= start self.end = end def __eq__(self, other): if not isinstance(other, self.__class__): None\",\n \"bool: True when successfully joined, False otherwise \\\"\\\"\\\" if self.contains(other): return True if\",\n \"def contains(self, cub): \\\"\\\"\\\"Tests if another cuboid is contained by this one Arguments:\",\n \"__iter__(self): \\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l\",\n \"or cub.left == self.right and cub.bottom == self.top and cub.outeye == self.ineye): return\",\n \"return True if other.contains(self): self.x = other.x self.y = other.y self.z = other.z\",\n \"self.z + self.depth) def intersects(self, cub, edges=False): \\\"\\\"\\\"Detect intersections between this cuboid and\",\n \"def __init__(self, start, end): \\\"\\\"\\\"Arguments: start (Point): Segment start point end (Point): Segment\",\n \"self.depth = depth self.x = x self.y = y self.z = z self.rid\",\n \"if there is no intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool):\",\n \"class Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end): \\\"\\\"\\\"Arguments: start (Point):\",\n \"__eq__(self, other): return (self.x == other.x and self.y == other.y and self.z ==\",\n \"return self.end.y - self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create\",\n \"class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an In-Depth segment given\",\n \"self.depth == self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x =\",\n \"def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \\\"\\\"\\\"Compare cuboids by volume\",\n \"min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right - left, top - bottom,\",\n \"edges, and the argument 'edges' is True the cuboid returned will have a\",\n \"width, height, depth, rid=None): \\\"\\\"\\\"Initiating the Cuboid Args: x (int, float): y (int,\",\n \"self.height == other.height and \\\\ self.outeye == other.outeye and self.depth == self.depth: x_min\",\n \"yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \\\"R({}, {}, {}, {},\",\n \"a volume of 0. Returns None if there is no intersection. Arguments: cub\",\n \"cub.right and self.bottom == cub.top and self.outeye == cub.ineye or self.left == cub.right\",\n \"== cub.top and self.outeye == cub.ineye or self.left == cub.right and cub.bottom ==\",\n \"and self.y == other.y and self.z == other.z) def __repr__(self): return \\\"P({}, {},\",\n \"this one. If the result is also a cuboid and the operation is\",\n \"\\\"\\\"\\\" if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left =\",\n \"intersects if (self.left == cub.right and self.bottom == cub.top and self.outeye == cub.ineye\",\n \"return (self.width == other.width and self.height == other.height and self.depth == other.depth and\",\n \"the intersection of this and cub If the cuboids are only touching by\",\n \"when successfully joined, False otherwise \\\"\\\"\\\" if self.contains(other): return True if other.contains(self): self.x\",\n \"cuboid and the operation is successful then this cuboid is modified to the\",\n \"+ cub.height <= self.y + self.height and cub.x + cub.width <= self.x +\",\n \"cub.left == self.right and cub.bottom == self.top and cub.outeye == self.ineye): return False\",\n \"right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return\",\n \"x coordinate\\\"\\\"\\\" return self.x + self.width @property def outeye(self): \\\"\\\"\\\"Cuboid farther from eye\",\n \"properties.\\\"\\\"\\\" if not isinstance(other, self.__class__): return False return (self.width == other.width and self.height\",\n \"== self.ineye): return False return True def intersection(self, cub, edges=False): \\\"\\\"\\\"Returns the cuboid\",\n \"return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate through cuboid\",\n \"# Discard corner intersects if (self.left == cub.right and self.bottom == cub.top and\",\n \"y self.z = z self.rid = rid @property def bottom(self): \\\"\\\"\\\"Cuboid bottom edge\",\n \"self.y, self.z) def distance(self, point): \\\"\\\"\\\"Calculate distance to another point\\\"\\\"\\\" return sqrt((self.x -\",\n \"False otherwise \\\"\\\"\\\" # Not even touching if (self.bottom > cub.top or self.top\",\n \"nearer from eye edge z coordinate\\\"\\\"\\\" return self.z + self.depth @property def corner_top_l(self):\",\n \"and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z\",\n \"self.contains(other): return True if other.contains(self): self.x = other.x self.y = other.y self.z =\",\n \"\\\"\\\"\\\" self.x = x self.y = y self.z = z def contains(self, cub):\",\n \"Point(start.x, start.y, start.z + length)) @property def length(self): return self.end.z - self.start.z class\",\n \"cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye)\",\n \"self.width == other.width and \\\\ self.outeye == other.outeye and self.depth == self.depth: y_min\",\n \"and self.outeye == cub.ineye or cub.left == self.right and cub.bottom == self.top and\",\n \"self.y = y_min self.height = y_max - y_min return True # Other cuboid\",\n \"cub.ineye or self.ineye < cub.outeye): return False # Discard edge intersects if not\",\n \"def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment):\",\n \"def length(self): return self.end.z - self.start.z class Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive class. x,\",\n \"\\\"\\\"\\\"Move Cuboid to x,y,z coordinates Arguments: x (int, float): X coordinate y (int,\",\n \"x_max - x_min return True # Other cuboid is Right/Left from this if\",\n \"and width >= 0 and depth >= 0) self.width = width self.height =\",\n \"y (int, float): Y coordinate z (int, float): Z coordinate \\\"\\\"\\\" self.x =\",\n \"self.width and cub.z + cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False):\",\n \"Cuboid Args: x (int, float): y (int, float): z (int, float): width (int,\",\n \"def __init__(self, start, length): \\\"\\\"\\\"Create an In-Depth segment given its bottom most end\",\n \"# Discard edge intersects if not edges: if (self.bottom == cub.top or self.top\",\n \"def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate\",\n \"\\\\ self.outeye == other.outeye and self.depth == self.depth: x_min = min(self.left, other.left) x_max\",\n \"self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an Horizontal segment\",\n \"max(self.ineye, other.ineye) self.z = z_min self.depth = z_max - z_min return True return\",\n \"coordinate\\\"\\\"\\\" return self.z @property def ineye(self): \\\"\\\"\\\"Cuboid nearer from eye edge z coordinate\\\"\\\"\\\"\",\n \"__hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate through\",\n \"left most end point and its length. Arguments: - start (Point): Starting Point\",\n \"\\\"P({}, {}, {})\\\".format(self.x, self.y, self.z) def distance(self, point): \\\"\\\"\\\"Calculate distance to another point\\\"\\\"\\\"\",\n \"and cub.outeye == self.ineye or self.left == cub.right and cub.bottom == self.top and\",\n \"if other.contains(self): self.x = other.x self.y = other.y self.z = other.z self.width =\",\n \"return \\\"S({}, {})\\\".format(self.start, self.end) @property def length_squared(self): \\\"\\\"\\\"Faster than length and useful for\",\n \"+ self.height @property def left(self): \\\"\\\"\\\"Cuboid left edge x coordinate\\\"\\\"\\\" return self.x @property\",\n \"def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye)\",\n \"@property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top,\",\n \"__slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y,\",\n \"there is no intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool): If\",\n \"was no intersection. \\\"\\\"\\\" if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom,\",\n \"self.y @property def top(self): \\\"\\\"\\\"Cuboid top edge y coordiante\\\"\\\"\\\" return self.y + self.height\",\n \"def intersects(self, cub, edges=False): \\\"\\\"\\\"Detect intersections between this cuboid and cub. Args: cub\",\n \"\\\"\\\"\\\"Cuboid bottom edge y coordinate\\\"\\\"\\\" return self.y @property def top(self): \\\"\\\"\\\"Cuboid top edge\",\n \"y self.z = z def contains(self, cub): \\\"\\\"\\\"Tests if another cuboid is contained\",\n \"Up/Down from this if self.left == other.left and self.width == other.width and \\\\\",\n \"even touching if (self.bottom > cub.top or self.top < cub.bottom or self.left >\",\n \"edge y coordiante\\\"\\\"\\\" return self.y + self.height @property def left(self): \\\"\\\"\\\"Cuboid left edge\",\n \"self.x + self.width @property def outeye(self): \\\"\\\"\\\"Cuboid farther from eye edge z coordinate\\\"\\\"\\\"\",\n \"or self.top == cub.bottom or self.left == cub.right or self.right == cub.left or\",\n \"touching cuboids as intersects or not Returns: bool: True if the cuboids intersect,\",\n \"to another point\\\"\\\"\\\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + (\",\n \"cuboid to this one. If the result is also a cuboid and the\",\n \"y (int, float): z (int, float): width (int, float): height (int, float): depth\",\n \"- start (Point): Starting Point - length (number): segment length \\\"\\\"\\\" assert(isinstance(start, Point)\",\n \"self.volume() < other.volume() def __eq__(self, other): \\\"\\\"\\\"Equal cuboids have same properties.\\\"\\\"\\\" if not\",\n \"self.top and cub.outeye == self.ineye or cub.left == self.right and self.bottom == cub.top\",\n \"# Not even touching if (self.bottom > cub.top or self.top < cub.bottom or\",\n \"end point \\\"\\\"\\\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end =\",\n \"assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end = end def __eq__(self,\",\n \"to join a cuboid to this one. If the result is also a\",\n \"left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self):\",\n \"+ self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return\",\n \"self.ineye or cub.left == self.right and cub.bottom == self.top and cub.outeye == self.ineye):\",\n \"sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2) def\",\n \"assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y,\",\n \"\\\"\\\"\\\"Cuboid nearer from eye edge z coordinate\\\"\\\"\\\" return self.z + self.depth @property def\",\n \"distance to another point\\\"\\\"\\\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 +\",\n \"self.x @property def right(self): \\\"\\\"\\\"Cuboid right edge x coordinate\\\"\\\"\\\" return self.x + self.width\",\n \"cub.height <= self.y + self.height and cub.x + cub.width <= self.x + self.width\",\n \"{}, {}, {}, {})\\\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \\\"\\\"\\\"Cuboid\",\n \"'z') def __init__(self, x, y, z): self.x = x self.y = y self.z\",\n \"self.outeye > cub.ineye or self.ineye < cub.outeye): return False # Discard edge intersects\",\n \"returned will have a volume of 0. Returns None if there is no\",\n \"cub): \\\"\\\"\\\"Tests if another cuboid is contained by this one Arguments: cub (Cuboid):\",\n \"intersections. edges (bool): Accept edge touching cuboids as intersects or not Returns: bool:\",\n \"== cub.right or self.right == cub.left or self.outeye == cub.ineye or self.ineye ==\",\n \"self.ineye or self.left == cub.right and cub.bottom == self.top and cub.outeye == self.ineye\",\n \"will have a volume of 0. Returns None if there is no intersection.\",\n \"length(self): return self.end.y - self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self, start, length):\",\n \"returned Returns: Cuboid: Intersection. None: There was no intersection. \\\"\\\"\\\" if not self.intersects(cub,\",\n \"left, bottom, outeye, right - left, top - bottom, ineye - outeye) def\",\n \"== cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye): return False #\",\n \"+ ( self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self,\",\n \"return (cub.y >= self.y and cub.x >= self.x and cub.z >= self.z and\",\n \"return self.z @property def ineye(self): \\\"\\\"\\\"Cuboid nearer from eye edge z coordinate\\\"\\\"\\\" return\",\n \"None return self.start == other.start and self.end == other.end def __repr__(self): return \\\"S({},\",\n \"self.__class__): return False return (self.width == other.width and self.height == other.height and self.depth\",\n \"otherwise \\\"\\\"\\\" return (cub.y >= self.y and cub.x >= self.x and cub.z >=\",\n \"other.depth and self.x == other.x and self.y == other.y and self.z == other.z)\",\n \"return \\\"R({}, {}, {}, {}, {}, {})\\\".format( self.x, self.y, self.z, self.width, self.height, self.depth)\",\n \"self.height == other.height and self.depth == other.depth and self.x == other.x and self.y\",\n \"max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right,\",\n \"__init__(self, start, length): \\\"\\\"\\\"Create an Horizontal segment given its left most end point\",\n \"self.bottom == cub.top and self.outeye == cub.ineye or self.left == cub.right and cub.bottom\",\n \"point\\\"\\\"\\\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z -\",\n \"<gh_stars>10-100 # -*- coding: utf-8 -*- from math import sqrt class Point(object): __slots__\",\n \"- point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2 class Segment(object):\",\n \"= y_max - y_min return True # Other cuboid is Right/Left from this\",\n \"outeye(self): \\\"\\\"\\\"Cuboid farther from eye edge z coordinate\\\"\\\"\\\" return self.z @property def ineye(self):\",\n \"touching by their edges, and the argument 'edges' is True the cuboid returned\",\n \"self.z - point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 + (self.y -\",\n \"coordinate\\\"\\\"\\\" return self.y @property def top(self): \\\"\\\"\\\"Cuboid top edge y coordiante\\\"\\\"\\\" return self.y\",\n \"cub, edges=False): \\\"\\\"\\\"Returns the cuboid resulting of the intersection of this and cub\",\n \"corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \\\"\\\"\\\"Compare cuboids by volume (used\",\n \"self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self,\",\n \"False # Other cuboid is Up/Down from this if self.left == other.left and\",\n \"return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def\",\n \"successfully joined, False otherwise \\\"\\\"\\\" if self.contains(other): return True if other.contains(self): self.x =\",\n \"== other.depth and self.x == other.x and self.y == other.y and self.z ==\",\n \"return self.y + self.height @property def left(self): \\\"\\\"\\\"Cuboid left edge x coordinate\\\"\\\"\\\" return\",\n \"some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self): return\",\n \"self.bottom == cub.top and cub.outeye == self.ineye or self.left == cub.right and cub.bottom\",\n \"max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def\",\n \"cub.bottom == self.top and cub.outeye == self.ineye): return False return True def intersection(self,\",\n \"self.y + self.height @property def left(self): \\\"\\\"\\\"Cuboid left edge x coordinate\\\"\\\"\\\" return self.x\",\n \"self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top\",\n \"\\\"\\\"\\\" assert(height >= 0 and width >= 0 and depth >= 0) self.width\",\n \"bottom, ineye - outeye) def join(self, other): \\\"\\\"\\\"Try to join a cuboid to\",\n \"\\\"\\\"\\\"Faster than length and useful for some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property def length(self):\",\n \"between this cuboid and cub. Args: cub (Cuboid): Cuboid to test for intersections.\",\n \"cub.width <= self.x + self.width and cub.z + cub.depth <= self.z + self.depth)\",\n \"return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \\\"\\\"\\\"Compare cuboids by volume (used for\",\n \"== other.bottom and self.height == other.height and \\\\ self.outeye == other.outeye and self.depth\",\n \"from math import sqrt class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self,\",\n \"self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right,\",\n \"# -*- coding: utf-8 -*- from math import sqrt class Point(object): __slots__ =\",\n \"float): Z coordinate \\\"\\\"\\\" self.x = x self.y = y self.z = z\",\n \"self.end.y - self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an\",\n \">= self.y and cub.x >= self.x and cub.z >= self.z and cub.y +\",\n \"edges=False): \\\"\\\"\\\"Detect intersections between this cuboid and cub. Args: cub (Cuboid): Cuboid to\",\n \"other.right) self.x = x_min self.width = x_max - x_min return True # Other\",\n \"length): \\\"\\\"\\\"Create a Vertical segment given its bottom most end point and its\",\n \"(self.left == cub.right and self.bottom == cub.top and self.outeye == cub.ineye or self.left\",\n \"not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property def\",\n \"(int, float): Y coordinate z (int, float): Z coordinate \\\"\\\"\\\" self.x = x\",\n \"Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end): \\\"\\\"\\\"Arguments: start (Point): Segment\",\n \"isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property def length(self):\",\n \"start, Point(start.x, start.y + length, start.z)) @property def length(self): return self.end.y - self.start.y\",\n \"if self.bottom == other.bottom and self.height == other.height and \\\\ self.left == other.left\",\n \"not self.intersects(other, edges=True): return False # Other cuboid is Up/Down from this if\",\n \"False # Discard edge intersects if not edges: if (self.bottom == cub.top or\",\n \"return False return True def intersection(self, cub, edges=False): \\\"\\\"\\\"Returns the cuboid resulting of\",\n \"\\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create a Vertical segment given its bottom\",\n \"other.outeye and self.depth == self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right)\",\n \"> cub.top or self.top < cub.bottom or self.left > cub.right or self.right <\",\n \"== self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min\",\n \"\\\"\\\"\\\"Compare cuboids by volume (used for sorting)\\\"\\\"\\\" return self.volume() < other.volume() def __eq__(self,\",\n \"are only touching by their edges, and the argument 'edges' is True the\",\n \"min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye,\",\n \">= self.z and cub.y + cub.height <= self.y + self.height and cub.x +\",\n \"def outeye(self): \\\"\\\"\\\"Cuboid farther from eye edge z coordinate\\\"\\\"\\\" return self.z @property def\",\n \"Returns: bool: True if the cuboids intersect, False otherwise \\\"\\\"\\\" # Not even\",\n \"Point) and isinstance(end, Point)) self.start = start self.end = end def __eq__(self, other):\",\n \"to x,y,z coordinates Arguments: x (int, float): X coordinate y (int, float): Y\",\n \"__repr__(self): return \\\"P({}, {}, {})\\\".format(self.x, self.y, self.z) def distance(self, point): \\\"\\\"\\\"Calculate distance to\",\n \"and self.depth == other.depth and self.x == other.x and self.y == other.y and\",\n \"height or width or depth will be returned Returns: Cuboid: Intersection. None: There\",\n \"return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\"\",\n \"self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return \\\"R({}, {}, {}, {}, {},\",\n \"farther from eye edge z coordinate\\\"\\\"\\\" return self.z @property def ineye(self): \\\"\\\"\\\"Cuboid nearer\",\n \"self.y + self.height and cub.x + cub.width <= self.x + self.width and cub.z\",\n \"cub.ineye or cub.left == self.right and self.bottom == cub.top and cub.outeye == self.ineye\",\n \"self.x == other.x and self.y == other.y and self.z == other.z) def __hash__(self):\",\n \"volume\\\"\\\"\\\" return self.width * self.height * self.depth def move(self, x, y, z): \\\"\\\"\\\"Move\",\n \"min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left,\",\n \"corners\\\"\\\"\\\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out\",\n \"distance(self, point): \\\"\\\"\\\"Calculate distance to another point\\\"\\\"\\\" return sqrt((self.x - point.x)**2 + (self.y\",\n \"return self.x @property def right(self): \\\"\\\"\\\"Cuboid right edge x coordinate\\\"\\\"\\\" return self.x +\",\n \"start.y + length, start.z)) @property def length(self): return self.end.y - self.start.y class DSegment(Segment):\",\n \"cub.top and self.outeye == cub.ineye or cub.left == self.right and cub.bottom == self.top\",\n \"= max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top) right =\",\n \"no intersection. \\\"\\\"\\\" if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom)\",\n \"assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length,\",\n \"return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def\",\n \"self.x = x self.y = y self.z = z def contains(self, cub): \\\"\\\"\\\"Tests\",\n \"\\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an In-Depth segment given its bottom\",\n \"other.x self.y = other.y self.z = other.z self.width = other.width self.height = other.height\",\n \"(identifier object): \\\"\\\"\\\" assert(height >= 0 and width >= 0 and depth >=\",\n \"coordinate\\\"\\\"\\\" return self.x + self.width @property def outeye(self): \\\"\\\"\\\"Cuboid farther from eye edge\",\n \"__eq__(self, other): if not isinstance(other, self.__class__): None return self.start == other.start and self.end\",\n \"cub.ineye or self.left == cub.right and cub.bottom == self.top and self.outeye == cub.ineye\",\n \"join a cuboid to this one. If the result is also a cuboid\",\n \"self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x)\",\n \"self.bottom == cub.top and self.outeye == cub.ineye or cub.left == self.right and cub.bottom\",\n \"= other.y self.z = other.z self.width = other.width self.height = other.height self.depth =\",\n \"== cub.ineye or self.ineye == cub.outeye): return False # Discard corner intersects if\",\n \"\\\"\\\"\\\"Cuboid top edge y coordiante\\\"\\\"\\\" return self.y + self.height @property def left(self): \\\"\\\"\\\"Cuboid\",\n \"\\\"R({}, {}, {}, {}, {}, {})\\\".format( self.x, self.y, self.z, self.width, self.height, self.depth) def\",\n \"self.x = x self.y = y self.z = z self.rid = rid @property\",\n \"touching if (self.bottom > cub.top or self.top < cub.bottom or self.left > cub.right\",\n \"for some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self):\",\n \"def length(self): return self.end.x - self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self, start,\",\n \"self.ineye < cub.outeye): return False # Discard edge intersects if not edges: if\",\n \"@property def length(self): return self.end.x - self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self,\",\n \"= ('start', 'end') def __init__(self, start, end): \\\"\\\"\\\"Arguments: start (Point): Segment start point\",\n \"= other.depth return True if not self.intersects(other, edges=True): return False # Other cuboid\",\n \"length, start.z)) @property def length(self): return self.end.y - self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\"\",\n \"Cuboid( left, bottom, outeye, right - left, top - bottom, ineye - outeye)\",\n \"= z def contains(self, cub): \\\"\\\"\\\"Tests if another cuboid is contained by this\",\n \"self.right == cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye): return False\",\n \"primitive class. x, y, z-> Lower right corner coordinates width - height -\",\n \"of the intersection of this and cub If the cuboids are only touching\",\n \"or self.left > cub.right or self.right < cub.left or self.outeye > cub.ineye or\",\n \"{})\\\".format(self.start, self.end) @property def length_squared(self): \\\"\\\"\\\"Faster than length and useful for some comparisons\\\"\\\"\\\"\",\n \"self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \\\"\\\"\\\"Compare cuboids\",\n \"(int, float): X coordinate y (int, float): Y coordinate z (int, float): Z\",\n \"'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y, z, width, height,\",\n \"Other cuboid is Right/Left from this if self.bottom == other.bottom and self.height ==\",\n \"\\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length,\",\n \"outeye) def join(self, other): \\\"\\\"\\\"Try to join a cuboid to this one. If\",\n \"cub.x >= self.x and cub.z >= self.z and cub.y + cub.height <= self.y\",\n \"and self.height == other.height and \\\\ self.outeye == other.outeye and self.depth == self.depth:\",\n \"\\\"\\\"\\\"Cuboid right edge x coordinate\\\"\\\"\\\" return self.x + self.width @property def outeye(self): \\\"\\\"\\\"Cuboid\",\n \"edge intersects if not edges: if (self.bottom == cub.top or self.top == cub.bottom\",\n \"self.width = other.width self.height = other.height self.depth = other.depth return True if not\",\n \"self.bottom == cub.top and cub.outeye == self.ineye or cub.left == self.right and cub.bottom\",\n \"length(self): return self.end.x - self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self, start, length):\",\n \"float): height (int, float): depth (int, float): rid (identifier object): \\\"\\\"\\\" assert(height >=\",\n \"Accept edge touching cuboids as intersects or not Returns: bool: True if the\",\n \"== cub.right and cub.bottom == self.top and self.outeye == cub.ineye or self.left ==\",\n \"Cuboid to x,y,z coordinates Arguments: x (int, float): X coordinate y (int, float):\",\n \"return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self,\",\n \"self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y =\",\n \"__init__(self, x, y, z, width, height, depth, rid=None): \\\"\\\"\\\"Initiating the Cuboid Args: x\",\n \"- x_min return True # Other cuboid is Right/Left from this if self.bottom\",\n \"self.z @property def ineye(self): \\\"\\\"\\\"Cuboid nearer from eye edge z coordinate\\\"\\\"\\\" return self.z\",\n \"< cub.outeye): return False # Discard edge intersects if not edges: if (self.bottom\",\n \"given its bottom most end point and its length. Arguments: - start (Point):\",\n \"and self.bottom == cub.top and cub.outeye == self.ineye or self.left == cub.right and\",\n \"start, Point(start.x, start.y, start.z + length)) @property def length(self): return self.end.z - self.start.z\",\n \"cub.outeye == self.ineye or self.left == cub.right and cub.bottom == self.top and cub.outeye\",\n \"= other.height self.depth = other.depth return True if not self.intersects(other, edges=True): return False\",\n \"max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z,\",\n \"self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): \\\"\\\"\\\"Compare\",\n \"intersections between this cuboid and cub. Args: cub (Cuboid): Cuboid to test for\",\n \"start point end (Point): Segment end point \\\"\\\"\\\" assert(isinstance(start, Point) and isinstance(end, Point))\",\n \"return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def\",\n \"def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye)\",\n \"the cuboids intersect, False otherwise \\\"\\\"\\\" # Not even touching if (self.bottom >\",\n \"@property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom,\",\n \"== self.top and cub.outeye == self.ineye or cub.left == self.right and self.bottom ==\",\n \"z coordinate\\\"\\\"\\\" return self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye)\",\n \"z-> Lower right corner coordinates width - height - depth - \\\"\\\"\\\" __slots__\",\n \"'end') def __init__(self, start, end): \\\"\\\"\\\"Arguments: start (Point): Segment start point end (Point):\",\n \"not isinstance(other, self.__class__): return False return (self.width == other.width and self.height == other.height\",\n \"== cub.right and self.bottom == cub.top and self.outeye == cub.ineye or self.left ==\",\n \"resulting of the intersection of this and cub If the cuboids are only\",\n \"\\\"\\\"\\\" return (cub.y >= self.y and cub.x >= self.x and cub.z >= self.z\",\n \"(self.y - point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point): return (self.x\",\n \"self.right and cub.bottom == self.top and self.outeye == cub.ineye or cub.left == self.right\",\n \"y_min return True # Other cuboid is Right/Left from this if self.bottom ==\",\n \"Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x, y, z): self.x =\",\n \"corner coordinates width - height - depth - \\\"\\\"\\\" __slots__ = ('width', 'height',\",\n \"y, z): \\\"\\\"\\\"Move Cuboid to x,y,z coordinates Arguments: x (int, float): X coordinate\",\n \"successful then this cuboid is modified to the union. Arguments: other (Cuboid): Cuboid\",\n \"cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye)\",\n \"return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def left(self): return\",\n \"hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\"\",\n \"min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min self.width = x_max -\",\n \"edge touching cuboids as intersects or not Returns: bool: True if the cuboids\",\n \"@property def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def\",\n \"and isinstance(end, Point)) self.start = start self.end = end def __eq__(self, other): if\",\n \"cub.ineye or self.left == cub.right and self.bottom == cub.top and cub.outeye == self.ineye\",\n \"@property def length(self): return self.end.y - self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self,\",\n \"and self.bottom == cub.top and cub.outeye == self.ineye or cub.left == self.right and\",\n \"= height self.depth = depth self.x = x self.y = y self.z =\",\n \"than length and useful for some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property def length(self): return\",\n \"if (self.bottom == cub.top or self.top == cub.bottom or self.left == cub.right or\",\n \"cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye): return False # Discard\",\n \"def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye)\",\n \"is Right/Left from this if self.bottom == other.bottom and self.height == other.height and\",\n \"self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width *\",\n \"== cub.bottom or self.left == cub.right or self.right == cub.left or self.outeye ==\",\n \"bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top) right\",\n \"outeye, right - left, top - bottom, ineye - outeye) def join(self, other):\",\n \"HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an Horizontal segment given its\",\n \"from eye edge z coordinate\\\"\\\"\\\" return self.z + self.depth @property def corner_top_l(self): return\",\n \"\\\"\\\"\\\"Equal cuboids have same properties.\\\"\\\"\\\" if not isinstance(other, self.__class__): return False return (self.width\",\n \"float): y (int, float): z (int, float): width (int, float): height (int, float):\",\n \"* self.depth def move(self, x, y, z): \\\"\\\"\\\"Move Cuboid to x,y,z coordinates Arguments:\",\n \"end point and its length. Arguments: - start (Point): Starting Point - length\",\n \"Y coordinate z (int, float): Z coordinate \\\"\\\"\\\" self.x = x self.y =\",\n \"self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left,\",\n \"left(self): \\\"\\\"\\\"Cuboid left edge x coordinate\\\"\\\"\\\" return self.x @property def right(self): \\\"\\\"\\\"Cuboid right\",\n \"z): self.x = x self.y = y self.z = z def __eq__(self, other):\",\n \"length (number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__(\",\n \"def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye)\",\n \"cuboid is contained by this one Arguments: cub (Cuboid): The other cuboiud Returns:\",\n \"'rid') def __init__(self, x, y, z, width, height, depth, rid=None): \\\"\\\"\\\"Initiating the Cuboid\",\n \"VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create a Vertical segment given its\",\n \"top(self): \\\"\\\"\\\"Cuboid top edge y coordiante\\\"\\\"\\\" return self.y + self.height @property def left(self):\",\n \"other): \\\"\\\"\\\"Compare cuboids by volume (used for sorting)\\\"\\\"\\\" return self.volume() < other.volume() def\",\n \"x self.y = y self.z = z def contains(self, cub): \\\"\\\"\\\"Tests if another\",\n \"if another cuboid is contained by this one Arguments: cub (Cuboid): The other\",\n \"__init__(self, start, end): \\\"\\\"\\\"Arguments: start (Point): Segment start point end (Point): Segment end\",\n \"@property def left(self): \\\"\\\"\\\"Cuboid left edge x coordinate\\\"\\\"\\\" return self.x @property def right(self):\",\n \"self.right < cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye): return False\",\n \"@property def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property\",\n \"through cuboid corners\\\"\\\"\\\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out\",\n \"same properties.\\\"\\\"\\\" if not isinstance(other, self.__class__): return False return (self.width == other.width and\",\n \"\\\"\\\"\\\"Create a Vertical segment given its bottom most end point and its length.\",\n \"and useful for some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property\",\n \"\\\"\\\"\\\" if self.contains(other): return True if other.contains(self): self.x = other.x self.y = other.y\",\n \"cub.right or self.right < cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye):\",\n \"self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z)\",\n \"Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive class. x, y, z-> Lower right corner coordinates width\",\n \"or self.ineye == cub.outeye): return False # Discard corner intersects if (self.left ==\",\n \"coordinates Arguments: x (int, float): X coordinate y (int, float): Y coordinate z\",\n \"length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x +\",\n \"or width or depth will be returned Returns: Cuboid: Intersection. None: There was\",\n \"(self.bottom > cub.top or self.top < cub.bottom or self.left > cub.right or self.right\",\n \"cuboid corners\\\"\\\"\\\" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield\",\n \"start.y, start.z + length)) @property def length(self): return self.end.z - self.start.z class Cuboid(object):\",\n \"self.x = x self.y = y self.z = z def __eq__(self, other): return\",\n \"float): Y coordinate z (int, float): Z coordinate \\\"\\\"\\\" self.x = x self.y\",\n \"of this and cub If the cuboids are only touching by their edges,\",\n \"__lt__(self, other): \\\"\\\"\\\"Compare cuboids by volume (used for sorting)\\\"\\\"\\\" return self.volume() < other.volume()\",\n \"intersects or not Returns: bool: True if the cuboids intersect, False otherwise \\\"\\\"\\\"\",\n \"cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right - left,\",\n \"@property def length_squared(self): \\\"\\\"\\\"Faster than length and useful for some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end)\",\n \"def length(self): return self.end.y - self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self, start,\",\n \"+ self.width and cub.z + cub.depth <= self.z + self.depth) def intersects(self, cub,\",\n \"coordinate y (int, float): Y coordinate z (int, float): Z coordinate \\\"\\\"\\\" self.x\",\n \"this one, False otherwise \\\"\\\"\\\" return (cub.y >= self.y and cub.x >= self.x\",\n \"other.height and \\\\ self.left == other.left and self.width == other.width: z_min = min(self.outeye,\",\n \"{})\\\".format(self.x, self.y, self.z) def distance(self, point): \\\"\\\"\\\"Calculate distance to another point\\\"\\\"\\\" return sqrt((self.x\",\n \"= x self.y = y self.z = z def contains(self, cub): \\\"\\\"\\\"Tests if\",\n \"self.z, self.width, self.height, self.depth)) def __iter__(self): \\\"\\\"\\\"Iterate through cuboid corners\\\"\\\"\\\" yield self.corner_top_l yield\",\n \"if not self.intersects(other, edges=True): return False # Other cuboid is Up/Down from this\",\n \"= width self.height = height self.depth = depth self.x = x self.y =\",\n \"to this one. If the result is also a cuboid and the operation\",\n \"joined, False otherwise \\\"\\\"\\\" if self.contains(other): return True if other.contains(self): self.x = other.x\",\n \"y, z, width, height, depth, rid=None): \\\"\\\"\\\"Initiating the Cuboid Args: x (int, float):\",\n \"self.depth def move(self, x, y, z): \\\"\\\"\\\"Move Cuboid to x,y,z coordinates Arguments: x\",\n \"- depth - \\\"\\\"\\\" __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid')\",\n \"isinstance(end, Point)) self.start = start self.end = end def __eq__(self, other): if not\",\n \"self.z == other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth))\",\n \"\\\"\\\"\\\"Try to join a cuboid to this one. If the result is also\",\n \"cuboid is Right/Left from this if self.bottom == other.bottom and self.height == other.height\",\n \"If true, touching edges are considered an intersection, and a cuboid of 0\",\n \"or cub.left == self.right and cub.bottom == self.top and self.outeye == cub.ineye or\",\n \"coordinate\\\"\\\"\\\" return self.x @property def right(self): \\\"\\\"\\\"Cuboid right edge x coordinate\\\"\\\"\\\" return self.x\",\n \"point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end') def\",\n \"return \\\"P({}, {}, {})\\\".format(self.x, self.y, self.z) def distance(self, point): \\\"\\\"\\\"Calculate distance to another\",\n \"(int, float): Z coordinate \\\"\\\"\\\" self.x = x self.y = y self.z =\",\n \"Vertical segment given its bottom most end point and its length. Arguments: -\",\n \"operation is successful then this cuboid is modified to the union. Arguments: other\",\n \"length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y\",\n \"z (int, float): Z coordinate \\\"\\\"\\\" self.x = x self.y = y self.z\",\n \"edge y coordinate\\\"\\\"\\\" return self.y @property def top(self): \\\"\\\"\\\"Cuboid top edge y coordiante\\\"\\\"\\\"\",\n \"and self.height == other.height and self.depth == other.depth and self.x == other.x and\",\n \"other.depth return True if not self.intersects(other, edges=True): return False # Other cuboid is\",\n \"cub.left == self.right and cub.bottom == self.top and self.outeye == cub.ineye or cub.left\",\n \"self.left == cub.right and self.bottom == cub.top and cub.outeye == self.ineye or self.left\",\n \"0 height or width or depth will be returned Returns: Cuboid: Intersection. None:\",\n \"if the cuboids intersect, False otherwise \\\"\\\"\\\" # Not even touching if (self.bottom\",\n \"cub If the cuboids are only touching by their edges, and the argument\",\n \"return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2)\",\n \"if it is inside this one, False otherwise \\\"\\\"\\\" return (cub.y >= self.y\",\n \"other.z) def __repr__(self): return \\\"P({}, {}, {})\\\".format(self.x, self.y, self.z) def distance(self, point): \\\"\\\"\\\"Calculate\",\n \"yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def\",\n \"self).__init__( start, Point(start.x + length, start.y, start.z)) @property def length(self): return self.end.x -\",\n \"self.__class__): None return self.start == other.start and self.end == other.end def __repr__(self): return\",\n \"def bottom(self): \\\"\\\"\\\"Cuboid bottom edge y coordinate\\\"\\\"\\\" return self.y @property def top(self): \\\"\\\"\\\"Cuboid\",\n \"def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self, start, length):\",\n \"def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def\",\n \"utf-8 -*- from math import sqrt class Point(object): __slots__ = ('x', 'y', 'z')\",\n \"Returns: bool: True when successfully joined, False otherwise \\\"\\\"\\\" if self.contains(other): return True\",\n \"or self.ineye < cub.outeye): return False # Discard edge intersects if not edges:\",\n \"other.x and self.y == other.y and self.z == other.z) def __hash__(self): return hash(\",\n \"or self.outeye > cub.ineye or self.ineye < cub.outeye): return False # Discard edge\",\n \"max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right -\",\n \"edges=edges): return None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top =\",\n \"(int, float): z (int, float): width (int, float): height (int, float): depth (int,\",\n \"and self.z == other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height,\",\n \"edges=True): return False # Other cuboid is Up/Down from this if self.left ==\",\n \"self.height, self.depth) def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width * self.height * self.depth def\",\n \"and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y\",\n \"Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length))\",\n \"coordinate z (int, float): Z coordinate \\\"\\\"\\\" self.x = x self.y = y\",\n \"and cub.outeye == self.ineye): return False return True def intersection(self, cub, edges=False): \\\"\\\"\\\"Returns\",\n \"this and cub If the cuboids are only touching by their edges, and\",\n \"corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def\",\n \"self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an In-Depth segment\",\n \"and self.height == other.height and \\\\ self.left == other.left and self.width == other.width:\",\n \"+ length, start.z)) @property def length(self): return self.end.y - self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth\",\n \"== other.height and \\\\ self.outeye == other.outeye and self.depth == self.depth: x_min =\",\n \"self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min self.width\",\n \"- length (number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment,\",\n \"(bool): If true, touching edges are considered an intersection, and a cuboid of\",\n \"contained by this one Arguments: cub (Cuboid): The other cuboiud Returns: bool: True\",\n \"cuboid is modified to the union. Arguments: other (Cuboid): Cuboid to join Returns:\",\n \"@property def ineye(self): \\\"\\\"\\\"Cuboid nearer from eye edge z coordinate\\\"\\\"\\\" return self.z +\",\n \"or self.left == cub.right and cub.bottom == self.top and self.outeye == cub.ineye or\",\n \"cuboids by volume (used for sorting)\\\"\\\"\\\" return self.volume() < other.volume() def __eq__(self, other):\",\n \"def distance(self, point): \\\"\\\"\\\"Calculate distance to another point\\\"\\\"\\\" return sqrt((self.x - point.x)**2 +\",\n \"self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x)\",\n \"Starting Point - length (number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length,\",\n \"(int, float): depth (int, float): rid (identifier object): \\\"\\\"\\\" assert(height >= 0 and\",\n \"cuboid is Up/Down from this if self.left == other.left and self.width == other.width\",\n \"not isinstance(other, self.__class__): None return self.start == other.start and self.end == other.end def\",\n \"cub (Cuboid): Cuboid to test for intersections. edges (bool): Accept edge touching cuboids\",\n \"self.depth) def intersects(self, cub, edges=False): \\\"\\\"\\\"Detect intersections between this cuboid and cub. Args:\",\n \"otherwise \\\"\\\"\\\" if self.contains(other): return True if other.contains(self): self.x = other.x self.y =\",\n \"yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return\",\n \"a cuboid and the operation is successful then this cuboid is modified to\",\n \"bottom, outeye, right - left, top - bottom, ineye - outeye) def join(self,\",\n \"point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__\",\n \"Point)) self.start = start self.end = end def __eq__(self, other): if not isinstance(other,\",\n \"self.left > cub.right or self.right < cub.left or self.outeye > cub.ineye or self.ineye\",\n \"and cub.bottom == self.top and self.outeye == cub.ineye or cub.left == self.right and\",\n \"Segment end point \\\"\\\"\\\" assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end\",\n \"< cub.bottom or self.left > cub.right or self.right < cub.left or self.outeye >\",\n \"point): \\\"\\\"\\\"Calculate distance to another point\\\"\\\"\\\" return sqrt((self.x - point.x)**2 + (self.y -\",\n \"one. If the result is also a cuboid and the operation is successful\",\n \"Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property def length(self): return\",\n \"= max(self.right, other.right) self.x = x_min self.width = x_max - x_min return True\",\n \"Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an Horizontal segment given its left most\",\n \"Discard edge intersects if not edges: if (self.bottom == cub.top or self.top ==\",\n \"height, depth, rid=None): \\\"\\\"\\\"Initiating the Cuboid Args: x (int, float): y (int, float):\",\n \"length): \\\"\\\"\\\"Create an Horizontal segment given its left most end point and its\",\n \"edge z coordinate\\\"\\\"\\\" return self.z @property def ineye(self): \\\"\\\"\\\"Cuboid nearer from eye edge\",\n \"Point - length (number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point))\",\n \"is successful then this cuboid is modified to the union. Arguments: other (Cuboid):\",\n \"= max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right\",\n \"= x self.y = y self.z = z def __eq__(self, other): return (self.x\",\n \"== other.height and \\\\ self.left == other.left and self.width == other.width: z_min =\",\n \"z, width, height, depth, rid=None): \\\"\\\"\\\"Initiating the Cuboid Args: x (int, float): y\",\n \"intersection(self, cub, edges=False): \\\"\\\"\\\"Returns the cuboid resulting of the intersection of this and\",\n \"== other.outeye and self.depth == self.depth: x_min = min(self.left, other.left) x_max = max(self.right,\",\n \"self.height == other.height and \\\\ self.left == other.left and self.width == other.width: z_min\",\n \"length): \\\"\\\"\\\"Create an In-Depth segment given its bottom most end point and its\",\n \"def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def bottom(self):\",\n \"- self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create a Vertical\",\n \"self.top and self.outeye == cub.ineye or self.left == cub.right and self.bottom == cub.top\",\n \"also a cuboid and the operation is successful then this cuboid is modified\",\n \"self.x and cub.z >= self.z and cub.y + cub.height <= self.y + self.height\",\n \"z (int, float): width (int, float): height (int, float): depth (int, float): rid\",\n \"and self.x == other.x and self.y == other.y and self.z == other.z) def\",\n \"Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other):\",\n \"Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self):\",\n \"min(self.start.z, self.end.z) class HSegment(Segment): \\\"\\\"\\\"Horizontal Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an Horizontal\",\n \"True if the cuboids intersect, False otherwise \\\"\\\"\\\" # Not even touching if\",\n \"(cub.y >= self.y and cub.x >= self.x and cub.z >= self.z and cub.y\",\n \"Horizontal segment given its left most end point and its length. Arguments: -\",\n \"self.y, self.z, self.width, self.height, self.depth) def volume(self): \\\"\\\"\\\"Cuboid volume\\\"\\\"\\\" return self.width * self.height\",\n \"\\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z\",\n \"cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom,\",\n \"Returns: bool: True if it is inside this one, False otherwise \\\"\\\"\\\" return\",\n \">= 0 and width >= 0 and depth >= 0) self.width = width\",\n \"by their edges, and the argument 'edges' is True the cuboid returned will\",\n \"length. Arguments: - start (Point): Starting Point - length (number): segment length \\\"\\\"\\\"\",\n \"cub.top and self.outeye == cub.ineye or self.left == cub.right and cub.bottom == self.top\",\n \"if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left = max(self.left,\",\n \"(Cuboid): The other cuboiud Returns: bool: True if it is inside this one,\",\n \"(Point): Segment start point end (Point): Segment end point \\\"\\\"\\\" assert(isinstance(start, Point) and\",\n \"False otherwise \\\"\\\"\\\" return (cub.y >= self.y and cub.x >= self.x and cub.z\",\n \"If the cuboids are only touching by their edges, and the argument 'edges'\",\n \"__slots__ = ('x', 'y', 'z') def __init__(self, x, y, z): self.x = x\",\n \"cub (Cuboid): The other cuboiud Returns: bool: True if it is inside this\",\n \"self.end = end def __eq__(self, other): if not isinstance(other, self.__class__): None return self.start\",\n \"cuboids are only touching by their edges, and the argument 'edges' is True\",\n \"and \\\\ self.outeye == other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom)\",\n \"'y', 'z', 'rid') def __init__(self, x, y, z, width, height, depth, rid=None): \\\"\\\"\\\"Initiating\",\n \"or self.left == cub.right and self.bottom == cub.top and cub.outeye == self.ineye or\",\n \"other.left and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye)\",\n \"= max(self.top, other.top) self.y = y_min self.height = y_max - y_min return True\",\n \"the Cuboid Args: x (int, float): y (int, float): z (int, float): width\",\n \"assert(height >= 0 and width >= 0 and depth >= 0) self.width =\",\n \"right(self): \\\"\\\"\\\"Cuboid right edge x coordinate\\\"\\\"\\\" return self.x + self.width @property def outeye(self):\",\n \"return self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def\",\n \"Arguments: cub (Cuboid): The other cuboid. edges (bool): If true, touching edges are\",\n \"y_max = max(self.top, other.top) self.y = y_min self.height = y_max - y_min return\",\n \"= min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min self.width = x_max\",\n \"cub. Args: cub (Cuboid): Cuboid to test for intersections. edges (bool): Accept edge\",\n \"self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right,\",\n \"(self.bottom == cub.top or self.top == cub.bottom or self.left == cub.right or self.right\",\n \"Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property def length(self): return\",\n \"(number): segment length \\\"\\\"\\\" assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start,\",\n \"0 and depth >= 0) self.width = width self.height = height self.depth =\",\n \"self.outeye == cub.ineye or cub.left == self.right and self.bottom == cub.top and cub.outeye\",\n \"other.height and self.depth == other.depth and self.x == other.x and self.y == other.y\",\n \"cuboids as intersects or not Returns: bool: True if the cuboids intersect, False\",\n \"corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property\",\n \"return True if not self.intersects(other, edges=True): return False # Other cuboid is Up/Down\",\n \"is contained by this one Arguments: cub (Cuboid): The other cuboiud Returns: bool:\",\n \"length and useful for some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end)\",\n \"self.y == other.y and self.z == other.z) def __hash__(self): return hash( (self.x, self.y,\",\n \"class. x, y, z-> Lower right corner coordinates width - height - depth\",\n \"or self.left == cub.right and cub.bottom == self.top and cub.outeye == self.ineye or\",\n \"otherwise \\\"\\\"\\\" # Not even touching if (self.bottom > cub.top or self.top <\",\n \"this if self.bottom == other.bottom and self.height == other.height and \\\\ self.left ==\",\n \"or not Returns: bool: True if the cuboids intersect, False otherwise \\\"\\\"\\\" #\",\n \"\\\"\\\"\\\"Cuboid farther from eye edge z coordinate\\\"\\\"\\\" return self.z @property def ineye(self): \\\"\\\"\\\"Cuboid\",\n \"cub.outeye == self.ineye): return False return True def intersection(self, cub, edges=False): \\\"\\\"\\\"Returns the\",\n \"to test for intersections. edges (bool): Accept edge touching cuboids as intersects or\",\n \"(Cuboid): The other cuboid. edges (bool): If true, touching edges are considered an\",\n \"bottom(self): \\\"\\\"\\\"Cuboid bottom edge y coordinate\\\"\\\"\\\" return self.y @property def top(self): \\\"\\\"\\\"Cuboid top\",\n \"= y_min self.height = y_max - y_min return True # Other cuboid is\",\n \"end): \\\"\\\"\\\"Arguments: start (Point): Segment start point end (Point): Segment end point \\\"\\\"\\\"\",\n \"self.ineye): return False return True def intersection(self, cub, edges=False): \\\"\\\"\\\"Returns the cuboid resulting\",\n \"Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self):\",\n \"z_max = max(self.ineye, other.ineye) self.z = z_min self.depth = z_max - z_min return\",\n \"x, y, z): self.x = x self.y = y self.z = z def\",\n \"z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth =\",\n \"Point(start.x + length, start.y, start.z)) @property def length(self): return self.end.x - self.start.x class\",\n \"__slots__ = ('start', 'end') def __init__(self, start, end): \\\"\\\"\\\"Arguments: start (Point): Segment start\",\n \"('start', 'end') def __init__(self, start, end): \\\"\\\"\\\"Arguments: start (Point): Segment start point end\",\n \"self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y)\",\n \"@property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top,\",\n \"test for intersections. edges (bool): Accept edge touching cuboids as intersects or not\",\n \"0) self.width = width self.height = height self.depth = depth self.x = x\",\n \"- left, top - bottom, ineye - outeye) def join(self, other): \\\"\\\"\\\"Try to\",\n \"if (self.bottom > cub.top or self.top < cub.bottom or self.left > cub.right or\",\n \"and self.outeye == cub.ineye or self.left == cub.right and cub.bottom == self.top and\",\n \"self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height\",\n \"self.x = x_min self.width = x_max - x_min return True # Other cuboid\",\n \"other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self):\",\n \"+ (self.y - point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__ =\",\n \"start (Point): Segment start point end (Point): Segment end point \\\"\\\"\\\" assert(isinstance(start, Point)\",\n \"edges: if (self.bottom == cub.top or self.top == cub.bottom or self.left == cub.right\",\n \"x, y, z): \\\"\\\"\\\"Move Cuboid to x,y,z coordinates Arguments: x (int, float): X\",\n \"Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property def length(self): return\",\n \"== cub.right and cub.bottom == self.top and cub.outeye == self.ineye or cub.left ==\",\n \"self.height = other.height self.depth = other.depth return True if not self.intersects(other, edges=True): return\",\n \"== other.left and self.width == other.width and \\\\ self.outeye == other.outeye and self.depth\",\n \"self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return\",\n \"max(self.top, other.top) self.y = y_min self.height = y_max - y_min return True #\",\n \"cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False): \\\"\\\"\\\"Detect intersections between this\",\n \"end def __eq__(self, other): if not isinstance(other, self.__class__): None return self.start == other.start\",\n \"self.outeye == other.outeye and self.depth == self.depth: x_min = min(self.left, other.left) x_max =\",\n \"x_min return True # Other cuboid is Right/Left from this if self.bottom ==\",\n \"= min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye =\",\n \"self.right and self.bottom == cub.top and self.outeye == cub.ineye or cub.left == self.right\",\n \"bool: True if it is inside this one, False otherwise \\\"\\\"\\\" return (cub.y\",\n \"the cuboid returned will have a volume of 0. Returns None if there\",\n \">= 0 and depth >= 0) self.width = width self.height = height self.depth\",\n \"x_min self.width = x_max - x_min return True # Other cuboid is Right/Left\",\n \"- self.start.y class DSegment(Segment): \\\"\\\"\\\"In-Depth Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create an In-Depth\",\n \"self.end.x - self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create a\",\n \"== cub.right and self.bottom == cub.top and cub.outeye == self.ineye or self.left ==\",\n \"- point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 + (self.y - point.y)**2\",\n \"corner intersects if (self.left == cub.right and self.bottom == cub.top and self.outeye ==\",\n \"True when successfully joined, False otherwise \\\"\\\"\\\" if self.contains(other): return True if other.contains(self):\",\n \"left = max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right) outeye\",\n \"(self.width == other.width and self.height == other.height and self.depth == other.depth and self.x\",\n \"self.top and cub.outeye == self.ineye): return False return True def intersection(self, cub, edges=False):\",\n \"float): X coordinate y (int, float): Y coordinate z (int, float): Z coordinate\",\n \"Cuboid to test for intersections. edges (bool): Accept edge touching cuboids as intersects\",\n \"start, length): \\\"\\\"\\\"Create an In-Depth segment given its bottom most end point and\",\n \"intersection, and a cuboid of 0 height or width or depth will be\",\n \"== self.ineye or cub.left == self.right and self.bottom == cub.top and self.outeye ==\",\n \"True def intersection(self, cub, edges=False): \\\"\\\"\\\"Returns the cuboid resulting of the intersection of\",\n \"edge x coordinate\\\"\\\"\\\" return self.x @property def right(self): \\\"\\\"\\\"Cuboid right edge x coordinate\\\"\\\"\\\"\",\n \"this one Arguments: cub (Cuboid): The other cuboiud Returns: bool: True if it\",\n \"= rid @property def bottom(self): \\\"\\\"\\\"Cuboid bottom edge y coordinate\\\"\\\"\\\" return self.y @property\",\n \"cub.right and cub.bottom == self.top and cub.outeye == self.ineye or cub.left == self.right\",\n \"self.z = other.z self.width = other.width self.height = other.height self.depth = other.depth return\",\n \"cub.right or self.right == cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye):\",\n \"y coordiante\\\"\\\"\\\" return self.y + self.height @property def left(self): \\\"\\\"\\\"Cuboid left edge x\",\n \"return True def intersection(self, cub, edges=False): \\\"\\\"\\\"Returns the cuboid resulting of the intersection\",\n \"volume of 0. Returns None if there is no intersection. Arguments: cub (Cuboid):\",\n \"another point\\\"\\\"\\\" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z\",\n \"self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right,\",\n \"float): width (int, float): height (int, float): depth (int, float): rid (identifier object):\",\n \"edge x coordinate\\\"\\\"\\\" return self.x + self.width @property def outeye(self): \\\"\\\"\\\"Cuboid farther from\",\n \"(self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2 class\",\n \"and cub.outeye == self.ineye or cub.left == self.right and self.bottom == cub.top and\",\n \"== other.y and self.z == other.z) def __repr__(self): return \\\"P({}, {}, {})\\\".format(self.x, self.y,\",\n \"Arguments: x (int, float): X coordinate y (int, float): Y coordinate z (int,\",\n \"== other.x and self.y == other.y and self.z == other.z) def __repr__(self): return\",\n \"modified to the union. Arguments: other (Cuboid): Cuboid to join Returns: bool: True\",\n \"min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z,\",\n \"self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right,\",\n \"self.z == other.z) def __repr__(self): return \\\"P({}, {}, {})\\\".format(self.x, self.y, self.z) def distance(self,\",\n \"Segment start point end (Point): Segment end point \\\"\\\"\\\" assert(isinstance(start, Point) and isinstance(end,\",\n \"it is inside this one, False otherwise \\\"\\\"\\\" return (cub.y >= self.y and\",\n \"and cub.z + cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False): \\\"\\\"\\\"Detect\",\n \"self.start.z class Cuboid(object): \\\"\\\"\\\"Basic cuboid primitive class. x, y, z-> Lower right corner\",\n \"right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def ineye(self):\",\n \"@property def right(self): \\\"\\\"\\\"Cuboid right edge x coordinate\\\"\\\"\\\" return self.x + self.width @property\",\n \"Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self):\",\n \"== other.height and self.depth == other.depth and self.x == other.x and self.y ==\",\n \"self.depth == other.depth and self.x == other.x and self.y == other.y and self.z\",\n \"cub, edges=False): \\\"\\\"\\\"Detect intersections between this cuboid and cub. Args: cub (Cuboid): Cuboid\",\n \"not Returns: bool: True if the cuboids intersect, False otherwise \\\"\\\"\\\" # Not\",\n \"x_max = max(self.right, other.right) self.x = x_min self.width = x_max - x_min return\",\n \"one Arguments: cub (Cuboid): The other cuboiud Returns: bool: True if it is\",\n \"and cub.z >= self.z and cub.y + cub.height <= self.y + self.height and\",\n \"is no intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool): If true,\",\n \"self.ineye == cub.outeye): return False # Discard corner intersects if (self.left == cub.right\",\n \"from this if self.bottom == other.bottom and self.height == other.height and \\\\ self.outeye\",\n \"and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property\",\n \"self.left == cub.right and cub.bottom == self.top and self.outeye == cub.ineye or self.left\",\n \"self.right and self.bottom == cub.top and cub.outeye == self.ineye or cub.left == self.right\",\n \"and self.z == other.z) def __repr__(self): return \\\"P({}, {}, {})\\\".format(self.x, self.y, self.z) def\",\n \"coordiante\\\"\\\"\\\" return self.y + self.height @property def left(self): \\\"\\\"\\\"Cuboid left edge x coordinate\\\"\\\"\\\"\",\n \"useful for some comparisons\\\"\\\"\\\" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def\",\n \"(int, float): height (int, float): depth (int, float): rid (identifier object): \\\"\\\"\\\" assert(height\",\n \"== cub.ineye or self.left == cub.right and self.bottom == cub.top and cub.outeye ==\",\n \"return True # Other cuboid is Right/Left from this if self.bottom == other.bottom\",\n \"== cub.ineye or cub.left == self.right and cub.bottom == self.top and self.outeye ==\",\n \"only touching by their edges, and the argument 'edges' is True the cuboid\",\n \"point): return (self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z -\",\n \"if self.left == other.left and self.width == other.width and \\\\ self.outeye == other.outeye\",\n \"cub.left == self.right and self.bottom == cub.top and self.outeye == cub.ineye or cub.left\",\n \"self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end):\",\n \"== other.z) def __repr__(self): return \\\"P({}, {}, {})\\\".format(self.x, self.y, self.z) def distance(self, point):\",\n \"self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return\",\n \"cub.top and cub.outeye == self.ineye or self.left == cub.right and cub.bottom == self.top\",\n \"None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top)\",\n \"start self.end = end def __eq__(self, other): if not isinstance(other, self.__class__): None return\",\n \"Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z))\",\n \"+ self.depth) def intersects(self, cub, edges=False): \\\"\\\"\\\"Detect intersections between this cuboid and cub.\",\n \"self.top and self.outeye == cub.ineye or cub.left == self.right and self.bottom == cub.top\",\n \"self.corner_bot_l_out def __repr__(self): return \\\"R({}, {}, {}, {}, {}, {})\\\".format( self.x, self.y, self.z,\",\n \"In-Depth segment given its bottom most end point and its length. Arguments: -\",\n \"(self.y - point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__ = ('start',\",\n \"from eye edge z coordinate\\\"\\\"\\\" return self.z @property def ineye(self): \\\"\\\"\\\"Cuboid nearer from\",\n \"edges (bool): If true, touching edges are considered an intersection, and a cuboid\",\n \"point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2\",\n \"an Horizontal segment given its left most end point and its length. Arguments:\",\n \"ineye(self): \\\"\\\"\\\"Cuboid nearer from eye edge z coordinate\\\"\\\"\\\" return self.z + self.depth @property\",\n \"- height - depth - \\\"\\\"\\\" __slots__ = ('width', 'height', 'depth', 'x', 'y',\",\n \"to join Returns: bool: True when successfully joined, False otherwise \\\"\\\"\\\" if self.contains(other):\",\n \"def move(self, x, y, z): \\\"\\\"\\\"Move Cuboid to x,y,z coordinates Arguments: x (int,\",\n \"self.rid = rid @property def bottom(self): \\\"\\\"\\\"Cuboid bottom edge y coordinate\\\"\\\"\\\" return self.y\",\n \"not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left)\",\n \"return self.end.x - self.start.x class VSegment(Segment): \\\"\\\"\\\"Vertical Segment\\\"\\\"\\\" def __init__(self, start, length): \\\"\\\"\\\"Create\",\n \"outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye,\",\n \"self.left == other.left and self.width == other.width and \\\\ self.outeye == other.outeye and\",\n \"= max(self.ineye, other.ineye) self.z = z_min self.depth = z_max - z_min return True\",\n \"other.z self.width = other.width self.height = other.height self.depth = other.depth return True if\",\n \"__repr__(self): return \\\"S({}, {})\\\".format(self.start, self.end) @property def length_squared(self): \\\"\\\"\\\"Faster than length and useful\",\n \"super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property def length(self): return self.end.y\",\n \"depth (int, float): rid (identifier object): \\\"\\\"\\\" assert(height >= 0 and width >=\",\n \"def distance_squared(self, point): return (self.x - point.x)**2 + (self.y - point.y)**2 + (\",\n \"not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property def\",\n \"def __lt__(self, other): \\\"\\\"\\\"Compare cuboids by volume (used for sorting)\\\"\\\"\\\" return self.volume() <\",\n \"Lower right corner coordinates width - height - depth - \\\"\\\"\\\" __slots__ =\",\n \"0 and width >= 0 and depth >= 0) self.width = width self.height\",\n \"self.intersects(other, edges=True): return False # Other cuboid is Up/Down from this if self.left\",\n \"= x self.y = y self.z = z self.rid = rid @property def\",\n \"eye edge z coordinate\\\"\\\"\\\" return self.z + self.depth @property def corner_top_l(self): return Point(self.left,\",\n \"@property def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property\",\n \"= z self.rid = rid @property def bottom(self): \\\"\\\"\\\"Cuboid bottom edge y coordinate\\\"\\\"\\\"\",\n \"return self.y @property def top(self): \\\"\\\"\\\"Cuboid top edge y coordiante\\\"\\\"\\\" return self.y +\",\n \"cub.bottom or self.left > cub.right or self.right < cub.left or self.outeye > cub.ineye\",\n \"def intersection(self, cub, edges=False): \\\"\\\"\\\"Returns the cuboid resulting of the intersection of this\",\n \"top = min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye\",\n \"and self.depth == self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x\",\n \"this if self.bottom == other.bottom and self.height == other.height and \\\\ self.outeye ==\",\n \"is also a cuboid and the operation is successful then this cuboid is\",\n \"not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property def\"\n]","isConversational":false}}},{"rowIdx":1196,"cells":{"ngram":{"kind":"list like","value":["fields that are omitted from # the request will not be modified in","recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving # all","# check that the images in the response so that's the path to","# pillow, this will import our image class which will let us then","two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the","of the # arguments you want to add # here we have single","key this will get the id of the created object. # Next what","that we retrieved # test full update # put: it will replace the","RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 =","key in the recipe # payload[key]: This will actually get the value of","if files exist on the system from PIL import Image # pillow, this","the # arguments you want to add # here we have single item","# add a new tag and what we're going to do is we're","the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images","on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2","name='Tag 2') payload = { 'title': 'Test recipe with two tags', 'tags': [tag1.id,","to use the sample recipe that gets created # it creates a named","uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) # creates","a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\" payload = {","# put: it will replace the object that we're updating with the full","the request that means if you exclude # any fields in the payload","# you've used it import os # this allows us to perform things","# test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3)","we have single item def sample_tag(user, name='Main course'): \"\"\"Create and return a sample","in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id):","45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id'])","that we're updating with the full # object that is provided in the","as I explained # because when we do a HTTP put if we","an object using the Django rest framework the # default behavior is that","in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the","to access it then it would # just be blank because you've already","# filter our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer =","the path exists for the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path))","we were returning the list view # or we wanted to simulate the","key)) # assertion for each one of these keys, check that it is","# for our viewset because we're going to have a detail action #","= sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe)","<reponame>tahmadvand/recipe_app_api from django.contrib.auth import get_user_model from django.test import TestCase from django.urls import reverse","different HTTP methods: put, patch # patch: Patch is used to update the","and then you pass in a list of the # arguments you want","get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial","the standard HTTP response code for creating objects # in an API. recipe","them by retrieving # all of the recipes from our database. res =","the # recipe with the ID of the recipe that we want to","for each one of these keys, check that it is # equal to","\"\"\"Test updating a recipe with put\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = {","for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the","should be accessible self.assertIn('image', res.data) # check that the path exists for the","class RecipeImageUploadTests(TestCase): # what happens at the setup of the test def setUp(self):","the beginning of the file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions","filtering is working # should only return the first two recipe # test","self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes with","image class which will let us then # create test images which we","return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url # you're going to","recipe.refresh_from_db() # refreshes the details in our recipe from the database # typically","updating with the full # object that is provided in the request that","return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument # when you","sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test","tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = {","the # database and then we're going to check the fields that #","img = Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) #","# somewhere in the system and then you can remove that file after","beginning of the file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions #","\"\"\"Test viewing a recipe detail\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id)","fields that it will change are the # fields that are provided and","/api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \"\"\"Return URL for recipe image upload\"\"\"","going to check # that is the correct value assigned to our recipe","already read up to the end # of the file so use this","args=[recipe_id]) # name of the end point that the default router will create","# convert the dictionary into the argument # when you use the two","detail_url(recipe.id) # the way that you update an object using the Django rest","API requests from core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer","will be the seeking will be done to the # end of the","of an object # there are two ways in which you can update","and mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} )","tags that we retrieved # test full update # put: it will replace","recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response code for","tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res","serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data)","URL in more # or less all the tests let's assign that as","are two ways in which you can update an object using the #","return a sample recipe\"\"\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price':","# Next what we're going to do is we're going to loop through","fields that are provided and any fields that are omitted from # the","then it would # just be blank because you've already read up to","tests let's assign that as a variable # at top of the class","variable # at top of the class in all capitals. # app :","self.client.get(RECIPES_URL) # filter our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer","test partial update and full update of an object # there are two","the only fields that it will change are the # fields that are","as ntf: img = Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG')","tearDown(self): self.recipe.image.delete() # make sure that our file system is kept clean after","patch # patch: Patch is used to update the fields that are provided","we want to update. self.client.patch(url, payload) # make request # We're going to","to write an image # to that file and then we're going to","cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3","# test recipes are limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>',","= { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price':","'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code,","the two asterisks when calling a # function it has the reverse effect.","separated list string and assign # it to the tags get parameter #","ntf.seek(0) # it's the way that Python reads files so because we've #","more # or less all the tests let's assign that as a variable","key in our payload object # getattr: that allows you to retrieve an","we create here and we're going # to replace it with a new","and what we're going to do is we're going # to swap out","the return result def test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes with specific ingredients\"\"\" recipe1 =",") self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test retrieving list of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're","update. self.client.patch(url, payload) # make request # We're going to retrieve an update","is the standard HTTP response code for creating objects # in an API.","also checking if files exist on the system from PIL import Image #","out this tag that we create here and we're going # to replace","# an object by passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self):","the first two recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2 =","it will return a dictionary containing # the created object This is how","the /temp folder # create a temporary file we're going to write an","will return a dictionary containing # the created object This is how I","app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \"\"\"Return URL","what happens at the setup of the test def setUp(self): self.client = APIClient()","Image # pillow, this will import our image class which will let us","assigned class RecipeImageUploadTests(TestCase): # what happens at the setup of the test def","through each # one of these keys and then we're going to check","test_upload_image_to_recipe(self): \"\"\"Test uploading an image to recipe\"\"\" url = image_upload_url(self.recipe.id) # going to","going to upload that file # through the API like you would with","access the URL in more # or less all the tests let's assign","the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \"\"\"Return","recipe with the ID of the recipe that we want to update. self.client.patch(url,","= {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will be replaced","sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id,","that won't change # unless you do refresh from dB if the values","temporary file on the system at a random # location usually in the","the end # of the file so use this seek function to set","URL for recipe image upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image","any fields in the payload those fields will actually be removed # from","new tag is in the tags that we retrieved # test full update","tags # assigned class RecipeImageUploadTests(TestCase): # what happens at the setup of the","ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2,","# make request # We're going to retrieve an update to the recipe","sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving # all of","to change a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add","going to write an image # to that file and then we're going","recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id,","detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all()","sample_recipe(user=self.user) # after the test runs it runs tear down def tearDown(self): self.recipe.image.delete()","one of these keys and then we're going to check # that is","self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \"\"\"Test viewing a recipe detail\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user))","tag so the existing tag that # we created won't be assigned to","= sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe with two tags',","def test_view_recipe_detail(self): \"\"\"Test viewing a recipe detail\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url","ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe","so if you try to access it then it would # just be","HTTP put if we omit a field # that should clear the value","an image def detail_url(recipe_id): \"\"\"Return recipe detail URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name","create # for our viewset because we're going to have a detail action","test_retrieve_recipes(self): \"\"\"Test retrieving list of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access","we created won't be assigned to it url = detail_url(recipe.id) # the way","the tags that we retrieved # test full update # put: it will","our API requests from core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer,","keys and then we're going to check # that is the correct value","to it url = detail_url(recipe.id) # the way that you update an object","with the full # object that is provided in the request that means","is the URL of the # recipe with the ID of the recipe","the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this","name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and","it will be the seeking will be done to the # end of","and just make sure they match what we expect. recipe.refresh_from_db() # refreshes the","{ 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url =","serializer = RecipeSerializer(recipes, many=True) # many=true: this is because we were returning the","test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe with ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 =","'<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test retrieving list of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user)","here and we're going # to replace it with a new tag payload","and any fields that are omitted from # the request will not be","payload = { 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 }","self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full update of an","to a model the details of that won't change # unless you do","assertion for each one of these keys, check that it is # equal","to check # that is the correct value assigned to our recipe model.","removing all of the test files that we create # delete the image","have a sample tag assigned should not have any tags # assigned class","object that we're updating with the full # object that is provided in","request that means if you exclude # any fields in the payload those","we're going # to replace it with a new tag payload = {'title':","import os # this allows us to perform things like # creating path","file and then we're going to upload that file # through the API","do refresh from dB if the values have changed # in the database.","black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python reads files","tag that we create here and we're going # to replace it with","class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe API access\"\"\" def setUp(self): self.client = APIClient() def","[ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED)","serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \"\"\"Test viewing a recipe","updating def test_full_update_recipe(self): \"\"\"Test updating a recipe with put\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user))","you create a new model and you have a # reference to a","self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self): \"\"\"Test returning","sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by the authenticated user","This is how I know that if we do res.data and # retrieve","recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag new tag is","explained # because when we do a HTTP put if we omit a","because we've # saved the file it will be the seeking will be","25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes,","location usually in the /temp folder # create a temporary file we're going","to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test uploading an invalid image\"\"\" url =","that's being updated. def test_partial_update_recipe(self): \"\"\"Test updating a recipe with patch\"\"\" # make","update and full update of an object # there are two ways in","= sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user,","details in our recipe from the database # typically when you create a","change a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a","the recipe self.assertEqual(tags.count(), 2) # because we expect two tags to be assigned.","in our payload object # getattr: that allows you to retrieve an attribute","files that we create # delete the image if it exists in the","self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we","format='JPEG') ntf.seek(0) # it's the way that Python reads files so because we've","# we created won't be assigned to it url = detail_url(recipe.id) # the","detail action # this is how you specify arguments with the reverse function","self.client = APIClient() def test_required_auth(self): \"\"\"Test the authenticaiton is required\"\"\" res = self.client.get(RECIPES_URL)","check that the path exists for the image that is saved to our","self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) # after the test runs","# should only return the first two recipe # test the response: serializer1","payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will","curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 =","a random # location usually in the /temp folder # create a temporary","the value of that field so now our recipe # that did have","to check the fields that # are assigned and just make sure they","self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all()","temp file # somewhere in the system and then you can remove that","the details of that won't change # unless you do refresh from dB","self.assertIn('image', res.data) # check that the path exists for the image that is","runs tear down def tearDown(self): self.recipe.image.delete() # make sure that our file system","in order to upload an image def detail_url(recipe_id): \"\"\"Return recipe detail URL\"\"\" return","with ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload","payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags =","asterisks when calling a # function it has the reverse effect. class PublicRecipeApiTests(TestCase):","exists for the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self):","actually get the value of the # key in our payload object #","class in all capitals. # app : identifier of the URL in the","going to run some assertions to check that it # uploaded correctly with","you're going to need the existing recipe ID in order to upload an","make request # We're going to retrieve an update to the recipe from","existing tag that # we created won't be assigned to it url =","# in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags)","that you're updating def test_full_update_recipe(self): \"\"\"Test updating a recipe with put\"\"\" recipe =","detail url def image_upload_url(recipe_id): \"\"\"Return URL for recipe image upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id])","# we're going to access them by retrieving # all of the recipes","sample_ingredient(user, name='Cinnamon'): \"\"\"Create and return a sample ingredient\"\"\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user,","sample recipe\"\"\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, }","the class in all capitals. # app : identifier of the URL in","an update to the recipe from the # database and then we're going","sample_recipe(user=self.user, title='Steak and mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id,","# getattr: that allows you to retrieve an attribute from # an object","in which you can update an object using the # API there's two","APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test retrieving list","add a new tag and what we're going to do is we're going","defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return","what we're going to do is we're going # to swap out this","# We're going to retrieve an update to the recipe from the #","# authenticate our user self.recipe = sample_recipe(user=self.user) # after the test runs it","= self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma","2) # because we expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2,","check # that is the correct value assigned to our recipe model. for","our image class which will let us then # create test images which","# in the payload so the only fields that it will change are","that are omitted from # the request will not be modified in the","sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price':","recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer =","ingredients\"\"\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore')","name=name) def sample_ingredient(user, name='Cinnamon'): \"\"\"Create and return a sample ingredient\"\"\" return Ingredient.objects.create(user=user, name=name)","because you've already read up to the end # of the file so","cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms')","path to # the image that should be accessible self.assertIn('image', res.data) # check","limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user)","that file # through the API like you would with a HTTP POST","= self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2)","create # delete the image if it exists in the recipe def test_upload_image_to_recipe(self):","would # just be blank because you've already read up to the end","15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients =","that Python reads files so because we've # saved the file it will","creates a named temporary file on the system at a random # location","you can update an object using the # API there's two different HTTP","list of the # arguments you want to add # here we have","= sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag = sample_tag(user=self.user,","access it then it would # just be blank because you've already read","a sample tag assigned should not have any tags # assigned class RecipeImageUploadTests(TestCase):","there are two ways in which you can update an object using the","an object using the # API there's two different HTTP methods: put, patch","database # typically when you create a new model and you have a","you use the detail URL so that is the URL of the #","with a new tag payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]}","argument # when you use the two asterisks when calling a # function","res = self.client.get(RECIPES_URL) # filter our recipes by the authenticated user recipes =","# make a request to change a field in our recipe. recipe =","saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test uploading an invalid image\"\"\" url","top of the class in all capitals. # app : identifier of the","to access the URL in more # or less all the tests let's","put: it will replace the object that we're updating with the full #","a HTTP put if we omit a field # that should clear the","a sample recipe\"\"\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00,","the details in our recipe from the database # typically when you create","= sample_recipe(user=self.user, title='Steak and mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients':","upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url # you're going","tag that # we created won't be assigned to it url = detail_url(recipe.id)","name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API res","'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients","will create # for our viewset because we're going to have a detail","single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\" payload = { 'title':","res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 =","with specific tags\"\"\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine","rest_framework import status from rest_framework.test import APIClient # use that for making our","all capitals. # app : identifier of the URL in the app #","post this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is","recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) #","default behavior is that it will return a dictionary containing # the created","= Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all() # retrieve the","self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe","assertions # refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check","or we wanted to simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK)","'<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test retrieving list of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) #","that is the correct value assigned to our recipe model. for key in","# test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 =","self.assertIn(tag2, tags) # check if the tags that we created as our sample","user self.recipe = sample_recipe(user=self.user) # after the test runs it runs tear down","which we can then upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since","that file after # you've used it import os # this allows us","generate our upload image url # you're going to need the existing recipe","# delete the image if it exists in the recipe def test_upload_image_to_recipe(self): \"\"\"Test","the database # typically when you create a new model and you have","import status from rest_framework.test import APIClient # use that for making our API","the existing recipe ID in order to upload an image def detail_url(recipe_id): \"\"\"Return","'<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by","tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the tags assigned","arguments with the reverse function # you just pass in args and then","ingredients) # test partial update and full update of an object # there","upload that file # through the API like you would with a HTTP","details of that won't change # unless you do refresh from dB if","res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we just want","and # retrieve the id key this will get the id of the","update the fields that are provided # in the payload so the only","these keys, check that it is # equal to the same key in","of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving #","make a request to change a field in our recipe. recipe = sample_recipe(user=self.user)","\"\"\"Test updating a recipe with patch\"\"\" # make a request to change a","object using the Django rest framework # view sets is you use the","# typically when you create a new model and you have a #","in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an object using","if you try to access it then it would # just be blank","in the /temp folder # create a temporary file we're going to write","assign that as a variable # at top of the class in all","import Image # pillow, this will import our image class which will let","will be done to the # end of the file so if you","name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips')","after the test runs it runs tear down def tearDown(self): self.recipe.image.delete() # make","URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response code for creating","get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes","URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def","get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test retrieving list of recipes\"\"\" sample_recipe(user=self.user)","5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price,","image that should be accessible self.assertIn('image', res.data) # check that the path exists","django.contrib.auth import get_user_model from django.test import TestCase from django.urls import reverse from rest_framework","this is because we were returning the list view # or we wanted","capitals. # app : identifier of the URL in the app # /api/recipe/recipes","the image that should be accessible self.assertIn('image', res.data) # check that the path","# through the API like you would with a HTTP POST and then","the authenticaiton is required\"\"\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated","Updated sample recipe', 'tags': [new_tag.id]} # tags will be replaced with this new","\"\"\"Test authenticated recipe API access\"\"\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user(","specify arguments with the reverse function # you just pass in args and","using the # API there's two different HTTP methods: put, patch # patch:","= sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe', 'time_minutes': 25,","in the recipe def test_upload_image_to_recipe(self): \"\"\"Test uploading an image to recipe\"\"\" url =","that were created with the recipe self.assertEqual(tags.count(), 2) # because we expect two","calling a # function it has the reverse effect. class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated","recipe # that did have a sample tag assigned should not have any","in the payload those fields will actually be removed # from the object","# creating path names and also checking if files exist on the system","view sets is you use the detail URL so that is the URL","the object that's being updated. def test_partial_update_recipe(self): \"\"\"Test updating a recipe with patch\"\"\"","the sample recipe that gets created # it creates a named temporary file","dB if the values have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags","# you're going to need the existing recipe ID in order to upload","is we're going # to swap out this tag that we create here","in the request that means if you exclude # any fields in the","expect. recipe.refresh_from_db() # refreshes the details in our recipe from the database #","file system is kept clean after our test # removing all of the","title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2)","import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call a function which","update an object using the # API there's two different HTTP methods: put,","is provided in the request that means if you exclude # any fields","# to that file and then we're going to upload that file #","serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\" payload =","a HTTP POST and then # we're going to run some assertions to","end of the file so if you try to access it then it","update # put: it will replace the object that we're updating with the","going to do is we're going to loop through each # one of","'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe =","recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post this","= sample_tag(user=self.user, name='Curry') # add a new tag and what we're going to","uploading an image to recipe\"\"\" url = image_upload_url(self.recipe.id) # going to use the","that for making our API requests from core.models import Recipe, Tag, Ingredient from","in this case we just want to serialize a single object self.assertEqual(res.data, serializer.data)","if our filtering is working # should only return the first two recipe","recipe\"\"\" url = image_upload_url(self.recipe.id) # going to use the sample recipe that gets","# assertion for each one of these keys, check that it is #","setup of the test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass')","self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags),","an object using the Django rest framework # view sets is you use","only return the first two recipe # test the response: serializer1 = RecipeSerializer(recipe1)","mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1","simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data)","sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user,","a temp file # somewhere in the system and then you can remove","going to access them by retrieving # all of the recipes from our","not have any tags # assigned class RecipeImageUploadTests(TestCase): # what happens at the","# fields that are provided and any fields that are omitted from #","two different HTTP methods: put, patch # patch: Patch is used to update","queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe with ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1')","exists in the recipe def test_upload_image_to_recipe(self): \"\"\"Test uploading an image to recipe\"\"\" url","fields will actually be removed # from the object that you're updating def","set # the pointer back to the beginning of the file res =","that we create here and we're going # to replace it with a","sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by the authenticated user recipes","in args and then you pass in a list of the # arguments","were returning the list view # or we wanted to simulate the list",") sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by the authenticated","\"\"\"Test returning recipes with specific tags\"\"\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2","to the recipe from the # database and then we're going to check","# this is the standard HTTP response code for creating objects # in","our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe","in the recipe # payload[key]: This will actually get the value of the","= self.client.get(RECIPES_URL) # filter our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user)","to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need to","since we're going to need to access the URL in more # or","{ 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload)","unauthenticated recipe API access\"\"\" def setUp(self): self.client = APIClient() def test_required_auth(self): \"\"\"Test the","RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need to access the URL","to need to access the URL in more # or less all the","and return a sample tag\"\"\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \"\"\"Create and","1) self.assertIn(new_tag, tags) # check that the tag new tag is in the","will then create a temp file # somewhere in the system and then","need to access the URL in more # or less all the tests","the payload so the only fields that it will change are the #","it's the way that Python reads files so because we've # saved the","= RecipeDetailSerializer(recipe) # in this case we just want to serialize a single","{ 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults)","read up to the end # of the file so use this seek","it exists in the recipe def test_upload_image_to_recipe(self): \"\"\"Test uploading an image to recipe\"\"\"","system is kept clean after our test # removing all of the test","will get the id of the created object. # Next what we're going","2') payload = { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes':","our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code,","we're going to write an image # to that file and then we're","recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 =","import tempfile # allows you to call a function which will then create","= sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe with ingredients', 'ingredients':","recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1","from # the request will not be modified in the object that's being","that we want to update. self.client.patch(url, payload) # make request # We're going","status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(),","def detail_url(recipe_id): \"\"\"Return recipe detail URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the","the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize","end point that the default router will create # for our viewset because","arguments you want to add # here we have single item def sample_tag(user,","by passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test creating a","'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id)","# the image that should be accessible self.assertIn('image', res.data) # check that the","the id of the created object. # Next what we're going to do","[tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED)","defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument # when","= detail_url(recipe.id) # the way that you update an object using the Django","file so use this seek function to set # the pointer back to","URL of the # recipe with the ID of the recipe that we","this allows us to perform things like # creating path names and also","# assertions # refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) #","that the path exists for the image that is saved to our model","to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags that","'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title'])","of these keys and then we're going to check # that is the","reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url # you're going to need","temporary file we're going to write an image # to that file and","you exclude # any fields in the payload those fields will actually be","viewset because we're going to have a detail action # this is how","a # function it has the reverse effect. class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe","it with a new tag payload = {'title': 'Partially Updated sample recipe', 'tags':","working # should only return the first two recipe # test the response:","in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test creating a recipe with","retrieve an update to the recipe from the # database and then we're","= self.client.post(RECIPES_URL, payload) # post this payload dictionary to our recipes URL. self.assertEqual(res.status_code,","from rest_framework.test import APIClient # use that for making our API requests from","invalid image\"\"\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)","random # location usually in the /temp folder # create a temporary file","sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1)","self.assertIn(new_tag, tags) # check that the tag new tag is in the tags","the reverse function # you just pass in args and then you pass","Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call","# it to the tags get parameter # if our filtering is working","} defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument #","def test_upload_image_bad_request(self): \"\"\"Test uploading an invalid image\"\"\" url = image_upload_url(self.recipe.id) res = self.client.post(url,","a list of the # arguments you want to add # here we","self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \"\"\"Test viewing a recipe detail\"\"\" recipe =","# the way that you update an object using the Django rest framework","POST and then # we're going to run some assertions to check that","are limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2)","sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry')","from the object that you're updating def test_full_update_recipe(self): \"\"\"Test updating a recipe with","file it will be the seeking will be done to the # end","we retrieved # test full update # put: it will replace the object","clear the value of that field so now our recipe # that did","ingredient\"\"\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \"\"\"Create and return a sample recipe\"\"\"","effect. class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe API access\"\"\" def setUp(self): self.client = APIClient()","from core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile","viewing a recipe detail\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res","status.HTTP_200_OK) # check that the images in the response so that's the path","accessible self.assertIn('image', res.data) # check that the path exists for the image that","with put\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample","image\"\"\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def","existing recipe ID in order to upload an image def detail_url(recipe_id): \"\"\"Return recipe","the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return","= { 'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30,","image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test uploading an","to retrieve an attribute from # an object by passing in a variable.","format='multipart') # assertions # refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK)","ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data)","list of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving","the file it will be the seeking will be done to the #","order to upload an image def detail_url(recipe_id): \"\"\"Return recipe detail URL\"\"\" return reverse('recipe:recipe-detail',","should only return the first two recipe # test the response: serializer1 =","detail URL so that is the URL of the # recipe with the","with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) # creates black square","if # they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data,","router will create # for our viewset because we're going to have a","to need the existing recipe ID in order to upload an image def","to upload an image def detail_url(recipe_id): \"\"\"Return recipe detail URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id])","should clear the value of that field so now our recipe # that","means if you exclude # any fields in the payload those fields will","= APIClient() def test_required_auth(self): \"\"\"Test the authenticaiton is required\"\"\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code,","object This is how I know that if we do res.data and #","get parameter # if our filtering is working # should only return the","this will import our image class which will let us then # create","self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the response so that's","url = image_upload_url(self.recipe.id) # going to use the sample recipe that gets created","title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this","and we're going to check if # they exist in the responses returned","the recipe from the # database and then we're going to check the","recipe\"\"\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params)","is required\"\"\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe API","to the # end of the file so if you try to access","back to the beginning of the file res = self.client.post(url, {'image': ntf}, format='multipart')","from django.urls import reverse from rest_framework import status from rest_framework.test import APIClient #","# check that the path exists for the image that is saved to","'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post this payload","to upload that file # through the API like you would with a","passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test creating a recipe","an object by passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test","sample recipe', 'tags': [new_tag.id]} # tags will be replaced with this new tag","the way that Python reads files so because we've # saved the file","os # this allows us to perform things like # creating path names","object using the Django rest framework the # default behavior is that it","just make sure they match what we expect. recipe.refresh_from_db() # refreshes the details","tag payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will","same as the tags that are in our queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test creating","django.urls import reverse from rest_framework import status from rest_framework.test import APIClient # use","return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \"\"\"Create and return a sample ingredient\"\"\" return","sample tag assigned should not have any tags # assigned class RecipeImageUploadTests(TestCase): #","recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) #","check the fields that # are assigned and just make sure they match","path exists for the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def","RecipeSerializer(recipes, many=True) # many=true: this is because we were returning the list view","the test files that we create # delete the image if it exists","a variable # at top of the class in all capitals. # app","then we're going to check # that is the correct value assigned to","authenticaiton is required\"\"\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe","# refreshes the details in our recipe from the database # typically when","the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res =","parameter # if our filtering is working # should only return the first","{'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma separated list string","at the setup of the test def setUp(self): self.client = APIClient() self.user =","action # this is how you specify arguments with the reverse function #","you specify arguments with the reverse function # you just pass in args","dictionary containing # the created object This is how I know that if","will be replaced with this new tag so the existing tag that #","object # getattr: that allows you to retrieve an attribute from # an","the Django rest framework the # default behavior is that it will return","status.HTTP_201_CREATED) # this is the standard HTTP response code for creating objects #","2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full update of","Next what we're going to do is we're going to loop through each","= RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data,","field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to","test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate","def tearDown(self): self.recipe.image.delete() # make sure that our file system is kept clean","status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test returning recipes with specific tags\"\"\" recipe1 = sample_recipe(user=self.user, title='Thai","add a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a","authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL)","get the id of the created object. # Next what we're going to","self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def","recipe tags = recipe.tags.all() # retrieve the tags that were created with the","an image to recipe\"\"\" url = image_upload_url(self.recipe.id) # going to use the sample","\"\"\"Test unauthenticated recipe API access\"\"\" def setUp(self): self.client = APIClient() def test_required_auth(self): \"\"\"Test","# allows you to call a function which will then create a temp","system and then you can remove that file after # you've used it","# in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an object","that are provided # in the payload so the only fields that it","self.client.patch(url, payload) # make request # We're going to retrieve an update to","RecipeImageUploadTests(TestCase): # what happens at the setup of the test def setUp(self): self.client","object that you're updating def test_full_update_recipe(self): \"\"\"Test updating a recipe with put\"\"\" recipe","done to the # end of the file so if you try to","each # one of these keys and then we're going to check #","we're going to run some assertions to check that it # uploaded correctly","then we're going to check the fields that # are assigned and just","serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes for user\"\"\" # test recipes are limited","we will check that the tags assigned are zero now as I explained","to call a function which will then create a temp file # somewhere","# many=true: this is because we were returning the list view # or","updated. def test_partial_update_recipe(self): \"\"\"Test updating a recipe with patch\"\"\" # make a request","is in the tags that we retrieved # test full update # put:","we create # delete the image if it exists in the recipe def","sure that our file system is kept clean after our test # removing","tags) # check that the tag new tag is in the tags that","of the file so if you try to access it then it would","the reverse effect. class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe API access\"\"\" def setUp(self): self.client","= sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} )","we created as our sample tags are # the same as the tags","def test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes with specific ingredients\"\"\" recipe1 = sample_recipe(user=self.user, title='Posh beans","with the ID of the recipe that we want to update. self.client.patch(url, payload)","that # are assigned and just make sure they match what we expect.","to perform things like # creating path names and also checking if files","This will actually get the value of the # key in our payload","return the first two recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2","then you pass in a list of the # arguments you want to","in more # or less all the tests let's assign that as a","= self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe API access\"\"\" def setUp(self):","class which will let us then # create test images which we can","our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the response","because we expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) #","specific ingredients\"\"\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken","'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert","required\"\"\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe API access\"\"\"","recipe with ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2')","create test images which we can then upload to our API RECIPES_URL =","request # We're going to retrieve an update to the recipe from the","this is how you specify arguments with the reverse function # you just","the way that you update an object using the Django rest framework #","# when you use the two asterisks when calling a # function it","reference to a model the details of that won't change # unless you","2') payload = { 'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id],","PIL import Image # pillow, this will import our image class which will","in all capitals. # app : identifier of the URL in the app","check that the tag new tag is in the tags that we retrieved","sample recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db()","pass in a list of the # arguments you want to add #","HTTP methods: put, patch # patch: Patch is used to update the fields","this case we just want to serialize a single object self.assertEqual(res.data, serializer.data) def","def setUp(self): self.client = APIClient() def test_required_auth(self): \"\"\"Test the authenticaiton is required\"\"\" res","our viewset because we're going to have a detail action # this is","framework the # default behavior is that it will return a dictionary containing","going to do is we're going # to swap out this tag that","# of the file so use this seek function to set # the","behavior is that it will return a dictionary containing # the created object","Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to","in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of these keys,","return result def test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes with specific ingredients\"\"\" recipe1 = sample_recipe(user=self.user,","I know that if we do res.data and # retrieve the id key","it then it would # just be blank because you've already read up","and return a sample ingredient\"\"\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \"\"\"Create and","# that should clear the value of that field so now our recipe","that means if you exclude # any fields in the payload those fields","going to check if # they exist in the responses returned self.assertIn(serializer1.data, res.data)","be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags that we","'{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma separated list string and","methods: put, patch # patch: Patch is used to update the fields that","payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0)","self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test retrieving list of","put if we omit a field # that should clear the value of","ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full update of an object","want to add # here we have single item def sample_tag(user, name='Main course'):","'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) #","to do is we're going # to swap out this tag that we","change are the # fields that are provided and any fields that are","will actually get the value of the # key in our payload object","use the sample recipe that gets created # it creates a named temporary","then we're going to upload that file # through the API like you","self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test returning recipes with specific tags\"\"\" recipe1 = sample_recipe(user=self.user,","self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the","replace it with a new tag payload = {'title': 'Partially Updated sample recipe',","is # equal to the same key in the recipe # payload[key]: This","an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an object using the","recipe with put\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated","is because we were returning the list view # or we wanted to","self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test uploading an invalid image\"\"\" url = image_upload_url(self.recipe.id) res =","know that if we do res.data and # retrieve the id key this","the # default behavior is that it will return a dictionary containing #","database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in","going # to replace it with a new tag payload = {'title': 'Partially","name=name) def sample_recipe(user, **params): \"\"\"Create and return a sample recipe\"\"\" defaults = {","the full # object that is provided in the request that means if","status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe API access\"\"\" def setUp(self): self.client = APIClient()","us then # create test images which we can then upload to our","res = self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the database for","recipes with specific ingredients\"\"\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 =","from the database # typically when you create a new model and you","it would # just be blank because you've already read up to the","we do a HTTP put if we omit a field # that should","test full update # put: it will replace the object that we're updating","recipes and we're going to check if # they exist in the responses","the end point that the default router will create # for our viewset","creating recipe with ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient","of the file so use this seek function to set # the pointer","# going to use the sample recipe that gets created # it creates","that the tag new tag is in the tags that we retrieved #","access\"\"\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user)","# app : identifier of the URL in the app # /api/recipe/recipes #","we do res.data and # retrieve the id key this will get the","sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and","is you use the detail URL so that is the URL of the","for making our API requests from core.models import Recipe, Tag, Ingredient from ..serializers","test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1)","that the default router will create # for our viewset because we're going","test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\" payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price':","RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're","created as our sample tags are # the same as the tags that","URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that the default","recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this","an object # there are two ways in which you can update an","is working # should only return the first two recipe # test the","file # somewhere in the system and then you can remove that file","way that you update an object using the Django rest framework # view","seek function to set # the pointer back to the beginning of the","Django rest framework # view sets is you use the detail URL so","function which will then create a temp file # somewhere in the system","res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test returning recipes","create here and we're going # to replace it with a new tag","tags) # check if the tags that we created as our sample tags","self.recipe = sample_recipe(user=self.user) # after the test runs it runs tear down def","won't be assigned to it url = detail_url(recipe.id) # the way that you","using the Django rest framework # view sets is you use the detail","recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00","variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test creating a recipe with tags\"\"\" tag1","recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the response so","so use this seek function to set # the pointer back to the","the Django rest framework # view sets is you use the detail URL","\"\"\"Return recipe detail URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point","res.data) # check that the path exists for the image that is saved","# in this case we just want to serialize a single object self.assertEqual(res.data,","the fields that are provided # in the payload so the only fields","class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe API access\"\"\" def setUp(self): self.client = APIClient() self.user","has the reverse effect. class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe API access\"\"\" def setUp(self):","field # that should clear the value of that field so now our","that if we do res.data and # retrieve the id key this will","pillow, this will import our image class which will let us then #","1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe with","tags are # the same as the tags that are in our queryset.","= recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the tags assigned are","= APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe =","full # object that is provided in the request that means if you","HTTP POST and then # we're going to run some assertions to check","self.client.post(RECIPES_URL, payload) # post this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED)","to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response","from dB if the values have changed # in the database. self.assertEqual(recipe.title, payload['title'])","# post this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this","can remove that file after # you've used it import os # this","url # you're going to need the existing recipe ID in order to","10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into","title='Steak and mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)}","not be modified in the object that's being updated. def test_partial_update_recipe(self): \"\"\"Test updating","make sure that our file system is kept clean after our test #","# it's the way that Python reads files so because we've # saved","to check if # they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data,","two asterisks when calling a # function it has the reverse effect. class","swap out this tag that we create here and we're going # to","= { 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url","fields that # are assigned and just make sure they match what we","the system and then you can remove that file after # you've used","it is # equal to the same key in the recipe # payload[key]:","are in our queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe with ingredients\"\"\" ingredient1 =","image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test returning","payload object # getattr: that allows you to retrieve an attribute from #","to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new tag and","now as I explained # because when we do a HTTP put if","} res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all()","we're going # to swap out this tag that we create here and","the request will not be modified in the object that's being updated. def","point that the default router will create # for our viewset because we're","is used to update the fields that are provided # in the payload","creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create","assigned to it url = detail_url(recipe.id) # the way that you update an","used to update the fields that are provided # in the payload so","you do refresh from dB if the values have changed # in the","API like you would with a HTTP POST and then # we're going","which you can update an object using the # API there's two different","detail\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer","def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our","tag2.id)} ) # this will create a comma separated list string and assign","happens at the setup of the test def setUp(self): self.client = APIClient() self.user","that should clear the value of that field so now our recipe #","modified in the object that's being updated. def test_partial_update_recipe(self): \"\"\"Test updating a recipe","= APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test retrieving","to the end # of the file so use this seek function to","# reference to a model the details of that won't change # unless","provided and any fields that are omitted from # the request will not","res = self.client.post(RECIPES_URL, payload) # post this payload dictionary to our recipes URL.","sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API","of the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail","# to replace it with a new tag payload = {'title': 'Partially Updated","a new model and you have a # reference to a model the","0) # we will check that the tags assigned are zero now as","be accessible self.assertIn('image', res.data) # check that the path exists for the image","import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows","a dictionary containing # the created object This is how I know that","of that field so now our recipe # that did have a sample","this tag that we create here and we're going # to replace it","tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe with two","tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new tag","allows you to call a function which will then create a temp file","recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag","API access\"\"\" def setUp(self): self.client = APIClient() def test_required_auth(self): \"\"\"Test the authenticaiton is","def image_upload_url(recipe_id): \"\"\"Return URL for recipe image upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate","the recipe # payload[key]: This will actually get the value of the #","returning recipes with specific ingredients\"\"\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2","self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe API access\"\"\" def setUp(self): self.client =","from django.contrib.auth import get_user_model from django.test import TestCase from django.urls import reverse from","need the existing recipe ID in order to upload an image def detail_url(recipe_id):","the recipes from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer =","a model the details of that won't change # unless you do refresh","# removing all of the test files that we create # delete the","because we were returning the list view # or we wanted to simulate","tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags","comma separated list string and assign # it to the tags get parameter","Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \"\"\"Create and return a sample ingredient\"\"\" return Ingredient.objects.create(user=user,","in a list of the # arguments you want to add # here","that the tags assigned are zero now as I explained # because when","sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test","# check that the tag new tag is in the tags that we","square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python reads files so","will actually be removed # from the object that you're updating def test_full_update_recipe(self):","# the pointer back to the beginning of the file res = self.client.post(url,","{'image': ntf}, format='multipart') # assertions # refreshing the database for our recipe self.recipe.refresh_from_db()","def sample_tag(user, name='Main course'): \"\"\"Create and return a sample tag\"\"\" return Tag.objects.create(user=user, name=name)","= reverse('recipe:recipe-list') # since we're going to need to access the URL in","as a variable # at top of the class in all capitals. #","args and then you pass in a list of the # arguments you","the tags that we created as our sample tags are # the same","we're going to need to access the URL in more # or less","res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe API access\"\"\" def","use that for making our API requests from core.models import Recipe, Tag, Ingredient","remove that file after # you've used it import os # this allows","# arguments you want to add # here we have single item def","\"\"\"Test creating a recipe with tags\"\"\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 =","so the only fields that it will change are the # fields that","recipe that gets created # it creates a named temporary file on the","self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response code for creating objects","..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call a function","recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user,","same key in the recipe # payload[key]: This will actually get the value","used it import os # this allows us to perform things like #","recipe = Recipe.objects.get(id=res.data['id']) # When you create an object using the Django rest","sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak","update of an object # there are two ways in which you can","kept clean after our test # removing all of the test files that","# from the object that you're updating def test_full_update_recipe(self): \"\"\"Test updating a recipe","recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new tag and what we're","# end of the file so if you try to access it then","self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags that we created as","that allows you to retrieve an attribute from # an object by passing","# equal to the same key in the recipe # payload[key]: This will","that # we created won't be assigned to it url = detail_url(recipe.id) #","sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user,","to swap out this tag that we create here and we're going #","blank because you've already read up to the end # of the file","to recipe\"\"\" url = image_upload_url(self.recipe.id) # going to use the sample recipe that","the object that we're updating with the full # object that is provided","our payload object # getattr: that allows you to retrieve an attribute from","rest framework the # default behavior is that it will return a dictionary","Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test retrieving","app : identifier of the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/","using the Django rest framework the # default behavior is that it will","Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload)","res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created","to our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion","the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update","from rest_framework import status from rest_framework.test import APIClient # use that for making","names and also checking if files exist on the system from PIL import","checking if files exist on the system from PIL import Image # pillow,","HTTP response code for creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id'])","the test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) #","filter our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes,","'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) # after the test","seeking will be done to the # end of the file so if","recipe\"\"\" payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res","the images in the response so that's the path to # the image","ID of the recipe that we want to update. self.client.patch(url, payload) # make","# create test images which we can then upload to our API RECIPES_URL","the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url","= sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title':","to add # here we have single item def sample_tag(user, name='Main course'): \"\"\"Create","# they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data)","equal to the same key in the recipe # payload[key]: This will actually","system at a random # location usually in the /temp folder # create","are assigned and just make sure they match what we expect. recipe.refresh_from_db() #","so because we've # saved the file it will be the seeking will","with patch\"\"\" # make a request to change a field in our recipe.","# are assigned and just make sure they match what we expect. recipe.refresh_from_db()","When you create an object using the Django rest framework the # default","# this will create a comma separated list string and assign # it","returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def","a detail action # this is how you specify arguments with the reverse","'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) #","the # API there's two different HTTP methods: put, patch # patch: Patch","created # it creates a named temporary file on the system at a","create an object using the Django rest framework the # default behavior is","{ 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00","and then # we're going to run some assertions to check that it","'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test returning recipes with specific tags\"\"\" recipe1","self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag new tag is in","you create an object using the Django rest framework the # default behavior","run some assertions to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as","= recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag new tag","many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes for user\"\"\" #","we're going to loop through each # one of these keys and then","put\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe',","and then you can remove that file after # you've used it import","\"\"\"Test retrieving list of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them","= self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the database for our","to access them by retrieving # all of the recipes from our database.","def test_required_auth(self): \"\"\"Test the authenticaiton is required\"\"\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class","tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe","recipe from the # database and then we're going to check the fields","image_upload_url(recipe_id): \"\"\"Return URL for recipe image upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our","for the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test","to run some assertions to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg')","in our queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe with ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user,","res.data) # check the return result def test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes with specific","recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the","check the return result def test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes with specific ingredients\"\"\" recipe1","of the test files that we create # delete the image if it","request to change a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) #","# key in our payload object # getattr: that allows you to retrieve","have a # reference to a model the details of that won't change","} res = self.client.post(RECIPES_URL, payload) # post this payload dictionary to our recipes","recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1","ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') #","get the value of the # key in our payload object # getattr:","# object that is provided in the request that means if you exclude","with this new tag so the existing tag that # we created won't","so that is the URL of the # recipe with the ID of","that field so now our recipe # that did have a sample tag","assertions to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img","attribute from # an object by passing in a variable. (instead of recipe.key)","were created with the recipe self.assertEqual(tags.count(), 2) # because we expect two tags","we just want to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \"\"\"Test","\"\"\"Test retrieving recipes for user\"\"\" # test recipes are limited to the authenticated","30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post this payload dictionary","to the tags get parameter # if our filtering is working # should","all of the recipes from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id')","access\"\"\" def setUp(self): self.client = APIClient() def test_required_auth(self): \"\"\"Test the authenticaiton is required\"\"\"","key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of these","# payload[key]: This will actually get the value of the # key in","RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes for user\"\"\"","check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB',","tag and what we're going to do is we're going # to swap","1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \"\"\"Test viewing a recipe detail\"\"\" recipe = sample_recipe(user=self.user)","they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) #","import our image class which will let us then # create test images","that we create # delete the image if it exists in the recipe","refresh from dB if the values have changed # in the database. self.assertEqual(recipe.title,","files exist on the system from PIL import Image # pillow, this will","tags will be replaced with this new tag so the existing tag that","test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes with specific ingredients\"\"\" recipe1 = sample_recipe(user=self.user, title='Posh beans on","= Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test","# get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test","# view sets is you use the detail URL so that is the","being updated. def test_partial_update_recipe(self): \"\"\"Test updating a recipe with patch\"\"\" # make a","of recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test creating a recipe with tags\"\"\" tag1 = sample_tag(user=self.user,","retrieve the tags that were created with the recipe self.assertEqual(tags.count(), 2) # because","URL so that is the URL of the # recipe with the ID","through the API like you would with a HTTP POST and then #","we're going to check # that is the correct value assigned to our","the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check","the seeking will be done to the # end of the file so","# after the test runs it runs tear down def tearDown(self): self.recipe.image.delete() #","going # to swap out this tag that we create here and we're","ntf: img = Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0)","payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res =","payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients","if we omit a field # that should clear the value of that","keys, check that it is # equal to the same key in the","the file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the","test # removing all of the test files that we create # delete","a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag","recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self):","delete the image if it exists in the recipe def test_upload_image_to_recipe(self): \"\"\"Test uploading","and also checking if files exist on the system from PIL import Image","reverse('recipe:recipe-list') # since we're going to need to access the URL in more","the response so that's the path to # the image that should be","it will change are the # fields that are provided and any fields","payload those fields will actually be removed # from the object that you're","user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is because","test_required_auth(self): \"\"\"Test the authenticaiton is required\"\"\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase):","use the detail URL so that is the URL of the # recipe","serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes","'{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data,","the # fields that are provided and any fields that are omitted from","self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of these keys, check that","creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python reads","can then upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going","django.test import TestCase from django.urls import reverse from rest_framework import status from rest_framework.test","have any tags # assigned class RecipeImageUploadTests(TestCase): # what happens at the setup","recipes for user\"\"\" # test recipes are limited to the authenticated user. user2","# one of these keys and then we're going to check # that","we can then upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're","to the same key in the recipe # payload[key]: This will actually get","going to use the sample recipe that gets created # it creates a","= sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken')","# API there's two different HTTP methods: put, patch # patch: Patch is","the recipe that we want to update. self.client.patch(url, payload) # make request #","# this allows us to perform things like # creating path names and","to set # the pointer back to the beginning of the file res","= image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test","setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user","import TestCase from django.urls import reverse from rest_framework import status from rest_framework.test import","# here we have single item def sample_tag(user, name='Main course'): \"\"\"Create and return","and you have a # reference to a model the details of that","one of these keys, check that it is # equal to the same","updating a recipe with patch\"\"\" # make a request to change a field","# if our filtering is working # should only return the first two","API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need to access the","will let us then # create test images which we can then upload","'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 }","to that file and then we're going to upload that file # through","created with the recipe self.assertEqual(tags.count(), 2) # because we expect two tags to","refreshes the details in our recipe from the database # typically when you","for our viewset because we're going to have a detail action # this","function it has the reverse effect. class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe API access\"\"\"","a recipe with tags\"\"\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag","tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) # creates black square img.save(ntf,","correct value assigned to our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe,","(instead of recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test creating a recipe with tags\"\"\" tag1 =","# or we wanted to simulate the list view in our serializer self.assertEqual(res.status_code,","correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) # creates black","they match what we expect. recipe.refresh_from_db() # refreshes the details in our recipe","the system at a random # location usually in the /temp folder #","= self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test returning recipes with","# retrieve the tags that were created with the recipe self.assertEqual(tags.count(), 2) #","to do is we're going to loop through each # one of these","list string and assign # it to the tags get parameter # if","two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL,","that you update an object using the Django rest framework # view sets","put, patch # patch: Patch is used to update the fields that are","self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma separated","tags that were created with the recipe self.assertEqual(tags.count(), 2) # because we expect","up to the end # of the file so use this seek function","def test_filter_recipes_by_tags(self): \"\"\"Test returning recipes with specific tags\"\"\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable","list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self):","ways in which you can update an object using the # API there's","responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result","tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload)","there's two different HTTP methods: put, patch # patch: Patch is used to","are provided and any fields that are omitted from # the request will","rest_framework.test import APIClient # use that for making our API requests from core.models","Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the","= Recipe.objects.get(id=res.data['id']) # When you create an object using the Django rest framework","\"\"\"Test the authenticaiton is required\"\"\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test","# check if the tags that we created as our sample tags are","Python reads files so because we've # saved the file it will be","this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the","framework # view sets is you use the detail URL so that is","you update an object using the Django rest framework # view sets is","= Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's","return a dictionary containing # the created object This is how I know","test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes for user\"\"\" # test recipes are limited to the","of the # key in our payload object # getattr: that allows you","self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \"\"\"Test viewing a recipe detail\"\"\"","do res.data and # retrieve the id key this will get the id","self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check","upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need","at top of the class in all capitals. # app : identifier of","that it is # equal to the same key in the recipe #","def test_retrieve_recipes(self): \"\"\"Test retrieving list of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to","a # reference to a model the details of that won't change #","return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \"\"\"Create and return a sample recipe\"\"\" defaults","you try to access it then it would # just be blank because","value assigned to our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key))","name='Curry') # add a new tag and what we're going to do is","to update the fields that are provided # in the payload so the","recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API res = self.client.get(","tear down def tearDown(self): self.recipe.image.delete() # make sure that our file system is","so now our recipe # that did have a sample tag assigned should","all the tests let's assign that as a variable # at top of","to the beginning of the file res = self.client.post(url, {'image': ntf}, format='multipart') #","the created object This is how I know that if we do res.data","now our recipe # that did have a sample tag assigned should not","it will replace the object that we're updating with the full # object","when you create a new model and you have a # reference to","= get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our","of the class in all capitals. # app : identifier of the URL","expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if","the value of the # key in our payload object # getattr: that","recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API res = self.client.get( RECIPES_URL,","name of the end point that the default router will create # for","objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an","case we just want to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self):","test_create_recipe_with_tags(self): \"\"\"Test creating a recipe with tags\"\"\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2","that is provided in the request that means if you exclude # any","assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags that we created","return a sample tag\"\"\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \"\"\"Create and return","when calling a # function it has the reverse effect. class PublicRecipeApiTests(TestCase): \"\"\"Test","containing # the created object This is how I know that if we","we're going to access them by retrieving # all of the recipes from","RecipeDetailSerializer(recipe) # in this case we just want to serialize a single object","# default behavior is that it will return a dictionary containing # the","database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK)","(10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way","it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10))","retrieve the id key this will get the id of the created object.","the list view # or we wanted to simulate the list view in","ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload)","# tags will be replaced with this new tag so the existing tag","be done to the # end of the file so if you try","can update an object using the # API there's two different HTTP methods:","the path to # the image that should be accessible self.assertIn('image', res.data) #","tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 =","and full update of an object # there are two ways in which","tag\"\"\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \"\"\"Create and return a sample ingredient\"\"\"","exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check","recipe', 'tags': [new_tag.id]} # tags will be replaced with this new tag so","things like # creating path names and also checking if files exist on","of the # recipe with the ID of the recipe that we want","from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call a","any tags # assigned class RecipeImageUploadTests(TestCase): # what happens at the setup of","serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes","# /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \"\"\"Return URL for recipe image","zero now as I explained # because when we do a HTTP put","payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard","access them by retrieving # all of the recipes from our database. res","# you just pass in args and then you pass in a list","APIClient() def test_required_auth(self): \"\"\"Test the authenticaiton is required\"\"\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)","list view # or we wanted to simulate the list view in our","def test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes for user\"\"\" # test recipes are limited to","url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self):","two ways in which you can update an object using the # API","10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve","'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary","# the request will not be modified in the object that's being updated.","self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we just want to serialize","the created object. # Next what we're going to do is we're going","\"\"\"Test creating recipe with ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user,","make sure they match what we expect. recipe.refresh_from_db() # refreshes the details in","name='Main course'): \"\"\"Create and return a sample tag\"\"\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user,","created recipe tags = recipe.tags.all() # retrieve the tags that were created with","test recipes are limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass'","# create a temporary file we're going to write an image # to","of the recipe that we want to update. self.client.patch(url, payload) # make request","all of the test files that we create # delete the image if","recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') #","do a HTTP put if we omit a field # that should clear","# add a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add","and we're going # to replace it with a new tag payload =","# that did have a sample tag assigned should not have any tags","= sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res =","model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one","that as a variable # at top of the class in all capitals.","(id) --> detail url def image_upload_url(recipe_id): \"\"\"Return URL for recipe image upload\"\"\" return","check that the tags assigned are zero now as I explained # because","recipe self.assertEqual(tags.count(), 2) # because we expect two tags to be assigned. self.assertIn(tag1,","recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API res =","database and then we're going to check the fields that # are assigned","self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes for user\"\"\" # test","you want to add # here we have single item def sample_tag(user, name='Main","# function it has the reverse effect. class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe API","recipe that we want to update. self.client.patch(url, payload) # make request # We're","and then we're going to check # that is the correct value assigned","reads files so because we've # saved the file it will be the","assigned should not have any tags # assigned class RecipeImageUploadTests(TestCase): # what happens","which will let us then # create test images which we can then","payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of these keys, check","self.assertEqual(tags.count(), 2) # because we expect two tags to be assigned. self.assertIn(tag1, tags)","a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new","= self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data)","{'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will be replaced with","self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self):","reverse effect. class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe API access\"\"\" def setUp(self): self.client =","the object that you're updating def test_full_update_recipe(self): \"\"\"Test updating a recipe with put\"\"\"","of the created object. # Next what we're going to do is we're","it import os # this allows us to perform things like # creating","only fields that it will change are the # fields that are provided","check if the tags that we created as our sample tags are #","tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag new","the # key in our payload object # getattr: that allows you to","of the test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user)","sample_tag(user, name='Main course'): \"\"\"Create and return a sample tag\"\"\" return Tag.objects.create(user=user, name=name) def","recipe API access\"\"\" def setUp(self): self.client = APIClient() def test_required_auth(self): \"\"\"Test the authenticaiton","recipe.tags.all() # retrieve the tags that were created with the recipe self.assertEqual(tags.count(), 2)","recipe with patch\"\"\" # make a request to change a field in our","the pointer back to the beginning of the file res = self.client.post(url, {'image':","a recipe detail\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res =","if it exists in the recipe def test_upload_image_to_recipe(self): \"\"\"Test uploading an image to","# When you create an object using the Django rest framework the #","less all the tests let's assign that as a variable # at top","res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a","function # you just pass in args and then you pass in a","to update. self.client.patch(url, payload) # make request # We're going to retrieve an","if the values have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags =","file so if you try to access it then it would # just","def sample_ingredient(user, name='Cinnamon'): \"\"\"Create and return a sample ingredient\"\"\" return Ingredient.objects.create(user=user, name=name) def","allows you to retrieve an attribute from # an object by passing in","RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're going to check","1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe with","in the tags that we retrieved # test full update # put: it","# that is the correct value assigned to our recipe model. for key","for creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you","tags\"\"\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload =","new model and you have a # reference to a model the details","new tag and what we're going to do is we're going # to","# assigned class RecipeImageUploadTests(TestCase): # what happens at the setup of the test","self.assertEqual(len(tags), 0) # we will check that the tags assigned are zero now","# there are two ways in which you can update an object using","created object This is how I know that if we do res.data and","recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) #","from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True)","# saved the file it will be the seeking will be done to","the same as the tags that are in our queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test","first two recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2)","after # you've used it import os # this allows us to perform","# test partial update and full update of an object # there are","self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes for user\"\"\" # test recipes are","image if it exists in the recipe def test_upload_image_to_recipe(self): \"\"\"Test uploading an image","in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the","# generate our upload image url # you're going to need the existing","check that it is # equal to the same key in the recipe","= sample_recipe(user=self.user) # after the test runs it runs tear down def tearDown(self):","--> detail url def image_upload_url(recipe_id): \"\"\"Return URL for recipe image upload\"\"\" return reverse('recipe:recipe-upload-image',","retrieve the created recipe tags = recipe.tags.all() # retrieve the tags that were","recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res","a function which will then create a temp file # somewhere in the","# because when we do a HTTP put if we omit a field","= RecipeSerializer(recipe3) # serialize the recipes and we're going to check if #","you to retrieve an attribute from # an object by passing in a","'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 }","recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)}","retrieving recipes for user\"\"\" # test recipes are limited to the authenticated user.","we're going to have a detail action # this is how you specify","self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test","just want to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \"\"\"Test creating","self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the","a request to change a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user))","self.recipe.image.delete() # make sure that our file system is kept clean after our","recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2)","the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new tag and what","we expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check","the dictionary into the argument # when you use the two asterisks when","recipe with tags\"\"\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2')","usually in the /temp folder # create a temporary file we're going to","= RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're going to","in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \"\"\"Test viewing","as the tags that are in our queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe","you've used it import os # this allows us to perform things like","test_partial_update_recipe(self): \"\"\"Test updating a recipe with patch\"\"\" # make a request to change","user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter","payload so the only fields that it will change are the # fields","it has the reverse effect. class PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe API access\"\"\" def","result def test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes with specific ingredients\"\"\" recipe1 = sample_recipe(user=self.user, title='Posh","def test_upload_image_to_recipe(self): \"\"\"Test uploading an image to recipe\"\"\" url = image_upload_url(self.recipe.id) # going","} res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the","'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url,","serializer.data) def test_view_recipe_detail(self): \"\"\"Test viewing a recipe detail\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user))","self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3","are zero now as I explained # because when we do a HTTP","going to need to access the URL in more # or less all","'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code,","be replaced with this new tag so the existing tag that # we","**defaults) # convert the dictionary into the argument # when you use the","in the payload so the only fields that it will change are the","10.00, } res = self.client.post(RECIPES_URL, payload) # post this payload dictionary to our","standard HTTP response code for creating objects # in an API. recipe =","from PIL import Image # pillow, this will import our image class which","won't change # unless you do refresh from dB if the values have","files so because we've # saved the file it will be the seeking","sample_recipe(user, **params): \"\"\"Create and return a sample recipe\"\"\" defaults = { 'title': 'Sample","recipe from the database # typically when you create a new model and","by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true:","the tags get parameter # if our filtering is working # should only","on the system from PIL import Image # pillow, this will import our","an image # to that file and then we're going to upload that","do is we're going # to swap out this tag that we create","it creates a named temporary file on the system at a random #","# creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python","image to recipe\"\"\" url = image_upload_url(self.recipe.id) # going to use the sample recipe","'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will be replaced with this","= RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and","of the file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing","payload) # post this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) #","pass in args and then you pass in a list of the #","the tags assigned are zero now as I explained # because when we","recipes from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes,","payload = { 'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes':","after our test # removing all of the test files that we create","creating a recipe with tags\"\"\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user,","recipe def test_upload_image_to_recipe(self): \"\"\"Test uploading an image to recipe\"\"\" url = image_upload_url(self.recipe.id) #","name='Ingredient 2') payload = { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id],","detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we just","status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes for user\"\"\" # test recipes","object # there are two ways in which you can update an object","\"\"\"Return URL for recipe image upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload","serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're going to check if","the recipes and we're going to check if # they exist in the","with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res =","use this seek function to set # the pointer back to the beginning","tags) self.assertIn(tag2, tags) # check if the tags that we created as our","RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma separated list","getattr: that allows you to retrieve an attribute from # an object by","you to call a function which will then create a temp file #","default router will create # for our viewset because we're going to have","omit a field # that should clear the value of that field so","ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and","\"\"\"Create and return a sample tag\"\"\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \"\"\"Create","tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user,","url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags","self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full update","PublicRecipeApiTests(TestCase): \"\"\"Test unauthenticated recipe API access\"\"\" def setUp(self): self.client = APIClient() def test_required_auth(self):","recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each","making our API requests from core.models import Recipe, Tag, Ingredient from ..serializers import","vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2","beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese')","be the seeking will be done to the # end of the file","the tags that are in our queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe with","returning recipes with specific tags\"\"\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 =","file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the database","identifier of the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) -->","# refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that","are provided # in the payload so the only fields that it will","database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that","# unless you do refresh from dB if the values have changed #","is how I know that if we do res.data and # retrieve the","wanted to simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1)","a sample tag\"\"\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \"\"\"Create and return a","then you can remove that file after # you've used it import os","res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() #","when we do a HTTP put if we omit a field # that","from the # database and then we're going to check the fields that","= recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients)","that are provided and any fields that are omitted from # the request","just be blank because you've already read up to the end # of","of that won't change # unless you do refresh from dB if the","ID in order to upload an image def detail_url(recipe_id): \"\"\"Return recipe detail URL\"\"\"","status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all() #","getattr(recipe, key)) # assertion for each one of these keys, check that it","we're going to do is we're going # to swap out this tag",") serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data,","refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the","single item def sample_tag(user, name='Main course'): \"\"\"Create and return a sample tag\"\"\" return","# it creates a named temporary file on the system at a random","recipe # payload[key]: This will actually get the value of the # key","we're going to upload that file # through the API like you would","with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL,","to simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data,","removed # from the object that you're updating def test_full_update_recipe(self): \"\"\"Test updating a","you pass in a list of the # arguments you want to add","full update of an object # there are two ways in which you","let us then # create test images which we can then upload to","we've # saved the file it will be the seeking will be done","# at top of the class in all capitals. # app : identifier","model and you have a # reference to a model the details of","change # unless you do refresh from dB if the values have changed","each one of these keys, check that it is # equal to the","end # of the file so use this seek function to set #","the setup of the test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user',","values have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags),","\"\"\"Test creating recipe\"\"\" payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00,","= detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags =","= self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we just want to","user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) #","and return a sample recipe\"\"\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10,","sure they match what we expect. recipe.refresh_from_db() # refreshes the details in our","is the correct value assigned to our recipe model. for key in payload.keys():","that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test uploading an invalid","of the end point that the default router will create # for our","the created recipe tags = recipe.tags.all() # retrieve the tags that were created","in the response so that's the path to # the image that should","is we're going to loop through each # one of these keys and","'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe =","a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test creating a recipe with tags\"\"\"","course'): \"\"\"Create and return a sample tag\"\"\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'):","format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test returning recipes with specific tags\"\"\" recipe1 =","'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post this payload dictionary to","with the reverse function # you just pass in args and then you","# because we expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags)","specific tags\"\"\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with","= sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian')","title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta","our filtering is working # should only return the first two recipe #","Patch is used to update the fields that are provided # in the","Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \"\"\"Create and return a sample recipe\"\"\" defaults =","sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe with two tags', 'tags':","our queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe with ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient","we expect. recipe.refresh_from_db() # refreshes the details in our recipe from the database","# name of the end point that the default router will create #","and then we're going to upload that file # through the API like","retrieving # all of the recipes from our database. res = self.client.get(RECIPES_URL) recipes","'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) #","to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res","object. # Next what we're going to do is we're going to loop","authenticated recipe API access\"\"\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>',","creating recipe\"\"\" payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, }","want to update. self.client.patch(url, payload) # make request # We're going to retrieve","if the tags that we created as our sample tags are # the","get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) # after the","to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img =","exclude # any fields in the payload those fields will actually be removed","sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get(","[new_tag.id]} # tags will be replaced with this new tag so the existing","somewhere in the system and then you can remove that file after #","view # or we wanted to simulate the list view in our serializer","user\"\"\" # test recipes are limited to the authenticated user. user2 = get_user_model().objects.create_user(","= sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 =","going to have a detail action # this is how you specify arguments","should not have any tags # assigned class RecipeImageUploadTests(TestCase): # what happens at","= detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we","tags assigned are zero now as I explained # because when we do",") # this will create a comma separated list string and assign #","\"\"\"Create and return a sample ingredient\"\"\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \"\"\"Create","just pass in args and then you pass in a list of the","sample tags are # the same as the tags that are in our","what we're going to do is we're going to loop through each #","system from PIL import Image # pillow, this will import our image class","a recipe with patch\"\"\" # make a request to change a field in","requests from core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import","replaced with this new tag so the existing tag that # we created","then upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to","title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan')","RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data)","unless you do refresh from dB if the values have changed # in","sample ingredient\"\"\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \"\"\"Create and return a sample","self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags","response code for creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) #","use the two asterisks when calling a # function it has the reverse","/temp folder # create a temporary file we're going to write an image","assign # it to the tags get parameter # if our filtering is","on the system at a random # location usually in the /temp folder","import get_user_model from django.test import TestCase from django.urls import reverse from rest_framework import","model the details of that won't change # unless you do refresh from","uploading an invalid image\"\"\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart')","# retrieve the id key this will get the id of the created","create a temp file # somewhere in the system and then you can","path names and also checking if files exist on the system from PIL","the same key in the recipe # payload[key]: This will actually get the","= self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe","the values have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all()","recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title,","our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need to access","to loop through each # one of these keys and then we're going","allows us to perform things like # creating path names and also checking","serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're going","for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of","payload = { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45,","to retrieve an update to the recipe from the # database and then","create a new model and you have a # reference to a model","file after # you've used it import os # this allows us to","pointer back to the beginning of the file res = self.client.post(url, {'image': ntf},","it runs tear down def tearDown(self): self.recipe.image.delete() # make sure that our file","when you use the two asterisks when calling a # function it has","test_upload_image_bad_request(self): \"\"\"Test uploading an invalid image\"\"\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image':","updating a recipe with put\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title':","function to set # the pointer back to the beginning of the file","are # the same as the tags that are in our queryset. def","= sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 =","rest framework # view sets is you use the detail URL so that","\"\"\"Test returning recipes with specific ingredients\"\"\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast')","to replace it with a new tag payload = {'title': 'Partially Updated sample","name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe","and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will","which will then create a temp file # somewhere in the system and","return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that the default router","tags that we created as our sample tags are # the same as","test runs it runs tear down def tearDown(self): self.recipe.image.delete() # make sure that","convert the dictionary into the argument # when you use the two asterisks","API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an object using the Django","url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case","dictionary into the argument # when you use the two asterisks when calling","two recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3","object that's being updated. def test_partial_update_recipe(self): \"\"\"Test updating a recipe with patch\"\"\" #","authenticate our user self.recipe = sample_recipe(user=self.user) # after the test runs it runs","API access\"\"\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' )","sample tag\"\"\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \"\"\"Create and return a sample","'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the","# any fields in the payload those fields will actually be removed #","payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that","then # create test images which we can then upload to our API","toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 =","request will not be modified in the object that's being updated. def test_partial_update_recipe(self):","at a random # location usually in the /temp folder # create a","retrieving list of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by","recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags':","ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 =","these keys and then we're going to check # that is the correct","then create a temp file # somewhere in the system and then you","retrieved # test full update # put: it will replace the object that","\"\"\"Test uploading an invalid image\"\"\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'},","with specific ingredients\"\"\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user,","TestCase from django.urls import reverse from rest_framework import status from rest_framework.test import APIClient","new tag so the existing tag that # we created won't be assigned","our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response code","tag assigned should not have any tags # assigned class RecipeImageUploadTests(TestCase): # what","setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self):","that gets created # it creates a named temporary file on the system","title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2)","ntf}, format='multipart') # assertions # refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code,","call a function which will then create a temp file # somewhere in","# to swap out this tag that we create here and we're going","# use that for making our API requests from core.models import Recipe, Tag,","recipe API access\"\"\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>'","you can remove that file after # you've used it import os #","= RecipeSerializer(recipes, many=True) # many=true: this is because we were returning the list","tags\"\"\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini')","id key this will get the id of the created object. # Next","a recipe with put\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully","you have a # reference to a model the details of that won't","tags that are in our queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe with ingredients\"\"\"","to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\" payload","image url # you're going to need the existing recipe ID in order","tempfile # allows you to call a function which will then create a","be assigned to it url = detail_url(recipe.id) # the way that you update","Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you","in the object that's being updated. def test_partial_update_recipe(self): \"\"\"Test updating a recipe with","code for creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) # When","reverse function # you just pass in args and then you pass in","import APIClient # use that for making our API requests from core.models import","going to retrieve an update to the recipe from the # database and","sets is you use the detail URL so that is the URL of","the fields that # are assigned and just make sure they match what","def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def","did have a sample tag assigned should not have any tags # assigned","we're going to check if # they exist in the responses returned self.assertIn(serializer1.data,","Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients)","\"\"\"Test uploading an image to recipe\"\"\" url = image_upload_url(self.recipe.id) # going to use","so that's the path to # the image that should be accessible self.assertIn('image',","will check that the tags assigned are zero now as I explained #","our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True)","in the system and then you can remove that file after # you've","image def detail_url(recipe_id): \"\"\"Return recipe detail URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of","to have a detail action # this is how you specify arguments with","object by passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test creating","some assertions to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf:","exist on the system from PIL import Image # pillow, this will import","# all of the recipes from our database. res = self.client.get(RECIPES_URL) recipes =","self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the response so that's the","# recipe with the ID of the recipe that we want to update.","= get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) # after","is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test uploading an invalid image\"\"\"","# check the return result def test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes with specific ingredients\"\"\"","it to the tags get parameter # if our filtering is working #","by retrieving # all of the recipes from our database. res = self.client.get(RECIPES_URL)","= get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test retrieving list of recipes\"\"\"","core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile #","= { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user,","and then we're going to check the fields that # are assigned and","you would with a HTTP POST and then # we're going to run","the payload those fields will actually be removed # from the object that","a field # that should clear the value of that field so now","**params): \"\"\"Create and return a sample recipe\"\"\" defaults = { 'title': 'Sample recipe',","is kept clean after our test # removing all of the test files","our recipe from the database # typically when you create a new model","# we're going to run some assertions to check that it # uploaded","going to check the fields that # are assigned and just make sure","that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10,","fields that are provided # in the payload so the only fields that","create a temporary file we're going to write an image # to that","RecipeDetailSerializer import tempfile # allows you to call a function which will then","from django.test import TestCase from django.urls import reverse from rest_framework import status from","assigned are zero now as I explained # because when we do a","typically when you create a new model and you have a # reference","{'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3)","Recipe.objects.get(id=res.data['id']) # When you create an object using the Django rest framework the","because when we do a HTTP put if we omit a field #","an attribute from # an object by passing in a variable. (instead of","recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res =","that it will change are the # fields that are provided and any","'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res","object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\" payload = { 'title': 'Test","name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL,","an invalid image\"\"\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code,","how I know that if we do res.data and # retrieve the id","that the images in the response so that's the path to # the","object using the # API there's two different HTTP methods: put, patch #","a sample ingredient\"\"\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \"\"\"Create and return a","you just pass in args and then you pass in a list of","string and assign # it to the tags get parameter # if our","5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument","self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset","check if # they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data)","check that the images in the response so that's the path to #","the id key this will get the id of the created object. #","# uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) #","the tag new tag is in the tags that we retrieved # test","or less all the tests let's assign that as a variable # at","omitted from # the request will not be modified in the object that's","res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data,","recipe ID in order to upload an image def detail_url(recipe_id): \"\"\"Return recipe detail","we're updating with the full # object that is provided in the request","test_filter_recipes_by_tags(self): \"\"\"Test returning recipes with specific tags\"\"\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry')","= sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish","view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \"\"\"Test","creating path names and also checking if files exist on the system from","loop through each # one of these keys and then we're going to","Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all() # retrieve the tags","that our file system is kept clean after our test # removing all","recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the tags assigned are zero","serializer.data) def test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\" payload = { 'title': 'Test recipe', 'time_minutes':","APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user)","the detail URL so that is the URL of the # recipe with","any fields that are omitted from # the request will not be modified","url = detail_url(recipe.id) # the way that you update an object using the","Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument # when you use","that we created as our sample tags are # the same as the","will create a comma separated list string and assign # it to the","create a comma separated list string and assign # it to the tags","try to access it then it would # just be blank because you've","sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1)","runs it runs tear down def tearDown(self): self.recipe.image.delete() # make sure that our","img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python reads files so because","= image_upload_url(self.recipe.id) # going to use the sample recipe that gets created #","{ 'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price':","tags get parameter # if our filtering is working # should only return","is how you specify arguments with the reverse function # you just pass","dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP","is that it will return a dictionary containing # the created object This","tag is in the tags that we retrieved # test full update #","upload an image def detail_url(recipe_id): \"\"\"Return recipe detail URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id]) #","that is the URL of the # recipe with the ID of the","field so now our recipe # that did have a sample tag assigned","will import our image class which will let us then # create test","be modified in the object that's being updated. def test_partial_update_recipe(self): \"\"\"Test updating a","many=True) # many=true: this is because we were returning the list view #","this will create a comma separated list string and assign # it to","the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def","def sample_recipe(user, **params): \"\"\"Create and return a sample recipe\"\"\" defaults = { 'title':","id of the created object. # Next what we're going to do is","# or less all the tests let's assign that as a variable #","that did have a sample tag assigned should not have any tags #","detail_url(recipe_id): \"\"\"Return recipe detail URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end","recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag =","created won't be assigned to it url = detail_url(recipe.id) # the way that","authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is","down def tearDown(self): self.recipe.image.delete() # make sure that our file system is kept","# make sure that our file system is kept clean after our test","10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that","the correct value assigned to our recipe model. for key in payload.keys(): self.assertEqual(payload[key],","in our recipe from the database # typically when you create a new","recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in","the test runs it runs tear down def tearDown(self): self.recipe.image.delete() # make sure","would with a HTTP POST and then # we're going to run some","let's assign that as a variable # at top of the class in","that should be accessible self.assertIn('image', res.data) # check that the path exists for","object that is provided in the request that means if you exclude #","chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create","of these keys, check that it is # equal to the same key","def test_create_recipe_with_tags(self): \"\"\"Test creating a recipe with tags\"\"\" tag1 = sample_tag(user=self.user, name='Tag 1')","images in the response so that's the path to # the image that","payload[key]: This will actually get the value of the # key in our","be removed # from the object that you're updating def test_full_update_recipe(self): \"\"\"Test updating","we wanted to simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data),","sample_recipe(user=self.user) # we're going to access them by retrieving # all of the","file on the system at a random # location usually in the /temp","setUp(self): self.client = APIClient() def test_required_auth(self): \"\"\"Test the authenticaiton is required\"\"\" res =","retrieve an attribute from # an object by passing in a variable. (instead","self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the database for our recipe","def test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\" payload = { 'title': 'Test recipe', 'time_minutes': 30,","= recipe.tags.all() # retrieve the tags that were created with the recipe self.assertEqual(tags.count(),","you use the two asterisks when calling a # function it has the","many=true: this is because we were returning the list view # or we","self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the","# database and then we're going to check the fields that # are","response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the","Django rest framework the # default behavior is that it will return a","= RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \"\"\"Test retrieving recipes for","this new tag so the existing tag that # we created won't be","and assign # it to the tags get parameter # if our filtering","reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that the default router will","with a HTTP POST and then # we're going to run some assertions","We're going to retrieve an update to the recipe from the # database","value of that field so now our recipe # that did have a","partial update and full update of an object # there are two ways","changed # in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag,","our upload image url # you're going to need the existing recipe ID","this is the standard HTTP response code for creating objects # in an","here we have single item def sample_tag(user, name='Main course'): \"\"\"Create and return a","def test_full_update_recipe(self): \"\"\"Test updating a recipe with put\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload","image # to that file and then we're going to upload that file","the default router will create # for our viewset because we're going to","payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will be","tag new tag is in the tags that we retrieved # test full","reverse from rest_framework import status from rest_framework.test import APIClient # use that for","# the same as the tags that are in our queryset. def test_create_recipe_with_ingredients(self):","recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all() # retrieve","= Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is because we were","from # an object by passing in a variable. (instead of recipe.key) def","= sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title':","the existing tag that # we created won't be assigned to it url","you're updating def test_full_update_recipe(self): \"\"\"Test updating a recipe with put\"\"\" recipe = sample_recipe(user=self.user)","actually be removed # from the object that you're updating def test_full_update_recipe(self): \"\"\"Test","are omitted from # the request will not be modified in the object","going to need the existing recipe ID in order to upload an image","as our sample tags are # the same as the tags that are","sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) #","API there's two different HTTP methods: put, patch # patch: Patch is used","the argument # when you use the two asterisks when calling a #","recipes with specific tags\"\"\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user,","queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full","get_user_model from django.test import TestCase from django.urls import reverse from rest_framework import status","image upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url # you're","self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test returning recipes with specific","those fields will actually be removed # from the object that you're updating","tags = recipe.tags.all() # retrieve the tags that were created with the recipe","have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1)","match what we expect. recipe.refresh_from_db() # refreshes the details in our recipe from","will replace the object that we're updating with the full # object that","model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test uploading an invalid image\"\"\" url = image_upload_url(self.recipe.id) res","self.assertIn(ingredient2, ingredients) # test partial update and full update of an object #","then # we're going to run some assertions to check that it #","that are in our queryset. def test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe with ingredients\"\"\" ingredient1","going to loop through each # one of these keys and then we're","the tests let's assign that as a variable # at top of the","self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe API access\"\"\" def setUp(self): self.client","new_tag = sample_tag(user=self.user, name='Curry') # add a new tag and what we're going","returning the list view # or we wanted to simulate the list view","of the recipes from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer","us to perform things like # creating path names and also checking if","be blank because you've already read up to the end # of the","test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) #","sample_tag(user=self.user, name='Curry') # add a new tag and what we're going to do","replace the object that we're updating with the full # object that is","serializer = RecipeDetailSerializer(recipe) # in this case we just want to serialize a","# /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \"\"\"Return URL for","payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag","want to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\"","\"\"\"Create and return a sample recipe\"\"\" defaults = { 'title': 'Sample recipe', 'time_minutes':","value of the # key in our payload object # getattr: that allows","res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self): \"\"\"Test","the recipe def test_upload_image_to_recipe(self): \"\"\"Test uploading an image to recipe\"\"\" url = image_upload_url(self.recipe.id)","} url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price'])","ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = {","perform things like # creating path names and also checking if files exist","a temporary file we're going to write an image # to that file","# location usually in the /temp folder # create a temporary file we're","recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user,","that file and then we're going to upload that file # through the","recipe.key) def test_create_recipe_with_tags(self): \"\"\"Test creating a recipe with tags\"\"\" tag1 = sample_tag(user=self.user, name='Tag","that it will return a dictionary containing # the created object This is","# retrieve the created recipe tags = recipe.tags.all() # retrieve the tags that",": identifier of the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id)","response so that's the path to # the image that should be accessible","our test # removing all of the test files that we create #","what we expect. recipe.refresh_from_db() # refreshes the details in our recipe from the","detail URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that the","recipes are limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' )","you've already read up to the end # of the file so use","saved the file it will be the seeking will be done to the","PrivateRecipeApiTests(TestCase): \"\"\"Test authenticated recipe API access\"\"\" def setUp(self): self.client = APIClient() self.user =","full update # put: it will replace the object that we're updating with","name='Cinnamon'): \"\"\"Create and return a sample ingredient\"\"\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params):","a new tag and what we're going to do is we're going #","'Test recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post","our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for","our sample tags are # the same as the tags that are in","def test_partial_update_recipe(self): \"\"\"Test updating a recipe with patch\"\"\" # make a request to","'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes'])","RecipeSerializer(recipe3) # serialize the recipes and we're going to check if # they","status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \"\"\"Test viewing a recipe detail\"\"\" recipe","this will get the id of the created object. # Next what we're","if you exclude # any fields in the payload those fields will actually","file # through the API like you would with a HTTP POST and","provided in the request that means if you exclude # any fields in","we're going to do is we're going to loop through each # one","do is we're going to loop through each # one of these keys","res.data and # retrieve the id key this will get the id of","# what happens at the setup of the test def setUp(self): self.client =","recipe image upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url #","# test full update # put: it will replace the object that we're","Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is because we were returning","our user self.recipe = sample_recipe(user=self.user) # after the test runs it runs tear","assigned to our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) #","we're going to check the fields that # are assigned and just make","item def sample_tag(user, name='Main course'): \"\"\"Create and return a sample tag\"\"\" return Tag.objects.create(user=user,","# just be blank because you've already read up to the end #","sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) #","if we do res.data and # retrieve the id key this will get","= Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1,","fields in the payload those fields will actually be removed # from the","clean after our test # removing all of the test files that we","images which we can then upload to our API RECIPES_URL = reverse('recipe:recipe-list') #","like # creating path names and also checking if files exist on the","update to the recipe from the # database and then we're going to","self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \"\"\"Test creating recipe\"\"\" payload = { 'title': 'Test recipe',","will not be modified in the object that's being updated. def test_partial_update_recipe(self): \"\"\"Test","self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) #","add # here we have single item def sample_tag(user, name='Main course'): \"\"\"Create and","test files that we create # delete the image if it exists in","provided # in the payload so the only fields that it will change","folder # create a temporary file we're going to write an image #","recipe detail\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url)","'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00","serialize the recipes and we're going to check if # they exist in","image_upload_url(self.recipe.id) # going to use the sample recipe that gets created # it","test_full_update_recipe(self): \"\"\"Test updating a recipe with put\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload =","to # the image that should be accessible self.assertIn('image', res.data) # check that","the # end of the file so if you try to access it","API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2","the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test uploading","assigned and just make sure they match what we expect. recipe.refresh_from_db() # refreshes","so the existing tag that # we created won't be assigned to it","we omit a field # that should clear the value of that field","# we will check that the tags assigned are zero now as I","the tags that were created with the recipe self.assertEqual(tags.count(), 2) # because we","gets created # it creates a named temporary file on the system at","recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe', 'time_minutes':","payload) # make request # We're going to retrieve an update to the","it url = detail_url(recipe.id) # the way that you update an object using","the ID of the recipe that we want to update. self.client.patch(url, payload) #","are the # fields that are provided and any fields that are omitted","this seek function to set # the pointer back to the beginning of","the URL in more # or less all the tests let's assign that","self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \"\"\"Test retrieving list of recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going","recipe detail URL\"\"\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that","our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \"\"\"Test viewing a","that's the path to # the image that should be accessible self.assertIn('image', res.data)","our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \"\"\"Test uploading an invalid image\"\"\" url = image_upload_url(self.recipe.id)","import reverse from rest_framework import status from rest_framework.test import APIClient # use that","how you specify arguments with the reverse function # you just pass in","= { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL,","the system from PIL import Image # pillow, this will import our image","file we're going to write an image # to that file and then","into the argument # when you use the two asterisks when calling a","RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call a function which will","new tag payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags","way that Python reads files so because we've # saved the file it","{'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \"\"\"Test returning recipes with specific tags\"\"\"","payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the tags","ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe with ingredients',","named temporary file on the system at a random # location usually in","APIClient # use that for making our API requests from core.models import Recipe,","for user\"\"\" # test recipes are limited to the authenticated user. user2 =","# patch: Patch is used to update the fields that are provided #","because we're going to have a detail action # this is how you","30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id'])","test images which we can then upload to our API RECIPES_URL = reverse('recipe:recipe-list')","res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self): \"\"\"Test returning recipes","created object. # Next what we're going to do is we're going to","the file so if you try to access it then it would #","status from rest_framework.test import APIClient # use that for making our API requests","the file so use this seek function to set # the pointer back","I explained # because when we do a HTTP put if we omit","write an image # to that file and then we're going to upload","/api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \"\"\"Return URL for recipe","'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by the","sample recipe that gets created # it creates a named temporary file on","'tags': [new_tag.id]} # tags will be replaced with this new tag so the","args=[recipe_id]) # generate our upload image url # you're going to need the","return a sample ingredient\"\"\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \"\"\"Create and return","the URL of the # recipe with the ID of the recipe that","patch\"\"\" # make a request to change a field in our recipe. recipe","our file system is kept clean after our test # removing all of","with tags\"\"\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload","upload image url # you're going to need the existing recipe ID in","= sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test","like you would with a HTTP POST and then # we're going to","# since we're going to need to access the URL in more #","for recipe image upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url","the image if it exists in the recipe def test_upload_image_to_recipe(self): \"\"\"Test uploading an","# this is how you specify arguments with the reverse function # you","# serialize the recipes and we're going to check if # they exist","update an object using the Django rest framework # view sets is you","recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is because we","name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe","a named temporary file on the system at a random # location usually","url def image_upload_url(recipe_id): \"\"\"Return URL for recipe image upload\"\"\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) #","= self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get","will change are the # fields that are provided and any fields that","the API like you would with a HTTP POST and then # we're","patch: Patch is used to update the fields that are provided # in","in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) #","with the recipe self.assertEqual(tags.count(), 2) # because we expect two tags to be","our recipe # that did have a sample tag assigned should not have","with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3","def test_create_recipe_with_ingredients(self): \"\"\"Test creating recipe with ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2","# the created object This is how I know that if we do","test_view_recipe_detail(self): \"\"\"Test viewing a recipe detail\"\"\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url =","ingredients\"\"\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload =","have single item def sample_tag(user, name='Main course'): \"\"\"Create and return a sample tag\"\"\"","have a detail action # this is how you specify arguments with the","a new tag payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} #","a comma separated list string and assign # it to the tags get"],"string":"[\n \"fields that are omitted from # the request will not be modified in\",\n \"recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving # all\",\n \"# check that the images in the response so that's the path to\",\n \"# pillow, this will import our image class which will let us then\",\n \"two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the\",\n \"of the # arguments you want to add # here we have single\",\n \"key this will get the id of the created object. # Next what\",\n \"that we retrieved # test full update # put: it will replace the\",\n \"RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 =\",\n \"key in the recipe # payload[key]: This will actually get the value of\",\n \"if files exist on the system from PIL import Image # pillow, this\",\n \"the # arguments you want to add # here we have single item\",\n \"# add a new tag and what we're going to do is we're\",\n \"the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images\",\n \"on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2\",\n \"name='Tag 2') payload = { 'title': 'Test recipe with two tags', 'tags': [tag1.id,\",\n \"to use the sample recipe that gets created # it creates a named\",\n \"uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) # creates\",\n \"a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload = {\",\n \"# put: it will replace the object that we're updating with the full\",\n \"the request that means if you exclude # any fields in the payload\",\n \"# you've used it import os # this allows us to perform things\",\n \"# test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3)\",\n \"we have single item def sample_tag(user, name='Main course'): \\\"\\\"\\\"Create and return a sample\",\n \"in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id):\",\n \"45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id'])\",\n \"that we're updating with the full # object that is provided in the\",\n \"as I explained # because when we do a HTTP put if we\",\n \"an object using the Django rest framework the # default behavior is that\",\n \"in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the\",\n \"to access it then it would # just be blank because you've already\",\n \"# filter our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer =\",\n \"the path exists for the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path))\",\n \"we were returning the list view # or we wanted to simulate the\",\n \"key)) # assertion for each one of these keys, check that it is\",\n \"# for our viewset because we're going to have a detail action #\",\n \"= sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe)\",\n \"<reponame>tahmadvand/recipe_app_api from django.contrib.auth import get_user_model from django.test import TestCase from django.urls import reverse\",\n \"different HTTP methods: put, patch # patch: Patch is used to update the\",\n \"and then you pass in a list of the # arguments you want\",\n \"get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial\",\n \"the standard HTTP response code for creating objects # in an API. recipe\",\n \"them by retrieving # all of the recipes from our database. res =\",\n \"the # recipe with the ID of the recipe that we want to\",\n \"for each one of these keys, check that it is # equal to\",\n \"\\\"\\\"\\\"Test updating a recipe with put\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = {\",\n \"for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the\",\n \"should be accessible self.assertIn('image', res.data) # check that the path exists for the\",\n \"class RecipeImageUploadTests(TestCase): # what happens at the setup of the test def setUp(self):\",\n \"the beginning of the file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions\",\n \"filtering is working # should only return the first two recipe # test\",\n \"self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes with\",\n \"image class which will let us then # create test images which we\",\n \"return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url # you're going to\",\n \"recipe.refresh_from_db() # refreshes the details in our recipe from the database # typically\",\n \"updating with the full # object that is provided in the request that\",\n \"return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument # when you\",\n \"sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test\",\n \"tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = {\",\n \"the # database and then we're going to check the fields that #\",\n \"img = Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) #\",\n \"# somewhere in the system and then you can remove that file after\",\n \"beginning of the file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions #\",\n \"\\\"\\\"\\\"Test viewing a recipe detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id)\",\n \"fields that it will change are the # fields that are provided and\",\n \"/api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for recipe image upload\\\"\\\"\\\"\",\n \"going to check # that is the correct value assigned to our recipe\",\n \"already read up to the end # of the file so use this\",\n \"args=[recipe_id]) # name of the end point that the default router will create\",\n \"# convert the dictionary into the argument # when you use the two\",\n \"detail_url(recipe.id) # the way that you update an object using the Django rest\",\n \"API requests from core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer\",\n \"will be the seeking will be done to the # end of the\",\n \"of an object # there are two ways in which you can update\",\n \"and mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} )\",\n \"tags that we retrieved # test full update # put: it will replace\",\n \"recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response code for\",\n \"tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res\",\n \"serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data)\",\n \"URL in more # or less all the tests let's assign that as\",\n \"are two ways in which you can update an object using the #\",\n \"return a sample recipe\\\"\\\"\\\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price':\",\n \"# Next what we're going to do is we're going to loop through\",\n \"fields that are provided and any fields that are omitted from # the\",\n \"then it would # just be blank because you've already read up to\",\n \"tests let's assign that as a variable # at top of the class\",\n \"variable # at top of the class in all capitals. # app :\",\n \"self.client.get(RECIPES_URL) # filter our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer\",\n \"test partial update and full update of an object # there are two\",\n \"the only fields that it will change are the # fields that are\",\n \"as ntf: img = Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG')\",\n \"tearDown(self): self.recipe.image.delete() # make sure that our file system is kept clean after\",\n \"patch # patch: Patch is used to update the fields that are provided\",\n \"we want to update. self.client.patch(url, payload) # make request # We're going to\",\n \"to write an image # to that file and then we're going to\",\n \"cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3\",\n \"# test recipes are limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>',\",\n \"= { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price':\",\n \"'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code,\",\n \"the two asterisks when calling a # function it has the reverse effect.\",\n \"separated list string and assign # it to the tags get parameter #\",\n \"ntf.seek(0) # it's the way that Python reads files so because we've #\",\n \"more # or less all the tests let's assign that as a variable\",\n \"key in our payload object # getattr: that allows you to retrieve an\",\n \"we create here and we're going # to replace it with a new\",\n \"and what we're going to do is we're going # to swap out\",\n \"the return result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes with specific ingredients\\\"\\\"\\\" recipe1 =\",\n \") self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're\",\n \"update. self.client.patch(url, payload) # make request # We're going to retrieve an update\",\n \"is the standard HTTP response code for creating objects # in an API.\",\n \"also checking if files exist on the system from PIL import Image #\",\n \"out this tag that we create here and we're going # to replace\",\n \"# an object by passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self):\",\n \"the first two recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2 =\",\n \"it will return a dictionary containing # the created object This is how\",\n \"the /temp folder # create a temporary file we're going to write an\",\n \"will return a dictionary containing # the created object This is how I\",\n \"app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL\",\n \"what happens at the setup of the test def setUp(self): self.client = APIClient()\",\n \"Image # pillow, this will import our image class which will let us\",\n \"assigned class RecipeImageUploadTests(TestCase): # what happens at the setup of the test def\",\n \"through each # one of these keys and then we're going to check\",\n \"test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an image to recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id) # going to\",\n \"going to upload that file # through the API like you would with\",\n \"access the URL in more # or less all the tests let's assign\",\n \"the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \\\"\\\"\\\"Return\",\n \"recipe with the ID of the recipe that we want to update. self.client.patch(url,\",\n \"= {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will be replaced\",\n \"sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id,\",\n \"that won't change # unless you do refresh from dB if the values\",\n \"temporary file on the system at a random # location usually in the\",\n \"the end # of the file so use this seek function to set\",\n \"URL for recipe image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image\",\n \"any fields in the payload those fields will actually be removed # from\",\n \"new tag is in the tags that we retrieved # test full update\",\n \"tags # assigned class RecipeImageUploadTests(TestCase): # what happens at the setup of the\",\n \"ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2,\",\n \"# make request # We're going to retrieve an update to the recipe\",\n \"sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving # all of\",\n \"to change a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add\",\n \"going to write an image # to that file and then we're going\",\n \"recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id,\",\n \"detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all()\",\n \"sample_recipe(user=self.user) # after the test runs it runs tear down def tearDown(self): self.recipe.image.delete()\",\n \"one of these keys and then we're going to check # that is\",\n \"self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a recipe detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user))\",\n \"tag so the existing tag that # we created won't be assigned to\",\n \"= sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe with two tags',\",\n \"def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a recipe detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url\",\n \"ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe\",\n \"so if you try to access it then it would # just be\",\n \"HTTP put if we omit a field # that should clear the value\",\n \"an image def detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name\",\n \"create # for our viewset because we're going to have a detail action\",\n \"test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access\",\n \"we created won't be assigned to it url = detail_url(recipe.id) # the way\",\n \"the tags that we retrieved # test full update # put: it will\",\n \"our API requests from core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer,\",\n \"keys and then we're going to check # that is the correct value\",\n \"to it url = detail_url(recipe.id) # the way that you update an object\",\n \"with the full # object that is provided in the request that means\",\n \"is the URL of the # recipe with the ID of the recipe\",\n \"the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this\",\n \"name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and\",\n \"it will be the seeking will be done to the # end of\",\n \"and just make sure they match what we expect. recipe.refresh_from_db() # refreshes the\",\n \"{ 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url =\",\n \"serializer = RecipeSerializer(recipes, many=True) # many=true: this is because we were returning the\",\n \"test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe with ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 =\",\n \"'<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user)\",\n \"here and we're going # to replace it with a new tag payload\",\n \"and any fields that are omitted from # the request will not be\",\n \"payload = { 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 }\",\n \"self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full update of an\",\n \"to a model the details of that won't change # unless you do\",\n \"assertion for each one of these keys, check that it is # equal\",\n \"to check # that is the correct value assigned to our recipe model.\",\n \"removing all of the test files that we create # delete the image\",\n \"have a sample tag assigned should not have any tags # assigned class\",\n \"object that we're updating with the full # object that is provided in\",\n \"request that means if you exclude # any fields in the payload those\",\n \"we're going # to replace it with a new tag payload = {'title':\",\n \"import os # this allows us to perform things like # creating path\",\n \"file and then we're going to upload that file # through the API\",\n \"do refresh from dB if the values have changed # in the database.\",\n \"black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python reads files\",\n \"tag that we create here and we're going # to replace it with\",\n \"class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() def\",\n \"[ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED)\",\n \"serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a recipe\",\n \"updating def test_full_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with put\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user))\",\n \"you create a new model and you have a # reference to a\",\n \"self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning\",\n \"sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by the authenticated user\",\n \"This is how I know that if we do res.data and # retrieve\",\n \"recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag new tag is\",\n \"explained # because when we do a HTTP put if we omit a\",\n \"because we've # saved the file it will be the seeking will be\",\n \"25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes,\",\n \"location usually in the /temp folder # create a temporary file we're going\",\n \"to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an invalid image\\\"\\\"\\\" url =\",\n \"that's being updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with patch\\\"\\\"\\\" # make\",\n \"update and full update of an object # there are two ways in\",\n \"= sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user,\",\n \"details in our recipe from the database # typically when you create a\",\n \"change a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a\",\n \"the recipe self.assertEqual(tags.count(), 2) # because we expect two tags to be assigned.\",\n \"in our payload object # getattr: that allows you to retrieve an attribute\",\n \"files that we create # delete the image if it exists in the\",\n \"self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we\",\n \"format='JPEG') ntf.seek(0) # it's the way that Python reads files so because we've\",\n \"# we created won't be assigned to it url = detail_url(recipe.id) # the\",\n \"detail action # this is how you specify arguments with the reverse function\",\n \"self.client = APIClient() def test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton is required\\\"\\\"\\\" res = self.client.get(RECIPES_URL)\",\n \"check that the path exists for the image that is saved to our\",\n \"self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) # after the test runs\",\n \"# should only return the first two recipe # test the response: serializer1\",\n \"payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will\",\n \"curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 =\",\n \"a random # location usually in the /temp folder # create a temporary\",\n \"the value of that field so now our recipe # that did have\",\n \"to check the fields that # are assigned and just make sure they\",\n \"self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all()\",\n \"temp file # somewhere in the system and then you can remove that\",\n \"the details of that won't change # unless you do refresh from dB\",\n \"self.assertIn('image', res.data) # check that the path exists for the image that is\",\n \"runs tear down def tearDown(self): self.recipe.image.delete() # make sure that our file system\",\n \"in order to upload an image def detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\" return\",\n \"with ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload\",\n \"payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags =\",\n \"asterisks when calling a # function it has the reverse effect. class PublicRecipeApiTests(TestCase):\",\n \"exists for the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self):\",\n \"actually get the value of the # key in our payload object #\",\n \"class in all capitals. # app : identifier of the URL in the\",\n \"going to run some assertions to check that it # uploaded correctly with\",\n \"you're going to need the existing recipe ID in order to upload an\",\n \"make request # We're going to retrieve an update to the recipe from\",\n \"existing tag that # we created won't be assigned to it url =\",\n \"# in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags)\",\n \"that you're updating def test_full_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with put\\\"\\\"\\\" recipe =\",\n \"detail url def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for recipe image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id])\",\n \"# we're going to access them by retrieving # all of the recipes\",\n \"sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and return a sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user,\",\n \"sample recipe\\\"\\\"\\\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, }\",\n \"the class in all capitals. # app : identifier of the URL in\",\n \"an update to the recipe from the # database and then we're going\",\n \"sample_recipe(user=self.user, title='Steak and mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id,\",\n \"# getattr: that allows you to retrieve an attribute from # an object\",\n \"in which you can update an object using the # API there's two\",\n \"APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list\",\n \"add a new tag and what we're going to do is we're going\",\n \"defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return\",\n \"what we're going to do is we're going # to swap out this\",\n \"# We're going to retrieve an update to the recipe from the #\",\n \"# authenticate our user self.recipe = sample_recipe(user=self.user) # after the test runs it\",\n \"= self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma\",\n \"2) # because we expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2,\",\n \"check # that is the correct value assigned to our recipe model. for\",\n \"our image class which will let us then # create test images which\",\n \"# in the payload so the only fields that it will change are\",\n \"that are omitted from # the request will not be modified in the\",\n \"sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price':\",\n \"recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer =\",\n \"ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore')\",\n \"name=name) def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and return a sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name)\",\n \"because you've already read up to the end # of the file so\",\n \"cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms')\",\n \"path to # the image that should be accessible self.assertIn('image', res.data) # check\",\n \"limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user)\",\n \"that file # through the API like you would with a HTTP POST\",\n \"= self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2)\",\n \"create # delete the image if it exists in the recipe def test_upload_image_to_recipe(self):\",\n \"would # just be blank because you've already read up to the end\",\n \"15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients =\",\n \"that Python reads files so because we've # saved the file it will\",\n \"creates a named temporary file on the system at a random # location\",\n \"you can update an object using the # API there's two different HTTP\",\n \"list of the # arguments you want to add # here we have\",\n \"= sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag = sample_tag(user=self.user,\",\n \"access it then it would # just be blank because you've already read\",\n \"a sample tag assigned should not have any tags # assigned class RecipeImageUploadTests(TestCase):\",\n \"there are two ways in which you can update an object using the\",\n \"an object using the # API there's two different HTTP methods: put, patch\",\n \"database # typically when you create a new model and you have a\",\n \"you use the detail URL so that is the URL of the #\",\n \"with a new tag payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]}\",\n \"argument # when you use the two asterisks when calling a # function\",\n \"res = self.client.get(RECIPES_URL) # filter our recipes by the authenticated user recipes =\",\n \"# make a request to change a field in our recipe. recipe =\",\n \"saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an invalid image\\\"\\\"\\\" url\",\n \"top of the class in all capitals. # app : identifier of the\",\n \"to access the URL in more # or less all the tests let's\",\n \"put: it will replace the object that we're updating with the full #\",\n \"a HTTP put if we omit a field # that should clear the\",\n \"a sample recipe\\\"\\\"\\\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00,\",\n \"the details in our recipe from the database # typically when you create\",\n \"= sample_recipe(user=self.user, title='Steak and mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients':\",\n \"upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url # you're going\",\n \"tag that # we created won't be assigned to it url = detail_url(recipe.id)\",\n \"name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API res\",\n \"'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients\",\n \"will create # for our viewset because we're going to have a detail\",\n \"single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload = { 'title':\",\n \"res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 =\",\n \"with specific tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine\",\n \"rest_framework import status from rest_framework.test import APIClient # use that for making our\",\n \"all capitals. # app : identifier of the URL in the app #\",\n \"post this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is\",\n \"recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) #\",\n \"default behavior is that it will return a dictionary containing # the created\",\n \"= Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all() # retrieve the\",\n \"self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe\",\n \"assertions # refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check\",\n \"or we wanted to simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"'<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) #\",\n \"that is the correct value assigned to our recipe model. for key in\",\n \"# test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 =\",\n \"self.assertIn(tag2, tags) # check if the tags that we created as our sample\",\n \"user self.recipe = sample_recipe(user=self.user) # after the test runs it runs tear down\",\n \"which we can then upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since\",\n \"that file after # you've used it import os # this allows us\",\n \"generate our upload image url # you're going to need the existing recipe\",\n \"# delete the image if it exists in the recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test\",\n \"the database # typically when you create a new model and you have\",\n \"import status from rest_framework.test import APIClient # use that for making our API\",\n \"the existing recipe ID in order to upload an image def detail_url(recipe_id): \\\"\\\"\\\"Return\",\n \"'<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by\",\n \"tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the tags assigned\",\n \"arguments with the reverse function # you just pass in args and then\",\n \"ingredients) # test partial update and full update of an object # there\",\n \"upload that file # through the API like you would with a HTTP\",\n \"details of that won't change # unless you do refresh from dB if\",\n \"res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we just want\",\n \"and # retrieve the id key this will get the id of the\",\n \"update the fields that are provided # in the payload so the only\",\n \"these keys, check that it is # equal to the same key in\",\n \"of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving #\",\n \"make a request to change a field in our recipe. recipe = sample_recipe(user=self.user)\",\n \"\\\"\\\"\\\"Test updating a recipe with patch\\\"\\\"\\\" # make a request to change a\",\n \"object using the Django rest framework # view sets is you use the\",\n \"# typically when you create a new model and you have a #\",\n \"in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an object using\",\n \"if you try to access it then it would # just be blank\",\n \"in the /temp folder # create a temporary file we're going to write\",\n \"assign that as a variable # at top of the class in all\",\n \"import Image # pillow, this will import our image class which will let\",\n \"will be done to the # end of the file so if you\",\n \"name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips')\",\n \"after the test runs it runs tear down def tearDown(self): self.recipe.image.delete() # make\",\n \"URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response code for creating\",\n \"get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes\",\n \"URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def\",\n \"get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list of recipes\\\"\\\"\\\" sample_recipe(user=self.user)\",\n \"5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price,\",\n \"image that should be accessible self.assertIn('image', res.data) # check that the path exists\",\n \"django.contrib.auth import get_user_model from django.test import TestCase from django.urls import reverse from rest_framework\",\n \"this is because we were returning the list view # or we wanted\",\n \"capitals. # app : identifier of the URL in the app # /api/recipe/recipes\",\n \"the image that should be accessible self.assertIn('image', res.data) # check that the path\",\n \"# through the API like you would with a HTTP POST and then\",\n \"the authenticaiton is required\\\"\\\"\\\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated\",\n \"Updated sample recipe', 'tags': [new_tag.id]} # tags will be replaced with this new\",\n \"\\\"\\\"\\\"Test authenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user(\",\n \"specify arguments with the reverse function # you just pass in args and\",\n \"using the # API there's two different HTTP methods: put, patch # patch:\",\n \"= sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe', 'time_minutes': 25,\",\n \"in the recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an image to recipe\\\"\\\"\\\" url =\",\n \"that were created with the recipe self.assertEqual(tags.count(), 2) # because we expect two\",\n \"calling a # function it has the reverse effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated\",\n \"recipe # that did have a sample tag assigned should not have any\",\n \"in the payload those fields will actually be removed # from the object\",\n \"# creating path names and also checking if files exist on the system\",\n \"view sets is you use the detail URL so that is the URL\",\n \"the object that's being updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with patch\\\"\\\"\\\"\",\n \"the sample recipe that gets created # it creates a named temporary file\",\n \"dB if the values have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags\",\n \"# you're going to need the existing recipe ID in order to upload\",\n \"is we're going # to swap out this tag that we create here\",\n \"in the request that means if you exclude # any fields in the\",\n \"expect. recipe.refresh_from_db() # refreshes the details in our recipe from the database #\",\n \"file system is kept clean after our test # removing all of the\",\n \"title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2)\",\n \"import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call a function which\",\n \"update an object using the # API there's two different HTTP methods: put,\",\n \"is provided in the request that means if you exclude # any fields\",\n \"# to that file and then we're going to upload that file #\",\n \"serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload =\",\n \"a HTTP POST and then # we're going to run some assertions to\",\n \"end of the file so if you try to access it then it\",\n \"update # put: it will replace the object that we're updating with the\",\n \"going to do is we're going to loop through each # one of\",\n \"'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe =\",\n \"recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post this\",\n \"= sample_tag(user=self.user, name='Curry') # add a new tag and what we're going to\",\n \"uploading an image to recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id) # going to use the\",\n \"that for making our API requests from core.models import Recipe, Tag, Ingredient from\",\n \"in this case we just want to serialize a single object self.assertEqual(res.data, serializer.data)\",\n \"if our filtering is working # should only return the first two recipe\",\n \"recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id) # going to use the sample recipe that gets\",\n \"# assertion for each one of these keys, check that it is #\",\n \"setup of the test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass')\",\n \"self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags),\",\n \"an object using the Django rest framework # view sets is you use\",\n \"only return the first two recipe # test the response: serializer1 = RecipeSerializer(recipe1)\",\n \"mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1\",\n \"simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data)\",\n \"sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user,\",\n \"a temp file # somewhere in the system and then you can remove\",\n \"going to access them by retrieving # all of the recipes from our\",\n \"not have any tags # assigned class RecipeImageUploadTests(TestCase): # what happens at the\",\n \"# fields that are provided and any fields that are omitted from #\",\n \"two different HTTP methods: put, patch # patch: Patch is used to update\",\n \"queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe with ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1')\",\n \"exists in the recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an image to recipe\\\"\\\"\\\" url\",\n \"fields will actually be removed # from the object that you're updating def\",\n \"set # the pointer back to the beginning of the file res =\",\n \"that we create here and we're going # to replace it with a\",\n \"sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by the authenticated user recipes\",\n \"in args and then you pass in a list of the # arguments\",\n \"were returning the list view # or we wanted to simulate the list\",\n \") sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by the authenticated\",\n \"\\\"\\\"\\\"Test returning recipes with specific tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2\",\n \"to the recipe from the # database and then we're going to check\",\n \"# this is the standard HTTP response code for creating objects # in\",\n \"our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe\",\n \"in the recipe # payload[key]: This will actually get the value of the\",\n \"= self.client.get(RECIPES_URL) # filter our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user)\",\n \"to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need to\",\n \"since we're going to need to access the URL in more # or\",\n \"{ 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload)\",\n \"unauthenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() def test_required_auth(self): \\\"\\\"\\\"Test the\",\n \"RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need to access the URL\",\n \"to need to access the URL in more # or less all the\",\n \"and return a sample tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and\",\n \"1) self.assertIn(new_tag, tags) # check that the tag new tag is in the\",\n \"will then create a temp file # somewhere in the system and then\",\n \"need to access the URL in more # or less all the tests\",\n \"the payload so the only fields that it will change are the #\",\n \"it's the way that Python reads files so because we've # saved the\",\n \"= RecipeDetailSerializer(recipe) # in this case we just want to serialize a single\",\n \"{ 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults)\",\n \"read up to the end # of the file so use this seek\",\n \"it exists in the recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an image to recipe\\\"\\\"\\\"\",\n \"system is kept clean after our test # removing all of the test\",\n \"will get the id of the created object. # Next what we're going\",\n \"2') payload = { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes':\",\n \"our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code,\",\n \"we're going to write an image # to that file and then we're\",\n \"recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 =\",\n \"import tempfile # allows you to call a function which will then create\",\n \"= sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe with ingredients', 'ingredients':\",\n \"recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1\",\n \"from # the request will not be modified in the object that's being\",\n \"that we want to update. self.client.patch(url, payload) # make request # We're going\",\n \"status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(),\",\n \"def detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the\",\n \"the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize\",\n \"end point that the default router will create # for our viewset because\",\n \"arguments you want to add # here we have single item def sample_tag(user,\",\n \"by passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a\",\n \"'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id)\",\n \"# the image that should be accessible self.assertIn('image', res.data) # check that the\",\n \"the id of the created object. # Next what we're going to do\",\n \"[tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED)\",\n \"defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument # when\",\n \"= detail_url(recipe.id) # the way that you update an object using the Django\",\n \"file so use this seek function to set # the pointer back to\",\n \"URL of the # recipe with the ID of the recipe that we\",\n \"this allows us to perform things like # creating path names and also\",\n \"# assertions # refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) #\",\n \"that the path exists for the image that is saved to our model\",\n \"to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags that\",\n \"'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title'])\",\n \"of these keys and then we're going to check # that is the\",\n \"reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url # you're going to need\",\n \"temporary file we're going to write an image # to that file and\",\n \"you exclude # any fields in the payload those fields will actually be\",\n \"viewset because we're going to have a detail action # this is how\",\n \"a # function it has the reverse effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe\",\n \"it with a new tag payload = {'title': 'Partially Updated sample recipe', 'tags':\",\n \"working # should only return the first two recipe # test the response:\",\n \"in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a recipe with\",\n \"retrieve an update to the recipe from the # database and then we're\",\n \"= self.client.post(RECIPES_URL, payload) # post this payload dictionary to our recipes URL. self.assertEqual(res.status_code,\",\n \"from rest_framework.test import APIClient # use that for making our API requests from\",\n \"invalid image\\\"\\\"\\\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\",\n \"random # location usually in the /temp folder # create a temporary file\",\n \"sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1)\",\n \"self.assertIn(new_tag, tags) # check that the tag new tag is in the tags\",\n \"the reverse function # you just pass in args and then you pass\",\n \"Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call\",\n \"# it to the tags get parameter # if our filtering is working\",\n \"} defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument #\",\n \"def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an invalid image\\\"\\\"\\\" url = image_upload_url(self.recipe.id) res = self.client.post(url,\",\n \"a list of the # arguments you want to add # here we\",\n \"self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a recipe detail\\\"\\\"\\\" recipe =\",\n \"# the way that you update an object using the Django rest framework\",\n \"POST and then # we're going to run some assertions to check that\",\n \"are limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2)\",\n \"sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry')\",\n \"from the object that you're updating def test_full_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with\",\n \"file it will be the seeking will be done to the # end\",\n \"we retrieved # test full update # put: it will replace the object\",\n \"clear the value of that field so now our recipe # that did\",\n \"ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \\\"\\\"\\\"Create and return a sample recipe\\\"\\\"\\\"\",\n \"effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient()\",\n \"from core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile\",\n \"viewing a recipe detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res\",\n \"status.HTTP_200_OK) # check that the images in the response so that's the path\",\n \"accessible self.assertIn('image', res.data) # check that the path exists for the image that\",\n \"with put\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample\",\n \"image\\\"\\\"\\\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def\",\n \"existing recipe ID in order to upload an image def detail_url(recipe_id): \\\"\\\"\\\"Return recipe\",\n \"the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return\",\n \"= { 'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30,\",\n \"image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an\",\n \"to retrieve an attribute from # an object by passing in a variable.\",\n \"format='multipart') # assertions # refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data)\",\n \"list of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving\",\n \"the file it will be the seeking will be done to the #\",\n \"order to upload an image def detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail',\",\n \"should only return the first two recipe # test the response: serializer1 =\",\n \"detail URL so that is the URL of the # recipe with the\",\n \"with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) # creates black square\",\n \"if # they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data,\",\n \"router will create # for our viewset because we're going to have a\",\n \"to need the existing recipe ID in order to upload an image def\",\n \"to upload an image def detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id])\",\n \"should clear the value of that field so now our recipe # that\",\n \"means if you exclude # any fields in the payload those fields will\",\n \"= APIClient() def test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton is required\\\"\\\"\\\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code,\",\n \"object This is how I know that if we do res.data and #\",\n \"get parameter # if our filtering is working # should only return the\",\n \"this will import our image class which will let us then # create\",\n \"self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the response so that's\",\n \"url = image_upload_url(self.recipe.id) # going to use the sample recipe that gets created\",\n \"title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this\",\n \"and we're going to check if # they exist in the responses returned\",\n \"the recipe from the # database and then we're going to check the\",\n \"recipe\\\"\\\"\\\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params)\",\n \"is required\\\"\\\"\\\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe API\",\n \"to the # end of the file so if you try to access\",\n \"back to the beginning of the file res = self.client.post(url, {'image': ntf}, format='multipart')\",\n \"from django.urls import reverse from rest_framework import status from rest_framework.test import APIClient #\",\n \"# check that the path exists for the image that is saved to\",\n \"'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post this payload\",\n \"to upload that file # through the API like you would with a\",\n \"passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a recipe\",\n \"an object by passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test\",\n \"sample recipe', 'tags': [new_tag.id]} # tags will be replaced with this new tag\",\n \"the way that Python reads files so because we've # saved the file\",\n \"os # this allows us to perform things like # creating path names\",\n \"object using the Django rest framework the # default behavior is that it\",\n \"just make sure they match what we expect. recipe.refresh_from_db() # refreshes the details\",\n \"tag payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will\",\n \"same as the tags that are in our queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating\",\n \"django.urls import reverse from rest_framework import status from rest_framework.test import APIClient # use\",\n \"return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and return a sample ingredient\\\"\\\"\\\" return\",\n \"sample tag assigned should not have any tags # assigned class RecipeImageUploadTests(TestCase): #\",\n \"recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) #\",\n \"check the fields that # are assigned and just make sure they match\",\n \"path exists for the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def\",\n \"RecipeSerializer(recipes, many=True) # many=true: this is because we were returning the list view\",\n \"the test files that we create # delete the image if it exists\",\n \"a variable # at top of the class in all capitals. # app\",\n \"then we're going to check # that is the correct value assigned to\",\n \"authenticaiton is required\\\"\\\"\\\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe\",\n \"# refreshes the details in our recipe from the database # typically when\",\n \"the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res =\",\n \"parameter # if our filtering is working # should only return the first\",\n \"{'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma separated list string\",\n \"at the setup of the test def setUp(self): self.client = APIClient() self.user =\",\n \"action # this is how you specify arguments with the reverse function #\",\n \"you specify arguments with the reverse function # you just pass in args\",\n \"dictionary containing # the created object This is how I know that if\",\n \"will be replaced with this new tag so the existing tag that #\",\n \"object # getattr: that allows you to retrieve an attribute from # an\",\n \"the Django rest framework the # default behavior is that it will return\",\n \"status.HTTP_201_CREATED) # this is the standard HTTP response code for creating objects #\",\n \"2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full update of\",\n \"Next what we're going to do is we're going to loop through each\",\n \"= RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data,\",\n \"field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to\",\n \"test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate\",\n \"def tearDown(self): self.recipe.image.delete() # make sure that our file system is kept clean\",\n \"status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes with specific tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Thai\",\n \"add a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a\",\n \"authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL)\",\n \"get the id of the created object. # Next what we're going to\",\n \"self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def\",\n \"recipe tags = recipe.tags.all() # retrieve the tags that were created with the\",\n \"an image to recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id) # going to use the sample\",\n \"\\\"\\\"\\\"Test unauthenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() def test_required_auth(self): \\\"\\\"\\\"Test\",\n \"# allows you to call a function which will then create a temp\",\n \"system and then you can remove that file after # you've used it\",\n \"# in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an object\",\n \"that are provided # in the payload so the only fields that it\",\n \"self.client.patch(url, payload) # make request # We're going to retrieve an update to\",\n \"RecipeImageUploadTests(TestCase): # what happens at the setup of the test def setUp(self): self.client\",\n \"object that you're updating def test_full_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with put\\\"\\\"\\\" recipe\",\n \"done to the # end of the file so if you try to\",\n \"each # one of these keys and then we're going to check #\",\n \"we're going to run some assertions to check that it # uploaded correctly\",\n \"then we're going to check the fields that # are assigned and just\",\n \"serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes for user\\\"\\\"\\\" # test recipes are limited\",\n \"we will check that the tags assigned are zero now as I explained\",\n \"to call a function which will then create a temp file # somewhere\",\n \"# many=true: this is because we were returning the list view # or\",\n \"updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with patch\\\"\\\"\\\" # make a request\",\n \"is in the tags that we retrieved # test full update # put:\",\n \"we create # delete the image if it exists in the recipe def\",\n \"sure that our file system is kept clean after our test # removing\",\n \"tags) # check that the tag new tag is in the tags that\",\n \"of the file so if you try to access it then it would\",\n \"the reverse effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client\",\n \"= sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} )\",\n \"we created as our sample tags are # the same as the tags\",\n \"def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes with specific ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Posh beans\",\n \"with the ID of the recipe that we want to update. self.client.patch(url, payload)\",\n \"that # are assigned and just make sure they match what we expect.\",\n \"to perform things like # creating path names and also checking if files\",\n \"This will actually get the value of the # key in our payload\",\n \"return the first two recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2\",\n \"then you pass in a list of the # arguments you want to\",\n \"in more # or less all the tests let's assign that as a\",\n \"= self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe API access\\\"\\\"\\\" def setUp(self):\",\n \"class which will let us then # create test images which we can\",\n \"our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the response\",\n \"because we expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) #\",\n \"specific ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken\",\n \"'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert\",\n \"required\\\"\\\"\\\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe API access\\\"\\\"\\\"\",\n \"recipe with ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2')\",\n \"create test images which we can then upload to our API RECIPES_URL =\",\n \"request # We're going to retrieve an update to the recipe from the\",\n \"this is how you specify arguments with the reverse function # you just\",\n \"the way that you update an object using the Django rest framework #\",\n \"# when you use the two asterisks when calling a # function it\",\n \"reference to a model the details of that won't change # unless you\",\n \"2') payload = { 'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id],\",\n \"PIL import Image # pillow, this will import our image class which will\",\n \"in all capitals. # app : identifier of the URL in the app\",\n \"check that the tag new tag is in the tags that we retrieved\",\n \"sample recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db()\",\n \"pass in a list of the # arguments you want to add #\",\n \"HTTP methods: put, patch # patch: Patch is used to update the fields\",\n \"this case we just want to serialize a single object self.assertEqual(res.data, serializer.data) def\",\n \"def setUp(self): self.client = APIClient() def test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton is required\\\"\\\"\\\" res\",\n \"our viewset because we're going to have a detail action # this is\",\n \"framework the # default behavior is that it will return a dictionary containing\",\n \"going to do is we're going # to swap out this tag that\",\n \"# of the file so use this seek function to set # the\",\n \"behavior is that it will return a dictionary containing # the created object\",\n \"Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to\",\n \"in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of these keys,\",\n \"return result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes with specific ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user,\",\n \"I know that if we do res.data and # retrieve the id key\",\n \"it then it would # just be blank because you've already read up\",\n \"and return a sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \\\"\\\"\\\"Create and\",\n \"# that should clear the value of that field so now our recipe\",\n \"that means if you exclude # any fields in the payload those fields\",\n \"going to check if # they exist in the responses returned self.assertIn(serializer1.data, res.data)\",\n \"be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags that we\",\n \"'{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma separated list string and\",\n \"methods: put, patch # patch: Patch is used to update the fields that\",\n \"payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0)\",\n \"self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list of\",\n \"put if we omit a field # that should clear the value of\",\n \"ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full update of an object\",\n \"want to add # here we have single item def sample_tag(user, name='Main course'):\",\n \"'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) #\",\n \"to do is we're going # to swap out this tag that we\",\n \"change are the # fields that are provided and any fields that are\",\n \"will actually get the value of the # key in our payload object\",\n \"use the sample recipe that gets created # it creates a named temporary\",\n \"then we're going to upload that file # through the API like you\",\n \"self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes with specific tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user,\",\n \"self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the\",\n \"replace it with a new tag payload = {'title': 'Partially Updated sample recipe',\",\n \"is # equal to the same key in the recipe # payload[key]: This\",\n \"an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an object using the\",\n \"recipe with put\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated\",\n \"is because we were returning the list view # or we wanted to\",\n \"self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an invalid image\\\"\\\"\\\" url = image_upload_url(self.recipe.id) res =\",\n \"know that if we do res.data and # retrieve the id key this\",\n \"the # default behavior is that it will return a dictionary containing #\",\n \"database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in\",\n \"going # to replace it with a new tag payload = {'title': 'Partially\",\n \"name=name) def sample_recipe(user, **params): \\\"\\\"\\\"Create and return a sample recipe\\\"\\\"\\\" defaults = {\",\n \"the full # object that is provided in the request that means if\",\n \"status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient()\",\n \"us then # create test images which we can then upload to our\",\n \"res = self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the database for\",\n \"recipes with specific ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 =\",\n \"from the database # typically when you create a new model and you\",\n \"it would # just be blank because you've already read up to the\",\n \"we do a HTTP put if we omit a field # that should\",\n \"test full update # put: it will replace the object that we're updating\",\n \"recipes and we're going to check if # they exist in the responses\",\n \"the end point that the default router will create # for our viewset\",\n \"creating recipe with ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient\",\n \"of the file so use this seek function to set # the pointer\",\n \"# going to use the sample recipe that gets created # it creates\",\n \"that the tag new tag is in the tags that we retrieved #\",\n \"access\\\"\\\"\\\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user)\",\n \"# app : identifier of the URL in the app # /api/recipe/recipes #\",\n \"we do res.data and # retrieve the id key this will get the\",\n \"sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and\",\n \"is you use the detail URL so that is the URL of the\",\n \"for making our API requests from core.models import Recipe, Tag, Ingredient from ..serializers\",\n \"test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1)\",\n \"that the default router will create # for our viewset because we're going\",\n \"test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price':\",\n \"RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're\",\n \"created as our sample tags are # the same as the tags that\",\n \"URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that the default\",\n \"recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this\",\n \"an object # there are two ways in which you can update an\",\n \"is working # should only return the first two recipe # test the\",\n \"file # somewhere in the system and then you can remove that file\",\n \"way that you update an object using the Django rest framework # view\",\n \"seek function to set # the pointer back to the beginning of the\",\n \"Django rest framework # view sets is you use the detail URL so\",\n \"function which will then create a temp file # somewhere in the system\",\n \"res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes\",\n \"create here and we're going # to replace it with a new tag\",\n \"tags) # check if the tags that we created as our sample tags\",\n \"self.recipe = sample_recipe(user=self.user) # after the test runs it runs tear down def\",\n \"won't be assigned to it url = detail_url(recipe.id) # the way that you\",\n \"using the Django rest framework # view sets is you use the detail\",\n \"recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00\",\n \"variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a recipe with tags\\\"\\\"\\\" tag1\",\n \"recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the response so\",\n \"so use this seek function to set # the pointer back to the\",\n \"the Django rest framework # view sets is you use the detail URL\",\n \"\\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point\",\n \"res.data) # check that the path exists for the image that is saved\",\n \"# in this case we just want to serialize a single object self.assertEqual(res.data,\",\n \"the fields that are provided # in the payload so the only fields\",\n \"class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() self.user\",\n \"has the reverse effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe API access\\\"\\\"\\\" def setUp(self):\",\n \"field # that should clear the value of that field so now our\",\n \"that if we do res.data and # retrieve the id key this will\",\n \"pillow, this will import our image class which will let us then #\",\n \"1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe with\",\n \"tags are # the same as the tags that are in our queryset.\",\n \"= recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the tags assigned are\",\n \"= APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe =\",\n \"full # object that is provided in the request that means if you\",\n \"HTTP POST and then # we're going to run some assertions to check\",\n \"self.client.post(RECIPES_URL, payload) # post this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED)\",\n \"to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response\",\n \"from dB if the values have changed # in the database. self.assertEqual(recipe.title, payload['title'])\",\n \"# post this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this\",\n \"can remove that file after # you've used it import os # this\",\n \"url # you're going to need the existing recipe ID in order to\",\n \"10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into\",\n \"title='Steak and mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)}\",\n \"not be modified in the object that's being updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating\",\n \"make sure that our file system is kept clean after our test #\",\n \"# it's the way that Python reads files so because we've # saved\",\n \"to check if # they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data,\",\n \"two asterisks when calling a # function it has the reverse effect. class\",\n \"swap out this tag that we create here and we're going # to\",\n \"= { 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url\",\n \"fields that # are assigned and just make sure they match what we\",\n \"the system and then you can remove that file after # you've used\",\n \"it is # equal to the same key in the recipe # payload[key]:\",\n \"are in our queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe with ingredients\\\"\\\"\\\" ingredient1 =\",\n \"image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning\",\n \"payload object # getattr: that allows you to retrieve an attribute from #\",\n \"to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new tag and\",\n \"now as I explained # because when we do a HTTP put if\",\n \"} res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all()\",\n \"we're going # to swap out this tag that we create here and\",\n \"the request will not be modified in the object that's being updated. def\",\n \"point that the default router will create # for our viewset because we're\",\n \"is used to update the fields that are provided # in the payload\",\n \"creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create\",\n \"assigned to it url = detail_url(recipe.id) # the way that you update an\",\n \"used to update the fields that are provided # in the payload so\",\n \"you do refresh from dB if the values have changed # in the\",\n \"API like you would with a HTTP POST and then # we're going\",\n \"which you can update an object using the # API there's two different\",\n \"detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer\",\n \"def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our\",\n \"tag2.id)} ) # this will create a comma separated list string and assign\",\n \"happens at the setup of the test def setUp(self): self.client = APIClient() self.user\",\n \"that should clear the value of that field so now our recipe #\",\n \"modified in the object that's being updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a recipe\",\n \"= APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving\",\n \"to the end # of the file so use this seek function to\",\n \"# reference to a model the details of that won't change # unless\",\n \"provided and any fields that are omitted from # the request will not\",\n \"res = self.client.post(RECIPES_URL, payload) # post this payload dictionary to our recipes URL.\",\n \"sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API\",\n \"of the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail\",\n \"# to replace it with a new tag payload = {'title': 'Partially Updated\",\n \"a new model and you have a # reference to a model the\",\n \"0) # we will check that the tags assigned are zero now as\",\n \"be accessible self.assertIn('image', res.data) # check that the path exists for the image\",\n \"import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows\",\n \"a dictionary containing # the created object This is how I know that\",\n \"of that field so now our recipe # that did have a sample\",\n \"this tag that we create here and we're going # to replace it\",\n \"tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe with two\",\n \"tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new tag\",\n \"allows you to call a function which will then create a temp file\",\n \"recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag\",\n \"API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() def test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton is\",\n \"def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for recipe image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate\",\n \"the recipe # payload[key]: This will actually get the value of the #\",\n \"returning recipes with specific ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2\",\n \"self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client =\",\n \"from django.contrib.auth import get_user_model from django.test import TestCase from django.urls import reverse from\",\n \"need the existing recipe ID in order to upload an image def detail_url(recipe_id):\",\n \"the recipes from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer =\",\n \"a model the details of that won't change # unless you do refresh\",\n \"# removing all of the test files that we create # delete the\",\n \"because we were returning the list view # or we wanted to simulate\",\n \"tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags\",\n \"comma separated list string and assign # it to the tags get parameter\",\n \"Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and return a sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user,\",\n \"in a list of the # arguments you want to add # here\",\n \"that the tags assigned are zero now as I explained # because when\",\n \"sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test\",\n \"# check that the tag new tag is in the tags that we\",\n \"square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python reads files so\",\n \"will actually be removed # from the object that you're updating def test_full_update_recipe(self):\",\n \"# the pointer back to the beginning of the file res = self.client.post(url,\",\n \"{'image': ntf}, format='multipart') # assertions # refreshing the database for our recipe self.recipe.refresh_from_db()\",\n \"def sample_tag(user, name='Main course'): \\\"\\\"\\\"Create and return a sample tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name)\",\n \"= reverse('recipe:recipe-list') # since we're going to need to access the URL in\",\n \"as a variable # at top of the class in all capitals. #\",\n \"args and then you pass in a list of the # arguments you\",\n \"the tags that we created as our sample tags are # the same\",\n \"we're going to need to access the URL in more # or less\",\n \"res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe API access\\\"\\\"\\\" def\",\n \"use that for making our API requests from core.models import Recipe, Tag, Ingredient\",\n \"remove that file after # you've used it import os # this allows\",\n \"# arguments you want to add # here we have single item def\",\n \"\\\"\\\"\\\"Test creating a recipe with tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 =\",\n \"so the only fields that it will change are the # fields that\",\n \"recipe that gets created # it creates a named temporary file on the\",\n \"self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response code for creating objects\",\n \"..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call a function\",\n \"recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user,\",\n \"same key in the recipe # payload[key]: This will actually get the value\",\n \"used it import os # this allows us to perform things like #\",\n \"recipe = Recipe.objects.get(id=res.data['id']) # When you create an object using the Django rest\",\n \"sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak\",\n \"update of an object # there are two ways in which you can\",\n \"kept clean after our test # removing all of the test files that\",\n \"# from the object that you're updating def test_full_update_recipe(self): \\\"\\\"\\\"Test updating a recipe\",\n \"recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new tag and what we're\",\n \"# end of the file so if you try to access it then\",\n \"self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags that we created as\",\n \"that allows you to retrieve an attribute from # an object by passing\",\n \"# equal to the same key in the recipe # payload[key]: This will\",\n \"that # we created won't be assigned to it url = detail_url(recipe.id) #\",\n \"sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user,\",\n \"to swap out this tag that we create here and we're going #\",\n \"blank because you've already read up to the end # of the file\",\n \"to recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id) # going to use the sample recipe that\",\n \"the object that we're updating with the full # object that is provided\",\n \"our payload object # getattr: that allows you to retrieve an attribute from\",\n \"rest framework the # default behavior is that it will return a dictionary\",\n \"Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving\",\n \"app : identifier of the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/\",\n \"using the Django rest framework the # default behavior is that it will\",\n \"Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload)\",\n \"res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created\",\n \"to our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion\",\n \"the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update\",\n \"from rest_framework import status from rest_framework.test import APIClient # use that for making\",\n \"names and also checking if files exist on the system from PIL import\",\n \"checking if files exist on the system from PIL import Image # pillow,\",\n \"HTTP response code for creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id'])\",\n \"the test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) #\",\n \"filter our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes,\",\n \"'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) # after the test\",\n \"seeking will be done to the # end of the file so if\",\n \"recipe\\\"\\\"\\\" payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res\",\n \"the images in the response so that's the path to # the image\",\n \"ID of the recipe that we want to update. self.client.patch(url, payload) # make\",\n \"# create test images which we can then upload to our API RECIPES_URL\",\n \"the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url\",\n \"= sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title':\",\n \"to add # here we have single item def sample_tag(user, name='Main course'): \\\"\\\"\\\"Create\",\n \"# they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data)\",\n \"equal to the same key in the recipe # payload[key]: This will actually\",\n \"system at a random # location usually in the /temp folder # create\",\n \"are assigned and just make sure they match what we expect. recipe.refresh_from_db() #\",\n \"so because we've # saved the file it will be the seeking will\",\n \"with patch\\\"\\\"\\\" # make a request to change a field in our recipe.\",\n \"# are assigned and just make sure they match what we expect. recipe.refresh_from_db()\",\n \"When you create an object using the Django rest framework the # default\",\n \"# this will create a comma separated list string and assign # it\",\n \"returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def\",\n \"a detail action # this is how you specify arguments with the reverse\",\n \"'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) #\",\n \"the # API there's two different HTTP methods: put, patch # patch: Patch\",\n \"created # it creates a named temporary file on the system at a\",\n \"create an object using the Django rest framework the # default behavior is\",\n \"{ 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00\",\n \"and then # we're going to run some assertions to check that it\",\n \"'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes with specific tags\\\"\\\"\\\" recipe1\",\n \"self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag new tag is in\",\n \"you create an object using the Django rest framework the # default behavior\",\n \"run some assertions to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as\",\n \"= recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag new tag\",\n \"many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes for user\\\"\\\"\\\" #\",\n \"we're going to loop through each # one of these keys and then\",\n \"put\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe',\",\n \"and then you can remove that file after # you've used it import\",\n \"\\\"\\\"\\\"Test retrieving list of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them\",\n \"= self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the database for our\",\n \"to access them by retrieving # all of the recipes from our database.\",\n \"def test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton is required\\\"\\\"\\\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class\",\n \"tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe\",\n \"recipe from the # database and then we're going to check the fields\",\n \"image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for recipe image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our\",\n \"for the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test\",\n \"to run some assertions to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg')\",\n \"in our queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe with ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user,\",\n \"res.data) # check the return result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes with specific\",\n \"recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the\",\n \"check the return result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes with specific ingredients\\\"\\\"\\\" recipe1\",\n \"of the test files that we create # delete the image if it\",\n \"request to change a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) #\",\n \"# key in our payload object # getattr: that allows you to retrieve\",\n \"have a # reference to a model the details of that won't change\",\n \"} res = self.client.post(RECIPES_URL, payload) # post this payload dictionary to our recipes\",\n \"recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1\",\n \"ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') #\",\n \"get the value of the # key in our payload object # getattr:\",\n \"# object that is provided in the request that means if you exclude\",\n \"with this new tag so the existing tag that # we created won't\",\n \"so that is the URL of the # recipe with the ID of\",\n \"that field so now our recipe # that did have a sample tag\",\n \"assertions to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img\",\n \"attribute from # an object by passing in a variable. (instead of recipe.key)\",\n \"were created with the recipe self.assertEqual(tags.count(), 2) # because we expect two tags\",\n \"we just want to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test\",\n \"\\\"\\\"\\\"Test retrieving recipes for user\\\"\\\"\\\" # test recipes are limited to the authenticated\",\n \"30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post this payload dictionary\",\n \"to the tags get parameter # if our filtering is working # should\",\n \"all of the recipes from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id')\",\n \"access\\\"\\\"\\\" def setUp(self): self.client = APIClient() def test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton is required\\\"\\\"\\\"\",\n \"key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of these\",\n \"# payload[key]: This will actually get the value of the # key in\",\n \"RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes for user\\\"\\\"\\\"\",\n \"check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB',\",\n \"tag and what we're going to do is we're going # to swap\",\n \"1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a recipe detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user)\",\n \"they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) #\",\n \"import our image class which will let us then # create test images\",\n \"that we create # delete the image if it exists in the recipe\",\n \"refresh from dB if the values have changed # in the database. self.assertEqual(recipe.title,\",\n \"files exist on the system from PIL import Image # pillow, this will\",\n \"tags will be replaced with this new tag so the existing tag that\",\n \"test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes with specific ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Posh beans on\",\n \"= Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test\",\n \"# get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test\",\n \"# view sets is you use the detail URL so that is the\",\n \"being updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with patch\\\"\\\"\\\" # make a\",\n \"of recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a recipe with tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user,\",\n \"retrieve the tags that were created with the recipe self.assertEqual(tags.count(), 2) # because\",\n \"URL so that is the URL of the # recipe with the ID\",\n \"through the API like you would with a HTTP POST and then #\",\n \"we're going to check # that is the correct value assigned to our\",\n \"the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check\",\n \"the seeking will be done to the # end of the file so\",\n \"# after the test runs it runs tear down def tearDown(self): self.recipe.image.delete() #\",\n \"going # to swap out this tag that we create here and we're\",\n \"ntf: img = Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0)\",\n \"payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res =\",\n \"payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients\",\n \"if we omit a field # that should clear the value of that\",\n \"keys, check that it is # equal to the same key in the\",\n \"the file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the\",\n \"test # removing all of the test files that we create # delete\",\n \"a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag\",\n \"recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self):\",\n \"delete the image if it exists in the recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading\",\n \"and also checking if files exist on the system from PIL import Image\",\n \"reverse('recipe:recipe-list') # since we're going to need to access the URL in more\",\n \"the response so that's the path to # the image that should be\",\n \"it will change are the # fields that are provided and any fields\",\n \"payload those fields will actually be removed # from the object that you're\",\n \"user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is because\",\n \"test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton is required\\\"\\\"\\\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase):\",\n \"use the detail URL so that is the URL of the # recipe\",\n \"serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes\",\n \"'{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data,\",\n \"the # fields that are provided and any fields that are omitted from\",\n \"self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of these keys, check that\",\n \"creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python reads\",\n \"can then upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going\",\n \"django.test import TestCase from django.urls import reverse from rest_framework import status from rest_framework.test\",\n \"have any tags # assigned class RecipeImageUploadTests(TestCase): # what happens at the setup\",\n \"recipes for user\\\"\\\"\\\" # test recipes are limited to the authenticated user. user2\",\n \"# one of these keys and then we're going to check # that\",\n \"we can then upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're\",\n \"to the same key in the recipe # payload[key]: This will actually get\",\n \"going to use the sample recipe that gets created # it creates a\",\n \"= sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken')\",\n \"# API there's two different HTTP methods: put, patch # patch: Patch is\",\n \"the recipe that we want to update. self.client.patch(url, payload) # make request #\",\n \"# this allows us to perform things like # creating path names and\",\n \"to set # the pointer back to the beginning of the file res\",\n \"= image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test\",\n \"setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user\",\n \"import TestCase from django.urls import reverse from rest_framework import status from rest_framework.test import\",\n \"# here we have single item def sample_tag(user, name='Main course'): \\\"\\\"\\\"Create and return\",\n \"and you have a # reference to a model the details of that\",\n \"one of these keys, check that it is # equal to the same\",\n \"updating a recipe with patch\\\"\\\"\\\" # make a request to change a field\",\n \"# if our filtering is working # should only return the first two\",\n \"API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need to access the\",\n \"will let us then # create test images which we can then upload\",\n \"'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 }\",\n \"to that file and then we're going to upload that file # through\",\n \"created with the recipe self.assertEqual(tags.count(), 2) # because we expect two tags to\",\n \"refreshes the details in our recipe from the database # typically when you\",\n \"for our viewset because we're going to have a detail action # this\",\n \"function it has the reverse effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe API access\\\"\\\"\\\"\",\n \"a recipe with tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag\",\n \"tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) # creates black square img.save(ntf,\",\n \"correct value assigned to our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe,\",\n \"(instead of recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a recipe with tags\\\"\\\"\\\" tag1 =\",\n \"# or we wanted to simulate the list view in our serializer self.assertEqual(res.status_code,\",\n \"correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) # creates black\",\n \"they match what we expect. recipe.refresh_from_db() # refreshes the details in our recipe\",\n \"the system at a random # location usually in the /temp folder #\",\n \"= self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes with\",\n \"# retrieve the tags that were created with the recipe self.assertEqual(tags.count(), 2) #\",\n \"to do is we're going to loop through each # one of these\",\n \"list string and assign # it to the tags get parameter # if\",\n \"two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL,\",\n \"that you update an object using the Django rest framework # view sets\",\n \"put, patch # patch: Patch is used to update the fields that are\",\n \"self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma separated\",\n \"tags that were created with the recipe self.assertEqual(tags.count(), 2) # because we expect\",\n \"up to the end # of the file so use this seek function\",\n \"def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes with specific tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable\",\n \"list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self):\",\n \"ways in which you can update an object using the # API there's\",\n \"responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result\",\n \"tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload)\",\n \"there's two different HTTP methods: put, patch # patch: Patch is used to\",\n \"are provided and any fields that are omitted from # the request will\",\n \"rest_framework.test import APIClient # use that for making our API requests from core.models\",\n \"Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the\",\n \"= Recipe.objects.get(id=res.data['id']) # When you create an object using the Django rest framework\",\n \"\\\"\\\"\\\"Test the authenticaiton is required\\\"\\\"\\\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test\",\n \"# check if the tags that we created as our sample tags are\",\n \"Python reads files so because we've # saved the file it will be\",\n \"this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the\",\n \"framework # view sets is you use the detail URL so that is\",\n \"you update an object using the Django rest framework # view sets is\",\n \"= Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's\",\n \"return a dictionary containing # the created object This is how I know\",\n \"test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes for user\\\"\\\"\\\" # test recipes are limited to the\",\n \"of the # key in our payload object # getattr: that allows you\",\n \"self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a recipe detail\\\"\\\"\\\"\",\n \"do res.data and # retrieve the id key this will get the id\",\n \"self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check\",\n \"upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need\",\n \"at top of the class in all capitals. # app : identifier of\",\n \"that it is # equal to the same key in the recipe #\",\n \"def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to\",\n \"a # reference to a model the details of that won't change #\",\n \"return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \\\"\\\"\\\"Create and return a sample recipe\\\"\\\"\\\" defaults\",\n \"you try to access it then it would # just be blank because\",\n \"value assigned to our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key))\",\n \"name='Curry') # add a new tag and what we're going to do is\",\n \"to update the fields that are provided # in the payload so the\",\n \"recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API res = self.client.get(\",\n \"tear down def tearDown(self): self.recipe.image.delete() # make sure that our file system is\",\n \"so now our recipe # that did have a sample tag assigned should\",\n \"all the tests let's assign that as a variable # at top of\",\n \"to the beginning of the file res = self.client.post(url, {'image': ntf}, format='multipart') #\",\n \"the created object This is how I know that if we do res.data\",\n \"now our recipe # that did have a sample tag assigned should not\",\n \"it will replace the object that we're updating with the full # object\",\n \"when you create a new model and you have a # reference to\",\n \"= get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our\",\n \"of the class in all capitals. # app : identifier of the URL\",\n \"expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if\",\n \"the value of the # key in our payload object # getattr: that\",\n \"recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API res = self.client.get( RECIPES_URL,\",\n \"name of the end point that the default router will create # for\",\n \"objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an\",\n \"case we just want to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self):\",\n \"test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a recipe with tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2\",\n \"that is provided in the request that means if you exclude # any\",\n \"assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags that we created\",\n \"return a sample tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and return\",\n \"when calling a # function it has the reverse effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test\",\n \"containing # the created object This is how I know that if we\",\n \"we're going to access them by retrieving # all of the recipes from\",\n \"RecipeDetailSerializer(recipe) # in this case we just want to serialize a single object\",\n \"# default behavior is that it will return a dictionary containing # the\",\n \"database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"(10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way\",\n \"it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10))\",\n \"retrieve the id key this will get the id of the created object.\",\n \"the list view # or we wanted to simulate the list view in\",\n \"ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload)\",\n \"# tags will be replaced with this new tag so the existing tag\",\n \"be done to the # end of the file so if you try\",\n \"can update an object using the # API there's two different HTTP methods:\",\n \"the path to # the image that should be accessible self.assertIn('image', res.data) #\",\n \"tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 =\",\n \"and full update of an object # there are two ways in which\",\n \"tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and return a sample ingredient\\\"\\\"\\\"\",\n \"exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check\",\n \"recipe', 'tags': [new_tag.id]} # tags will be replaced with this new tag so\",\n \"things like # creating path names and also checking if files exist on\",\n \"of the # recipe with the ID of the recipe that we want\",\n \"from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call a\",\n \"any tags # assigned class RecipeImageUploadTests(TestCase): # what happens at the setup of\",\n \"serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes\",\n \"# /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for recipe image\",\n \"zero now as I explained # because when we do a HTTP put\",\n \"payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard\",\n \"access them by retrieving # all of the recipes from our database. res\",\n \"# you just pass in args and then you pass in a list\",\n \"APIClient() def test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton is required\\\"\\\"\\\" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"list view # or we wanted to simulate the list view in our\",\n \"def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes for user\\\"\\\"\\\" # test recipes are limited to\",\n \"url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self):\",\n \"two ways in which you can update an object using the # API\",\n \"10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve\",\n \"'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary\",\n \"# the request will not be modified in the object that's being updated.\",\n \"self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we just want to serialize\",\n \"the created object. # Next what we're going to do is we're going\",\n \"\\\"\\\"\\\"Test creating recipe with ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user,\",\n \"make sure they match what we expect. recipe.refresh_from_db() # refreshes the details in\",\n \"name='Main course'): \\\"\\\"\\\"Create and return a sample tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user,\",\n \"created recipe tags = recipe.tags.all() # retrieve the tags that were created with\",\n \"test recipes are limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass'\",\n \"# create a temporary file we're going to write an image # to\",\n \"of the recipe that we want to update. self.client.patch(url, payload) # make request\",\n \"all of the test files that we create # delete the image if\",\n \"recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') #\",\n \"do a HTTP put if we omit a field # that should clear\",\n \"# add a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add\",\n \"and we're going # to replace it with a new tag payload =\",\n \"# that did have a sample tag assigned should not have any tags\",\n \"= sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res =\",\n \"model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one\",\n \"that as a variable # at top of the class in all capitals.\",\n \"(id) --> detail url def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for recipe image upload\\\"\\\"\\\" return\",\n \"check that the tags assigned are zero now as I explained # because\",\n \"recipe self.assertEqual(tags.count(), 2) # because we expect two tags to be assigned. self.assertIn(tag1,\",\n \"recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API res =\",\n \"database and then we're going to check the fields that # are assigned\",\n \"self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes for user\\\"\\\"\\\" # test\",\n \"you want to add # here we have single item def sample_tag(user, name='Main\",\n \"# function it has the reverse effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe API\",\n \"recipe that we want to update. self.client.patch(url, payload) # make request # We're\",\n \"and then we're going to check # that is the correct value assigned\",\n \"reads files so because we've # saved the file it will be the\",\n \"assigned should not have any tags # assigned class RecipeImageUploadTests(TestCase): # what happens\",\n \"which will let us then # create test images which we can then\",\n \"payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of these keys, check\",\n \"self.assertEqual(tags.count(), 2) # because we expect two tags to be assigned. self.assertIn(tag1, tags)\",\n \"a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new\",\n \"= self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data)\",\n \"{'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will be replaced with\",\n \"self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self):\",\n \"reverse effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client =\",\n \"the object that you're updating def test_full_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with put\\\"\\\"\\\"\",\n \"of the created object. # Next what we're going to do is we're\",\n \"it import os # this allows us to perform things like # creating\",\n \"only fields that it will change are the # fields that are provided\",\n \"check if the tags that we created as our sample tags are #\",\n \"tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag new\",\n \"the # key in our payload object # getattr: that allows you to\",\n \"of the test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user)\",\n \"sample_tag(user, name='Main course'): \\\"\\\"\\\"Create and return a sample tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name) def\",\n \"recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() def test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton\",\n \"recipe.tags.all() # retrieve the tags that were created with the recipe self.assertEqual(tags.count(), 2)\",\n \"recipe with patch\\\"\\\"\\\" # make a request to change a field in our\",\n \"the pointer back to the beginning of the file res = self.client.post(url, {'image':\",\n \"a recipe detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res =\",\n \"if it exists in the recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an image to\",\n \"# When you create an object using the Django rest framework the #\",\n \"less all the tests let's assign that as a variable # at top\",\n \"res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a\",\n \"function # you just pass in args and then you pass in a\",\n \"to update. self.client.patch(url, payload) # make request # We're going to retrieve an\",\n \"if the values have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags =\",\n \"file so if you try to access it then it would # just\",\n \"def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and return a sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name) def\",\n \"allows you to retrieve an attribute from # an object by passing in\",\n \"RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're going to check\",\n \"1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe with\",\n \"in the tags that we retrieved # test full update # put: it\",\n \"# that is the correct value assigned to our recipe model. for key\",\n \"for creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you\",\n \"tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload =\",\n \"new model and you have a # reference to a model the details\",\n \"new tag and what we're going to do is we're going # to\",\n \"# assigned class RecipeImageUploadTests(TestCase): # what happens at the setup of the test\",\n \"self.assertEqual(len(tags), 0) # we will check that the tags assigned are zero now\",\n \"# there are two ways in which you can update an object using\",\n \"created object This is how I know that if we do res.data and\",\n \"recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) #\",\n \"from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True)\",\n \"# saved the file it will be the seeking will be done to\",\n \"the same as the tags that are in our queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test\",\n \"first two recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2)\",\n \"after # you've used it import os # this allows us to perform\",\n \"# test partial update and full update of an object # there are\",\n \"self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes for user\\\"\\\"\\\" # test recipes are\",\n \"image if it exists in the recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an image\",\n \"in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the\",\n \"# generate our upload image url # you're going to need the existing\",\n \"check that it is # equal to the same key in the recipe\",\n \"= sample_recipe(user=self.user) # after the test runs it runs tear down def tearDown(self):\",\n \"--> detail url def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for recipe image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image',\",\n \"retrieve the created recipe tags = recipe.tags.all() # retrieve the tags that were\",\n \"recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res\",\n \"a function which will then create a temp file # somewhere in the\",\n \"# because when we do a HTTP put if we omit a field\",\n \"= RecipeSerializer(recipe3) # serialize the recipes and we're going to check if #\",\n \"you to retrieve an attribute from # an object by passing in a\",\n \"'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 }\",\n \"recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)}\",\n \"retrieving recipes for user\\\"\\\"\\\" # test recipes are limited to the authenticated user.\",\n \"we're going to have a detail action # this is how you specify\",\n \"self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test\",\n \"just want to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating\",\n \"self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the\",\n \"a request to change a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user))\",\n \"self.recipe.image.delete() # make sure that our file system is kept clean after our\",\n \"recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2)\",\n \"the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new tag and what\",\n \"we expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check\",\n \"the dictionary into the argument # when you use the two asterisks when\",\n \"recipe with tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2')\",\n \"usually in the /temp folder # create a temporary file we're going to\",\n \"= RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're going to\",\n \"in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing\",\n \"as the tags that are in our queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe\",\n \"you've used it import os # this allows us to perform things like\",\n \"test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with patch\\\"\\\"\\\" # make a request to change\",\n \"user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter\",\n \"payload so the only fields that it will change are the # fields\",\n \"it has the reverse effect. class PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe API access\\\"\\\"\\\" def\",\n \"result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes with specific ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Posh\",\n \"def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an image to recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id) # going\",\n \"} res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the\",\n \"'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url,\",\n \"serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a recipe detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user))\",\n \"self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3\",\n \"are zero now as I explained # because when we do a HTTP\",\n \"going to need to access the URL in more # or less all\",\n \"'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code,\",\n \"be replaced with this new tag so the existing tag that # we\",\n \"**defaults) # convert the dictionary into the argument # when you use the\",\n \"in the payload so the only fields that it will change are the\",\n \"10.00, } res = self.client.post(RECIPES_URL, payload) # post this payload dictionary to our\",\n \"standard HTTP response code for creating objects # in an API. recipe =\",\n \"from PIL import Image # pillow, this will import our image class which\",\n \"won't change # unless you do refresh from dB if the values have\",\n \"files so because we've # saved the file it will be the seeking\",\n \"sample_recipe(user, **params): \\\"\\\"\\\"Create and return a sample recipe\\\"\\\"\\\" defaults = { 'title': 'Sample\",\n \"recipe from the database # typically when you create a new model and\",\n \"by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true:\",\n \"the tags get parameter # if our filtering is working # should only\",\n \"on the system from PIL import Image # pillow, this will import our\",\n \"an image # to that file and then we're going to upload that\",\n \"do is we're going # to swap out this tag that we create\",\n \"it creates a named temporary file on the system at a random #\",\n \"# creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python\",\n \"image to recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id) # going to use the sample recipe\",\n \"'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will be replaced with this\",\n \"= RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and\",\n \"of the file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing\",\n \"payload) # post this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) #\",\n \"pass in args and then you pass in a list of the #\",\n \"the tags assigned are zero now as I explained # because when we\",\n \"recipes from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes,\",\n \"payload = { 'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes':\",\n \"after our test # removing all of the test files that we create\",\n \"creating a recipe with tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user,\",\n \"recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an image to recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id) #\",\n \"name='Ingredient 2') payload = { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id],\",\n \"detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we just\",\n \"status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes for user\\\"\\\"\\\" # test recipes\",\n \"object # there are two ways in which you can update an object\",\n \"\\\"\\\"\\\"Return URL for recipe image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload\",\n \"serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're going to check if\",\n \"the recipes and we're going to check if # they exist in the\",\n \"with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res =\",\n \"use this seek function to set # the pointer back to the beginning\",\n \"tags) self.assertIn(tag2, tags) # check if the tags that we created as our\",\n \"RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma separated list\",\n \"getattr: that allows you to retrieve an attribute from # an object by\",\n \"you to call a function which will then create a temp file #\",\n \"default router will create # for our viewset because we're going to have\",\n \"omit a field # that should clear the value of that field so\",\n \"ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and\",\n \"\\\"\\\"\\\"Create and return a sample tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create\",\n \"tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user,\",\n \"url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags\",\n \"self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full update\",\n \"PublicRecipeApiTests(TestCase): \\\"\\\"\\\"Test unauthenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() def test_required_auth(self):\",\n \"recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each\",\n \"making our API requests from core.models import Recipe, Tag, Ingredient from ..serializers import\",\n \"vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2\",\n \"beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese')\",\n \"be the seeking will be done to the # end of the file\",\n \"the tags that are in our queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe with\",\n \"returning recipes with specific tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 =\",\n \"file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the database\",\n \"identifier of the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) -->\",\n \"# refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that\",\n \"are provided # in the payload so the only fields that it will\",\n \"database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that\",\n \"# unless you do refresh from dB if the values have changed #\",\n \"is how I know that if we do res.data and # retrieve the\",\n \"wanted to simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1)\",\n \"a sample tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and return a\",\n \"then you can remove that file after # you've used it import os\",\n \"res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() #\",\n \"when we do a HTTP put if we omit a field # that\",\n \"from the # database and then we're going to check the fields that\",\n \"= recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients)\",\n \"that are provided and any fields that are omitted from # the request\",\n \"just be blank because you've already read up to the end # of\",\n \"of that won't change # unless you do refresh from dB if the\",\n \"ID in order to upload an image def detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\"\",\n \"status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all() #\",\n \"getattr(recipe, key)) # assertion for each one of these keys, check that it\",\n \"we're going to do is we're going # to swap out this tag\",\n \") serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data,\",\n \"refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the\",\n \"single item def sample_tag(user, name='Main course'): \\\"\\\"\\\"Create and return a sample tag\\\"\\\"\\\" return\",\n \"# it creates a named temporary file on the system at a random\",\n \"recipe # payload[key]: This will actually get the value of the # key\",\n \"we're going to upload that file # through the API like you would\",\n \"with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL,\",\n \"to simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data,\",\n \"removed # from the object that you're updating def test_full_update_recipe(self): \\\"\\\"\\\"Test updating a\",\n \"you pass in a list of the # arguments you want to add\",\n \"full update of an object # there are two ways in which you\",\n \"let us then # create test images which we can then upload to\",\n \"we've # saved the file it will be the seeking will be done\",\n \"# at top of the class in all capitals. # app : identifier\",\n \"model and you have a # reference to a model the details of\",\n \"change # unless you do refresh from dB if the values have changed\",\n \"each one of these keys, check that it is # equal to the\",\n \"end # of the file so use this seek function to set #\",\n \"the setup of the test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user',\",\n \"values have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags),\",\n \"\\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00,\",\n \"= detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags =\",\n \"= self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we just want to\",\n \"user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) #\",\n \"and return a sample recipe\\\"\\\"\\\" defaults = { 'title': 'Sample recipe', 'time_minutes': 10,\",\n \"sure they match what we expect. recipe.refresh_from_db() # refreshes the details in our\",\n \"is the correct value assigned to our recipe model. for key in payload.keys():\",\n \"that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an invalid\",\n \"of the end point that the default router will create # for our\",\n \"the created recipe tags = recipe.tags.all() # retrieve the tags that were created\",\n \"in the response so that's the path to # the image that should\",\n \"is we're going to loop through each # one of these keys and\",\n \"'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe =\",\n \"a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a recipe with tags\\\"\\\"\\\"\",\n \"course'): \\\"\\\"\\\"Create and return a sample tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'):\",\n \"format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes with specific tags\\\"\\\"\\\" recipe1 =\",\n \"'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post this payload dictionary to\",\n \"with the reverse function # you just pass in args and then you\",\n \"# because we expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags)\",\n \"specific tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with\",\n \"= sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian')\",\n \"title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta\",\n \"our filtering is working # should only return the first two recipe #\",\n \"Patch is used to update the fields that are provided # in the\",\n \"Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \\\"\\\"\\\"Create and return a sample recipe\\\"\\\"\\\" defaults =\",\n \"sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe with two tags', 'tags':\",\n \"our queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe with ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient\",\n \"we expect. recipe.refresh_from_db() # refreshes the details in our recipe from the database\",\n \"# name of the end point that the default router will create #\",\n \"and then we're going to upload that file # through the API like\",\n \"retrieving # all of the recipes from our database. res = self.client.get(RECIPES_URL) recipes\",\n \"'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) #\",\n \"to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res\",\n \"object. # Next what we're going to do is we're going to loop\",\n \"authenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>',\",\n \"creating recipe\\\"\\\"\\\" payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, }\",\n \"want to update. self.client.patch(url, payload) # make request # We're going to retrieve\",\n \"if the tags that we created as our sample tags are # the\",\n \"get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) # after the\",\n \"to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img =\",\n \"exclude # any fields in the payload those fields will actually be removed\",\n \"sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get(\",\n \"[new_tag.id]} # tags will be replaced with this new tag so the existing\",\n \"somewhere in the system and then you can remove that file after #\",\n \"view # or we wanted to simulate the list view in our serializer\",\n \"user\\\"\\\"\\\" # test recipes are limited to the authenticated user. user2 = get_user_model().objects.create_user(\",\n \"= sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 =\",\n \"going to have a detail action # this is how you specify arguments\",\n \"should not have any tags # assigned class RecipeImageUploadTests(TestCase): # what happens at\",\n \"= detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we\",\n \"tags assigned are zero now as I explained # because when we do\",\n \") # this will create a comma separated list string and assign #\",\n \"\\\"\\\"\\\"Create and return a sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \\\"\\\"\\\"Create\",\n \"just pass in args and then you pass in a list of the\",\n \"sample tags are # the same as the tags that are in our\",\n \"what we're going to do is we're going to loop through each #\",\n \"system from PIL import Image # pillow, this will import our image class\",\n \"a recipe with patch\\\"\\\"\\\" # make a request to change a field in\",\n \"requests from core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import\",\n \"replaced with this new tag so the existing tag that # we created\",\n \"then upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to\",\n \"title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan')\",\n \"RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data)\",\n \"unless you do refresh from dB if the values have changed # in\",\n \"sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \\\"\\\"\\\"Create and return a sample\",\n \"self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags\",\n \"response code for creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) #\",\n \"use the two asterisks when calling a # function it has the reverse\",\n \"/temp folder # create a temporary file we're going to write an image\",\n \"assign # it to the tags get parameter # if our filtering is\",\n \"on the system at a random # location usually in the /temp folder\",\n \"import get_user_model from django.test import TestCase from django.urls import reverse from rest_framework import\",\n \"model the details of that won't change # unless you do refresh from\",\n \"uploading an invalid image\\\"\\\"\\\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart')\",\n \"# retrieve the id key this will get the id of the created\",\n \"create a temp file # somewhere in the system and then you can\",\n \"path names and also checking if files exist on the system from PIL\",\n \"the same key in the recipe # payload[key]: This will actually get the\",\n \"= self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe\",\n \"the values have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all()\",\n \"recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title,\",\n \"our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need to access\",\n \"to loop through each # one of these keys and then we're going\",\n \"allows us to perform things like # creating path names and also checking\",\n \"serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're going\",\n \"for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of\",\n \"payload = { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45,\",\n \"to retrieve an update to the recipe from the # database and then\",\n \"create a new model and you have a # reference to a model\",\n \"file after # you've used it import os # this allows us to\",\n \"pointer back to the beginning of the file res = self.client.post(url, {'image': ntf},\",\n \"it runs tear down def tearDown(self): self.recipe.image.delete() # make sure that our file\",\n \"when you use the two asterisks when calling a # function it has\",\n \"test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an invalid image\\\"\\\"\\\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image':\",\n \"updating a recipe with put\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title':\",\n \"function to set # the pointer back to the beginning of the file\",\n \"are # the same as the tags that are in our queryset. def\",\n \"= sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 =\",\n \"rest framework # view sets is you use the detail URL so that\",\n \"\\\"\\\"\\\"Test returning recipes with specific ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast')\",\n \"to replace it with a new tag payload = {'title': 'Partially Updated sample\",\n \"name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe\",\n \"and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will\",\n \"which will then create a temp file # somewhere in the system and\",\n \"return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that the default router\",\n \"tags that we created as our sample tags are # the same as\",\n \"test runs it runs tear down def tearDown(self): self.recipe.image.delete() # make sure that\",\n \"convert the dictionary into the argument # when you use the two asterisks\",\n \"API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an object using the Django\",\n \"url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case\",\n \"dictionary into the argument # when you use the two asterisks when calling\",\n \"two recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3\",\n \"object that's being updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with patch\\\"\\\"\\\" #\",\n \"authenticate our user self.recipe = sample_recipe(user=self.user) # after the test runs it runs\",\n \"API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' )\",\n \"sample tag\\\"\\\"\\\" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): \\\"\\\"\\\"Create and return a sample\",\n \"'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the\",\n \"# any fields in the payload those fields will actually be removed #\",\n \"payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that\",\n \"then # create test images which we can then upload to our API\",\n \"toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 =\",\n \"request will not be modified in the object that's being updated. def test_partial_update_recipe(self):\",\n \"at a random # location usually in the /temp folder # create a\",\n \"retrieving list of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by\",\n \"recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags':\",\n \"ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 =\",\n \"these keys and then we're going to check # that is the correct\",\n \"then create a temp file # somewhere in the system and then you\",\n \"retrieved # test full update # put: it will replace the object that\",\n \"\\\"\\\"\\\"Test uploading an invalid image\\\"\\\"\\\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'},\",\n \"with specific ingredients\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user,\",\n \"TestCase from django.urls import reverse from rest_framework import status from rest_framework.test import APIClient\",\n \"new tag so the existing tag that # we created won't be assigned\",\n \"our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response code\",\n \"tag assigned should not have any tags # assigned class RecipeImageUploadTests(TestCase): # what\",\n \"setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self):\",\n \"that gets created # it creates a named temporary file on the system\",\n \"title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2)\",\n \"ntf}, format='multipart') # assertions # refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code,\",\n \"call a function which will then create a temp file # somewhere in\",\n \"# to swap out this tag that we create here and we're going\",\n \"# use that for making our API requests from core.models import Recipe, Tag,\",\n \"recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>'\",\n \"you can remove that file after # you've used it import os #\",\n \"= RecipeSerializer(recipes, many=True) # many=true: this is because we were returning the list\",\n \"tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini')\",\n \"id key this will get the id of the created object. # Next\",\n \"a recipe with put\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully\",\n \"you have a # reference to a model the details of that won't\",\n \"tags that are in our queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe with ingredients\\\"\\\"\\\"\",\n \"to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload\",\n \"image url # you're going to need the existing recipe ID in order\",\n \"tempfile # allows you to call a function which will then create a\",\n \"be assigned to it url = detail_url(recipe.id) # the way that you update\",\n \"Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you\",\n \"in the object that's being updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with\",\n \"code for creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) # When\",\n \"reverse function # you just pass in args and then you pass in\",\n \"import APIClient # use that for making our API requests from core.models import\",\n \"going to retrieve an update to the recipe from the # database and\",\n \"sets is you use the detail URL so that is the URL of\",\n \"the fields that # are assigned and just make sure they match what\",\n \"def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def\",\n \"did have a sample tag assigned should not have any tags # assigned\",\n \"we're going to check if # they exist in the responses returned self.assertIn(serializer1.data,\",\n \"Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients)\",\n \"\\\"\\\"\\\"Test uploading an image to recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id) # going to use\",\n \"so that's the path to # the image that should be accessible self.assertIn('image',\",\n \"will check that the tags assigned are zero now as I explained #\",\n \"our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True)\",\n \"in the system and then you can remove that file after # you've\",\n \"image def detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of\",\n \"to have a detail action # this is how you specify arguments with\",\n \"object by passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating\",\n \"some assertions to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf:\",\n \"exist on the system from PIL import Image # pillow, this will import\",\n \"# all of the recipes from our database. res = self.client.get(RECIPES_URL) recipes =\",\n \"self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the response so that's the\",\n \"# recipe with the ID of the recipe that we want to update.\",\n \"= get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) # after\",\n \"is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an invalid image\\\"\\\"\\\"\",\n \"# check the return result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes with specific ingredients\\\"\\\"\\\"\",\n \"it to the tags get parameter # if our filtering is working #\",\n \"by retrieving # all of the recipes from our database. res = self.client.get(RECIPES_URL)\",\n \"= get_user_model().objects.create_user( '<EMAIL>', '<PASSWORD>' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list of recipes\\\"\\\"\\\"\",\n \"core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile #\",\n \"= { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user,\",\n \"and then we're going to check the fields that # are assigned and\",\n \"you would with a HTTP POST and then # we're going to run\",\n \"the payload those fields will actually be removed # from the object that\",\n \"a field # that should clear the value of that field so now\",\n \"**params): \\\"\\\"\\\"Create and return a sample recipe\\\"\\\"\\\" defaults = { 'title': 'Sample recipe',\",\n \"is kept clean after our test # removing all of the test files\",\n \"our recipe from the database # typically when you create a new model\",\n \"# we're going to run some assertions to check that it # uploaded\",\n \"going to check the fields that # are assigned and just make sure\",\n \"that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10,\",\n \"fields that are provided # in the payload so the only fields that\",\n \"create a temporary file we're going to write an image # to that\",\n \"RecipeDetailSerializer import tempfile # allows you to call a function which will then\",\n \"from django.test import TestCase from django.urls import reverse from rest_framework import status from\",\n \"assigned are zero now as I explained # because when we do a\",\n \"typically when you create a new model and you have a # reference\",\n \"{'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3)\",\n \"Recipe.objects.get(id=res.data['id']) # When you create an object using the Django rest framework the\",\n \"because when we do a HTTP put if we omit a field #\",\n \"an attribute from # an object by passing in a variable. (instead of\",\n \"recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res =\",\n \"that it will change are the # fields that are provided and any\",\n \"'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res\",\n \"object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload = { 'title': 'Test\",\n \"name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL,\",\n \"an invalid image\\\"\\\"\\\" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code,\",\n \"how I know that if we do res.data and # retrieve the id\",\n \"that the images in the response so that's the path to # the\",\n \"object using the # API there's two different HTTP methods: put, patch #\",\n \"a sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \\\"\\\"\\\"Create and return a\",\n \"you just pass in args and then you pass in a list of\",\n \"string and assign # it to the tags get parameter # if our\",\n \"5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument\",\n \"self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset\",\n \"check if # they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data)\",\n \"check that the images in the response so that's the path to #\",\n \"the id key this will get the id of the created object. #\",\n \"# uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) #\",\n \"the tag new tag is in the tags that we retrieved # test\",\n \"or less all the tests let's assign that as a variable # at\",\n \"omitted from # the request will not be modified in the object that's\",\n \"res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data,\",\n \"recipe ID in order to upload an image def detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail\",\n \"we're updating with the full # object that is provided in the request\",\n \"test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes with specific tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry')\",\n \"= sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish\",\n \"view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test\",\n \"creating path names and also checking if files exist on the system from\",\n \"loop through each # one of these keys and then we're going to\",\n \"Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all() # retrieve the tags\",\n \"that our file system is kept clean after our test # removing all\",\n \"recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the tags assigned are zero\",\n \"serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload = { 'title': 'Test recipe', 'time_minutes':\",\n \"APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user)\",\n \"the detail URL so that is the URL of the # recipe with\",\n \"any fields that are omitted from # the request will not be modified\",\n \"url = detail_url(recipe.id) # the way that you update an object using the\",\n \"Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument # when you use\",\n \"that we created as our sample tags are # the same as the\",\n \"will create a comma separated list string and assign # it to the\",\n \"create a comma separated list string and assign # it to the tags\",\n \"try to access it then it would # just be blank because you've\",\n \"sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1)\",\n \"runs it runs tear down def tearDown(self): self.recipe.image.delete() # make sure that our\",\n \"img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python reads files so because\",\n \"= image_upload_url(self.recipe.id) # going to use the sample recipe that gets created #\",\n \"{ 'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price':\",\n \"tags get parameter # if our filtering is working # should only return\",\n \"is how you specify arguments with the reverse function # you just pass\",\n \"dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP\",\n \"is that it will return a dictionary containing # the created object This\",\n \"tag is in the tags that we retrieved # test full update #\",\n \"upload an image def detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id]) #\",\n \"that is the URL of the # recipe with the ID of the\",\n \"field so now our recipe # that did have a sample tag assigned\",\n \"will import our image class which will let us then # create test\",\n \"be modified in the object that's being updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a\",\n \"many=True) # many=true: this is because we were returning the list view #\",\n \"this will create a comma separated list string and assign # it to\",\n \"the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def\",\n \"def sample_recipe(user, **params): \\\"\\\"\\\"Create and return a sample recipe\\\"\\\"\\\" defaults = { 'title':\",\n \"id of the created object. # Next what we're going to do is\",\n \"# or less all the tests let's assign that as a variable #\",\n \"that did have a sample tag assigned should not have any tags #\",\n \"detail_url(recipe_id): \\\"\\\"\\\"Return recipe detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end\",\n \"recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag =\",\n \"created won't be assigned to it url = detail_url(recipe.id) # the way that\",\n \"authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is\",\n \"down def tearDown(self): self.recipe.image.delete() # make sure that our file system is kept\",\n \"# make sure that our file system is kept clean after our test\",\n \"10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that\",\n \"the correct value assigned to our recipe model. for key in payload.keys(): self.assertEqual(payload[key],\",\n \"in our recipe from the database # typically when you create a new\",\n \"recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in\",\n \"the test runs it runs tear down def tearDown(self): self.recipe.image.delete() # make sure\",\n \"would with a HTTP POST and then # we're going to run some\",\n \"let's assign that as a variable # at top of the class in\",\n \"that should be accessible self.assertIn('image', res.data) # check that the path exists for\",\n \"object that is provided in the request that means if you exclude #\",\n \"chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create\",\n \"of these keys, check that it is # equal to the same key\",\n \"def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a recipe with tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user, name='Tag 1')\",\n \"images in the response so that's the path to # the image that\",\n \"payload[key]: This will actually get the value of the # key in our\",\n \"be removed # from the object that you're updating def test_full_update_recipe(self): \\\"\\\"\\\"Test updating\",\n \"we wanted to simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data),\",\n \"sample_recipe(user=self.user) # we're going to access them by retrieving # all of the\",\n \"file on the system at a random # location usually in the /temp\",\n \"setUp(self): self.client = APIClient() def test_required_auth(self): \\\"\\\"\\\"Test the authenticaiton is required\\\"\\\"\\\" res =\",\n \"retrieve an attribute from # an object by passing in a variable. (instead\",\n \"self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the database for our recipe\",\n \"def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload = { 'title': 'Test recipe', 'time_minutes': 30,\",\n \"= recipe.tags.all() # retrieve the tags that were created with the recipe self.assertEqual(tags.count(),\",\n \"you use the two asterisks when calling a # function it has the\",\n \"many=true: this is because we were returning the list view # or we\",\n \"self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the\",\n \"# database and then we're going to check the fields that # are\",\n \"response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the\",\n \"Django rest framework the # default behavior is that it will return a\",\n \"= RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): \\\"\\\"\\\"Test retrieving recipes for\",\n \"this new tag so the existing tag that # we created won't be\",\n \"and assign # it to the tags get parameter # if our filtering\",\n \"reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that the default router will\",\n \"with a HTTP POST and then # we're going to run some assertions\",\n \"We're going to retrieve an update to the recipe from the # database\",\n \"value of that field so now our recipe # that did have a\",\n \"partial update and full update of an object # there are two ways\",\n \"changed # in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag,\",\n \"our upload image url # you're going to need the existing recipe ID\",\n \"this is the standard HTTP response code for creating objects # in an\",\n \"here we have single item def sample_tag(user, name='Main course'): \\\"\\\"\\\"Create and return a\",\n \"def test_full_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with put\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload\",\n \"image # to that file and then we're going to upload that file\",\n \"the default router will create # for our viewset because we're going to\",\n \"payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will be\",\n \"tag new tag is in the tags that we retrieved # test full\",\n \"reverse from rest_framework import status from rest_framework.test import APIClient # use that for\",\n \"# the same as the tags that are in our queryset. def test_create_recipe_with_ingredients(self):\",\n \"recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all() # retrieve\",\n \"= Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is because we were\",\n \"from # an object by passing in a variable. (instead of recipe.key) def\",\n \"= sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title':\",\n \"the existing tag that # we created won't be assigned to it url\",\n \"you're updating def test_full_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with put\\\"\\\"\\\" recipe = sample_recipe(user=self.user)\",\n \"actually be removed # from the object that you're updating def test_full_update_recipe(self): \\\"\\\"\\\"Test\",\n \"are omitted from # the request will not be modified in the object\",\n \"going to need the existing recipe ID in order to upload an image\",\n \"as our sample tags are # the same as the tags that are\",\n \"sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) #\",\n \"API there's two different HTTP methods: put, patch # patch: Patch is used\",\n \"the argument # when you use the two asterisks when calling a #\",\n \"recipes with specific tags\\\"\\\"\\\" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user,\",\n \"queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full\",\n \"get_user_model from django.test import TestCase from django.urls import reverse from rest_framework import status\",\n \"image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url # you're\",\n \"self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes with specific\",\n \"those fields will actually be removed # from the object that you're updating\",\n \"tags = recipe.tags.all() # retrieve the tags that were created with the recipe\",\n \"have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1)\",\n \"match what we expect. recipe.refresh_from_db() # refreshes the details in our recipe from\",\n \"will replace the object that we're updating with the full # object that\",\n \"model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an invalid image\\\"\\\"\\\" url = image_upload_url(self.recipe.id) res\",\n \"self.assertIn(ingredient2, ingredients) # test partial update and full update of an object #\",\n \"then # we're going to run some assertions to check that it #\",\n \"that are in our queryset. def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe with ingredients\\\"\\\"\\\" ingredient1\",\n \"going to loop through each # one of these keys and then we're\",\n \"the tests let's assign that as a variable # at top of the\",\n \"self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client\",\n \"new_tag = sample_tag(user=self.user, name='Curry') # add a new tag and what we're going\",\n \"returning the list view # or we wanted to simulate the list view\",\n \"of the recipes from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer\",\n \"us to perform things like # creating path names and also checking if\",\n \"be blank because you've already read up to the end # of the\",\n \"test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) #\",\n \"sample_tag(user=self.user, name='Curry') # add a new tag and what we're going to do\",\n \"replace the object that we're updating with the full # object that is\",\n \"serializer = RecipeDetailSerializer(recipe) # in this case we just want to serialize a\",\n \"# /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for\",\n \"payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag\",\n \"want to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\"\",\n \"\\\"\\\"\\\"Create and return a sample recipe\\\"\\\"\\\" defaults = { 'title': 'Sample recipe', 'time_minutes':\",\n \"value of the # key in our payload object # getattr: that allows\",\n \"res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test\",\n \"the recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an image to recipe\\\"\\\"\\\" url = image_upload_url(self.recipe.id)\",\n \"} url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price'])\",\n \"ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = {\",\n \"perform things like # creating path names and also checking if files exist\",\n \"a temporary file we're going to write an image # to that file\",\n \"# location usually in the /temp folder # create a temporary file we're\",\n \"recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user,\",\n \"that file and then we're going to upload that file # through the\",\n \"recipe.key) def test_create_recipe_with_tags(self): \\\"\\\"\\\"Test creating a recipe with tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user, name='Tag\",\n \"that it will return a dictionary containing # the created object This is\",\n \"# retrieve the created recipe tags = recipe.tags.all() # retrieve the tags that\",\n \": identifier of the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id)\",\n \"response so that's the path to # the image that should be accessible\",\n \"our test # removing all of the test files that we create #\",\n \"what we expect. recipe.refresh_from_db() # refreshes the details in our recipe from the\",\n \"detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that the\",\n \"recipes are limited to the authenticated user. user2 = get_user_model().objects.create_user( '<EMAIL>', 'pass' )\",\n \"you've already read up to the end # of the file so use\",\n \"saved the file it will be the seeking will be done to the\",\n \"PrivateRecipeApiTests(TestCase): \\\"\\\"\\\"Test authenticated recipe API access\\\"\\\"\\\" def setUp(self): self.client = APIClient() self.user =\",\n \"full update # put: it will replace the object that we're updating with\",\n \"name='Cinnamon'): \\\"\\\"\\\"Create and return a sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params):\",\n \"a new tag and what we're going to do is we're going #\",\n \"'Test recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post\",\n \"our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for\",\n \"our sample tags are # the same as the tags that are in\",\n \"def test_partial_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with patch\\\"\\\"\\\" # make a request to\",\n \"'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes'])\",\n \"RecipeSerializer(recipe3) # serialize the recipes and we're going to check if # they\",\n \"status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a recipe detail\\\"\\\"\\\" recipe\",\n \"this will get the id of the created object. # Next what we're\",\n \"if you exclude # any fields in the payload those fields will actually\",\n \"file # through the API like you would with a HTTP POST and\",\n \"provided in the request that means if you exclude # any fields in\",\n \"we're going to do is we're going to loop through each # one\",\n \"do is we're going to loop through each # one of these keys\",\n \"res.data and # retrieve the id key this will get the id of\",\n \"# what happens at the setup of the test def setUp(self): self.client =\",\n \"recipe image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url #\",\n \"# test full update # put: it will replace the object that we're\",\n \"Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is because we were returning\",\n \"our user self.recipe = sample_recipe(user=self.user) # after the test runs it runs tear\",\n \"assigned to our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) #\",\n \"we're going to check the fields that # are assigned and just make\",\n \"item def sample_tag(user, name='Main course'): \\\"\\\"\\\"Create and return a sample tag\\\"\\\"\\\" return Tag.objects.create(user=user,\",\n \"# just be blank because you've already read up to the end #\",\n \"sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) #\",\n \"if we do res.data and # retrieve the id key this will get\",\n \"= Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1,\",\n \"fields in the payload those fields will actually be removed # from the\",\n \"clean after our test # removing all of the test files that we\",\n \"images which we can then upload to our API RECIPES_URL = reverse('recipe:recipe-list') #\",\n \"like # creating path names and also checking if files exist on the\",\n \"update to the recipe from the # database and then we're going to\",\n \"self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): \\\"\\\"\\\"Test creating recipe\\\"\\\"\\\" payload = { 'title': 'Test recipe',\",\n \"will not be modified in the object that's being updated. def test_partial_update_recipe(self): \\\"\\\"\\\"Test\",\n \"self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) #\",\n \"add # here we have single item def sample_tag(user, name='Main course'): \\\"\\\"\\\"Create and\",\n \"test files that we create # delete the image if it exists in\",\n \"provided # in the payload so the only fields that it will change\",\n \"folder # create a temporary file we're going to write an image #\",\n \"recipe detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url)\",\n \"'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00\",\n \"serialize the recipes and we're going to check if # they exist in\",\n \"image_upload_url(self.recipe.id) # going to use the sample recipe that gets created # it\",\n \"test_full_update_recipe(self): \\\"\\\"\\\"Test updating a recipe with put\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload =\",\n \"to # the image that should be accessible self.assertIn('image', res.data) # check that\",\n \"the # end of the file so if you try to access it\",\n \"API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2\",\n \"the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading\",\n \"assigned and just make sure they match what we expect. recipe.refresh_from_db() # refreshes\",\n \"so the existing tag that # we created won't be assigned to it\",\n \"we omit a field # that should clear the value of that field\",\n \"# we will check that the tags assigned are zero now as I\",\n \"the tags that were created with the recipe self.assertEqual(tags.count(), 2) # because we\",\n \"gets created # it creates a named temporary file on the system at\",\n \"recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe', 'time_minutes':\",\n \"payload) # make request # We're going to retrieve an update to the\",\n \"it url = detail_url(recipe.id) # the way that you update an object using\",\n \"the ID of the recipe that we want to update. self.client.patch(url, payload) #\",\n \"are the # fields that are provided and any fields that are omitted\",\n \"this seek function to set # the pointer back to the beginning of\",\n \"the URL in more # or less all the tests let's assign that\",\n \"self.client.force_authenticate(self.user) def test_retrieve_recipes(self): \\\"\\\"\\\"Test retrieving list of recipes\\\"\\\"\\\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going\",\n \"recipe detail URL\\\"\\\"\\\" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that\",\n \"our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a\",\n \"that's the path to # the image that should be accessible self.assertIn('image', res.data)\",\n \"our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): \\\"\\\"\\\"Test uploading an invalid image\\\"\\\"\\\" url = image_upload_url(self.recipe.id)\",\n \"import reverse from rest_framework import status from rest_framework.test import APIClient # use that\",\n \"how you specify arguments with the reverse function # you just pass in\",\n \"= { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL,\",\n \"the system from PIL import Image # pillow, this will import our image\",\n \"file we're going to write an image # to that file and then\",\n \"into the argument # when you use the two asterisks when calling a\",\n \"RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call a function which will\",\n \"new tag payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags\",\n \"way that Python reads files so because we've # saved the file it\",\n \"{'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): \\\"\\\"\\\"Test returning recipes with specific tags\\\"\\\"\\\"\",\n \"payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the tags\",\n \"ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe with ingredients',\",\n \"named temporary file on the system at a random # location usually in\",\n \"APIClient # use that for making our API requests from core.models import Recipe,\",\n \"for user\\\"\\\"\\\" # test recipes are limited to the authenticated user. user2 =\",\n \"# patch: Patch is used to update the fields that are provided #\",\n \"because we're going to have a detail action # this is how you\",\n \"30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id'])\",\n \"test images which we can then upload to our API RECIPES_URL = reverse('recipe:recipe-list')\",\n \"res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self): \\\"\\\"\\\"Test returning recipes\",\n \"created object. # Next what we're going to do is we're going to\",\n \"the file so if you try to access it then it would #\",\n \"status from rest_framework.test import APIClient # use that for making our API requests\",\n \"the file so use this seek function to set # the pointer back\",\n \"I explained # because when we do a HTTP put if we omit\",\n \"write an image # to that file and then we're going to upload\",\n \"/api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for recipe\",\n \"'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by the\",\n \"sample recipe that gets created # it creates a named temporary file on\",\n \"'tags': [new_tag.id]} # tags will be replaced with this new tag so the\",\n \"args=[recipe_id]) # generate our upload image url # you're going to need the\",\n \"return a sample ingredient\\\"\\\"\\\" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): \\\"\\\"\\\"Create and return\",\n \"the URL of the # recipe with the ID of the recipe that\",\n \"patch\\\"\\\"\\\" # make a request to change a field in our recipe. recipe\",\n \"our file system is kept clean after our test # removing all of\",\n \"with tags\\\"\\\"\\\" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload\",\n \"upload image url # you're going to need the existing recipe ID in\",\n \"= sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test\",\n \"like you would with a HTTP POST and then # we're going to\",\n \"# since we're going to need to access the URL in more #\",\n \"for recipe image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url\",\n \"the image if it exists in the recipe def test_upload_image_to_recipe(self): \\\"\\\"\\\"Test uploading an\",\n \"# this is how you specify arguments with the reverse function # you\",\n \"# serialize the recipes and we're going to check if # they exist\",\n \"update an object using the Django rest framework # view sets is you\",\n \"recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is because we\",\n \"name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe\",\n \"a named temporary file on the system at a random # location usually\",\n \"url def image_upload_url(recipe_id): \\\"\\\"\\\"Return URL for recipe image upload\\\"\\\"\\\" return reverse('recipe:recipe-upload-image', args=[recipe_id]) #\",\n \"= self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get\",\n \"will change are the # fields that are provided and any fields that\",\n \"the API like you would with a HTTP POST and then # we're\",\n \"patch: Patch is used to update the fields that are provided # in\",\n \"in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) #\",\n \"with the recipe self.assertEqual(tags.count(), 2) # because we expect two tags to be\",\n \"our recipe # that did have a sample tag assigned should not have\",\n \"with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3\",\n \"def test_create_recipe_with_ingredients(self): \\\"\\\"\\\"Test creating recipe with ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2\",\n \"# the created object This is how I know that if we do\",\n \"test_view_recipe_detail(self): \\\"\\\"\\\"Test viewing a recipe detail\\\"\\\"\\\" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url =\",\n \"ingredients\\\"\\\"\\\" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload =\",\n \"have single item def sample_tag(user, name='Main course'): \\\"\\\"\\\"Create and return a sample tag\\\"\\\"\\\"\",\n \"have a detail action # this is how you specify arguments with the\",\n \"a new tag payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} #\",\n \"a comma separated list string and assign # it to the tags get\"\n]","isConversational":false}}},{"rowIdx":1197,"cells":{"ngram":{"kind":"list like","value":["multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2): print(\"Division = \",num1/num2) addition() subtraction() multiplication()","\",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2): print(\"Division = \",num1/num2)","division(num1,num2): print(\"Division = \",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day 8","print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2): print(\"Division = \",num1/num2) addition() subtraction() multiplication() division()","something to it and handlethe subsequent errorusing Exception Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i","\")) b=int(input(\"Enter second number = \")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\"Addition","def subtraction(num1,num2): print(\"Subtraction = \",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def","input for you # any kind of function you want to build def","python program to open a file in read only mode and # try","print(\"Subtraction = \",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2): print(\"Division","program to open a file in read only mode and # try writing","for you # any kind of function you want to build def getInput(calculate_arg_fuc):","try writing something to it and handlethe subsequent errorusing Exception Handling try: f=open(\"abc.txt\",\"r\");","# you need to develop a python program to open a file in","Day 8 # write a decorator function for taking input for you #","handlethe subsequent errorusing Exception Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am prajval\"); f.close(); except:","you # any kind of function you want to build def getInput(calculate_arg_fuc): def","Exception Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am prajval\"); f.close(); except: print(\"File is in","you need to develop a python program to open a file in read","function you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter two numbers \")","8 # write a decorator function for taking input for you # any","write a decorator function for taking input for you # any kind of","= \")) b=int(input(\"Enter second number = \")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2):","writing something to it and handlethe subsequent errorusing Exception Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy,","and handlethe subsequent errorusing Exception Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am prajval\"); f.close();","decorator function for taking input for you # any kind of function you","and # try writing something to it and handlethe subsequent errorusing Exception Handling","to develop a python program to open a file in read only mode","# Assignment 2 day 8 # you need to develop a python program","\",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day 8 # you need","open a file in read only mode and # try writing something to","function for taking input for you # any kind of function you want","file in read only mode and # try writing something to it and","@getInput def subtraction(num1,num2): print(\"Subtraction = \",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput","getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter two numbers \") a=int(input(\"Enter first number = \")) b=int(input(\"Enter","= \",num1+num2) @getInput def subtraction(num1,num2): print(\"Subtraction = \",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication =","division() # Assignment 2 day 8 # you need to develop a python","day 8 # you need to develop a python program to open a","wrap_function @getInput def addition(num1,num2): print(\"Addition = \",num1+num2) @getInput def subtraction(num1,num2): print(\"Subtraction = \",num1-num2)","# Assignment 1 Day 8 # write a decorator function for taking input","@getInput def multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2): print(\"Division = \",num1/num2) addition()","kind of function you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter two","want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter two numbers \") a=int(input(\"Enter first","read only mode and # try writing something to it and handlethe subsequent","of function you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter two numbers","to it and handlethe subsequent errorusing Exception Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am","print(\"Division = \",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day 8 #","two numbers \") a=int(input(\"Enter first number = \")) b=int(input(\"Enter second number = \"))","\",num1*num2) @getInput def division(num1,num2): print(\"Division = \",num1/num2) addition() subtraction() multiplication() division() # Assignment","it and handlethe subsequent errorusing Exception Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am prajval\");","addition() subtraction() multiplication() division() # Assignment 2 day 8 # you need to","only mode and # try writing something to it and handlethe subsequent errorusing","= \",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2): print(\"Division =","numbers \") a=int(input(\"Enter first number = \")) b=int(input(\"Enter second number = \")) calculate_arg_fuc(a,b)","print(\"Addition = \",num1+num2) @getInput def subtraction(num1,num2): print(\"Subtraction = \",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication","subtraction(num1,num2): print(\"Subtraction = \",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2):","@getInput def division(num1,num2): print(\"Division = \",num1/num2) addition() subtraction() multiplication() division() # Assignment 2","errorusing Exception Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am prajval\"); f.close(); except: print(\"File is","to open a file in read only mode and # try writing something","subsequent errorusing Exception Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am prajval\"); f.close(); except: print(\"File","number = \")) b=int(input(\"Enter second number = \")) calculate_arg_fuc(a,b) return wrap_function @getInput def","def wrap_function(): print(\"Enter two numbers \") a=int(input(\"Enter first number = \")) b=int(input(\"Enter second","\") a=int(input(\"Enter first number = \")) b=int(input(\"Enter second number = \")) calculate_arg_fuc(a,b) return","= \",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day 8 # you","def getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter two numbers \") a=int(input(\"Enter first number = \"))","\",num1+num2) @getInput def subtraction(num1,num2): print(\"Subtraction = \",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication = \",num1*num2)","number = \")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\"Addition = \",num1+num2) @getInput","<gh_stars>0 # Assignment 1 Day 8 # write a decorator function for taking","any kind of function you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter","to build def getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter two numbers \") a=int(input(\"Enter first number","first number = \")) b=int(input(\"Enter second number = \")) calculate_arg_fuc(a,b) return wrap_function @getInput","def addition(num1,num2): print(\"Addition = \",num1+num2) @getInput def subtraction(num1,num2): print(\"Subtraction = \",num1-num2) @getInput def","Assignment 2 day 8 # you need to develop a python program to","= \")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\"Addition = \",num1+num2) @getInput def","taking input for you # any kind of function you want to build","build def getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter two numbers \") a=int(input(\"Enter first number =","need to develop a python program to open a file in read only","def multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2): print(\"Division = \",num1/num2) addition() subtraction()","subtraction() multiplication() division() # Assignment 2 day 8 # you need to develop","# any kind of function you want to build def getInput(calculate_arg_fuc): def wrap_function():","calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\"Addition = \",num1+num2) @getInput def subtraction(num1,num2): print(\"Subtraction","a decorator function for taking input for you # any kind of function","addition(num1,num2): print(\"Addition = \",num1+num2) @getInput def subtraction(num1,num2): print(\"Subtraction = \",num1-num2) @getInput def multiplication(num1,num2):","print(\"Enter two numbers \") a=int(input(\"Enter first number = \")) b=int(input(\"Enter second number =","b=int(input(\"Enter second number = \")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\"Addition =","1 Day 8 # write a decorator function for taking input for you","\")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\"Addition = \",num1+num2) @getInput def subtraction(num1,num2):","mode and # try writing something to it and handlethe subsequent errorusing Exception","second number = \")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\"Addition = \",num1+num2)","you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\"Enter two numbers \") a=int(input(\"Enter","def division(num1,num2): print(\"Division = \",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day","develop a python program to open a file in read only mode and","try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am prajval\"); f.close(); except: print(\"File is in read only","a python program to open a file in read only mode and #","f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am prajval\"); f.close(); except: print(\"File is in read only mode...\")","return wrap_function @getInput def addition(num1,num2): print(\"Addition = \",num1+num2) @getInput def subtraction(num1,num2): print(\"Subtraction =","@getInput def addition(num1,num2): print(\"Addition = \",num1+num2) @getInput def subtraction(num1,num2): print(\"Subtraction = \",num1-num2) @getInput","a file in read only mode and # try writing something to it","for taking input for you # any kind of function you want to","= \",num1*num2) @getInput def division(num1,num2): print(\"Division = \",num1/num2) addition() subtraction() multiplication() division() #","# try writing something to it and handlethe subsequent errorusing Exception Handling try:","wrap_function(): print(\"Enter two numbers \") a=int(input(\"Enter first number = \")) b=int(input(\"Enter second number","Handling try: f=open(\"abc.txt\",\"r\"); f.write(\"Heyy, i am prajval\"); f.close(); except: print(\"File is in read","# write a decorator function for taking input for you # any kind","in read only mode and # try writing something to it and handlethe","multiplication() division() # Assignment 2 day 8 # you need to develop a","2 day 8 # you need to develop a python program to open","8 # you need to develop a python program to open a file","a=int(input(\"Enter first number = \")) b=int(input(\"Enter second number = \")) calculate_arg_fuc(a,b) return wrap_function","Assignment 1 Day 8 # write a decorator function for taking input for"],"string":"[\n \"multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2) @getInput def division(num1,num2): print(\\\"Division = \\\",num1/num2) addition() subtraction() multiplication()\",\n \"\\\",num1-num2) @getInput def multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2) @getInput def division(num1,num2): print(\\\"Division = \\\",num1/num2)\",\n \"division(num1,num2): print(\\\"Division = \\\",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day 8\",\n \"print(\\\"Multiplication = \\\",num1*num2) @getInput def division(num1,num2): print(\\\"Division = \\\",num1/num2) addition() subtraction() multiplication() division()\",\n \"something to it and handlethe subsequent errorusing Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i\",\n \"\\\")) b=int(input(\\\"Enter second number = \\\")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\\\"Addition\",\n \"def subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2) @getInput def multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2) @getInput def\",\n \"input for you # any kind of function you want to build def\",\n \"python program to open a file in read only mode and # try\",\n \"print(\\\"Subtraction = \\\",num1-num2) @getInput def multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2) @getInput def division(num1,num2): print(\\\"Division\",\n \"program to open a file in read only mode and # try writing\",\n \"for you # any kind of function you want to build def getInput(calculate_arg_fuc):\",\n \"try writing something to it and handlethe subsequent errorusing Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\");\",\n \"# you need to develop a python program to open a file in\",\n \"Day 8 # write a decorator function for taking input for you #\",\n \"handlethe subsequent errorusing Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am prajval\\\"); f.close(); except:\",\n \"you # any kind of function you want to build def getInput(calculate_arg_fuc): def\",\n \"Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am prajval\\\"); f.close(); except: print(\\\"File is in\",\n \"you need to develop a python program to open a file in read\",\n \"function you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter two numbers \\\")\",\n \"8 # write a decorator function for taking input for you # any\",\n \"write a decorator function for taking input for you # any kind of\",\n \"= \\\")) b=int(input(\\\"Enter second number = \\\")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2):\",\n \"writing something to it and handlethe subsequent errorusing Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy,\",\n \"and handlethe subsequent errorusing Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am prajval\\\"); f.close();\",\n \"decorator function for taking input for you # any kind of function you\",\n \"and # try writing something to it and handlethe subsequent errorusing Exception Handling\",\n \"to develop a python program to open a file in read only mode\",\n \"# Assignment 2 day 8 # you need to develop a python program\",\n \"\\\",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day 8 # you need\",\n \"open a file in read only mode and # try writing something to\",\n \"function for taking input for you # any kind of function you want\",\n \"file in read only mode and # try writing something to it and\",\n \"@getInput def subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2) @getInput def multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2) @getInput\",\n \"getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter two numbers \\\") a=int(input(\\\"Enter first number = \\\")) b=int(input(\\\"Enter\",\n \"= \\\",num1+num2) @getInput def subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2) @getInput def multiplication(num1,num2): print(\\\"Multiplication =\",\n \"division() # Assignment 2 day 8 # you need to develop a python\",\n \"day 8 # you need to develop a python program to open a\",\n \"wrap_function @getInput def addition(num1,num2): print(\\\"Addition = \\\",num1+num2) @getInput def subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2)\",\n \"# Assignment 1 Day 8 # write a decorator function for taking input\",\n \"@getInput def multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2) @getInput def division(num1,num2): print(\\\"Division = \\\",num1/num2) addition()\",\n \"kind of function you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter two\",\n \"want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter two numbers \\\") a=int(input(\\\"Enter first\",\n \"read only mode and # try writing something to it and handlethe subsequent\",\n \"of function you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter two numbers\",\n \"to it and handlethe subsequent errorusing Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am\",\n \"print(\\\"Division = \\\",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day 8 #\",\n \"two numbers \\\") a=int(input(\\\"Enter first number = \\\")) b=int(input(\\\"Enter second number = \\\"))\",\n \"\\\",num1*num2) @getInput def division(num1,num2): print(\\\"Division = \\\",num1/num2) addition() subtraction() multiplication() division() # Assignment\",\n \"it and handlethe subsequent errorusing Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am prajval\\\");\",\n \"addition() subtraction() multiplication() division() # Assignment 2 day 8 # you need to\",\n \"only mode and # try writing something to it and handlethe subsequent errorusing\",\n \"= \\\",num1-num2) @getInput def multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2) @getInput def division(num1,num2): print(\\\"Division =\",\n \"numbers \\\") a=int(input(\\\"Enter first number = \\\")) b=int(input(\\\"Enter second number = \\\")) calculate_arg_fuc(a,b)\",\n \"print(\\\"Addition = \\\",num1+num2) @getInput def subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2) @getInput def multiplication(num1,num2): print(\\\"Multiplication\",\n \"subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2) @getInput def multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2) @getInput def division(num1,num2):\",\n \"@getInput def division(num1,num2): print(\\\"Division = \\\",num1/num2) addition() subtraction() multiplication() division() # Assignment 2\",\n \"errorusing Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am prajval\\\"); f.close(); except: print(\\\"File is\",\n \"to open a file in read only mode and # try writing something\",\n \"subsequent errorusing Exception Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am prajval\\\"); f.close(); except: print(\\\"File\",\n \"number = \\\")) b=int(input(\\\"Enter second number = \\\")) calculate_arg_fuc(a,b) return wrap_function @getInput def\",\n \"def wrap_function(): print(\\\"Enter two numbers \\\") a=int(input(\\\"Enter first number = \\\")) b=int(input(\\\"Enter second\",\n \"\\\") a=int(input(\\\"Enter first number = \\\")) b=int(input(\\\"Enter second number = \\\")) calculate_arg_fuc(a,b) return\",\n \"= \\\",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day 8 # you\",\n \"def getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter two numbers \\\") a=int(input(\\\"Enter first number = \\\"))\",\n \"\\\",num1+num2) @getInput def subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2) @getInput def multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2)\",\n \"number = \\\")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\\\"Addition = \\\",num1+num2) @getInput\",\n \"<gh_stars>0 # Assignment 1 Day 8 # write a decorator function for taking\",\n \"any kind of function you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter\",\n \"to build def getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter two numbers \\\") a=int(input(\\\"Enter first number\",\n \"first number = \\\")) b=int(input(\\\"Enter second number = \\\")) calculate_arg_fuc(a,b) return wrap_function @getInput\",\n \"def addition(num1,num2): print(\\\"Addition = \\\",num1+num2) @getInput def subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2) @getInput def\",\n \"Assignment 2 day 8 # you need to develop a python program to\",\n \"= \\\")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\\\"Addition = \\\",num1+num2) @getInput def\",\n \"taking input for you # any kind of function you want to build\",\n \"build def getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter two numbers \\\") a=int(input(\\\"Enter first number =\",\n \"need to develop a python program to open a file in read only\",\n \"def multiplication(num1,num2): print(\\\"Multiplication = \\\",num1*num2) @getInput def division(num1,num2): print(\\\"Division = \\\",num1/num2) addition() subtraction()\",\n \"subtraction() multiplication() division() # Assignment 2 day 8 # you need to develop\",\n \"# any kind of function you want to build def getInput(calculate_arg_fuc): def wrap_function():\",\n \"calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\\\"Addition = \\\",num1+num2) @getInput def subtraction(num1,num2): print(\\\"Subtraction\",\n \"a decorator function for taking input for you # any kind of function\",\n \"addition(num1,num2): print(\\\"Addition = \\\",num1+num2) @getInput def subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2) @getInput def multiplication(num1,num2):\",\n \"print(\\\"Enter two numbers \\\") a=int(input(\\\"Enter first number = \\\")) b=int(input(\\\"Enter second number =\",\n \"b=int(input(\\\"Enter second number = \\\")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\\\"Addition =\",\n \"1 Day 8 # write a decorator function for taking input for you\",\n \"\\\")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\\\"Addition = \\\",num1+num2) @getInput def subtraction(num1,num2):\",\n \"mode and # try writing something to it and handlethe subsequent errorusing Exception\",\n \"second number = \\\")) calculate_arg_fuc(a,b) return wrap_function @getInput def addition(num1,num2): print(\\\"Addition = \\\",num1+num2)\",\n \"you want to build def getInput(calculate_arg_fuc): def wrap_function(): print(\\\"Enter two numbers \\\") a=int(input(\\\"Enter\",\n \"def division(num1,num2): print(\\\"Division = \\\",num1/num2) addition() subtraction() multiplication() division() # Assignment 2 day\",\n \"develop a python program to open a file in read only mode and\",\n \"try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am prajval\\\"); f.close(); except: print(\\\"File is in read only\",\n \"a python program to open a file in read only mode and #\",\n \"f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am prajval\\\"); f.close(); except: print(\\\"File is in read only mode...\\\")\",\n \"return wrap_function @getInput def addition(num1,num2): print(\\\"Addition = \\\",num1+num2) @getInput def subtraction(num1,num2): print(\\\"Subtraction =\",\n \"@getInput def addition(num1,num2): print(\\\"Addition = \\\",num1+num2) @getInput def subtraction(num1,num2): print(\\\"Subtraction = \\\",num1-num2) @getInput\",\n \"a file in read only mode and # try writing something to it\",\n \"for taking input for you # any kind of function you want to\",\n \"= \\\",num1*num2) @getInput def division(num1,num2): print(\\\"Division = \\\",num1/num2) addition() subtraction() multiplication() division() #\",\n \"# try writing something to it and handlethe subsequent errorusing Exception Handling try:\",\n \"wrap_function(): print(\\\"Enter two numbers \\\") a=int(input(\\\"Enter first number = \\\")) b=int(input(\\\"Enter second number\",\n \"Handling try: f=open(\\\"abc.txt\\\",\\\"r\\\"); f.write(\\\"Heyy, i am prajval\\\"); f.close(); except: print(\\\"File is in read\",\n \"# write a decorator function for taking input for you # any kind\",\n \"in read only mode and # try writing something to it and handlethe\",\n \"multiplication() division() # Assignment 2 day 8 # you need to develop a\",\n \"2 day 8 # you need to develop a python program to open\",\n \"8 # you need to develop a python program to open a file\",\n \"a=int(input(\\\"Enter first number = \\\")) b=int(input(\\\"Enter second number = \\\")) calculate_arg_fuc(a,b) return wrap_function\",\n \"Assignment 1 Day 8 # write a decorator function for taking input for\"\n]","isConversational":false}}},{"rowIdx":1198,"cells":{"ngram":{"kind":"list like","value":["Parameters ---------- naxes : int Number of axes. axes_type : str One of","utils as astutil from astropy import coordinates as coord from astropy.extern import six","object\"\"\" raise NotImplementedError(\"Subclasses may implement this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Representation Parameters","is None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame,","axes_names = tuple([\"\"] * naxes) self._axes_names = axes_names if name is None: self._name","unit is None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes","= tuple([u.Unit(au) for au in unit]) if axes_names is not None: if isinstance(axes_names,","len(axes_order): raise ValueError(\"Incorrect numbering of axes, \" \"axes_order should contain unique numbers, \"","unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral Frame Parameters ---------- axes_order","this frame. name : str Name of this frame. \"\"\" def __init__(self, naxes,","if name is None: self._name = self.__class__.__name__ else: self._name = name if reference_position","is not None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def","if self.reference_frame is not None: fmt += \", reference_frame={0}\".format(self.reference_frame) fmt += \")>\" return","Represents one or more frames. Parameters ---------- frames : list List of frames","else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is not None: if","unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type","or iterable of those Units on axes. axes_names : list Names of the","this frame. name : str Name of this frame. \"\"\" def __init__(self, axes_order=(0,","self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args):","[\"SPATIAL\", \"SPATIAL\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for","WCS pipeline The transform between start_frame and the current frame is used to","in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \"\"\" A 2D","__all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in","in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes = len(_axes_names)","(unit,) if len(unit) != naxes: raise ValueError(\"Number of units does not match number","name=name) def coordinates(self, *args): \"\"\" Create a SkyCoord object. Parameters ---------- args :","axes. axes_names : list Names of the axes in this frame. name :","a 3-clause BSD style license - see LICENSE.rst \"\"\" Defines coordinate frames and","* naxes) self._axes_names = axes_names if name is None: self._name = self.__class__.__name__ else:","axes_names is None: axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if","naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names,","self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = []","this frame.\"\"\" self._name = val @property def naxes(self): \"\"\" The number of axes","str Name for this frame. \"\"\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None,","naxes = 2 if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names","Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform","BSD style license - see LICENSE.rst \"\"\" Defines coordinate frames and ties them",": list Names of the axes in this frame. name : str Name","val @property def naxes(self): \"\"\" The number of axes intheis frame.\"\"\" return self._naxes","i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \"\"\" A","from astropy import units as u from astropy import utils as astutil from","self._name is not None: return self._name else: return self.__class__.__name__ @property def name(self): \"\"\"","of those Units on axes. axes_names : list Names of the axes in","of this frame.\"\"\" self._name = val @property def naxes(self): \"\"\" The number of","axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \"\"\" Create a SkyCoord object.",": str Spectral axis name. name : str Name for this frame. \"\"\"","ValueError(\"Number of axes names does not match number of axes.\") else: axes_names =","astutil from astropy import coordinates as coord from astropy.extern import six from .","necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral","if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if","in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the","['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self,","numbers, \" \"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def","\"\"\" # Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise","# Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class","\"\"\" Create a SkyCoord object. Parameters ---------- args : float inputs to wcs.input_frame","Name of this frame. \"\"\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'),","\"\"\" from __future__ import absolute_import, division, unicode_literals, print_function import numpy as np from","unicode_literals, print_function import numpy as np from astropy import units as u from","in self.axes_order] coo = tuple([arg * un for arg, un in zip(args, self.unit)])",": str or units.Unit instance or iterable of those Units on axes. axes_names","coordinates(self, *args): \"\"\" Create a SkyCoord object. Parameters ---------- args : float inputs","\"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\" Base class","objects). unit : str or units.Unit instance Spectral unit. axes_names : str Spectral","not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if","__init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is not","self._name else: return self.__class__.__name__ @property def name(self): \"\"\" A custom name of this","import numpy as np from astropy import units as u from astropy import","un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\"Incorrect numbering of axes, \" \"axes_order should","list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame in","name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else: return","= tuple([\"\"] * naxes) self._axes_names = axes_names if name is None: self._name =","*args): if np.isscalar(args): return args * self.unit[0] else: return args[0] * self.unit[0] class","\"\"\" Represents one or more frames. Parameters ---------- frames : list List of","for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype","= list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit = _unit if axes_order","reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else: return args[0]","self._naxes @property def unit(self): \"\"\"The unit of this frame.\"\"\" return self._unit @property def","of axes in the frame.\"\"\" return self._axes_names @property def axes_order(self): \"\"\" A tuple","reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type,","if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) !=","@property def axes_order(self): \"\"\" A tuple of indices which map inputs to axes.\"\"\"","in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un, n in","name of this frame.\"\"\" self._name = val @property def naxes(self): \"\"\" The number","to world coordinate objects). unit : str or units.Unit instance Spectral unit. axes_names","not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if len(unit)","reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args):","naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \"\"\"","\"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\" Base class for CoordinateFrames. Parameters","super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit,","in the input data that corresponds to this axis. unit : list of","self._name = name if reference_position is not None: self._reference_position = reference_position else: self._reference_position","self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is","unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \"\"\" Create a SkyCoord object. Parameters ----------","fmt += \", reference_frame={0}\".format(self.reference_frame) fmt += \")>\" return fmt def __str__(self): if self._name","this frame. \"\"\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2","= tuple(axes_type) self._reference_frame = reference_frame if unit is not None: if astutil.isiterable(unit): unit","List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this","\"\"\" A tuple of indices which map inputs to axes.\"\"\" return self._axes_order @property","\"\"\" Celestial Frame Representation Parameters ---------- axes_order : tuple of int A dimension","return self._unit @property def axes_names(self): \"\"\" Names of axes in the frame.\"\"\" return","of int A dimension in the input data that corresponds to this axis.","frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this frame. \"\"\"","return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \"\"\" Computes which axes","axes.\") else: self._unit = tuple([u.Unit(au) for au in unit]) if axes_names is not","as coord from astropy.extern import six from . import utils as gwutils __all__","six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit","!= naxes: raise ValueError(\"Number of units does not match number of axes.\") else:","axes_type : str One of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order : tuple of int","inputs: outputs. \"\"\" sep = self._separable(start_frame) inputs = [] for ax in self.axes_order:","if unit is not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit =","axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \"\"\"","'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\",","of axes.\") else: axes_names = tuple([\"\"] * naxes) self._axes_names = axes_names if name","in self.frames: fargs = [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs))","+= \", reference_frame={0}\".format(self.reference_frame) fmt += \")>\" return fmt def __str__(self): if self._name is","this frame. name : str Name of this frame. \"\"\" def __init__(self, axes_order=None,","astropy.units.Unit Unit for each axis. axes_names : list Names of the axes in","= [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\",","utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper()","Create a SkyCoord object. Parameters ---------- args : float inputs to wcs.input_frame \"\"\"","class CompositeFrame(CoordinateFrame): \"\"\" Represents one or more frames. Parameters ---------- frames : list","transform between start_frame and the current frame is used to compute the mapping","or units.Unit instance Spectral unit. axes_names : str Spectral axis name. name :","names does not match number of axes.\") else: axes_names = tuple([\"\"] * naxes)",": float inputs to wcs.input_frame \"\"\" # Reorder axes if necesary. try: return","that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with","unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type,","Spectral axis name. name : str Name for this frame. \"\"\" def __init__(self,","\"SPATIAL\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for i","frame (usually used with output_frame to convert to world coordinate objects). reference_position :","for frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes))","objects). reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list","is not None: fmt += \", reference_frame={0}\".format(self.reference_frame) fmt += \")>\" return fmt def","or units.Unit instance or iterable of those Units on axes. axes_names : list","return coo class Frame2D(CoordinateFrame): \"\"\" A 2D coordinate frame. Parameters ---------- axes_order :","Parameters ---------- frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name","see LICENSE.rst \"\"\" Defines coordinate frames and ties them to data axes. \"\"\"",": str One of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order : tuple of int A","\"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\" Base class for CoordinateFrames. Parameters ----------","= frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit","from . import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame']","in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] =","self).__init__() def __repr__(self): fmt = '<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names,","the WCS pipeline The transform between start_frame and the current frame is used","return self.__class__.__name__ @property def name(self): \"\"\" A custom name of this frame.\"\"\" return","@property def axes_names(self): \"\"\" Names of axes in the frame.\"\"\" return self._axes_names @property","data that corresponds to this axis. unit : list of astropy.units.Unit Unit for","name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self,","axes_names=None, name=None): naxes = 2 if reference_frame is not None: if reference_frame.name.upper() in","for each axis. axes_names : list Names of the axes in this frame.","fargs = [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo","to axes.\"\"\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self): try:","in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order =","A 2D coordinate frame. Parameters ---------- axes_order : tuple of int A dimension","else: return self.__class__.__name__ @property def name(self): \"\"\" A custom name of this frame.\"\"\"","data axes. \"\"\" from __future__ import absolute_import, division, unicode_literals, print_function import numpy as","coordinate objects). reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION` unit :","axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt +=","in the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference","this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert","None: self._name = self.__class__.__name__ else: self._name = name if reference_position is not None:","start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform between start_frame","= un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\"Incorrect numbering of axes, \" \"axes_order","the current frame is used to compute the mapping inputs: outputs. \"\"\" sep","axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to","frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \"\"\" A 2D coordinate frame. Parameters ----------","---------- axes_order : tuple of int A dimension in the input data that","as np from astropy import units as u from astropy import utils as","frame. Parameters ---------- axes_order : tuple of int A dimension in the input","not None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self):","str Name of this frame. \"\"\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x',","axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for i in","[args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame):","Reference frame (usually used with output_frame to convert to world coordinate objects). reference_position","axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type =","not None: fmt += \", reference_frame={0}\".format(self.reference_frame) fmt += \")>\" return fmt def __str__(self):","return self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position","unit : str or units.Unit instance or iterable of those Units on axes.","\"\"\" Names of axes in the frame.\"\"\" return self._axes_names @property def axes_order(self): \"\"\"","class CoordinateFrame(object): \"\"\" Base class for CoordinateFrames. Parameters ---------- naxes : int Number","naxes: raise ValueError(\"Number of units does not match number of axes.\") else: self._unit","fmt = '<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position","style license - see LICENSE.rst \"\"\" Defines coordinate frames and ties them to",": int Number of axes. axes_type : str One of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"]","for i in self.axes_order] coo = tuple([arg * un for arg, un in","axes_order : tuple or int A dimension in the input data that corresponds","\"\"\" Defines coordinate frames and ties them to data axes. \"\"\" from __future__","Name for this frame. \"\"\" def __init__(self, frames, name=None): self._frames = frames[:] naxes","Unit for each axis. axes_names : list Names of the axes in this","= 2 if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names =","object. Parameters ---------- args : float inputs to wcs.input_frame \"\"\" # Reorder axes","unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\"Incorrect numbering of axes, \"","Parameters ---------- axes_order : tuple of int A dimension in the input data","frame in self.frames: fargs = [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order)","frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral Frame Parameters ---------- axes_order :","def unit(self): \"\"\"The unit of this frame.\"\"\" return self._unit @property def axes_names(self): \"\"\"","gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame","\"\"\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\" return self._axes_type def","__init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame,","coo = tuple([arg * un for arg, un in zip(args, self.unit)]) return coo","axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\"Number","axes_names if name is None: self._name = self.__class__.__name__ else: self._name = name if","import coordinates as coord from astropy.extern import six from . import utils as","used with output_frame to convert to world coordinate objects). unit : str or","str or units.Unit instance Spectral unit. axes_names : str Spectral axis name. name","axis. axes_names : list Names of the axes in this frame. name :","else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\" Represents one or more frames.","list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for","tuple([\"\"] * naxes) self._axes_names = axes_names if name is None: self._name = self.__class__.__name__","for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \"\"\" Type of","self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt += ',","name(self): \"\"\" A custom name of this frame.\"\"\" return self._name @name.setter def name(self,","axes in `start_frame` contribute to each axis in the current frame. Parameters ----------","float inputs to wcs.input_frame \"\"\" # Reorder axes if necesary. try: return coord.SkyCoord(*args,","Reference position. unit : str or units.Unit instance or iterable of those Units","six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise","reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\"{1}\", unit={2},","\"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return","\"\"\" Base class for CoordinateFrames. Parameters ---------- naxes : int Number of axes.","dimension in the input data that corresponds to this axis. unit : list","[\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order : tuple of int A dimension in the input","corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame","in the WCS pipeline The transform between start_frame and the current frame is","n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind]","one or more frames. Parameters ---------- frames : list List of frames (TimeFrame,","\"\"\" The number of axes intheis frame.\"\"\" return self._naxes @property def unit(self): \"\"\"The","is None: self._name = self.__class__.__name__ else: self._name = name if reference_position is not","frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) !=","self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \"\"\" Computes which axes in","the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS","instance or iterable of those Units on axes. axes_names : list Names of","sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names =","zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un","self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def","\"\"\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\",","naxes(self): \"\"\" The number of axes intheis frame.\"\"\" return self._naxes @property def unit(self):","'y'), name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args):","axes in the frame.\"\"\" return self._axes_names @property def axes_order(self): \"\"\" A tuple of","*args): args = [args[i] for i in self.axes_order] coo = tuple([arg * un","self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self):","axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] =","__str__(self): if self._name is not None: return self._name else: return self.__class__.__name__ @property def","super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \"\"\" Create","str Reference position. unit : str or units.Unit instance or iterable of those","self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt =","* self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\" Represents one or more frames. Parameters ---------- frames","world coordinate objects). reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION` unit","custom name of this frame.\"\"\" self._name = val @property def naxes(self): \"\"\" The","None: if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if len(unit) !=","self._name = val @property def naxes(self): \"\"\" The number of axes intheis frame.\"\"\"","frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un if","CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Representation Parameters ---------- axes_order : tuple of int A","return inputs @property def axes_type(self): \"\"\" Type of this frame : 'SPATIAL', 'SPECTRAL',",": str Reference position. unit : str or units.Unit instance or iterable of","is None: axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit","Representation Parameters ---------- axes_order : tuple of int A dimension in the input","corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str","intheis frame.\"\"\" return self._naxes @property def unit(self): \"\"\"The unit of this frame.\"\"\" return","\"axes_order should contain unique numbers, \" \"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit,","tuple(range(naxes)) if unit is None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL']","not match number of axes.\") else: axes_names = tuple([\"\"] * naxes) self._axes_names =","unit : list of astropy.units.Unit Unit for each axis. axes_names : list Names","axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order)","[] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \"\"\" Type","outputs. \"\"\" sep = self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0]))","axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if","ValueError(\"Incorrect numbering of axes, \" \"axes_order should contain unique numbers, \" \"got {}.\".format(axes_order))","input_axes(self, start_frame=None): \"\"\" Computes which axes in `start_frame` contribute to each axis in","frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\",","and the current frame is used to compute the mapping inputs: outputs. \"\"\"","---------- naxes : int Number of axes. axes_type : str One of [\"SPATIAL\",","naxes = sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes))","in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \"\"\" Type of this frame","- see LICENSE.rst \"\"\" Defines coordinate frames and ties them to data axes.","reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position. unit :","axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self,","'<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not","frame. name : str Name of this frame. \"\"\" def __init__(self, axes_order=None, reference_frame=None,","naxes: raise ValueError(\"Number of axes names does not match number of axes.\") else:","this frame.\"\"\" return self._unit @property def axes_names(self): \"\"\" Names of axes in the","implement this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Representation Parameters ---------- axes_order : tuple","def frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo =","unit = _unit if axes_order is None: axes_order = tuple(range(naxes)) if unit is","'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\",","self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt += ', reference_position=\"{0}\"'.format(self.reference_position)",": str Name of this frame. \"\"\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None,","= tuple(unit) else: unit = (unit,) if len(unit) != naxes: raise ValueError(\"Number of","\"\"\" sep = self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return","name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for i in self.axes_order]","astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position. unit : str or","if len(unit) != naxes: raise ValueError(\"Number of units does not match number of","self.frames: fargs = [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return","frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] =","LICENSE.rst \"\"\" Defines coordinate frames and ties them to data axes. \"\"\" from","more frames. Parameters ---------- frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame,","used to compute the mapping inputs: outputs. \"\"\" sep = self._separable(start_frame) inputs =","\"\"\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame","(usually used with output_frame to convert to world coordinate objects). reference_position : str","astropy import coordinates as coord from astropy.extern import six from . import utils","print_function import numpy as np from astropy import units as u from astropy","The number of axes intheis frame.\"\"\" return self._naxes @property def unit(self): \"\"\"The unit","inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self):","isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes:","axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self): return repr(self.frames)","SpectralFrame, CoordinateFrame). name : str Name for this frame. \"\"\" def __init__(self, frames,","if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\"Incorrect numbering of axes, \" \"axes_order should contain","or more frames. Parameters ---------- frames : list List of frames (TimeFrame, CelestialFrame,","if len(axes_names) != naxes: raise ValueError(\"Number of axes names does not match number","of axes. axes_type : str One of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order : tuple","Name of this frame. \"\"\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes","_unit: unit = _unit if axes_order is None: axes_order = tuple(range(naxes)) if unit","Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame):","if unit is None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] *","self._name @name.setter def name(self, val): \"\"\" A custom name of this frame.\"\"\" self._name","None: axes_order = tuple(range(naxes)) if unit is None: unit = tuple([u.degree] * naxes)","reference_position is not None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__()","= [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \"\"\"","+= ', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \", reference_frame={0}\".format(self.reference_frame) fmt","self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \"\"\" Create a","inputs to axes.\"\"\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self):","def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is","STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object):","import units as u from astropy import utils as astutil from astropy import","CoordinateFrame(object): \"\"\" Base class for CoordinateFrames. Parameters ---------- naxes : int Number of","Name of this frame. \"\"\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None,","np.isscalar(args): return args * self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\"","'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\" return self._axes_type def coordinates(self, *args): \"\"\" Create world coordinates","the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference","list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for frame in frames:","except: raise class SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral Frame Parameters ---------- axes_order : tuple","int A dimension in the input data that corresponds to this axis. unit","naxes : int Number of axes. axes_type : str One of [\"SPATIAL\", \"SPECTRAL\",","self._frames = frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes))","to each axis in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A","@property def unit(self): \"\"\"The unit of this frame.\"\"\" return self._unit @property def axes_names(self):","*args): \"\"\" Create world coordinates object\"\"\" raise NotImplementedError(\"Subclasses may implement this\") class CelestialFrame(CoordinateFrame):","def name(self): \"\"\" A custom name of this frame.\"\"\" return self._name @name.setter def","self._unit = tuple([u.Unit(au) for au in unit]) if axes_names is not None: if","except AttributeError: return None def input_axes(self, start_frame=None): \"\"\" Computes which axes in `start_frame`","args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\" Represents one or more frames. Parameters ----------","return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral Frame Parameters","A tuple of indices which map inputs to axes.\"\"\" return self._axes_order @property def","axes names does not match number of axes.\") else: axes_names = tuple([\"\"] *",": str Name for this frame. \"\"\" def __init__(self, frames, name=None): self._frames =","*args): coo = [] for frame in self.frames: fargs = [args[i] for i","name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args","unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else:","* naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args):","reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \", reference_frame={0}\".format(self.reference_frame) fmt += \")>\"","1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order, name=name, axes_names=axes_names,","A dimension in the input data that corresponds to this axis. unit :","coordinates object\"\"\" raise NotImplementedError(\"Subclasses may implement this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Representation","if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame",": str Name of this frame. \"\"\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None,","unit : str or units.Unit instance Spectral unit. axes_names : str Spectral axis","@property def naxes(self): \"\"\" The number of axes intheis frame.\"\"\" return self._naxes @property","frame. name : str Name of this frame. \"\"\" def __init__(self, naxes, axes_type,","raise NotImplementedError(\"Subclasses may implement this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Representation Parameters ----------","Name for this frame. \"\"\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None):","for au in unit]) if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names","is not None: return self._name else: return self.__class__.__name__ @property def name(self): \"\"\" A","None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes,","= tuple(range(naxes)) if unit is None: unit = tuple([u.degree] * naxes) axes_type =","axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit,","tuple or int A dimension in the input data that corresponds to this","\"\"\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes =","axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for","unit is None and _unit: unit = _unit if axes_order is None: axes_order","- one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each axis.","unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position)","class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Representation Parameters ---------- axes_order : tuple of int","\"\"\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order,","return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo = [] for","axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args",": 'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\" return self._axes_type def coordinates(self, *args): \"\"\" Create world","not None: return self._name else: return self.__class__.__name__ @property def name(self): \"\"\" A custom","i in self.axes_order] coo = tuple([arg * un for arg, un in zip(args,","coordinate objects). unit : str or units.Unit instance Spectral unit. axes_names : str","tuple of int A dimension in the input data that corresponds to this","raise ValueError(\"Number of units does not match number of axes.\") else: self._unit =","this frame. \"\"\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1,","axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position. unit","None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance')","str Name for this frame. \"\"\" def __init__(self, frames, name=None): self._frames = frames[:]","not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names:","None: fmt += ', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \",","axes. \"\"\" from __future__ import absolute_import, division, unicode_literals, print_function import numpy as np","frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit =","val): \"\"\" A custom name of this frame.\"\"\" self._name = val @property def","with output_frame to convert to world coordinate objects). reference_position : str Reference position","axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\"Incorrect numbering","that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position :","self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position except","self.__class__.__name__ else: self._name = name if reference_position is not None: self._reference_position = reference_position","from astropy import utils as astutil from astropy import coordinates as coord from","axes_names : str Spectral axis name. name : str Name for this frame.","instance Spectral unit. axes_names : str Spectral axis name. name : str Name","reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance'","frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order))","coordinates(self, *args): coo = [] for frame in self.frames: fargs = [args[i] for","in unit]) if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,)","= sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names","contain unique numbers, \" \"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name)","if self._name is not None: return self._name else: return self.__class__.__name__ @property def name(self):","frame. \"\"\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes","axes in this frame. name : str Name of this frame. \"\"\" def","list of astropy.units.Unit Unit for each axis. axes_names : list Names of the","ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \"\"\" Type of this","unit is not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,)","frame. reference_position : str Reference position. unit : str or units.Unit instance or","inputs @property def axes_type(self): \"\"\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'.","world coordinate objects). unit : str or units.Unit instance Spectral unit. axes_names :","axes_names=axes_names, name=name) def coordinates(self, *args): \"\"\" Create a SkyCoord object. Parameters ---------- args","A frame in the WCS pipeline The transform between start_frame and the current","frame in the WCS pipeline The transform between start_frame and the current frame","---------- axes_order : tuple or int A dimension in the input data that",". import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES","+= \")>\" return fmt def __str__(self): if self._name is not None: return self._name","3-clause BSD style license - see LICENSE.rst \"\"\" Defines coordinate frames and ties","{}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames","the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame","One of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order : tuple of int A dimension in","\"\"\" Computes which axes in `start_frame` contribute to each axis in the current","None and _unit: unit = _unit if axes_order is None: axes_order = tuple(range(naxes))","in this frame. name : str Name of this frame. \"\"\" def __init__(self,",": str Name of this frame. \"\"\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix),","return self._name else: return self.__class__.__name__ @property def name(self): \"\"\" A custom name of",": ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform between start_frame and","with output_frame to convert to world coordinate objects). unit : str or units.Unit","data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used","args * self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\" Represents one","frames and ties them to data axes. \"\"\" from __future__ import absolute_import, division,","frame. name : str Name of this frame. \"\"\" def __init__(self, axes_order=(0, 1),","return self._naxes @property def unit(self): \"\"\"The unit of this frame.\"\"\" return self._unit @property","as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for","The transform between start_frame and the current frame is used to compute the","Spectral Frame Parameters ---------- axes_order : tuple or int A dimension in the","np from astropy import units as u from astropy import utils as astutil","reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name,","axes_names is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names =","args : float inputs to wcs.input_frame \"\"\" # Reorder axes if necesary. try:","axes_order is None: axes_order = tuple(range(naxes)) if unit is None: unit = tuple([u.degree]","this frame. \"\"\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None):","str Name of this frame. \"\"\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None):","= reference_frame if unit is not None: if astutil.isiterable(unit): unit = tuple(unit) else:",": str Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit","AttributeError: return None def input_axes(self, start_frame=None): \"\"\" Computes which axes in `start_frame` contribute","input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame.","= self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property","return self._name @name.setter def name(self, val): \"\"\" A custom name of this frame.\"\"\"","NotImplementedError(\"Subclasses may implement this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Representation Parameters ---------- axes_order","\", reference_frame={0}\".format(self.reference_frame) fmt += \")>\" return fmt def __str__(self): if self._name is not","`STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each axis. axes_names : list","(TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this frame. \"\"\" def","repr(self.frames) def coordinates(self, *args): coo = [] for frame in self.frames: fargs =","---------- args : float inputs to wcs.input_frame \"\"\" # Reorder axes if necesary.","start_frame and the current frame is used to compute the mapping inputs: outputs.","astropy import utils as astutil from astropy import coordinates as coord from astropy.extern","Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for","the frame.\"\"\" return self._axes_names @property def axes_order(self): \"\"\" A tuple of indices which","try: return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \"\"\" Computes which","name : str Name for this frame. \"\"\" def __init__(self, frames, name=None): self._frames",": astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to world coordinate","\" \"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self):","def name(self, val): \"\"\" A custom name of this frame.\"\"\" self._name = val","in `start_frame` contribute to each axis in the current frame. Parameters ---------- start_frame","= len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit =","axes.\") else: axes_names = tuple([\"\"] * naxes) self._axes_names = axes_names if name is","int Number of axes. axes_type : str One of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order","len(unit) != naxes: raise ValueError(\"Number of units does not match number of axes.\")","A custom name of this frame.\"\"\" return self._name @name.setter def name(self, val): \"\"\"","= list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order)","len(np.unique(axes_order)) != len(axes_order): raise ValueError(\"Incorrect numbering of axes, \" \"axes_order should contain unique","is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in","self.reference_frame is not None: fmt += \", reference_frame={0}\".format(self.reference_frame) fmt += \")>\" return fmt","\"TIME\"] axes_order : tuple of int A dimension in the input data that","ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind]","= name if reference_position is not None: self._reference_position = reference_position else: self._reference_position =","= (unit,) if len(unit) != naxes: raise ValueError(\"Number of units does not match","= None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__,","of the axes in this frame. name : str Name of this frame.","license - see LICENSE.rst \"\"\" Defines coordinate frames and ties them to data","self._unit @property def axes_names(self): \"\"\" Names of axes in the frame.\"\"\" return self._axes_names","list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names","None: fmt += \", reference_frame={0}\".format(self.reference_frame) fmt += \")>\" return fmt def __str__(self): if","= (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is not","if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if len(unit) != naxes:","self.__class__.__name__ @property def name(self): \"\"\" A custom name of this frame.\"\"\" return self._name","tuple(axes_type) self._reference_frame = reference_frame if unit is not None: if astutil.isiterable(unit): unit =","frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The","reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is not None: if","unit(self): \"\"\"The unit of this frame.\"\"\" return self._unit @property def axes_names(self): \"\"\" Names","axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order, name=name,","unit]) if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else:","of axes, \" \"axes_order should contain unique numbers, \" \"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes,","units.Unit instance Spectral unit. axes_names : str Spectral axis name. name : str","else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\"Number of axes names","frame is used to compute the mapping inputs: outputs. \"\"\" sep = self._separable(start_frame)","* self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\" Represents one or","for CoordinateFrames. Parameters ---------- naxes : int Number of axes. axes_type : str","unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None:","import utils as astutil from astropy import coordinates as coord from astropy.extern import","\"SPECTRAL\", \"TIME\"] axes_order : tuple of int A dimension in the input data","reference_position(self): try: return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \"\"\" Computes","as u from astropy import utils as astutil from astropy import coordinates as","axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name)","if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \"\"\" Represents","axis. unit : list of astropy.units.Unit Unit for each axis. axes_names : list","name : str Name of this frame. \"\"\" def __init__(self, axes_order=None, reference_frame=None, unit=None,","def naxes(self): \"\"\" The number of axes intheis frame.\"\"\" return self._naxes @property def","= self.__class__.__name__ else: self._name = name if reference_position is not None: self._reference_position =","_unit if axes_order is None: axes_order = tuple(range(naxes)) if unit is None: unit","name : str Name for this frame. \"\"\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None,","name(self, val): \"\"\" A custom name of this frame.\"\"\" self._name = val @property","unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame in frames:","axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for i in self.axes_order] coo","frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo = []","class for CoordinateFrames. Parameters ---------- naxes : int Number of axes. axes_type :","STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\",","axes intheis frame.\"\"\" return self._naxes @property def unit(self): \"\"\"The unit of this frame.\"\"\"","not None: fmt += ', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is not None: fmt +=","Defines coordinate frames and ties them to data axes. \"\"\" from __future__ import","A reference frame. reference_position : str Reference position. unit : str or units.Unit","axes_order = tuple(range(naxes)) if unit is None: unit = tuple([u.degree] * naxes) axes_type","self._reference_frame = reference_frame if unit is not None: if astutil.isiterable(unit): unit = tuple(unit)","try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral Frame","axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self): return","else: self._unit = tuple([u.Unit(au) for au in unit]) if axes_names is not None:","else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\"{1}\", unit={2}, axes_names={3},","output_frame to convert to world coordinate objects). unit : str or units.Unit instance","* naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit,","_axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit:","\"\"\" Represents Spectral Frame Parameters ---------- axes_order : tuple or int A dimension","un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n","Names of the axes in this frame. name : str Name of this",": tuple or int A dimension in the input data that corresponds to","(usually used with output_frame to convert to world coordinate objects). unit : str","unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is not None: if reference_frame.name.upper()","= val @property def naxes(self): \"\"\" The number of axes intheis frame.\"\"\" return","# Licensed under a 3-clause BSD style license - see LICENSE.rst \"\"\" Defines","coordinates as coord from astropy.extern import six from . import utils as gwutils","axes. axes_type : str One of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order : tuple of","---------- frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name :","\" \"axes_order should contain unique numbers, \" \"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order,","SkyCoord object. Parameters ---------- args : float inputs to wcs.input_frame \"\"\" # Reorder","unique numbers, \" \"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property","is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names)","_axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes = len(_axes_names) _unit =","coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else: return args[0] * self.unit[0]","self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i]","input data that corresponds to this axis. unit : list of astropy.units.Unit Unit","in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"]","unit. axes_names : str Spectral axis name. name : str Name for this","= list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for frame in","axes_order(self): \"\"\" A tuple of indices which map inputs to axes.\"\"\" return self._axes_order","of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this frame.","None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt","---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform between","current frame is used to compute the mapping inputs: outputs. \"\"\" sep =","frames. Parameters ---------- frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame).","@property def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError:","on axes. axes_names : list Names of the axes in this frame. name","does not match number of axes.\") else: self._unit = tuple([u.Unit(au) for au in","super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if","convert to world coordinate objects). unit : str or units.Unit instance Spectral unit.","'SPECTRAL', 'TIME'. \"\"\" return self._axes_type def coordinates(self, *args): \"\"\" Create world coordinates object\"\"\"","self.axes_names, self.axes_order) if self.reference_position is not None: fmt += ', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame","units does not match number of axes.\") else: self._unit = tuple([u.Unit(au) for au","None: return self._name else: return self.__class__.__name__ @property def name(self): \"\"\" A custom name","[\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\" Base","astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to world coordinate objects).","reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names","pipeline The transform between start_frame and the current frame is used to compute","= n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\"Incorrect numbering of","of this frame. \"\"\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None,","def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"],","int A dimension in the input data that corresponds to this axis. reference_frame","axes_type(self): \"\"\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\" return self._axes_type","Create world coordinates object\"\"\" raise NotImplementedError(\"Subclasses may implement this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial","return self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError: return None def","world coordinates object\"\"\" raise NotImplementedError(\"Subclasses may implement this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame","= [] for frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind,","[args[i] for i in self.axes_order] coo = tuple([arg * un for arg, un","as astutil from astropy import coordinates as coord from astropy.extern import six from",": list of astropy.units.Unit Unit for each axis. axes_names : list Names of","class SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral Frame Parameters ---------- axes_order : tuple or int","__init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame in self._frames])","str or units.Unit instance or iterable of those Units on axes. axes_names :","self._axes_names = axes_names if name is None: self._name = self.__class__.__name__ else: self._name =","is used to compute the mapping inputs: outputs. \"\"\" sep = self._separable(start_frame) inputs","if unit is None and _unit: unit = _unit if axes_order is None:","the axes in this frame. name : str Name of this frame. \"\"\"","self.axes_order) if self.reference_position is not None: fmt += ', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is","coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class","Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\" return self._axes_type def coordinates(self,","@property def name(self): \"\"\" A custom name of this frame.\"\"\" return self._name @name.setter","self._axes_names @property def axes_order(self): \"\"\" A tuple of indices which map inputs to","_axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names","SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral Frame Parameters ---------- axes_order : tuple or int A","import six from . import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame',","str One of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order : tuple of int A dimension","axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \"\"\" Create a SkyCoord","def coordinates(self, *args): coo = [] for frame in self.frames: fargs = [args[i]","return self._axes_type def coordinates(self, *args): \"\"\" Create world coordinates object\"\"\" raise NotImplementedError(\"Subclasses may","= ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def","units as u from astropy import utils as astutil from astropy import coordinates","output_frame to convert to world coordinate objects). reference_position : str Reference position -","None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names)","def __str__(self): if self._name is not None: return self._name else: return self.__class__.__name__ @property","axes_names : list Names of the axes in this frame. name : str","is not None: fmt += ', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is not None: fmt","tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\"Number of axes names does not match","self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format(","\"\"\" return self._axes_type def coordinates(self, *args): \"\"\" Create world coordinates object\"\"\" raise NotImplementedError(\"Subclasses","axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def","= [args[i] for i in self.axes_order] coo = tuple([arg * un for arg,","def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes","unit = (unit,) if len(unit) != naxes: raise ValueError(\"Number of units does not","iterable of those Units on axes. axes_names : list Names of the axes","is None and _unit: unit = _unit if axes_order is None: axes_order =","A dimension in the input data that corresponds to this axis. reference_frame :","= list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame","= ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__]","a SkyCoord object. Parameters ---------- args : float inputs to wcs.input_frame \"\"\" #","isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if","coord from astropy.extern import six from . import utils as gwutils __all__ =","def coordinates(self, *args): \"\"\" Create world coordinates object\"\"\" raise NotImplementedError(\"Subclasses may implement this\")","\"\"\" Create world coordinates object\"\"\" raise NotImplementedError(\"Subclasses may implement this\") class CelestialFrame(CoordinateFrame): \"\"\"","def coordinates(self, *args): args = [args[i] for i in self.axes_order] coo = tuple([arg","name=None): naxes = 2 if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES:","axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args *","to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference","them to data axes. \"\"\" from __future__ import absolute_import, division, unicode_literals, print_function import","tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame =","custom name of this frame.\"\"\" return self._name @name.setter def name(self, val): \"\"\" A","compute the mapping inputs: outputs. \"\"\" sep = self._separable(start_frame) inputs = [] for","between start_frame and the current frame is used to compute the mapping inputs:","position. unit : str or units.Unit instance or iterable of those Units on","one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each axis. axes_names","number of axes intheis frame.\"\"\" return self._naxes @property def unit(self): \"\"\"The unit of","axes_names = list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for frame","reference_frame={0}\".format(self.reference_frame) fmt += \")>\" return fmt def __str__(self): if self._name is not None:","= [] for frame in self.frames: fargs = [args[i] for i in frame.axes_order]","axis in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in","def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else: return args[0] *","and ties them to data axes. \"\"\" from __future__ import absolute_import, division, unicode_literals,","def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError: return","mapping inputs: outputs. \"\"\" sep = self._separable(start_frame) inputs = [] for ax in","= tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\"Number of axes names does not","of astropy.units.Unit Unit for each axis. axes_names : list Names of the axes","numbering of axes, \" \"axes_order should contain unique numbers, \" \"got {}.\".format(axes_order)) super(CompositeFrame,","contribute to each axis in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame","[frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\",","au in unit]) if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names =","print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \"\"\" A 2D coordinate frame.","axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\"Number of axes names does","coo class Frame2D(CoordinateFrame): \"\"\" A 2D coordinate frame. Parameters ---------- axes_order : tuple","def axes_type(self): \"\"\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\" return","class Frame2D(CoordinateFrame): \"\"\" A 2D coordinate frame. Parameters ---------- axes_order : tuple of","u from astropy import utils as astutil from astropy import coordinates as coord","tuple(unit) else: unit = (unit,) if len(unit) != naxes: raise ValueError(\"Number of units","raise ValueError(\"Incorrect numbering of axes, \" \"axes_order should contain unique numbers, \" \"got","reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list of","= axes_names if name is None: self._name = self.__class__.__name__ else: self._name = name","= _unit if axes_order is None: axes_order = tuple(range(naxes)) if unit is None:","fmt += \")>\" return fmt def __str__(self): if self._name is not None: return","axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order):","list Names of the axes in this frame. name : str Name of","Licensed under a 3-clause BSD style license - see LICENSE.rst \"\"\" Defines coordinate","return args * self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\" Represents","which map inputs to axes.\"\"\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property","map inputs to axes.\"\"\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property def","axes_order : tuple of int A dimension in the input data that corresponds","\")>\" return fmt def __str__(self): if self._name is not None: return self._name else:","\"\"\" A custom name of this frame.\"\"\" self._name = val @property def naxes(self):",": astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position. unit : str","\"\"\" def __init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame","self._name = self.__class__.__name__ else: self._name = name if reference_position is not None: self._reference_position","frame. \"\"\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if","naxes) self._axes_names = axes_names if name is None: self._name = self.__class__.__name__ else: self._name","for frame in frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit,","axes.\"\"\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return","!= naxes: raise ValueError(\"Number of axes names does not match number of axes.\")","return self._axes_names @property def axes_order(self): \"\"\" A tuple of indices which map inputs","naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type =","name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,)","def reference_position(self): try: return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \"\"\"","str Spectral axis name. name : str Name for this frame. \"\"\" def","axis name. name : str Name for this frame. \"\"\" def __init__(self, axes_order=(0,),","Frame2D(CoordinateFrame): \"\"\" A 2D coordinate frame. Parameters ---------- axes_order : tuple of int","name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type =","self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError: return None def input_axes(self,","current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline","frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order","does not match number of axes.\") else: axes_names = tuple([\"\"] * naxes) self._axes_names","name=name) @property def frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args):","def input_axes(self, start_frame=None): \"\"\" Computes which axes in `start_frame` contribute to each axis","in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is","u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order, name=name, axes_names=axes_names, unit=unit) def","is not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if","Base class for CoordinateFrames. Parameters ---------- naxes : int Number of axes. axes_type","this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position.","tuple of indices which map inputs to axes.\"\"\" return self._axes_order @property def reference_frame(self):","frame (usually used with output_frame to convert to world coordinate objects). unit :","of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\" return self._axes_type def coordinates(self, *args):","axes_names(self): \"\"\" Names of axes in the frame.\"\"\" return self._axes_names @property def axes_order(self):","Computes which axes in `start_frame` contribute to each axis in the current frame.","args = [args[i] for i in self.axes_order] coo = tuple([arg * un for","may implement this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Representation Parameters ---------- axes_order :","\"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\" Base class for CoordinateFrames. Parameters ---------- naxes :","data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position","str Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit","reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError: return None","name : str Name of this frame. \"\"\" def __init__(self, axes_order=(0, 1), unit=(u.pix,","of indices which map inputs to axes.\"\"\" return self._axes_order @property def reference_frame(self): return","super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args =","self.reference_position is not None: fmt += ', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is not None:","and _unit: unit = _unit if axes_order is None: axes_order = tuple(range(naxes)) if","reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0]","coo = [] for frame in self.frames: fargs = [args[i] for i in","from astropy import coordinates as coord from astropy.extern import six from . import","def coordinates(self, *args): \"\"\" Create a SkyCoord object. Parameters ---------- args : float","axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type,","to data axes. \"\"\" from __future__ import absolute_import, division, unicode_literals, print_function import numpy","start_frame=None): \"\"\" Computes which axes in `start_frame` contribute to each axis in the","self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\" Represents one or more frames. Parameters ---------- frames :","number of axes.\") else: self._unit = tuple([u.Unit(au) for au in unit]) if axes_names","CoordinateFrames. Parameters ---------- naxes : int Number of axes. axes_type : str One","of this frame. \"\"\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None):","None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name,","for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \"\"\"","reference_frame if unit is not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit","\"\"\"The unit of this frame.\"\"\" return self._unit @property def axes_names(self): \"\"\" Names of","else: axes_names = tuple([\"\"] * naxes) self._axes_names = axes_names if name is None:","axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\"Incorrect","if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes","CoordinateFrame). name : str Name for this frame. \"\"\" def __init__(self, frames, name=None):","for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\",","reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types):","raise class SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral Frame Parameters ---------- axes_order : tuple or","inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \"\"\" Type of this frame : 'SPATIAL',","number of axes.\") else: axes_names = tuple([\"\"] * naxes) self._axes_names = axes_names if","(axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is not None:","astropy.extern import six from . import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame',","self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type)","is None: axes_order = tuple(range(naxes)) if unit is None: unit = tuple([u.degree] *","axes, \" \"axes_order should contain unique numbers, \" \"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type,","unit=unit) def coordinates(self, *args): args = [args[i] for i in self.axes_order] coo =","Reference frame (usually used with output_frame to convert to world coordinate objects). unit","of axes.\") else: self._unit = tuple([u.Unit(au) for au in unit]) if axes_names is","coordinate frames and ties them to data axes. \"\"\" from __future__ import absolute_import,","tuple([u.Unit(au) for au in unit]) if axes_names is not None: if isinstance(axes_names, six.string_types):","None: axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is","Parameters ---------- args : float inputs to wcs.input_frame \"\"\" # Reorder axes if","sep = self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs","Frame Representation Parameters ---------- axes_order : tuple of int A dimension in the","Units on axes. axes_names : list Names of the axes in this frame.","return repr(self.frames) def coordinates(self, *args): coo = [] for frame in self.frames: fargs","of units does not match number of axes.\") else: self._unit = tuple([u.Unit(au) for","frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un, n","for frame in self.frames: fargs = [args[i] for i in frame.axes_order] print(frame, fargs,","this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Representation Parameters ---------- axes_order : tuple of","= _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and","of this frame. \"\"\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes =","[] for frame in self.frames: fargs = [args[i] for i in frame.axes_order] print(frame,","name is None: self._name = self.__class__.__name__ else: self._name = name if reference_position is","axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is not None:","def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo = [] for frame in","the mapping inputs: outputs. \"\"\" sep = self._separable(start_frame) inputs = [] for ax","def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names,","len(axes_names) != naxes: raise ValueError(\"Number of axes names does not match number of","of axes names does not match number of axes.\") else: axes_names = tuple([\"\"]","frame.\"\"\" return self._name @name.setter def name(self, val): \"\"\" A custom name of this","division, unicode_literals, print_function import numpy as np from astropy import units as u","to convert to world coordinate objects). reference_position : str Reference position - one","frame. \"\"\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\"SPECTRAL\",","'TIME'. \"\"\" return self._axes_type def coordinates(self, *args): \"\"\" Create world coordinates object\"\"\" raise","str Name of this frame. \"\"\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None,",": list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name","frame. \"\"\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2,","A custom name of this frame.\"\"\" self._name = val @property def naxes(self): \"\"\"","numpy as np from astropy import units as u from astropy import utils","self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt += ', reference_position=\"{0}\"'.format(self.reference_position) if","frame.\"\"\" return self._naxes @property def unit(self): \"\"\"The unit of this frame.\"\"\" return self._unit","coordinates(self, *args): args = [args[i] for i in self.axes_order] coo = tuple([arg *","ties them to data axes. \"\"\" from __future__ import absolute_import, division, unicode_literals, print_function","__init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order,","to compute the mapping inputs: outputs. \"\"\" sep = self._separable(start_frame) inputs = []","__repr__(self): return repr(self.frames) def coordinates(self, *args): coo = [] for frame in self.frames:","to this axis. unit : list of astropy.units.Unit Unit for each axis. axes_names","ValueError(\"Number of units does not match number of axes.\") else: self._unit = tuple([u.Unit(au)","axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None","if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if","under a 3-clause BSD style license - see LICENSE.rst \"\"\" Defines coordinate frames","self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is not None: if astutil.isiterable(unit):","(axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\"Number of axes","of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each axis. axes_names :","indices which map inputs to axes.\"\"\" return self._axes_order @property def reference_frame(self): return self._reference_frame","coordinates(self, *args): \"\"\" Create world coordinates object\"\"\" raise NotImplementedError(\"Subclasses may implement this\") class","\"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\" Base class for CoordinateFrames. Parameters ---------- naxes","= [\"GEOCENTER\", \"BARYCENTER\", \"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\"","coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \"\"\" A 2D coordinate frame. Parameters ---------- axes_order","this frame. \"\"\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D,","\"\"\" A 2D coordinate frame. Parameters ---------- axes_order : tuple of int A","those Units on axes. axes_names : list Names of the axes in this","in the frame.\"\"\" return self._axes_names @property def axes_order(self): \"\"\" A tuple of indices","name : str Name of this frame. \"\"\" def __init__(self, naxes, axes_type, axes_order,","naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit",": tuple of int A dimension in the input data that corresponds to","raise ValueError(\"Number of axes names does not match number of axes.\") else: axes_names","wcs.input_frame \"\"\" # Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except:","Number of axes. axes_type : str One of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order :",": str Name for this frame. \"\"\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None,","Represents Spectral Frame Parameters ---------- axes_order : tuple or int A dimension in","inputs to wcs.input_frame \"\"\" # Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit,","super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def","self.axes_order] coo = tuple([arg * un for arg, un in zip(args, self.unit)]) return","naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order =","__future__ import absolute_import, division, unicode_literals, print_function import numpy as np from astropy import","self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\" Represents one or more","or int A dimension in the input data that corresponds to this axis.","frame.\"\"\" return self._axes_names @property def axes_order(self): \"\"\" A tuple of indices which map","fmt def __str__(self): if self._name is not None: return self._name else: return self.__class__.__name__","to wcs.input_frame \"\"\" # Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame)","Celestial Frame Representation Parameters ---------- axes_order : tuple of int A dimension in","= '<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is","dimension in the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames","coordinate frame. Parameters ---------- axes_order : tuple of int A dimension in the","Parameters ---------- axes_order : tuple or int A dimension in the input data","absolute_import, division, unicode_literals, print_function import numpy as np from astropy import units as","units.Unit instance or iterable of those Units on axes. axes_names : list Names",": str or units.Unit instance Spectral unit. axes_names : str Spectral axis name.","unit = tuple(unit) else: unit = (unit,) if len(unit) != naxes: raise ValueError(\"Number","used with output_frame to convert to world coordinate objects). reference_position : str Reference","= tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order,","__init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order","self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else:","fmt += ', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \", reference_frame={0}\".format(self.reference_frame)","Names of axes in the frame.\"\"\" return self._axes_names @property def axes_order(self): \"\"\" A","n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\"Incorrect numbering of axes,","def __init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame in","\"\"\" A custom name of this frame.\"\"\" return self._name @name.setter def name(self, val):","name. name : str Name for this frame. \"\"\" def __init__(self, axes_order=(0,), reference_frame=None,","@property def frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo","this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\" return self._axes_type def coordinates(self, *args): \"\"\"","= tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame","= axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise","CompositeFrame(CoordinateFrame): \"\"\" Represents one or more frames. Parameters ---------- frames : list List","\"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\" Base class for CoordinateFrames.","else: unit = (unit,) if len(unit) != naxes: raise ValueError(\"Number of units does","of axes intheis frame.\"\"\" return self._naxes @property def unit(self): \"\"\"The unit of this","~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform between start_frame and the","reference frame. reference_position : str Reference position. unit : str or units.Unit instance","else: self._name = name if reference_position is not None: self._reference_position = reference_position else:","frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \"\"\" A 2D coordinate","the input data that corresponds to this axis. unit : list of astropy.units.Unit","if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names","astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if len(unit) != naxes: raise","Spectral unit. axes_names : str Spectral axis name. name : str Name for","def __repr__(self): fmt = '<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order)","reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to world","frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str","return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \"\"\" Represents one or more frames. Parameters","not match number of axes.\") else: self._unit = tuple([u.Unit(au) for au in unit])","corresponds to this axis. unit : list of astropy.units.Unit Unit for each axis.","for this frame. \"\"\" def __init__(self, frames, name=None): self._frames = frames[:] naxes =","frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type","unit of this frame.\"\"\" return self._unit @property def axes_names(self): \"\"\" Names of axes","= naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type","in the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A","self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo = [] for frame","this axis. unit : list of astropy.units.Unit Unit for each axis. axes_names :","for this frame. \"\"\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame,","convert to world coordinate objects). reference_position : str Reference position - one of","position - one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each","2D coordinate frame. Parameters ---------- axes_order : tuple of int A dimension in","tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame,","to convert to world coordinate objects). unit : str or units.Unit instance Spectral","= [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class","to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to","match number of axes.\") else: self._unit = tuple([u.Unit(au) for au in unit]) if","name if reference_position is not None: self._reference_position = reference_position else: self._reference_position = None","of this frame.\"\"\" return self._name @name.setter def name(self, val): \"\"\" A custom name","in frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind]","', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \", reference_frame={0}\".format(self.reference_frame) fmt +=","Frame Parameters ---------- axes_order : tuple or int A dimension in the input","which axes in `start_frame` contribute to each axis in the current frame. Parameters","len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit = _unit","six from . import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame',","if axes_names is None: axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values())","if np.isscalar(args): return args * self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame):","axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for","!= len(axes_order): raise ValueError(\"Incorrect numbering of axes, \" \"axes_order should contain unique numbers,","unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self): return repr(self.frames) def","return None def input_axes(self, start_frame=None): \"\"\" Computes which axes in `start_frame` contribute to","self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \"\"\" Type of this frame :","frame in frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names):","list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit = _unit if axes_order is","2 if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values())","'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\"GEOCENTER\",","fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \"\"\" A 2D coordinate frame. Parameters","CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this frame. \"\"\" def __init__(self,","of this frame.\"\"\" return self._unit @property def axes_names(self): \"\"\" Names of axes in","of [\"SPATIAL\", \"SPECTRAL\", \"TIME\"] axes_order : tuple of int A dimension in the","['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION","input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually","= (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\"Number of","coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \"\"\" Represents Spectral Frame Parameters ----------","`start_frame` contribute to each axis in the current frame. Parameters ---------- start_frame :","@property def reference_position(self): try: return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None):","def axes_order(self): \"\"\" A tuple of indices which map inputs to axes.\"\"\" return","should contain unique numbers, \" \"got {}.\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names,","to world coordinate objects). reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION`","for frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un,","reference_position : str Reference position. unit : str or units.Unit instance or iterable","from astropy.extern import six from . import utils as gwutils __all__ = ['Frame2D',","def axes_names(self): \"\"\" Names of axes in the frame.\"\"\" return self._axes_names @property def","@property def axes_type(self): \"\"\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\"","STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None:","[] for frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype,","\"HELIOCENTER\", \"TOPOCENTER\", \"LSR\", \"LSRK\", \"LSRD\", \"GALACTIC_CENTER\", \"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\" Base class for","'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes =","each axis. axes_names : list Names of the axes in this frame. name","None def input_axes(self, start_frame=None): \"\"\" Computes which axes in `start_frame` contribute to each","astropy import units as u from astropy import utils as astutil from astropy","frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \"\"\" return self._axes_type def coordinates(self, *args): \"\"\" Create","*args): \"\"\" Create a SkyCoord object. Parameters ---------- args : float inputs to","frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un, n in zip(frame.axes_order,","self._axes_type def coordinates(self, *args): \"\"\" Create world coordinates object\"\"\" raise NotImplementedError(\"Subclasses may implement","'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION =","reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \"\"\" Create a SkyCoord object. Parameters","@name.setter def name(self, val): \"\"\" A custom name of this frame.\"\"\" self._name =","__repr__(self): fmt = '<{0}(name=\"{1}\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if","axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt","match number of axes.\") else: axes_names = tuple([\"\"] * naxes) self._axes_names = axes_names","from __future__ import absolute_import, division, unicode_literals, print_function import numpy as np from astropy","if reference_position is not None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame,","if self.reference_position is not None: fmt += ', reference_position=\"{0}\"'.format(self.reference_position) if self.reference_frame is not","return fmt def __str__(self): if self._name is not None: return self._name else: return","this frame. \"\"\" def __init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes","that corresponds to this axis. unit : list of astropy.units.Unit Unit for each","_unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit = _unit if","frame.\"\"\" return self._unit @property def axes_names(self): \"\"\" Names of axes in the frame.\"\"\"","\"LOCAL_GROUP_CENTER\"] class CoordinateFrame(object): \"\"\" Base class for CoordinateFrames. Parameters ---------- naxes : int","name of this frame.\"\"\" return self._name @name.setter def name(self, val): \"\"\" A custom","if axes_order is None: axes_order = tuple(range(naxes)) if unit is None: unit =","axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self,","= list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names =","list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for","this frame.\"\"\" return self._name @name.setter def name(self, val): \"\"\" A custom name of","frame.\"\"\" self._name = val @property def naxes(self): \"\"\" The number of axes intheis","axes_type=\"SPECTRAL\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return","import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES =","frame. \"\"\" def __init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for","= reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\"{1}\",","_axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes = len(_axes_names) _unit","each axis in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame","import absolute_import, division, unicode_literals, print_function import numpy as np from astropy import units","unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\"SPATIAL\", \"SPATIAL\"], axes_order, name=name, axes_names=axes_names, unit=unit)"],"string":"[\n \"Parameters ---------- naxes : int Number of axes. axes_type : str One of\",\n \"utils as astutil from astropy import coordinates as coord from astropy.extern import six\",\n \"object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may implement this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame Representation Parameters\",\n \"is None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame,\",\n \"axes_names = tuple([\\\"\\\"] * naxes) self._axes_names = axes_names if name is None: self._name\",\n \"unit is None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes\",\n \"= tuple([u.Unit(au) for au in unit]) if axes_names is not None: if isinstance(axes_names,\",\n \"len(axes_order): raise ValueError(\\\"Incorrect numbering of axes, \\\" \\\"axes_order should contain unique numbers, \\\"\",\n \"unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral Frame Parameters ---------- axes_order\",\n \"this frame. name : str Name of this frame. \\\"\\\"\\\" def __init__(self, naxes,\",\n \"if name is None: self._name = self.__class__.__name__ else: self._name = name if reference_position\",\n \"is not None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def\",\n \"if self.reference_frame is not None: fmt += \\\", reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\" return\",\n \"Represents one or more frames. Parameters ---------- frames : list List of frames\",\n \"else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is not None: if\",\n \"unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type\",\n \"or iterable of those Units on axes. axes_names : list Names of the\",\n \"this frame. name : str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,\",\n \"self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args):\",\n \"[\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for\",\n \"WCS pipeline The transform between start_frame and the current frame is used to\",\n \"in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D\",\n \"__all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in\",\n \"in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes = len(_axes_names)\",\n \"(unit,) if len(unit) != naxes: raise ValueError(\\\"Number of units does not match number\",\n \"name=name) def coordinates(self, *args): \\\"\\\"\\\" Create a SkyCoord object. Parameters ---------- args :\",\n \"axes. axes_names : list Names of the axes in this frame. name :\",\n \"a 3-clause BSD style license - see LICENSE.rst \\\"\\\"\\\" Defines coordinate frames and\",\n \"* naxes) self._axes_names = axes_names if name is None: self._name = self.__class__.__name__ else:\",\n \"axes_names is None: axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if\",\n \"naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names,\",\n \"self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = []\",\n \"this frame.\\\"\\\"\\\" self._name = val @property def naxes(self): \\\"\\\"\\\" The number of axes\",\n \"str Name for this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None,\",\n \"naxes = 2 if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names\",\n \"Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform\",\n \"BSD style license - see LICENSE.rst \\\"\\\"\\\" Defines coordinate frames and ties them\",\n \": list Names of the axes in this frame. name : str Name\",\n \"val @property def naxes(self): \\\"\\\"\\\" The number of axes intheis frame.\\\"\\\"\\\" return self._naxes\",\n \"i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \\\"\\\"\\\" A\",\n \"from astropy import units as u from astropy import utils as astutil from\",\n \"self._name is not None: return self._name else: return self.__class__.__name__ @property def name(self): \\\"\\\"\\\"\",\n \"of those Units on axes. axes_names : list Names of the axes in\",\n \"of this frame.\\\"\\\"\\\" self._name = val @property def naxes(self): \\\"\\\"\\\" The number of\",\n \"axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \\\"\\\"\\\" Create a SkyCoord object.\",\n \": str Spectral axis name. name : str Name for this frame. \\\"\\\"\\\"\",\n \"ValueError(\\\"Number of axes names does not match number of axes.\\\") else: axes_names =\",\n \"astutil from astropy import coordinates as coord from astropy.extern import six from .\",\n \"necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral\",\n \"if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if\",\n \"in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the\",\n \"['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self,\",\n \"numbers, \\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def\",\n \"\\\"\\\"\\\" # Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise\",\n \"# Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class\",\n \"\\\"\\\"\\\" Create a SkyCoord object. Parameters ---------- args : float inputs to wcs.input_frame\",\n \"Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'),\",\n \"\\\"\\\"\\\" from __future__ import absolute_import, division, unicode_literals, print_function import numpy as np from\",\n \"unicode_literals, print_function import numpy as np from astropy import units as u from\",\n \"in self.axes_order] coo = tuple([arg * un for arg, un in zip(args, self.unit)])\",\n \": str or units.Unit instance or iterable of those Units on axes. axes_names\",\n \"coordinates(self, *args): \\\"\\\"\\\" Create a SkyCoord object. Parameters ---------- args : float inputs\",\n \"\\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\" Base class\",\n \"objects). unit : str or units.Unit instance Spectral unit. axes_names : str Spectral\",\n \"not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if\",\n \"__init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is not\",\n \"self._name else: return self.__class__.__name__ @property def name(self): \\\"\\\"\\\" A custom name of this\",\n \"import numpy as np from astropy import units as u from astropy import\",\n \"un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\\\"Incorrect numbering of axes, \\\" \\\"axes_order should\",\n \"list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame in\",\n \"name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else: return\",\n \"= tuple([\\\"\\\"] * naxes) self._axes_names = axes_names if name is None: self._name =\",\n \"*args): if np.isscalar(args): return args * self.unit[0] else: return args[0] * self.unit[0] class\",\n \"\\\"\\\"\\\" Represents one or more frames. Parameters ---------- frames : list List of\",\n \"for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype\",\n \"= list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit = _unit if axes_order\",\n \"reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else: return args[0]\",\n \"self._naxes @property def unit(self): \\\"\\\"\\\"The unit of this frame.\\\"\\\"\\\" return self._unit @property def\",\n \"of axes in the frame.\\\"\\\"\\\" return self._axes_names @property def axes_order(self): \\\"\\\"\\\" A tuple\",\n \"reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type,\",\n \"if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) !=\",\n \"@property def axes_order(self): \\\"\\\"\\\" A tuple of indices which map inputs to axes.\\\"\\\"\\\"\",\n \"in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un, n in\",\n \"name of this frame.\\\"\\\"\\\" self._name = val @property def naxes(self): \\\"\\\"\\\" The number\",\n \"to world coordinate objects). unit : str or units.Unit instance Spectral unit. axes_names\",\n \"not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if len(unit)\",\n \"reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args):\",\n \"naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \\\"\\\"\\\"\",\n \"\\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\" Base class for CoordinateFrames. Parameters\",\n \"super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit,\",\n \"in the input data that corresponds to this axis. unit : list of\",\n \"self._name = name if reference_position is not None: self._reference_position = reference_position else: self._reference_position\",\n \"self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is\",\n \"unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \\\"\\\"\\\" Create a SkyCoord object. Parameters ----------\",\n \"fmt += \\\", reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\" return fmt def __str__(self): if self._name\",\n \"this frame. \\\"\\\"\\\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2\",\n \"= tuple(axes_type) self._reference_frame = reference_frame if unit is not None: if astutil.isiterable(unit): unit\",\n \"List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this\",\n \"\\\"\\\"\\\" A tuple of indices which map inputs to axes.\\\"\\\"\\\" return self._axes_order @property\",\n \"\\\"\\\"\\\" Celestial Frame Representation Parameters ---------- axes_order : tuple of int A dimension\",\n \"return self._unit @property def axes_names(self): \\\"\\\"\\\" Names of axes in the frame.\\\"\\\"\\\" return\",\n \"of int A dimension in the input data that corresponds to this axis.\",\n \"frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this frame. \\\"\\\"\\\"\",\n \"return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \\\"\\\"\\\" Computes which axes\",\n \"axes.\\\") else: self._unit = tuple([u.Unit(au) for au in unit]) if axes_names is not\",\n \"as coord from astropy.extern import six from . import utils as gwutils __all__\",\n \"six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit\",\n \"!= naxes: raise ValueError(\\\"Number of units does not match number of axes.\\\") else:\",\n \"axes_type : str One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order : tuple of int\",\n \"inputs: outputs. \\\"\\\"\\\" sep = self._separable(start_frame) inputs = [] for ax in self.axes_order:\",\n \"if unit is not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit =\",\n \"axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\"\",\n \"'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\",\",\n \"of axes.\\\") else: axes_names = tuple([\\\"\\\"] * naxes) self._axes_names = axes_names if name\",\n \"in self.frames: fargs = [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs))\",\n \"+= \\\", reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\" return fmt def __str__(self): if self._name is\",\n \"this frame. name : str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=None,\",\n \"astropy.units.Unit Unit for each axis. axes_names : list Names of the axes in\",\n \"= [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\",\",\n \"utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper()\",\n \"Create a SkyCoord object. Parameters ---------- args : float inputs to wcs.input_frame \\\"\\\"\\\"\",\n \"class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one or more frames. Parameters ---------- frames : list\",\n \"transform between start_frame and the current frame is used to compute the mapping\",\n \"or units.Unit instance Spectral unit. axes_names : str Spectral axis name. name :\",\n \"names does not match number of axes.\\\") else: axes_names = tuple([\\\"\\\"] * naxes)\",\n \": float inputs to wcs.input_frame \\\"\\\"\\\" # Reorder axes if necesary. try: return\",\n \"that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with\",\n \"unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type,\",\n \"Spectral axis name. name : str Name for this frame. \\\"\\\"\\\" def __init__(self,\",\n \"\\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for i\",\n \"frame (usually used with output_frame to convert to world coordinate objects). reference_position :\",\n \"for frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes))\",\n \"objects). reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list\",\n \"is not None: fmt += \\\", reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\" return fmt def\",\n \"or units.Unit instance or iterable of those Units on axes. axes_names : list\",\n \"return coo class Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D coordinate frame. Parameters ---------- axes_order :\",\n \"Parameters ---------- frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name\",\n \"see LICENSE.rst \\\"\\\"\\\" Defines coordinate frames and ties them to data axes. \\\"\\\"\\\"\",\n \": str One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order : tuple of int A\",\n \"\\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\" Base class for CoordinateFrames. Parameters ----------\",\n \"= frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit\",\n \"from . import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame']\",\n \"in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] =\",\n \"self).__init__() def __repr__(self): fmt = '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names,\",\n \"the WCS pipeline The transform between start_frame and the current frame is used\",\n \"return self.__class__.__name__ @property def name(self): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" return\",\n \"@property def axes_names(self): \\\"\\\"\\\" Names of axes in the frame.\\\"\\\"\\\" return self._axes_names @property\",\n \"data that corresponds to this axis. unit : list of astropy.units.Unit Unit for\",\n \"name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self,\",\n \"axes_names=None, name=None): naxes = 2 if reference_frame is not None: if reference_frame.name.upper() in\",\n \"for each axis. axes_names : list Names of the axes in this frame.\",\n \"fargs = [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo\",\n \"to axes.\\\"\\\"\\\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self): try:\",\n \"in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order =\",\n \"A 2D coordinate frame. Parameters ---------- axes_order : tuple of int A dimension\",\n \"else: return self.__class__.__name__ @property def name(self): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\"\",\n \"data axes. \\\"\\\"\\\" from __future__ import absolute_import, division, unicode_literals, print_function import numpy as\",\n \"coordinate objects). reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION` unit :\",\n \"axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt +=\",\n \"in the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference\",\n \"this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert\",\n \"None: self._name = self.__class__.__name__ else: self._name = name if reference_position is not None:\",\n \"start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform between start_frame\",\n \"= un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\\\"Incorrect numbering of axes, \\\" \\\"axes_order\",\n \"the current frame is used to compute the mapping inputs: outputs. \\\"\\\"\\\" sep\",\n \"axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to\",\n \"frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D coordinate frame. Parameters ----------\",\n \"---------- axes_order : tuple of int A dimension in the input data that\",\n \"as np from astropy import units as u from astropy import utils as\",\n \"frame. Parameters ---------- axes_order : tuple of int A dimension in the input\",\n \"not None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self):\",\n \"str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x',\",\n \"axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for i in\",\n \"[args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame):\",\n \"Reference frame (usually used with output_frame to convert to world coordinate objects). reference_position\",\n \"axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type =\",\n \"not None: fmt += \\\", reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\" return fmt def __str__(self):\",\n \"return self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position\",\n \"unit : str or units.Unit instance or iterable of those Units on axes.\",\n \"\\\"\\\"\\\" Names of axes in the frame.\\\"\\\"\\\" return self._axes_names @property def axes_order(self): \\\"\\\"\\\"\",\n \"class CoordinateFrame(object): \\\"\\\"\\\" Base class for CoordinateFrames. Parameters ---------- naxes : int Number\",\n \"naxes: raise ValueError(\\\"Number of units does not match number of axes.\\\") else: self._unit\",\n \"fmt = '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position\",\n \"style license - see LICENSE.rst \\\"\\\"\\\" Defines coordinate frames and ties them to\",\n \": int Number of axes. axes_type : str One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"]\",\n \"for i in self.axes_order] coo = tuple([arg * un for arg, un in\",\n \"axes_order : tuple or int A dimension in the input data that corresponds\",\n \"\\\"\\\"\\\" Defines coordinate frames and ties them to data axes. \\\"\\\"\\\" from __future__\",\n \"Name for this frame. \\\"\\\"\\\" def __init__(self, frames, name=None): self._frames = frames[:] naxes\",\n \"Unit for each axis. axes_names : list Names of the axes in this\",\n \"= 2 if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names =\",\n \"object. Parameters ---------- args : float inputs to wcs.input_frame \\\"\\\"\\\" # Reorder axes\",\n \"unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\\\"Incorrect numbering of axes, \\\"\",\n \"Parameters ---------- axes_order : tuple of int A dimension in the input data\",\n \"frame in self.frames: fargs = [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order)\",\n \"frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral Frame Parameters ---------- axes_order :\",\n \"def unit(self): \\\"\\\"\\\"The unit of this frame.\\\"\\\"\\\" return self._unit @property def axes_names(self): \\\"\\\"\\\"\",\n \"gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame\",\n \"\\\"\\\"\\\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\" return self._axes_type def\",\n \"__init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame,\",\n \"coo = tuple([arg * un for arg, un in zip(args, self.unit)]) return coo\",\n \"axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\\\"Number\",\n \"axes_names if name is None: self._name = self.__class__.__name__ else: self._name = name if\",\n \"import coordinates as coord from astropy.extern import six from . import utils as\",\n \"used with output_frame to convert to world coordinate objects). unit : str or\",\n \"str or units.Unit instance Spectral unit. axes_names : str Spectral axis name. name\",\n \"axis. axes_names : list Names of the axes in this frame. name :\",\n \"else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one or more frames.\",\n \"list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for\",\n \"tuple([\\\"\\\"] * naxes) self._axes_names = axes_names if name is None: self._name = self.__class__.__name__\",\n \"for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \\\"\\\"\\\" Type of\",\n \"self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt += ',\",\n \"name(self): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" return self._name @name.setter def name(self,\",\n \"axes in `start_frame` contribute to each axis in the current frame. Parameters ----------\",\n \"float inputs to wcs.input_frame \\\"\\\"\\\" # Reorder axes if necesary. try: return coord.SkyCoord(*args,\",\n \"Reference position. unit : str or units.Unit instance or iterable of those Units\",\n \"six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise\",\n \"reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\\\"{1}\\\", unit={2},\",\n \"\\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return\",\n \"\\\"\\\"\\\" Base class for CoordinateFrames. Parameters ---------- naxes : int Number of axes.\",\n \"dimension in the input data that corresponds to this axis. unit : list\",\n \"[\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order : tuple of int A dimension in the input\",\n \"corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame\",\n \"in the WCS pipeline The transform between start_frame and the current frame is\",\n \"n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind]\",\n \"one or more frames. Parameters ---------- frames : list List of frames (TimeFrame,\",\n \"\\\"\\\"\\\" The number of axes intheis frame.\\\"\\\"\\\" return self._naxes @property def unit(self): \\\"\\\"\\\"The\",\n \"is None: self._name = self.__class__.__name__ else: self._name = name if reference_position is not\",\n \"frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) !=\",\n \"self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \\\"\\\"\\\" Computes which axes in\",\n \"the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS\",\n \"instance or iterable of those Units on axes. axes_names : list Names of\",\n \"sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names =\",\n \"zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un\",\n \"self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def\",\n \"\\\"\\\"\\\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\",\",\n \"naxes(self): \\\"\\\"\\\" The number of axes intheis frame.\\\"\\\"\\\" return self._naxes @property def unit(self):\",\n \"'y'), name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args):\",\n \"axes in the frame.\\\"\\\"\\\" return self._axes_names @property def axes_order(self): \\\"\\\"\\\" A tuple of\",\n \"*args): args = [args[i] for i in self.axes_order] coo = tuple([arg * un\",\n \"self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self):\",\n \"axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] =\",\n \"__str__(self): if self._name is not None: return self._name else: return self.__class__.__name__ @property def\",\n \"super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \\\"\\\"\\\" Create\",\n \"str Reference position. unit : str or units.Unit instance or iterable of those\",\n \"self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt =\",\n \"* self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one or more frames. Parameters ---------- frames\",\n \"world coordinate objects). reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION` unit\",\n \"custom name of this frame.\\\"\\\"\\\" self._name = val @property def naxes(self): \\\"\\\"\\\" The\",\n \"None: if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if len(unit) !=\",\n \"self._name = val @property def naxes(self): \\\"\\\"\\\" The number of axes intheis frame.\\\"\\\"\\\"\",\n \"frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un if\",\n \"CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame Representation Parameters ---------- axes_order : tuple of int A\",\n \"return inputs @property def axes_type(self): \\\"\\\"\\\" Type of this frame : 'SPATIAL', 'SPECTRAL',\",\n \": str Reference position. unit : str or units.Unit instance or iterable of\",\n \"is None: axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit\",\n \"Representation Parameters ---------- axes_order : tuple of int A dimension in the input\",\n \"corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str\",\n \"intheis frame.\\\"\\\"\\\" return self._naxes @property def unit(self): \\\"\\\"\\\"The unit of this frame.\\\"\\\"\\\" return\",\n \"\\\"axes_order should contain unique numbers, \\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit,\",\n \"tuple(range(naxes)) if unit is None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL']\",\n \"not match number of axes.\\\") else: axes_names = tuple([\\\"\\\"] * naxes) self._axes_names =\",\n \"unit : list of astropy.units.Unit Unit for each axis. axes_names : list Names\",\n \"axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order)\",\n \"[] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \\\"\\\"\\\" Type\",\n \"outputs. \\\"\\\"\\\" sep = self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0]))\",\n \"axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if\",\n \"ValueError(\\\"Incorrect numbering of axes, \\\" \\\"axes_order should contain unique numbers, \\\" \\\"got {}.\\\".format(axes_order))\",\n \"input_axes(self, start_frame=None): \\\"\\\"\\\" Computes which axes in `start_frame` contribute to each axis in\",\n \"frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\",\",\n \"and the current frame is used to compute the mapping inputs: outputs. \\\"\\\"\\\"\",\n \"---------- naxes : int Number of axes. axes_type : str One of [\\\"SPATIAL\\\",\",\n \"naxes = sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes))\",\n \"in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \\\"\\\"\\\" Type of this frame\",\n \"- see LICENSE.rst \\\"\\\"\\\" Defines coordinate frames and ties them to data axes.\",\n \"reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position. unit :\",\n \"axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self,\",\n \"'<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not\",\n \"frame. name : str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=None, reference_frame=None,\",\n \"naxes: raise ValueError(\\\"Number of axes names does not match number of axes.\\\") else:\",\n \"this frame.\\\"\\\"\\\" return self._unit @property def axes_names(self): \\\"\\\"\\\" Names of axes in the\",\n \"implement this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame Representation Parameters ---------- axes_order : tuple\",\n \"def frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo =\",\n \"unit = _unit if axes_order is None: axes_order = tuple(range(naxes)) if unit is\",\n \"'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\",\",\n \"self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position)\",\n \": str Name of this frame. \\\"\\\"\\\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None,\",\n \"= tuple(unit) else: unit = (unit,) if len(unit) != naxes: raise ValueError(\\\"Number of\",\n \"\\\"\\\"\\\" sep = self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return\",\n \"name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for i in self.axes_order]\",\n \"astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position. unit : str or\",\n \"if len(unit) != naxes: raise ValueError(\\\"Number of units does not match number of\",\n \"self.frames: fargs = [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return\",\n \"frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] =\",\n \"LICENSE.rst \\\"\\\"\\\" Defines coordinate frames and ties them to data axes. \\\"\\\"\\\" from\",\n \"more frames. Parameters ---------- frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame,\",\n \"used to compute the mapping inputs: outputs. \\\"\\\"\\\" sep = self._separable(start_frame) inputs =\",\n \"\\\"\\\"\\\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame\",\n \"(usually used with output_frame to convert to world coordinate objects). reference_position : str\",\n \"astropy import coordinates as coord from astropy.extern import six from . import utils\",\n \"print_function import numpy as np from astropy import units as u from astropy\",\n \"The number of axes intheis frame.\\\"\\\"\\\" return self._naxes @property def unit(self): \\\"\\\"\\\"The unit\",\n \"inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self):\",\n \"isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes:\",\n \"axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self): return repr(self.frames)\",\n \"SpectralFrame, CoordinateFrame). name : str Name for this frame. \\\"\\\"\\\" def __init__(self, frames,\",\n \"if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\\\"Incorrect numbering of axes, \\\" \\\"axes_order should contain\",\n \"or more frames. Parameters ---------- frames : list List of frames (TimeFrame, CelestialFrame,\",\n \"if len(axes_names) != naxes: raise ValueError(\\\"Number of axes names does not match number\",\n \"of axes. axes_type : str One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order : tuple\",\n \"Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes\",\n \"_unit: unit = _unit if axes_order is None: axes_order = tuple(range(naxes)) if unit\",\n \"Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame):\",\n \"if unit is None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] *\",\n \"self._name @name.setter def name(self, val): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" self._name\",\n \"None: axes_order = tuple(range(naxes)) if unit is None: unit = tuple([u.degree] * naxes)\",\n \"reference_position is not None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__()\",\n \"= [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \\\"\\\"\\\"\",\n \"+= ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \\\", reference_frame={0}\\\".format(self.reference_frame) fmt\",\n \"self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \\\"\\\"\\\" Create a\",\n \"inputs to axes.\\\"\\\"\\\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self):\",\n \"def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is\",\n \"STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object):\",\n \"import units as u from astropy import utils as astutil from astropy import\",\n \"CoordinateFrame(object): \\\"\\\"\\\" Base class for CoordinateFrames. Parameters ---------- naxes : int Number of\",\n \"Name of this frame. \\\"\\\"\\\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None,\",\n \"np.isscalar(args): return args * self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\"\",\n \"'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\" return self._axes_type def coordinates(self, *args): \\\"\\\"\\\" Create world coordinates\",\n \"the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference\",\n \"list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for frame in frames:\",\n \"except: raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral Frame Parameters ---------- axes_order : tuple\",\n \"int A dimension in the input data that corresponds to this axis. unit\",\n \"naxes : int Number of axes. axes_type : str One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\",\",\n \"self._frames = frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes))\",\n \"to each axis in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A\",\n \"@property def unit(self): \\\"\\\"\\\"The unit of this frame.\\\"\\\"\\\" return self._unit @property def axes_names(self):\",\n \"*args): \\\"\\\"\\\" Create world coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may implement this\\\") class CelestialFrame(CoordinateFrame):\",\n \"def name(self): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" return self._name @name.setter def\",\n \"self._unit = tuple([u.Unit(au) for au in unit]) if axes_names is not None: if\",\n \"except AttributeError: return None def input_axes(self, start_frame=None): \\\"\\\"\\\" Computes which axes in `start_frame`\",\n \"args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one or more frames. Parameters ----------\",\n \"return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral Frame Parameters\",\n \"A tuple of indices which map inputs to axes.\\\"\\\"\\\" return self._axes_order @property def\",\n \"axes names does not match number of axes.\\\") else: axes_names = tuple([\\\"\\\"] *\",\n \": str Name for this frame. \\\"\\\"\\\" def __init__(self, frames, name=None): self._frames =\",\n \"*args): coo = [] for frame in self.frames: fargs = [args[i] for i\",\n \"name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args\",\n \"unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else:\",\n \"* naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args):\",\n \"reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \\\", reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\"\",\n \"1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names,\",\n \"A dimension in the input data that corresponds to this axis. unit :\",\n \"coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may implement this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame Representation\",\n \"if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame\",\n \": str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None,\",\n \"unit : str or units.Unit instance Spectral unit. axes_names : str Spectral axis\",\n \"@property def naxes(self): \\\"\\\"\\\" The number of axes intheis frame.\\\"\\\"\\\" return self._naxes @property\",\n \"frame. name : str Name of this frame. \\\"\\\"\\\" def __init__(self, naxes, axes_type,\",\n \"raise NotImplementedError(\\\"Subclasses may implement this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame Representation Parameters ----------\",\n \"Name for this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None):\",\n \"for au in unit]) if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names\",\n \"is not None: return self._name else: return self.__class__.__name__ @property def name(self): \\\"\\\"\\\" A\",\n \"None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes,\",\n \"= tuple(range(naxes)) if unit is None: unit = tuple([u.degree] * naxes) axes_type =\",\n \"axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit,\",\n \"tuple or int A dimension in the input data that corresponds to this\",\n \"\\\"\\\"\\\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes =\",\n \"axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for\",\n \"unit is None and _unit: unit = _unit if axes_order is None: axes_order\",\n \"- one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each axis.\",\n \"unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position)\",\n \"class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame Representation Parameters ---------- axes_order : tuple of int\",\n \"\\\"\\\"\\\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order,\",\n \"return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo = [] for\",\n \"axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args\",\n \": 'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\" return self._axes_type def coordinates(self, *args): \\\"\\\"\\\" Create world\",\n \"not None: return self._name else: return self.__class__.__name__ @property def name(self): \\\"\\\"\\\" A custom\",\n \"i in self.axes_order] coo = tuple([arg * un for arg, un in zip(args,\",\n \"coordinate objects). unit : str or units.Unit instance Spectral unit. axes_names : str\",\n \"tuple of int A dimension in the input data that corresponds to this\",\n \"raise ValueError(\\\"Number of units does not match number of axes.\\\") else: self._unit =\",\n \"this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1,\",\n \"axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position. unit\",\n \"None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance')\",\n \"str Name for this frame. \\\"\\\"\\\" def __init__(self, frames, name=None): self._frames = frames[:]\",\n \"not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names:\",\n \"None: fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \\\",\",\n \"axes. \\\"\\\"\\\" from __future__ import absolute_import, division, unicode_literals, print_function import numpy as np\",\n \"frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit =\",\n \"val): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" self._name = val @property def\",\n \"with output_frame to convert to world coordinate objects). reference_position : str Reference position\",\n \"axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\\\"Incorrect numbering\",\n \"that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position :\",\n \"self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position except\",\n \"self.__class__.__name__ else: self._name = name if reference_position is not None: self._reference_position = reference_position\",\n \"from astropy import utils as astutil from astropy import coordinates as coord from\",\n \"axes_names : str Spectral axis name. name : str Name for this frame.\",\n \"instance Spectral unit. axes_names : str Spectral axis name. name : str Name\",\n \"reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance'\",\n \"frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order))\",\n \"coordinates(self, *args): coo = [] for frame in self.frames: fargs = [args[i] for\",\n \"in unit]) if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,)\",\n \"= sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names\",\n \"contain unique numbers, \\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name)\",\n \"if self._name is not None: return self._name else: return self.__class__.__name__ @property def name(self):\",\n \"frame. \\\"\\\"\\\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes\",\n \"axes in this frame. name : str Name of this frame. \\\"\\\"\\\" def\",\n \"list of astropy.units.Unit Unit for each axis. axes_names : list Names of the\",\n \"ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \\\"\\\"\\\" Type of this\",\n \"unit is not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,)\",\n \"frame. reference_position : str Reference position. unit : str or units.Unit instance or\",\n \"inputs @property def axes_type(self): \\\"\\\"\\\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'.\",\n \"world coordinate objects). unit : str or units.Unit instance Spectral unit. axes_names :\",\n \"axes_names=axes_names, name=name) def coordinates(self, *args): \\\"\\\"\\\" Create a SkyCoord object. Parameters ---------- args\",\n \"A frame in the WCS pipeline The transform between start_frame and the current\",\n \"frame in the WCS pipeline The transform between start_frame and the current frame\",\n \"---------- axes_order : tuple or int A dimension in the input data that\",\n \". import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES\",\n \"+= \\\")>\\\" return fmt def __str__(self): if self._name is not None: return self._name\",\n \"3-clause BSD style license - see LICENSE.rst \\\"\\\"\\\" Defines coordinate frames and ties\",\n \"{}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames\",\n \"the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame\",\n \"One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order : tuple of int A dimension in\",\n \"\\\"\\\"\\\" Computes which axes in `start_frame` contribute to each axis in the current\",\n \"None and _unit: unit = _unit if axes_order is None: axes_order = tuple(range(naxes))\",\n \"in this frame. name : str Name of this frame. \\\"\\\"\\\" def __init__(self,\",\n \": str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix),\",\n \"return self._name else: return self.__class__.__name__ @property def name(self): \\\"\\\"\\\" A custom name of\",\n \": ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform between start_frame and\",\n \"with output_frame to convert to world coordinate objects). unit : str or units.Unit\",\n \"data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used\",\n \"args * self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one\",\n \"frames and ties them to data axes. \\\"\\\"\\\" from __future__ import absolute_import, division,\",\n \"frame. name : str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0, 1),\",\n \"return self._naxes @property def unit(self): \\\"\\\"\\\"The unit of this frame.\\\"\\\"\\\" return self._unit @property\",\n \"as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for\",\n \"The transform between start_frame and the current frame is used to compute the\",\n \"Spectral Frame Parameters ---------- axes_order : tuple or int A dimension in the\",\n \"np from astropy import units as u from astropy import utils as astutil\",\n \"reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name,\",\n \"axes_names is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names =\",\n \"args : float inputs to wcs.input_frame \\\"\\\"\\\" # Reorder axes if necesary. try:\",\n \"axes_order is None: axes_order = tuple(range(naxes)) if unit is None: unit = tuple([u.degree]\",\n \"this frame. \\\"\\\"\\\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None):\",\n \"str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None):\",\n \"= reference_frame if unit is not None: if astutil.isiterable(unit): unit = tuple(unit) else:\",\n \": str Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit\",\n \"AttributeError: return None def input_axes(self, start_frame=None): \\\"\\\"\\\" Computes which axes in `start_frame` contribute\",\n \"input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame.\",\n \"= self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property\",\n \"return self._name @name.setter def name(self, val): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\"\",\n \"NotImplementedError(\\\"Subclasses may implement this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame Representation Parameters ---------- axes_order\",\n \"\\\", reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\" return fmt def __str__(self): if self._name is not\",\n \"`STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each axis. axes_names : list\",\n \"(TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this frame. \\\"\\\"\\\" def\",\n \"repr(self.frames) def coordinates(self, *args): coo = [] for frame in self.frames: fargs =\",\n \"---------- args : float inputs to wcs.input_frame \\\"\\\"\\\" # Reorder axes if necesary.\",\n \"start_frame and the current frame is used to compute the mapping inputs: outputs.\",\n \"astropy import utils as astutil from astropy import coordinates as coord from astropy.extern\",\n \"Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for\",\n \"the frame.\\\"\\\"\\\" return self._axes_names @property def axes_order(self): \\\"\\\"\\\" A tuple of indices which\",\n \"try: return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \\\"\\\"\\\" Computes which\",\n \"name : str Name for this frame. \\\"\\\"\\\" def __init__(self, frames, name=None): self._frames\",\n \": astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to world coordinate\",\n \"\\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self):\",\n \"def name(self, val): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" self._name = val\",\n \"in `start_frame` contribute to each axis in the current frame. Parameters ---------- start_frame\",\n \"= len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit =\",\n \"axes.\\\") else: axes_names = tuple([\\\"\\\"] * naxes) self._axes_names = axes_names if name is\",\n \"int Number of axes. axes_type : str One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order\",\n \"len(unit) != naxes: raise ValueError(\\\"Number of units does not match number of axes.\\\")\",\n \"A custom name of this frame.\\\"\\\"\\\" return self._name @name.setter def name(self, val): \\\"\\\"\\\"\",\n \"= list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order)\",\n \"len(np.unique(axes_order)) != len(axes_order): raise ValueError(\\\"Incorrect numbering of axes, \\\" \\\"axes_order should contain unique\",\n \"is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in\",\n \"self.reference_frame is not None: fmt += \\\", reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\" return fmt\",\n \"\\\"TIME\\\"] axes_order : tuple of int A dimension in the input data that\",\n \"ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind]\",\n \"= name if reference_position is not None: self._reference_position = reference_position else: self._reference_position =\",\n \"= (unit,) if len(unit) != naxes: raise ValueError(\\\"Number of units does not match\",\n \"= None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__,\",\n \"of the axes in this frame. name : str Name of this frame.\",\n \"license - see LICENSE.rst \\\"\\\"\\\" Defines coordinate frames and ties them to data\",\n \"self._unit @property def axes_names(self): \\\"\\\"\\\" Names of axes in the frame.\\\"\\\"\\\" return self._axes_names\",\n \"list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names\",\n \"None: fmt += \\\", reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\" return fmt def __str__(self): if\",\n \"= (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is not\",\n \"if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if len(unit) != naxes:\",\n \"self.__class__.__name__ @property def name(self): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" return self._name\",\n \"tuple(axes_type) self._reference_frame = reference_frame if unit is not None: if astutil.isiterable(unit): unit =\",\n \"frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The\",\n \"reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is not None: if\",\n \"unit(self): \\\"\\\"\\\"The unit of this frame.\\\"\\\"\\\" return self._unit @property def axes_names(self): \\\"\\\"\\\" Names\",\n \"axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name,\",\n \"unit]) if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else:\",\n \"of axes, \\\" \\\"axes_order should contain unique numbers, \\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes,\",\n \"units.Unit instance Spectral unit. axes_names : str Spectral axis name. name : str\",\n \"else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\\\"Number of axes names\",\n \"frame is used to compute the mapping inputs: outputs. \\\"\\\"\\\" sep = self._separable(start_frame)\",\n \"* self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one or\",\n \"for CoordinateFrames. Parameters ---------- naxes : int Number of axes. axes_type : str\",\n \"unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None:\",\n \"import utils as astutil from astropy import coordinates as coord from astropy.extern import\",\n \"\\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order : tuple of int A dimension in the input data\",\n \"reference_position(self): try: return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \\\"\\\"\\\" Computes\",\n \"as u from astropy import utils as astutil from astropy import coordinates as\",\n \"axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name)\",\n \"if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents\",\n \"axis. unit : list of astropy.units.Unit Unit for each axis. axes_names : list\",\n \"name : str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=None, reference_frame=None, unit=None,\",\n \"def naxes(self): \\\"\\\"\\\" The number of axes intheis frame.\\\"\\\"\\\" return self._naxes @property def\",\n \"= self.__class__.__name__ else: self._name = name if reference_position is not None: self._reference_position =\",\n \"_unit if axes_order is None: axes_order = tuple(range(naxes)) if unit is None: unit\",\n \"name : str Name for this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None,\",\n \"name(self, val): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" self._name = val @property\",\n \"unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame in frames:\",\n \"axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for i in self.axes_order] coo\",\n \"frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo = []\",\n \"class for CoordinateFrames. Parameters ---------- naxes : int Number of axes. axes_type :\",\n \"STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\",\",\n \"axes intheis frame.\\\"\\\"\\\" return self._naxes @property def unit(self): \\\"\\\"\\\"The unit of this frame.\\\"\\\"\\\"\",\n \"not None: fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is not None: fmt +=\",\n \"Defines coordinate frames and ties them to data axes. \\\"\\\"\\\" from __future__ import\",\n \"A reference frame. reference_position : str Reference position. unit : str or units.Unit\",\n \"axes_order = tuple(range(naxes)) if unit is None: unit = tuple([u.degree] * naxes) axes_type\",\n \"self._reference_frame = reference_frame if unit is not None: if astutil.isiterable(unit): unit = tuple(unit)\",\n \"try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral Frame\",\n \"axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self): return\",\n \"else: self._unit = tuple([u.Unit(au) for au in unit]) if axes_names is not None:\",\n \"else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3},\",\n \"output_frame to convert to world coordinate objects). unit : str or units.Unit instance\",\n \"* naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit,\",\n \"_axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit:\",\n \"\\\"\\\"\\\" Represents Spectral Frame Parameters ---------- axes_order : tuple or int A dimension\",\n \"un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n\",\n \"Names of the axes in this frame. name : str Name of this\",\n \": tuple or int A dimension in the input data that corresponds to\",\n \"(usually used with output_frame to convert to world coordinate objects). unit : str\",\n \"unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is not None: if reference_frame.name.upper()\",\n \"= val @property def naxes(self): \\\"\\\"\\\" The number of axes intheis frame.\\\"\\\"\\\" return\",\n \"# Licensed under a 3-clause BSD style license - see LICENSE.rst \\\"\\\"\\\" Defines\",\n \"coordinates as coord from astropy.extern import six from . import utils as gwutils\",\n \"axes. axes_type : str One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order : tuple of\",\n \"---------- frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name :\",\n \"\\\" \\\"axes_order should contain unique numbers, \\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order,\",\n \"SkyCoord object. Parameters ---------- args : float inputs to wcs.input_frame \\\"\\\"\\\" # Reorder\",\n \"unique numbers, \\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property\",\n \"is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names)\",\n \"_axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes = len(_axes_names) _unit =\",\n \"coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else: return args[0] * self.unit[0]\",\n \"self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i]\",\n \"input data that corresponds to this axis. unit : list of astropy.units.Unit Unit\",\n \"in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"]\",\n \"unit. axes_names : str Spectral axis name. name : str Name for this\",\n \"= list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for frame in\",\n \"axes_order(self): \\\"\\\"\\\" A tuple of indices which map inputs to axes.\\\"\\\"\\\" return self._axes_order\",\n \"of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this frame.\",\n \"None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt\",\n \"---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform between\",\n \"current frame is used to compute the mapping inputs: outputs. \\\"\\\"\\\" sep =\",\n \"frames. Parameters ---------- frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame).\",\n \"@property def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError:\",\n \"on axes. axes_names : list Names of the axes in this frame. name\",\n \"does not match number of axes.\\\") else: self._unit = tuple([u.Unit(au) for au in\",\n \"super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if\",\n \"convert to world coordinate objects). unit : str or units.Unit instance Spectral unit.\",\n \"'SPECTRAL', 'TIME'. \\\"\\\"\\\" return self._axes_type def coordinates(self, *args): \\\"\\\"\\\" Create world coordinates object\\\"\\\"\\\"\",\n \"self.axes_names, self.axes_order) if self.reference_position is not None: fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame\",\n \"units does not match number of axes.\\\") else: self._unit = tuple([u.Unit(au) for au\",\n \"None: return self._name else: return self.__class__.__name__ @property def name(self): \\\"\\\"\\\" A custom name\",\n \"[\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\" Base\",\n \"astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to world coordinate objects).\",\n \"reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names\",\n \"pipeline The transform between start_frame and the current frame is used to compute\",\n \"= n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\\\"Incorrect numbering of\",\n \"of this frame. \\\"\\\"\\\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None,\",\n \"def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"],\",\n \"int A dimension in the input data that corresponds to this axis. reference_frame\",\n \"axes_type(self): \\\"\\\"\\\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\" return self._axes_type\",\n \"Create world coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may implement this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial\",\n \"return self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError: return None def\",\n \"world coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may implement this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame\",\n \"= [] for frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind,\",\n \"[args[i] for i in self.axes_order] coo = tuple([arg * un for arg, un\",\n \"as astutil from astropy import coordinates as coord from astropy.extern import six from\",\n \": list of astropy.units.Unit Unit for each axis. axes_names : list Names of\",\n \"class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral Frame Parameters ---------- axes_order : tuple or int\",\n \"__init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame in self._frames])\",\n \"str or units.Unit instance or iterable of those Units on axes. axes_names :\",\n \"self._axes_names = axes_names if name is None: self._name = self.__class__.__name__ else: self._name =\",\n \"is used to compute the mapping inputs: outputs. \\\"\\\"\\\" sep = self._separable(start_frame) inputs\",\n \"if unit is None and _unit: unit = _unit if axes_order is None:\",\n \"the axes in this frame. name : str Name of this frame. \\\"\\\"\\\"\",\n \"self.axes_order) if self.reference_position is not None: fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is\",\n \"coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class\",\n \"Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\" return self._axes_type def coordinates(self,\",\n \"@property def name(self): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" return self._name @name.setter\",\n \"self._axes_names @property def axes_order(self): \\\"\\\"\\\" A tuple of indices which map inputs to\",\n \"_axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names\",\n \"SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral Frame Parameters ---------- axes_order : tuple or int A\",\n \"import six from . import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame',\",\n \"str One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order : tuple of int A dimension\",\n \"axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \\\"\\\"\\\" Create a SkyCoord\",\n \"def coordinates(self, *args): coo = [] for frame in self.frames: fargs = [args[i]\",\n \"return self._axes_type def coordinates(self, *args): \\\"\\\"\\\" Create world coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may\",\n \"= ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def\",\n \"units as u from astropy import utils as astutil from astropy import coordinates\",\n \"output_frame to convert to world coordinate objects). reference_position : str Reference position -\",\n \"None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names)\",\n \"def __str__(self): if self._name is not None: return self._name else: return self.__class__.__name__ @property\",\n \"axes_names : list Names of the axes in this frame. name : str\",\n \"is not None: fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is not None: fmt\",\n \"tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\\\"Number of axes names does not match\",\n \"self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format(\",\n \"\\\"\\\"\\\" return self._axes_type def coordinates(self, *args): \\\"\\\"\\\" Create world coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses\",\n \"axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def\",\n \"= [args[i] for i in self.axes_order] coo = tuple([arg * un for arg,\",\n \"def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes\",\n \"unit = (unit,) if len(unit) != naxes: raise ValueError(\\\"Number of units does not\",\n \"iterable of those Units on axes. axes_names : list Names of the axes\",\n \"is None and _unit: unit = _unit if axes_order is None: axes_order =\",\n \"A dimension in the input data that corresponds to this axis. reference_frame :\",\n \"= list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame\",\n \"= ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__]\",\n \"a SkyCoord object. Parameters ---------- args : float inputs to wcs.input_frame \\\"\\\"\\\" #\",\n \"isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if\",\n \"coord from astropy.extern import six from . import utils as gwutils __all__ =\",\n \"def coordinates(self, *args): \\\"\\\"\\\" Create world coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may implement this\\\")\",\n \"\\\"\\\"\\\" Create world coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may implement this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\"\",\n \"def coordinates(self, *args): args = [args[i] for i in self.axes_order] coo = tuple([arg\",\n \"name=None): naxes = 2 if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES:\",\n \"axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args *\",\n \"to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference\",\n \"them to data axes. \\\"\\\"\\\" from __future__ import absolute_import, division, unicode_literals, print_function import\",\n \"tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame =\",\n \"custom name of this frame.\\\"\\\"\\\" return self._name @name.setter def name(self, val): \\\"\\\"\\\" A\",\n \"compute the mapping inputs: outputs. \\\"\\\"\\\" sep = self._separable(start_frame) inputs = [] for\",\n \"between start_frame and the current frame is used to compute the mapping inputs:\",\n \"position. unit : str or units.Unit instance or iterable of those Units on\",\n \"one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each axis. axes_names\",\n \"number of axes intheis frame.\\\"\\\"\\\" return self._naxes @property def unit(self): \\\"\\\"\\\"The unit of\",\n \"axes_names = list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for frame\",\n \"reference_frame={0}\\\".format(self.reference_frame) fmt += \\\")>\\\" return fmt def __str__(self): if self._name is not None:\",\n \"= [] for frame in self.frames: fargs = [args[i] for i in frame.axes_order]\",\n \"axis in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in\",\n \"def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else: return args[0] *\",\n \"and ties them to data axes. \\\"\\\"\\\" from __future__ import absolute_import, division, unicode_literals,\",\n \"def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError: return\",\n \"mapping inputs: outputs. \\\"\\\"\\\" sep = self._separable(start_frame) inputs = [] for ax in\",\n \"= tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\\\"Number of axes names does not\",\n \"of astropy.units.Unit Unit for each axis. axes_names : list Names of the axes\",\n \"numbering of axes, \\\" \\\"axes_order should contain unique numbers, \\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame,\",\n \"contribute to each axis in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame\",\n \"[frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\",\",\n \"au in unit]) if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names =\",\n \"print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D coordinate frame.\",\n \"axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\\\"Number of axes names does\",\n \"coo class Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D coordinate frame. Parameters ---------- axes_order : tuple\",\n \"def axes_type(self): \\\"\\\"\\\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\" return\",\n \"class Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D coordinate frame. Parameters ---------- axes_order : tuple of\",\n \"u from astropy import utils as astutil from astropy import coordinates as coord\",\n \"tuple(unit) else: unit = (unit,) if len(unit) != naxes: raise ValueError(\\\"Number of units\",\n \"raise ValueError(\\\"Incorrect numbering of axes, \\\" \\\"axes_order should contain unique numbers, \\\" \\\"got\",\n \"reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list of\",\n \"= axes_names if name is None: self._name = self.__class__.__name__ else: self._name = name\",\n \"= _unit if axes_order is None: axes_order = tuple(range(naxes)) if unit is None:\",\n \"fmt += \\\")>\\\" return fmt def __str__(self): if self._name is not None: return\",\n \"axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order):\",\n \"list Names of the axes in this frame. name : str Name of\",\n \"Licensed under a 3-clause BSD style license - see LICENSE.rst \\\"\\\"\\\" Defines coordinate\",\n \"return args * self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents\",\n \"which map inputs to axes.\\\"\\\"\\\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property\",\n \"map inputs to axes.\\\"\\\"\\\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property def\",\n \"axes_order : tuple of int A dimension in the input data that corresponds\",\n \"\\\")>\\\" return fmt def __str__(self): if self._name is not None: return self._name else:\",\n \"\\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" self._name = val @property def naxes(self):\",\n \": astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position. unit : str\",\n \"\\\"\\\"\\\" def __init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame\",\n \"self._name = self.__class__.__name__ else: self._name = name if reference_position is not None: self._reference_position\",\n \"frame. \\\"\\\"\\\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if\",\n \"naxes) self._axes_names = axes_names if name is None: self._name = self.__class__.__name__ else: self._name\",\n \"for frame in frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit,\",\n \"axes.\\\"\\\"\\\" return self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return\",\n \"!= naxes: raise ValueError(\\\"Number of axes names does not match number of axes.\\\")\",\n \"return self._axes_names @property def axes_order(self): \\\"\\\"\\\" A tuple of indices which map inputs\",\n \"naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type =\",\n \"name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,)\",\n \"def reference_position(self): try: return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): \\\"\\\"\\\"\",\n \"str Spectral axis name. name : str Name for this frame. \\\"\\\"\\\" def\",\n \"axis name. name : str Name for this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,),\",\n \"Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D coordinate frame. Parameters ---------- axes_order : tuple of int\",\n \"name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type =\",\n \"self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError: return None def input_axes(self,\",\n \"current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline\",\n \"frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order\",\n \"does not match number of axes.\\\") else: axes_names = tuple([\\\"\\\"] * naxes) self._axes_names\",\n \"name=name) @property def frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args):\",\n \"def input_axes(self, start_frame=None): \\\"\\\"\\\" Computes which axes in `start_frame` contribute to each axis\",\n \"in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is\",\n \"u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names, unit=unit) def\",\n \"is not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if\",\n \"Base class for CoordinateFrames. Parameters ---------- naxes : int Number of axes. axes_type\",\n \"this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position.\",\n \"tuple of indices which map inputs to axes.\\\"\\\"\\\" return self._axes_order @property def reference_frame(self):\",\n \"frame (usually used with output_frame to convert to world coordinate objects). unit :\",\n \"of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\" return self._axes_type def coordinates(self, *args):\",\n \"axes_names(self): \\\"\\\"\\\" Names of axes in the frame.\\\"\\\"\\\" return self._axes_names @property def axes_order(self):\",\n \"Computes which axes in `start_frame` contribute to each axis in the current frame.\",\n \"args = [args[i] for i in self.axes_order] coo = tuple([arg * un for\",\n \"may implement this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame Representation Parameters ---------- axes_order :\",\n \"\\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\" Base class for CoordinateFrames. Parameters ---------- naxes :\",\n \"data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position\",\n \"str Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit\",\n \"reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError: return None\",\n \"name : str Name of this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0, 1), unit=(u.pix,\",\n \"of indices which map inputs to axes.\\\"\\\"\\\" return self._axes_order @property def reference_frame(self): return\",\n \"super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args =\",\n \"self.reference_position is not None: fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is not None:\",\n \"and _unit: unit = _unit if axes_order is None: axes_order = tuple(range(naxes)) if\",\n \"reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0]\",\n \"coo = [] for frame in self.frames: fargs = [args[i] for i in\",\n \"from astropy import coordinates as coord from astropy.extern import six from . import\",\n \"def coordinates(self, *args): \\\"\\\"\\\" Create a SkyCoord object. Parameters ---------- args : float\",\n \"axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type,\",\n \"to data axes. \\\"\\\"\\\" from __future__ import absolute_import, division, unicode_literals, print_function import numpy\",\n \"start_frame=None): \\\"\\\"\\\" Computes which axes in `start_frame` contribute to each axis in the\",\n \"self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one or more frames. Parameters ---------- frames :\",\n \"number of axes.\\\") else: self._unit = tuple([u.Unit(au) for au in unit]) if axes_names\",\n \"CoordinateFrames. Parameters ---------- naxes : int Number of axes. axes_type : str One\",\n \"of this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None):\",\n \"None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name,\",\n \"for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \\\"\\\"\\\"\",\n \"reference_frame if unit is not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit\",\n \"\\\"\\\"\\\"The unit of this frame.\\\"\\\"\\\" return self._unit @property def axes_names(self): \\\"\\\"\\\" Names of\",\n \"else: axes_names = tuple([\\\"\\\"] * naxes) self._axes_names = axes_names if name is None:\",\n \"axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\\\"Incorrect\",\n \"if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes\",\n \"CoordinateFrame). name : str Name for this frame. \\\"\\\"\\\" def __init__(self, frames, name=None):\",\n \"for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\",\",\n \"reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types):\",\n \"raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral Frame Parameters ---------- axes_order : tuple or\",\n \"inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \\\"\\\"\\\" Type of this frame : 'SPATIAL',\",\n \"number of axes.\\\") else: axes_names = tuple([\\\"\\\"] * naxes) self._axes_names = axes_names if\",\n \"(axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is not None:\",\n \"astropy.extern import six from . import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame',\",\n \"self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type)\",\n \"is None: axes_order = tuple(range(naxes)) if unit is None: unit = tuple([u.degree] *\",\n \"axes, \\\" \\\"axes_order should contain unique numbers, \\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type,\",\n \"unit=unit) def coordinates(self, *args): args = [args[i] for i in self.axes_order] coo =\",\n \"Reference frame (usually used with output_frame to convert to world coordinate objects). unit\",\n \"of axes.\\\") else: self._unit = tuple([u.Unit(au) for au in unit]) if axes_names is\",\n \"coordinate frames and ties them to data axes. \\\"\\\"\\\" from __future__ import absolute_import,\",\n \"tuple([u.Unit(au) for au in unit]) if axes_names is not None: if isinstance(axes_names, six.string_types):\",\n \"None: axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is\",\n \"Parameters ---------- args : float inputs to wcs.input_frame \\\"\\\"\\\" # Reorder axes if\",\n \"sep = self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs\",\n \"Frame Representation Parameters ---------- axes_order : tuple of int A dimension in the\",\n \"Units on axes. axes_names : list Names of the axes in this frame.\",\n \"return repr(self.frames) def coordinates(self, *args): coo = [] for frame in self.frames: fargs\",\n \"of units does not match number of axes.\\\") else: self._unit = tuple([u.Unit(au) for\",\n \"frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un, n\",\n \"for frame in self.frames: fargs = [args[i] for i in frame.axes_order] print(frame, fargs,\",\n \"this\\\") class CelestialFrame(CoordinateFrame): \\\"\\\"\\\" Celestial Frame Representation Parameters ---------- axes_order : tuple of\",\n \"= _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and\",\n \"of this frame. \\\"\\\"\\\" def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes =\",\n \"[] for frame in self.frames: fargs = [args[i] for i in frame.axes_order] print(frame,\",\n \"name is None: self._name = self.__class__.__name__ else: self._name = name if reference_position is\",\n \"axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is not None:\",\n \"def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo = [] for frame in\",\n \"the mapping inputs: outputs. \\\"\\\"\\\" sep = self._separable(start_frame) inputs = [] for ax\",\n \"def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names,\",\n \"len(axes_names) != naxes: raise ValueError(\\\"Number of axes names does not match number of\",\n \"of axes names does not match number of axes.\\\") else: axes_names = tuple([\\\"\\\"]\",\n \"frame.\\\"\\\"\\\" return self._name @name.setter def name(self, val): \\\"\\\"\\\" A custom name of this\",\n \"division, unicode_literals, print_function import numpy as np from astropy import units as u\",\n \"to convert to world coordinate objects). reference_position : str Reference position - one\",\n \"frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type=\\\"SPECTRAL\\\",\",\n \"'TIME'. \\\"\\\"\\\" return self._axes_type def coordinates(self, *args): \\\"\\\"\\\" Create world coordinates object\\\"\\\"\\\" raise\",\n \"str Name of this frame. \\\"\\\"\\\" def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None,\",\n \": list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name\",\n \"frame. \\\"\\\"\\\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2,\",\n \"A custom name of this frame.\\\"\\\"\\\" self._name = val @property def naxes(self): \\\"\\\"\\\"\",\n \"numpy as np from astropy import units as u from astropy import utils\",\n \"self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if\",\n \"frame.\\\"\\\"\\\" return self._naxes @property def unit(self): \\\"\\\"\\\"The unit of this frame.\\\"\\\"\\\" return self._unit\",\n \"coordinates(self, *args): args = [args[i] for i in self.axes_order] coo = tuple([arg *\",\n \"ties them to data axes. \\\"\\\"\\\" from __future__ import absolute_import, division, unicode_literals, print_function\",\n \"__init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order,\",\n \"to compute the mapping inputs: outputs. \\\"\\\"\\\" sep = self._separable(start_frame) inputs = []\",\n \"__repr__(self): return repr(self.frames) def coordinates(self, *args): coo = [] for frame in self.frames:\",\n \"to this axis. unit : list of astropy.units.Unit Unit for each axis. axes_names\",\n \"ValueError(\\\"Number of units does not match number of axes.\\\") else: self._unit = tuple([u.Unit(au)\",\n \"axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None\",\n \"if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if\",\n \"under a 3-clause BSD style license - see LICENSE.rst \\\"\\\"\\\" Defines coordinate frames\",\n \"self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is not None: if astutil.isiterable(unit):\",\n \"(axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\\\"Number of axes\",\n \"of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each axis. axes_names :\",\n \"indices which map inputs to axes.\\\"\\\"\\\" return self._axes_order @property def reference_frame(self): return self._reference_frame\",\n \"coordinates(self, *args): \\\"\\\"\\\" Create world coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may implement this\\\") class\",\n \"\\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\" Base class for CoordinateFrames. Parameters ---------- naxes\",\n \"= [\\\"GEOCENTER\\\", \\\"BARYCENTER\\\", \\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\"\",\n \"coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D coordinate frame. Parameters ---------- axes_order\",\n \"this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D,\",\n \"\\\"\\\"\\\" A 2D coordinate frame. Parameters ---------- axes_order : tuple of int A\",\n \"those Units on axes. axes_names : list Names of the axes in this\",\n \"in the frame.\\\"\\\"\\\" return self._axes_names @property def axes_order(self): \\\"\\\"\\\" A tuple of indices\",\n \"name : str Name of this frame. \\\"\\\"\\\" def __init__(self, naxes, axes_type, axes_order,\",\n \"naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit\",\n \": tuple of int A dimension in the input data that corresponds to\",\n \"raise ValueError(\\\"Number of axes names does not match number of axes.\\\") else: axes_names\",\n \"wcs.input_frame \\\"\\\"\\\" # Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except:\",\n \"Number of axes. axes_type : str One of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order :\",\n \": str Name for this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None,\",\n \"Represents Spectral Frame Parameters ---------- axes_order : tuple or int A dimension in\",\n \"inputs to wcs.input_frame \\\"\\\"\\\" # Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit,\",\n \"super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def\",\n \"self.axes_order] coo = tuple([arg * un for arg, un in zip(args, self.unit)]) return\",\n \"naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order =\",\n \"__future__ import absolute_import, division, unicode_literals, print_function import numpy as np from astropy import\",\n \"self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one or more\",\n \"or int A dimension in the input data that corresponds to this axis.\",\n \"frame.\\\"\\\"\\\" return self._axes_names @property def axes_order(self): \\\"\\\"\\\" A tuple of indices which map\",\n \"fmt def __str__(self): if self._name is not None: return self._name else: return self.__class__.__name__\",\n \"to wcs.input_frame \\\"\\\"\\\" # Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame)\",\n \"Celestial Frame Representation Parameters ---------- axes_order : tuple of int A dimension in\",\n \"= '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is\",\n \"dimension in the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames\",\n \"coordinate frame. Parameters ---------- axes_order : tuple of int A dimension in the\",\n \"Parameters ---------- axes_order : tuple or int A dimension in the input data\",\n \"absolute_import, division, unicode_literals, print_function import numpy as np from astropy import units as\",\n \"units.Unit instance or iterable of those Units on axes. axes_names : list Names\",\n \": str or units.Unit instance Spectral unit. axes_names : str Spectral axis name.\",\n \"unit = tuple(unit) else: unit = (unit,) if len(unit) != naxes: raise ValueError(\\\"Number\",\n \"used with output_frame to convert to world coordinate objects). reference_position : str Reference\",\n \"= tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order,\",\n \"__init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order\",\n \"self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else:\",\n \"fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \\\", reference_frame={0}\\\".format(self.reference_frame)\",\n \"Names of axes in the frame.\\\"\\\"\\\" return self._axes_names @property def axes_order(self): \\\"\\\"\\\" A\",\n \"n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError(\\\"Incorrect numbering of axes,\",\n \"def __init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame in\",\n \"\\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" return self._name @name.setter def name(self, val):\",\n \"name. name : str Name for this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,), reference_frame=None,\",\n \"@property def frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo\",\n \"this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\" return self._axes_type def coordinates(self, *args): \\\"\\\"\\\"\",\n \"= tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame\",\n \"= axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise\",\n \"CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one or more frames. Parameters ---------- frames : list List\",\n \"\\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\" Base class for CoordinateFrames.\",\n \"else: unit = (unit,) if len(unit) != naxes: raise ValueError(\\\"Number of units does\",\n \"of axes intheis frame.\\\"\\\"\\\" return self._naxes @property def unit(self): \\\"\\\"\\\"The unit of this\",\n \"~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform between start_frame and the\",\n \"reference frame. reference_position : str Reference position. unit : str or units.Unit instance\",\n \"else: self._name = name if reference_position is not None: self._reference_position = reference_position else:\",\n \"frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D coordinate\",\n \"the input data that corresponds to this axis. unit : list of astropy.units.Unit\",\n \"if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names\",\n \"astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if len(unit) != naxes: raise\",\n \"Spectral unit. axes_names : str Spectral axis name. name : str Name for\",\n \"def __repr__(self): fmt = '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order)\",\n \"reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to world\",\n \"frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str\",\n \"return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): \\\"\\\"\\\" Represents one or more frames. Parameters\",\n \"not match number of axes.\\\") else: self._unit = tuple([u.Unit(au) for au in unit])\",\n \"corresponds to this axis. unit : list of astropy.units.Unit Unit for each axis.\",\n \"for this frame. \\\"\\\"\\\" def __init__(self, frames, name=None): self._frames = frames[:] naxes =\",\n \"frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type\",\n \"unit of this frame.\\\"\\\"\\\" return self._unit @property def axes_names(self): \\\"\\\"\\\" Names of axes\",\n \"= naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type\",\n \"in the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A\",\n \"self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo = [] for frame\",\n \"this axis. unit : list of astropy.units.Unit Unit for each axis. axes_names :\",\n \"for this frame. \\\"\\\"\\\" def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame,\",\n \"convert to world coordinate objects). reference_position : str Reference position - one of\",\n \"position - one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each\",\n \"2D coordinate frame. Parameters ---------- axes_order : tuple of int A dimension in\",\n \"tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame,\",\n \"to convert to world coordinate objects). unit : str or units.Unit instance Spectral\",\n \"= [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class\",\n \"to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to\",\n \"match number of axes.\\\") else: self._unit = tuple([u.Unit(au) for au in unit]) if\",\n \"name if reference_position is not None: self._reference_position = reference_position else: self._reference_position = None\",\n \"of this frame.\\\"\\\"\\\" return self._name @name.setter def name(self, val): \\\"\\\"\\\" A custom name\",\n \"in frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind]\",\n \"', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is not None: fmt += \\\", reference_frame={0}\\\".format(self.reference_frame) fmt +=\",\n \"Frame Parameters ---------- axes_order : tuple or int A dimension in the input\",\n \"which axes in `start_frame` contribute to each axis in the current frame. Parameters\",\n \"len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit = _unit\",\n \"six from . import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame',\",\n \"if axes_names is None: axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values())\",\n \"if np.isscalar(args): return args * self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame):\",\n \"axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for\",\n \"!= len(axes_order): raise ValueError(\\\"Incorrect numbering of axes, \\\" \\\"axes_order should contain unique numbers,\",\n \"unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self): return repr(self.frames) def\",\n \"return None def input_axes(self, start_frame=None): \\\"\\\"\\\" Computes which axes in `start_frame` contribute to\",\n \"self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): \\\"\\\"\\\" Type of this frame :\",\n \"frame in frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names):\",\n \"list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit = _unit if axes_order is\",\n \"2 if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values())\",\n \"'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = [\\\"GEOCENTER\\\",\",\n \"fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): \\\"\\\"\\\" A 2D coordinate frame. Parameters\",\n \"CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this frame. \\\"\\\"\\\" def __init__(self,\",\n \"of this frame.\\\"\\\"\\\" return self._unit @property def axes_names(self): \\\"\\\"\\\" Names of axes in\",\n \"of [\\\"SPATIAL\\\", \\\"SPECTRAL\\\", \\\"TIME\\\"] axes_order : tuple of int A dimension in the\",\n \"['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION\",\n \"input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually\",\n \"= (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError(\\\"Number of\",\n \"coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): \\\"\\\"\\\" Represents Spectral Frame Parameters ----------\",\n \"`start_frame` contribute to each axis in the current frame. Parameters ---------- start_frame :\",\n \"@property def reference_position(self): try: return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None):\",\n \"def axes_order(self): \\\"\\\"\\\" A tuple of indices which map inputs to axes.\\\"\\\"\\\" return\",\n \"should contain unique numbers, \\\" \\\"got {}.\\\".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names,\",\n \"to world coordinate objects). reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION`\",\n \"for frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un,\",\n \"reference_position : str Reference position. unit : str or units.Unit instance or iterable\",\n \"from astropy.extern import six from . import utils as gwutils __all__ = ['Frame2D',\",\n \"def axes_names(self): \\\"\\\"\\\" Names of axes in the frame.\\\"\\\"\\\" return self._axes_names @property def\",\n \"@property def axes_type(self): \\\"\\\"\\\" Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\"\",\n \"STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None:\",\n \"[] for frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype,\",\n \"\\\"HELIOCENTER\\\", \\\"TOPOCENTER\\\", \\\"LSR\\\", \\\"LSRK\\\", \\\"LSRD\\\", \\\"GALACTIC_CENTER\\\", \\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\" Base class for\",\n \"'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes =\",\n \"each axis. axes_names : list Names of the axes in this frame. name\",\n \"None def input_axes(self, start_frame=None): \\\"\\\"\\\" Computes which axes in `start_frame` contribute to each\",\n \"astropy import units as u from astropy import utils as astutil from astropy\",\n \"frame : 'SPATIAL', 'SPECTRAL', 'TIME'. \\\"\\\"\\\" return self._axes_type def coordinates(self, *args): \\\"\\\"\\\" Create\",\n \"*args): \\\"\\\"\\\" Create a SkyCoord object. Parameters ---------- args : float inputs to\",\n \"frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un, n in zip(frame.axes_order,\",\n \"self._axes_type def coordinates(self, *args): \\\"\\\"\\\" Create world coordinates object\\\"\\\"\\\" raise NotImplementedError(\\\"Subclasses may implement\",\n \"'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION =\",\n \"reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): \\\"\\\"\\\" Create a SkyCoord object. Parameters\",\n \"@name.setter def name(self, val): \\\"\\\"\\\" A custom name of this frame.\\\"\\\"\\\" self._name =\",\n \"__repr__(self): fmt = '<{0}(name=\\\"{1}\\\", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if\",\n \"axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt\",\n \"match number of axes.\\\") else: axes_names = tuple([\\\"\\\"] * naxes) self._axes_names = axes_names\",\n \"from __future__ import absolute_import, division, unicode_literals, print_function import numpy as np from astropy\",\n \"if reference_position is not None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame,\",\n \"if self.reference_position is not None: fmt += ', reference_position=\\\"{0}\\\"'.format(self.reference_position) if self.reference_frame is not\",\n \"return fmt def __str__(self): if self._name is not None: return self._name else: return\",\n \"this frame. \\\"\\\"\\\" def __init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes\",\n \"that corresponds to this axis. unit : list of astropy.units.Unit Unit for each\",\n \"_unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit = _unit if\",\n \"frame.\\\"\\\"\\\" return self._unit @property def axes_names(self): \\\"\\\"\\\" Names of axes in the frame.\\\"\\\"\\\"\",\n \"\\\"LOCAL_GROUP_CENTER\\\"] class CoordinateFrame(object): \\\"\\\"\\\" Base class for CoordinateFrames. Parameters ---------- naxes : int\",\n \"name of this frame.\\\"\\\"\\\" return self._name @name.setter def name(self, val): \\\"\\\"\\\" A custom\",\n \"if axes_order is None: axes_order = tuple(range(naxes)) if unit is None: unit =\",\n \"axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self,\",\n \"= list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names =\",\n \"list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for\",\n \"this frame.\\\"\\\"\\\" return self._name @name.setter def name(self, val): \\\"\\\"\\\" A custom name of\",\n \"frame.\\\"\\\"\\\" self._name = val @property def naxes(self): \\\"\\\"\\\" The number of axes intheis\",\n \"axes_type=\\\"SPECTRAL\\\", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return\",\n \"import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES =\",\n \"frame. \\\"\\\"\\\" def __init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for\",\n \"= reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name=\\\"{1}\\\",\",\n \"_axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes = len(_axes_names) _unit\",\n \"each axis in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame\",\n \"import absolute_import, division, unicode_literals, print_function import numpy as np from astropy import units\",\n \"unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, [\\\"SPATIAL\\\", \\\"SPATIAL\\\"], axes_order, name=name, axes_names=axes_names, unit=unit)\"\n]","isConversational":false}}},{"rowIdx":1199,"cells":{"ngram":{"kind":"list like","value":["modifierModoItem, hostModoItem): \"\"\" Allows for attaching modifier to locator type item. Attached item","Parameters ---------- modoItem : modo.Item The constraint modo item. \"\"\" Operation = TransformConstraintOperation","self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\" Tests if this is rotation constraint. Returns -------","deleted together with locator they are attached to. Parameters ---------- modifierModoItem : modo.Item","pass an item that is not a group. This doesn't throw an error","that is not a group. This doesn't throw an error and it doesn't","def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\"","Passing this item is mandatory. However, if you don't want to add constraints","represents Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item The constraint modo","import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\" Allows for attaching","name='TransformConstraint'): \"\"\" Adds new transform constraint to the scene. Parameters ---------- assemblyItem :","list (you can unfold it with a little plus icons next to item","= 'scl' class CMTransformConstraint(object): \"\"\" This class represents Transform Constraint channel modifier. Parameters","def modoItem(self): return self._item # -------- Private methods def __init__(self, modoItem): if modoItem.type","This is assembly item to which the constraint will be added. Passing this","attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item that you want to","= 'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \"\"\" This class","it'll be under this item in item list. It'll also get deleted when","it doesn't add constraint to any groups either. hostItem : modo.Item Constraint can","bool \"\"\" return self.operation == self.Operation.ROTATION @property def offset(self): \"\"\" Gets the constraint","\"\"\" Tests if this is rotation constraint. Returns ------- bool \"\"\" return self.operation","self.operation == self.Operation.ROTATION @property def offset(self): \"\"\" Gets the constraint offset vector. Returns","add constraints to any assembly pass an item that is not a group.","Parameters ---------- modifierModoItem : modo.Item Modifier item that you want to attach. hostModoItem","constraint to any groups either. hostItem : modo.Item Constraint can be attached to","return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\" Tests if this is rotation constraint. Returns","Returns ------- bool \"\"\" return self.operation == self.Operation.ROTATION @property def offset(self): \"\"\" Gets","if this is rotation constraint. Returns ------- bool \"\"\" return self.operation == self.Operation.ROTATION","modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False,","want to add constraints to any assembly pass an item that is not","such that it'll be under this item in item list. It'll also get","they are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item that you","offsetVec : modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2],","modifier. Parameters ---------- modoItem : modo.Item The constraint modo item. \"\"\" Operation =","Returns ------- CMTransformConstraint \"\"\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false' %","to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot'","self.Operation.ROTATION @property def offset(self): \"\"\" Gets the constraint offset vector. Returns ------- modo.Vector3","run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\" Allows for","in item list (you can unfold it with a little plus icons next","class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\" Allows for attaching modifier to","class CMTransformConstraint(object): \"\"\" This class represents Transform Constraint channel modifier. Parameters ---------- modoItem","to an item such that it'll be under this item in item list.","a little plus icons next to item name in item list). Attached modifiers","constraint to the scene. Parameters ---------- assemblyItem : modo.Item This is assembly item","Sets new offset for the constraint. Parameters ---------- offsetVec : modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0],","Allows for attaching modifier to locator type item. Attached item will show up","attached to an item such that it'll be under this item in item","return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \"\"\" Sets new offset for",": modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0,","This class represents Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item The","constraint offset vector. Returns ------- modo.Vector3 \"\"\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get()","0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private methods def","\"\"\" return self.operation == self.Operation.ROTATION @property def offset(self): \"\"\" Gets the constraint offset","= TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \"\"\" Adds new transform constraint","Returns ------- str One of TransformConstraintOperation constants. \"\"\" return self._item.channel('operation').get() @property def inputChannel(self):","if you don't want to add constraints to any assembly pass an item","(you can unfold it with a little plus icons next to item name","this item in item list. It'll also get deleted when the host item","Attached item will show up under the locator item in item list (you","hostModoItem : modo.Item Locator type item you want to attach modifier to. \"\"\"","modo.Item The constraint modo item. \"\"\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem,","select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name =","host item is deleted. name : str Name for new constraint item. Returns","CMTransformConstraint \"\"\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false' % assemblyItem.id) cnsItem","methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise TypeError self._item = modoItem","str One of TransformConstraintOperation constants. \"\"\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput')","transform constraint to the scene. Parameters ---------- assemblyItem : modo.Item This is assembly","\"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE","attach modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION","you want to attach modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION","to item name in item list). Attached modifiers are getting deleted together with","The constraint modo item. \"\"\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem,","% assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property","channel modifier. Parameters ---------- modoItem : modo.Item The constraint modo item. \"\"\" Operation","item from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\"","plus icons next to item name in item list). Attached modifiers are getting","= self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter","be attached to an item such that it'll be under this item in","any groups either. hostItem : modo.Item Constraint can be attached to an item","def operation(self): \"\"\" Gets the type of the constraint. Returns ------- str One","POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \"\"\" This","locator they are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item that","action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return","item in item list (you can unfold it with a little plus icons","x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z)","unfold it with a little plus icons next to item name in item","self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item","item. Attached item will show up under the locator item in item list","be added. Passing this item is mandatory. However, if you don't want to","offsetVec): \"\"\" Sets new offset for the constraint. Parameters ---------- offsetVec : modo.Vector3","offset for the constraint. Parameters ---------- offsetVec : modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False,","def attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\" Allows for attaching modifier to locator type item.","= 'rot' SCALE = 'scl' class CMTransformConstraint(object): \"\"\" This class represents Transform Constraint","icons next to item name in item list). Attached modifiers are getting deleted","of TransformConstraintOperation constants. \"\"\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def","key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # --------","to add constraints to any assembly pass an item that is not a","type of the constraint. Returns ------- str One of TransformConstraintOperation constants. \"\"\" return","'rot' SCALE = 'scl' class CMTransformConstraint(object): \"\"\" This class represents Transform Constraint channel","Constraint channel modifier. Parameters ---------- modoItem : modo.Item The constraint modo item. \"\"\"","to which the constraint will be added. Passing this item is mandatory. However,","class represents Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item The constraint","new transform constraint to the scene. Parameters ---------- assemblyItem : modo.Item This is","Parameters ---------- assemblyItem : modo.Item This is assembly item to which the constraint","self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private methods","assemblyItem : modo.Item This is assembly item to which the constraint will be","don't want to add constraints to any assembly pass an item that is","will show up under the locator item in item list (you can unfold","item is mandatory. However, if you don't want to add constraints to any","with locator they are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item","new(cls, assemblyItem, hostItem, name='TransformConstraint'): \"\"\" Adds new transform constraint to the scene. Parameters","to any assembly pass an item that is not a group. This doesn't","a group. This doesn't throw an error and it doesn't add constraint to","Attached modifiers are getting deleted together with locator they are attached to. Parameters","modo.Item Modifier item that you want to attach. hostModoItem : modo.Item Locator type","def isRotationConstraint(self): \"\"\" Tests if this is rotation constraint. Returns ------- bool \"\"\"","offset(self, offsetVec): \"\"\" Sets new offset for the constraint. Parameters ---------- offsetVec :","------- modo.Vector3 \"\"\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return","---------- modifierModoItem : modo.Item Modifier item that you want to attach. hostModoItem :","Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \"\"\" Adds new transform",": modo.Item The constraint modo item. \"\"\" Operation = TransformConstraintOperation @classmethod def new(cls,","Modifier item that you want to attach. hostModoItem : modo.Item Locator type item","want to attach. hostModoItem : modo.Item Locator type item you want to attach","error and it doesn't add constraint to any groups either. hostItem : modo.Item","the constraint offset vector. Returns ------- modo.Vector3 \"\"\" x = self._item.channel('offset.X').get() y =","modo.Item Locator type item you want to attach modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem,","import item from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem):","TransformConstraintOperation constants. \"\"\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self):","attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\" Allows for attaching modifier to locator type item. Attached","new constraint item. Returns ------- CMTransformConstraint \"\"\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\"","new offset for the constraint. Parameters ---------- offsetVec : modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0,","itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name","item list). Attached modifiers are getting deleted together with locator they are attached","modo item. \"\"\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \"\"\"","= self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec):","---------- modoItem : modo.Item The constraint modo item. \"\"\" Operation = TransformConstraintOperation @classmethod","self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \"\"\" Sets new offset","SCALE = 'scl' class CMTransformConstraint(object): \"\"\" This class represents Transform Constraint channel modifier.","that you want to attach. hostModoItem : modo.Item Locator type item you want","want to attach modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION =","be under this item in item list. It'll also get deleted when the","type item. Attached item will show up under the locator item in item","outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\" Tests if this is rotation constraint.","return CMTransformConstraint(cnsItem) @property def operation(self): \"\"\" Gets the type of the constraint. Returns","are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item that you want","self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def",": modo.Item Modifier item that you want to attach. hostModoItem : modo.Item Locator","from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\" Allows","constraint. Returns ------- str One of TransformConstraintOperation constants. \"\"\" return self._item.channel('operation').get() @property def","run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem,","are getting deleted together with locator they are attached to. Parameters ---------- modifierModoItem","operation(self): \"\"\" Gets the type of the constraint. Returns ------- str One of","insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem)","---------- assemblyItem : modo.Item This is assembly item to which the constraint will","constraint will be added. Passing this item is mandatory. However, if you don't","item. \"\"\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \"\"\" Adds","= name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \"\"\" Gets the type","Adds new transform constraint to the scene. Parameters ---------- assemblyItem : modo.Item This","throw an error and it doesn't add constraint to any groups either. hostItem","# -------- Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise TypeError","constraint modo item. \"\"\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'):","name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \"\"\" Gets the type of","However, if you don't want to add constraints to any assembly pass an","vector. Returns ------- modo.Vector3 \"\"\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z =","in item list). Attached modifiers are getting deleted together with locator they are","inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\" Tests","Returns ------- modo.Vector3 \"\"\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get()","under the locator item in item list (you can unfold it with a","in item list. It'll also get deleted when the host item is deleted.","attaching modifier to locator type item. Attached item will show up under the","def offset(self, offsetVec): \"\"\" Sets new offset for the constraint. Parameters ---------- offsetVec","item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE =","assembly pass an item that is not a group. This doesn't throw an",": modo.Item Locator type item you want to attach modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem,","attach. hostModoItem : modo.Item Locator type item you want to attach modifier to.","= self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \"\"\" Sets new","item is deleted. name : str Name for new constraint item. Returns -------","\"\"\" Gets the constraint offset vector. Returns ------- modo.Vector3 \"\"\" x = self._item.channel('offset.X').get()","this item is mandatory. However, if you don't want to add constraints to","assemblyItem, hostItem, name='TransformConstraint'): \"\"\" Adds new transform constraint to the scene. Parameters ----------","modifier to locator type item. Attached item will show up under the locator","Parameters ---------- offsetVec : modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False,","rotation constraint. Returns ------- bool \"\"\" return self.operation == self.Operation.ROTATION @property def offset(self):","the constraint will be added. Passing this item is mandatory. However, if you","get deleted when the host item is deleted. name : str Name for","assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def","@property def modoItem(self): return self._item # -------- Private methods def __init__(self, modoItem): if","groups either. hostItem : modo.Item Constraint can be attached to an item such","cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \"\"\" Gets the","\"\"\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false' % assemblyItem.id) cnsItem =","modifiers are getting deleted together with locator they are attached to. Parameters ----------","hostItem : modo.Item Constraint can be attached to an item such that it'll","item list. It'll also get deleted when the host item is deleted. name","an error and it doesn't add constraint to any groups either. hostItem :","is not a group. This doesn't throw an error and it doesn't add","\"\"\" This class represents Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item","is mandatory. However, if you don't want to add constraints to any assembly","to any groups either. hostItem : modo.Item Constraint can be attached to an","ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \"\"\" Gets the type of the","which the constraint will be added. Passing this item is mandatory. However, if","hostItem, name='TransformConstraint'): \"\"\" Adds new transform constraint to the scene. Parameters ---------- assemblyItem","the constraint. Returns ------- str One of TransformConstraintOperation constants. \"\"\" return self._item.channel('operation').get() @property","@property def offset(self): \"\"\" Gets the constraint offset vector. Returns ------- modo.Vector3 \"\"\"","Locator type item you want to attach modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods')","It'll also get deleted when the host item is deleted. name : str","TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \"\"\" Adds new transform constraint to","the type of the constraint. Returns ------- str One of TransformConstraintOperation constants. \"\"\"","== self.Operation.ROTATION @property def offset(self): \"\"\" Gets the constraint offset vector. Returns -------","show up under the locator item in item list (you can unfold it","= itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \"\"\"","that it'll be under this item in item list. It'll also get deleted","------- str One of TransformConstraintOperation constants. \"\"\" return self._item.channel('operation').get() @property def inputChannel(self): return","group. This doesn't throw an error and it doesn't add constraint to any","@classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \"\"\" Adds new transform constraint to the","CMTransformConstraint(cnsItem) @property def operation(self): \"\"\" Gets the type of the constraint. Returns -------","will be added. Passing this item is mandatory. However, if you don't want","constraint. Parameters ---------- offsetVec : modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0,","added. Passing this item is mandatory. However, if you don't want to add","item list (you can unfold it with a little plus icons next to","list). Attached modifiers are getting deleted together with locator they are attached to.","itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0]",": modo.Item This is assembly item to which the constraint will be added.","\"\"\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y,","list. It'll also get deleted when the host item is deleted. name :","Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise TypeError self._item =","also get deleted when the host item is deleted. name : str Name","action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private","offset(self): \"\"\" Gets the constraint offset vector. Returns ------- modo.Vector3 \"\"\" x =","modifierModoItem : modo.Item Modifier item that you want to attach. hostModoItem : modo.Item","item to which the constraint will be added. Passing this item is mandatory.","\"\"\" Allows for attaching modifier to locator type item. Attached item will show","add constraint to any groups either. hostItem : modo.Item Constraint can be attached","locator type item. Attached item will show up under the locator item in","to attach modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos'","constraints to any assembly pass an item that is not a group. This","One of TransformConstraintOperation constants. \"\"\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property","modo import select import item from run import run class ChannelModifierUtils(object): @classmethod def","Gets the constraint offset vector. Returns ------- modo.Vector3 \"\"\" x = self._item.channel('offset.X').get() y","\"\"\" Sets new offset for the constraint. Parameters ---------- offsetVec : modo.Vector3 \"\"\"","Name for new constraint item. Returns ------- CMTransformConstraint \"\"\" itemSelection = select.ItemSelection() itemSelection.clear()",": str Name for new constraint item. Returns ------- CMTransformConstraint \"\"\" itemSelection =","constraint item. Returns ------- CMTransformConstraint \"\"\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s}","------- CMTransformConstraint \"\"\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false' % assemblyItem.id)","z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \"\"\" Sets","return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property","@property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self):","when the host item is deleted. name : str Name for new constraint","-------- Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise TypeError self._item","modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \"\"\" Sets new offset for the","modoItem(self): return self._item # -------- Private methods def __init__(self, modoItem): if modoItem.type !=","up under the locator item in item list (you can unfold it with","constraint. Returns ------- bool \"\"\" return self.operation == self.Operation.ROTATION @property def offset(self): \"\"\"","with a little plus icons next to item name in item list). Attached","import select import item from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls,","either. hostItem : modo.Item Constraint can be attached to an item such that","Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item The constraint modo item.","item that you want to attach. hostModoItem : modo.Item Locator type item you","0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def","Tests if this is rotation constraint. Returns ------- bool \"\"\" return self.operation ==","lx import modo import select import item from run import run class ChannelModifierUtils(object):","def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\" Tests if this is rotation","you want to attach. hostModoItem : modo.Item Locator type item you want to","hostModoItem): \"\"\" Allows for attaching modifier to locator type item. Attached item will","is assembly item to which the constraint will be added. Passing this item","\"\"\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput')","item. Returns ------- CMTransformConstraint \"\"\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false'","this is rotation constraint. Returns ------- bool \"\"\" return self.operation == self.Operation.ROTATION @property","modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION =","scene. Parameters ---------- assemblyItem : modo.Item This is assembly item to which the","def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \"\"\" Adds new transform constraint to the scene.","modoItem : modo.Item The constraint modo item. \"\"\" Operation = TransformConstraintOperation @classmethod def","modo.Item This is assembly item to which the constraint will be added. Passing","---------- offsetVec : modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP)","to the scene. Parameters ---------- assemblyItem : modo.Item This is assembly item to","class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object):","run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\" Allows for attaching modifier","to locator type item. Attached item will show up under the locator item","item name in item list). Attached modifiers are getting deleted together with locator","item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return","for the constraint. Parameters ---------- offsetVec : modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP)","item that is not a group. This doesn't throw an error and it","it with a little plus icons next to item name in item list).","\"cmTransformConstraint:rot\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem)","you don't want to add constraints to any assembly pass an item that","and it doesn't add constraint to any groups either. hostItem : modo.Item Constraint","modo.Vector3 \"\"\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x,","the scene. Parameters ---------- assemblyItem : modo.Item This is assembly item to which","not a group. This doesn't throw an error and it doesn't add constraint","name : str Name for new constraint item. Returns ------- CMTransformConstraint \"\"\" itemSelection","item will show up under the locator item in item list (you can","under this item in item list. It'll also get deleted when the host","self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def","return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\" Tests if","@property def operation(self): \"\"\" Gets the type of the constraint. Returns ------- str","action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private methods def __init__(self, modoItem):","assembly item to which the constraint will be added. Passing this item is","select import item from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem,","CMTransformConstraint(object): \"\"\" This class represents Transform Constraint channel modifier. Parameters ---------- modoItem :","next to item name in item list). Attached modifiers are getting deleted together","mandatory. However, if you don't want to add constraints to any assembly pass","itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \"\"\" Gets","'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl' class","any assembly pass an item that is not a group. This doesn't throw","self._item # -------- Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise","an item that is not a group. This doesn't throw an error and","item such that it'll be under this item in item list. It'll also","@property def isRotationConstraint(self): \"\"\" Tests if this is rotation constraint. Returns ------- bool","the constraint. Parameters ---------- offsetVec : modo.Vector3 \"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1],","is rotation constraint. Returns ------- bool \"\"\" return self.operation == self.Operation.ROTATION @property def","type item you want to attach modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class","0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item #","cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self):","key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self):","Gets the type of the constraint. Returns ------- str One of TransformConstraintOperation constants.","item you want to attach modifier to. \"\"\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object):","the locator item in item list (you can unfold it with a little","\"\"\" Adds new transform constraint to the scene. Parameters ---------- assemblyItem : modo.Item","modo.Item Constraint can be attached to an item such that it'll be under","y, z) @offset.setter def offset(self, offsetVec): \"\"\" Sets new offset for the constraint.","This doesn't throw an error and it doesn't add constraint to any groups","------- bool \"\"\" return self.operation == self.Operation.ROTATION @property def offset(self): \"\"\" Gets the","little plus icons next to item name in item list). Attached modifiers are","an item such that it'll be under this item in item list. It'll","deleted. name : str Name for new constraint item. Returns ------- CMTransformConstraint \"\"\"","y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self,","return self.operation == self.Operation.ROTATION @property def offset(self): \"\"\" Gets the constraint offset vector.","\"\"\" Gets the type of the constraint. Returns ------- str One of TransformConstraintOperation","self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \"\"\"","key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private methods def __init__(self,","str Name for new constraint item. Returns ------- CMTransformConstraint \"\"\" itemSelection = select.ItemSelection()","self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\" Tests if this","\"\"\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \"\"\" Adds new","can be attached to an item such that it'll be under this item","z) @offset.setter def offset(self, offsetVec): \"\"\" Sets new offset for the constraint. Parameters","@offset.setter def offset(self, offsetVec): \"\"\" Sets new offset for the constraint. Parameters ----------","@classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\" Allows for attaching modifier to locator type","hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl'","\"\"\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP)","doesn't throw an error and it doesn't add constraint to any groups either.","'scl' class CMTransformConstraint(object): \"\"\" This class represents Transform Constraint channel modifier. Parameters ----------","together with locator they are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier","offset vector. Returns ------- modo.Vector3 \"\"\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z","self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property","name in item list). Attached modifiers are getting deleted together with locator they","'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \"\"\" This class represents","item in item list. It'll also get deleted when the host item is","ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \"\"\" Allows for attaching modifier to locator","TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \"\"\"","for new constraint item. Returns ------- CMTransformConstraint \"\"\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create","import modo import select import item from run import run class ChannelModifierUtils(object): @classmethod","hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \"\"\" Gets the type of the constraint.","@property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\" Tests if this is","is deleted. name : str Name for new constraint item. Returns ------- CMTransformConstraint","doesn't add constraint to any groups either. hostItem : modo.Item Constraint can be","ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \"\"\" This class represents Transform","import lx import modo import select import item from run import run class","getting deleted together with locator they are attached to. Parameters ---------- modifierModoItem :","to attach. hostModoItem : modo.Item Locator type item you want to attach modifier","of the constraint. Returns ------- str One of TransformConstraintOperation constants. \"\"\" return self._item.channel('operation').get()","locator item in item list (you can unfold it with a little plus","to. Parameters ---------- modifierModoItem : modo.Item Modifier item that you want to attach.","the host item is deleted. name : str Name for new constraint item.","return self._item # -------- Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint':",": modo.Item Constraint can be attached to an item such that it'll be","can unfold it with a little plus icons next to item name in","def offset(self): \"\"\" Gets the constraint offset vector. Returns ------- modo.Vector3 \"\"\" x","constants. \"\"\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return","isRotationConstraint(self): \"\"\" Tests if this is rotation constraint. Returns ------- bool \"\"\" return","Constraint can be attached to an item such that it'll be under this","= select.ItemSelection() itemSelection.clear() run('modifier.create \"cmTransformConstraint:rot\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\"cmTransformConstraint\")[0] cnsItem.name","for attaching modifier to locator type item. Attached item will show up under","deleted when the host item is deleted. name : str Name for new"],"string":"[\n \"modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows for attaching modifier to locator type item. Attached item\",\n \"Parameters ---------- modoItem : modo.Item The constraint modo item. \\\"\\\"\\\" Operation = TransformConstraintOperation\",\n \"self._item.channel('matrixOutput') @property def isRotationConstraint(self): \\\"\\\"\\\" Tests if this is rotation constraint. Returns -------\",\n \"deleted together with locator they are attached to. Parameters ---------- modifierModoItem : modo.Item\",\n \"pass an item that is not a group. This doesn't throw an error\",\n \"that is not a group. This doesn't throw an error and it doesn't\",\n \"def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \\\"\\\"\\\"\",\n \"Passing this item is mandatory. However, if you don't want to add constraints\",\n \"represents Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item The constraint modo\",\n \"import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows for attaching\",\n \"name='TransformConstraint'): \\\"\\\"\\\" Adds new transform constraint to the scene. Parameters ---------- assemblyItem :\",\n \"list (you can unfold it with a little plus icons next to item\",\n \"= 'scl' class CMTransformConstraint(object): \\\"\\\"\\\" This class represents Transform Constraint channel modifier. Parameters\",\n \"def modoItem(self): return self._item # -------- Private methods def __init__(self, modoItem): if modoItem.type\",\n \"This is assembly item to which the constraint will be added. Passing this\",\n \"attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item that you want to\",\n \"= 'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \\\"\\\"\\\" This class\",\n \"it'll be under this item in item list. It'll also get deleted when\",\n \"it doesn't add constraint to any groups either. hostItem : modo.Item Constraint can\",\n \"bool \\\"\\\"\\\" return self.operation == self.Operation.ROTATION @property def offset(self): \\\"\\\"\\\" Gets the constraint\",\n \"\\\"\\\"\\\" Tests if this is rotation constraint. Returns ------- bool \\\"\\\"\\\" return self.operation\",\n \"self.operation == self.Operation.ROTATION @property def offset(self): \\\"\\\"\\\" Gets the constraint offset vector. Returns\",\n \"add constraints to any assembly pass an item that is not a group.\",\n \"Parameters ---------- modifierModoItem : modo.Item Modifier item that you want to attach. hostModoItem\",\n \"constraint to any groups either. hostItem : modo.Item Constraint can be attached to\",\n \"return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \\\"\\\"\\\" Tests if this is rotation constraint. Returns\",\n \"Returns ------- bool \\\"\\\"\\\" return self.operation == self.Operation.ROTATION @property def offset(self): \\\"\\\"\\\" Gets\",\n \"if this is rotation constraint. Returns ------- bool \\\"\\\"\\\" return self.operation == self.Operation.ROTATION\",\n \"modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False,\",\n \"want to add constraints to any assembly pass an item that is not\",\n \"such that it'll be under this item in item list. It'll also get\",\n \"they are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item that you\",\n \"offsetVec : modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2],\",\n \"modifier. Parameters ---------- modoItem : modo.Item The constraint modo item. \\\"\\\"\\\" Operation =\",\n \"Returns ------- CMTransformConstraint \\\"\\\"\\\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' %\",\n \"to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot'\",\n \"self.Operation.ROTATION @property def offset(self): \\\"\\\"\\\" Gets the constraint offset vector. Returns ------- modo.Vector3\",\n \"run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows for\",\n \"in item list (you can unfold it with a little plus icons next\",\n \"class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows for attaching modifier to\",\n \"class CMTransformConstraint(object): \\\"\\\"\\\" This class represents Transform Constraint channel modifier. Parameters ---------- modoItem\",\n \"to an item such that it'll be under this item in item list.\",\n \"a little plus icons next to item name in item list). Attached modifiers\",\n \"constraint to the scene. Parameters ---------- assemblyItem : modo.Item This is assembly item\",\n \"Sets new offset for the constraint. Parameters ---------- offsetVec : modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0],\",\n \"Allows for attaching modifier to locator type item. Attached item will show up\",\n \"attached to an item such that it'll be under this item in item\",\n \"return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \\\"\\\"\\\" Sets new offset for\",\n \": modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0,\",\n \"This class represents Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item The\",\n \"constraint offset vector. Returns ------- modo.Vector3 \\\"\\\"\\\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get()\",\n \"0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private methods def\",\n \"\\\"\\\"\\\" return self.operation == self.Operation.ROTATION @property def offset(self): \\\"\\\"\\\" Gets the constraint offset\",\n \"= TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds new transform constraint\",\n \"Returns ------- str One of TransformConstraintOperation constants. \\\"\\\"\\\" return self._item.channel('operation').get() @property def inputChannel(self):\",\n \"if you don't want to add constraints to any assembly pass an item\",\n \"(you can unfold it with a little plus icons next to item name\",\n \"this item in item list. It'll also get deleted when the host item\",\n \"Attached item will show up under the locator item in item list (you\",\n \"hostModoItem : modo.Item Locator type item you want to attach modifier to. \\\"\\\"\\\"\",\n \"modo.Item The constraint modo item. \\\"\\\"\\\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem,\",\n \"select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name =\",\n \"host item is deleted. name : str Name for new constraint item. Returns\",\n \"CMTransformConstraint \\\"\\\"\\\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' % assemblyItem.id) cnsItem\",\n \"methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise TypeError self._item = modoItem\",\n \"str One of TransformConstraintOperation constants. \\\"\\\"\\\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput')\",\n \"transform constraint to the scene. Parameters ---------- assemblyItem : modo.Item This is assembly\",\n \"\\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE\",\n \"attach modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION\",\n \"you want to attach modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION\",\n \"to item name in item list). Attached modifiers are getting deleted together with\",\n \"The constraint modo item. \\\"\\\"\\\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem,\",\n \"% assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property\",\n \"channel modifier. Parameters ---------- modoItem : modo.Item The constraint modo item. \\\"\\\"\\\" Operation\",\n \"item from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\"\",\n \"plus icons next to item name in item list). Attached modifiers are getting\",\n \"= self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter\",\n \"be attached to an item such that it'll be under this item in\",\n \"any groups either. hostItem : modo.Item Constraint can be attached to an item\",\n \"def operation(self): \\\"\\\"\\\" Gets the type of the constraint. Returns ------- str One\",\n \"POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \\\"\\\"\\\" This\",\n \"locator they are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item that\",\n \"action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return\",\n \"item in item list (you can unfold it with a little plus icons\",\n \"x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z)\",\n \"unfold it with a little plus icons next to item name in item\",\n \"self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item\",\n \"item. Attached item will show up under the locator item in item list\",\n \"be added. Passing this item is mandatory. However, if you don't want to\",\n \"offsetVec): \\\"\\\"\\\" Sets new offset for the constraint. Parameters ---------- offsetVec : modo.Vector3\",\n \"offset for the constraint. Parameters ---------- offsetVec : modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False,\",\n \"def attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows for attaching modifier to locator type item.\",\n \"= 'rot' SCALE = 'scl' class CMTransformConstraint(object): \\\"\\\"\\\" This class represents Transform Constraint\",\n \"icons next to item name in item list). Attached modifiers are getting deleted\",\n \"of TransformConstraintOperation constants. \\\"\\\"\\\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def\",\n \"key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # --------\",\n \"to add constraints to any assembly pass an item that is not a\",\n \"type of the constraint. Returns ------- str One of TransformConstraintOperation constants. \\\"\\\"\\\" return\",\n \"'rot' SCALE = 'scl' class CMTransformConstraint(object): \\\"\\\"\\\" This class represents Transform Constraint channel\",\n \"Constraint channel modifier. Parameters ---------- modoItem : modo.Item The constraint modo item. \\\"\\\"\\\"\",\n \"to which the constraint will be added. Passing this item is mandatory. However,\",\n \"class represents Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item The constraint\",\n \"new transform constraint to the scene. Parameters ---------- assemblyItem : modo.Item This is\",\n \"Parameters ---------- assemblyItem : modo.Item This is assembly item to which the constraint\",\n \"self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private methods\",\n \"assemblyItem : modo.Item This is assembly item to which the constraint will be\",\n \"don't want to add constraints to any assembly pass an item that is\",\n \"will show up under the locator item in item list (you can unfold\",\n \"item is mandatory. However, if you don't want to add constraints to any\",\n \"with locator they are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item\",\n \"new(cls, assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds new transform constraint to the scene. Parameters\",\n \"to any assembly pass an item that is not a group. This doesn't\",\n \"a group. This doesn't throw an error and it doesn't add constraint to\",\n \"Attached modifiers are getting deleted together with locator they are attached to. Parameters\",\n \"modo.Item Modifier item that you want to attach. hostModoItem : modo.Item Locator type\",\n \"def isRotationConstraint(self): \\\"\\\"\\\" Tests if this is rotation constraint. Returns ------- bool \\\"\\\"\\\"\",\n \"offset(self, offsetVec): \\\"\\\"\\\" Sets new offset for the constraint. Parameters ---------- offsetVec :\",\n \"------- modo.Vector3 \\\"\\\"\\\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return\",\n \"---------- modifierModoItem : modo.Item Modifier item that you want to attach. hostModoItem :\",\n \"Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds new transform\",\n \": modo.Item The constraint modo item. \\\"\\\"\\\" Operation = TransformConstraintOperation @classmethod def new(cls,\",\n \"Modifier item that you want to attach. hostModoItem : modo.Item Locator type item\",\n \"want to attach. hostModoItem : modo.Item Locator type item you want to attach\",\n \"error and it doesn't add constraint to any groups either. hostItem : modo.Item\",\n \"the constraint offset vector. Returns ------- modo.Vector3 \\\"\\\"\\\" x = self._item.channel('offset.X').get() y =\",\n \"modo.Item Locator type item you want to attach modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem,\",\n \"import item from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem):\",\n \"TransformConstraintOperation constants. \\\"\\\"\\\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self):\",\n \"attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows for attaching modifier to locator type item. Attached\",\n \"new constraint item. Returns ------- CMTransformConstraint \\\"\\\"\\\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\"\",\n \"new offset for the constraint. Parameters ---------- offsetVec : modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0,\",\n \"itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name\",\n \"item list). Attached modifiers are getting deleted together with locator they are attached\",\n \"modo item. \\\"\\\"\\\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\"\",\n \"= self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec):\",\n \"---------- modoItem : modo.Item The constraint modo item. \\\"\\\"\\\" Operation = TransformConstraintOperation @classmethod\",\n \"self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \\\"\\\"\\\" Sets new offset\",\n \"SCALE = 'scl' class CMTransformConstraint(object): \\\"\\\"\\\" This class represents Transform Constraint channel modifier.\",\n \"that you want to attach. hostModoItem : modo.Item Locator type item you want\",\n \"want to attach modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION =\",\n \"be under this item in item list. It'll also get deleted when the\",\n \"type item. Attached item will show up under the locator item in item\",\n \"outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \\\"\\\"\\\" Tests if this is rotation constraint.\",\n \"return CMTransformConstraint(cnsItem) @property def operation(self): \\\"\\\"\\\" Gets the type of the constraint. Returns\",\n \"are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier item that you want\",\n \"self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def\",\n \": modo.Item Modifier item that you want to attach. hostModoItem : modo.Item Locator\",\n \"from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows\",\n \"constraint. Returns ------- str One of TransformConstraintOperation constants. \\\"\\\"\\\" return self._item.channel('operation').get() @property def\",\n \"run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem,\",\n \"are getting deleted together with locator they are attached to. Parameters ---------- modifierModoItem\",\n \"operation(self): \\\"\\\"\\\" Gets the type of the constraint. Returns ------- str One of\",\n \"insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem)\",\n \"---------- assemblyItem : modo.Item This is assembly item to which the constraint will\",\n \"constraint will be added. Passing this item is mandatory. However, if you don't\",\n \"item. \\\"\\\"\\\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds\",\n \"= name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \\\"\\\"\\\" Gets the type\",\n \"Adds new transform constraint to the scene. Parameters ---------- assemblyItem : modo.Item This\",\n \"throw an error and it doesn't add constraint to any groups either. hostItem\",\n \"# -------- Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise TypeError\",\n \"constraint modo item. \\\"\\\"\\\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'):\",\n \"name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \\\"\\\"\\\" Gets the type of\",\n \"However, if you don't want to add constraints to any assembly pass an\",\n \"vector. Returns ------- modo.Vector3 \\\"\\\"\\\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z =\",\n \"in item list). Attached modifiers are getting deleted together with locator they are\",\n \"inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \\\"\\\"\\\" Tests\",\n \"Returns ------- modo.Vector3 \\\"\\\"\\\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get()\",\n \"under the locator item in item list (you can unfold it with a\",\n \"in item list. It'll also get deleted when the host item is deleted.\",\n \"attaching modifier to locator type item. Attached item will show up under the\",\n \"def offset(self, offsetVec): \\\"\\\"\\\" Sets new offset for the constraint. Parameters ---------- offsetVec\",\n \"item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE =\",\n \"assembly pass an item that is not a group. This doesn't throw an\",\n \": modo.Item Locator type item you want to attach modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem,\",\n \"attach. hostModoItem : modo.Item Locator type item you want to attach modifier to.\",\n \"= self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \\\"\\\"\\\" Sets new\",\n \"item is deleted. name : str Name for new constraint item. Returns -------\",\n \"\\\"\\\"\\\" Gets the constraint offset vector. Returns ------- modo.Vector3 \\\"\\\"\\\" x = self._item.channel('offset.X').get()\",\n \"this item is mandatory. However, if you don't want to add constraints to\",\n \"assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds new transform constraint to the scene. Parameters ----------\",\n \"modifier to locator type item. Attached item will show up under the locator\",\n \"Parameters ---------- offsetVec : modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False,\",\n \"rotation constraint. Returns ------- bool \\\"\\\"\\\" return self.operation == self.Operation.ROTATION @property def offset(self):\",\n \"the constraint will be added. Passing this item is mandatory. However, if you\",\n \"get deleted when the host item is deleted. name : str Name for\",\n \"assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def\",\n \"@property def modoItem(self): return self._item # -------- Private methods def __init__(self, modoItem): if\",\n \"groups either. hostItem : modo.Item Constraint can be attached to an item such\",\n \"cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \\\"\\\"\\\" Gets the\",\n \"\\\"\\\"\\\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' % assemblyItem.id) cnsItem =\",\n \"modifiers are getting deleted together with locator they are attached to. Parameters ----------\",\n \"hostItem : modo.Item Constraint can be attached to an item such that it'll\",\n \"item list. It'll also get deleted when the host item is deleted. name\",\n \"an error and it doesn't add constraint to any groups either. hostItem :\",\n \"is not a group. This doesn't throw an error and it doesn't add\",\n \"\\\"\\\"\\\" This class represents Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item\",\n \"is mandatory. However, if you don't want to add constraints to any assembly\",\n \"to any groups either. hostItem : modo.Item Constraint can be attached to an\",\n \"ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \\\"\\\"\\\" Gets the type of the\",\n \"which the constraint will be added. Passing this item is mandatory. However, if\",\n \"hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds new transform constraint to the scene. Parameters ---------- assemblyItem\",\n \"the constraint. Returns ------- str One of TransformConstraintOperation constants. \\\"\\\"\\\" return self._item.channel('operation').get() @property\",\n \"@property def offset(self): \\\"\\\"\\\" Gets the constraint offset vector. Returns ------- modo.Vector3 \\\"\\\"\\\"\",\n \"Locator type item you want to attach modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods')\",\n \"It'll also get deleted when the host item is deleted. name : str\",\n \"TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds new transform constraint to\",\n \"the type of the constraint. Returns ------- str One of TransformConstraintOperation constants. \\\"\\\"\\\"\",\n \"== self.Operation.ROTATION @property def offset(self): \\\"\\\"\\\" Gets the constraint offset vector. Returns -------\",\n \"show up under the locator item in item list (you can unfold it\",\n \"= itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \\\"\\\"\\\"\",\n \"that it'll be under this item in item list. It'll also get deleted\",\n \"------- str One of TransformConstraintOperation constants. \\\"\\\"\\\" return self._item.channel('operation').get() @property def inputChannel(self): return\",\n \"group. This doesn't throw an error and it doesn't add constraint to any\",\n \"@classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds new transform constraint to the\",\n \"CMTransformConstraint(cnsItem) @property def operation(self): \\\"\\\"\\\" Gets the type of the constraint. Returns -------\",\n \"will be added. Passing this item is mandatory. However, if you don't want\",\n \"constraint. Parameters ---------- offsetVec : modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0,\",\n \"added. Passing this item is mandatory. However, if you don't want to add\",\n \"item list (you can unfold it with a little plus icons next to\",\n \"list). Attached modifiers are getting deleted together with locator they are attached to.\",\n \"itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0]\",\n \": modo.Item This is assembly item to which the constraint will be added.\",\n \"\\\"\\\"\\\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y,\",\n \"list. It'll also get deleted when the host item is deleted. name :\",\n \"Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise TypeError self._item =\",\n \"also get deleted when the host item is deleted. name : str Name\",\n \"action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private\",\n \"offset(self): \\\"\\\"\\\" Gets the constraint offset vector. Returns ------- modo.Vector3 \\\"\\\"\\\" x =\",\n \"modifierModoItem : modo.Item Modifier item that you want to attach. hostModoItem : modo.Item\",\n \"item to which the constraint will be added. Passing this item is mandatory.\",\n \"\\\"\\\"\\\" Allows for attaching modifier to locator type item. Attached item will show\",\n \"add constraint to any groups either. hostItem : modo.Item Constraint can be attached\",\n \"locator type item. Attached item will show up under the locator item in\",\n \"to attach modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos'\",\n \"constraints to any assembly pass an item that is not a group. This\",\n \"One of TransformConstraintOperation constants. \\\"\\\"\\\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property\",\n \"modo import select import item from run import run class ChannelModifierUtils(object): @classmethod def\",\n \"Gets the constraint offset vector. Returns ------- modo.Vector3 \\\"\\\"\\\" x = self._item.channel('offset.X').get() y\",\n \"\\\"\\\"\\\" Sets new offset for the constraint. Parameters ---------- offsetVec : modo.Vector3 \\\"\\\"\\\"\",\n \"Name for new constraint item. Returns ------- CMTransformConstraint \\\"\\\"\\\" itemSelection = select.ItemSelection() itemSelection.clear()\",\n \": str Name for new constraint item. Returns ------- CMTransformConstraint \\\"\\\"\\\" itemSelection =\",\n \"constraint item. Returns ------- CMTransformConstraint \\\"\\\"\\\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s}\",\n \"------- CMTransformConstraint \\\"\\\"\\\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' % assemblyItem.id)\",\n \"z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \\\"\\\"\\\" Sets\",\n \"return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property\",\n \"@property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self):\",\n \"when the host item is deleted. name : str Name for new constraint\",\n \"-------- Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise TypeError self._item\",\n \"modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \\\"\\\"\\\" Sets new offset for the\",\n \"modoItem(self): return self._item # -------- Private methods def __init__(self, modoItem): if modoItem.type !=\",\n \"up under the locator item in item list (you can unfold it with\",\n \"constraint. Returns ------- bool \\\"\\\"\\\" return self.operation == self.Operation.ROTATION @property def offset(self): \\\"\\\"\\\"\",\n \"with a little plus icons next to item name in item list). Attached\",\n \"import select import item from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls,\",\n \"either. hostItem : modo.Item Constraint can be attached to an item such that\",\n \"Transform Constraint channel modifier. Parameters ---------- modoItem : modo.Item The constraint modo item.\",\n \"item that you want to attach. hostModoItem : modo.Item Locator type item you\",\n \"0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def\",\n \"Tests if this is rotation constraint. Returns ------- bool \\\"\\\"\\\" return self.operation ==\",\n \"lx import modo import select import item from run import run class ChannelModifierUtils(object):\",\n \"def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \\\"\\\"\\\" Tests if this is rotation\",\n \"you want to attach. hostModoItem : modo.Item Locator type item you want to\",\n \"hostModoItem): \\\"\\\"\\\" Allows for attaching modifier to locator type item. Attached item will\",\n \"is assembly item to which the constraint will be added. Passing this item\",\n \"\\\"\\\"\\\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput')\",\n \"item. Returns ------- CMTransformConstraint \\\"\\\"\\\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false'\",\n \"this is rotation constraint. Returns ------- bool \\\"\\\"\\\" return self.operation == self.Operation.ROTATION @property\",\n \"modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION =\",\n \"scene. Parameters ---------- assemblyItem : modo.Item This is assembly item to which the\",\n \"def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds new transform constraint to the scene.\",\n \"modoItem : modo.Item The constraint modo item. \\\"\\\"\\\" Operation = TransformConstraintOperation @classmethod def\",\n \"modo.Item This is assembly item to which the constraint will be added. Passing\",\n \"---------- offsetVec : modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP)\",\n \"to the scene. Parameters ---------- assemblyItem : modo.Item This is assembly item to\",\n \"class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object):\",\n \"run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows for attaching modifier\",\n \"to locator type item. Attached item will show up under the locator item\",\n \"item name in item list). Attached modifiers are getting deleted together with locator\",\n \"item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return\",\n \"for the constraint. Parameters ---------- offsetVec : modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP)\",\n \"item that is not a group. This doesn't throw an error and it\",\n \"it with a little plus icons next to item name in item list).\",\n \"\\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem)\",\n \"you don't want to add constraints to any assembly pass an item that\",\n \"and it doesn't add constraint to any groups either. hostItem : modo.Item Constraint\",\n \"modo.Vector3 \\\"\\\"\\\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x,\",\n \"the scene. Parameters ---------- assemblyItem : modo.Item This is assembly item to which\",\n \"not a group. This doesn't throw an error and it doesn't add constraint\",\n \"name : str Name for new constraint item. Returns ------- CMTransformConstraint \\\"\\\"\\\" itemSelection\",\n \"item will show up under the locator item in item list (you can\",\n \"under this item in item list. It'll also get deleted when the host\",\n \"self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def\",\n \"return self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \\\"\\\"\\\" Tests if\",\n \"@property def operation(self): \\\"\\\"\\\" Gets the type of the constraint. Returns ------- str\",\n \"action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private methods def __init__(self, modoItem):\",\n \"assembly item to which the constraint will be added. Passing this item is\",\n \"select import item from run import run class ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem,\",\n \"CMTransformConstraint(object): \\\"\\\"\\\" This class represents Transform Constraint channel modifier. Parameters ---------- modoItem :\",\n \"next to item name in item list). Attached modifiers are getting deleted together\",\n \"mandatory. However, if you don't want to add constraints to any assembly pass\",\n \"itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \\\"\\\"\\\" Gets\",\n \"'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl' class\",\n \"any assembly pass an item that is not a group. This doesn't throw\",\n \"self._item # -------- Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint': raise\",\n \"an item that is not a group. This doesn't throw an error and\",\n \"item such that it'll be under this item in item list. It'll also\",\n \"@property def isRotationConstraint(self): \\\"\\\"\\\" Tests if this is rotation constraint. Returns ------- bool\",\n \"the constraint. Parameters ---------- offsetVec : modo.Vector3 \\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1],\",\n \"is rotation constraint. Returns ------- bool \\\"\\\"\\\" return self.operation == self.Operation.ROTATION @property def\",\n \"type item you want to attach modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class\",\n \"0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item #\",\n \"cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name = name ChannelModifierUtils.attachModifierToItem(cnsItem, hostItem) return CMTransformConstraint(cnsItem) @property def operation(self):\",\n \"key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self):\",\n \"Gets the type of the constraint. Returns ------- str One of TransformConstraintOperation constants.\",\n \"item you want to attach modifier to. \\\"\\\"\\\" item.ItemUtils.addForwardGraphConnections(modifierModoItem, hostModoItem, 'chanMods') class TransformConstraintOperation(object):\",\n \"the locator item in item list (you can unfold it with a little\",\n \"\\\"\\\"\\\" Adds new transform constraint to the scene. Parameters ---------- assemblyItem : modo.Item\",\n \"modo.Item Constraint can be attached to an item such that it'll be under\",\n \"y, z) @offset.setter def offset(self, offsetVec): \\\"\\\"\\\" Sets new offset for the constraint.\",\n \"This doesn't throw an error and it doesn't add constraint to any groups\",\n \"------- bool \\\"\\\"\\\" return self.operation == self.Operation.ROTATION @property def offset(self): \\\"\\\"\\\" Gets the\",\n \"little plus icons next to item name in item list). Attached modifiers are\",\n \"an item such that it'll be under this item in item list. It'll\",\n \"deleted. name : str Name for new constraint item. Returns ------- CMTransformConstraint \\\"\\\"\\\"\",\n \"y = self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self,\",\n \"return self.operation == self.Operation.ROTATION @property def offset(self): \\\"\\\"\\\" Gets the constraint offset vector.\",\n \"\\\"\\\"\\\" Gets the type of the constraint. Returns ------- str One of TransformConstraintOperation\",\n \"self._item.channel('offset.Y').get() z = self._item.channel('offset.Z').get() return modo.Vector3(x, y, z) @offset.setter def offset(self, offsetVec): \\\"\\\"\\\"\",\n \"key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property def modoItem(self): return self._item # -------- Private methods def __init__(self,\",\n \"str Name for new constraint item. Returns ------- CMTransformConstraint \\\"\\\"\\\" itemSelection = select.ItemSelection()\",\n \"self._item.channel('matrixInput') @property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \\\"\\\"\\\" Tests if this\",\n \"\\\"\\\"\\\" Operation = TransformConstraintOperation @classmethod def new(cls, assemblyItem, hostItem, name='TransformConstraint'): \\\"\\\"\\\" Adds new\",\n \"can be attached to an item such that it'll be under this item\",\n \"z) @offset.setter def offset(self, offsetVec): \\\"\\\"\\\" Sets new offset for the constraint. Parameters\",\n \"@offset.setter def offset(self, offsetVec): \\\"\\\"\\\" Sets new offset for the constraint. Parameters ----------\",\n \"@classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows for attaching modifier to locator type\",\n \"hostModoItem, 'chanMods') class TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl'\",\n \"\\\"\\\"\\\" self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP)\",\n \"doesn't throw an error and it doesn't add constraint to any groups either.\",\n \"'scl' class CMTransformConstraint(object): \\\"\\\"\\\" This class represents Transform Constraint channel modifier. Parameters ----------\",\n \"together with locator they are attached to. Parameters ---------- modifierModoItem : modo.Item Modifier\",\n \"offset vector. Returns ------- modo.Vector3 \\\"\\\"\\\" x = self._item.channel('offset.X').get() y = self._item.channel('offset.Y').get() z\",\n \"self._item.channel('offset.X').set(offsetVec[0], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Y').set(offsetVec[1], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) self._item.channel('offset.Z').set(offsetVec[2], 0.0, key=False, action=lx.symbol.s_ACTIONLAYER_SETUP) @property\",\n \"name in item list). Attached modifiers are getting deleted together with locator they\",\n \"'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \\\"\\\"\\\" This class represents\",\n \"item in item list. It'll also get deleted when the host item is\",\n \"ChannelModifierUtils(object): @classmethod def attachModifierToItem(cls, modifierModoItem, hostModoItem): \\\"\\\"\\\" Allows for attaching modifier to locator\",\n \"TransformConstraintOperation(object): POSITION = 'pos' ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \\\"\\\"\\\"\",\n \"for new constraint item. Returns ------- CMTransformConstraint \\\"\\\"\\\" itemSelection = select.ItemSelection() itemSelection.clear() run('modifier.create\",\n \"import modo import select import item from run import run class ChannelModifierUtils(object): @classmethod\",\n \"hostItem) return CMTransformConstraint(cnsItem) @property def operation(self): \\\"\\\"\\\" Gets the type of the constraint.\",\n \"@property def outputChannel(self): return self._item.channel('matrixOutput') @property def isRotationConstraint(self): \\\"\\\"\\\" Tests if this is\",\n \"is deleted. name : str Name for new constraint item. Returns ------- CMTransformConstraint\",\n \"doesn't add constraint to any groups either. hostItem : modo.Item Constraint can be\",\n \"ROTATION = 'rot' SCALE = 'scl' class CMTransformConstraint(object): \\\"\\\"\\\" This class represents Transform\",\n \"import lx import modo import select import item from run import run class\",\n \"getting deleted together with locator they are attached to. Parameters ---------- modifierModoItem :\",\n \"to attach. hostModoItem : modo.Item Locator type item you want to attach modifier\",\n \"of the constraint. Returns ------- str One of TransformConstraintOperation constants. \\\"\\\"\\\" return self._item.channel('operation').get()\",\n \"locator item in item list (you can unfold it with a little plus\",\n \"to. Parameters ---------- modifierModoItem : modo.Item Modifier item that you want to attach.\",\n \"the host item is deleted. name : str Name for new constraint item.\",\n \"return self._item # -------- Private methods def __init__(self, modoItem): if modoItem.type != 'cmTransformConstraint':\",\n \": modo.Item Constraint can be attached to an item such that it'll be\",\n \"can unfold it with a little plus icons next to item name in\",\n \"def offset(self): \\\"\\\"\\\" Gets the constraint offset vector. Returns ------- modo.Vector3 \\\"\\\"\\\" x\",\n \"constants. \\\"\\\"\\\" return self._item.channel('operation').get() @property def inputChannel(self): return self._item.channel('matrixInput') @property def outputChannel(self): return\",\n \"isRotationConstraint(self): \\\"\\\"\\\" Tests if this is rotation constraint. Returns ------- bool \\\"\\\"\\\" return\",\n \"Constraint can be attached to an item such that it'll be under this\",\n \"= select.ItemSelection() itemSelection.clear() run('modifier.create \\\"cmTransformConstraint:rot\\\" item:{%s} insert:false' % assemblyItem.id) cnsItem = itemSelection.getOfTypeModo(\\\"cmTransformConstraint\\\")[0] cnsItem.name\",\n \"for attaching modifier to locator type item. Attached item will show up under\",\n \"deleted when the host item is deleted. name : str Name for new\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":11,"numItemsPerPage":100,"numTotalItems":12866649,"offset":1100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDQ4MDc2Mywic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMyIsImV4cCI6MTc3NDQ4NDM2MywiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.UEKV2MNd_1k9kSx29U-QQH9xqXskagx4DFfsq3W_pv7zY58iPY7uwYnKueapyw8E9e7tlU7jrmTZFw-1PwhqAA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":149232926747}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 67.8k |
|---|
[
"my_32bit_int - my_64bit_int - my_double - my_string - my_binary - my_string_string_map - my_string_enum_map",
"not None: oprot.writeFieldBegin('my_byte', TType.BYTE, 2) oprot.writeByte(self.my_byte) oprot.writeFieldEnd() if self.my_16bit_int is not None: oprot.writeFieldBegin('my_16bit_int',",
"9: if ftype == ... |
[
"copy_data('external_file_08.txt','data_file.txt') write_config_file(mode='update',reuse_flag='false',fast_match='true',file='data_file.txt') self.doTest(32) def test_33_gpload_reuse_table_merge_mode_with_fast_match_and_external_schema(self): \"33 gpload update mode with fast match and",
"psql -d reuse_gptest -c ... |
[
"'dot' { 'd': 3 }, # 4. 'digit' after 'dot' (‘blank’ before 'dot')",
"'d': 7, ' ': 8 }, # 7. 'digit' after 'e' { '",
"3. 'digit' after 'dot' { 'd': 3 }, # 4. 'digit' after 'dot'",
"{ 'd': 7 }, # 6. 'sign' after 'e' { 'd': 7, '",
"'e': 5, ' ': 8 }, # 3. 'digit' after 'dot' { 'd':",
"8. end with 'blank' ] p... |
[
"u'start': u'192.168.27.3', u'end': u'192.168.27.254' } ], u'host_routes': [], u'ip_version': 4, u'gateway_ip': u'192.168.27.2', u'cidr':",
"= neutron_exceptions.NeutronException() self.neutron_client_mock.side_effect = exc self.assertRaises(self.provider.NetworkProviderException, self.provider._get_neutron_clien... |
[] |
[
"tag, 'post_list': post_list, 'sidebars': SideBar.get_all(), } context.update(Category.get_navs()) return render(request, 'blog/list.html', context=context) def post_detail(request,",
"post_list, category=Post.get_by_category(category_id) else: post_list = Post.latest_posts() context = { 'category': category, 'ta... |
[
"May 7 11:38:18 2016 @author: thomasbarillot VMI control \"\"\" from ctypes import cdll",
"#%% print test.GetFilename() #%% test.setFilename('20161115_1841.dat') print test.GetFilename() #%% test.StartAcquisitionPrev() #%% test.StopAcquisition() #%% img=test.RecallImagePrev()",
"\"\"\" from ctypes import cdll #... |
[
"version('1.0.1h', sha256='9d1c8a9836aa63e2c6adb684186cbd4371c9e9dcc01d6e3bb447abf2d4d3d093') version('1.0.1e', sha256='f74f15e8c8ff11aa3d5bb5f276d202ec18d7246e95f961db76054199c69c1ae3') variant('systemcerts', default=True, description='Use system certificates') depends_on('zlib') depends_on('perl@5.14.0:', type=('... |
[
"message list if no parent profiler. \"\"\" if self._finished or self.disable: return self._finished",
"%0.4f ms\", self._name, (ptime.time() - self._firstTime) * 1000) type(self)._depth -= 1 if self._depth",
"method qualifier = caller_object_type.__name__ func_qualname = qualifier + \".\" + caller_frame.f_code... |
[
"user.USERNAME_FIELD = 'email' user.email = 'test_email' assert BaseProcessor('entity-id').get_user_id(user) == 'test_email' def test_extract_user_id_configure_by_settings(self, settings):",
"settings.SAML_IDP_DJANGO_USERNAME_FIELD = 'first_name' user = User() user.first_name = 'test_first_name' assert BaseProces... |
[
"== ele: is_node_deleted = True prev_node.next = temp_head.next break prev_node = temp_head temp_head",
"None: if temp_head.data == ele: is_node_deleted = True prev_node.next = temp_head.next break prev_node",
"= self.head while temp_head.next is not None: temp_head = temp_head.next; temp_head.next = new_node;"... |
[] |
[
"response_header: Optional[str] class Result(BaseModel): url_key: str = Field(alias=\"urlkey\") timestamp: datetime url: str mime:",
"Field(alias=\"urlkey\") timestamp: datetime url: str mime: str mime_detected: str = Field(alias=\"mime-detected\") status: int",
"pages in this index for a given url.\"\"\" index... |
[
"errors.OperationFailed) if _errno == errno.EACCES and sys.platform == \"win32\": if getattr(exc_value, 'args', None)",
"errors.ResourceNotFound, errno.ESRCH: errors.ResourceNotFound, errno.ENOTEMPTY: errors.DirectoryNotEmpty, errno.EEXIST: errors.FileExists, 183: errors.DirectoryExists, #errno.ENOTDIR: errors.Di... |
[
"and the following disclaimer in the documentation # and/or other materials provided with",
"%s\" % status.brief()) exit(0) print (\"\\n\") print (\">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\") print (\">>> Demo End\") print",
"EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #... |
[
"=parsed_json['image2'] _read=parsed_json['read'] if _read: if _image1 is not None and _image2 is not",
"on_message(client, userdata, msg): try: parsed_json=json.loads(convertJson(msg.payload)) if msg.topic==\"detection_find\": print(parsed_json) _image1 =parsed_json['image1'] _image2 =parsed_json['image2'] _read... |
[
"left_sum = 0 for n in A[:-1]: left_sum += n v = abs(total",
"v = abs(total - 2*left_sum) if v < m: m = v return",
"= float('inf') left_sum = 0 for n in A[:-1]: left_sum += n v",
"A[:-1]: left_sum += n v = abs(total - 2*left_sum) if v < m:",
"m = float('inf') left_sum = 0 for n in A[:-1]: left_sum += n",
... |
[
"page accordingly. soup = BeautifulSoup(page.content.raw) for img_tag in soup.findAll('img'): if img_tag['src'] == path:",
"different formats that pep2html.py outputs \"\"\" check_paths() data = { 'title': None, }",
"actual pep, so we return both Page objects. \"\"\" pep0_content = convert_pep0() pep0_page,",
... |
[] |
[] |
[
"0x100: hexstr += \"00\" else: hexstr += \"%02X\" % LE expected = hexstr",
"+= pysatl.Utils.hexstr(data, separator=\"\") if LE>0: if case4e: if LE == 0x10000: hexstr +=",
"P1=0x22, P2=0x33)) check(\"00 11 22 33\", CAPDU(CLA=0x00, INS=0x11, P1=0x22, P2=0x33)) check(\"0x00,0x11,0x22,0x33\", CAPDU(CLA=0x00, INS=0x... |
[
"tan, atan, pi, acos, sqrt, exp, log10 import sys, os import copy import",
"bootstrapping_stats = parallel_Mdim_bootstrapping(Globals.n_bootstrapping) print \"\\n//BOOTSTRAPPING:// {1.0, 0.1, 0.01, 0.001}%\" print \"FAP Levels:\", fap_levels(bootstrapping_stats)",
"zip(*comb_rv_err) \"\"\" #allowing repetition ... |
[
"of Index Servers number_of_links = 5 # number of links to be sent",
"total number rows of Index Servers number_of_links = 5 # number of links",
"number of links to be sent to Crawler number_of_chunks = 5 # number",
"all constants used by the device manager Author: - <NAME> (<EMAIL> at <EMAIL>",
"= 3 # tota... |
[
"dict( size=0, sigma=0.6, eps=-15, phi=10e8, k=2.5, gamma=0.97 ) def gen_xdog_image(src, dst): gray =",
"g2 = cv2.GaussianBlur(image, (size, size), sigma*k) return g1 - gamma * g2 def",
"255 # This config is found by the author # modify if not",
"eps /= 255 d = DoG(image, size, sigma, k, gamma) d /= d.max()",... |
[
"comma separated).\") flags.DEFINE_string( \"output_dir\", None, \"The output directory where the model checkpoints will",
"None, \"The Cloud TPU to use for training. This should be either the",
"FLAGS = flags.FLAGS ## Required parameters flags.DEFINE_string( \"config_file\", 'configs/base.json', \"The config j... |
[
"print(\"ERROR: %s is not a valid model!\" % model) return a = InformationGain(gp,",
"rng=rng, normalize_output=False, normalize_input=True, lower=lower, upper=upper) elif model == \"gp_mcmc\": gp = GaussianProcessMCMC(kernel, prior=prior,",
"= [inc for inc in bo.incumbents] results[\"incumbent_values\"] = [val... |
[
"migrations, models class Migration(migrations.Migration): dependencies = [ ('app', '0095_bisericapage_utitle'), ] operations = [",
"class Migration(migrations.Migration): dependencies = [ ('app', '0095_bisericapage_utitle'), ] operations = [ migrations.AddField( model_name='bisericapage',",
"dependencies = [ (... |
[
"<reponame>bcgov/mds from app.api.utils.models_mixins import Base from app.extensions import db class MMSSurfaceBulkSampleActivity(Base): __tablename__ =",
"app.api.utils.models_mixins import Base from app.extensions import db class MMSSurfaceBulkSampleActivity(Base): __tablename__ = \"surface_bulk_sample_activit... |
[
"Enum class RemoteControlLock(Enum): OFF = 0 ON = 1 def map_to_state(data: int): return",
"self._send_command(self._command, 255)) async def set_state(self, state: RemoteControlLock): return map_to_state(await self._send_command(self._command, state.value)) def on(self):",
"RemoteControlLock(Enum): OFF = 0 ON =... |
[
"class PowerOf2: def start(self): number=int(sys.argv[1]) print(number) for i in Utility().powerof2(number): print(i) return PowerOf2().start()",
"com.bridgelabz.utility.Utility import Utility class PowerOf2: def start(self): number=int(sys.argv[1]) print(number) for i in Utility().powerof2(number):",
"import U... |
[
"+ \"//Job_Openings.xlsx\" print(\"-\" * 75, \"-\" * 75, \"\\n\\t\\t\\t\\t\\t\\t\\t JOB WEB SCRAPER\", \"-\"",
"\"-\" * 75, sep=\"\\n\") workbook, worksheet = excel.load_xlsx(file_path) last_scrape_date = excel.get_first_job_date(worksheet) last_scrape_date =",
"already exists then append the jobs not already i... |
[
"(sorted) list of decoders to include in block n, assuming decoders are split",
"to use for the given filename\"\"\" return filename.replace(\"/\", \"_\").replace(\".\", \"_\").upper() + \"_\" def",
"number of code blocks to break classes into.\"\"\" num_blocks = len(separators) + 1",
"\"\"\" HEADER_BOILERPLA... |
[
"\"\"\" num_images = 100 images_array = np.zeros((num_images, np.prod(image_shape)), dtype=np.float32) targets_array = np.zeros(num_images, dtype=int)",
"as a Mx3x32x32 numpy array. \"\"\" return load_img(path, img_nums, (3, 32, 32)) def",
"\"\"\" Loads a image in the human-readable format. Args: path: The path... |
[
"self.frame.setFrameShape(QFrame.StyledPanel) self.frame.setFrameShadow(QFrame.Raised) self.frame.setLineWidth(0) self.label = QLabel(self.frame) self.label.setObjectName(u\"label\") self.label.setGeometry(QRect(0, 0, 720, 425)) self.label.setLineWidth(0) self.label.setPixmap(QPixmap(u\"img/SS_logo.jpg\"))",
"330... |
[
"keys = (keys,) keystr = ', PRIMARY KEY (%s)' % ','.join(keys) template =",
"to DBAPI2.0 compliant format\"\"\" args = [sql] if params is not None: if",
"\"\"\" Make a data_frame's column type align with an sql_table column types Need",
"copy=False) # Handle date parsing if col_name in parse_dates: try: fmt =... |
[
"str :rtype: int \"\"\" cnt, tail = 0, len(s) - 1 while tail",
"def XXX(self, s): \"\"\" :type s: str :rtype: int \"\"\" cnt, tail =",
"len(s) - 1 while tail >= 0 and s[tail] == ' ': tail",
"while tail >= 0 and s[tail] != ' ': cnt += 1 tail",
"tail >= 0 and s[tail] == ' ': tail -= 1 while tail",
"\"\"\" c... |
[
"def get_magics(object: Object) -> Dictionary: \"\"\"Gets the magic methods of an object.\"\"\" return",
"metaclass to customize the behavior of all classes.\"\"\" def __new__(self, name: str, bases:",
"**keys - Any Keyword arguments to pass in \"\"\" # Creating a new",
"= value # Adds attributes to __protect... |
[
"for bank in banks: check( c.rpc( \"BK_register\", {\"bank_id\": bank.ccf_id, \"country\": bank.country}, ), result=bank.ccf_id,",
"primary.node_client() as mc: check_commit = infra.checker.Checker(mc) with primary.user_client(format=\"msgpack\", user_id=manager.name) as c: check( c.rpc(",
"add(parser): parser.... |
[
"H_dir for h in H_norms] m_avg = hysteresis(sim, H_vals, fun=lambda sim: sim.m_average, **kwargs)",
"its docstring for more details). fun: callable The user can pass a function",
"all values in H_ext_list are exhausted. Note: The fields in H_ext_list are applied",
"return values of `fun` after each stage. Oth... |
[
"brush = QtGui.QBrush(QtGui.QColor(51, 51, 51)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush) gradient = QtGui.QRadialGradient(0.3, -0.4,",
"brush = QtGui.QBrush(QtGui.QColor(84, 84, 84)) brush.setStyle(QtCore.Qt.SolidPattern) palette.setBrush(QtG... |
[
"from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('myApp', '0016_usergroup_buyer'), ]",
"Migration(migrations.Migration): dependencies = [ ('myApp', '0016_usergroup_buyer'), ] operations = [ migrations.CreateModel( name='Chat', fields=[",
"models class Migration(... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"KIND, either express or implied. # See the License for the specific language",
"Unless required by applicable law or agreed to in writing, software # distributed",
"You may obtain a copy of the License at # # http://www.apache.org... |
[
"config: configuration settings :rtype: tuple :return: pfxfile, passphrase, sha1 tp \"\"\" pfxfile =",
"settings.batch_shipyard_encryption_pfx_sha1_thumbprint( config) # manually get thumbprint of pfx if not exists in config",
"bool shell: execute with shell :param bool tty: allocate pseudo-tty :param tuple ssh... |
[
"2 USE_DROPOUT = False KEEP_PROB = 1.0 SHOW_SCORE_THRSHOLD = 0.6 # only show",
"channel is BGR IMG_SHORT_SIDE_LEN = 800 IMG_MAX_LENGTH = 1333 CLASS_NUM = 80 #",
"= 20 SMRY_ITER = 200 SAVE_WEIGHTS_INTE = 80000 SUMMARY_PATH = ROOT_PATH + '/output/summary'",
"SHOW_SCORE_THRSHOLD = 0.6 # only show in tensorboard ... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"Project Authors. # # Licensed under the Apache License, Version 2.0 (the \"License\");",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"delta.exceptions as exceptions from delta.testing.utils ... |
[
"= int(input(\"Enter the no of rows\")) c = int(input(\"Enter the no of columns\"))",
"check_matrix(first_matrix,sec_matrix): if(first_matrix==sec_matrix): print(\"same\") else: print(\"not same\") print(\"Enter the 1st matrix\") first_matrix = matrix_form()",
"else: print(\"not same\") print(\"Enter the 1st ma... |
[
"import os.path import csv import numpy as np from time import time from",
"hf = h5py.File(subject+'_ef.h5', 'w') hf.create_dataset('Y', data=Y) hf.create_dataset('Z', data=Z) hf.close() session = Session(aws_access_key_id=config[0],aws_secret_access_key=config[1]) s3",
"as K from keras.utils import multi_gpu_m... |
[
"= alpha*chi**2 return (r0*vr0/np.sqrt(mu))*chi*(1 - alpha*chi**2*S(z)) + \\ (1 - alpha*r0)*chi**2*C(z) + r0",
"''' Solve Kepler's Equation in the form containing Eccentric Anomaly (E), eccentricity (e),",
"alpha) r_1 = f*r_0 + g*v_0 r1 = np.linalg.norm(r_1) fd = calc_fd(mu, r1,",
"= np.linalg.norm(v_0) # (km... |
[
"'pressure', 'sampletable', 'pilz', 'detector', 'collimation', ] devices = dict( mcasink = device('nicos_mlz.pgaa.devices.MCASink', settypes",
"settypes = {'point'}, detectors = ['_60p', 'LEGe'], ), chnsink = device('nicos_mlz.pgaa.devices.CHNSink', settypes =",
"= \"\"\" SetDetectors('_60p', 'LEGe') SetEnviron... |
[
"w = tvm.size_var(\"n\"), 10, 224, 224 x = relay.var(\"x\", relay.ty.TensorType((n, c, h, w),",
"kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype) ref_res = topi.testing.conv2d_nchw_python( data.astype(out_dtype), kernel.astype(out_dtype), 1, padding, groups=groups)",
"run_infer_type(y) asser... |
[
"return tokenizer.Subtokenizer(temp_file.name, reserved_tokens=[]) def test_encode(self): vocab_list = [\"123_\", \"test\", \"ing_\"] subtokenizer = self._init_subtokenizer(vocab_list)",
"reserved_tokens = [\"reserved\", \"tokens\"] vocab_list = tokenizer._generate_subtokens(token_counts, alphabet, min_count, num... |
[] |
[
"2020 Soil, Inc. from soil.openstack.base import DataBase from soil.openstack.base import SourceBase class SnapshotData(DataBase):",
"data\"\"\" def __init__(self, data): self.data = data['snapshot'] class Snapshot(SourceBase): \"\"\"A class for openstack",
"status = snapshot_info['snapshot']['status'] if statu... |
[
"all((data['username'], data['password'])): login = data['username'] if login not in results: results[login] = {}",
"Pypykatz(ModuleInfo): \"\"\" Pypykatz dumps all secrets from the lsass.exe memory It does not",
"password for i in ['credman_creds', 'ssp_creds', 'livessp_creds', 'tspkg_creds', 'wdigest_creds']:... |
[
"return self._make_release(tracks) def test_parse_media_for_tracks(self): tracks = [self._make_track('TITLE ONE', '1', '01:01'), self._make_track('TITLE TWO', '2',",
"3) def test_parse_minimal_release(self): \"\"\"Test parsing of a release with the minimal amount of",
"1) self.assertEqual(t[2].medium_index, 1) ... |
[
"= self.length - 1 \"\"\"Reports item at the front of self @return item",
"rotate queue\"\"\" def rotate(self, rotation): for i in range(rotation): temp = self.stack[0] self.stack",
"(represented by a list with pop and append)\"\"\" class Queue: def __init__(self): self.stack",
"= self.stack[0] self.stack = s... |
[
"io.StringIO() with open(config_file) as fin: for line in fin: if line.startswith('['): section =",
"'darknet' return mygraph if __name__ == '__main__': config_path = sys.argv[1] weights_path = sys.argv[2]",
"'BiasAdd' node.input = [prev_output] node.input_norm = node.input #node.attr = [] node.bias = conv_bias... |
[
"class Song(models.Model): user = models.ForeignKey(User, default=1,on_delete=models.CASCADE) album = models.ForeignKey(Album, on_delete=models.CASCADE, null=True) song_title =",
"models.ForeignKey(User, default=1,on_delete=models.CASCADE) album = models.ForeignKey(Album, on_delete=models.CASCADE, null=True) song... |
[
"cur = db.cursor() try: for row in df.itertuples(): ts = row.Time / 1000",
"def connect_to_db(): config = json.load(open('dbconn.json'))[\"mysql\"] db = M.connect(host = config[\"host\"], user = config[\"user\"],",
"import calendar import requests import pandas as pd import json import os.path import",
"time ... |
[
"\"\"\"Configures a Kafka Connector for Postgres Station data\"\"\" import json import logging import",
"connect connector...\") resp = requests.get(f\"{KAFKA_CONNECT_URL}/{CONNECTOR_NAME}\") if resp.status_code == 200: logging.debug(\"Connector already created skipping",
"\"value.converter\": \"org.apache.kafk... |
[
"Sampler logger = logging.getLogger(__name__) def load_word_embeddings(file): embeddings_index = {} fin = io.open(file, 'r',",
"'type': type, 'head': head, 'subject': ex.subj_text, 'object': ex.obj_text, 'subject_pos': subj_position, 'object_pos': obj_position, 'relation':",
"knn_size) sentence.knn_words = knn_... |
[
"Cython from Cython.Build import build_ext cmdclass[\"build_ext\"] = build_ext module_src = \"cgranges/python/cgranges.pyx\" except ImportError:",
"\"\"\" setup_kwargs.update( { \"ext_modules\": [ Extension( \"cgranges\", sources=[module_src, \"cgranges/cgranges.c\"], depends=[ \"cgranges/cgranges.h\", \"cgranges... |
[] |
[
"'applying_cream', 'archery', 'arm_wrestling', 'arranging_flowers', 'assembling_computer', 'auctioning', 'baby_waking_up', 'baking_cookies', 'balloon_blowing', 'bandaging', 'barbequing', 'bartending', 'beatboxing',",
"'playing_xylophone', 'pole_vault', 'presenting_weather_forecast', 'pull_ups', 'pumping_fist', 'p... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"permissions and # limitations under the License. from datetime import datetime from qf_lib.common.tickers.tickers",
"under the License. from datet... |
[
"value == wtypes.Unset: self._audit_uuid = wtypes.Unset elif value and self._audit_uuid != value: try:",
"action_plans with detail. :param marker: pagination marker for large data sets. :param limit:",
"Unless required by applicable law or agreed to in writing, software # distributed",
"[{'description': 'Test... |
[
"**options) @albums.route('/albums') def albums_route(): options = { \"edit\": False } return render_template(\"albums.html\", **options)",
"__name__, template_folder='templates') @albums.route('/albums/edit') def albums_edit_route(): options = { \"edit\": True } return render_template(\"albums.html\",",
"<repo... |
[
"import debug_logger from dfirtrack_main.models import Division class DivisionList(LoginRequiredMixin, ListView): login_url = '/login' model",
"import DivisionForm from dfirtrack_main.logger.default_logger import debug_logger from dfirtrack_main.models import Division class DivisionList(LoginRequiredMixin, ListVi... |
[
"torch.backends.cudnn.deterministic = True # 保证每次结果一样 start_time = time.time() print(\"Loading data...\") vocab, train_data, dev_data,",
"help='random or pre_trained') parser.add_argument('--word', default=False, type=bool, help='True for word, False for char') args",
"torch.manual_seed(1) torch.cuda.manual_see... |
[
"'resource-0-description': 'new resource name', 'resource-0-resource': resource_one.resource, 'resource-1-id': resource_two.id, 'resource-1-DELETE': True, 'resource-1-description': resource_two.description, 'resource-1-resource':",
"response.status_code == 200 with scope(event=submission.event): submission.refres... |
[
"3 MB_MAX_WRITE_REGNUM = 123 MB_MAX_READ_REGNUM = 125 MB_MAX_REG_ADDR = 65535 MB_MAX_REG_VAL = 65535",
"mb_bsp.alarm_cb.status_timeout == 1: print('*** Test FAILED: ', status , ' timeout ***') mb_bsp.alarm_cb.status_timeout",
"= pdu.copy() bytecount = regnum_l << 1 pdu.append(bytecount) for i in range(regnum_l)... |
[
"num_unanswered = ndb.IntegerProperty() total_question_views = ndb.IntegerProperty() status = ndb.StringProperty() status_string = ndb.StringProperty() def",
"result = {} result['requested_time'] = dthandler(self.requested_time) result['num_tagged_questions'] = self.num_tagged_questions result['num_questions_by_t... |
[
"True: start = time.clock() while time.clock() - start < busyTime: pass time.sleep(busyTime /",
"while True: start = time.clock() while time.clock() - start < busyTime: pass time.sleep(busyTime",
"-*- import time busyTime = 10 idleTime = busyTime while True: start =",
"import time busyTime = 10 idleTime = bus... |
[
"\"\"\" Initializes a BaseMap instance :param auth_client: Auth client \"\"\" super(BaseMap, self).__init__(auth_client) def",
"endpoint = (self.Meta.collection_endpoint + \"{template_id}\"). \\ format(template_id=self.template_id) if (auth is not None): endpoint",
"to download data :rtype: str :raise: CartoExc... |
[
"kv_client.kv_client import KVClient def main(): kvSlave = KVClient(1, \"127.0.0.1\", 3456) kvSlave.start() if __name__",
"def main(): kvSlave = KVClient(1, \"127.0.0.1\", 3456) kvSlave.start() if __name__ == \"__main__\": main()",
"KVClient def main(): kvSlave = KVClient(1, \"127.0.0.1\", 3456) kvSlave.start()... |
[
"mode, input_size, offset, sparse, include_last_offset, device): self.embedding = torch.nn.EmbeddingBag( num_embeddings=embeddingbags, embedding_dim=dim, mode=mode, include_last_offset=include_last_offset,",
"op_bench import torch import numpy from . import configs \"\"\"EmbeddingBag Operator Benchmark\"\"\" clas... |
[
"import backend as F from .. import ndarray as nd def _farthest_point_sampler(data, batch_size,",
"Returns ------- a 1-D tensor A vector with each element that indicates the",
"number of points to sample in each batch. dist : tensor Pre-allocated tensor",
"The number of points to sample in each batch. dist : ... |
[
"None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods",
"last_modified_time = getmtime(str(self.path)) if require_update \\ or 'sha256' not in self.metadata \\ or",
"not self.path.exists(): return None with open(str(... |
[
"If you want to process the gradient before applying then call `tf.GradientTape` and",
"hold the slot value. This can be useful if you want to log",
"without the optimizer; # it's a dependency hypergraph with edges of the form",
"dimension of `values` (as in an IndexedSlices object). Returns: A tuple of (`sum... |
[
"<PASSWORD>' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert",
"url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v... |
[
"a rule to parse body as xml for requests with /broker/xml uri xml_rule",
"some specific rules for crs_group in waf_policy_obj.get(\"crs_groups\", []): group_id = get_id_from_group(crs_group) if group_id",
"\"description\": \"WAF will buffer the whole request body first and then release to",
"\"allowlist-star... |
[
"(False, \"/category,5/department,3440/department,3443/\", \"id\", 3443), (True, \"/category,5/\", \"category_id\", 5), (False, \"/category,5/\", \"category_id\", 5), (True,",
"attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping... |
[
"run_benchmark, # get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks import runtools,",
"# get_benchmarks, # post_benchmark, # plot_benchmark, # ) from votesim.benchmarks import runtools, simple",
"# from votesim.benchmarks.benchrunner import ( # run_benchmark, # get_benchmarks, # p... |
[
"= str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines,",
"total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_... |
[
"Crown Copyright 2017-2021 Met Office. # All rights reserved. # # Redistribution and",
"and the following disclaimer in the documentation # and/or other materials provided with",
"EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES",
"has a multi valued threshold coordinate.... |
[
"self.auth = auth def add_item( self, name, price, quantity, image, category_id, reorder_point, auth):",
"available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = \"\"\"UPDATE",
"self.conn = model.conn self.cur = model.cur query = \"\"\"INSERT INTO items(name, price, quantity,",
"... |
[
"else: # Simple class name; find from the pre-calculated mappings. if text not",
"javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name to",
"options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(opti... |
[
"for lock_name in lock_names: self.locker.init(lock_name) def loop(self): self.running = True while self.running: self.run()",
"__init__(self, manager, interval, *locks): self.manager = manager self.storage = manager.storage self.interval = interval",
"interval def init_locker(self, *lock_names): self.locker = ... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading... |
[] |
[
"Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] =",
"in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i,",
"... |
[
"param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name,",
"self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm):",
">>> loss_adv.backward() # 反向传播，并在正常的... |
[
"= torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f:",
"OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detect... |
[
"import ChanceConstraintConstant # to do list # 0. implement zero, Hessian term #",
"3. scaling with repsect to mesh (design + uncertainty), trace, variance reduction, #bfgs",
"samples and states at different design # 2. table for variance reduction #",
"CostFunctionalQuadraticMultiPDE # from .chanceConstrain... |
[
"from get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD",
"ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return std*z + mean",
"return transform_train def get_transforms_valid(): transform_valid = Compos... |
[
"z def contains(self, cub): \"\"\"Tests if another cuboid is contained by this one",
"start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment): \"\"\"Vertical Segment\"\"\" def",
"None if there is no intersection. Arguments: cub (Cuboid): The other cuboid. edges",
"the ar... |
[
"fields that are omitted from # the request will not be modified in",
"recipes\"\"\" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving # all",
"# check that the images in the response so that's the path to",
"# pillow, this will import our image class which... |
[
"multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2): print(\"Division = \",num1/num2) addition() subtraction() multiplication()",
"\",num1-num2) @getInput def multiplication(num1,num2): print(\"Multiplication = \",num1*num2) @getInput def division(num1,num2): print... |
[
"Parameters ---------- naxes : int Number of axes. axes_type : str One of",
"utils as astutil from astropy import coordinates as coord from astropy.extern import six",
"object\"\"\" raise NotImplementedError(\"Subclasses may implement this\") class CelestialFrame(CoordinateFrame): \"\"\" Celestial Frame Represe... |
[
"modifierModoItem, hostModoItem): \"\"\" Allows for attaching modifier to locator type item. Attached item",
"Parameters ---------- modoItem : modo.Item The constraint modo item. \"\"\" Operation = TransformConstraintOperation",
"self._item.channel('matrixOutput') @property def isRotationConstraint(self): \"\"\... |