id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
188,533 | import os
from pkg_resources import parse_version
from setuptools import find_packages, setup
The provided code snippet includes necessary dependencies for implementing the `parse_requirements` function. Write a Python function `def parse_requirements(fname='requirements.txt', with_version=True)` to solve the followin... | Parse the package dependencies listed in a file but strips specific versioning information. Args: fname (str): path to the file with_version (bool, default=False): if True include version specs Returns: List[str]: list of requirements items CommandLine: python -c "import setup; print(setup.parse_requirements())" |
188,534 | import os
from pkg_resources import parse_version
from setuptools import find_packages, setup
EXT_TYPE = ''
try:
import torch
from torch.utils.cpp_extension import BuildExtension
cmd_class = {'build_ext': BuildExtension}
EXT_TYPE = 'torch'
except ModuleNotFoundError:
cmd_class = {}
print('Skip b... | null |
188,535 | import os
import subprocess
import sys
import pytorch_sphinx_theme
from m2r import MdInclude
from recommonmark.transform import AutoStructify
from sphinx.builders.html import StandaloneHTMLBuilder
def generate_doxygen_xml(app):
try:
folder = '../cppapi'
retcode = subprocess.call('cd %s; doxygen' % f... | null |
188,537 | from typing import Tuple
version_info = parse_version_info(__version__)
The provided code snippet includes necessary dependencies for implementing the `parse_version_info` function. Write a Python function `def parse_version_info(version_str: str) -> Tuple` to solve the following problem:
Parse version from a string. ... | Parse version from a string. Args: version_str (str): A string represents a version info. Returns: tuple: A sequence of integer and string represents version. |
188,538 | from typing import Dict, Iterable, Optional, Union
import onnx
from .core import PIPELINE_MANAGER
The provided code snippet includes necessary dependencies for implementing the `extract_model` function. Write a Python function `def extract_model(model: Union[str, onnx.ModelProto], start_marker: Union... | Extract partition-model from an ONNX model. The partition-model is defined by the names of the input and output tensors exactly. Examples: >>> from mmdeploy.apis import extract_model >>> model = 'work_dir/fastrcnn.onnx' >>> start_marker = 'detector:input' >>> end_marker = ['extract_feat:output', 'multiclass_nms[0]:inpu... |
188,539 | from copy import deepcopy
from typing import Optional, Union
from mmengine import Config
from .core import PIPELINE_MANAGER, no_mp
The provided code snippet includes necessary dependencies for implementing the `create_calib_input_data` function. Write a Python function `def create_calib_input_data(calib_file: str, ... | Create dataset for post-training quantization. Args: calib_file (str): The output calibration data file. deploy_cfg (str | Config): Deployment config file or Config object. model_cfg (str | Config): Model config file or Config object. model_checkpoint (str): A checkpoint path of PyTorch model, defaults to `None`. datas... |
188,540 | import os.path as osp
from typing import Any, Optional, Union
import mmengine
from mmdeploy.apis.core.pipeline_manager import PIPELINE_MANAGER, no_mp
class no_mp:
"""The context manager used to disable multiprocess."""
def __init__(self, manager: PipelineManager = PIPELINE_MANAGER) -> None:
self._mana... | Convert PyTorch model to torchscript model. Args: img (str | np.ndarray | torch.Tensor): Input image used to assist converting model. work_dir (str): A working directory to save files. save_file (str): Filename to save torchscript model. deploy_cfg (str | mmengine.Config): Deployment config file or Config object. model... |
188,541 | from typing import Optional, Sequence, Union
import mmengine
import numpy as np
import torch
from mmdeploy.utils import Backend, get_backend, get_input_shape, load_config
The provided code snippet includes necessary dependencies for implementing the `visualize_model` function. Write a Python function `def visualize_mo... | Run inference with PyTorch or backend model and show results. Examples: >>> from mmdeploy.apis import visualize_model >>> model_cfg = ('mmdetection/configs/fcos/' 'fcos_r50_caffe_fpn_gn-head_1x_coco.py') >>> deploy_cfg = ('configs/mmdet/detection/' 'detection_onnxruntime_dynamic.py') >>> model = 'work_dir/fcos.onnx' >>... |
188,542 | from typing import Any, Sequence, Union
import mmengine
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `inference_model` function. Write a Python function `def inference_model(model_cfg: Union[str, mmengine.Config], deploy_cfg: Union[str, mmengine.... | Run inference with PyTorch or backend model and show results. Examples: >>> from mmdeploy.apis import inference_model >>> model_cfg = ('mmdetection/configs/fcos/' 'fcos_r50_caffe_fpn_gn-head_1x_coco.py') >>> deploy_cfg = ('configs/mmdet/detection/' 'detection_onnxruntime_dynamic.py') >>> backend_files = ['work_dir/fcos... |
188,543 | import os.path as osp
from typing import Any, Optional, Union
import mmengine
from .core import PIPELINE_MANAGER
class no_mp:
"""The context manager used to disable multiprocess."""
def __init__(self, manager: PipelineManager = PIPELINE_MANAGER) -> None:
self._manager = manager
self._old_enabl... | Convert PyTorch model to ONNX model. Examples: >>> from mmdeploy.apis import torch2onnx >>> img = 'demo.jpg' >>> work_dir = 'work_dir' >>> save_file = 'fcos.onnx' >>> deploy_cfg = ('configs/mmdet/detection/' 'detection_onnxruntime_dynamic.py') >>> model_cfg = ('mmdetection/configs/fcos/' 'fcos_r50_caffe_fpn_gn-head_1x_... |
188,544 | import importlib
import inspect
import logging
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Sequence, Union
from mmdeploy.utils import get_root_logger
The provided code snippet includes necessary dependencies for implementing the `_get_func_name` function. Write a Python function... | get function name. |
188,545 | from typing import Dict, Iterable, Optional, Union
import onnx
import onnx.helper
import onnx.utils
from mmdeploy.apis.core import PIPELINE_MANAGER
from mmdeploy.core.optimizers import (attribute_to_dict, create_extractor,
get_new_name, parse_extractor_io_string,
... | Extract partition-model from an ONNX model. The partition-model is defined by the names of the input and output tensors exactly. Examples: >>> from mmdeploy.apis import extract_model >>> model = 'work_dir/fastrcnn.onnx' >>> start_marker = 'detector:input' >>> end_marker = ['extract_feat:output', 'multiclass_nms[0]:inpu... |
188,546 | from typing import Callable
import torch
from mmdeploy.core import FUNCTION_REWRITER
def update_squeeze_unsqueeze_opset13_pass(graph, params_dict, torch_out):
"""Update Squeeze/Unsqueeze axes for opset13."""
for node in graph.nodes():
if node.kind() in ['onnx::Squeeze', 'onnx::Unsqueeze'] and \
... | Rewriter of _model_to_graph, add custom passes. |
188,547 | from typing import Callable
import torch
from mmdeploy.core import FUNCTION_REWRITER
The provided code snippet includes necessary dependencies for implementing the `jit_pass_onnx_deduplicate_initializers__disable` function. Write a Python function `def jit_pass_onnx_deduplicate_initializers__disable(graph, param_dict,... | This pass will disable TensorRT topk export. disable for TensorRT. |
188,548 | from typing import Callable
import torch
from mmdeploy.core import FUNCTION_REWRITER
The provided code snippet includes necessary dependencies for implementing the `jit_pass_onnx_autograd_function_process__disable` function. Write a Python function `def jit_pass_onnx_autograd_function_process__disable(graph)` to solve... | Disable process autograph function. |
188,549 | from typing import Dict, List
import mmengine
from mmdeploy.backend.openvino import ModelOptimizerOptions
from mmdeploy.utils import get_model_inputs
from mmdeploy.utils.config_utils import get_backend_config, get_ir_config
def update_input_names(input_info: Dict[str, List],
input_names: List[str... | Get the input names and shapes from the configs for OpenVINO Model Optimizer. Args: deploy_cfg (mmengine.Config): Deployment config. Returns: Dict[str, List]: A dict that stores the names and shapes of input. |
188,550 | from typing import Dict, List
import mmengine
from mmdeploy.backend.openvino import ModelOptimizerOptions
from mmdeploy.utils import get_model_inputs
from mmdeploy.utils.config_utils import get_backend_config, get_ir_config
def get_backend_config(deploy_cfg: Union[str, mmengine.Config]) -> Dict:
"""Get the backend... | Get additional parameters for the Model Optimizer from the deploy config. Args: deploy_cfg (mmengine.Config): Deployment config. Returns: ModelOptimizerOptions: A class that will contain additional arguments. |
188,551 | from copy import deepcopy
from typing import Callable, Dict, Optional
import torch
from torch.utils.data import DataLoader
from ..core import PIPELINE_MANAGER
The provided code snippet includes necessary dependencies for implementing the `create_calib_input_data` function. Write a Python function `def create_calib_inp... | Create calibration table. Examples: >>> from mmdeploy.apis.utils import create_calib_input_data >>> from mmdeploy.utils import get_calib_filename, load_config >>> deploy_cfg = 'configs/mmdet/detection/' 'detection_tensorrt-int8_dynamic-320x320-1344x1344.py' >>> deploy_cfg = load_config(deploy_cfg)[0] >>> calib_file = g... |
188,552 | import logging
from typing import Any, Optional, Sequence
import mmengine
from mmdeploy.codebase import BaseTask, get_codebase_class, import_codebase
from mmdeploy.utils import (get_backend, get_codebase, get_task_type,
parse_device_id)
from mmdeploy.utils.config_utils import get_codebase_ex... | Build a task processor to manage the deployment pipeline. Args: model_cfg (str | mmengine.Config): Model config file. deploy_cfg (str | mmengine.Config): Deployment config file. device (str): A string specifying device type. Returns: BaseTask: A task processor. |
188,553 | import logging
from typing import Any, Optional, Sequence
import mmengine
from mmdeploy.codebase import BaseTask, get_codebase_class, import_codebase
from mmdeploy.utils import (get_backend, get_codebase, get_task_type,
parse_device_id)
from mmdeploy.utils.config_utils import get_codebase_ex... | Get the predefined partition config. Notes: Currently only support mmdet codebase. Args: deploy_cfg (mmengine.Config): use deploy config to get the codebase and task type. partition_type (str): A string specifying partition type. Returns: dict: A dictionary of partition config. |
188,554 | import logging
from typing import Any, Optional, Sequence
import mmengine
from mmdeploy.codebase import BaseTask, get_codebase_class, import_codebase
from mmdeploy.utils import (get_backend, get_codebase, get_task_type,
parse_device_id)
from mmdeploy.utils.config_utils import get_codebase_ex... | Convert intermediate representation to given backend. Args: backend_name (str): The name of the backend. ir_files (Sequence[str]): The intermediate representation files. work_dir (str): The work directory, backend files and logs should be save in this directory. deploy_cfg (Any): The deploy config. log_level (int, opti... |
188,555 | import re
import onnx
from packaging import version
The provided code snippet includes necessary dependencies for implementing the `parse_extractor_io_string` function. Write a Python function `def parse_extractor_io_string(io_str) -> tuple` to solve the following problem:
Parse IO string for extractor.
Here is the f... | Parse IO string for extractor. |
188,556 | import re
import onnx
from packaging import version
def _dfs_search_reachable_nodes_fast(self, node_output_name, graph_input_nodes,
reachable_nodes):
"""Using DFS to search reachable nodes."""
outputs = {}
for index, node in enumerate(self.graph.node):
for name i... | Create Extractor for ONNX. Args: model (onnx.ModelProto): An input onnx model. Returns: onnx.utils.Extractor: Extractor for the onnx. |
188,557 | import inspect
from typing import Any, Callable, Dict, Optional, Sequence
import torch
from mmdeploy.core.rewriters import FUNCTION_REWRITER
from mmdeploy.utils import IR, cfg_apply_marks, get_partition_config
MARK_FUNCTION_COUNT = dict()
The provided code snippet includes necessary dependencies for implementing the `... | Reset counter of mark function. |
188,558 | import inspect
from typing import Any, Callable, Dict, Optional, Sequence
import torch
from mmdeploy.core.rewriters import FUNCTION_REWRITER
from mmdeploy.utils import IR, cfg_apply_marks, get_partition_config
The provided code snippet includes necessary dependencies for implementing the `mark_symbolic` function. Writ... | Rewrite symbolic of mark op. |
188,559 | import inspect
from typing import Any, Callable, Dict, Optional, Sequence
import torch
from mmdeploy.core.rewriters import FUNCTION_REWRITER
from mmdeploy.utils import IR, cfg_apply_marks, get_partition_config
The provided code snippet includes necessary dependencies for implementing the `forward_of_mark` function. Wr... | Rewrite forward of mark op. |
188,560 | import inspect
from typing import Any, Callable, Dict, Optional, Sequence
import torch
from mmdeploy.core.rewriters import FUNCTION_REWRITER
from mmdeploy.utils import IR, cfg_apply_marks, get_partition_config
The provided code snippet includes necessary dependencies for implementing the `remove_mark__torchscript` fun... | Disable all marks for TorchScript backend. As the Node `mark` is not able to be traced, we just return original input for the function `mark_tensors`. Args: xs (Any): Input structure which contains tensor. |
188,561 | import inspect
from typing import Any, Callable, Dict, Optional, Sequence
import torch
from mmdeploy.core.rewriters import FUNCTION_REWRITER
from mmdeploy.utils import IR, cfg_apply_marks, get_partition_config
MARK_FUNCTION_COUNT = dict()
def mark_tensors(xs: Any, func: str, func_id: int, io_type: str, ctx: Any,
... | The decorator used to add mark node. Mark node can be used to support model partition. Args: func_name (str): The name of the function where marks come from. inputs (Sequence[str]): The input names of the marks. The final name \ might have suffix if inputs is list or dictionary. outputs (Sequence[str]): The output name... |
188,562 | from typing import Callable, Dict, Iterable, Optional
import onnx
from onnx.helper import get_attribute_value
from mmdeploy.utils import get_root_logger
def attribute_to_dict(attr: onnx.AttributeProto) -> Dict:
"""Convert onnx op attribute to dict.
Args:
attr (onnx.AttributeProto): Input onnx op attribu... | Check whether a mark is unused. Args: marks (Iterable[onnx.NodeProto]): A list of onnx NodeProto. Returns: Callable: The function to check if a mark node is in `marks`. |
188,563 | from typing import Callable, Dict, Iterable, Optional
import onnx
from onnx.helper import get_attribute_value
from mmdeploy.utils import get_root_logger
The provided code snippet includes necessary dependencies for implementing the `get_new_name` function. Write a Python function `def get_new_name(attrs: Dict[str, str... | Get new name for a node. Args: attrs (Dict[str, str]): A dict contains attributes of an ONNX node. mark_name (str): The input mark op name. Default is ''. name_map (Dict[str, str]): A mapping of node names, defaults to `None`. Returns: str: The new node name. |
188,564 | from typing import Callable, Dict, Iterable, Optional
import onnx
from onnx.helper import get_attribute_value
from mmdeploy.utils import get_root_logger
The provided code snippet includes necessary dependencies for implementing the `rename_value` function. Write a Python function `def rename_value(model: onnx.ModelPro... | Rename a node in an ONNX model. Args: model (onnx.ModelProto): Input onnx model. old_name (str): Original node name in the model. new_name (str): New node name in the model. |
188,565 | from typing import Callable, Dict, Iterable, Optional
import onnx
from onnx.helper import get_attribute_value
from mmdeploy.utils import get_root_logger
def remove_nodes(model: onnx.ModelProto,
predicate: Callable) -> onnx.ModelProto:
"""Remove nodes from ONNX model.
Args:
model (onnx.M... | Remove identity node from an ONNX model. Args: model (onnx.ModelProto): Input onnx model. |
188,566 | from typing import Callable, Dict, Iterable, Optional
import onnx
from onnx.helper import get_attribute_value
from mmdeploy.utils import get_root_logger
The provided code snippet includes necessary dependencies for implementing the `remove_imports` function. Write a Python function `def remove_imports(model: onnx.Mode... | Remove useless imports from an ONNX model. The domain like `mmdeploy` might influence model conversion for some backends. Args: model (onnx.ModelProto): Input onnx model. |
188,567 | from typing import Dict
import mmengine
import torch.nn as nn
from mmdeploy.utils.constants import IR, Backend
from .function_rewriter import FunctionRewriter
from .module_rewriter import ModuleRewriter
from .rewriter_utils import collect_env
from .symbolic_rewriter import SymbolicRewriter
MODULE_REWRITER = REWRITER_MA... | Patch the model, replace the modules that can be rewritten. Note that the original model will be modified permanently. Args: model (torch.nn.Module): The model to patch. cfg (Dict): Config dictionary of deployment. backend (str): The inference engine name. ir (IR): The intermeditate representation name. recursive (bool... |
188,568 | import functools
import inspect
import types
import warnings
from abc import ABCMeta, abstractmethod
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import mmdeploy
from mmdeploy.utils.constants import IR, Backend
The provided code snippet includes necessary dependencie... | Evaluate the string as Python script. Args: path (str): The path to evaluate. Returns: Any: The result of evaluation. |
188,569 | import functools
import inspect
import types
import warnings
from abc import ABCMeta, abstractmethod
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import mmdeploy
from mmdeploy.utils.constants import IR, Backend
The provided code snippet includes necessary dependencie... | Import and evaluate a function. If the function is defined in a class, evaluate the class additionally. Args: path (str): The path to evaluate. Returns: Callable: The function of evaluation. type: The class of evaluation if the function is defined in a class, or None. |
188,570 | import functools
import inspect
import types
import warnings
from abc import ABCMeta, abstractmethod
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import mmdeploy
from mmdeploy.utils.constants import IR, Backend
class IR(AdvancedEnum):
"""Define intermediate repre... | Collect current environment information, including backend, ir, codebase version, etc. Rewriters will be checked according to env infos. Args: backend (Backend): Current backend. ir (IR): Current IR. Returns: Dict: Record the value of Backend and IR as well as the versions of libraries. |
188,571 | import functools
import inspect
import types
import warnings
from abc import ABCMeta, abstractmethod
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import mmdeploy
from mmdeploy.utils.constants import IR, Backend
The provided code snippet includes necessary dependencie... | get function name. |
188,572 | import functools
import inspect
import types
import warnings
from abc import ABCMeta, abstractmethod
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import mmdeploy
from mmdeploy.utils.constants import IR, Backend
The provided code snippet includes necessary dependencie... | get func of frame. |
188,573 | import functools
import inspect
import types
import warnings
from abc import ABCMeta, abstractmethod
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import mmdeploy
from mmdeploy.utils.constants import IR, Backend
The provided code snippet includes necessary dependencie... | get frame name. |
188,574 | import functools
import inspect
import types
import warnings
from abc import ABCMeta, abstractmethod
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import mmdeploy
from mmdeploy.utils.constants import IR, Backend
The provided code snippet includes necessary dependencie... | Copy the function. |
188,575 | import types
from collections import defaultdict
from typing import (Any, Callable, Dict, List, MutableSequence, Optional,
Tuple, Union)
from mmdeploy.utils import IR, Backend, get_root_logger
from .rewriter_utils import (Checker, ContextCaller, RewriterRegistry,
copy_fu... | Rewrite a function by executing a python statement. Args: origin_func_path (str): The path to origin function. rewrite_func (Callable): The new function instance. ignore_refs (Tuple[Any]): These refs will be ignored. ignore_keys (Tuple[str]): object with these keys will be ignored. |
188,576 | import types
from collections import defaultdict
from typing import (Any, Callable, Dict, List, MutableSequence, Optional,
Tuple, Union)
from mmdeploy.utils import IR, Backend, get_root_logger
from .rewriter_utils import (Checker, ContextCaller, RewriterRegistry,
copy_fu... | Delete a function that is denoted by a path. Args: path (str): The path to evaluate. |
188,577 | import types
from collections import defaultdict
from typing import (Any, Callable, Dict, List, MutableSequence, Optional,
Tuple, Union)
from mmdeploy.utils import IR, Backend, get_root_logger
from .rewriter_utils import (Checker, ContextCaller, RewriterRegistry,
copy_fu... | If a function is wrapped by torch.fx.wrap, its copy also needs to be wrapped by torch.fx.wrap. |
188,578 | from torch.onnx.symbolic_helper import parse_args
from mmdeploy.core import SYMBOLIC_REWRITER
from mmdeploy.utils import Backend, get_backend
def grid_sampler(g,
input,
grid,
interpolation_mode,
padding_mode,
align_corners=False):
... | Register default symbolic function for `grid_sampler`. Add support to grid_sample to ONNX. |
188,579 | from torch.onnx import symbolic_helper
from mmdeploy.core import SYMBOLIC_REWRITER
from mmdeploy.utils import Backend
def gelu__ncnn_pt111(g, self):
"""gelu for torch<=1.12."""
return g.op('mmdeploy::Gelu', self)
'gelu', is_pytorch=True, backend=Backend.NCNN.value)
The provided code snippet includes necess... | Support export GELU with ncnn backend. |
188,580 |
The provided code snippet includes necessary dependencies for implementing the `hardsigmoid__default` function. Write a Python function `def hardsigmoid__default(g, self)` to solve the following problem:
Support export hardsigmoid This rewrite enable export hardsigmoid in torch<=1.8.2.
Here is the function:
def har... | Support export hardsigmoid This rewrite enable export hardsigmoid in torch<=1.8.2. |
188,581 | import sys
from torch.onnx.symbolic_helper import _slice_helper, parse_args
from mmdeploy.core import SYMBOLIC_REWRITER
def roll(g, self, shifts, dims):
"""Symbolic function for `roll`."""
assert len(shifts) == len(dims)
result = self
for i in range(len(shifts)):
shapes = []
shape = _sli... | Support export roll to ONNX with PyTorch version 1.10-. |
188,582 | from torch.onnx.symbolic_helper import parse_args
from mmdeploy.core import SYMBOLIC_REWRITER
from mmdeploy.utils import Backend
The provided code snippet includes necessary dependencies for implementing the `layer_norm__default` function. Write a Python function `def layer_norm__default(g, input, normalized_shape, we... | Symbolic function for `layer_norm` Layer norm with torch<=1.12 might lead to wrong output shapes. Add keepdims=1 to each ReduceMean node to correct the shape. |
188,583 | from torch.onnx.symbolic_helper import parse_args
from mmdeploy.core import SYMBOLIC_REWRITER
from mmdeploy.utils import Backend
def _layer_norm_ncnn(g, input, normalized_shape, weight, bias, eps,
cudnn_enable):
"""Symbolic function for `layer_norm`.
PyTorch does not support export layer_no... | Register default symbolic function for `layer_norm`. Add support to layer_norm to ONNX. |
188,584 | import torch
import torch.onnx.symbolic_helper as sym_help
from mmdeploy.core import SYMBOLIC_REWRITER
from mmdeploy.utils import get_ir_config
The provided code snippet includes necessary dependencies for implementing the `squeeze__default` function. Write a Python function `def squeeze__default(g, self, dim=None)` t... | Register default symbolic function for `squeeze`. squeeze might be exported with IF node in ONNX, which is not supported in lots of backend. |
188,585 | import torch
from torch.onnx.symbolic_helper import (_get_tensor_dim_size, _get_tensor_rank,
_unimplemented, _unsqueeze_helper,
parse_args)
from mmdeploy.core import SYMBOLIC_REWRITER
def instance_norm(g, input, num_groups, weight, bias, ep... | Register symbolic function for TensorRT backend. Notes: Instance normalization is implemented in group norm in pytorch. |
188,586 | import warnings
import torch
import torch.onnx.symbolic_helper as sym_help
from torch.onnx.symbolic_helper import _unimplemented
from torch.onnx.symbolic_opset9 import unused
from mmdeploy.core import FUNCTION_REWRITER
warnings.warn(
'Exporting a model to ONNX with a batch_size other than 1, ' +
'wi... | rewrite of _generic_rnn for ncnn. `g.op` will add some nodes for h0 and c0 in LSTM. which is not supported in ncnn. So we add a custom domain to avoid it. |
188,587 |
The provided code snippet includes necessary dependencies for implementing the `adaptive_avg_pool2d__ncnn` function. Write a Python function `def adaptive_avg_pool2d__ncnn(g, x, output_size)` to solve the following problem:
Register ncnn symbolic function for `adaptive_avg_pool2d`. Align symbolic of adaptive_avg_pool... | Register ncnn symbolic function for `adaptive_avg_pool2d`. Align symbolic of adaptive_avg_pool2d in ncnn. |
188,588 | from torch.onnx.symbolic_helper import parse_args
from mmdeploy.core import SYMBOLIC_REWRITER
from mmdeploy.utils import Backend
def linear_no_bias(g, input, weight):
"""Symbolic function for `linear` without bias.
PyTorch `nn.Linear` will be exported as ONNX node 'Gemm'.
"""
return g.op(
'Gemm'... | Support export linear This rewrite enable export Gemm. |
188,589 | import math
from typing import Optional, Tuple
import torch
from torch import Tensor
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils.constants import Backend
class ScaledDotProductAttentionTRT(torch.autograd.Function):
"""Caller of scale dot product attention."""
def forward(ctx,
... | Rewrite for custom ops. |
188,590 | import math
from typing import Optional, Tuple
import torch
from torch import Tensor
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils.constants import Backend
The provided code snippet includes necessary dependencies for implementing the `scaled_dot_product_attention__default` function. Write a Python f... | Rewrite to export to onnx on torch>=2.0.0. |
188,591 |
The provided code snippet includes necessary dependencies for implementing the `group_norm__ncnn` function. Write a Python function `def group_norm__ncnn( input: torch.Tensor, num_groups: int, weight: Union[torch.Tensor, torch.NoneType] = None, bias: Union[torch.Tensor, torch.NoneType] = None, eps... | Rewrite `group_norm` for ncnn backend. InstanceNorm in ncnn require input with shape [C, H, W]. So we have to reshape the input tensor before it. |
188,592 | import torch
import torch.onnx.symbolic_helper as sym_help
from packaging.version import parse as version_parse
from mmdeploy.core import FUNCTION_REWRITER
The provided code snippet includes necessary dependencies for implementing the `_prepare_onnx_paddings__tensorrt` function. Write a Python function `def _prepare_o... | Rewrite `_prepare_onnx_paddings` for TensorRT backend. For codes like `x = torch.nn.ZeroPad2d((0, a, 0, b))(x)`, where a and b are variables of torch.tensor, onnx2tensorrt raises errors like `INVALID_NODE: Invalid Node - Pad_`. Generate paddings in ONNX order based on pad in pytorch. Args: input: the input tensor. pad:... |
188,593 | import torch
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import IR
func_name='torch.Tensor.chunk', backend='ncnn')
def chunk__ncnn(self, num_chunks: int, dim: int = 0) -> torch.Tensor:
"""Rewrite `chunk` for NCNN backend.
Chunk in ncnn are not supported, so it should be rewritten.
""... | Rewrite `chunk` for NCNN backend. Chunk in ncnn are not supported, so it should be rewritten. |
188,594 | import torch
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import IR
func_name='torch.Tensor.chunk', backend='ncnn')
def chunk__ncnn(self, num_chunks: int, dim: int = 0) -> torch.Tensor:
"""Rewrite `chunk` for NCNN backend.
Chunk in ncnn are not supported, so it should be rewritten.
""... | Rewrite `chunk` for Torchscript. Replace chunk op with split op |
188,595 | import torch
from mmdeploy.core import FUNCTION_REWRITER
func_name='torch.Tensor.size', backend='ncnn')
def tensor__size__ncnn(self, *args):
"""Rewrite `size` for ncnn backend.
ONNX Shape node is not supported in ncnn. This function return integer
instead of Torch.Size to avoid ONNX Shape node.
"""
... | Rewrite `size` for ncnn backend. ONNX Shape node is not supported in ncnn. This function return integer instead of Torch.Size to avoid ONNX Shape node. |
188,596 |
The provided code snippet includes necessary dependencies for implementing the `tensor__size__ascend` function. Write a Python function `def tensor__size__ascend(self, *args)` to solve the following problem:
Rewrite `size` for ascens backend. Support negative index.
Here is the function:
def tensor__size__ascend(se... | Rewrite `size` for ascens backend. Support negative index. |
188,597 |
The provided code snippet includes necessary dependencies for implementing the `normalize__ncnn` function. Write a Python function `def normalize__ncnn(input: torch.Tensor, p: int = 2, dim: int = 1, eps: float = 1e-12, *args, ... | Rewrite `normalize` for ncnn backend. Make sure L2 norm on channel dim and be exported to ncnn correctly. |
188,598 |
The provided code snippet includes necessary dependencies for implementing the `norm__ncnn` function. Write a Python function `def norm__ncnn(input: torch.Tensor, p: Optional[Union[int, str]] = 'fro', dim: Optional[Union[int, Sequence]] = None, keepdim: Optional[bool] = Fa... | Rewrite `torch.norm` for ncnn backend. Rewrite torch.norm when p is Frobenius norm to avoid FP16 exceed in ncnn Android platform. |
188,599 | from typing import Iterable
import torch
from mmdeploy.core import FUNCTION_REWRITER
func_name='torch.Tensor.__getitem__', backend='ascend')
def tensor__getitem__ascend(self, key) -> torch.Tensor:
"""Rewrite `getitem` for ascend backend.
Ascend does not support negative select
"""
ctx = FUNCTION_REW... | Rewrite `getitem` for ascend backend. Ascend does not support negative select |
188,600 | import torch
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import Backend
func_name='torch.Tensor.clip', backend=Backend.COREML.value)
func_name='torch.clip', backend=Backend.COREML.value)
func_name='torch.Tensor.clamp', backend=Backend.COREML.value)
func_name='torch.clamp', backend=Ba... | Rewrite `clip` for coreml backend. Cast data type. |
188,601 | from typing import Optional, Tuple, Union
import torch
from torch.autograd import Function
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import Backend, get_root_logger
ctx = FUNCTION_REWRITER.get_context()
input_size = input.shape
return ctx.origin_func(
input,
None,
... | Rewrite `interpolate` for ncnn backend. ncnn require `size` should be constant in ONNX Node. We use `scale_factor` instead of `size` to avoid dynamic size. |
188,602 | from typing import Optional, Tuple, Union
import torch
from torch.autograd import Function
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import Backend, get_root_logger
ctx = FUNCTION_REWRITER.get_context()
input_size = input.shape
return ctx.origin_func(
input,
None,
... | Rewrite `interpolate` for rknn backend. rknn require `size` should be constant in ONNX Node. We use `scale_factor` instead of `size` to avoid dynamic size. |
188,603 | from typing import Optional, Tuple, Union
import torch
from torch.autograd import Function
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import Backend, get_root_logger
ctx = FUNCTION_REWRITER.get_context()
input_size = input.shape
return ctx.origin_func(
input,
None,
... | Register default symbolic function for `interpolate`. |
188,604 |
The provided code snippet includes necessary dependencies for implementing the `mod__tensorrt` function. Write a Python function `def mod__tensorrt(input: torch.Tensor, other: Union[torch.Tensor, torch.NumberType], *args, **kwargs) -> torch.Tensor`... | Rewrite `mod` when exporting model to ONNX for TensorRT backend. |
188,605 | from typing import Sequence
import torch
from packaging.version import parse
from mmdeploy.core import FUNCTION_REWRITER, SYMBOLIC_REWRITER
if parse(torch.__version__) >= parse('1.12.0'):
The provided code snippet includes necessary dependencies for implementing the `tensor__setitem__default` function. Write a Python ... | Rewrite `setitem` to ease the index put. |
188,606 | from typing import Sequence
import torch
from packaging.version import parse
from mmdeploy.core import FUNCTION_REWRITER, SYMBOLIC_REWRITER
def copy__default(g, x, y, non_blocking):
return x | null |
188,607 | import torch
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import Backend
func_name='torch.Tensor.flatten', backend=Backend.NCNN.value)
func_name='torch.flatten', backend=Backend.NCNN.value)
func_name='torch.Tensor.flatten', backend=Backend.COREML.value)
func_name='torch.flatten', back... | Rewrite `flatten` for coreml backend. Use reshape instead of flatten |
188,608 | from typing import Optional
import torch
from mmdeploy.core import FUNCTION_REWRITER
The provided code snippet includes necessary dependencies for implementing the `any__default` function. Write a Python function `def any__default(input: torch.Tensor, dim: Optional[str] = None, keepdi... | Rewrite `any` for ONNX. |
188,609 |
The provided code snippet includes necessary dependencies for implementing the `topk__dynamic` function. Write a Python function `def topk__dynamic(input: torch.Tensor, k: int, dim: Optional[int] = None, largest: bool = True, sorted: bool = True)... | Rewrite `topk` for default backend. Cast k to tensor and make sure k is smaller than input.shape[dim]. |
188,610 |
TENSORRT_MAX_TOPK = 3840
The provided code snippet includes necessary dependencies for implementing the `topk__tensorrt` function. Write a Python function `def topk__tensorrt(input: torch.Tensor, k: int, dim: Optional[int] = None, largest: bool = True, ... | Rewrite `topk` for TensorRT backend. TensorRT does not support topk with dynamic k. This function cast k to constant integer. |
188,611 |
The provided code snippet includes necessary dependencies for implementing the `topk__coreml` function. Write a Python function `def topk__coreml(input: torch.Tensor, k: int, dim: Optional[int] = None, largest: bool = True, sorted: bool = True)` to s... | Rewrite `topk` for coreml. Cast k to tensor and make sure k is smaller than input.shape[dim]. |
188,612 | from torch import Tensor
from mmdeploy.core import FUNCTION_REWRITER
The provided code snippet includes necessary dependencies for implementing the `copy__default` function. Write a Python function `def copy__default(tensor: Tensor, *args, **kwargs) -> Tensor` to solve the following problem:
Rewrite `copy.deepcopy` fo... | Rewrite `copy.deepcopy` for default backend. Replace it with tensor.clone(), or may raise `NYI: Named tensors are not supported with the tracer` |
188,613 |
The provided code snippet includes necessary dependencies for implementing the `masked_fill__onnxruntime` function. Write a Python function `def masked_fill__onnxruntime( input, mask: torch.Tensor, value: Union[torch.Tensor, Number]) -> torch.Tensor` to solve th... | Rewrite `masked_fill` for onnxruntime backend. SATRN model as example, when value is set to `float('-inf')`, the results of ORT inferencing turns out to be NAN. |
188,614 |
The provided code snippet includes necessary dependencies for implementing the `tensor__repeat__tensorrt` function. Write a Python function `def tensor__repeat__tensorrt(input: torch.Tensor, *size: Union[torch.Size, Sequence[int]])` to solve the following... | Rewrite `repeat` for TensorRT backend. Some layers in TensorRT can not be applied on batch axis. add extra axis before operation and remove it afterward. |
188,615 | import torch
from mmdeploy.core import FUNCTION_REWRITER
func_name='torch.Tensor.expand', backend='ncnn')
def expand__ncnn(self, *sizes) -> torch.Tensor:
"""Rewrite `expand` for NCNN backend.
Do not expand on batch dim for tensor with ndim >= 3
"""
ctx = FUNCTION_REWRITER.get_context()
if self.n... | Rewrite `expand` for NCNN backend. Do not expand on batch dim for tensor with ndim >= 3 |
188,616 | import torch
from torch.types import Number
from mmdeploy.core import FUNCTION_REWRITER
The provided code snippet includes necessary dependencies for implementing the `linspace__default` function. Write a Python function `def linspace__default(start: Number, end: Number, steps: int = None, **kwargs)` to solve the foll... | Rewrite `linspace` for onnxruntime. |
188,617 | from typing import Sequence
import torch
from torch import Tensor
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import get_dynamic_axes
The provided code snippet includes necessary dependencies for implementing the `cat__tensorrt` function. Write a Python function `def cat__tensorrt(tensors: Sequence... | Rewrite `cat` for TensorRT backend. cat in TensorRT does not support bool or uint8 type when input is dynamic. |
188,618 | import torch
from mmdeploy.core import FUNCTION_REWRITER
func_name='torch.Tensor.__getattribute__', backend='ncnn')
def tensor__getattribute__ncnn(self: torch.Tensor, name: str):
"""Rewrite `__getattribute__` of `torch.Tensor` for ncnn backend.
Shape node is not supported by ncnn. This function transform dy... | Rewrite `__getattribute__` of `torch.Tensor` for ncnn backend. Shape node is not supported by ncnn. This function transform dynamic shape to constant shape. |
188,619 | import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import Backend, get_root_logger, is_dynamic_shape
The provided code snippet includes necessary dependencies for implementing the `adaptive_avg_pool2d__default` function. Write a Pyth... | Rewrite `adaptive_avg_pool2d` for default backend. |
188,620 | import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import Backend, get_root_logger, is_dynamic_shape
The provided code snippet includes necessary dependencies for implementing the `adaptive_avg_pool2d__ncnn` function. Write a Python ... | Rewrite `adaptive_avg_pool2d` for ncnn and torchscript backend. |
188,621 | import torch
from mmdeploy.core import FUNCTION_REWRITER
The provided code snippet includes necessary dependencies for implementing the `atan2__default` function. Write a Python function `def atan2__default( input1: torch.Tensor, input2: torch.Tensor, )` to solve the following problem:
Rewrite `atan2` for defa... | Rewrite `atan2` for default backend. |
188,622 | import torch
from mmdeploy.core import FUNCTION_REWRITER
The provided code snippet includes necessary dependencies for implementing the `triu__default` function. Write a Python function `def triu__default(input: torch.Tensor, diagonal: int = 0, *args, **kwargs) -> ... | Rewrite `triu` for exporting model to ONNX. |
188,623 | from typing import Optional, Union
import torch
from mmdeploy.core import FUNCTION_REWRITER
class GemmOp(torch.autograd.Function):
"""Create onnx::Gemm op."""
def forward(ctx, input, weight, bias=None):
out = input @ weight.transpose(0, 1)
if bias is not None:
out += bias
ret... | Rewrite `linear` for ncnn backend. The broadcast rules are different between ncnn and PyTorch. This function add extra reshape and transpose to support linear operation of different input shape. |
188,624 | import torch
from torch import Tensor
from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import Backend
class MultiHeadAttentionop(torch.autograd.Function):
"""Create onnx::MultiHeadAttention op."""
def forward(ctx, q: Tensor, k: Tensor, v: Tensor, q_weight: Tensor,
q_bias: Tensor, ... | Rewrite `forward` of MultiheadAttention used in vision_transformer for ncnn backend. Args: query (Tensor): The input query with shape [num_queries, bs, embed_dims] if self.batch_first is False, else [bs, num_queries embed_dims]. key (Tensor): The key tensor with shape [num_keys, bs, embed_dims] if self.batch_first is F... |
188,625 | from mmdeploy.core import FUNCTION_REWRITER
from mmdeploy.utils import Backend
The provided code snippet includes necessary dependencies for implementing the `patch_embed__forward__ncnn` function. Write a Python function `def patch_embed__forward__ncnn(self, x)` to solve the following problem:
Rewrite `forward` of Pat... | Rewrite `forward` of PatchEmbed for ncnn backend. Args: x (Tensor): Has shape (B, C, H, W). In most case, C is 3. Returns: tuple: Contains merged results and its spatial shape. - x (Tensor): Has shape (B, out_h * out_w, embed_dims) - out_size (tuple[int]): Spatial shape of x, arrange as (out_h, out_w). |
188,626 |
The provided code snippet includes necessary dependencies for implementing the `roi_align_rotated_default` function. Write a Python function `def roi_align_rotated_default(g, input: Tensor, rois: Tensor, output_size: List[int], spatial_scale: float, sampling... | Rewrite symbolic function for default backend. Replace onnx::RoIAlignRotated with mmdeploy::MMCVRoIAlignRotated. Args: ctx (ContextCaller): The context with additional information. g (Graph): The traced onnx graph. input (Tensor): Input tensor, 4-D feature map of shape (N, C, H, W). rois (Tensor): Bx5 boxes. First colu... |
188,627 |
The provided code snippet includes necessary dependencies for implementing the `roi_align_default` function. Write a Python function `def roi_align_default(g, input: Tensor, rois: Tensor, output_size: List[int], spatial_scale: float, sampling_ratio: int, pool_mode: str, ali... | Rewrite symbolic function for default backend. Replace onnx::RoiAlign with mmcv::MMCVRoiAlign for PPLNN. For ONNXRuntime, align operation get done outside the inference engine for opset versions lower than 16. By default, onnx::RoiAlign get replaced to mmdeploy::MMCVRoiAlign. Args: ctx (ContextCaller): The context with... |
188,628 | import torch
from packaging import version
from torch import Tensor
from torch.onnx import symbolic_helper as sym_help
from mmdeploy.core import FUNCTION_REWRITER, mark
from mmdeploy.utils import IR, is_dynamic_batch
from mmdeploy.utils.constants import Backend
from .nms_match import multiclass_nms_match
from .nms_rota... | Create a dummy onnx::NonMaxSuppression op while exporting to ONNX. This function helps exporting to onnx with batch and multiclass NMS op. It only supports class-agnostic detection results. That is, the scores is of shape (N, num_bboxes, num_classes) and the boxes is of shape (N, num_boxes, 4). Args: boxes (Tensor): Th... |
188,629 | import torch
from packaging import version
from torch import Tensor
from torch.onnx import symbolic_helper as sym_help
from mmdeploy.core import FUNCTION_REWRITER, mark
from mmdeploy.utils import IR, is_dynamic_batch
from mmdeploy.utils.constants import Backend
from .nms_match import multiclass_nms_match
from .nms_rota... | Wrapper for `multiclass_nms` with TensorRT. Args: ctx (ContextCaller): The context with additional information. boxes (Tensor): The bounding boxes of shape [N, num_boxes, 4]. scores (Tensor): The detection scores of shape [N, num_boxes, num_classes]. max_output_boxes_per_class (int): Maximum number of output boxes per ... |
188,630 | import torch
from packaging import version
from torch import Tensor
from torch.onnx import symbolic_helper as sym_help
from mmdeploy.core import FUNCTION_REWRITER, mark
from mmdeploy.utils import IR, is_dynamic_batch
from mmdeploy.utils.constants import Backend
from .nms_match import multiclass_nms_match
from .nms_rota... | rewrite for coreml batched nms. Use coreml_nms from custom ops. |
188,631 | import torch
from packaging import version
from torch import Tensor
from torch.onnx import symbolic_helper as sym_help
from mmdeploy.core import FUNCTION_REWRITER, mark
from mmdeploy.utils import IR, is_dynamic_batch
from mmdeploy.utils.constants import Backend
from .nms_match import multiclass_nms_match
from .nms_rota... | rewrite for torchscript batched nms. Use batched_nms from torchvision instead of custom nms. |
188,632 | import torch
from packaging import version
from torch import Tensor
from torch.onnx import symbolic_helper as sym_help
from mmdeploy.core import FUNCTION_REWRITER, mark
from mmdeploy.utils import IR, is_dynamic_batch
from mmdeploy.utils.constants import Backend
from .nms_match import multiclass_nms_match
from .nms_rota... | Wrapper for `multiclass_nms` with Ascend. Args: ctx (ContextCaller): The context with additional information. boxes (Tensor): The bounding boxes of shape [N, num_boxes, 4]. scores (Tensor): The detection scores of shape [N, num_boxes, num_classes]. max_output_boxes_per_class (int): Maximum number of output boxes per cl... |
188,633 | import torch
from torch import Tensor
import mmdeploy
from mmdeploy.core import FUNCTION_REWRITER, mark
class TRTBatchedRotatedNMSop(torch.autograd.Function):
"""Create mmdeploy::TRTBatchedRotatedNMSop op for TensorRT backend.
NMS in ONNX supports dynamic outputs. This class helps replace
onnx::NonMaxSuppre... | Wrapper for `multiclass_nms` with TensorRT. Args: ctx (ContextCaller): The context with additional information. boxes (Tensor): The bounding boxes of shape [N, num_boxes, 5]. scores (Tensor): The detection scores of shape [N, num_boxes, num_classes]. max_output_boxes_per_class (int): Maximum number of output boxes per ... |
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