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class SparseMaxPool2d(SparseMaxPool):
def __init__(self, kernel_size, stride=1, padding=0, dilation=1):
super(SparseMaxPool2d, self).__init__(2, kernel_size, stride, padding, dilation)
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class SparseMaxPool3d(SparseMaxPool):
def __init__(self, kernel_size, stride=1, padding=0, dilation=1):
super(SparseMaxPool3d, self).__init__(3, kernel_size, stride, padding, dilation)
|
class SyncBatchNormFunction(Function):
@staticmethod
def symbolic(g, input, running_mean, running_var, weight, bias, momentum, eps, group, group_size, stats_mode):
return g.op('mmcv::MMCVSyncBatchNorm', input, running_mean, running_var, weight, bias, momentum_f=momentum, eps_f=eps, group_i=group, gro... |
@NORM_LAYERS.register_module(name='MMSyncBN')
class SyncBatchNorm(Module):
"Synchronized Batch Normalization.\n\n Args:\n num_features (int): number of features/chennels in input tensor\n eps (float, optional): a value added to the denominator for numerical\n stability. Defaults to 1e-... |
class ThreeInterpolate(Function):
'Performs weighted linear interpolation on 3 features.\n\n Please refer to `Paper of PointNet++ <https://arxiv.org/abs/1706.02413>`_\n for more details.\n '
@staticmethod
def forward(ctx, features: torch.Tensor, indices: torch.Tensor, weight: torch.Tensor) -> to... |
class ThreeNN(Function):
'Find the top-3 nearest neighbors of the target set from the source set.\n\n Please refer to `Paper of PointNet++ <https://arxiv.org/abs/1706.02413>`_\n for more details.\n '
@staticmethod
def forward(ctx, target: torch.Tensor, source: torch.Tensor) -> Tuple[(torch.Tenso... |
class TINShiftFunction(Function):
@staticmethod
def forward(ctx, input, shift):
C = input.size(2)
num_segments = shift.size(1)
if (((C // num_segments) <= 0) or ((C % num_segments) != 0)):
raise ValueError(f'C should be a multiple of num_segments, but got C={C} and num_seg... |
class TINShift(nn.Module):
'Temporal Interlace Shift.\n\n Temporal Interlace shift is a differentiable temporal-wise frame shifting\n which is proposed in "Temporal Interlacing Network"\n\n Please refer to `Temporal Interlacing Network\n <https://arxiv.org/abs/2001.06499>`_ for more details.\n\n Co... |
class _Voxelization(Function):
@staticmethod
def forward(ctx, points, voxel_size, coors_range, max_points=35, max_voxels=20000, deterministic=True):
'Convert kitti points(N, >=3) to voxels.\n\n Args:\n points (torch.Tensor): [N, ndim]. Points[:, :3] contain xyz points\n ... |
class Voxelization(nn.Module):
'Convert kitti points(N, >=3) to voxels.\n\n Please refer to `Point-Voxel CNN for Efficient 3D Deep Learning\n <https://arxiv.org/abs/1907.03739>`_ for more details.\n\n Args:\n voxel_size (tuple or float): The size of voxel with the shape of [3].\n point_clou... |
def scatter(input, devices, streams=None):
'Scatters tensor across multiple GPUs.'
if (streams is None):
streams = ([None] * len(devices))
if isinstance(input, list):
chunk_size = (((len(input) - 1) // len(devices)) + 1)
outputs = [scatter(input[i], [devices[(i // chunk_size)]], [s... |
def synchronize_stream(output, devices, streams):
if isinstance(output, list):
chunk_size = (len(output) // len(devices))
for i in range(len(devices)):
for j in range(chunk_size):
synchronize_stream(output[((i * chunk_size) + j)], [devices[i]], [streams[i]])
elif is... |
def get_input_device(input):
if isinstance(input, list):
for item in input:
input_device = get_input_device(item)
if (input_device != (- 1)):
return input_device
return (- 1)
elif isinstance(input, torch.Tensor):
return (input.get_device() if inp... |
class Scatter():
@staticmethod
def forward(target_gpus, input):
input_device = get_input_device(input)
streams = None
if ((input_device == (- 1)) and (target_gpus != [(- 1)])):
streams = [_get_stream(device) for device in target_gpus]
outputs = scatter(input, targe... |
def collate(batch, samples_per_gpu=1):
'Puts each data field into a tensor/DataContainer with outer dimension\n batch size.\n\n Extend default_collate to add support for\n :type:`~mmcv.parallel.DataContainer`. There are 3 cases.\n\n 1. cpu_only = True, e.g., meta data\n 2. cpu_only = False, stack =... |
def assert_tensor_type(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
if (not isinstance(args[0].data, torch.Tensor)):
raise AttributeError(f'{args[0].__class__.__name__} has no attribute {func.__name__} for type {args[0].datatype}')
return func(*args, **kwargs)
r... |
class DataContainer():
'A container for any type of objects.\n\n Typically tensors will be stacked in the collate function and sliced along\n some dimension in the scatter function. This behavior has some limitations.\n 1. All tensors have to be the same size.\n 2. Types are limited (numpy array or Te... |
class MMDataParallel(DataParallel):
'The DataParallel module that supports DataContainer.\n\n MMDataParallel has two main differences with PyTorch DataParallel:\n\n - It supports a custom type :class:`DataContainer` which allows more\n flexible control of input data during both GPU and CPU inference.\n... |
class MMDistributedDataParallel(DistributedDataParallel):
'The DDP module that supports DataContainer.\n\n MMDDP has two main differences with PyTorch DDP:\n\n - It supports a custom type :class:`DataContainer` which allows more\n flexible control of input data.\n - It implement two APIs ``train_ste... |
@MODULE_WRAPPERS.register_module()
class MMDistributedDataParallel(nn.Module):
def __init__(self, module, dim=0, broadcast_buffers=True, bucket_cap_mb=25):
super(MMDistributedDataParallel, self).__init__()
self.module = module
self.dim = dim
self.broadcast_buffers = broadcast_buff... |
def scatter(inputs, target_gpus, dim=0):
'Scatter inputs to target gpus.\n\n The only difference from original :func:`scatter` is to add support for\n :type:`~mmcv.parallel.DataContainer`.\n '
def scatter_map(obj):
if isinstance(obj, torch.Tensor):
if (target_gpus != [(- 1)]):
... |
def scatter_kwargs(inputs, kwargs, target_gpus, dim=0):
'Scatter with support for kwargs dictionary.'
inputs = (scatter(inputs, target_gpus, dim) if inputs else [])
kwargs = (scatter(kwargs, target_gpus, dim) if kwargs else [])
if (len(inputs) < len(kwargs)):
inputs.extend([() for _ in range((... |
def is_module_wrapper(module):
'Check if a module is a module wrapper.\n\n The following 3 modules in MMCV (and their subclasses) are regarded as\n module wrappers: DataParallel, DistributedDataParallel,\n MMDistributedDataParallel (the deprecated version). You may add you own\n module wrapper by regi... |
class BaseModule(nn.Module, metaclass=ABCMeta):
'Base module for all modules in openmmlab.\n\n ``BaseModule`` is a wrapper of ``torch.nn.Module`` with additional\n functionality of parameter initialization. Compared with\n ``torch.nn.Module``, ``BaseModule`` mainly adds three attributes.\n\n - ``init_... |
class Sequential(BaseModule, nn.Sequential):
'Sequential module in openmmlab.\n\n Args:\n init_cfg (dict, optional): Initialization config dict.\n '
def __init__(self, *args, init_cfg=None):
BaseModule.__init__(self, init_cfg)
nn.Sequential.__init__(self, *args)
|
class ModuleList(BaseModule, nn.ModuleList):
'ModuleList in openmmlab.\n\n Args:\n modules (iterable, optional): an iterable of modules to add.\n init_cfg (dict, optional): Initialization config dict.\n '
def __init__(self, modules=None, init_cfg=None):
BaseModule.__init__(self, i... |
class ModuleDict(BaseModule, nn.ModuleDict):
'ModuleDict in openmmlab.\n\n Args:\n modules (dict, optional): a mapping (dictionary) of (string: module)\n or an iterable of key-value pairs of type (string, module).\n init_cfg (dict, optional): Initialization config dict.\n '
def... |
class BaseRunner(metaclass=ABCMeta):
'The base class of Runner, a training helper for PyTorch.\n\n All subclasses should implement the following APIs:\n\n - ``run()``\n - ``train()``\n - ``val()``\n - ``save_checkpoint()``\n\n Args:\n model (:obj:`torch.nn.Module`): The model to be run.\n... |
def build_runner_constructor(cfg):
return RUNNER_BUILDERS.build(cfg)
|
def build_runner(cfg, default_args=None):
runner_cfg = copy.deepcopy(cfg)
constructor_type = runner_cfg.pop('constructor', 'DefaultRunnerConstructor')
runner_constructor = build_runner_constructor(dict(type=constructor_type, runner_cfg=runner_cfg, default_args=default_args))
runner = runner_constructo... |
def _get_mmcv_home():
mmcv_home = os.path.expanduser(os.getenv(ENV_MMCV_HOME, os.path.join(os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))
mkdir_or_exist(mmcv_home)
return mmcv_home
|
def load_state_dict(module, state_dict, strict=False, logger=None):
"Load state_dict to a module.\n\n This method is modified from :meth:`torch.nn.Module.load_state_dict`.\n Default value for ``strict`` is set to ``False`` and the message for\n param mismatch will be shown even if strict is False.\n\n ... |
def get_torchvision_models():
model_urls = dict()
for (_, name, ispkg) in pkgutil.walk_packages(torchvision.models.__path__):
if ispkg:
continue
_zoo = import_module(f'torchvision.models.{name}')
if hasattr(_zoo, 'model_urls'):
_urls = getattr(_zoo, 'model_urls'... |
def get_external_models():
mmcv_home = _get_mmcv_home()
default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')
default_urls = load_file(default_json_path)
assert isinstance(default_urls, dict)
external_json_path = osp.join(mmcv_home, 'open_mmlab.json')
if osp.exists(extern... |
def get_mmcls_models():
mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json')
mmcls_urls = load_file(mmcls_json_path)
return mmcls_urls
|
def get_deprecated_model_names():
deprecate_json_path = osp.join(mmcv.__path__[0], 'model_zoo/deprecated.json')
deprecate_urls = load_file(deprecate_json_path)
assert isinstance(deprecate_urls, dict)
return deprecate_urls
|
def _process_mmcls_checkpoint(checkpoint):
if ('state_dict' in checkpoint):
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
new_state_dict = OrderedDict()
for (k, v) in state_dict.items():
if k.startswith('backbone.'):
new_state_dict[k[9:]] = v
... |
class CheckpointLoader():
'A general checkpoint loader to manage all schemes.'
_schemes = {}
@classmethod
def _register_scheme(cls, prefixes, loader, force=False):
if isinstance(prefixes, str):
prefixes = [prefixes]
else:
assert isinstance(prefixes, (list, tupl... |
@CheckpointLoader.register_scheme(prefixes='')
def load_from_local(filename, map_location):
'load checkpoint by local file path.\n\n Args:\n filename (str): local checkpoint file path\n map_location (str, optional): Same as :func:`torch.load`.\n\n Returns:\n dict or OrderedDict: The loa... |
@CheckpointLoader.register_scheme(prefixes=('http://', 'https://'))
def load_from_http(filename, map_location=None, model_dir=None):
'load checkpoint through HTTP or HTTPS scheme path. In distributed\n setting, this function only download checkpoint at local rank 0.\n\n Args:\n filename (str): checkp... |
@CheckpointLoader.register_scheme(prefixes='pavi://')
def load_from_pavi(filename, map_location=None):
'load checkpoint through the file path prefixed with pavi. In distributed\n setting, this function download ckpt at all ranks to different temporary\n directories.\n\n Args:\n filename (str): che... |
@CheckpointLoader.register_scheme(prefixes='(\\S+\\:)?s3://')
def load_from_ceph(filename, map_location=None, backend='petrel'):
"load checkpoint through the file path prefixed with s3. In distributed\n setting, this function download ckpt at all ranks to different temporary\n directories.\n\n Note:\n ... |
@CheckpointLoader.register_scheme(prefixes=('modelzoo://', 'torchvision://'))
def load_from_torchvision(filename, map_location=None):
'load checkpoint through the file path prefixed with modelzoo or\n torchvision.\n\n Args:\n filename (str): checkpoint file path with modelzoo or\n torchvis... |
@CheckpointLoader.register_scheme(prefixes=('open-mmlab://', 'openmmlab://'))
def load_from_openmmlab(filename, map_location=None):
'load checkpoint through the file path prefixed with open-mmlab or\n openmmlab.\n\n Args:\n filename (str): checkpoint file path with open-mmlab or\n openmmlab pr... |
@CheckpointLoader.register_scheme(prefixes='mmcls://')
def load_from_mmcls(filename, map_location=None):
'load checkpoint through the file path prefixed with mmcls.\n\n Args:\n filename (str): checkpoint file path with mmcls prefix\n map_location (str, optional): Same as :func:`torch.load`.\n\n ... |
def _load_checkpoint(filename, map_location=None, logger=None):
'Load checkpoint from somewhere (modelzoo, file, url).\n\n Args:\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for\n details.\n ... |
def _load_checkpoint_with_prefix(prefix, filename, map_location=None):
'Load partial pretrained model with specific prefix.\n\n Args:\n prefix (str): The prefix of sub-module.\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to `... |
def load_checkpoint(model, filename, map_location=None, strict=False, logger=None, revise_keys=[('^module\\.', '')]):
"Load checkpoint from a file or URI.\n\n Args:\n model (Module): Module to load checkpoint.\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``ope... |
def weights_to_cpu(state_dict):
'Copy a model state_dict to cpu.\n\n Args:\n state_dict (OrderedDict): Model weights on GPU.\n\n Returns:\n OrderedDict: Model weights on GPU.\n '
state_dict_cpu = OrderedDict()
for (key, val) in state_dict.items():
state_dict_cpu[key] = val.c... |
def _save_to_state_dict(module, destination, prefix, keep_vars):
'Saves module state to `destination` dictionary.\n\n This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.\n\n Args:\n module (nn.Module): The module to generate state_dict.\n destination (dict): A dict where ... |
def get_state_dict(module, destination=None, prefix='', keep_vars=False):
'Returns a dictionary containing a whole state of the module.\n\n Both parameters and persistent buffers (e.g. running averages) are\n included. Keys are corresponding parameter and buffer names.\n\n This method is modified from :m... |
def save_checkpoint(model, filename, optimizer=None, meta=None, file_client_args=None):
'Save checkpoint to file.\n\n The checkpoint will have 3 fields: ``meta``, ``state_dict`` and\n ``optimizer``. By default ``meta`` will contain version and time info.\n\n Args:\n model (Module): Module whose pa... |
@RUNNER_BUILDERS.register_module()
class DefaultRunnerConstructor():
"Default constructor for runners.\n\n Custom existing `Runner` like `EpocBasedRunner` though `RunnerConstructor`.\n For example, We can inject some new properties and functions for `Runner`.\n\n Example:\n >>> from mmcv.runner im... |
def init_dist(launcher, backend='nccl', **kwargs):
if (mp.get_start_method(allow_none=True) is None):
mp.set_start_method('spawn')
if (launcher == 'pytorch'):
_init_dist_pytorch(backend, **kwargs)
elif (launcher == 'mpi'):
_init_dist_mpi(backend, **kwargs)
elif (launcher == 'sl... |
def _init_dist_pytorch(backend, **kwargs):
rank = int(os.environ['RANK'])
num_gpus = torch.cuda.device_count()
torch.cuda.set_device((rank % num_gpus))
dist.init_process_group(backend=backend, **kwargs)
|
def _init_dist_mpi(backend, **kwargs):
rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
num_gpus = torch.cuda.device_count()
torch.cuda.set_device((rank % num_gpus))
dist.init_process_group(backend=backend, **kwargs)
|
def _init_dist_slurm(backend, port=None):
'Initialize slurm distributed training environment.\n\n If argument ``port`` is not specified, then the master port will be system\n environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system\n environment variable, then a default port ``29500`` wi... |
def get_dist_info():
if (dist.is_available() and dist.is_initialized()):
rank = dist.get_rank()
world_size = dist.get_world_size()
else:
rank = 0
world_size = 1
return (rank, world_size)
|
def master_only(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
(rank, _) = get_dist_info()
if (rank == 0):
return func(*args, **kwargs)
return wrapper
|
def allreduce_params(params, coalesce=True, bucket_size_mb=(- 1)):
'Allreduce parameters.\n\n Args:\n params (list[torch.Parameters]): List of parameters or buffers of a\n model.\n coalesce (bool, optional): Whether allreduce parameters as a whole.\n Defaults to True.\n ... |
def allreduce_grads(params, coalesce=True, bucket_size_mb=(- 1)):
'Allreduce gradients.\n\n Args:\n params (list[torch.Parameters]): List of parameters of a model\n coalesce (bool, optional): Whether allreduce parameters as a whole.\n Defaults to True.\n bucket_size_mb (int, opt... |
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=(- 1)):
if (bucket_size_mb > 0):
bucket_size_bytes = ((bucket_size_mb * 1024) * 1024)
buckets = _take_tensors(tensors, bucket_size_bytes)
else:
buckets = OrderedDict()
for tensor in tensors:
tp = tensor.ty... |
@RUNNERS.register_module()
class EpochBasedRunner(BaseRunner):
'Epoch-based Runner.\n\n This runner train models epoch by epoch.\n '
def run_iter(self, data_batch, train_mode, **kwargs):
if (self.batch_processor is not None):
outputs = self.batch_processor(self.model, data_batch, tr... |
@RUNNERS.register_module()
class Runner(EpochBasedRunner):
'Deprecated name of EpochBasedRunner.'
def __init__(self, *args, **kwargs):
warnings.warn('Runner was deprecated, please use EpochBasedRunner instead', DeprecationWarning)
super().__init__(*args, **kwargs)
|
def cast_tensor_type(inputs, src_type, dst_type):
'Recursively convert Tensor in inputs from src_type to dst_type.\n\n Note:\n In v1.4.4 and later, ``cast_tersor_type`` will only convert the\n torch.Tensor which is consistent with ``src_type`` to the ``dst_type``.\n Before v1.4.4, it ignor... |
def auto_fp16(apply_to=None, out_fp32=False):
"Decorator to enable fp16 training automatically.\n\n This decorator is useful when you write custom modules and want to support\n mixed precision training. If inputs arguments are fp32 tensors, they will\n be converted to fp16 automatically. Arguments other ... |
def force_fp32(apply_to=None, out_fp16=False):
"Decorator to convert input arguments to fp32 in force.\n\n This decorator is useful when you write custom modules and want to support\n mixed precision training. If there are some inputs that must be processed\n in fp32 mode, then this decorator can handle ... |
def allreduce_grads(params, coalesce=True, bucket_size_mb=(- 1)):
warnings.warning('"mmcv.runner.fp16_utils.allreduce_grads" is deprecated, and will be removed in v2.8. Please switch to "mmcv.runner.allreduce_grads', DeprecationWarning)
_allreduce_grads(params, coalesce=coalesce, bucket_size_mb=bucket_size_mb... |
def wrap_fp16_model(model):
'Wrap the FP32 model to FP16.\n\n If you are using PyTorch >= 1.6, torch.cuda.amp is used as the\n backend, otherwise, original mmcv implementation will be adopted.\n\n For PyTorch >= 1.6, this function will\n 1. Set fp16 flag inside the model to True.\n\n Otherwise:\n ... |
def patch_norm_fp32(module):
'Recursively convert normalization layers from FP16 to FP32.\n\n Args:\n module (nn.Module): The modules to be converted in FP16.\n\n Returns:\n nn.Module: The converted module, the normalization layers have been\n converted to FP32.\n '
if isinst... |
def patch_forward_method(func, src_type, dst_type, convert_output=True):
'Patch the forward method of a module.\n\n Args:\n func (callable): The original forward method.\n src_type (torch.dtype): Type of input arguments to be converted from.\n dst_type (torch.dtype): Type of input argument... |
class LossScaler():
'Class that manages loss scaling in mixed precision training which\n supports both dynamic or static mode.\n\n The implementation refers to\n https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/loss_scaler.py.\n Indirectly, by supplying ``mode=\'dynamic\'`` for dynamic loss ... |
@HOOKS.register_module()
class CheckpointHook(Hook):
'Save checkpoints periodically.\n\n Args:\n interval (int): The saving period. If ``by_epoch=True``, interval\n indicates epochs, otherwise it indicates iterations.\n Default: -1, which means "never".\n by_epoch (bool): Sa... |
@HOOKS.register_module()
class ClosureHook(Hook):
def __init__(self, fn_name, fn):
assert hasattr(self, fn_name)
assert callable(fn)
setattr(self, fn_name, fn)
|
@HOOKS.register_module()
class EMAHook(Hook):
'Exponential Moving Average Hook.\n\n Use Exponential Moving Average on all parameters of model in training\n process. All parameters have a ema backup, which update by the formula\n as below. EMAHook takes priority over EvalHook and CheckpointSaverHook.\n\n ... |
class EvalHook(Hook):
"Non-Distributed evaluation hook.\n\n This hook will regularly perform evaluation in a given interval when\n performing in non-distributed environment.\n\n Args:\n dataloader (DataLoader): A PyTorch dataloader, whose dataset has\n implemented ``evaluate`` function.... |
class DistEvalHook(EvalHook):
"Distributed evaluation hook.\n\n This hook will regularly perform evaluation in a given interval when\n performing in distributed environment.\n\n Args:\n dataloader (DataLoader): A PyTorch dataloader, whose dataset has\n implemented ``evaluate`` function.... |
class Hook():
stages = ('before_run', 'before_train_epoch', 'before_train_iter', 'after_train_iter', 'after_train_epoch', 'before_val_epoch', 'before_val_iter', 'after_val_iter', 'after_val_epoch', 'after_run')
def before_run(self, runner):
pass
def after_run(self, runner):
pass
def... |
@HOOKS.register_module()
class IterTimerHook(Hook):
def before_epoch(self, runner):
self.t = time.time()
def before_iter(self, runner):
runner.log_buffer.update({'data_time': (time.time() - self.t)})
def after_iter(self, runner):
runner.log_buffer.update({'time': (time.time() - ... |
class LoggerHook(Hook):
'Base class for logger hooks.\n\n Args:\n interval (int): Logging interval (every k iterations). Default 10.\n ignore_last (bool): Ignore the log of last iterations in each epoch\n if less than `interval`. Default True.\n reset_flag (bool): Whether to cle... |
@HOOKS.register_module()
class DvcliveLoggerHook(LoggerHook):
'Class to log metrics with dvclive.\n\n It requires `dvclive`_ to be installed.\n\n Args:\n model_file (str): Default None. If not None, after each epoch the\n model will be saved to {model_file}.\n interval (int): Loggin... |
@HOOKS.register_module()
class MlflowLoggerHook(LoggerHook):
'Class to log metrics and (optionally) a trained model to MLflow.\n\n It requires `MLflow`_ to be installed.\n\n Args:\n exp_name (str, optional): Name of the experiment to be used.\n Default None. If not None, set the active exp... |
@HOOKS.register_module()
class NeptuneLoggerHook(LoggerHook):
"Class to log metrics to NeptuneAI.\n\n It requires `Neptune`_ to be installed.\n\n Args:\n init_kwargs (dict): a dict contains the initialization keys as below:\n\n - project (str): Name of a project in a form of\n ... |
@HOOKS.register_module()
class PaviLoggerHook(LoggerHook):
"Class to visual model, log metrics (for internal use).\n\n Args:\n init_kwargs (dict): A dict contains the initialization keys.\n add_graph (bool): Whether to visual model. Default: False.\n add_last_ckpt (bool): Whether to save c... |
@HOOKS.register_module()
class SegmindLoggerHook(LoggerHook):
'Class to log metrics to Segmind.\n\n It requires `Segmind`_ to be installed.\n\n Args:\n interval (int): Logging interval (every k iterations). Default: 10.\n ignore_last (bool): Ignore the log of last iterations in each epoch\n ... |
@HOOKS.register_module()
class TensorboardLoggerHook(LoggerHook):
'Class to log metrics to Tensorboard.\n\n Args:\n log_dir (string): Save directory location. Default: None. If default\n values are used, directory location is ``runner.work_dir``/tf_logs.\n interval (int): Logging inter... |
@HOOKS.register_module()
class TextLoggerHook(LoggerHook):
"Logger hook in text.\n\n In this logger hook, the information will be printed on terminal and\n saved in json file.\n\n Args:\n by_epoch (bool, optional): Whether EpochBasedRunner is used.\n Default: True.\n interval (in... |
@HOOKS.register_module()
class WandbLoggerHook(LoggerHook):
"Class to log metrics with wandb.\n\n It requires `wandb`_ to be installed.\n\n\n Args:\n init_kwargs (dict): A dict contains the initialization keys. Check\n https://docs.wandb.ai/ref/python/init for more init arguments.\n ... |
class LrUpdaterHook(Hook):
"LR Scheduler in MMCV.\n\n Args:\n by_epoch (bool): LR changes epoch by epoch\n warmup (string): Type of warmup used. It can be None(use no warmup),\n 'constant', 'linear' or 'exp'\n warmup_iters (int): The number of iterations or epochs that warmup\n ... |
@HOOKS.register_module()
class FixedLrUpdaterHook(LrUpdaterHook):
def __init__(self, **kwargs):
super(FixedLrUpdaterHook, self).__init__(**kwargs)
def get_lr(self, runner, base_lr):
return base_lr
|
@HOOKS.register_module()
class StepLrUpdaterHook(LrUpdaterHook):
"Step LR scheduler with min_lr clipping.\n\n Args:\n step (int | list[int]): Step to decay the LR. If an int value is given,\n regard it as the decay interval. If a list is given, decay LR at\n these steps.\n g... |
@HOOKS.register_module()
class ExpLrUpdaterHook(LrUpdaterHook):
def __init__(self, gamma, **kwargs):
self.gamma = gamma
super(ExpLrUpdaterHook, self).__init__(**kwargs)
def get_lr(self, runner, base_lr):
progress = (runner.epoch if self.by_epoch else runner.iter)
return (base... |
@HOOKS.register_module()
class PolyLrUpdaterHook(LrUpdaterHook):
def __init__(self, power=1.0, min_lr=0.0, **kwargs):
self.power = power
self.min_lr = min_lr
super(PolyLrUpdaterHook, self).__init__(**kwargs)
def get_lr(self, runner, base_lr):
if self.by_epoch:
pro... |
@HOOKS.register_module()
class InvLrUpdaterHook(LrUpdaterHook):
def __init__(self, gamma, power=1.0, **kwargs):
self.gamma = gamma
self.power = power
super(InvLrUpdaterHook, self).__init__(**kwargs)
def get_lr(self, runner, base_lr):
progress = (runner.epoch if self.by_epoch ... |
@HOOKS.register_module()
class CosineAnnealingLrUpdaterHook(LrUpdaterHook):
def __init__(self, min_lr=None, min_lr_ratio=None, **kwargs):
assert ((min_lr is None) ^ (min_lr_ratio is None))
self.min_lr = min_lr
self.min_lr_ratio = min_lr_ratio
super(CosineAnnealingLrUpdaterHook, se... |
@HOOKS.register_module()
class FlatCosineAnnealingLrUpdaterHook(LrUpdaterHook):
'Flat + Cosine lr schedule.\n\n Modified from https://github.com/fastai/fastai/blob/master/fastai/callback/schedule.py#L128 # noqa: E501\n\n Args:\n start_percent (float): When to start annealing the learning rate\n ... |
@HOOKS.register_module()
class CosineRestartLrUpdaterHook(LrUpdaterHook):
'Cosine annealing with restarts learning rate scheme.\n\n Args:\n periods (list[int]): Periods for each cosine anneling cycle.\n restart_weights (list[float], optional): Restart weights at each\n restart iteratio... |
def get_position_from_periods(iteration, cumulative_periods):
'Get the position from a period list.\n\n It will return the index of the right-closest number in the period list.\n For example, the cumulative_periods = [100, 200, 300, 400],\n if iteration == 50, return 0;\n if iteration == 210, return 2... |
@HOOKS.register_module()
class CyclicLrUpdaterHook(LrUpdaterHook):
"Cyclic LR Scheduler.\n\n Implement the cyclical learning rate policy (CLR) described in\n https://arxiv.org/pdf/1506.01186.pdf\n\n Different from the original paper, we use cosine annealing rather than\n triangular policy inside a cyc... |
@HOOKS.register_module()
class OneCycleLrUpdaterHook(LrUpdaterHook):
"One Cycle LR Scheduler.\n\n The 1cycle learning rate policy changes the learning rate after every\n batch. The one cycle learning rate policy is described in\n https://arxiv.org/pdf/1708.07120.pdf\n\n Args:\n max_lr (float or... |
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