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131 lines
4.8 KiB
131 lines
4.8 KiB
# Copyright (c) OpenMMLab. All rights reserved.
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from abc import ABCMeta, abstractmethod
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from collections import OrderedDict
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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class BasePose(nn.Module, metaclass=ABCMeta):
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"""Base class for pose detectors.
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All recognizers should subclass it.
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All subclass should overwrite:
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Methods:`forward_train`, supporting to forward when training.
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Methods:`forward_test`, supporting to forward when testing.
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Args:
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backbone (dict): Backbone modules to extract feature.
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head (dict): Head modules to give output.
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train_cfg (dict): Config for training. Default: None.
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test_cfg (dict): Config for testing. Default: None.
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"""
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@abstractmethod
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def forward_train(self, img, img_metas, **kwargs):
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"""Defines the computation performed at training."""
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@abstractmethod
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def forward_test(self, img, img_metas, **kwargs):
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"""Defines the computation performed at testing."""
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@abstractmethod
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def forward(self, img, img_metas, return_loss=True, **kwargs):
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"""Forward function."""
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@staticmethod
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def _parse_losses(losses):
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"""Parse the raw outputs (losses) of the network.
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Args:
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losses (dict): Raw output of the network, which usually contain
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losses and other necessary information.
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Returns:
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tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor \
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which may be a weighted sum of all losses, log_vars \
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contains all the variables to be sent to the logger.
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"""
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log_vars = OrderedDict()
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for loss_name, loss_value in losses.items():
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if isinstance(loss_value, torch.Tensor):
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log_vars[loss_name] = loss_value.mean()
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elif isinstance(loss_value, float):
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log_vars[loss_name] = loss_value
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elif isinstance(loss_value, list):
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log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
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else:
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raise TypeError(
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f'{loss_name} is not a tensor or list of tensors or float')
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loss = sum(_value for _key, _value in log_vars.items()
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if 'loss' in _key)
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log_vars['loss'] = loss
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for loss_name, loss_value in log_vars.items():
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# reduce loss when distributed training
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if not isinstance(loss_value, float):
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if dist.is_available() and dist.is_initialized():
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loss_value = loss_value.data.clone()
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dist.all_reduce(loss_value.div_(dist.get_world_size()))
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log_vars[loss_name] = loss_value.item()
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else:
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log_vars[loss_name] = loss_value
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return loss, log_vars
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def train_step(self, data_batch, optimizer, **kwargs):
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"""The iteration step during training.
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This method defines an iteration step during training, except for the
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back propagation and optimizer updating, which are done in an optimizer
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hook. Note that in some complicated cases or models, the whole process
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including back propagation and optimizer updating is also defined in
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this method, such as GAN.
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Args:
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data_batch (dict): The output of dataloader.
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optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
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runner is passed to ``train_step()``. This argument is unused
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and reserved.
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Returns:
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dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
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``num_samples``.
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``loss`` is a tensor for back propagation, which can be a
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weighted sum of multiple losses.
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``log_vars`` contains all the variables to be sent to the
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logger.
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``num_samples`` indicates the batch size (when the model is
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DDP, it means the batch size on each GPU), which is used for
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averaging the logs.
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"""
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losses = self.forward(**data_batch)
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loss, log_vars = self._parse_losses(losses)
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outputs = dict(
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loss=loss,
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log_vars=log_vars,
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num_samples=len(next(iter(data_batch.values()))))
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return outputs
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def val_step(self, data_batch, optimizer, **kwargs):
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"""The iteration step during validation.
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This method shares the same signature as :func:`train_step`, but used
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during val epochs. Note that the evaluation after training epochs is
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not implemented with this method, but an evaluation hook.
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"""
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results = self.forward(return_loss=False, **data_batch)
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outputs = dict(results=results)
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return outputs
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@abstractmethod
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def show_result(self, **kwargs):
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"""Visualize the results."""
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raise NotImplementedError
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