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