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| from abc import ABCMeta, abstractmethod | |
| from collections import OrderedDict | |
| import torch | |
| import torch.distributed as dist | |
| from mmcv.runner import BaseModule | |
| class BaseArchitecture(BaseModule, metaclass=ABCMeta): | |
| """Base class for mmhuman3d architecture.""" | |
| def __init__(self, init_cfg=None): | |
| super(BaseArchitecture, self).__init__(init_cfg) | |
| def forward_train(self, **kwargs): | |
| pass | |
| def forward_test(self, **kwargs): | |
| pass | |
| def _parse_losses(self, 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, 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') | |
| 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 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() | |
| return loss, log_vars | |
| def train_step(self, data, optimizer): | |
| """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 (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(**data) | |
| loss, log_vars = self._parse_losses(losses) | |
| outputs = dict(loss=loss, | |
| log_vars=log_vars, | |
| num_samples=len(data['img_metas'])) | |
| return outputs | |
| def val_step(self, data, optimizer=None): | |
| """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. | |
| """ | |
| losses = self(**data) | |
| loss, log_vars = self._parse_losses(losses) | |
| outputs = dict(loss=loss, | |
| log_vars=log_vars, | |
| num_samples=len(data['img_metas'])) | |
| return outputs | |
| def forward(self, **kwargs): | |
| if self.training: | |
| return self.forward_train(**kwargs) | |
| else: | |
| return self.forward_test(**kwargs) | |