174 lines
6.6 KiB
Python
174 lines
6.6 KiB
Python
# 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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from typing import Sequence
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import torch
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import torch.distributed as dist
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from mmcv.runner import BaseModule, auto_fp16
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class BaseClassifier(BaseModule, metaclass=ABCMeta):
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"""Base class for classifiers."""
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def __init__(self, init_cfg=None):
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super(BaseClassifier, self).__init__(init_cfg)
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self.fp16_enabled = False
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@property
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def with_neck(self):
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return hasattr(self, 'neck') and self.neck is not None
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@property
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def with_head(self):
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return hasattr(self, 'head') and self.head is not None
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@abstractmethod
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def extract_feat(self, imgs, stage=None):
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pass
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def extract_feats(self, imgs, stage=None):
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assert isinstance(imgs, Sequence)
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kwargs = {} if stage is None else {'stage': stage}
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for img in imgs:
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yield self.extract_feat(img, **kwargs)
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@abstractmethod
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def forward_train(self, imgs, **kwargs):
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"""
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Args:
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img (list[Tensor]): List of tensors of shape (1, C, H, W).
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Typically these should be mean centered and std scaled.
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kwargs (keyword arguments): Specific to concrete implementation.
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"""
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pass
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@abstractmethod
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def simple_test(self, img, **kwargs):
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pass
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def forward_test(self, imgs, **kwargs):
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"""
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Args:
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imgs (List[Tensor]): the outer list indicates test-time
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augmentations and inner Tensor should have a shape NxCxHxW,
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which contains all images in the batch.
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"""
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if isinstance(imgs, torch.Tensor):
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imgs = [imgs]
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for var, name in [(imgs, 'imgs')]:
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if not isinstance(var, list):
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raise TypeError(f'{name} must be a list, but got {type(var)}')
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if len(imgs) == 1:
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return self.simple_test(imgs[0], **kwargs)
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else:
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raise NotImplementedError('aug_test has not been implemented')
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@auto_fp16(apply_to=('img', ))
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def forward(self, img, return_loss=True, **kwargs):
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"""Calls either forward_train or forward_test depending on whether
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return_loss=True.
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Note this setting will change the expected inputs. When
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`return_loss=True`, img and img_meta are single-nested (i.e. Tensor and
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List[dict]), and when `resturn_loss=False`, img and img_meta should be
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double nested (i.e. List[Tensor], List[List[dict]]), with the outer
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list indicating test time augmentations.
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"""
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if return_loss:
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return self.forward_train(img, **kwargs)
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else:
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return self.forward_test(img, **kwargs)
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def _parse_losses(self, losses):
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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, list):
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log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
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elif isinstance(loss_value, dict):
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for name, value in loss_value.items():
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log_vars[name] = 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')
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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 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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return loss, log_vars
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def train_step(self, data, optimizer=None, **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 are also defined in
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this method, such as GAN.
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Args:
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data (dict): The output of dataloader.
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optimizer (:obj:`torch.optim.Optimizer` | dict, optional): The
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optimizer of runner is passed to ``train_step()``. This
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argument is unused and reserved.
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Returns:
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dict: Dict of outputs. The following fields are contained.
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- loss (torch.Tensor): A tensor for back propagation, which \
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can be a weighted sum of multiple losses.
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- log_vars (dict): Dict contains all the variables to be sent \
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to the logger.
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- num_samples (int): Indicates the batch size (when the model \
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is DDP, it means the batch size on each GPU), which is \
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used for averaging the logs.
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"""
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losses = self(**data)
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loss, log_vars = self._parse_losses(losses)
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outputs = dict(
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loss=loss, log_vars=log_vars, num_samples=len(data['img'].data))
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return outputs
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def val_step(self, data, optimizer=None, **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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Args:
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data (dict): The output of dataloader.
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optimizer (:obj:`torch.optim.Optimizer` | dict, optional): The
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optimizer of runner is passed to ``train_step()``. This
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argument is unused and reserved.
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Returns:
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dict: Dict of outputs. The following fields are contained.
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- loss (torch.Tensor): A tensor for back propagation, which \
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can be a weighted sum of multiple losses.
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- log_vars (dict): Dict contains all the variables to be sent \
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to the logger.
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- num_samples (int): Indicates the batch size (when the model \
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is DDP, it means the batch size on each GPU), which is \
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used for averaging the logs.
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"""
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losses = self(**data)
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loss, log_vars = self._parse_losses(losses)
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outputs = dict(
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loss=loss, log_vars=log_vars, num_samples=len(data['img'].data))
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return outputs
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