219 lines
7.7 KiB
Python
219 lines
7.7 KiB
Python
import warnings
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from abc import ABCMeta, abstractmethod
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from collections import OrderedDict
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import cv2
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import mmcv
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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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from mmcv import color_val
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from mmcv.utils import print_log
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class BaseClassifier(nn.Module, metaclass=ABCMeta):
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"""Base class for classifiers"""
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def __init__(self):
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super(BaseClassifier, self).__init__()
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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):
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pass
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def extract_feats(self, imgs):
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assert isinstance(imgs, list)
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for img in imgs:
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yield self.extract_feat(img)
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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 init_weights(self, pretrained=None):
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if pretrained is not None:
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print_log(f'load model from: {pretrained}', logger='root')
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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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def forward(self, img, return_loss=True, **kwargs):
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"""
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Calls either forward_train or forward_test depending on whether
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return_loss=True. Note this setting will change the expected inputs.
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When `return_loss=True`, img and img_meta are single-nested (i.e.
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Tensor and List[dict]), and when `resturn_loss=False`, img and img_meta
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should be double nested (i.e. List[Tensor], List[List[dict]]), with
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the outer 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):
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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): 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(**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):
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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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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 show_result(self,
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img,
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result,
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text_color='green',
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font_scale=0.5,
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row_width=20,
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show=False,
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win_name='',
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wait_time=0,
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out_file=None):
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"""Draw `result` over `img`.
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Args:
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img (str or Tensor): The image to be displayed.
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result (Tensor): The classification results to draw over `img`.
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text_color (str or tuple or :obj:`Color`): Color of texts.
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font_scale (float): Font scales of texts.
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row_width (int): width between each row of results on the image.
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show (bool): Whether to show the image.
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Default: False.
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win_name (str): The window name.
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wait_time (int): Value of waitKey param.
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Default: 0.
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out_file (str or None): The filename to write the image.
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Default: None.
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Returns:
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img (Tensor): Only if not `show` or `out_file`
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"""
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img = mmcv.imread(img)
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img = img.copy()
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# write results on left-top of the image
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x, y = 0, row_width
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text_color = color_val(text_color)
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for k, v in result.items():
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if isinstance(v, float):
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v = f'{v:.2f}'
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label_text = f'{k}: {v}'
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cv2.putText(img, label_text, (x, y), cv2.FONT_HERSHEY_COMPLEX,
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font_scale, text_color)
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y += row_width
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# if out_file specified, do not show image in window
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if out_file is not None:
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show = False
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if show:
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mmcv.imshow(img, win_name, wait_time)
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if out_file is not None:
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mmcv.imwrite(img, out_file)
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if not (show or out_file):
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warnings.warn('show==False and out_file is not specified, only '
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'result image will be returned')
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return img
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