import logging import warnings from abc import ABCMeta, abstractmethod from collections import OrderedDict import annotator.mmpkg.mmcv as mmcv import numpy as np import torch import torch.distributed as dist import torch.nn as nn from annotator.mmpkg.mmcv.runner import auto_fp16 class BaseSegmentor(nn.Module): """Base class for segmentors.""" __metaclass__ = ABCMeta def __init__(self): super(BaseSegmentor, self).__init__() self.fp16_enabled = False @property def with_neck(self): """bool: whether the segmentor has neck""" return hasattr(self, 'neck') and self.neck is not None @property def with_auxiliary_head(self): """bool: whether the segmentor has auxiliary head""" return hasattr(self, 'auxiliary_head') and self.auxiliary_head is not None @property def with_decode_head(self): """bool: whether the segmentor has decode head""" return hasattr(self, 'decode_head') and self.decode_head is not None @abstractmethod def extract_feat(self, imgs): """Placeholder for extract features from images.""" pass @abstractmethod def encode_decode(self, img, img_metas): """Placeholder for encode images with backbone and decode into a semantic segmentation map of the same size as input.""" pass @abstractmethod def forward_train(self, imgs, img_metas, **kwargs): """Placeholder for Forward function for training.""" pass @abstractmethod def simple_test(self, img, img_meta, **kwargs): """Placeholder for single image test.""" pass @abstractmethod def aug_test(self, imgs, img_metas, **kwargs): """Placeholder for augmentation test.""" pass def init_weights(self, pretrained=None): """Initialize the weights in segmentor. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to None. """ if pretrained is not None: logger = logging.getLogger() logger.info(f'load model from: {pretrained}') def forward_test(self, imgs, img_metas, **kwargs): """ Args: imgs (List[Tensor]): the outer list indicates test-time augmentations and inner Tensor should have a shape NxCxHxW, which contains all images in the batch. img_metas (List[List[dict]]): the outer list indicates test-time augs (multiscale, flip, etc.) and the inner list indicates images in a batch. """ for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: if not isinstance(var, list): raise TypeError(f'{name} must be a list, but got ' f'{type(var)}') num_augs = len(imgs) if num_augs != len(img_metas): raise ValueError(f'num of augmentations ({len(imgs)}) != ' f'num of image meta ({len(img_metas)})') # all images in the same aug batch all of the same ori_shape and pad # shape for img_meta in img_metas: ori_shapes = [_['ori_shape'] for _ in img_meta] assert all(shape == ori_shapes[0] for shape in ori_shapes) img_shapes = [_['img_shape'] for _ in img_meta] assert all(shape == img_shapes[0] for shape in img_shapes) pad_shapes = [_['pad_shape'] for _ in img_meta] assert all(shape == pad_shapes[0] for shape in pad_shapes) if num_augs == 1: return self.simple_test(imgs[0], img_metas[0], **kwargs) else: return self.aug_test(imgs, img_metas, **kwargs) @auto_fp16(apply_to=('img', )) def forward(self, img, img_metas, return_loss=True, **kwargs): """Calls either :func:`forward_train` or :func:`forward_test` depending on whether ``return_loss`` is ``True``. Note this setting will change the expected inputs. When ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor and List[dict]), and when ``resturn_loss=False``, img and img_meta should be double nested (i.e. List[Tensor], List[List[dict]]), with the outer list indicating test time augmentations. """ if return_loss: return self.forward_train(img, img_metas, **kwargs) else: return self.forward_test(img, img_metas, **kwargs) 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 (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_batch) loss, log_vars = self._parse_losses(losses) outputs = dict( loss=loss, log_vars=log_vars, num_samples=len(data_batch['img_metas'])) return outputs def val_step(self, data_batch, **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. """ output = self(**data_batch, **kwargs) return output @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, 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 show_result(self, img, result, palette=None, win_name='', show=False, wait_time=0, out_file=None, opacity=0.5): """Draw `result` over `img`. Args: img (str or Tensor): The image to be displayed. result (Tensor): The semantic segmentation results to draw over `img`. palette (list[list[int]]] | np.ndarray | None): The palette of segmentation map. If None is given, random palette will be generated. Default: None win_name (str): The window name. wait_time (int): Value of waitKey param. Default: 0. show (bool): Whether to show the image. Default: False. out_file (str or None): The filename to write the image. Default: None. opacity(float): Opacity of painted segmentation map. Default 0.5. Must be in (0, 1] range. Returns: img (Tensor): Only if not `show` or `out_file` """ img = mmcv.imread(img) img = img.copy() seg = result[0] if palette is None: if self.PALETTE is None: palette = np.random.randint( 0, 255, size=(len(self.CLASSES), 3)) else: palette = self.PALETTE palette = np.array(palette) assert palette.shape[0] == len(self.CLASSES) assert palette.shape[1] == 3 assert len(palette.shape) == 2 assert 0 < opacity <= 1.0 color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) for label, color in enumerate(palette): color_seg[seg == label, :] = color # convert to BGR color_seg = color_seg[..., ::-1] img = img * (1 - opacity) + color_seg * opacity img = img.astype(np.uint8) # if out_file specified, do not show image in window if out_file is not None: show = False if show: mmcv.imshow(img, win_name, wait_time) if out_file is not None: mmcv.imwrite(img, out_file) if not (show or out_file): warnings.warn('show==False and out_file is not specified, only ' 'result image will be returned') return img