from abc import ABCMeta, abstractmethod from collections import OrderedDict import mmcv import numpy as np import torch import torch.distributed as dist import torch.nn as nn from mmcv.runner import auto_fp16 from mmcv.utils import print_log from mmdet.core.visualization import imshow_det_bboxes from mmdet.utils import get_root_logger class BaseDetector(nn.Module, metaclass=ABCMeta): """Base class for detectors.""" def __init__(self): super(BaseDetector, self).__init__() self.fp16_enabled = False @property def with_neck(self): """bool: whether the detector has a neck""" return hasattr(self, 'neck') and self.neck is not None # TODO: these properties need to be carefully handled # for both single stage & two stage detectors @property def with_shared_head(self): """bool: whether the detector has a shared head in the RoI Head""" return hasattr(self, 'roi_head') and self.roi_head.with_shared_head @property def with_bbox(self): """bool: whether the detector has a bbox head""" return ((hasattr(self, 'roi_head') and self.roi_head.with_bbox) or (hasattr(self, 'bbox_head') and self.bbox_head is not None)) @property def with_mask(self): """bool: whether the detector has a mask head""" return ((hasattr(self, 'roi_head') and self.roi_head.with_mask) or (hasattr(self, 'mask_head') and self.mask_head is not None)) @abstractmethod def extract_feat(self, imgs): """Extract features from images.""" pass def extract_feats(self, imgs): """Extract features from multiple images. Args: imgs (list[torch.Tensor]): A list of images. The images are augmented from the same image but in different ways. Returns: list[torch.Tensor]: Features of different images """ assert isinstance(imgs, list) return [self.extract_feat(img) for img in imgs] def forward_train(self, imgs, img_metas, **kwargs): """ Args: img (list[Tensor]): List of tensors of shape (1, C, H, W). Typically these should be mean centered and std scaled. img_metas (list[dict]): List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys, see :class:`mmdet.datasets.pipelines.Collect`. kwargs (keyword arguments): Specific to concrete implementation. """ # NOTE the batched image size information may be useful, e.g. # in DETR, this is needed for the construction of masks, which is # then used for the transformer_head. batch_input_shape = tuple(imgs[0].size()[-2:]) for img_meta in img_metas: img_meta['batch_input_shape'] = batch_input_shape async def async_simple_test(self, img, img_metas, **kwargs): raise NotImplementedError @abstractmethod def simple_test(self, img, img_metas, **kwargs): pass @abstractmethod def aug_test(self, imgs, img_metas, **kwargs): """Test function with test time augmentation.""" pass def init_weights(self, pretrained=None): """Initialize the weights in detector. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to None. """ if pretrained is not None: logger = get_root_logger() print_log(f'load model from: {pretrained}', logger=logger) async def aforward_test(self, *, img, img_metas, **kwargs): for var, name in [(img, 'img'), (img_metas, 'img_metas')]: if not isinstance(var, list): raise TypeError(f'{name} must be a list, but got {type(var)}') num_augs = len(img) if num_augs != len(img_metas): raise ValueError(f'num of augmentations ({len(img)}) ' f'!= num of image metas ({len(img_metas)})') # TODO: remove the restriction of samples_per_gpu == 1 when prepared samples_per_gpu = img[0].size(0) assert samples_per_gpu == 1 if num_augs == 1: return await self.async_simple_test(img[0], img_metas[0], **kwargs) else: raise NotImplementedError 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 {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)})') # NOTE the batched image size information may be useful, e.g. # in DETR, this is needed for the construction of masks, which is # then used for the transformer_head. for img, img_meta in zip(imgs, img_metas): batch_size = len(img_meta) for img_id in range(batch_size): img_meta[img_id]['batch_input_shape'] = tuple(img.size()[-2:]) if num_augs == 1: # proposals (List[List[Tensor]]): the outer list indicates # test-time augs (multiscale, flip, etc.) and the inner list # indicates images in a batch. # The Tensor should have a shape Px4, where P is the number of # proposals. if 'proposals' in kwargs: kwargs['proposals'] = kwargs['proposals'][0] return self.simple_test(imgs[0], img_metas[0], **kwargs) else: assert imgs[0].size(0) == 1, 'aug test does not support ' \ 'inference with batch size ' \ f'{imgs[0].size(0)}' # TODO: support test augmentation for predefined proposals assert 'proposals' not in kwargs 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 _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 infomation. 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): """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 show_result(self, img, result, score_thr=0.3, bbox_color=(72, 101, 241), text_color=(72, 101, 241), mask_color=None, thickness=2, font_size=13, win_name='', show=False, wait_time=0, out_file=None): """Draw `result` over `img`. Args: img (str or Tensor): The image to be displayed. result (Tensor or tuple): The results to draw over `img` bbox_result or (bbox_result, segm_result). score_thr (float, optional): Minimum score of bboxes to be shown. Default: 0.3. bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines. The tuple of color should be in BGR order. Default: 'green' text_color (str or tuple(int) or :obj:`Color`):Color of texts. The tuple of color should be in BGR order. Default: 'green' mask_color (None or str or tuple(int) or :obj:`Color`): Color of masks. The tuple of color should be in BGR order. Default: None thickness (int): Thickness of lines. Default: 2 font_size (int): Font size of texts. Default: 13 win_name (str): The window name. Default: '' wait_time (float): 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. Returns: img (Tensor): Only if not `show` or `out_file` """ img = mmcv.imread(img) img = img.copy() if isinstance(result, tuple): bbox_result, segm_result = result if isinstance(segm_result, tuple): segm_result = segm_result[0] # ms rcnn else: bbox_result, segm_result = result, None bboxes = np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in enumerate(bbox_result) ] labels = np.concatenate(labels) # draw segmentation masks segms = None if segm_result is not None and len(labels) > 0: # non empty segms = mmcv.concat_list(segm_result) if isinstance(segms[0], torch.Tensor): segms = torch.stack(segms, dim=0).detach().cpu().numpy() else: segms = np.stack(segms, axis=0) # if out_file specified, do not show image in window if out_file is not None: show = False # draw bounding boxes img = imshow_det_bboxes( img, bboxes, labels, segms, class_names=self.CLASSES, score_thr=score_thr, bbox_color=bbox_color, text_color=text_color, mask_color=mask_color, thickness=thickness, font_size=font_size, win_name=win_name, show=show, wait_time=wait_time, out_file=out_file) if not (show or out_file): return img