Spaces:
Runtime error
Runtime error
| import mmcv | |
| from mmcv.image import tensor2imgs | |
| from mmdet.core import bbox_mapping | |
| from ..builder import DETECTORS, build_backbone, build_head, build_neck | |
| from .base import BaseDetector | |
| class RPN(BaseDetector): | |
| """Implementation of Region Proposal Network.""" | |
| def __init__(self, | |
| backbone, | |
| neck, | |
| rpn_head, | |
| train_cfg, | |
| test_cfg, | |
| pretrained=None): | |
| super(RPN, self).__init__() | |
| self.backbone = build_backbone(backbone) | |
| self.neck = build_neck(neck) if neck is not None else None | |
| rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None | |
| rpn_head.update(train_cfg=rpn_train_cfg) | |
| rpn_head.update(test_cfg=test_cfg.rpn) | |
| self.rpn_head = build_head(rpn_head) | |
| self.train_cfg = train_cfg | |
| self.test_cfg = test_cfg | |
| self.init_weights(pretrained=pretrained) | |
| def init_weights(self, pretrained=None): | |
| """Initialize the weights in detector. | |
| Args: | |
| pretrained (str, optional): Path to pre-trained weights. | |
| Defaults to None. | |
| """ | |
| super(RPN, self).init_weights(pretrained) | |
| self.backbone.init_weights(pretrained=pretrained) | |
| if self.with_neck: | |
| self.neck.init_weights() | |
| self.rpn_head.init_weights() | |
| def extract_feat(self, img): | |
| """Extract features. | |
| Args: | |
| img (torch.Tensor): Image tensor with shape (n, c, h ,w). | |
| Returns: | |
| list[torch.Tensor]: Multi-level features that may have | |
| different resolutions. | |
| """ | |
| x = self.backbone(img) | |
| if self.with_neck: | |
| x = self.neck(x) | |
| return x | |
| def forward_dummy(self, img): | |
| """Dummy forward function.""" | |
| x = self.extract_feat(img) | |
| rpn_outs = self.rpn_head(x) | |
| return rpn_outs | |
| def forward_train(self, | |
| img, | |
| img_metas, | |
| gt_bboxes=None, | |
| gt_bboxes_ignore=None): | |
| """ | |
| Args: | |
| img (Tensor): Input images of shape (N, C, H, W). | |
| Typically these should be mean centered and std scaled. | |
| img_metas (list[dict]): A 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`. | |
| gt_bboxes (list[Tensor]): Each item are the truth boxes for each | |
| image in [tl_x, tl_y, br_x, br_y] format. | |
| gt_bboxes_ignore (None | list[Tensor]): Specify which bounding | |
| boxes can be ignored when computing the loss. | |
| Returns: | |
| dict[str, Tensor]: A dictionary of loss components. | |
| """ | |
| if (isinstance(self.train_cfg.rpn, dict) | |
| and self.train_cfg.rpn.get('debug', False)): | |
| self.rpn_head.debug_imgs = tensor2imgs(img) | |
| x = self.extract_feat(img) | |
| losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None, | |
| gt_bboxes_ignore) | |
| return losses | |
| def simple_test(self, img, img_metas, rescale=False): | |
| """Test function without test time augmentation. | |
| Args: | |
| imgs (list[torch.Tensor]): List of multiple images | |
| img_metas (list[dict]): List of image information. | |
| rescale (bool, optional): Whether to rescale the results. | |
| Defaults to False. | |
| Returns: | |
| list[np.ndarray]: proposals | |
| """ | |
| x = self.extract_feat(img) | |
| proposal_list = self.rpn_head.simple_test_rpn(x, img_metas) | |
| if rescale: | |
| for proposals, meta in zip(proposal_list, img_metas): | |
| proposals[:, :4] /= proposals.new_tensor(meta['scale_factor']) | |
| return [proposal.cpu().numpy() for proposal in proposal_list] | |
| def aug_test(self, imgs, img_metas, rescale=False): | |
| """Test function with test time augmentation. | |
| Args: | |
| imgs (list[torch.Tensor]): List of multiple images | |
| img_metas (list[dict]): List of image information. | |
| rescale (bool, optional): Whether to rescale the results. | |
| Defaults to False. | |
| Returns: | |
| list[np.ndarray]: proposals | |
| """ | |
| proposal_list = self.rpn_head.aug_test_rpn( | |
| self.extract_feats(imgs), img_metas) | |
| if not rescale: | |
| for proposals, img_meta in zip(proposal_list, img_metas[0]): | |
| img_shape = img_meta['img_shape'] | |
| scale_factor = img_meta['scale_factor'] | |
| flip = img_meta['flip'] | |
| flip_direction = img_meta['flip_direction'] | |
| proposals[:, :4] = bbox_mapping(proposals[:, :4], img_shape, | |
| scale_factor, flip, | |
| flip_direction) | |
| return [proposal.cpu().numpy() for proposal in proposal_list] | |
| def show_result(self, data, result, top_k=20, **kwargs): | |
| """Show RPN proposals on the image. | |
| Args: | |
| data (str or np.ndarray): Image filename or loaded image. | |
| result (Tensor or tuple): The results to draw over `img` | |
| bbox_result or (bbox_result, segm_result). | |
| top_k (int): Plot the first k bboxes only | |
| if set positive. Default: 20 | |
| Returns: | |
| np.ndarray: The image with bboxes drawn on it. | |
| """ | |
| mmcv.imshow_bboxes(data, result, top_k=top_k) | |