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| from ..builder import DETECTORS | |
| from .two_stage import TwoStageDetector | |
| class SparseRCNN(TwoStageDetector): | |
| r"""Implementation of `Sparse R-CNN: End-to-End Object Detection with | |
| Learnable Proposals <https://arxiv.org/abs/2011.12450>`_""" | |
| def __init__(self, *args, **kwargs): | |
| super(SparseRCNN, self).__init__(*args, **kwargs) | |
| assert self.with_rpn, 'Sparse R-CNN do not support external proposals' | |
| def forward_train(self, | |
| img, | |
| img_metas, | |
| gt_bboxes, | |
| gt_labels, | |
| gt_bboxes_ignore=None, | |
| gt_masks=None, | |
| proposals=None, | |
| **kwargs): | |
| """Forward function of SparseR-CNN in train stage. | |
| Args: | |
| img (Tensor): of shape (N, C, H, W) encoding input images. | |
| 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`. | |
| gt_bboxes (list[Tensor]): Ground truth bboxes for each image with | |
| shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. | |
| gt_labels (list[Tensor]): class indices corresponding to each box | |
| gt_bboxes_ignore (None | list[Tensor): specify which bounding | |
| boxes can be ignored when computing the loss. | |
| gt_masks (List[Tensor], optional) : Segmentation masks for | |
| each box. But we don't support it in this architecture. | |
| proposals (List[Tensor], optional): override rpn proposals with | |
| custom proposals. Use when `with_rpn` is False. | |
| Returns: | |
| dict[str, Tensor]: a dictionary of loss components | |
| """ | |
| assert proposals is None, 'Sparse R-CNN does not support' \ | |
| ' external proposals' | |
| assert gt_masks is None, 'Sparse R-CNN does not instance segmentation' | |
| x = self.extract_feat(img) | |
| proposal_boxes, proposal_features, imgs_whwh = \ | |
| self.rpn_head.forward_train(x, img_metas) | |
| roi_losses = self.roi_head.forward_train( | |
| x, | |
| proposal_boxes, | |
| proposal_features, | |
| img_metas, | |
| gt_bboxes, | |
| gt_labels, | |
| gt_bboxes_ignore=gt_bboxes_ignore, | |
| gt_masks=gt_masks, | |
| imgs_whwh=imgs_whwh) | |
| return roi_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): Whether to rescale the results. | |
| Defaults to False. | |
| Returns: | |
| list[list[np.ndarray]]: BBox results of each image and classes. | |
| The outer list corresponds to each image. The inner list | |
| corresponds to each class. | |
| """ | |
| x = self.extract_feat(img) | |
| proposal_boxes, proposal_features, imgs_whwh = \ | |
| self.rpn_head.simple_test_rpn(x, img_metas) | |
| bbox_results = self.roi_head.simple_test( | |
| x, | |
| proposal_boxes, | |
| proposal_features, | |
| img_metas, | |
| imgs_whwh=imgs_whwh, | |
| rescale=rescale) | |
| return bbox_results | |
| def forward_dummy(self, img): | |
| """Used for computing network flops. | |
| See `mmdetection/tools/analysis_tools/get_flops.py` | |
| """ | |
| # backbone | |
| x = self.extract_feat(img) | |
| # rpn | |
| num_imgs = len(img) | |
| dummy_img_metas = [ | |
| dict(img_shape=(800, 1333, 3)) for _ in range(num_imgs) | |
| ] | |
| proposal_boxes, proposal_features, imgs_whwh = \ | |
| self.rpn_head.simple_test_rpn(x, dummy_img_metas) | |
| # roi_head | |
| roi_outs = self.roi_head.forward_dummy(x, proposal_boxes, | |
| proposal_features, | |
| dummy_img_metas) | |
| return roi_outs | |