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from ..builder import DETECTORS |
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from .two_stage import TwoStageDetector |
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@DETECTORS.register_module() |
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class SparseRCNN(TwoStageDetector): |
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r"""Implementation of `Sparse R-CNN: End-to-End Object Detection with |
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Learnable Proposals <https://arxiv.org/abs/2011.12450>`_""" |
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def __init__(self, *args, **kwargs): |
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super(SparseRCNN, self).__init__(*args, **kwargs) |
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assert self.with_rpn, 'Sparse R-CNN do not support external proposals' |
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def forward_train(self, |
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img, |
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img_metas, |
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gt_bboxes, |
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gt_labels, |
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gt_bboxes_ignore=None, |
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gt_masks=None, |
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proposals=None, |
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**kwargs): |
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"""Forward function of SparseR-CNN in train stage. |
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Args: |
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img (Tensor): of shape (N, C, H, W) encoding input images. |
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Typically these should be mean centered and std scaled. |
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img_metas (list[dict]): list of image info dict where each dict |
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has: 'img_shape', 'scale_factor', 'flip', and may also contain |
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'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. |
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For details on the values of these keys see |
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:class:`mmdet.datasets.pipelines.Collect`. |
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gt_bboxes (list[Tensor]): Ground truth bboxes for each image with |
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shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. |
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gt_labels (list[Tensor]): class indices corresponding to each box |
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gt_bboxes_ignore (None | list[Tensor): specify which bounding |
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boxes can be ignored when computing the loss. |
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gt_masks (List[Tensor], optional) : Segmentation masks for |
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each box. But we don't support it in this architecture. |
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proposals (List[Tensor], optional): override rpn proposals with |
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custom proposals. Use when `with_rpn` is False. |
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Returns: |
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dict[str, Tensor]: a dictionary of loss components |
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""" |
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assert proposals is None, 'Sparse R-CNN does not support' \ |
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' external proposals' |
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assert gt_masks is None, 'Sparse R-CNN does not instance segmentation' |
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x = self.extract_feat(img) |
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proposal_boxes, proposal_features, imgs_whwh = \ |
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self.rpn_head.forward_train(x, img_metas) |
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roi_losses = self.roi_head.forward_train( |
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x, |
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proposal_boxes, |
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proposal_features, |
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img_metas, |
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gt_bboxes, |
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gt_labels, |
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gt_bboxes_ignore=gt_bboxes_ignore, |
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gt_masks=gt_masks, |
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imgs_whwh=imgs_whwh) |
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return roi_losses |
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def simple_test(self, img, img_metas, rescale=False): |
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"""Test function without test time augmentation. |
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Args: |
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imgs (list[torch.Tensor]): List of multiple images |
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img_metas (list[dict]): List of image information. |
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rescale (bool): Whether to rescale the results. |
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Defaults to False. |
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Returns: |
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list[list[np.ndarray]]: BBox results of each image and classes. |
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The outer list corresponds to each image. The inner list |
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corresponds to each class. |
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""" |
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x = self.extract_feat(img) |
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proposal_boxes, proposal_features, imgs_whwh = \ |
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self.rpn_head.simple_test_rpn(x, img_metas) |
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bbox_results = self.roi_head.simple_test( |
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x, |
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proposal_boxes, |
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proposal_features, |
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img_metas, |
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imgs_whwh=imgs_whwh, |
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rescale=rescale) |
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return bbox_results |
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def forward_dummy(self, img): |
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"""Used for computing network flops. |
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See `mmdetection/tools/analysis_tools/get_flops.py` |
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""" |
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x = self.extract_feat(img) |
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num_imgs = len(img) |
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dummy_img_metas = [ |
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dict(img_shape=(800, 1333, 3)) for _ in range(num_imgs) |
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] |
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proposal_boxes, proposal_features, imgs_whwh = \ |
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self.rpn_head.simple_test_rpn(x, dummy_img_metas) |
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roi_outs = self.roi_head.forward_dummy(x, proposal_boxes, |
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proposal_features, |
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dummy_img_metas) |
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return roi_outs |
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