mirror of https://github.com/hero-y/BHRL
111 lines
4.3 KiB
Python
111 lines
4.3 KiB
Python
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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# backbone
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x = self.extract_feat(img)
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# rpn
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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_head
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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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