Source code for mmdet.models.detectors.sparse_rcnn

from ..builder import DETECTORS
from .two_stage import TwoStageDetector

[docs]@DETECTORS.register_module() class SparseRCNN(TwoStageDetector): r"""Implementation of `Sparse R-CNN: End-to-End Object Detection with Learnable Proposals <>`_""" def __init__(self, *args, **kwargs): super(SparseRCNN, self).__init__(*args, **kwargs) assert self.with_rpn, 'Sparse R-CNN do not support external proposals'
[docs] 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
[docs] 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
[docs] def forward_dummy(self, img): """Used for computing network flops. See `mmdetection/tools/analysis_tools/` """ # 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