mirror of https://github.com/hero-y/BHRL
117 lines
4.4 KiB
Python
117 lines
4.4 KiB
Python
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import torch
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from mmdet.core import bbox2result
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from ..builder import DETECTORS, build_head
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from .single_stage import SingleStageDetector
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@DETECTORS.register_module()
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class YOLACT(SingleStageDetector):
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"""Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>`_"""
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def __init__(self,
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backbone,
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neck,
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bbox_head,
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segm_head,
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mask_head,
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train_cfg=None,
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test_cfg=None,
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pretrained=None,
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init_cfg=None):
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super(YOLACT, self).__init__(backbone, neck, bbox_head, train_cfg,
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test_cfg, pretrained, init_cfg)
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self.segm_head = build_head(segm_head)
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self.mask_head = build_head(mask_head)
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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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raise NotImplementedError
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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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"""
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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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`mmdet/datasets/pipelines/formatting.py: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 (None | Tensor) : true segmentation masks for each box
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used if the architecture supports a segmentation task.
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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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# convert Bitmap mask or Polygon Mask to Tensor here
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gt_masks = [
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gt_mask.to_tensor(dtype=torch.uint8, device=img.device)
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for gt_mask in gt_masks
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]
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x = self.extract_feat(img)
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cls_score, bbox_pred, coeff_pred = self.bbox_head(x)
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bbox_head_loss_inputs = (cls_score, bbox_pred) + (gt_bboxes, gt_labels,
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img_metas)
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losses, sampling_results = self.bbox_head.loss(
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*bbox_head_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
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segm_head_outs = self.segm_head(x[0])
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loss_segm = self.segm_head.loss(segm_head_outs, gt_masks, gt_labels)
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losses.update(loss_segm)
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mask_pred = self.mask_head(x[0], coeff_pred, gt_bboxes, img_metas,
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sampling_results)
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loss_mask = self.mask_head.loss(mask_pred, gt_masks, gt_bboxes,
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img_metas, sampling_results)
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losses.update(loss_mask)
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# check NaN and Inf
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for loss_name in losses.keys():
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assert torch.isfinite(torch.stack(losses[loss_name]))\
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.all().item(), '{} becomes infinite or NaN!'\
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.format(loss_name)
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return 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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feat = self.extract_feat(img)
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det_bboxes, det_labels, det_coeffs = self.bbox_head.simple_test(
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feat, img_metas, rescale=rescale)
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bbox_results = [
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bbox2result(det_bbox, det_label, self.bbox_head.num_classes)
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for det_bbox, det_label in zip(det_bboxes, det_labels)
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]
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segm_results = self.mask_head.simple_test(
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feat,
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det_bboxes,
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det_labels,
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det_coeffs,
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img_metas,
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rescale=rescale)
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return list(zip(bbox_results, segm_results))
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def aug_test(self, imgs, img_metas, rescale=False):
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"""Test with augmentations."""
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raise NotImplementedError(
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'YOLACT does not support test-time augmentation')
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