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| import torch | |
| from mmdet.core import bbox2result | |
| from ..builder import DETECTORS, build_head | |
| from .single_stage import SingleStageDetector | |
| class YOLACT(SingleStageDetector): | |
| """Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>`_""" | |
| def __init__(self, | |
| backbone, | |
| neck, | |
| bbox_head, | |
| segm_head, | |
| mask_head, | |
| train_cfg=None, | |
| test_cfg=None, | |
| pretrained=None): | |
| super(YOLACT, self).__init__(backbone, neck, bbox_head, train_cfg, | |
| test_cfg, pretrained) | |
| self.segm_head = build_head(segm_head) | |
| self.mask_head = build_head(mask_head) | |
| self.init_segm_mask_weights() | |
| def init_segm_mask_weights(self): | |
| """Initialize weights of the YOLACT segm head and YOLACT mask head.""" | |
| self.segm_head.init_weights() | |
| self.mask_head.init_weights() | |
| def forward_dummy(self, img): | |
| """Used for computing network flops. | |
| See `mmdetection/tools/analysis_tools/get_flops.py` | |
| """ | |
| raise NotImplementedError | |
| def forward_train(self, | |
| img, | |
| img_metas, | |
| gt_bboxes, | |
| gt_labels, | |
| gt_bboxes_ignore=None, | |
| gt_masks=None): | |
| """ | |
| 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 | |
| `mmdet/datasets/pipelines/formatting.py: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 (None | Tensor) : true segmentation masks for each box | |
| used if the architecture supports a segmentation task. | |
| Returns: | |
| dict[str, Tensor]: a dictionary of loss components | |
| """ | |
| # convert Bitmap mask or Polygon Mask to Tensor here | |
| gt_masks = [ | |
| gt_mask.to_tensor(dtype=torch.uint8, device=img.device) | |
| for gt_mask in gt_masks | |
| ] | |
| x = self.extract_feat(img) | |
| cls_score, bbox_pred, coeff_pred = self.bbox_head(x) | |
| bbox_head_loss_inputs = (cls_score, bbox_pred) + (gt_bboxes, gt_labels, | |
| img_metas) | |
| losses, sampling_results = self.bbox_head.loss( | |
| *bbox_head_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) | |
| segm_head_outs = self.segm_head(x[0]) | |
| loss_segm = self.segm_head.loss(segm_head_outs, gt_masks, gt_labels) | |
| losses.update(loss_segm) | |
| mask_pred = self.mask_head(x[0], coeff_pred, gt_bboxes, img_metas, | |
| sampling_results) | |
| loss_mask = self.mask_head.loss(mask_pred, gt_masks, gt_bboxes, | |
| img_metas, sampling_results) | |
| losses.update(loss_mask) | |
| # check NaN and Inf | |
| for loss_name in losses.keys(): | |
| assert torch.isfinite(torch.stack(losses[loss_name]))\ | |
| .all().item(), '{} becomes infinite or NaN!'\ | |
| .format(loss_name) | |
| return losses | |
| def simple_test(self, img, img_metas, rescale=False): | |
| """Test function without test time augmentation.""" | |
| x = self.extract_feat(img) | |
| cls_score, bbox_pred, coeff_pred = self.bbox_head(x) | |
| bbox_inputs = (cls_score, bbox_pred, | |
| coeff_pred) + (img_metas, self.test_cfg, rescale) | |
| det_bboxes, det_labels, det_coeffs = self.bbox_head.get_bboxes( | |
| *bbox_inputs) | |
| bbox_results = [ | |
| bbox2result(det_bbox, det_label, self.bbox_head.num_classes) | |
| for det_bbox, det_label in zip(det_bboxes, det_labels) | |
| ] | |
| num_imgs = len(img_metas) | |
| scale_factors = tuple(meta['scale_factor'] for meta in img_metas) | |
| if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): | |
| segm_results = [[[] for _ in range(self.mask_head.num_classes)] | |
| for _ in range(num_imgs)] | |
| else: | |
| # if det_bboxes is rescaled to the original image size, we need to | |
| # rescale it back to the testing scale to obtain RoIs. | |
| if rescale and not isinstance(scale_factors[0], float): | |
| scale_factors = [ | |
| torch.from_numpy(scale_factor).to(det_bboxes[0].device) | |
| for scale_factor in scale_factors | |
| ] | |
| _bboxes = [ | |
| det_bboxes[i][:, :4] * | |
| scale_factors[i] if rescale else det_bboxes[i][:, :4] | |
| for i in range(len(det_bboxes)) | |
| ] | |
| mask_preds = self.mask_head(x[0], det_coeffs, _bboxes, img_metas) | |
| # apply mask post-processing to each image individually | |
| segm_results = [] | |
| for i in range(num_imgs): | |
| if det_bboxes[i].shape[0] == 0: | |
| segm_results.append( | |
| [[] for _ in range(self.mask_head.num_classes)]) | |
| else: | |
| segm_result = self.mask_head.get_seg_masks( | |
| mask_preds[i], det_labels[i], img_metas[i], rescale) | |
| segm_results.append(segm_result) | |
| return list(zip(bbox_results, segm_results)) | |
| def aug_test(self, imgs, img_metas, rescale=False): | |
| """Test with augmentations.""" | |
| raise NotImplementedError | |