pytorch

a6bb8b52 - Reduce error context from 10 -> 3 (#26765)

Reduce error context from 10 -> 3 (#26765) Summary: 10 lines of error context (on both sides) is overkill, especially now that we have line numbers. With a compilation stack of a couple functions, it becomes a pain to scroll to the top of the stack to see the real error every time. This also fixes class names in the compilation stack to a format of `ClassName.method_name` instead of the the full qualified name Old output ``` clip_boxes_to_image(Tensor boxes, (int, int) size) -> (Tensor): Expected a value of type 'Tuple[int, int]' for argument 'size' but instead found type 'Tuple[int, int, int]'. : at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:365:20 top_n_idx = self._get_top_n_idx(objectness, num_anchors_per_level) batch_idx = torch.arange(num_images, device=device)[:, None] objectness = objectness[batch_idx, top_n_idx] levels = levels[batch_idx, top_n_idx] proposals = proposals[batch_idx, top_n_idx] final_boxes = [] final_scores = [] for boxes, scores, lvl, img_shape in zip(proposals, objectness, levels, image_shapes): boxes = box_ops.clip_boxes_to_image(boxes, img_shape) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE keep = box_ops.remove_small_boxes(boxes, self.min_size) boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep] # non-maximum suppression, independently done per level keep = box_ops.batched_nms(boxes, scores, lvl, self.nms_thresh) # keep only topk scoring predictions keep = keep[:self.post_nms_top_n] boxes, scores = boxes[keep], scores[keep] final_boxes.append(boxes) final_scores.append(scores) 'RegionProposalNetwork.filter_proposals' is being compiled since it was called from 'RegionProposalNetwork.forward' at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:446:8 num_images = len(anchors) num_anchors_per_level = [o[0].numel() for o in objectness] objectness, pred_bbox_deltas = \ concat_box_prediction_layers(objectness, pred_bbox_deltas) # apply pred_bbox_deltas to anchors to obtain the decoded proposals # note that we detach the deltas because Faster R-CNN do not backprop through # the proposals proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors) proposals = proposals.view(num_images, -1, 4) boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE losses = {} if self.training: assert targets is not None labels, matched_gt_boxes = self.assign_targets_to_anchors(anchors, targets) regression_targets = self.box_coder.encode(matched_gt_boxes, anchors) loss_objectness, loss_rpn_box_reg = self.compute_loss( objectness, pred_bbox_deltas, labels, regression_targets) losses = { 'RegionProposalNetwork.forward' is being compiled since it was called from 'MaskRCNN.forward' at /home/davidriazati/dev/vision/torchvision/models/detection/generalized_rcnn.py:53:8 """ if self.training and targets is None: raise ValueError("In training mode, targets should be passed") original_image_sizes = [(img.shape[-2], img.shape[-3]) for img in images] images, targets = self.transform(images, targets) features = self.backbone(images.tensors) if isinstance(features, torch.Tensor): features = OrderedDict([(0, features)]) proposals, proposal_losses = self.rpn(images, features, targets) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets) detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes) losses = {} losses.update(detector_losses) losses.update(proposal_losses) # TODO: multiple return types?? # if self.training: ``` New output ``` RuntimeError: clip_boxes_to_image(Tensor boxes, (int, int) size) -> (Tensor): Expected a value of type 'Tuple[int, int]' for argument 'size' but instead found type 'Tuple[int, int, int]'. : at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:365:20 final_scores = [] for boxes, scores, lvl, img_shape in zip(proposals, objectness, levels, image_shapes): boxes = box_ops.clip_boxes_to_image(boxes, img_shape) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE keep = box_ops.remove_small_boxes(boxes, self.min_size) boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep] 'RegionProposalNetwork.filter_proposals' is being compiled since it was called from 'RegionProposalNetwork.forward' at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:446:8 proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors) proposals = proposals.view(num_images, -1, 4) boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE losses = {} 'RegionProposalNetwork.forward' is being compiled since it was called from 'MaskRCNN.forward' at /home/davidriazati/dev/vision/torchvision/models/detection/generalized_rcnn.py:53:8 if isinstance(features, torch.Tensor): features = OrderedDict([(0, features)]) proposals, proposal_losses = self.rpn(images, features, targets) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets) detections = self.transform.postprocess ``` ](https://our.intern.facebook.com/intern/diff/17560963/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/26765 Pulled By: driazati Differential Revision: D17560963 fbshipit-source-id: e463548744b505ca17f0158079b80e08fda47d49