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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| from typing import Any, List | |
| import torch | |
| from torch.nn import functional as F | |
| from detectron2.config import CfgNode | |
| from detectron2.structures import Instances | |
| from .utils import resample_data | |
| class SegmentationLoss: | |
| """ | |
| Segmentation loss as cross-entropy for raw unnormalized scores given ground truth | |
| labels. Segmentation ground truth labels are defined for the bounding box of | |
| interest at some fixed resolution [S, S], where | |
| S = MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE. | |
| """ | |
| def __init__(self, cfg: CfgNode): | |
| """ | |
| Initialize segmentation loss from configuration options | |
| Args: | |
| cfg (CfgNode): configuration options | |
| """ | |
| self.heatmap_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE | |
| self.n_segm_chan = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS | |
| def __call__( | |
| self, | |
| proposals_with_gt: List[Instances], | |
| densepose_predictor_outputs: Any, | |
| packed_annotations: Any, | |
| ) -> torch.Tensor: | |
| """ | |
| Compute segmentation loss as cross-entropy on aligned segmentation | |
| ground truth and estimated scores. | |
| Args: | |
| proposals_with_gt (list of Instances): detections with associated ground truth data | |
| densepose_predictor_outputs: an object of a dataclass that contains predictor outputs | |
| with estimated values; assumed to have the following attributes: | |
| * coarse_segm - coarse segmentation estimates, tensor of shape [N, D, S, S] | |
| packed_annotations: packed annotations for efficient loss computation; | |
| the following attributes are used: | |
| - coarse_segm_gt | |
| - bbox_xywh_gt | |
| - bbox_xywh_est | |
| """ | |
| if packed_annotations.coarse_segm_gt is None: | |
| return self.fake_value(densepose_predictor_outputs) | |
| coarse_segm_est = densepose_predictor_outputs.coarse_segm[packed_annotations.bbox_indices] | |
| with torch.no_grad(): | |
| coarse_segm_gt = resample_data( | |
| packed_annotations.coarse_segm_gt.unsqueeze(1), | |
| packed_annotations.bbox_xywh_gt, | |
| packed_annotations.bbox_xywh_est, | |
| self.heatmap_size, | |
| self.heatmap_size, | |
| mode="nearest", | |
| padding_mode="zeros", | |
| ).squeeze(1) | |
| if self.n_segm_chan == 2: | |
| coarse_segm_gt = coarse_segm_gt > 0 | |
| return F.cross_entropy(coarse_segm_est, coarse_segm_gt.long()) | |
| def fake_value(self, densepose_predictor_outputs: Any) -> torch.Tensor: | |
| """ | |
| Fake segmentation loss used when no suitable ground truth data | |
| was found in a batch. The loss has a value 0 and is primarily used to | |
| construct the computation graph, so that `DistributedDataParallel` | |
| has similar graphs on all GPUs and can perform reduction properly. | |
| Args: | |
| densepose_predictor_outputs: DensePose predictor outputs, an object | |
| of a dataclass that is assumed to have `coarse_segm` | |
| attribute | |
| Return: | |
| Zero value loss with proper computation graph | |
| """ | |
| return densepose_predictor_outputs.coarse_segm.sum() * 0 | |