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Running
on
Zero
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| from typing import Any, List | |
| import torch | |
| from detectron2.config import CfgNode | |
| from detectron2.structures import Instances | |
| from .mask import MaskLoss | |
| from .segm import SegmentationLoss | |
| class MaskOrSegmentationLoss: | |
| """ | |
| Mask or segmentation loss as cross-entropy for raw unnormalized scores | |
| given ground truth labels. Ground truth labels are either defined by coarse | |
| segmentation annotation, or by mask annotation, depending on the config | |
| value MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS | |
| """ | |
| def __init__(self, cfg: CfgNode): | |
| """ | |
| Initialize segmentation loss from configuration options | |
| Args: | |
| cfg (CfgNode): configuration options | |
| """ | |
| self.segm_trained_by_masks = ( | |
| cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS | |
| ) | |
| if self.segm_trained_by_masks: | |
| self.mask_loss = MaskLoss() | |
| self.segm_loss = SegmentationLoss(cfg) | |
| def __call__( | |
| self, | |
| proposals_with_gt: List[Instances], | |
| densepose_predictor_outputs: Any, | |
| packed_annotations: Any, | |
| ) -> torch.Tensor: | |
| """ | |
| Compute segmentation loss as cross-entropy between aligned unnormalized | |
| score estimates and ground truth; with ground truth given | |
| either by masks, or by coarse segmentation annotations. | |
| 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 | |
| Return: | |
| tensor: loss value as cross-entropy for raw unnormalized scores | |
| given ground truth labels | |
| """ | |
| if self.segm_trained_by_masks: | |
| return self.mask_loss(proposals_with_gt, densepose_predictor_outputs) | |
| return self.segm_loss( | |
| proposals_with_gt, densepose_predictor_outputs, packed_annotations | |
| ) | |
| 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 | |