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from typing import Any, List
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import torch
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from detectron2.config import CfgNode
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from detectron2.structures import Instances
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from .mask import MaskLoss
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from .segm import SegmentationLoss
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class MaskOrSegmentationLoss:
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"""
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Mask or segmentation loss as cross-entropy for raw unnormalized scores
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given ground truth labels. Ground truth labels are either defined by coarse
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segmentation annotation, or by mask annotation, depending on the config
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value MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS
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"""
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def __init__(self, cfg: CfgNode):
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"""
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Initialize segmentation loss from configuration options
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Args:
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cfg (CfgNode): configuration options
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"""
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self.segm_trained_by_masks = cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS
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if self.segm_trained_by_masks:
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self.mask_loss = MaskLoss()
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self.segm_loss = SegmentationLoss(cfg)
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def __call__(
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self,
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proposals_with_gt: List[Instances],
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densepose_predictor_outputs: Any,
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packed_annotations: Any,
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) -> torch.Tensor:
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"""
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Compute segmentation loss as cross-entropy between aligned unnormalized
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score estimates and ground truth; with ground truth given
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either by masks, or by coarse segmentation annotations.
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Args:
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proposals_with_gt (list of Instances): detections with associated ground truth data
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densepose_predictor_outputs: an object of a dataclass that contains predictor outputs
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with estimated values; assumed to have the following attributes:
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* coarse_segm - coarse segmentation estimates, tensor of shape [N, D, S, S]
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packed_annotations: packed annotations for efficient loss computation
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Return:
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tensor: loss value as cross-entropy for raw unnormalized scores
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given ground truth labels
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"""
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if self.segm_trained_by_masks:
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return self.mask_loss(proposals_with_gt, densepose_predictor_outputs)
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return self.segm_loss(proposals_with_gt, densepose_predictor_outputs, packed_annotations)
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def fake_value(self, densepose_predictor_outputs: Any) -> torch.Tensor:
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"""
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Fake segmentation loss used when no suitable ground truth data
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was found in a batch. The loss has a value 0 and is primarily used to
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construct the computation graph, so that `DistributedDataParallel`
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has similar graphs on all GPUs and can perform reduction properly.
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Args:
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densepose_predictor_outputs: DensePose predictor outputs, an object
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of a dataclass that is assumed to have `coarse_segm`
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attribute
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Return:
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Zero value loss with proper computation graph
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"""
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return densepose_predictor_outputs.coarse_segm.sum() * 0
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