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from typing import Any, List |
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import torch |
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from torch.nn import functional as F |
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from detectron2.config import CfgNode |
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from detectron2.structures import Instances |
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from .utils import resample_data |
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class SegmentationLoss: |
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""" |
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Segmentation loss as cross-entropy for raw unnormalized scores given ground truth |
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labels. Segmentation ground truth labels are defined for the bounding box of |
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interest at some fixed resolution [S, S], where |
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S = MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE. |
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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.heatmap_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE |
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self.n_segm_chan = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS |
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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 on aligned segmentation |
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ground truth and estimated scores. |
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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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the following attributes are used: |
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- coarse_segm_gt |
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- bbox_xywh_gt |
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- bbox_xywh_est |
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""" |
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if packed_annotations.coarse_segm_gt is None: |
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return self.fake_value(densepose_predictor_outputs) |
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coarse_segm_est = densepose_predictor_outputs.coarse_segm[packed_annotations.bbox_indices] |
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with torch.no_grad(): |
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coarse_segm_gt = resample_data( |
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packed_annotations.coarse_segm_gt.unsqueeze(1), |
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packed_annotations.bbox_xywh_gt, |
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packed_annotations.bbox_xywh_est, |
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self.heatmap_size, |
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self.heatmap_size, |
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mode="nearest", |
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padding_mode="zeros", |
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).squeeze(1) |
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if self.n_segm_chan == 2: |
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coarse_segm_gt = coarse_segm_gt > 0 |
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return F.cross_entropy(coarse_segm_est, coarse_segm_gt.long()) |
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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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