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| from dataclasses import dataclass
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| from typing import Union
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| import torch
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| @dataclass
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| class DensePoseEmbeddingPredictorOutput:
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| """
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| Predictor output that contains embedding and coarse segmentation data:
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| * embedding: float tensor of size [N, D, H, W], contains estimated embeddings
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| * coarse_segm: float tensor of size [N, K, H, W]
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| Here D = MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE
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| K = MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS
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| """
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| embedding: torch.Tensor
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| coarse_segm: torch.Tensor
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| def __len__(self):
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| """
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| Number of instances (N) in the output
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| """
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| return self.coarse_segm.size(0)
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| def __getitem__(
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| self, item: Union[int, slice, torch.BoolTensor]
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| ) -> "DensePoseEmbeddingPredictorOutput":
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| """
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| Get outputs for the selected instance(s)
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| Args:
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| item (int or slice or tensor): selected items
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| """
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| if isinstance(item, int):
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| return DensePoseEmbeddingPredictorOutput(
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| coarse_segm=self.coarse_segm[item].unsqueeze(0),
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| embedding=self.embedding[item].unsqueeze(0),
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| )
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| else:
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| return DensePoseEmbeddingPredictorOutput(
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| coarse_segm=self.coarse_segm[item], embedding=self.embedding[item]
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| )
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| def to(self, device: torch.device):
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| """
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| Transfers all tensors to the given device
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| """
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| coarse_segm = self.coarse_segm.to(device)
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| embedding = self.embedding.to(device)
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| return DensePoseEmbeddingPredictorOutput(coarse_segm=coarse_segm, embedding=embedding)
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|
