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