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# Copyright (c) Facebook, Inc. and its affiliates.
from dataclasses import fields
from typing import Any, List
import torch
from detectron2.structures import Instances
def densepose_inference(densepose_predictor_output: Any, detections: List[Instances]) -> None:
"""
Splits DensePose predictor outputs into chunks, each chunk corresponds to
detections on one image. Predictor output chunks are stored in `pred_densepose`
attribute of the corresponding `Instances` object.
Args:
densepose_predictor_output: a dataclass instance (can be of different types,
depending on predictor used for inference). Each field can be `None`
(if the corresponding output was not inferred) or a tensor of size
[N, ...], where N = N_1 + N_2 + .. + N_k is a total number of
detections on all images, N_1 is the number of detections on image 1,
N_2 is the number of detections on image 2, etc.
detections: a list of objects of type `Instance`, k-th object corresponds
to detections on k-th image.
"""
k = 0
for detection_i in detections:
if densepose_predictor_output is None:
# don't add `pred_densepose` attribute
continue
n_i = detection_i.__len__()
PredictorOutput = type(densepose_predictor_output)
output_i_dict = {}
# we assume here that `densepose_predictor_output` is a dataclass object
for field in fields(densepose_predictor_output):
field_value = getattr(densepose_predictor_output, field.name)
# slice tensors
if isinstance(field_value, torch.Tensor):
output_i_dict[field.name] = field_value[k : k + n_i]
# leave others as is
else:
output_i_dict[field.name] = field_value
detection_i.pred_densepose = PredictorOutput(**output_i_dict)
k += n_i
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