densepose/modeling/inference.py

# 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