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# Copyright (c) Facebook, Inc. and its affiliates.
from dataclasses import fields
import torch
from densepose.structures import DensePoseChartPredictorOutput, DensePoseTransformData
def densepose_chart_predictor_output_hflip(
densepose_predictor_output: DensePoseChartPredictorOutput,
transform_data: DensePoseTransformData,
) -> DensePoseChartPredictorOutput:
"""
Change to take into account a Horizontal flip.
"""
if len(densepose_predictor_output) > 0:
PredictorOutput = type(densepose_predictor_output)
output_dict = {}
for field in fields(densepose_predictor_output):
field_value = getattr(densepose_predictor_output, field.name)
# flip tensors
if isinstance(field_value, torch.Tensor):
setattr(densepose_predictor_output, field.name, torch.flip(field_value, [3]))
densepose_predictor_output = _flip_iuv_semantics_tensor(
densepose_predictor_output, transform_data
)
densepose_predictor_output = _flip_segm_semantics_tensor(
densepose_predictor_output, transform_data
)
for field in fields(densepose_predictor_output):
output_dict[field.name] = getattr(densepose_predictor_output, field.name)
return PredictorOutput(**output_dict)
else:
return densepose_predictor_output
def _flip_iuv_semantics_tensor(
densepose_predictor_output: DensePoseChartPredictorOutput,
dp_transform_data: DensePoseTransformData,
) -> DensePoseChartPredictorOutput:
point_label_symmetries = dp_transform_data.point_label_symmetries
uv_symmetries = dp_transform_data.uv_symmetries
N, C, H, W = densepose_predictor_output.u.shape
u_loc = (densepose_predictor_output.u[:, 1:, :, :].clamp(0, 1) * 255).long()
v_loc = (densepose_predictor_output.v[:, 1:, :, :].clamp(0, 1) * 255).long()
Iindex = torch.arange(C - 1, device=densepose_predictor_output.u.device)[
None, :, None, None
].expand(N, C - 1, H, W)
densepose_predictor_output.u[:, 1:, :, :] = uv_symmetries["U_transforms"][Iindex, v_loc, u_loc]
densepose_predictor_output.v[:, 1:, :, :] = uv_symmetries["V_transforms"][Iindex, v_loc, u_loc]
for el in ["fine_segm", "u", "v"]:
densepose_predictor_output.__dict__[el] = densepose_predictor_output.__dict__[el][
:, point_label_symmetries, :, :
]
return densepose_predictor_output
def _flip_segm_semantics_tensor(
densepose_predictor_output: DensePoseChartPredictorOutput, dp_transform_data
):
if densepose_predictor_output.coarse_segm.shape[1] > 2:
densepose_predictor_output.coarse_segm = densepose_predictor_output.coarse_segm[
:, dp_transform_data.mask_label_symmetries, :, :
]
return densepose_predictor_output
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