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