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| import torch
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| from torch import nn
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| from detectron2.config import CfgNode
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| from detectron2.layers import ConvTranspose2d, interpolate
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| from ...structures import DensePoseChartPredictorOutput
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| from ..utils import initialize_module_params
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| from .registry import DENSEPOSE_PREDICTOR_REGISTRY
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| @DENSEPOSE_PREDICTOR_REGISTRY.register()
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| class DensePoseChartPredictor(nn.Module):
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| """
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| Predictor (last layers of a DensePose model) that takes DensePose head outputs as an input
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| and produces 4 tensors which represent DensePose results for predefined body parts
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| (patches / charts):
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| * coarse segmentation, a tensor of shape [N, K, Hout, Wout]
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| * fine segmentation, a tensor of shape [N, C, Hout, Wout]
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| * U coordinates, a tensor of shape [N, C, Hout, Wout]
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| * V coordinates, a tensor of shape [N, C, Hout, Wout]
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| where
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| - N is the number of instances
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| - K is the number of coarse segmentation channels (
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| 2 = foreground / background,
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| 15 = one of 14 body parts / background)
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| - C is the number of fine segmentation channels (
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| 24 fine body parts / background)
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| - Hout and Wout are height and width of predictions
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| """
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| def __init__(self, cfg: CfgNode, input_channels: int):
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| """
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| Initialize predictor using configuration options
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| Args:
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| cfg (CfgNode): configuration options
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| input_channels (int): input tensor size along the channel dimension
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| """
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| super().__init__()
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| dim_in = input_channels
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| n_segm_chan = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS
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| dim_out_patches = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES + 1
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| kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL
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| self.ann_index_lowres = ConvTranspose2d(
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| dim_in, n_segm_chan, kernel_size, stride=2, padding=int(kernel_size / 2 - 1)
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| )
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| self.index_uv_lowres = ConvTranspose2d(
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| dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1)
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| )
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| self.u_lowres = ConvTranspose2d(
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| dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1)
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| )
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| self.v_lowres = ConvTranspose2d(
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| dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1)
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| )
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| self.scale_factor = cfg.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE
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| initialize_module_params(self)
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| def interp2d(self, tensor_nchw: torch.Tensor):
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| """
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| Bilinear interpolation method to be used for upscaling
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| Args:
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| tensor_nchw (tensor): tensor of shape (N, C, H, W)
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| Return:
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| tensor of shape (N, C, Hout, Wout), where Hout and Wout are computed
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| by applying the scale factor to H and W
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| """
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| return interpolate(
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| tensor_nchw, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
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| )
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| def forward(self, head_outputs: torch.Tensor):
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| """
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| Perform forward step on DensePose head outputs
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| Args:
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| head_outputs (tensor): DensePose head outputs, tensor of shape [N, D, H, W]
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| Return:
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| An instance of DensePoseChartPredictorOutput
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| """
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| return DensePoseChartPredictorOutput(
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| coarse_segm=self.interp2d(self.ann_index_lowres(head_outputs)),
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| fine_segm=self.interp2d(self.index_uv_lowres(head_outputs)),
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| u=self.interp2d(self.u_lowres(head_outputs)),
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| v=self.interp2d(self.v_lowres(head_outputs)),
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| )
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|
