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