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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| import torch | |
| from torch import nn | |
| from detectron2.config import CfgNode | |
| from detectron2.layers import ConvTranspose2d, interpolate | |
| from ...structures import DensePoseEmbeddingPredictorOutput | |
| from ..utils import initialize_module_params | |
| from .registry import DENSEPOSE_PREDICTOR_REGISTRY | |
| class DensePoseEmbeddingPredictor(nn.Module): | |
| """ | |
| Last layers of a DensePose model that take DensePose head outputs as an input | |
| and produce model outputs for continuous surface embeddings (CSE). | |
| """ | |
| 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 | |
| embed_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE | |
| kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL | |
| # coarse segmentation | |
| self.coarse_segm_lowres = ConvTranspose2d( | |
| dim_in, n_segm_chan, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) | |
| ) | |
| # embedding | |
| self.embed_lowres = ConvTranspose2d( | |
| dim_in, embed_size, 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): | |
| """ | |
| Perform forward step on DensePose head outputs | |
| Args: | |
| head_outputs (tensor): DensePose head outputs, tensor of shape [N, D, H, W] | |
| """ | |
| embed_lowres = self.embed_lowres(head_outputs) | |
| coarse_segm_lowres = self.coarse_segm_lowres(head_outputs) | |
| embed = self.interp2d(embed_lowres) | |
| coarse_segm = self.interp2d(coarse_segm_lowres) | |
| return DensePoseEmbeddingPredictorOutput(embedding=embed, coarse_segm=coarse_segm) | |