| |
|
|
| 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 |
|
|
|
|
| @DENSEPOSE_PREDICTOR_REGISTRY.register() |
| 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 |
| |
| self.coarse_segm_lowres = ConvTranspose2d( |
| dim_in, n_segm_chan, kernel_size, stride=2, padding=int(kernel_size / 2 - 1) |
| ) |
| |
| 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) |
|
|
