densepose/modeling/predictors/cse.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

# pyre-unsafe

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
        # 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)