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Running
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Zero
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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
@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)
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