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# Copyright (c) Facebook, Inc. and its affiliates.
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import CfgNode
from detectron2.layers import Conv2d
from ..utils import initialize_module_params
from .registry import ROI_DENSEPOSE_HEAD_REGISTRY
@ROI_DENSEPOSE_HEAD_REGISTRY.register()
class DensePoseV1ConvXHead(nn.Module):
"""
Fully convolutional DensePose head.
"""
def __init__(self, cfg: CfgNode, input_channels: int):
"""
Initialize DensePose fully convolutional head
Args:
cfg (CfgNode): configuration options
input_channels (int): number of input channels
"""
super(DensePoseV1ConvXHead, self).__init__()
# fmt: off
hidden_dim = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM
kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL
self.n_stacked_convs = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS
# fmt: on
pad_size = kernel_size // 2
n_channels = input_channels
for i in range(self.n_stacked_convs):
layer = Conv2d(n_channels, hidden_dim, kernel_size, stride=1, padding=pad_size)
layer_name = self._get_layer_name(i)
self.add_module(layer_name, layer)
n_channels = hidden_dim
self.n_out_channels = n_channels
initialize_module_params(self)
def forward(self, features: torch.Tensor):
"""
Apply DensePose fully convolutional head to the input features
Args:
features (tensor): input features
Result:
A tensor of DensePose head outputs
"""
x = features
output = x
for i in range(self.n_stacked_convs):
layer_name = self._get_layer_name(i)
x = getattr(self, layer_name)(x)
x = F.relu(x)
output = x
return output
def _get_layer_name(self, i: int):
layer_name = "body_conv_fcn{}".format(i + 1)
return layer_name
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