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# Copyright (c) Facebook, Inc. and its affiliates.
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import CfgNode
from detectron2.layers import Conv2d
from .registry import ROI_DENSEPOSE_HEAD_REGISTRY
@ROI_DENSEPOSE_HEAD_REGISTRY.register()
class DensePoseDeepLabHead(nn.Module):
"""
DensePose head using DeepLabV3 model from
"Rethinking Atrous Convolution for Semantic Image Segmentation"
<https://arxiv.org/abs/1706.05587>.
"""
def __init__(self, cfg: CfgNode, input_channels: int):
super(DensePoseDeepLabHead, self).__init__()
# fmt: off
hidden_dim = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM
kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL
norm = cfg.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NORM
self.n_stacked_convs = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS
self.use_nonlocal = cfg.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NONLOCAL_ON
# fmt: on
pad_size = kernel_size // 2
n_channels = input_channels
self.ASPP = ASPP(input_channels, [6, 12, 56], n_channels) # 6, 12, 56
self.add_module("ASPP", self.ASPP)
if self.use_nonlocal:
self.NLBlock = NONLocalBlock2D(input_channels, bn_layer=True)
self.add_module("NLBlock", self.NLBlock)
# weight_init.c2_msra_fill(self.ASPP)
for i in range(self.n_stacked_convs):
norm_module = nn.GroupNorm(32, hidden_dim) if norm == "GN" else None
layer = Conv2d(
n_channels,
hidden_dim,
kernel_size,
stride=1,
padding=pad_size,
bias=not norm,
norm=norm_module,
)
weight_init.c2_msra_fill(layer)
n_channels = hidden_dim
layer_name = self._get_layer_name(i)
self.add_module(layer_name, layer)
self.n_out_channels = hidden_dim
# initialize_module_params(self)
def forward(self, features):
x0 = features
x = self.ASPP(x0)
if self.use_nonlocal:
x = self.NLBlock(x)
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
# Copied from
# https://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/deeplabv3.py
# See https://arxiv.org/pdf/1706.05587.pdf for details
class ASPPConv(nn.Sequential):
def __init__(self, in_channels, out_channels, dilation):
modules = [
nn.Conv2d(
in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False
),
nn.GroupNorm(32, out_channels),
nn.ReLU(),
]
super(ASPPConv, self).__init__(*modules)
class ASPPPooling(nn.Sequential):
def __init__(self, in_channels, out_channels):
super(ASPPPooling, self).__init__(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, out_channels, 1, bias=False),
nn.GroupNorm(32, out_channels),
nn.ReLU(),
)
def forward(self, x):
size = x.shape[-2:]
x = super(ASPPPooling, self).forward(x)
return F.interpolate(x, size=size, mode="bilinear", align_corners=False)
class ASPP(nn.Module):
def __init__(self, in_channels, atrous_rates, out_channels):
super(ASPP, self).__init__()
modules = []
modules.append(
nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1, bias=False),
nn.GroupNorm(32, out_channels),
nn.ReLU(),
)
)
rate1, rate2, rate3 = tuple(atrous_rates)
modules.append(ASPPConv(in_channels, out_channels, rate1))
modules.append(ASPPConv(in_channels, out_channels, rate2))
modules.append(ASPPConv(in_channels, out_channels, rate3))
modules.append(ASPPPooling(in_channels, out_channels))
self.convs = nn.ModuleList(modules)
self.project = nn.Sequential(
nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),
# nn.BatchNorm2d(out_channels),
nn.ReLU()
# nn.Dropout(0.5)
)
def forward(self, x):
res = []
for conv in self.convs:
res.append(conv(x))
res = torch.cat(res, dim=1)
return self.project(res)
# copied from
# https://github.com/AlexHex7/Non-local_pytorch/blob/master/lib/non_local_embedded_gaussian.py
# See https://arxiv.org/abs/1711.07971 for details
class _NonLocalBlockND(nn.Module):
def __init__(
self, in_channels, inter_channels=None, dimension=3, sub_sample=True, bn_layer=True
):
super(_NonLocalBlockND, self).__init__()
assert dimension in [1, 2, 3]
self.dimension = dimension
self.sub_sample = sub_sample
self.in_channels = in_channels
self.inter_channels = inter_channels
if self.inter_channels is None:
self.inter_channels = in_channels // 2
if self.inter_channels == 0:
self.inter_channels = 1
if dimension == 3:
conv_nd = nn.Conv3d
max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
bn = nn.GroupNorm # (32, hidden_dim) #nn.BatchNorm3d
elif dimension == 2:
conv_nd = nn.Conv2d
max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
bn = nn.GroupNorm # (32, hidden_dim)nn.BatchNorm2d
else:
conv_nd = nn.Conv1d
max_pool_layer = nn.MaxPool1d(kernel_size=2)
bn = nn.GroupNorm # (32, hidden_dim)nn.BatchNorm1d
self.g = conv_nd(
in_channels=self.in_channels,
out_channels=self.inter_channels,
kernel_size=1,
stride=1,
padding=0,
)
if bn_layer:
self.W = nn.Sequential(
conv_nd(
in_channels=self.inter_channels,
out_channels=self.in_channels,
kernel_size=1,
stride=1,
padding=0,
),
bn(32, self.in_channels),
)
nn.init.constant_(self.W[1].weight, 0)
nn.init.constant_(self.W[1].bias, 0)
else:
self.W = conv_nd(
in_channels=self.inter_channels,
out_channels=self.in_channels,
kernel_size=1,
stride=1,
padding=0,
)
nn.init.constant_(self.W.weight, 0)
nn.init.constant_(self.W.bias, 0)
self.theta = conv_nd(
in_channels=self.in_channels,
out_channels=self.inter_channels,
kernel_size=1,
stride=1,
padding=0,
)
self.phi = conv_nd(
in_channels=self.in_channels,
out_channels=self.inter_channels,
kernel_size=1,
stride=1,
padding=0,
)
if sub_sample:
self.g = nn.Sequential(self.g, max_pool_layer)
self.phi = nn.Sequential(self.phi, max_pool_layer)
def forward(self, x):
"""
:param x: (b, c, t, h, w)
:return:
"""
batch_size = x.size(0)
g_x = self.g(x).view(batch_size, self.inter_channels, -1)
g_x = g_x.permute(0, 2, 1)
theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)
theta_x = theta_x.permute(0, 2, 1)
phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)
f = torch.matmul(theta_x, phi_x)
f_div_C = F.softmax(f, dim=-1)
y = torch.matmul(f_div_C, g_x)
y = y.permute(0, 2, 1).contiguous()
y = y.view(batch_size, self.inter_channels, *x.size()[2:])
W_y = self.W(y)
z = W_y + x
return z
class NONLocalBlock2D(_NonLocalBlockND):
def __init__(self, in_channels, inter_channels=None, sub_sample=True, bn_layer=True):
super(NONLocalBlock2D, self).__init__(
in_channels,
inter_channels=inter_channels,
dimension=2,
sub_sample=sub_sample,
bn_layer=bn_layer,
)
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