import torch
import torch.nn as nn
import torch.nn.functional as F
import math
def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3,
stride=strd, padding=padding, bias=bias)
class ConvBlock(nn.Module):
def __init__(self, in_planes, out_planes):
super(ConvBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = conv3x3(in_planes, int(out_planes / 2))
self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
if in_planes != out_planes:
self.downsample = nn.Sequential(
nn.BatchNorm2d(in_planes),
nn.ReLU(True),
nn.Conv2d(in_planes, out_planes,
kernel_size=1, stride=1, bias=False),
)
else:
self.downsample = None
def forward(self, x):
residual = x
out1 = self.bn1(x)
out1 = F.relu(out1, True)
out1 = self.conv1(out1)
out2 = self.bn2(out1)
out2 = F.relu(out2, True)
out2 = self.conv2(out2)
out3 = self.bn3(out2)
out3 = F.relu(out3, True)
out3 = self.conv3(out3)
out3 = torch.cat((out1, out2, out3), 1)
if self.downsample is not None:
residual = self.downsample(residual)
out3 += residual
return out3
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class HourGlass(nn.Module):
def __init__(self, num_modules, depth, num_features):
super(HourGlass, self).__init__()
self.num_modules = num_modules
self.depth = depth
self.features = num_features
self._generate_network(self.depth)
def _generate_network(self, level):
self.add_module('b1_' + str(level), ConvBlock(self.features, self.features))
self.add_module('b2_' + str(level), ConvBlock(self.features, self.features))
if level > 1:
self._generate_network(level - 1)
else:
self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features))
self.add_module('b3_' + str(level), ConvBlock(self.features, self.features))
def _forward(self, level, inp):
# Upper branch
up1 = inp
up1 = self._modules['b1_' + str(level)](up1)
# Lower branch
low1 = F.avg_pool2d(inp, 2, stride=2)
low1 = self._modules['b2_' + str(level)](low1)
if level > 1:
low2 = self._forward(level - 1, low1)
else:
low2 = low1
low2 = self._modules['b2_plus_' + str(level)](low2)
low3 = low2
low3 = self._modules['b3_' + str(level)](low3)
up2 = F.interpolate(low3, scale_factor=2, mode='nearest')
return up1 + up2
def forward(self, x):
return self._forward(self.depth, x)
class FAN(nn.Module):
def __init__(self, num_modules=1):
super(FAN, self).__init__()
self.num_modules = num_modules
# Base part
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = ConvBlock(64, 128)
self.conv3 = ConvBlock(128, 128)
self.conv4 = ConvBlock(128, 256)
# Stacking part
for hg_module in range(self.num_modules):
self.add_module('m' + str(hg_module), HourGlass(1, 4, 256))
self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))
self.add_module('conv_last' + str(hg_module),
nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
self.add_module('l' + str(hg_module), nn.Conv2d(256,
68, kernel_size=1, stride=1, padding=0))
if hg_module < self.num_modules - 1:
self.add_module(
'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
self.add_module('al' + str(hg_module), nn.Conv2d(68,
256, kernel_size=1, stride=1, padding=0))
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)), True)
x = F.avg_pool2d(self.conv2(x), 2, stride=2)
x = self.conv3(x)
x = self.conv4(x)
previous = x
outputs = []
for i in range(self.num_modules):
hg = self._modules['m' + str(i)](previous)
ll = hg
ll = self._modules['top_m_' + str(i)](ll)
ll = F.relu(self._modules['bn_end' + str(i)]
(self._modules['conv_last' + str(i)](ll)), True)
# Predict heatmaps
tmp_out = self._modules['l' + str(i)](ll)
outputs.append(tmp_out)
if i < self.num_modules - 1:
ll = self._modules['bl' + str(i)](ll)
tmp_out_ = self._modules['al' + str(i)](tmp_out)
previous += ll + tmp_out_
return outputs
class ResNetDepth(nn.Module):
def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68):
self.inplanes = 64
super(ResNetDepth, self).__init__()
self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
