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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as weight_init
def conv3x3(in_planes, out_planes, stride=1, padding=1, bias=False):
"""3x3 convolution with padding."""
return nn.Conv2d(in_planes, out_planes, kernel_size=(3, 3), stride=(stride, stride), padding=padding, bias=bias)
def conv1x1(in_planes, out_planes, stride=1, padding=0, bias=False):
"""1x1 convolution with padding."""
return nn.Conv2d(in_planes, out_planes, kernel_size=(1, 1), stride=(stride, stride), padding=padding, bias=bias)
class ConvBlock(nn.Module):
def __init__(self, in_planes, out_planes, lightweight = False):
super(ConvBlock, self).__init__()
if lightweight:
self.conv1 = conv1x1(in_planes, int(out_planes / 2))
self.conv2 = conv1x1(int(out_planes / 2), int(out_planes / 4))
self.conv3 = conv1x1(int(out_planes / 4), int(out_planes / 4))
else:
self.conv1 = conv3x3(in_planes, int(out_planes / 2))
self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
self.bn1 = nn.BatchNorm2d(int(out_planes / 2))
self.bn2 = nn.BatchNorm2d(int(out_planes / 4))
self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
if in_planes != out_planes:
self.downsample = nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=(1, 1), stride=(1, 1), bias=False),
nn.BatchNorm2d(out_planes),
nn.ReLU6(True),
)
else:
self.downsample = None
def forward(self, x):
residual = x
out1 = self.conv1(x)
out1 = self.bn1(out1)
out1 = F.relu6(out1, True)
out2 = self.conv2(out1)
out2 = self.bn2(out2)
out2 = F.relu6(out2, True)
out3 = self.conv3(out2)
out3 = self.bn3(out3)
out3 = F.relu6(out3, True)
out3 = torch.cat((out1, out2, out3), 1)
if self.downsample is not None:
residual = self.downsample(residual)
out3 += residual
return out3
class HourGlass(nn.Module):
def __init__(self, num_modules, depth, num_features, lightweight = False):
super(HourGlass, self).__init__()
self.num_modules = num_modules
self.depth = depth
self.features = num_features
self.lightweight = lightweight
self._generate_network(self.depth)
def _generate_network(self, level):
self.add_module('b1_' + str(level), ConvBlock(self.features, self.features, lightweight=self.lightweight))
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.max_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 QFAN(nn.Module):
def __init__(self, num_modules=1, num_in=3, num_features = 128, num_out=68, return_features=False):
super(QFAN, self).__init__()
self.num_modules = num_modules
self.num_in = num_in
self.num_features = num_features
self.num_out = num_out
self.return_features = return_features
# Base part
self.conv1 = nn.Conv2d(self.num_in, int(self.num_features / 2), kernel_size=(7, 7), stride=(2, 2), padding=3)
self.bn1 = nn.BatchNorm2d(int(self.num_features / 2))
self.conv2 = ConvBlock(int(self.num_features / 2), int(self.num_features / 2))
self.conv3 = ConvBlock(int(self.num_features / 2), self.num_features)
self.conv4 = ConvBlock(self.num_features, self.num_features)
# Stacking part
for hg_module in range(self.num_modules):
self.add_module('m' + str(hg_module), HourGlass(1, 4, self.num_features))
self.add_module('top_m_' + str(hg_module), ConvBlock(self.num_features, self.num_features))
self.add_module('conv_last' + str(hg_module),
nn.Conv2d(self.num_features, self.num_features, kernel_size=(1, 1), stride=(1, 1),
padding=0))
self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(self.num_features))
self.add_module('l' + str(hg_module), nn.Conv2d(self.num_features,
self.num_out, kernel_size=(1, 1), stride=(1, 1), padding=0))
if hg_module < self.num_modules - 1:
self.add_module(
'bl' + str(hg_module), nn.Conv2d(self.num_features, self.num_features, kernel_size=(1, 1),
stride=(1, 1), padding=0))
self.add_module('al' + str(hg_module), nn.Conv2d(self.num_out, self.num_features, kernel_size=(1, 1),
stride=(1, 1), padding=0))
def forward(self, x):
features = []
x = F.relu(self.bn1(self.conv1(x)), True)
x = F.max_pool2d(self.conv2(x), 2, stride=2)
x = self.conv3(x)
x = self.conv4(x)
if self.return_features:
features.append(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 = previous + ll + tmp_out_
if self.return_features:
return outputs, features
else:
return outputs
def init_weights(net, init_type='normal', gain=0.02):
def init_func(m):
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
weight_init.normal_(m.weight.data, 0.0, gain)
elif init_type == 'xavier':
weight_init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == 'kaiming':
weight_init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
weight_init.orthogonal_(m.weight.data, gain=gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
weight_init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1:
weight_init.normal_(m.weight.data, 1.0, gain)
weight_init.constant_(m.bias.data, 0.0)
net.apply(init_func)
class FANAU(nn.Module):
def __init__(self, num_modules=1, num_features = 128, n_points=66, block=ConvBlock):
super(FANAU, self).__init__()
self.num_modules = 1
self.num_features = num_features
self.fan = QFAN(num_modules = self.num_modules, return_features=True)
block = eval(block) if isinstance(block,str) else block
# input features
self.conv1 = nn.Sequential(nn.Conv2d(68, self.num_features, 1, 1), nn.BatchNorm2d(self.num_features), nn.ReLU6())
self.conv2 = nn.Sequential(nn.Conv2d(self.num_features, self.num_features, 1, 1),
nn.BatchNorm2d(self.num_features), nn.ReLU6())
self.net = HourGlass(1,4, self.num_features, lightweight=True)
self.conv_last = nn.Sequential(nn.Conv2d(self.num_features, self.num_features, 1, 1),
nn.BatchNorm2d(self.num_features), nn.ReLU6())
self.l = nn.Conv2d(self.num_features, n_points, 1, 1)
init_weights(self)
def forward(self, x):
self.fan.eval()
# with torch.no_grad():
output, features = self.fan(x)
# print(len(output), len(features))
# print(output[0].shape, features[0].shape)
out = output[-1]
x = self.conv1(out) + self.conv2(features[0])
x = self.net(x)
x = self.conv_last(x)
x = self.l(x)
# print(x.shape)
# quit()
return x
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