import torch
import torch.nn as nn
import torch.nn.functional as F
from Options_all import BaseOptions
opt = BaseOptions().parse()
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 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.dropout = nn.Dropout(0.5)
self._generate_network(self.depth)
def _generate_network(self, level):
self.add_module('b1_' + str(level), ConvBlock(256, 256))
self.add_module('b2_' + str(level), ConvBlock(256, 256))
if level > 1:
self._generate_network(level - 1)
else:
self.add_module('b2_plus_' + str(level), ConvBlock(256, 256))
self.add_module('b3_' + str(level), ConvBlock(256, 256))
def _forward(self, level, inp):
# Upper branch
up1 = inp
up1 = self._modules['b1_' + str(level)](up1)
up1 = self.dropout(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)
up1size = up1.size()
rescale_size = (up1size[2], up1size[3])
up2 = F.upsample(low3, size=rescale_size, mode='bilinear')
return up1 + up2
def forward(self, x):
return self._forward(self.depth, x)
class FAN_use(nn.Module):
def __init__(self, config=opt):
super(FAN_use, self).__init__()
self.num_modules = 1
# 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
hg_module = 0
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('l' + str(hg_module), nn.Conv2d(256,
68, kernel_size=1, stride=1, padding=0))
self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
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))
# if config.load_pretrain:
# self.load_pretrain(config.fan_pretrain_path2)
self.conv5 = nn.Conv2d(256, 256, kernel_size=5, stride=3, padding=1)
self.avgpool = nn.MaxPool2d((2, 2), 2)
self.conv6 = nn.Conv2d(68, 1, 3, 2, 1)
self.fc = nn.Linear(1024, 256)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)), True)
x = F.max_pool2d(self.conv2(x), 2)
x = self.conv3(x)
x = self.conv4(x)
previous = x
i = 0
hg = self._modules['m' + str(i)](previous)
ll = hg
ll = self._modules['top_m_' + str(i)](ll)
ll = self._modules['bn_end' + str(i)](self._modules['conv_last' + str(i)](ll))
tmp_out = self._modules['l' + str(i)](F.relu(ll))
return tmp_out
def load_pretrain(self, pretrain_path):
check_point = torch.load(pretrain_path)
self.load_state_dict(check_point['check_point'])
class FanFusion(nn.Module):
def __init__(self, opt=opt):
super(FanFusion, self).__init__()
self.opt = opt
self.model = FAN_use(opt)
self.conv6 = nn.Conv2d(68, 1, 3, 2, 1)
self.fc = nn.Linear(1024, 256)
self.bn1 = nn.BatchNorm2d(68)
self.relu = nn.ReLU(True)
def _forward(self, x):
net = self.model.forward(x)
net = self.relu(self.bn1(net))
net = self.conv6(net)
net = net.view(-1, 1024)
net = self.relu(net)
net = self.fc(net)
return net
def forward(self, x):
x0 = x.view(-1, self.opt.image_channel_size, self.opt.image_size, self.opt.image_size)
net = self._forward(x0)
net = net.view(x.size(0), -1, 256)
return net
