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D_e_U.py
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import torch
from data_new import *
from torch import nn
from torch.nn import init
import torch.nn.functional as F
from torch.autograd import Variable
from config import *
# vgg choice
base = {'dss': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']}
# extend vgg choice --- follow the paper, you can change it
extra = {'dss': [(64, 128, 3, [8, 16, 32, 64]), (128, 128, 3, [4, 8, 16, 32]), (256, 256, 5, [8, 16]),
(512, 256, 5, [4, 8]), (512, 512, 5, []), (512, 512, 7, [])]}
# vgg16
def vgg(cfg, i, batch_norm=False):
layers = []
in_channels = i
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return layers
# extend vgg: side outputs
class FeatLayer(nn.Module):
def __init__(self, in_channel, channel, k):
super(FeatLayer, self).__init__()
#print("side out:", "k",k)
self.main = nn.Sequential(nn.Conv2d(in_channel, channel, k, 1, k // 2), nn.ReLU(inplace=True),
nn.Conv2d(channel, channel, k, 1, k // 2), nn.ReLU(inplace=True),
nn.Dropout()
)
self.o =nn.Conv2d(channel, 1, 1, 1)
self.o2 = nn.Conv2d(channel, 1, 1, 1)
#self.o3 = nn.Conv2d(channel, 1, 1, 1)
def forward(self, x):
y=self.main(x)
y1 = self.o(y)
y2=self.o2(y)
#y3 = self.o3(y)
return (y,y1,y2)
# extra part
def extra_layer(vgg, cfg):
feat_layers, concat_layers, concat_layers_2, scale = [], [],[], 1
for k, v in enumerate(cfg):
#print("k:", k)
feat_layers += [FeatLayer(v[0], v[1], v[2])]
scale *= 2
return vgg, feat_layers
class Generator(torch.nn.Module):
def __init__(self, input_dim, num_filter, output_dim):
super(Generator, self).__init__()
# Decoder
self.linear = nn.Linear(100,1024)
self.deconv1 = DeconvBlock(num_filter * 8, num_filter * 8, dropout=True)
#self.deconv2 = DeconvBlock(num_filter * 8 * 2, num_filter * 8, dropout=True)
#self.deconv3 = DeconvBlock(num_filter * 8 * 2, num_filter * 8, dropout=True)
#self.deconv4 = DeconvBlock(num_filter * 8 * 2, num_filter * 8)
self.deconv2 = DeconvBlock(num_filter * 8 * 2, num_filter * 4)
self.deconv3 = DeconvBlock(num_filter * 4 * 2, num_filter * 2)
self.deconv4 = DeconvBlock(num_filter * 2 * 2, num_filter)
self.deconv5 = DeconvBlock(num_filter * 2, output_dim, batch_norm=False)
def forward(self, x):
# Encoder
enc1 = self.conv1(x)
enc2 = self.conv2(enc1)
enc3 = self.conv3(enc2)
enc4 = self.conv4(enc3)
enc5 = self.conv5(enc4)
#enc6 = self.conv6(enc5)
#enc7 = self.conv7(enc6)
#enc8 = self.conv8(enc7)
# Decoder with skip-connections
dec1 = self.deconv1(enc5)
dec1 = torch.cat([dec1, enc4], 1)
dec2 = self.deconv2(dec1)
dec2 = torch.cat([dec2, enc3], 1)
dec3 = self.deconv3(dec2)
dec3 = torch.cat([dec3, enc2], 1)
dec4 = self.deconv4(dec3)
dec4 = torch.cat([dec4, enc1], 1)
dec5 = self.deconv5(dec4)
#dec5 = torch.cat([dec5, enc3], 1)
#dec6 = self.deconv6(dec5)
#dec6 = torch.cat([dec6, enc2], 1)
#dec7 = self.deconv7(dec6)
#dec7 = torch.cat([dec7, enc1], 1)
#dec8 = self.deconv8(dec7)
out = torch.nn.Sigmoid()(dec5)
return out
class ResidualBlock(nn.Module):
"""
Residual Block
"""
def __init__(self, num_filters, kernel_size, padding=1, nonlinearity=nn.ReLU, dropout=0.2, dilation=1,
batchNormObject=nn.BatchNorm2d):
super(ResidualBlock, self).__init__()
num_hidden_filters = num_filters
self.conv1 = nn.Conv2d(num_filters, num_hidden_filters, kernel_size=kernel_size, stride=1, padding=padding,
dilation=dilation)
self.dropout = nn.Dropout2d(dropout)
self.nonlinearity = nonlinearity(inplace=False)
self.batch_norm1 = batchNormObject(num_hidden_filters)
self.conv2 = nn.Conv2d(num_hidden_filters, num_hidden_filters, kernel_size=kernel_size, stride=1,
padding=padding, dilation=dilation)
self.batch_norm2 = batchNormObject(num_filters)
def forward(self, og_x):
x = og_x
x = self.dropout(x)
x = self.conv1(og_x)
# x = self.batch_norm1(x)
x = self.nonlinearity(x)
x = self.conv2(x)
out = og_x + x
# out = self.batch_norm2(out)
out = self.nonlinearity(out)
return out
class DeconvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=4, stride=2, padding=1, batch_norm=False, dropout=False):
super(DeconvBlock, self).__init__()
self.deconv = torch.nn.ConvTranspose2d(input_size, output_size, kernel_size, stride, padding)
self.bn = torch.nn.BatchNorm2d(output_size)
self.drop = torch.nn.Dropout(0.2)
self.relu = torch.nn.ReLU(True)
self.batch_norm = batch_norm
self.dropout = dropout
def forward(self, x):
if self.batch_norm:
out = self.bn(self.deconv(self.relu(x)))
else:
out = self.deconv(self.relu(x))
if self.dropout:
return self.drop(out)
else:
return out
class D_U(nn.ModuleList):
def __init__(self):
super(D_U,self).__init__()
#self.up = []
self.up0=DeconvBlock(input_size=512,output_size=256,batch_norm=True)
self.up1=DeconvBlock(input_size=512, output_size=256, batch_norm=True)
self.up2=DeconvBlock(input_size=512,output_size=128,batch_norm=True)
self.up3=DeconvBlock(input_size=256,output_size=128,batch_norm=True)
self.extract0=nn.ConvTranspose2d(256, 1, 8,8)
self.discrim=nn.ConvTranspose2d(256,1,4,4)
self.extract1 =nn.ConvTranspose2d(256, 1, 4,4)
self.extract2 = nn.ConvTranspose2d(128, 1, 2, 2)
self.extract3 =nn.ConvTranspose2d(128, 1, 1, 1)
self.extract4 = nn.Conv2d(256,1,1,1)
def forward(self, features):
mask,e = [],[]
x = features[4]
x1 = self.up0(x)
mask.append(nn.Sigmoid()(self.extract0(x1)))
DIC = self.discrim(x1)
x2 = self.up1(torch.cat([features[3],x1],1))
e.append(nn.Sigmoid()(self.extract1(x2)))
x3 = self.up2(torch.cat([features[2],x2],1))
mask.append(nn.Sigmoid()(self.extract2(x3)))
x4 = self.up3(torch.cat([features[1],x3],1))
e.append(nn.Sigmoid()(self.extract3(x4)))
mask.append(nn.Sigmoid()(self.extract4(torch.cat([features[0],x4],1))))
return mask,e,DIC
# DSS network
class DSS(nn.Module):
def __init__(self, base, feat_layers, nums):
super(DSS, self).__init__()
self.extract = [3, 8, 15, 22, 29]
#print('------connect',connect)
self.n=nums
self.base = nn.ModuleList(base)
self.feat = nn.ModuleList(feat_layers)
self.up =nn.ModuleList()
self.up2 = nn.ModuleList()
self.up.append(nn.Conv2d(1,1,1))
k = 2.
for i in range(5):
self.up.append(nn.ConvTranspose2d(1, 1, k,k))
k = 2 * k
self.up2.append(nn.Conv2d(1, 1, 1))
k2 = 2.
for i in range(5):
self.up2.append(nn.ConvTranspose2d(1, 1, k2, k2))
k2 = 2 * k2
self.pool = nn.AvgPool2d(3, 1, 1)
self.pool2 =nn.AvgPool2d(3, 1, 1)
def forward(self, x, label=None):
m,e, prob, y, y1, y2,num = [],[],list(),list(),list(),list(), 0
for k in range(len(self.base)):
x = self.base[k](x)
#print(k,x.size())
if k in self.extract:
(t,t1,t2)=self.feat[num](x)
y.append(t)
y1.append(t1)
y2.append(t2)
#y3.append(t3)
num += 1
# side output
#print(len(y3))
y1.append(self.feat[num](self.pool(x))[1])
y2.append(self.feat[num](self.pool2(x))[2])
for i in range(3):
e.append(self.up[2*i](y1[2*i]))
e[i] = nn.Sigmoid()(e[i])
#print(1,y1[i].size())
for i in range(3):
m.append(self.up2[2*i+1](y2[2*i+1]))
m[i] = nn.Sigmoid()(m[i])
#print(i)
return (y,m,e)
def initialize_weights(net):
for m in net.modules():
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0, 0.05)
m.bias.data.zero_()
elif isinstance(m, nn.ConvTranspose2d):
m.weight.data.normal_(0, 0.05)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.05)
m.bias.data.zero_()
class DSE(nn.Module):
def __init__(self):
super(DSE, self).__init__()
self.net = DSS(*extra_layer(vgg(base['dss'], 3), extra['dss']),nums =BATCH_SIZE)
def forward(self, input):
x = self.net(input)
return x
if __name__ == '__main__':
net = DSE()
net.train()
net.cuda()
net2 = D_U().cuda()
x = Variable(torch.rand(1,3,256,256)).cuda()
(out,y1,y2) = net(x)
m,e,T = net2(out)
print(len(e))
print(out[0].size())
print(len(out))
#with SummaryWriter(comment='DSS') as w:
# w.add_graph(net,(x, ))