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models.py.bak
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import torch
import torch.nn as nn
class Conv_BN_Relu_first(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,padding,groups,bias):
super(Conv_BN_Relu_first,self).__init__()
kernel_size = 3
padding = 1
features = 64
groups =1
self.conv = nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding,groups=groups, bias=False)
self.bn = nn.BatchNorm2d(features)
self.relu = nn.ReLU(inplace=True)
def forward(self,x):
return self.relu(self.bn(self.conv(x)))
class Conv_BN_Relu_other(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,padding,groups,bias):
super(Conv_BN_Relu_other,self).__init__()
kernel_size = 3
padding = 1
features = out_channels
groups =1
self.conv = nn.Conv2d(in_channels=in_channels, out_channels=features, kernel_size=kernel_size, padding=padding,groups=groups, bias=False)
self.bn = nn.BatchNorm2d(features)
self.relu = nn.ReLU(inplace=True)
def forward(self,x):
return self.relu(self.bn(self.conv(x)))
class Conv(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,padding,groups,bais):
super(Conv,self).__init__()
kernel_size = 3
padding = 1
features = 1
groups =1
self.conv = nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding,groups=groups, bias=False)
def forward(self,x):
return self.conv(x)
class Self_Attn(nn.Module):
def __init__(self,in_dim):
super(Self_Attn,self).__init__()
self.chanel_in = in_dim
self.query_conv = nn.Conv2d(in_channels=in_dim,out_channels=in_dim//8,kernel_size=1)
self.key_conv = nn.Conv2d(in_channels=in_dim,out_channels=in_dim//8,kernel_size=1)
self.value_conv = nn.Conv2d(in_channels=in_dim,out_channels=in_dim,kernel_size=1)
self.gamma=nn.Parameter(torch.zeros(1))
self.softmax=nn.Softmax(dim=-1)
def forward(self,x):
m_batchsize, C, width,height = x.size()
proj_query = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1)
proj_key = self.key_conv(x).view(m_batchsize,-1,width*height)
print proj_query.size()
print proj_key.size()
energy = torch.bmm(proj_query,proj_key)
attention = self.softmax(energy)
proj_value = self.value_conv(x).view(m_batchsize,-1,width*height)
out = torch.bmm(proj_value,attention.permute(0,2,1))
out = out.view(m_batchsize,C,width,height)
out = self.gamma*out + x
return out, attention
class ADNet(nn.Module):
def __init__(self, channels, num_of_layers=15):
super(DnCNN, self).__init__()
kernel_size = 3
padding = 1
features = 64
groups =1
layers = []
kernel_size1 = 1
self.conv1_1 = nn.Sequential(nn.Conv2d(in_channels=channels,out_channels=features,kernel_size=kernel_size,padding=padding,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_2 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=2,groups=groups,bias=False,dilation=2),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_3 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_4 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_5 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=2,groups=groups,bias=False,dilation=2),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_6 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_7 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=padding,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_8 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_9 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=2,groups=groups,bias=False,dilation=2),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_10 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_11 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_12 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=2,groups=groups,bias=False,dilation=2),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_13 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=padding,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_14 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=padding,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_15 = nn.Sequential(nn.Conv2d(in_channels=features,out_channels=features,kernel_size=kernel_size,padding=1,groups=groups,bias=False),nn.BatchNorm2d(features),nn.ReLU(inplace=True))
self.conv1_16 = nn.Conv2d(in_channels=features,out_channels=3,kernel_size=kernel_size,padding=1,groups=groups,bias=False)
self.conv3 = nn.Conv2d(in_channels=6,out_channels=3,kernel_size=1,stride=1,padding=0,groups=1,bias=True)
self.ReLU = nn.ReLU(inplace=True)
self.Tanh= nn.Tanh()
self.sigmoid = nn.Sigmoid()
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, (2 / (9.0 * 64)) ** 0.5)
if isinstance(m, nn.BatchNorm2d):
m.weight.data.normal_(0, (2 / (9.0 * 64)) ** 0.5)
clip_b = 0.025
w = m.weight.data.shape[0]
for j in range(w):
if m.weight.data[j] >= 0 and m.weight.data[j] < clip_b:
m.weight.data[j] = clip_b
elif m.weight.data[j] > -clip_b and m.weight.data[j] < 0:
m.weight.data[j] = -clip_b
m.running_var.fill_(0.01)
def _make_layers(self, block,features, kernel_size, num_of_layers, padding=1, groups=1, bias=False):
layers = []
for _ in range(num_of_layers):
layers.append(block(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, groups=groups, bias=bias))
return nn.Sequential(*layers)
def forward(self, x):
input = x
x1 = self.conv1_1(x)
x1 = self.conv1_2(x1)
x1 = self.conv1_3(x1)
x1 = self.conv1_4(x1)
x1 = self.conv1_5(x1)
x1 = self.conv1_6(x1)
x1 = self.conv1_7(x1)
x1t = self.conv1_8(x1)
x1 = self.conv1_9(x1t)
x1 = self.conv1_10(x1)
x1 = self.conv1_11(x1)
x1 = self.conv1_12(x1)
x1 = self.conv1_13(x1)
x1 = self.conv1_14(x1)
x1 = self.conv1_15(x1)
x1 = self.conv1_16(x1)
out = torch.cat([x,x1],1)
out= self.Tanh(out)
out = self.conv3(out)
out = out*x1
out2 = x - out
return out2