如何在 Keras 中实现 LadderNet（2 个 U-Net）？ （以可用的 PyTorch 脚本作为参考）
How to implement LadderNet (2 U-Nets) in Keras? (With available PyTorch script as reference)
我正在尝试在 Keras 中实现 LadderNet (https://arxiv.org/abs/1810.07810) 的架构，仅提供 PyTorch 版本作为参考。论文中的架构由 2 个 U-Net 组成：
LadderNet架构的PyTorch实现代码（取自https://github.com/juntang-zhuang/LadderNet/blob/master/src/LadderNetv65.py) and Keras' implementation of U-Net (obtained from https://github.com/zhixuhao/unet/blob/master/model.py）分别为：
drop = 0.25
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=True)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
if inplanes!= planes:
self.conv0 = conv3x3(inplanes,planes)
self.inplanes = inplanes
self.planes = planes
self.conv1 = conv3x3(planes, planes, stride)
#self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
#self.conv2 = conv3x3(planes, planes)
#self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.drop = nn.Dropout2d(p=drop)
def forward(self, x):
if self.inplanes != self.planes:
x = self.conv0(x)
x = F.relu(x)
out = self.conv1(x)
#out = self.bn1(out)
out = self.relu(out)
out = self.drop(out)
out1 = self.conv1(out)
#out1 = self.relu(out1)
out2 = out1 + x
return F.relu(out2)
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 * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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 Initial_LadderBlock(nn.Module):
def __init__(self,planes,layers,kernel=3,block=BasicBlock,inplanes = 3):
super().__init__()
self.planes = planes
self.layers = layers
self.kernel = kernel
self.padding = int((kernel-1)/2)
self.inconv = nn.Conv2d(in_channels=inplanes,out_channels=planes,
kernel_size=3,stride=1,padding=1,bias=True)
# create module list for down branch
self.down_module_list = nn.ModuleList()
for i in range(0,layers):
self.down_module_list.append(block(planes*(2**i),planes*(2**i)))
# use strided conv instead of poooling
self.down_conv_list = nn.ModuleList()
for i in range(0,layers):
self.down_conv_list.append(nn.Conv2d(planes*2**i,planes*2**(i+1),stride=2,kernel_size=kernel,padding=self.padding))
# create module for bottom block
self.bottom = block(planes*(2**layers),planes*(2**layers))
# create module list for up branch
self.up_conv_list = nn.ModuleList()
self.up_dense_list = nn.ModuleList()
for i in range(0, layers):
self.up_conv_list.append(nn.ConvTranspose2d(in_channels=planes*2**(layers-i), out_channels=planes*2**max(0,layers-i-1), kernel_size=3,
stride=2,padding=1,output_padding=1,bias=True))
self.up_dense_list.append(block(planes*2**max(0,layers-i-1),planes*2**max(0,layers-i-1)))
def forward(self, x):
out = self.inconv(x)
out = F.relu(out)
down_out = []
# down branch
for i in range(0,self.layers):
out = self.down_module_list[i](out)
down_out.append(out)
out = self.down_conv_list[i](out)
out = F.relu(out)
# bottom branch
out = self.bottom(out)
bottom = out
# up branch
up_out = []
up_out.append(bottom)
for j in range(0,self.layers):
out = self.up_conv_list[j](out) + down_out[self.layers-j-1]
#out = F.relu(out)
out = self.up_dense_list[j](out)
up_out.append(out)
return up_out
class LadderBlock(nn.Module):
def __init__(self,planes,layers,kernel=3,block=BasicBlock,inplanes = 3):
super().__init__()
self.planes = planes
self.layers = layers
self.kernel = kernel
self.padding = int((kernel-1)/2)
self.inconv = block(planes,planes)
# create module list for down branch
self.down_module_list = nn.ModuleList()
for i in range(0,layers):
self.down_module_list.append(block(planes*(2**i),planes*(2**i)))
# use strided conv instead of poooling
self.down_conv_list = nn.ModuleList()
for i in range(0,layers):
self.down_conv_list.append(nn.Conv2d(planes*2**i,planes*2**(i+1),stride=2,kernel_size=kernel,padding=self.padding))
# create module for bottom block
self.bottom = block(planes*(2**layers),planes*(2**layers))
# create module list for up branch
self.up_conv_list = nn.ModuleList()
self.up_dense_list = nn.ModuleList()
for i in range(0, layers):
self.up_conv_list.append(nn.ConvTranspose2d(planes*2**(layers-i), planes*2**max(0,layers-i-1), kernel_size=3,
stride=2,padding=1,output_padding=1,bias=True))
self.up_dense_list.append(block(planes*2**max(0,layers-i-1),planes*2**max(0,layers-i-1)))
def forward(self, x):
out = self.inconv(x[-1])
down_out = []
# down branch
for i in range(0,self.layers):
out = out + x[-i-1]
out = self.down_module_list[i](out)
down_out.append(out)
out = self.down_conv_list[i](out)
out = F.relu(out)
# bottom branch
out = self.bottom(out)
bottom = out
# up branch
up_out = []
up_out.append(bottom)
for j in range(0,self.layers):
out = self.up_conv_list[j](out) + down_out[self.layers-j-1]
#out = F.relu(out)
out = self.up_dense_list[j](out)
up_out.append(out)
return up_out
class Final_LadderBlock(nn.Module):
def __init__(self,planes,layers,kernel=3,block=BasicBlock,inplanes = 3):
super().__init__()
self.block = LadderBlock(planes,layers,kernel=kernel,block=block)
def forward(self, x):
out = self.block(x)
return out[-1]
class LadderNetv6(nn.Module):
def __init__(self,layers=3,filters=16,num_classes=2,inplanes=3):
super().__init__()
self.initial_block = Initial_LadderBlock(planes=filters,layers=layers,inplanes=inplanes)
#self.middle_block = LadderBlock(planes=filters,layers=layers)
self.final_block = Final_LadderBlock(planes=filters,layers=layers)
self.final = nn.Conv2d(in_channels=filters,out_channels=num_classes,kernel_size=1)
def forward(self,x):
out = self.initial_block(x)
#out = self.middle_block(out)
out = self.final_block(out)
out = self.final(out)
#out = F.relu(out)
out = F.log_softmax(out,dim=1)
return out
和
def unet(pretrained_weights = None,input_size = (256,256,1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
我是 PyTorch 的新手，我还在熟悉 Keras 和 PyTorch 之间的转换，我也希望以上内容可以帮助我完成这个转换。
关于 LadderNet 在 Keras 中的实现，如果我对这篇论文的理解是正确的，它只是简单地并排叠加了 2 个 U-Net（命名为 LaddderNetKeras），如下所示：
def LadderNetKeras(pretrained_weights = None,input_size = (256,256,1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
# SECOND U-NET
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv10)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
谢谢您，我们将不胜感激！
此处提供了 Keras 中的 laddernet 实现：https://github.com/divamgupta/ladder_network_keras/blob/master/ladder_net.py。以此为起点，我已经成功地使用了这个存储库。
我正在尝试在 Keras 中实现 LadderNet (https://arxiv.org/abs/1810.07810) 的架构，仅提供 PyTorch 版本作为参考。论文中的架构由 2 个 U-Net 组成：
LadderNet架构的PyTorch实现代码（取自https://github.com/juntang-zhuang/LadderNet/blob/master/src/LadderNetv65.py) and Keras' implementation of U-Net (obtained from https://github.com/zhixuhao/unet/blob/master/model.py）分别为：
drop = 0.25
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=True)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
if inplanes!= planes:
self.conv0 = conv3x3(inplanes,planes)
self.inplanes = inplanes
self.planes = planes
self.conv1 = conv3x3(planes, planes, stride)
#self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
#self.conv2 = conv3x3(planes, planes)
#self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.drop = nn.Dropout2d(p=drop)
def forward(self, x):
if self.inplanes != self.planes:
x = self.conv0(x)
x = F.relu(x)
out = self.conv1(x)
#out = self.bn1(out)
out = self.relu(out)
out = self.drop(out)
out1 = self.conv1(out)
#out1 = self.relu(out1)
out2 = out1 + x
return F.relu(out2)
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 * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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 Initial_LadderBlock(nn.Module):
def __init__(self,planes,layers,kernel=3,block=BasicBlock,inplanes = 3):
super().__init__()
self.planes = planes
self.layers = layers
self.kernel = kernel
self.padding = int((kernel-1)/2)
self.inconv = nn.Conv2d(in_channels=inplanes,out_channels=planes,
kernel_size=3,stride=1,padding=1,bias=True)
# create module list for down branch
self.down_module_list = nn.ModuleList()
for i in range(0,layers):
self.down_module_list.append(block(planes*(2**i),planes*(2**i)))
# use strided conv instead of poooling
self.down_conv_list = nn.ModuleList()
for i in range(0,layers):
self.down_conv_list.append(nn.Conv2d(planes*2**i,planes*2**(i+1),stride=2,kernel_size=kernel,padding=self.padding))
# create module for bottom block
self.bottom = block(planes*(2**layers),planes*(2**layers))
# create module list for up branch
self.up_conv_list = nn.ModuleList()
self.up_dense_list = nn.ModuleList()
for i in range(0, layers):
self.up_conv_list.append(nn.ConvTranspose2d(in_channels=planes*2**(layers-i), out_channels=planes*2**max(0,layers-i-1), kernel_size=3,
stride=2,padding=1,output_padding=1,bias=True))
self.up_dense_list.append(block(planes*2**max(0,layers-i-1),planes*2**max(0,layers-i-1)))
def forward(self, x):
out = self.inconv(x)
out = F.relu(out)
down_out = []
# down branch
for i in range(0,self.layers):
out = self.down_module_list[i](out)
down_out.append(out)
out = self.down_conv_list[i](out)
out = F.relu(out)
# bottom branch
out = self.bottom(out)
bottom = out
# up branch
up_out = []
up_out.append(bottom)
for j in range(0,self.layers):
out = self.up_conv_list[j](out) + down_out[self.layers-j-1]
#out = F.relu(out)
out = self.up_dense_list[j](out)
up_out.append(out)
return up_out
class LadderBlock(nn.Module):
def __init__(self,planes,layers,kernel=3,block=BasicBlock,inplanes = 3):
super().__init__()
self.planes = planes
self.layers = layers
self.kernel = kernel
self.padding = int((kernel-1)/2)
self.inconv = block(planes,planes)
# create module list for down branch
self.down_module_list = nn.ModuleList()
for i in range(0,layers):
self.down_module_list.append(block(planes*(2**i),planes*(2**i)))
# use strided conv instead of poooling
self.down_conv_list = nn.ModuleList()
for i in range(0,layers):
self.down_conv_list.append(nn.Conv2d(planes*2**i,planes*2**(i+1),stride=2,kernel_size=kernel,padding=self.padding))
# create module for bottom block
self.bottom = block(planes*(2**layers),planes*(2**layers))
# create module list for up branch
self.up_conv_list = nn.ModuleList()
self.up_dense_list = nn.ModuleList()
for i in range(0, layers):
self.up_conv_list.append(nn.ConvTranspose2d(planes*2**(layers-i), planes*2**max(0,layers-i-1), kernel_size=3,
stride=2,padding=1,output_padding=1,bias=True))
self.up_dense_list.append(block(planes*2**max(0,layers-i-1),planes*2**max(0,layers-i-1)))
def forward(self, x):
out = self.inconv(x[-1])
down_out = []
# down branch
for i in range(0,self.layers):
out = out + x[-i-1]
out = self.down_module_list[i](out)
down_out.append(out)
out = self.down_conv_list[i](out)
out = F.relu(out)
# bottom branch
out = self.bottom(out)
bottom = out
# up branch
up_out = []
up_out.append(bottom)
for j in range(0,self.layers):
out = self.up_conv_list[j](out) + down_out[self.layers-j-1]
#out = F.relu(out)
out = self.up_dense_list[j](out)
up_out.append(out)
return up_out
class Final_LadderBlock(nn.Module):
def __init__(self,planes,layers,kernel=3,block=BasicBlock,inplanes = 3):
super().__init__()
self.block = LadderBlock(planes,layers,kernel=kernel,block=block)
def forward(self, x):
out = self.block(x)
return out[-1]
class LadderNetv6(nn.Module):
def __init__(self,layers=3,filters=16,num_classes=2,inplanes=3):
super().__init__()
self.initial_block = Initial_LadderBlock(planes=filters,layers=layers,inplanes=inplanes)
#self.middle_block = LadderBlock(planes=filters,layers=layers)
self.final_block = Final_LadderBlock(planes=filters,layers=layers)
self.final = nn.Conv2d(in_channels=filters,out_channels=num_classes,kernel_size=1)
def forward(self,x):
out = self.initial_block(x)
#out = self.middle_block(out)
out = self.final_block(out)
out = self.final(out)
#out = F.relu(out)
out = F.log_softmax(out,dim=1)
return out
和
def unet(pretrained_weights = None,input_size = (256,256,1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
我是 PyTorch 的新手，我还在熟悉 Keras 和 PyTorch 之间的转换，我也希望以上内容可以帮助我完成这个转换。
关于 LadderNet 在 Keras 中的实现，如果我对这篇论文的理解是正确的，它只是简单地并排叠加了 2 个 U-Net（命名为 LaddderNetKeras），如下所示：
def LadderNetKeras(pretrained_weights = None,input_size = (256,256,1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
# SECOND U-NET
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv10)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
谢谢您，我们将不胜感激！
此处提供了 Keras 中的 laddernet 实现：https://github.com/divamgupta/ladder_network_keras/blob/master/ladder_net.py。以此为起点，我已经成功地使用了这个存储库。