def stack_fn(x): x = stack1(x, 64, 3, stride1=1, name='conv2') x = stack1(x, 128, 4, name='conv3') x = stack1(x, 256, 6, name='conv4') return stack1(x, 512, 3, name='conv5')
时间: 2023-08-22 12:10:04 浏览: 21
这是一个用于构建ResNet的函数,它调用了stack1函数四次,分别构建了四个堆叠的残差块。第一个堆叠的残差块包含3个残差块,输出通道数为64。第二个堆叠的残差块包含4个残差块,输出通道数为128。第三个堆叠的残差块包含6个残差块,输出通道数为256。最后一个堆叠的残差块包含3个残差块,输出通道数为512。这个函数的输入是一个张量x,输出是最后一个堆叠的残差块的输出。
相关问题
请帮我编写下面网络的前向传播class UResNet_P(nn.Module):#ResNet_P编码器,参数量0.60M,595355 #def __init__(self): def __init__(self,in_channels=3, out_channels=3, init_features=32): super(UResNet_P,self).__init__() self.edge_detector=Edge_Detector() features = init_features self.pool = nn.MaxPool2d(kernel_size=2, stride=2) self.encoder1 = conv_block(in_channels, features) self.encoder2 = conv_block(features, features * 2) self.encoder3 = conv_block(features * 2, features * 4, dropout=True) self.residual_layer=self.stack_layer(Res_Block,16) self.conv=conv_block(features * 4, features * 8, dropout=True) self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2) self.decoder3 = conv_block(features * 8, features * 4) self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2) # 128 => 64 self.decoder2 = conv_block(features * 4, features * 2) self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2) # 64 => 32 self.decoder1 = conv_block(features * 2, features) self.conv = nn.Conv2d(in_channels=features, out_channels=out_channels, kernel_size=1) self.input = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1, bias=False) self.output = nn.Conv2d(in_channels=32, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False) self.relu=nn.ReLU(inplace=True)
好的,这是 `UResNet_P` 网络的前向传播代码:
```python
import torch.nn as nn
class UResNet_P(nn.Module):
def __init__(self, in_channels=3, out_channels=3, init_features=32):
super(UResNet_P, self).__init__()
self.edge_detector = Edge_Detector()
features = init_features
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder1 = conv_block(in_channels, features)
self.encoder2 = conv_block(features, features * 2)
self.encoder3 = conv_block(features * 2, features * 4, dropout=True)
self.residual_layer = self.stack_layer(Res_Block, 16)
self.conv = conv_block(features * 4, features * 8, dropout=True)
self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2)
self.decoder3 = conv_block(features * 8, features * 4)
self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2)
self.decoder2 = conv_block(features * 4, features * 2)
self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2)
self.decoder1 = conv_block(features * 2, features)
self.conv = nn.Conv2d(in_channels=features, out_channels=out_channels, kernel_size=1)
self.input = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1, bias=False)
self.output = nn.Conv2d(in_channels=32, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
edge = self.edge_detector(x)
x = self.input(x)
x = self.relu(x)
e1 = self.encoder1(x)
e2 = self.encoder2(self.pool(e1))
e3 = self.encoder3(self.pool(e2))
r = self.residual_layer(e3)
d = self.conv(torch.cat((e3, self.upconv3(r)), dim=1))
d = self.decoder3(d)
d = self.upconv2(d)
d = self.decoder2(torch.cat((e2, d), dim=1))
d = self.upconv1(d)
d = self.decoder1(torch.cat((e1, d), dim=1))
out = self.conv(d)
out = self.relu(out)
out = self.output(self.pool(out))
return out
```
这个网络包含了很多层,但是大部分都是相同的结构:卷积层 + ReLU 激活函数,或者是反卷积层。我这里只列出了 `conv_block` 和 `Res_Block` 的代码,因为这两个函数较为复杂,其他层都比较简单。你需要自己实现这两个函数,或者是从其他地方获取它们的代码。
def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None): """A residual block. Arguments: x: input tensor. filters: integer, filters of the bottleneck layer. kernel_size: default 3, kernel size of the bottleneck layer. stride: default 1, stride of the first layer. conv_shortcut: default True, use convolution shortcut if True, otherwise identity shortcut. name: string, block label. Returns: Output tensor for the residual block. """ bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 if conv_shortcut: shortcut = layers.Conv2D( 4 * filters, 1, strides=stride, name=name + '_0_conv')(x) shortcut = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut) else: shortcut = x #第一个卷积结构 x = layers.Conv2D(filters, 1, strides=stride, name=name + '_1_conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x) x = layers.Activation('relu', name=name + '_1_relu')(x) #第二个卷积结构 x = layers.Conv2D( filters, kernel_size, padding='SAME', name=name + '_2_conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x) x = layers.Activation('relu', name=name + '_2_relu')(x) #第三个卷积结构 x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x) x = layers.Add(name=name + '_add')([shortcut, x]) x = layers.Activation('relu', name=name + '_out')(x) return x def stack1(x, filters, blocks, stride1=2, name=None): """A set of stacked residual blocks. Arguments: x: input tensor. filters: integer, filters of the bottleneck layer in a block. blocks: integer, blocks in the stacked blocks. stride1: default 2, stride of the first layer in the first block. name: string, stack label. Returns: Output tensor for the stacked blocks. """ x = block1(x, filters, stride=stride1, name=name + '_block1') for i in range(2, blocks + 1): x = block1(x, filters, conv_shortcut=False, name=name + '_block' + str(i)) return x
这是一个用于构建深度残差网络(ResNet)的函数,包含了两个子函数:block1和stack1。其中block1是一个残差块,stack1是一组堆叠的残差块。在ResNet中,每个残差块由三个卷积层组成,其中第一个卷积层可以使用1x1卷积进行下采样,第三个卷积层的输出通道数是第二个卷积层的四倍。每个残差块的输出是输入和最后一个卷积层的输出的和,并经过ReLU激活函数。stack1函数调用block1函数构建一个堆叠的残差块,可以使用stride1参数指定第一个残差块的下采样步长。