class GhostModule(nn.Module): def __init__(self, input_channels, output_channels, kernel_size=1, ratio=2): super(GhostModule, self).__init__() self.output_channels = output_channels self.hidden_channels = output_channels // ratio self.primary_conv = nn.Sequential( nn.Conv2d(input_channels, self.hidden_channels, kernel_size, bias=False), nn.BatchNorm2d(self.hidden_channels), nn.ReLU(inplace=True) ) self.cheap_operation = nn.Sequential( nn.Conv2d(self.hidden_channels, self.hidden_channels, kernel_size, groups=self.hidden_channels, bias=False), nn.BatchNorm2d(self.hidden_channels), nn.ReLU(inplace=True) ) self.secondary_conv = nn.Sequential( nn.Conv2d(self.hidden_channels, self.output_channels - self.hidden_channels, kernel_size, bias=False), nn.BatchNorm2d(self.output_channels - self.hidden_channels) ) def forward(self, x): x1 = self.primary_conv(x) x2 = self.cheap_operation(x1) x3 = self.secondary_conv(torch.cat([x1, x2], dim=1)) return torch.cat([x1, x3], dim=1)
时间: 2024-04-27 20:25:31 浏览: 10
这是一个 PyTorch 的神经网络模块,该模块名为 GhostModule。该模块包含三个卷积层:主要卷积层、廉价卷积操作和次要卷积层。其中主要卷积层是一个标准的卷积操作,廉价卷积操作是一种深度可分离卷积,次要卷积层是一个标准的卷积操作。这个模块的目的是在减少计算成本的同时保持模型的准确性。你有没有其他问题呢?
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。
class CNN(nn.Module): def __init__(self,input_size,output_size): super(CNN, self).__init__() self.B = B self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Sequential( nn.Conv1d(in_channels=input_size, out_channels=64, kernel_size=2), # 24 - 2 + 1 = 23 nn.ReLU(), nn.MaxPool1d(kernel_size=2, stride=1), # 23 - 2 + 1 = 22 ) self.conv2 = nn.Sequential( nn.Conv1d(in_channels=64, out_channels=128, kernel_size=2), # 22 - 2 + 1 = 21 nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=1), # 21 - 2 + 1 = 20 ) self.Linear1 = nn.Linear(self.B * 127 * 20, self.B * 50) self.Linear2 = nn.Linear(self.B * 50 , output_size) def forward(self, x): # [batch_size, n_features, data_len] x = x.permute(0, 2, 1) x = self.conv1(x) x = self.conv2(x) x = x.view(-1) x = self.Linear1(x) x = self.relu(x) x = self.Linear2(x) x = x.view(x.shape[0], -1) return x
这段代码定义了一个CNN模型,它包含了两个卷积层和两个全连接层。下面是对代码的解释:
```python
class CNN(nn.Module):
def __init__(self, input_size, output_size):
super(CNN, self).__init__()
self.B = B
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Sequential(
nn.Conv1d(in_channels=input_size, out_channels=64, kernel_size=2), # 24 - 2 + 1 = 23
nn.ReLU(),
nn.MaxPool1d(kernel_size=2, stride=1), # 23 - 2 + 1 = 22
)
self.conv2 = nn.Sequential(
nn.Conv1d(in_channels=64, out_channels=128, kernel_size=2), # 22 - 2 + 1 = 21
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=1), # 21 - 2 + 1 = 20
)
self.Linear1 = nn.Linear(self.B * 127 * 20, self.B * 50)
self.Linear2 = nn.Linear(self.B * 50 , output_size)
def forward(self, x):
# [batch_size, n_features, data_len]
x = x.permute(0, 2, 1)
x = self.conv1(x)
x = self.conv2(x)
x = x.view(-1)
x = self.Linear1(x)
x = self.relu(x)
x = self.Linear2(x)
x = x.view(x.shape[0], -1)
return x
```
在构造函数`__init__`中,模型初始化了一些参数并定义了网络的各个层。其中,`self.conv1`是一个包含了一个卷积层、ReLU激活函数和最大池化层的序列。`self.conv2`也是一个类似的序列。`self.Linear1`和`self.Linear2`分别是两个全连接层。
在前向传播函数`forward`中,输入数据首先进行形状变换,然后通过卷积层和激活函数进行特征提取和降维。之后,将特征展平并通过全连接层进行预测。最后,输出结果进行形状变换以匹配预期的输出形状。
需要注意的是,代码中的一些变量(如`B`)没有给出具体的定义,你可能需要根据自己的需求进行修改。
希望这个解释对你有所帮助!如果还有其他问题,请随时提问。