nn.ConvTranspose2d(in_channels=3, out_channels=3, kernel_size=2, stride=2, padding=0)
时间: 2024-05-17 13:12:13 浏览: 13
这是一个使用反卷积操作的二维卷积层,其输入通道数为3,输出通道数也为3,卷积核大小为2x2,步长为2,边缘填充为0。反卷积层可以将输入张量的大小放大,实现上采样的功能。在这个例子中,输入张量大小为[N,3,H,W],经过这个反卷积层后,输出张量大小为[N,3,2H,2W]。其中,N为批次大小,H和W分别为输入张量的高和宽。
相关问题
nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=3, stride=2, padding=1),经过这层卷积层,输出大小有何变化
假设输入大小为 `(batch_size, 256, H_in, W_in)`,其中 `batch_size` 表示批量大小, `H_in` 和 `W_in` 分别表示输入的高度和宽度。该卷积层的超参数为 `in_channels=256`(输入通道数)、`out_channels=128`(输出通道数)、`kernel_size=3`(卷积核大小)、`stride=2`(步长大小)和 `padding=1`(填充大小)。
根据转置卷积层的计算方式,输出大小可以通过以下公式计算:
$H_{out} = (H_{in}-1) \times stride - 2 \times padding + kernel\_size$
$W_{out} = (W_{in}-1) \times stride - 2 \times padding + kernel\_size$
因此,该卷积层的输出大小为 `(batch_size, 128, H_out, W_out)`,其中 `H_out = (H_in-1) * stride - 2 * padding + kernel_size = (H_in-1) * 2 - 2 * 1 + 3 = 2H_in - 1`, `W_out = (W_in-1) * stride - 2 * padding + kernel_size = (W_in-1) * 2 - 2 * 1 + 3 = 2W_in - 1`。所以,该卷积层输出的特征图的高度和宽度都增加了2倍,分别为输入大小的2倍减1。
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。
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