pytroch 残差块 代码
时间: 2024-05-07 22:19:23 浏览: 11
以下是使用 PyTorch 实现的一个简单的残差块:
```python
import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
```
在这个实现中,我们定义了一个名为 `ResidualBlock` 的类,它继承自 `nn.Module`。我们通过定义 `__init__` 方法来初始化该块的卷积层、标准化层、激活函数和下采样层(如果需要)。接着,我们定义了 `forward` 方法,它接受输入张量 `x`,并使用残差连接将其传递到下一层。在 `forward` 方法中,我们首先通过卷积、标准化和激活函数来对输入进行变换,然后将其与恒等映射相加,并再次应用激活函数。最后,我们返回输出张量 `out`。