给我一个实现了ECA的pytorch代码

以下是一个简单的 PyTorch 代码示例，演示了如何实现 ECA 卷积层：

import torch.nn as nn
import torch.nn.functional as F

class ECALayer(nn.Module):
    def __init__(self, channels, gamma=2, b=1):
        super(ECALayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(channels, channels, kernel_size=1, stride=1)
        self.sigmoid = nn.Sigmoid()
        self.gamma = gamma
        self.b = b

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.conv(y.unsqueeze(-1)).squeeze(-1)
        y = self.sigmoid(y).view(b, c, 1, 1)
        return self.b + self.gamma * y * x

class ECAConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, gamma=2, b=1):
        super(ECAConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias)
        self.eca = ECALayer(out_channels, gamma=gamma, b=b)

    def forward(self, x):
        x = self.conv(x)
        x = self.eca(x)
        return x

在这个实现中，我们定义了两个类：`ECALayer` 和 `ECAConv2d`。`ECALayer` 实现了 ECA 模块，它首先对输入进行全局平均池化，然后通过一个 1D 卷积层和 Sigmoid 激活函数计算通道注意力权重，最后乘以输入，得到 ECA 之后的输出。`ECAConv2d` 则是将 ECA 模块集成到了卷积层中，将输入先通过一个标准卷积层进行计算，然后再通过 `ECALayer` 进行 ECA 处理，得到最终输出。