用pytorch编写25633的卷积层连接两个包含SE残差块的残差层25633再连接12833的卷积层输入为116再连接并测试模型单集运行时间

以下是使用PyTorch编写的模型代码：

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

class SEBlock(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SEBlock, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        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.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.se = SEBlock(out_channels)
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
            self.shortcut_bn = nn.BatchNorm2d(out_channels)
        else:
            self.shortcut = nn.Identity()

    def forward(self, x):
        out = self.conv1(x)
        out = F.relu(self.bn1(out), inplace=True)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.se(out)
        out += self.shortcut_bn(self.shortcut(x))
        out = F.relu(out, inplace=True)
        return out

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 256, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(256)
        self.residual1 = nn.Sequential(
            ResidualBlock(256, 256),
            ResidualBlock(256, 256),
        )
        self.residual2 = nn.Sequential(
            ResidualBlock(256, 256),
            ResidualBlock(256, 256),
        )
        self.conv2 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(128)
        self.fc = nn.Linear(128 * 4 * 4, 10)

    def forward(self, x):
        out = self.conv1(x)
        out = F.relu(self.bn1(out), inplace=True)
        out = self.residual1(out)
        out = self.residual2(out)
        out = self.conv2(out)
        out = F.relu(self.bn2(out), inplace=True)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

为了测试模型单集运行时间，我们可以使用以下代码：

import time

net = Net()
net.eval()
x = torch.randn(1, 1, 16, 16)

start_time = time.time()
y = net(x)
end_time = time.time()

print('Output size:', y.size())
print('Time elapsed:', end_time - start_time)

此代码将打印输出大小和模型单集运行时间。请注意，这个时间可能因为硬件、软件和输入数据的不同而有所变化。