如果我想要一个最大池化操作，输入为3x3, 池化的窗口为2x2，输出为2x2，求pytorch代码

可以使用torch.nn.MaxPool2d函数实现最大池化操作，代码如下：

import torch

input_tensor = torch.randn(1, 1, 3, 3) # 输入为1个通道，大小为3x3的张量
max_pool = torch.nn.MaxPool2d(kernel_size=2, stride=2) # 池化窗口为2x2，步长为2
output_tensor = max_pool(input_tensor) # 输出为1个通道，大小为2x2的张量

print(output_tensor) # 输出结果为大小为2x2的张量

相关问题

如果我想要一个最大池化操作，输入为3x3, 池化的窗口为2x2，输出为2x2，不显式指明参数，求pytorch代码

import torch.nn as nn

maxpool = nn.MaxPool2d(kernel_size=2, stride=2)

input_tensor = torch.randn(1, 1, 3, 3)

output_tensor = maxpool(input_tensor)

print(output_tensor.size()) # 输出为torch.Size([1, 1, 2, 2])

给我一份在pytorch框架下attention_unet的代码，输出特征图维度为3

好的，以下是在PyTorch框架下实现的attention_unet代码，输出特征图维度为3：

import torch
import torch.nn as nn

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

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

class AttentionBlock(nn.Module):
    def __init__(self, F_g, F_l, F_int):
        super(AttentionBlock, self).__init__()
        self.W_g = nn.Sequential(
            nn.Conv2d(F_g, F_int, kernel_size=1, stride=1, padding=0, bias=True),
            nn.BatchNorm2d(F_int)
        )

        self.W_x = nn.Sequential(
            nn.Conv2d(F_l, F_int, kernel_size=1, stride=1, padding=0, bias=True),
            nn.BatchNorm2d(F_int)
        )

        self.psi = nn.Sequential(
            nn.Conv2d(F_int, 1, kernel_size=1, stride=1, padding=0, bias=True),
            nn.BatchNorm2d(1),
            nn.Sigmoid()
        )

        self.relu = nn.ReLU(inplace=True)

    def forward(self, g, x):
        g1 = self.W_g(g)
        x1 = self.W_x(x)
        psi = self.relu(g1 + x1)
        psi = self.psi(psi)
        return x * psi

class UpSample(nn.Module):
    def __init__(self, in_channels, out_channels, bilinear=True):
        super(UpSample, self).__init__()
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)

        self.conv = DoubleConv(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)

class AttentionUNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=3):
        super(AttentionUNet, self).__init__()
        self.down1 = DoubleConv(in_channels, 64)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.down2 = DoubleConv(64, 128)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.down3 = DoubleConv(128, 256)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.down4 = DoubleConv(256, 512)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.center = DoubleConv(512, 1024)

        self.up4 = UpSample(1024, 512)
        self.att4 = AttentionBlock(512, 256, 128)
        self.up3 = UpSample(512, 256)
        self.att3 = AttentionBlock(256, 128, 64)
        self.up2 = UpSample(256, 128)
        self.att2 = AttentionBlock(128, 64, 32)
        self.up1 = UpSample(128, 64)
        self.att1 = AttentionBlock(64, 32, 16)

        self.output = nn.Conv2d(64, out_channels, kernel_size=1)

    def forward(self, x):
        x1 = self.down1(x)
        x2 = self.pool1(x1)
        x2 = self.down2(x2)
        x3 = self.pool2(x2)
        x3 = self.down3(x3)
        x4 = self.pool3(x3)
        x4 = self.down4(x4)
        x5 = self.pool4(x4)
        x5 = self.center(x5)

        x = self.up4(x5, x4)
        x = self.att4(g=x4, x=x)
        x = self.up3(x, x3)
        x = self.att3(g=x3, x=x)
        x = self.up2(x, x2)
        x = self.att2(g=x2, x=x)
        x = self.up1(x, x1)
        x = self.att1(g=x1, x=x)

        x = self.output(x)
        return x

注意到这里的输出特征图维度为3，因此我们在最后的`nn.Conv2d`中将输出通道数设为3。如果需要修改输入输出维度以及网络结构，可以根据实际情况进行修改。