3D卷积pytorch代码

以下是一个示例的3D卷积pytorch代码：

import torch
import torch.nn as nn

class Conv3DNet(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size):
        super(Conv3DNet, self).__init__()
        self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool3d(kernel_size=(2, 2, 2))
        self.conv2 = nn.Conv3d(out_channels, out_channels, kernel_size)
        self.fc1 = nn.Linear(out_channels * 10 * 10 * 10, 100)
        self.fc2 = nn.Linear(100, 2)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool(x)
        x = x.view(-1, self.num_flat_features(x))
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

此代码实现了一个3D卷积神经网络，其中包含两个3D卷积层，两个全连接层和一个ReLU激活函数。网络的输入是3D张量，输出是一个二元分类问题。