帮我生成一个pytorch框架下的三维卷积模型，可以依次输入.mat格式的数据块进行模型训练。

可以使用 PyTorch 中的 nn.Module 来生成三维卷积模型，以下是示例代码：

import torch.nn as nn

class Conv3DModel(nn.Module):
    def __init__(self):
        super(Conv3DModel, self).__init__()
        self.conv1 = nn.Conv3d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv3d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool = nn.MaxPool3d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 4 * 4 * 4, 512)
        self.relu3 = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool(x)
        x = x.view(-1, 64 * 4 * 4 * 4)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        return x

这个示例模型有两个卷积层和两个全连接层。在模型训练时，可以使用 PyTorch 中的 DataLoader 加载 .mat 格式的数据块进行训练，如下所示：

import torch.utils.data as data_utils
import numpy as np
import scipy.io

# Load .mat data block
data = scipy.io.loadmat('data.mat')
data = np.array(data['data']).astype(np.float32)

# Create DataLoader for training
batch_size = 32
train_data = data_utils.TensorDataset(torch.from_numpy(data))
train_loader = data_utils.DataLoader(train_data, batch_size=batch_size, shuffle=True)

# Train the model
model = Conv3DModel()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

num_epochs = 10
for epoch in range(num_epochs):
    for i, data in enumerate(train_loader, 0):
        inputs = data[0]
        labels = torch.randint(0, 10, (batch_size,)).long()
        
        optimizer.zero_grad()
        
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

可以根据实际情况修改模型结构、数据加载方式、损失函数等参数，实现自己需要的三维卷积模型训练。