python利用torch库，torch.nn库等，设计一个可以通过不同大小颗粒度的多通道卷积神经网络

在Python中，使用PyTorch库构建一个多通道、多尺度的卷积神经网络（Convolutional Neural Network, CNN），通常会涉及以下几个步骤：

1. **导入所需库**：

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

2. **定义模块**：
- **基础模块**（例如，单通道卷积层）：

     class BasicBlock(nn.Module):
         def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
             super(BasicBlock, self).__init__()
             self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
             self.bn1 = nn.BatchNorm2d(out_channels)
             self.relu = nn.ReLU()
             self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size, stride, padding)
             self.bn2 = nn.BatchNorm2d(out_channels)

         def forward(self, x):
             out = self.conv1(x)
             out = self.bn1(out)
             out = self.relu(out)
             out = self.conv2(out)
             out = self.bn2(out)
             return out + x  # 使用残差连接

3. **多尺度处理**：
- 可能通过调整输入数据的尺度，例如使用`nn.MaxPool2d`结合上采样操作来适应不同粒度：

     def multi_scale_block(input, block, scales=(1, 2, 4)):
         output = []
         for scale in scales:
             if scale > 1:
                 input = F.interpolate(input, scale_factor=scale, mode='nearest')
             output.append(block(input))
         return torch.cat(output, dim=1)  # 合并通道

4. **创建整个网络结构**：

     class MultiScaleCNN(nn.Module):
         def __init__(self, num_channels, num_classes, scales):
             super(MultiScaleCNN, self).__init__()
             self.in_channels = num_channels
             self.scales = scales
             self.block = BasicBlock(num_channels, num_channels * 2)
             self.final_layer = nn.Sequential(
                 nn.AdaptiveAvgPool2d((1, 1)),
                 nn.Flatten(),
                 nn.Linear(num_channels * len(scales), num_classes)
             )

         def forward(self, x):
             features = multi_scale_block(x, self.block, self.scales)
             out = self.final_layer(features)
             return out

5. **训练模型**：

     model = MultiScaleCNN(num_channels=3, num_classes=10, scales=(1, 2, 4))
     # ...
     criterion = nn.CrossEntropyLoss()
     optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
     # ...
     model.train()  # 或者model.eval()取决于是否在训练阶段