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卷积神经网络

在Python中，利用PyTorch库（特别是torch和torch.nn库）构建一个可以处理不同大小颗粒度的卷积神经网络（Convolutional Neural Network，CNN），通常涉及以下几个步骤：

1. 导入所需模块：

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

2. 定义一个可调整粒度的卷积层模块（例如，通过动态图或混合精度支持）：

class AdaptiveConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1):
        super(AdaptiveConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation)
        self.pooling = nn.MaxPool2d(kernel_size=(kernel_size, kernel_size), stride=stride)

    def forward(self, x, adaptive_kernel_size):
        return self.pooling(F.conv2d(x, self.conv.weight, bias=self.conv.bias, stride=self.conv.stride,
                                      padding=self.conv.padding, dilation=self.conv.dilation))

3. 设计主模型，其中包含一个或多个这样的自适应卷积层，可能还包含全连接层、池化层和批量归一化层：

class ResNetLikeBlock(nn.Module):
    def __init__(self, in_channels, out_channels, dropout_rate=0.5):
        super(ResNetLikeBlock, self).__init__()
        self.conv1 = AdaptiveConv(in_channels, out_channels // 2, kernel_size=3, stride=1)
        self.bn1 = nn.BatchNorm2d(out_channels // 2)
        self.relu1 = nn.ReLU()
        self.conv2 = AdaptiveConv(out_channels // 2, out_channels, kernel_size=3, stride=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.dropout = nn.Dropout(dropout_rate)

    def forward(self, x, kernel_size):
        residual = x
        out = self.conv1(x, kernel_size)
        out = self.bn1(out)
        out = self.relu1(out)
        out = self.conv2(out, kernel_size)
        out = self.bn2(out)
        if residual.shape == out.shape:
            out += residual
        else:
            out = self.dropout(out) + residual
        return out

# 示例模型结构
class CNNWithAdaptiveConv(nn.Module):
    def __init__(self, num_layers, input_shape, output_classes):
        super(CNNWithAdaptiveConv, self).__init__()
        channels = [64, 128, 256]
        self.adaptive_convs = nn.ModuleList([AdaptiveConv(input_shape[0], c, 3) for c in channels])
        self.resblocks = nn.Sequential(*[ResNetLikeBlock(channels[i], channels[i+1]) for i in range(len(channels)-1)])
        self.fc = nn.Linear(channels[-1] * (input_shape[1] // 8) ** 2, output_classes)

    def forward(self, x, kernel_sizes):
        x = [conv(x, kernel_size) for conv, kernel_size in zip(self.adaptive_convs, kernel_sizes)]
        x = torch.cat(x, dim=1)
        x = self.resblocks(x, kernel_sizes)
        x = x.mean(dim=[2, 3])  # Global average pooling
        x = self.fc(x)
        return x

在这个例子中，`kernel_sizes`是一个列表，表示每个自适应卷积层应该使用的可变大小。