写出使用torchsummary库中的summary函数计算模型大小的代码

当然可以，以下是使用torchsummary库中的summary函数计算模型大小的代码示例：

import torch
from torchsummary import summary

model = some_model() # 替换成你自己的模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

summary(model, input_size=(batch_size, input_channels, height, width), device=device)

其中，`some_model()`是你自己创建的模型；`batch_size`是你选择的批大小；`input_channels`、`height`和`width`是你选择的输入张量的通道数、高度和宽度。当你运行该代码时，你将在输出中看到类似下面的内容：

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1            [128, 64, 56, 56]           9,408
       BatchNorm2d-2            [128, 64, 56, 56]             128
              ReLU-3            [128, 64, 56, 56]               0
         MaxPool2d-4            [128, 64, 28, 28]               0
            Conv2d-5           [128, 128, 28, 28]          73,728
       BatchNorm2d-6           [128, 128, 28, 28]             256
              ReLU-7           [128, 128, 28, 28]               0
         MaxPool2d-8           [128, 128, 14, 14]               0
            Conv2d-9           [128, 256, 14, 14]         294,912
      BatchNorm2d-10           [128, 256, 14, 14]             512
             ReLU-11           [128, 256, 14, 14]               0
           Conv2d-12           [128, 256, 14, 14]         589,824
      BatchNorm2d-13           [128, 256, 14, 14]             512
             ReLU-14           [128, 256, 14, 14]               0
        MaxPool2d-15             [128, 256, 7, 7]               0
           Linear-16                 [128, 1024]      25,690,880
             ReLU-17                 [128, 1024]               0
          Dropout-18                 [128, 1024]               0
           Linear-19                   [128, 10]          10,250
================================================================
Total params: 26,669,890
Trainable params: 26,669,890
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 73.78
Forward/backward pass size (MB): 10848.50
Params size (MB): 101.69
Estimated Total Size (MB): 10923.97
-----------------------------------------------------------------

这些输出提供了你的模型各层的输出形状和参数数量，以及整个模型所需的总参数数量和总内存大小。记得替换掉`some_model()`、`batch_size`、`input_channels`、`height`和`width`等变量以符合你的情况。