Pytorch 中打印网络结构及其参数的方法与实现

1. print 直接输出网络结构

print(model)

print 只能打印最基本的网络结构，显示每一层的操作，输出结果如下：

Classifier(
  (cnn): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (6): ReLU()
    (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (8): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (10): ReLU()
    (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (12): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (14): ReLU()
    (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (17): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (18): ReLU()
    (19): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (fc): Sequential(
    (0): Linear(in_features=8192, out_features=1024, bias=True)
    (1): ReLU()
    (2): Linear(in_features=1024, out_features=512, bias=True)
    (3): ReLU()
    (4): Linear(in_features=512, out_features=11, bias=True)
  )
)

2. from torchsummary import summary

summary(model,(3,128,128))

torchsummary 中的 summary 可以打印每一层的shape，参数量，输出结果如下：

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 128, 128]           1,792
       BatchNorm2d-2         [-1, 64, 128, 128]             128
              ReLU-3         [-1, 64, 128, 128]               0
         MaxPool2d-4           [-1, 64, 64, 64]               0
            Conv2d-5          [-1, 128, 64, 64]          73,856
       BatchNorm2d-6          [-1, 128, 64, 64]             256
              ReLU-7          [-1, 128, 64, 64]               0
         MaxPool2d-8          [-1, 128, 32, 32]               0
            Conv2d-9          [-1, 256, 32, 32]         295,168
      BatchNorm2d-10          [-1, 256, 32, 32]             512
             ReLU-11          [-1, 256, 32, 32]               0
        MaxPool2d-12          [-1, 256, 16, 16]               0
           Conv2d-13          [-1, 512, 16, 16]       1,180,160
      BatchNorm2d-14          [-1, 512, 16, 16]           1,024
             ReLU-15          [-1, 512, 16, 16]               0
        MaxPool2d-16            [-1, 512, 8, 8]               0
           Conv2d-17            [-1, 512, 8, 8]       2,359,808
      BatchNorm2d-18            [-1, 512, 8, 8]           1,024
             ReLU-19            [-1, 512, 8, 8]               0
        MaxPool2d-20            [-1, 512, 4, 4]               0
           Linear-21                 [-1, 1024]       8,389,632
             ReLU-22                 [-1, 1024]               0
           Linear-23                  [-1, 512]         524,800
             ReLU-24                  [-1, 512]               0
           Linear-25                   [-1, 11]           5,643
================================================================
Total params: 12,833,803
Trainable params: 12,833,803
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.19
Forward/backward pass size (MB): 49.59
Params size (MB): 48.96
Estimated Total Size (MB): 98.73
----------------------------------------------------------------

3. from torchinfo import summary

注意使用时需要给定 batchsize

summary(model,(1, 3,128,128))

torchinfo 的 summary 更加友好，我个人觉得是 print 和 torchsummary 的 summary 的结合体！推荐！！！输出结果如下：

==================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==================================================================================
Classifier                               [1, 11]                   --
├─Sequential: 1-1                        [1, 512, 4, 4]            --
│    └─Conv2d: 2-1                       [1, 64, 128, 128]         1,792
│    └─BatchNorm2d: 2-2                  [1, 64, 128, 128]         128
│    └─ReLU: 2-3                         [1, 64, 128, 128]         --
│    └─MaxPool2d: 2-4                    [1, 64, 64, 64]           --
│    └─Conv2d: 2-5                       [1, 128, 64, 64]          73,856
│    └─BatchNorm2d: 2-6                  [1, 128, 64, 64]          256
│    └─ReLU: 2-7                         [1, 128, 64, 64]          --
│    └─MaxPool2d: 2-8                    [1, 128, 32, 32]          --
│    └─Conv2d: 2-9                       [1, 256, 32, 32]          295,168
│    └─BatchNorm2d: 2-10                 [1, 256, 32, 32]          512
│    └─ReLU: 2-11                        [1, 256, 32, 32]          --
│    └─MaxPool2d: 2-12                   [1, 256, 16, 16]          --
│    └─Conv2d: 2-13                      [1, 512, 16, 16]          1,180,160
│    └─BatchNorm2d: 2-14                 [1, 512, 16, 16]          1,024
│    └─ReLU: 2-15                        [1, 512, 16, 16]          --
│    └─MaxPool2d: 2-16                   [1, 512, 8, 8]            --
│    └─Conv2d: 2-17                      [1, 512, 8, 8]            2,359,808
│    └─BatchNorm2d: 2-18                 [1, 512, 8, 8]            1,024
│    └─ReLU: 2-19                        [1, 512, 8, 8]            --
│    └─MaxPool2d: 2-20                   [1, 512, 4, 4]            --
├─Sequential: 1-2                        [1, 11]                   --
│    └─Linear: 2-21                      [1, 1024]                 8,389,632
│    └─ReLU: 2-22                        [1, 1024]                 --
│    └─Linear: 2-23                      [1, 512]                  524,800
│    └─ReLU: 2-24                        [1, 512]                  --
│    └─Linear: 2-25                      [1, 11]                   5,643
==================================================================================
Total params: 12,833,803
Trainable params: 12,833,803
Non-trainable params: 0
Total mult-adds (G): 1.10
==================================================================================
Input size (MB): 0.20
Forward/backward pass size (MB): 31.99
Params size (MB): 51.34
Estimated Total Size (MB): 83.53
==================================================================================

4. Netron 可视化网络结构

    model = 自己的网络

    # 针对有网络模型，但还没有训练保存 .pth 文件的情况
    input = torch.randn(1, 3, 224, 224)  # 随机生成一个输入
    modelpath = "./demo.onnx"  # 定义模型结构保存的路径
    torch.onnx.export(model, input, modelpath)  # 导出并保存
    netron.start(modelpath)

    # #  针对已经存在网络模型 .pth 文件的情况
    # import netron
    #
    # modelpath = "./demo.onnx"  # 定义模型数据保存的路径
    # netron.start(modelpath)  # 输出网络结构

但是在我电脑上有个bug就是网页打不开（mac m1）

最后我下载了neutron的app，成功打开，链接：https://gitcode.net/mirrors/lutzroeder/netron?utm_source=csdn_github_accelerator

标签： pytorch 深度学习人工智能

本文转载自: https://blog.csdn.net/like_jmo/article/details/126903727
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Pytorch 中打印网络结构及其参数的方法与实现

1. print 直接输出网络结构

2. from torchsummary import summary

3. from torchinfo import summary

4. Netron 可视化网络结构

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