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
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