现在用的这个：
net = load_model(net, args.trained_model, args.cpu)
for name, param in net.named_parameters():
print(param.size(),name, )
下面这个报错：
Pytorch网络结构可视化
使用这个网址，超级强悍
https://lutzroeder.github.io/netron/
可以通过以下的命令进行安装
pip install graphviz
pip install torch torchvision
pip install tensorwatch
载入库
import sys
import torch
import tensorwatch as tw
import torchvision.models
网络结构可视化
model = torchvision.models.alexnet()
tw.draw_model(model, [1, 3, 224, 224])
载入alexnet，draw_model函数需要传入三个参数，第一个为model，第二个参数为input_shape，第三个参数为orientation，可以选择’LR’或者’TB’，分别代表左右布局与上下布局。
在notebook中，执行完上面的代码会显示如下的图，将网络的结构及各个层的name和shape进行了可视化。
统计网络参数
可以通过model_stats方法统计各层的参数情况。
tw.model_stats(alexnet_model, [1, 3, 224, 224])
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
alexnet_model.features
Sequential(
(0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): ReLU(inplace=True)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
alexnet_model.classifier
Sequential(
(0): Dropout(p=0.5)
(1): Linear(in_features=9216, out_features=4096, bias=True)
(2): ReLU(inplace=True)
(3): Dropout(p=0.5)
(4): Linear(in_features=4096, out_features=4096, bias=True)
(5): ReLU(inplace=True)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
参考
https://mp.weixin.qq.com/s/EJDSJYsXAxBzVom3NSBRnA
https://github.com/microsoft/tensorwatch
