How to iterate over layers in Pytorch
Let's say I have a network model object called m. Now I have no prior information about the number of layers this network has. How can create a for loop to iterate over its layer?
I am looking for something like:

Weight=[]
for layer in m._modules:
    Weight.append(layer.weight)


Let's say you have the following neural network.

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

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # define the forward function 
        return x


Now, let's print the size of the weight parameters associated with each NN layer.

model = Net()
for name, param in model.named_parameters():
    print(name, param.size())


Output:

conv1.weight torch.Size([6, 1, 5, 5])
conv1.bias torch.Size([6])
conv2.weight torch.Size([16, 6, 5, 5])
conv2.bias torch.Size([16])
fc1.weight torch.Size([120, 400])
fc1.bias torch.Size([120])
fc2.weight torch.Size([84, 120])
fc2.bias torch.Size([84])
fc3.weight torch.Size([10, 84])
fc3.bias torch.Size([10])


I hope you can extend the example to fulfill your needs.
