7、深入剖析PyTorch nn.Module源码

1. 重要类

• nn.module --> 所有神经网络的父类，自定义神经网络需要继承此类，并且自定义__init__,forward函数即可：

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @FileName  :MyModelNet.py
# @Time      :2024/11/20 13:38
# @Author    :Jason Zhang
import torch
from torch import nnclass NeuralNetwork(nn.Module):def __init__(self):super(NeuralNetwork,self).__init__()self.flatten = nn.Flatten()self.linear_relu_stack = nn.Sequential(nn.Linear(28 * 28, 512),nn.ReLU(),nn.Linear(512, 512),nn.ReLU(),nn.Linear(512, 10))def forward(self, x):x = self.flatten(x)logits = self.linear_relu_stack(x)return logitsif __name__ == "__main__":run_code = 0x_row = 28x_column = 28x_total = x_row * x_columnx = torch.arange(x_total, dtype=torch.float).reshape((1, x_row, x_column))my_net = NeuralNetwork()y = my_net(x)print(f"y.shape={y.shape}")print(my_net)

• 结果：

y.shape=torch.Size([1, 10])
NeuralNetwork((flatten): Flatten(start_dim=1, end_dim=-1)(linear_relu_stack): Sequential((0): Linear(in_features=784, out_features=512, bias=True)(1): ReLU()(2): Linear(in_features=512, out_features=512, bias=True)(3): ReLU()(4): Linear(in_features=512, out_features=10, bias=True))
)

2. add_modules

通过add_modules在旧的网络里面添加新的网络

• 重点： 用nn.ModuleList自带的insert,新的网络继承自老网络中，直接用按位置插入
• python

import torch
from torch import nn
from pytorch_model_summary import summarytorch.manual_seed(2323)class MyModel(nn.Module):def __init__(self):super(MyModel, self).__init__()self.flatten = nn.Flatten()self.block = nn.ModuleList([nn.Linear(28 * 28, 512),nn.ReLU(),nn.Linear(512, 10)])def forward(self, x):x = self.flatten(x)for layer in self.block:x = layer(x)return xclass MyNewNet(MyModel):def __init__(self):super(MyNewNet, self).__init__()self.block.insert(2, nn.Linear(512, 256))  # 插入新层self.block.insert(3, nn.ReLU())  # 插入新的激活函数self.block.insert(4, nn.Linear(256, 512))  # 插入另一层self.block.insert(5, nn.ReLU())  # 插入激活函数if __name__ == "__main__":# 测试原始模型my_model = MyModel()print("Original Model:")print(summary(my_model, torch.ones((1, 28, 28))))# 测试新模型my_new_model = MyNewNet()print("\nNew Model:")print(summary(my_new_model, torch.ones((1, 28, 28))))

• 结果：

Original Model:
-----------------------------------------------------------------------Layer (type)        Output Shape         Param #     Tr. Param #
=======================================================================Flatten-1            [1, 784]               0               0Linear-2            [1, 512]         401,920         401,920ReLU-3            [1, 512]               0               0Linear-4             [1, 10]           5,130           5,130
=======================================================================
Total params: 407,050
Trainable params: 407,050
Non-trainable params: 0
-----------------------------------------------------------------------New Model:
-----------------------------------------------------------------------Layer (type)        Output Shape         Param #     Tr. Param #
=======================================================================Flatten-1            [1, 784]               0               0Linear-2            [1, 512]         401,920         401,920ReLU-3            [1, 512]               0               0Linear-4            [1, 256]         131,328         131,328ReLU-5            [1, 256]               0               0Linear-6            [1, 512]         131,584         131,584ReLU-7            [1, 512]               0               0Linear-8             [1, 10]           5,130           5,130
=======================================================================
Total params: 669,962
Trainable params: 669,962
Non-trainable params: 0
-----------------------------------------------------------------------

3. Apply(fn)

模型权重weight,bias 的初始化

• python

import torch.nn as nn
import torchclass MyAwesomeModel(nn.Module):def __init__(self):super(MyAwesomeModel, self).__init__()self.fc1 = nn.Linear(3, 4)self.fc2 = nn.Linear(4, 5)self.fc3 = nn.Linear(5, 6)# 定义初始化函数
@torch.no_grad()
def init_weights(m):print(m)if type(m) == nn.Linear:m.weight.fill_(1.0)print(m.weight)# 创建神经网络实例
model = MyAwesomeModel()# 应用初始化权值函数到神经网络上
model.apply(init_weights)

• 结果：

Linear(in_features=3, out_features=4, bias=True)
Parameter containing:
tensor([[1., 1., 1.],[1., 1., 1.],[1., 1., 1.],[1., 1., 1.]], requires_grad=True)
Linear(in_features=4, out_features=5, bias=True)
Parameter containing:
tensor([[1., 1., 1., 1.],[1., 1., 1., 1.],[1., 1., 1., 1.],[1., 1., 1., 1.],[1., 1., 1., 1.]], requires_grad=True)
Linear(in_features=5, out_features=6, bias=True)
Parameter containing:
tensor([[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.],[1., 1., 1., 1., 1.]], requires_grad=True)
MyAwesomeModel((fc1): Linear(in_features=3, out_features=4, bias=True)(fc2): Linear(in_features=4, out_features=5, bias=True)(fc3): Linear(in_features=5, out_features=6, bias=True)
)Process finished with exit code 0

4. register_buffer

将模型中添加常数项。比如加1

• python:

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @FileName  :RegisterBuffer.py
# @Time      :2024/11/23 19:21
# @Author    :Jason Zhang
import torch
from torch import nnclass MyNet(nn.Module):def __init__(self):super(MyNet, self).__init__()self.register_buffer("my_buffer_a", torch.ones(2, 3))def forward(self, x):x = x + self.my_buffer_areturn xif __name__ == "__main__":run_code = 0my_test = MyNet()in_x = torch.arange(6).reshape((2, 3))y = my_test(in_x)print(f"x=\n{in_x}")print(f"y=\n{y}")

• 结果：

x=
tensor([[0, 1, 2],[3, 4, 5]])
y=
tensor([[1., 2., 3.],[4., 5., 6.]])

5. nn.Parameters&register_parameters

• python

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @FileName  :ParameterTest.py
# @Time      :2024/11/23 19:37
# @Author    :Jason Zhang
import torch
from torch import nnclass MyModule(nn.Module):def __init__(self, in_size, out_size):self.in_size = in_sizeself.out_size = out_sizesuper(MyModule, self).__init__()self.test = torch.rand(self.in_size, self.out_size)self.linear = nn.Linear(self.in_size, self.out_size)def forward(self, x):x = self.linear(x)return xclass MyModuleRegister(nn.Module):def __init__(self, in_size, out_size):self.in_size = in_sizeself.out_size = out_sizesuper(MyModuleRegister, self).__init__()self.test = torch.rand(self.in_size, self.out_size)self.linear = nn.Linear(self.in_size, self.out_size)def forward(self, x):x = self.linear(x)return xclass MyModulePara(nn.Module):def __init__(self, in_size, out_size):self.in_size = in_sizeself.out_size = out_sizesuper(MyModulePara, self).__init__()self.test = nn.Parameter(torch.rand(self.in_size, self.out_size))self.linear = nn.Linear(self.in_size, self.out_size)def forward(self, x):x = self.linear(x)return xif __name__ == "__main__":run_code = 0test_in = 4test_out = 6my_test = MyModule(test_in, test_out)my_test_para = MyModulePara(test_in, test_out)test_list = list(my_test.named_parameters())test_list_para = list(my_test_para.named_parameters())my_test_register = MyModuleRegister(test_in, test_out)para_register = nn.Parameter(torch.rand(test_in, test_out))my_test_register.register_parameter('para_add_register', para_register)test_list_para_register = list(my_test_register.named_parameters())print(f"*" * 50)print(f"test_list=\n{test_list}")print(f"*" * 50)print(f"*" * 50)print(f"test_list_para=\n{test_list_para}")print(f"*" * 50)print(f"*" * 50)print(f"test_list_para_register=\n{test_list_para_register}")print(f"*" * 50)

• 结果：

**************************************************
test_list=
[('linear.weight', Parameter containing:
tensor([[ 0.3805, -0.3368,  0.2348,  0.4525],[-0.4557, -0.3344,  0.1368, -0.3471],[-0.3961,  0.3302,  0.1904, -0.0111],[ 0.4542, -0.3325, -0.3782,  0.0376],[ 0.2083, -0.3113, -0.3447, -0.1503],[ 0.0343,  0.0410, -0.4216, -0.4793]], requires_grad=True)), ('linear.bias', Parameter containing:
tensor([-0.3465, -0.4510,  0.4919,  0.1967, -0.1366, -0.2496],requires_grad=True))]
**************************************************
**************************************************
test_list_para=
[('test', Parameter containing:
tensor([[0.1353, 0.9934, 0.0462, 0.2103, 0.3410, 0.0814],[0.7509, 0.2573, 0.8030, 0.0952, 0.1381, 0.5360],[0.1972, 0.1241, 0.5597, 0.2691, 0.3226, 0.0660],[0.3333, 0.8031, 0.9226, 0.4290, 0.3660, 0.6159]], requires_grad=True)), ('linear.weight', Parameter containing:
tensor([[-0.0633, -0.4030, -0.4962,  0.1928],[-0.1707,  0.2259,  0.0373, -0.0317],[ 0.4523,  0.2439, -0.1376, -0.3323],[ 0.3215,  0.1283,  0.0729,  0.3912],[ 0.0262, -0.1087,  0.4721, -0.1661],[-0.1055, -0.2199, -0.4974, -0.3444]], requires_grad=True)), ('linear.bias', Parameter containing:
tensor([ 0.3702, -0.0142, -0.2098, -0.0910, -0.2323, -0.0546],requires_grad=True))]
**************************************************
**************************************************
test_list_para_register=
[('para_add_register', Parameter containing:
tensor([[0.2428, 0.1388, 0.6612, 0.4215, 0.0215, 0.2618],[0.4234, 0.0160, 0.8947, 0.4784, 0.4403, 0.4800],[0.8845, 0.1469, 0.6894, 0.7050, 0.5911, 0.7702],[0.7694, 0.0491, 0.3583, 0.4451, 0.2282, 0.4293]], requires_grad=True)), ('linear.weight', Parameter containing:
tensor([[ 0.1358, -0.4704, -0.4181, -0.4504],[ 0.0903,  0.3235, -0.3164, -0.4163],[ 0.1342,  0.3108,  0.0612, -0.2910],[ 0.3527,  0.3397, -0.0414, -0.0408],[-0.4877,  0.1925, -0.2912, -0.2239],[-0.0081, -0.1730,  0.0921, -0.4210]], requires_grad=True)), ('linear.bias', Parameter containing:
tensor([-0.2194,  0.2233, -0.4950, -0.3260, -0.0206, -0.0197],requires_grad=True))]
**************************************************

6. 后续测试

• register_module
• get_submodule
• get_parameter