【pytorch笔记】搭建实战和Sequential使用

前言

• 使用教程来自小土堆pytorch教程
• 配置环境：torch2.0.1+cu118与对应torchaudio和torchvision

背景知识

• 此次练手选取该论文中的提到的网络结构。如图：
• 练手目的：熟悉用pytorch正确搭建神经网络

开干！

引入库

import torch
import torch.nn as nn

定义神经网络 - sequential类使用

• 不用sequential类的办法

class toy_model(nn.Module):

    def __init__(self):
        super().__init__()
        # 不加上面这行会报错：
        # AttributeError: cannot assign module before Module.__init__() call

        self.conv1 = nn.Conv2d(3, 32, 5, padding=2)
        # 这里padding是多少需要自己根据公式来计算
        # 实际上 奇数卷积核 直接用kernel_size // 2计算
        # 注意公式中是 H W  而不是通道数

        self.maxpool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(32, 32, 5, padding=2)
        self.maxpool2 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(32, 64, 5, padding=2)
        self.maxpool3 = nn.MaxPool2d(2)
        self.flatten = nn.Flatten()
        # 被拉平后 变成了一维的 64*4*4 = 1024 数组

        self.linear1 = nn.Linear(1024, 64)
        self.linear2 = nn.Linear(64, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.maxpool3(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.linear2(x)

        return x

• 用sequential类的办法

class toy_model(nn.Module):

    def __init__(self):
        super().__init__()
        # 不加上面这行会报错：
        # AttributeError: cannot assign module before Module.__init__() call

        self.model = nn.Sequential(
            nn.Conv2d(3, 32, 5, padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, 5, padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 5, padding=2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(1024, 64),
            nn.Linear(64, 10)
        )

    def forward(self, x):
        x = self.model(x)
        return x

必须写上super().__init__()，要不然难绷报错AttributeError: cannot assign module before Module.__init__() call

结果测试

• 使用代码

  if __name__ == '__main__':
    mymodel = toy_model()
    input = torch.ones((64, 3, 32, 32))
    output = mymodel(input)
    print(mymodel)
    print(output.shape)
  

• 不用sequential类的办法

toy_model(
  (conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear1): Linear(in_features=1024, out_features=64, bias=True)
  (linear2): Linear(in_features=64, out_features=10, bias=True)
)
torch.Size([64, 10])

• 用sequential类的办法

toy_model(
  (model): Sequential(
    (0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Flatten(start_dim=1, end_dim=-1)
    (7): Linear(in_features=1024, out_features=64, bias=True)
    (8): Linear(in_features=64, out_features=10, bias=True)
  )
)
torch.Size([64, 10])