2022-03-14thorough-pytorch-模型定义-优快云博客

本文链接：https://blog.youkuaiyun.com/lucifer80861/article/details/123491867

本文介绍了如何使用PyTorch中的Sequential、ModuleList和ModuleDict来组织模型结构，包括它们的用法、特点和在U-Net模型中的应用实例。涵盖了从基础操作到高级模型定义的全面指南。

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thorough pytorch

thorough pytorch
通过Sequential，ModuleList和ModuleDict三种方式定义PyTorch模型。

Sequential

对已定义好的模型顺序执行，不需要同时写__init__和forward

import torch.nn as nn
net = nn.Sequential(
        nn.Linear(784, 256),
        nn.ReLU(),
        nn.Linear(256, 10), 
        )
print(net)

OrderedDict格式的输入，有层名称的

import collections
import torch.nn as nn
net2 = nn.Sequential(collections.OrderedDict([
          ('fc1', nn.Linear(784, 256)),
          ('relu1', nn.ReLU()),
          ('fc2', nn.Linear(256, 10))
          ]))
print(net2)

ModuleList和ModuleDict

这俩只定义了模型，并没有规定执行顺序，需要init和foward，里面的层可以重复使用，ModuleDict就是加了个层名称

net = nn.ModuleList([nn.Linear(784, 256), nn.ReLU()])
net.append(nn.Linear(256, 10)) # # 类似List的append操作
print(net[-1])  # 类似List的索引访问
print(net)

net = nn.ModuleDict({
    'linear': nn.Linear(784, 256),
    'act': nn.ReLU(),
})
net['output'] = nn.Linear(256, 10) # 添加
print(net['linear']) # 访问
print(net.output)
print(net)

class model(nn.Module):
  def __init__(self, ...):
    self.modulelist = ...
    ...
    
  def forward(self, x):
    for layer in self.modulelist:
      x = layer(x)
    return x

U-Net模型中一个模块的实现

class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels, mid_channels=None):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)