pytorch构建网络模型的4种方法

• Z时代
• 2024-01-10
• 分类：综合

利用pytorch来构建网络模型有很多种方法，以下简单列出其中的四种。

假设构建一个网络模型如下：

卷积层--》Relu层--》池化层--》全连接层--》Relu层--》全连接层

首先导入几种方法用到的包：

import torch
import torch.nn.functional as F
from collections import OrderedDict

第一种方法

# Method 1 -----------------------------------------
class Net1(torch.nn.Module):
  def __init__(self):
    super(Net1, self).__init__()
    self.conv1 = torch.nn.Conv2d(3, 32, 3, 1, 1)
    self.dense1 = torch.nn.Linear(32 * 3 * 3, 128)
    self.dense2 = torch.nn.Linear(128, 10)
  def forward(self, x):
    x = F.max_pool2d(F.relu(self.conv(x)), 2)
    x = x.view(x.size(0), -1)
    x = F.relu(self.dense1(x))
    x = self.dense2(x)
    return x
print("Method 1:")
model1 = Net1()
print(model1)

这种方法比较常用，早期的教程通常就是使用这种方法。

第二种方法

# Method 2 ------------------------------------------
class Net2(torch.nn.Module):
  def __init__(self):
    super(Net2, self).__init__()
    self.conv = torch.nn.Sequential(
      torch.nn.Conv2d(3, 32, 3, 1, 1),
      torch.nn.ReLU(),
      torch.nn.MaxPool2d(2))
    self.dense = torch.nn.Sequential(
      torch.nn.Linear(32 * 3 * 3, 128),
      torch.nn.ReLU(),
      torch.nn.Linear(128, 10)
    )
  def forward(self, x):
    conv_out = self.conv1(x)
    res = conv_out.view(conv_out.size(0), -1)
    out = self.dense(res)
    return out
print("Method 2:")
model2 = Net2()
print(model2)

这种方法利用torch.nn.Sequential（）容器进行快速搭建，模型的各层被顺序添加到容器中。缺点是每层的编号是默认的阿拉伯数字，不易区分。

第三种方法：

# Method 3 -------------------------------
class Net3(torch.nn.Module):
  def __init__(self):
    super(Net3, self).__init__()
    self.conv=torch.nn.Sequential()
    self.conv.add_module("conv1",torch.nn.Conv2d(3, 32, 3, 1, 1))
    self.conv.add_module("relu1",torch.nn.ReLU())
    self.conv.add_module("pool1",torch.nn.MaxPool2d(2))
    self.dense = torch.nn.Sequential()
    self.dense.add_module("dense1",torch.nn.Linear(32 * 3 * 3, 128))
    self.dense.add_module("relu2",torch.nn.ReLU())
    self.dense.add_module("dense2",torch.nn.Linear(128, 10))
  def forward(self, x):
    conv_out = self.conv1(x)
    res = conv_out.view(conv_out.size(0), -1)
    out = self.dense(res)
    return out
print("Method 3:")
model3 = Net3()
print(model3)

这种方法是对第二种方法的改进：通过add_module()添加每一层，并且为每一层增加了一个单独的名字。 

第四种方法：

# Method 4 ------------------------------------------
class Net4(torch.nn.Module):
  def __init__(self):
    super(Net4, self).__init__()
    self.conv = torch.nn.Sequential(
      OrderedDict(
        [
          ("conv1", torch.nn.Conv2d(3, 32, 3, 1, 1)),
          ("relu1", torch.nn.ReLU()),
          ("pool", torch.nn.MaxPool2d(2))
        ]
      ))
    self.dense = torch.nn.Sequential(
      OrderedDict([
        ("dense1", torch.nn.Linear(32 * 3 * 3, 128)),
        ("relu2", torch.nn.ReLU()),
        ("dense2", torch.nn.Linear(128, 10))
      ])
    )
  def forward(self, x):
    conv_out = self.conv1(x)
    res = conv_out.view(conv_out.size(0), -1)
    out = self.dense(res)
    return out
print("Method 4:")
model4 = Net4()
print(model4)

是第三种方法的另外一种写法，通过字典的形式添加每一层，并且设置单独的层名称。

完整代码：

import torch
import torch.nn.functional as F
from collections import OrderedDict
# Method 1 -----------------------------------------
class Net1(torch.nn.Module):
  def __init__(self):
    super(Net1, self).__init__()
    self.conv1 = torch.nn.Conv2d(3, 32, 3, 1, 1)
    self.dense1 = torch.nn.Linear(32 * 3 * 3, 128)
    self.dense2 = torch.nn.Linear(128, 10)
  def forward(self, x):
    x = F.max_pool2d(F.relu(self.conv(x)), 2)
    x = x.view(x.size(0), -1)
    x = F.relu(self.dense1(x))
    x = self.dense2()
    return x
print("Method 1:")
model1 = Net1()
print(model1)
# Method 2 ------------------------------------------
class Net2(torch.nn.Module):
  def __init__(self):
    super(Net2, self).__init__()
    self.conv = torch.nn.Sequential(
      torch.nn.Conv2d(3, 32, 3, 1, 1),
      torch.nn.ReLU(),
      torch.nn.MaxPool2d(2))
    self.dense = torch.nn.Sequential(
      torch.nn.Linear(32 * 3 * 3, 128),
      torch.nn.ReLU(),
      torch.nn.Linear(128, 10)
    )
  def forward(self, x):
    conv_out = self.conv1(x)
    res = conv_out.view(conv_out.size(0), -1)
    out = self.dense(res)
    return out
print("Method 2:")
model2 = Net2()
print(model2)
# Method 3 -------------------------------
class Net3(torch.nn.Module):
  def __init__(self):
    super(Net3, self).__init__()
    self.conv=torch.nn.Sequential()
    self.conv.add_module("conv1",torch.nn.Conv2d(3, 32, 3, 1, 1))
    self.conv.add_module("relu1",torch.nn.ReLU())
    self.conv.add_module("pool1",torch.nn.MaxPool2d(2))
    self.dense = torch.nn.Sequential()
    self.dense.add_module("dense1",torch.nn.Linear(32 * 3 * 3, 128))
    self.dense.add_module("relu2",torch.nn.ReLU())
    self.dense.add_module("dense2",torch.nn.Linear(128, 10))
  def forward(self, x):
    conv_out = self.conv1(x)
    res = conv_out.view(conv_out.size(0), -1)
    out = self.dense(res)
    return out
print("Method 3:")
model3 = Net3()
print(model3)
# Method 4 ------------------------------------------
class Net4(torch.nn.Module):
  def __init__(self):
    super(Net4, self).__init__()
    self.conv = torch.nn.Sequential(
      OrderedDict(
        [
          ("conv1", torch.nn.Conv2d(3, 32, 3, 1, 1)),
          ("relu1", torch.nn.ReLU()),
          ("pool", torch.nn.MaxPool2d(2))
        ]
      ))
    self.dense = torch.nn.Sequential(
      OrderedDict([
        ("dense1", torch.nn.Linear(32 * 3 * 3, 128)),
        ("relu2", torch.nn.ReLU()),
        ("dense2", torch.nn.Linear(128, 10))
      ])
    )
  def forward(self, x):
    conv_out = self.conv1(x)
    res = conv_out.view(conv_out.size(0), -1)
    out = self.dense(res)
    return out
print("Method 4:")
model4 = Net4()
print(model4)

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