pytorch搭建网络模型的4种方法

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

 

model4 = Net4()

print("Method 4:")

print(model4)