Pytorch创建模型的多种方法

目录

• Method 1
• Method 2
• Method 3
• Method 4
• Reference

网络结构：

conv &＃8211;> relu &＃8211;> pool &＃8211;> FC &＃8212; > relu &＃8211;> FC

导入包

import torch
import torch.nn.functional as F
from collections import OrderedDict
from torchsummary import summaryMethod 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):
# [2, 3, 6, 6]
x = F.max_pool2d(F.relu(self.conv1(x)), 2)
x = x.view(x.size(0), -1)
x = F.relu(self.dense1(x))
x = self.dense2(x)
return x
print("Method 1:")
summary(Net1(), (3, 6, 6))

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):
# [2, 3, 6, 6]
x = self.conv(x)
x = x.view(x.size(0), -1)
x = self.dense(x)
return x
print("Method 2:")
summary(Net2(), (3, 6, 6))


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):
# [2, 3, 6, 6]
x = self.conv(x)
x = x.view(x.size(0), -1)
x = self.dense(x)
return x
print("Method 3:")
#summary(Net3(), (3, 6, 6))
print(Net3())

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。

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):
# [2, 3, 6, 6]
x = self.conv(x)
x = x.view(x.size(0), -1)
x = self.dense(x)
return x
print("Method 4:")
#summary(Net4(), (3, 6, 6))
print(Net4())

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。

https://www.cnblogs.com/denny402/p/7593301.html