【pytorch笔记】第十一篇 预训练模型的修改，保存及加载_imagenet21 k 预训练模型加载

预训练模型的修改，保存及加载

torchvision体统了很多常用的模型，可以自动下载供使用。

1. 下载网络模型

import torchvision

#trauin_data = torchvision.datasets.ImageNet("./dataset",split="train",download=True,transform=torchvision.transforms.ToTensor())   # 这个数据集没有办法再公开的访问了    
vgg16_true = torchvision.models.vgg16(pretrained=True) # 下载卷积层对应的参数是多少、池化层对应的参数时多少，这些参数时ImageNet训练好了的
vgg16_false = torchvision.models.vgg16(pretrained=False) # 没有预训练的参数
print("ok")
print(vgg16_true)
1
2
3
4
5
6
7

Output exceeds the size limit. Open the full output data in a text editor
ok
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace=True)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace=True)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace=True)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace=True)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace=True)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace=True)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace=True)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace=True)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
...
    (5): Dropout(p=0.5, inplace=False)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

2. 查看函数用法

import torchvision
help(torchvision.models.vgg16)
1
2

Help on function vgg16 in module torchvision.models.vgg:

vgg16(pretrained:bool=False, progress:bool=True, **kwargs:Any) -> torchvision.models.vgg.VGG
    VGG 16-layer model (configuration "D")
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
    The required minimum input size of the model is 32x32.
    
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
1
2
3
4
5
6
7
8
9
10

3. 网络模型添加

import torchvision
from torch import nn

dataset = torchvision.datasets.CIFAR10("./dataset",train=True,transform=torchvision.transforms.ToTensor(),download=True)       
vgg16_true = torchvision.models.vgg16(pretrained=True) # 下载卷积层对应的参数是多少、池化层对应的参数时多少，这些参数时ImageNet训练好了的
vgg16_true.add_module('add_linear',nn.Linear(1000,10)) # 在VGG16后面添加一个线性层，使得输出为适应CIFAR10的输出，CIFAR10需要输出10个种类

print(vgg16_true)
1
2
3
4
5
6
7
8

Output exceeds the size limit. Open the full output data in a text editor
Files already downloaded and verified
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace=True)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace=True)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace=True)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace=True)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace=True)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace=True)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace=True)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace=True)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
...
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
  (add_linear): Linear(in_features=1000, out_features=10, bias=True)
)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

4. 网络模型修改

import torchvision
from torch import nn

vgg16_false = torchvision.models.vgg16(pretrained=False) # 没有预训练的参数     
print(vgg16_false)
vgg16_false.classifier[6] = nn.Linear(4096,10)
print(vgg16_false)
1
2
3
4
5
6
7

Output exceeds the size limit. Open the full output data in a text editor
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace=True)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace=True)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace=True)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace=True)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace=True)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace=True)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace=True)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace=True)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace=True)
...
    (5): Dropout(p=0.5, inplace=False)
    (6): Linear(in_features=4096, out_features=10, bias=True)
  )
)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

5 模型保存与加载

几种常用的保存和加载方式

5.1 模型结构 + 模型参数

• 模型保存：

import torchvision
import torch
vgg16 = torchvision.models.vgg16(pretrained=False)
torch.save(vgg16,"./model/vgg16_method.pth") # 保存方式：模型结构 + 模型参数      
print(vgg16)
1
2
3
4
5

• 模型加载

import torch
model = torch.load("./model/vgg16_method.pth")
print(model)
1
2
3

5.2 模型参数（只保存模型参数，不保存模型结构，官方推荐方式）

• 模型参数保存

import torchvision
import torch
vgg16 = torchvision.models.vgg16(pretrained=False)
torch.save(vgg16.state_dict(),"./model/vgg16_method.pth") # 模型参数（官方推荐）,不再保存网络模型结构  
print(vgg16)
1
2
3
4
5

• 模型加载
  此时注意，模型保存时只保存了参数，模型的结构并没有保存，因此在导入参数之前先要搭建模型结构。

import torch
import model_save import * # 它就相当于把 model_save.py 里的网络模型定义写到这里了
    
#model = Myodel# 不需要写这一步，不需要创建网络模型    

model = torch.load("vgg16_method.pth")
print(model)
1
2
3
4
5
6
7