Save and Load the Model 保存和加载模型
文章目录
- [Save and Load the Model 保存和加载模型](#Save and Load the Model 保存和加载模型)
- [Saving and Loading Model Weights 保存和加载模型权重](#Saving and Loading Model Weights 保存和加载模型权重)
- [Saving and Loading Models with Shapes 保存和加载带有形状的模型](#Saving and Loading Models with Shapes 保存和加载带有形状的模型)
- [Related Tutorials 相关教程](#Related Tutorials 相关教程)
- [References 参考资料](#References 参考资料)
- Github
在本节中,我们将了解如何通过保存、加载和运行模型预测来持久保存模型状态。
python
import torch
import torchvision.models as models
Saving and Loading Model Weights 保存和加载模型权重
PyTorch 模型将学习到的参数存储在内部状态字典中,称为state_dict
。这些可以通过torch.save
方法保存 :
python
model = models.vgg16(weights='IMAGENET1K_V1')
torch.save(model.state_dict(), 'model_weights.pth')
Out:
python
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/vgg16-397923af.pth
0%| | 0.00/528M [00:00<?, ?B/s]
3%|2 | 13.6M/528M [00:00<00:03, 143MB/s]
5%|5 | 28.0M/528M [00:00<00:03, 147MB/s]
8%|8 | 42.5M/528M [00:00<00:03, 149MB/s]
11%|# | 56.9M/528M [00:00<00:03, 150MB/s]
13%|#3 | 71.2M/528M [00:00<00:03, 150MB/s]
16%|#6 | 85.7M/528M [00:00<00:03, 151MB/s]
19%|#8 | 100M/528M [00:00<00:02, 151MB/s]
22%|##1 | 115M/528M [00:00<00:02, 151MB/s]
24%|##4 | 129M/528M [00:00<00:02, 151MB/s]
27%|##7 | 143M/528M [00:01<00:02, 151MB/s]
30%|##9 | 158M/528M [00:01<00:02, 152MB/s]
33%|###2 | 172M/528M [00:01<00:02, 151MB/s]
35%|###5 | 187M/528M [00:01<00:02, 151MB/s]
38%|###8 | 201M/528M [00:01<00:02, 151MB/s]
41%|#### | 216M/528M [00:01<00:02, 151MB/s]
44%|####3 | 230M/528M [00:01<00:02, 152MB/s]
46%|####6 | 245M/528M [00:01<00:01, 152MB/s]
49%|####9 | 259M/528M [00:01<00:01, 152MB/s]
52%|#####1 | 274M/528M [00:01<00:01, 152MB/s]
55%|#####4 | 288M/528M [00:02<00:01, 152MB/s]
57%|#####7 | 303M/528M [00:02<00:01, 152MB/s]
60%|###### | 317M/528M [00:02<00:01, 152MB/s]
63%|######2 | 332M/528M [00:02<00:01, 152MB/s]
66%|######5 | 346M/528M [00:02<00:01, 151MB/s]
68%|######8 | 361M/528M [00:02<00:01, 151MB/s]
71%|#######1 | 375M/528M [00:02<00:01, 151MB/s]
74%|#######3 | 390M/528M [00:02<00:00, 151MB/s]
77%|#######6 | 404M/528M [00:02<00:00, 151MB/s]
79%|#######9 | 418M/528M [00:02<00:00, 151MB/s]
82%|########2 | 433M/528M [00:03<00:00, 151MB/s]
85%|########4 | 447M/528M [00:03<00:00, 151MB/s]
88%|########7 | 462M/528M [00:03<00:00, 151MB/s]
90%|######### | 476M/528M [00:03<00:00, 151MB/s]
93%|#########2| 491M/528M [00:03<00:00, 151MB/s]
96%|#########5| 505M/528M [00:03<00:00, 151MB/s]
98%|#########8| 520M/528M [00:03<00:00, 151MB/s]
100%|##########| 528M/528M [00:03<00:00, 151MB/s]
要加载模型权重,您需要先创建同一模型的实例,然后使用load_state_dict()
方法加载参数。
python
model = models.vgg16() # we do not specify ``weights``, i.e. create untrained model
model.load_state_dict(torch.load('model_weights.pth'))
model.eval()
Out:
python
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)
(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU(inplace=True)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU(inplace=True)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace=True)
(30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU(inplace=True)
(2): Dropout(p=0.5, inplace=False)
(3): Linear(in_features=4096, out_features=4096, bias=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
)
笔记
请务必在推理之前,调用model.eval()
方法,将 dropout 和批量归一化层设置为评估模式。如果不这样做将会产生不一致的推理结果。
Saving and Loading Models with Shapes 保存和加载带有形状的模型
加载模型权重时,我们需要首先实例化模型类,因为该类定义了网络的结构。我们可能希望将此类的结构与模型一起保存,在这种情况下,我们可以将model
(而不是model.state_dict()
)传递给保存函数:
python
torch.save(model, 'model.pth')
然后我们可以像这样加载模型:
python
model = torch.load('model.pth')
笔记
此方法在序列化模型时使用 Python pickle模块,因此它依赖于加载模型时可用的实际类定义。
Related Tutorials 相关教程
- Saving and Loading a General Checkpoint in PyTorch
- [Tips for loading an nn.Module from a checkpoint](https://pytorch.org/tutorials/recipes/recipes/module_load_state_dict_tips.html?highlight=loading nn module from checkpoint)
References 参考资料
Save and Load the Model --- PyTorch Tutorials 2.2.0+cu121 documentation
Save and Load the Model --- PyTorch Tutorials 2.2.0+cu121 documentation
Github
storm-ice/Get_started_with_PyTorch