【深度学习PyTorch入门】7.Save and Load the Model 保存和加载模型

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)
- 笔记
[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)
- 笔记
[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 复制代码

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 复制代码

model = models.vgg16(weights='IMAGENET1K_V1')
torch.save(model.state_dict(), 'model_weights.pth')

Out:

python 复制代码

Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/vgg16-397923af.pth

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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 复制代码

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 复制代码

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 复制代码

torch.save(model, 'model.pth')

然后我们可以像这样加载模型：

python 复制代码

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。

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)$

Save and Load the Model --- PyTorch Tutorials 2.2.0+cu121 documentation

Github

storm-ice/Get_started_with_PyTorch

【深度学习PyTorch入门】7.Save and Load the Model 保存和加载模型

Save and Load the Model 保存和加载模型

文章目录

Saving and Loading Model Weights 保存和加载模型权重

笔记

Saving and Loading Models with Shapes 保存和加载带有形状的模型

笔记

Related Tutorials 相关教程

Github