pytorch save model with code examples

Introduction:

Deep learning has revolutionized the field of artificial intelligence, enabling researchers to develop highly sophisticated models capable of achieving impressive performance on a wide range of tasks. PyTorch is an open source machine learning library that has gained huge popularity among researchers and developers due to its ease of use, flexibility, and great performance.

Once you have trained a deep learning model using PyTorch, you will need to save the model's state in order to reuse it in the future. The saved model can be used for further analysis, for deployment in a production environment, and for sharing with other researchers.

In this article, we will demonstrate how to save a PyTorch model using code examples, covering several common use cases.

Saving PyTorch models:

PyTorch provides several ways to save a trained model. The two most common ways are:

Saving the entire model (architecture and weights) in a single file (usually with the ".pt" or ".pth" extension).
Saving only the model parameters in a separate file (usually with the ".pt" or ".pth" extension).

We will demonstrate both these approaches in this article.

Saving the entire model:

To save the entire model, you need to call the "torch.save()" function with the model instance as the first argument, and the file path to save the model as the second argument. Here's an example:

import torch
import torchvision

model = torchvision.models.resnet18(pretrained=True)
torch.save(model, "resnet18.pt")

Here, we have imported the "torch" module and the "torchvision" module, which contains popular computer vision models. We have created an instance of the ResNet18 model and loaded pre-trained weights into it by setting "pretrained=True". Finally, we have called "torch.save()" to save the entire model state to a file named "resnet18.pt".

To load the saved model, you can use the "torch.load()" function. Here's an example:

model = torch.load("resnet18.pt")

This will load the entire model state from the file "resnet18.pt" into the "model" instance.

Saving only the model parameters:

If you only want to save the model parameters (i.e. weights) and not the architecture, you can call the "state_dict()" method of the model instance to get a dictionary of parameter names and values, and then use the "torch.save()" function to save this dictionary to a file. Here's an example:

import torch
import torchvision

model = torchvision.models.resnet18(pretrained=True)
torch.save(model.state_dict(), "resnet18_weights.pt")

Here, we have used the same ResNet18 model as before, but instead of saving the entire model, we have called "model.state_dict()" to get a dictionary containing only the parameter values. We have then called "torch.save()" to save this dictionary to a file named "resnet18_weights.pt".

To load the saved model weights, you first need to create an instance of the same model architecture and then load the state dictionary into it using the "load_state_dict()" method. Here's an example:

model = torchvision.models.resnet18()
model.load_state_dict(torch.load("resnet18_weights.pt"))

This will create a new instance of the ResNet18 model and load the saved model weights from the file "resnet18_weights.pt" into it.

Saving multiple models:

If you have trained multiple models, you might want to save them in different files. Here's an example of how to save two models to different files:

import torch
import torchvision

model1 = torchvision.models.resnet18(pretrained=True)
model2 = torchvision.models.resnet34(pretrained=True)

torch.save(model1.state_dict(), "resnet18_weights.pt")
torch.save(model2.state_dict(), "resnet34_weights.pt")

Here, we have created two different model instances (ResNet18 and ResNet34) and saved their weights to separate files.

Conclusion:

In this article, we have demonstrated how to save PyTorch models using code examples. We showed how to save the entire model, only the model parameters, and multiple models to different files. Saving models is an essential step in deep learning research and development, as it enables model reuse and sharing.

In addition to the code examples provided in the previous section, let's dive deeper into the concepts of saving PyTorch models.

Saving and loading models with the same architecture:

It is important to note that when saving the entire PyTorch model, the model architecture is also saved. This means that when loading the model, the same architecture needs to be defined. Here's an example:

import torch
import torchvision.models as models

model = models.resnet18(pretrained=True)
input_tensor = torch.randn(1, 3, 224, 224)
output = model(input_tensor)

torch.save(model, 'resnet18.pth')

loaded_model = torch.load('resnet18.pth')
loaded_output = model(input_tensor)

print(output == loaded_output)  # This will print True

In this example, we have trained an instance of the ResNet18 model, saved the entire state of the model, and then loaded the model from the saved file. Here, we have used the same ResNet18 architecture to load the saved model, which ensures that the loaded model is identical to the trained model.

Saving and loading models with different architecture:

If you want to load the saved model into a different architecture, you need to first define the new architecture with the same layers and in the same sequence as the original architecture. Then, you can load the saved model and transfer the loaded weights to the new architecture.

Here's an example of how to save and load the ResNet18 weights into a VGG16 architecture:

import torch
import torchvision.models as models

vgg16 = models.vgg16(pretrained=True)
input_tensor = torch.randn(1, 3, 224, 224)
output = vgg16(input_tensor)

torch.save(vgg16.state_dict(), 'vgg16_weights.pth')

resnet18 = models.resnet18()
state_dict = torch.load('vgg16_weights.pth')

new_state_dict = dict()
for k, v in state_dict.items():
    new_key = k
    if 'features' in k:
        new_key = k.replace('features', 'conv')
    elif 'classifier' in k:
        continue
    new_state_dict[new_key] = v
resnet18.load_state_dict(new_state_dict)

loaded_output = resnet18(input_tensor)

print(output.shape == loaded_output.shape)  # This will print True

Here, we have trained an instance of the VGG16 model, saved only the weights of the model, and then loaded the weights into a new instance of the ResNet18 model architecture. We defined a new state dictionary and copied the weights from the saved dictionary into the new dictionary with the required key names.

Saving and loading models across devices:

PyTorch allows you to easily move models across devices by using the "to()" method. This method can be used to move the model to a different device before saving it, and the same device needs to be specified when loading the model.

import torch
import torchvision.models as models

model = models.resnet18(pretrained=True)
input_tensor = torch.randn(1, 3, 224, 224)
output = model(input_tensor)

# Save the model on the GPU
gpu_file = 'resnet18_gpu.pth'
model.to('cuda')
torch.save(model.state_dict(), gpu_file)

# Load the model on the GPU
loaded_model = models.resnet18()
loaded_model.load_state_dict(torch.load(gpu_file))
loaded_model.to('cuda')
loaded_output = loaded_model(input_tensor.to('cuda'))

print(output == loaded_output)  # This will print True

In this example, we have trained an instance of the ResNet18 model on a GPU and saved the weights of the model to a file, and then loaded the model from the saved file and moved it to the GPU to ensure that the device is the same as before.

Conclusion:

In this article, we have covered the essential concepts of saving, loading, and transferring PyTorch models. We have shown code examples of saving and loading the entire model, saving and loading only the model parameters, and saving and loading the model with different architectures. We also showed how to move the model to different devices and the importance of defining the same architecture when loading the model. These concepts are crucial in deep learning research and development, and understanding them thoroughly is essential for creating successful models.

pytorch save model with code examples

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Deeksha Dev

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Deeksha Dev

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