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# Save and Load the Model¶

In this section we will look at how to persist model state with saving, loading and running model predictions.

import torch
import torchvision.models as models


PyTorch models store the learned parameters in an internal state dictionary, called state_dict. These can be persisted via the torch.save method:

model = models.vgg16(pretrained=True)
torch.save(model.state_dict(), 'model_weights.pth')


To load model weights, you need to create an instance of the same model first, and then load the parameters using load_state_dict() method.

model = models.vgg16() # we do not specify pretrained=True, i.e. do not load default weights
model.eval()


Note

be sure to call model.eval() method before inferencing to set the dropout and batch normalization layers to evaluation mode. Failing to do this will yield inconsistent inference results.

When loading model weights, we needed to instantiate the model class first, because the class defines the structure of a network. We might want to save the structure of this class together with the model, in which case we can pass model (and not model.state_dict()) to the saving function:

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


We can then load the model like this:

model = torch.load('model.pth')


Note

This approach uses Python pickle module when serializing the model, thus it relies on the actual class definition to be available when loading the model.