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How to load existing weights in PyTorch

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  • 13
    Can we train a classification model which has weights available from different dataset and continue the training using new dataset also making use of available weights?
      July 22, 2021 8:27 PM IST
    0
  • 346 3
    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. be sure to call model. eval() method before inferencing to set the dropout and batch normalization layers to evaluation mode.

    import torch
import torch.onnx as onnx
import torchvision.models as models

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.load_state_dict(torch.load('model_weights.pth'))
model.eval()

      July 30, 2021 2:55 PM IST
    0
  • 319 1

    Save/Load state_dict (Recommended)

    torch.save(model.state_dict(), PATH)

     

    Load:


    model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()​
      October 25, 2021 2:03 PM IST
    0
  • 259
    Saving & Loading Model Across Devices
    1. Save on GPU, Load on CPU. Save: torch. save(model. state_dict(), PATH) Load: device = torch. 
    2. Save on GPU, Load on GPU. Save: torch. save(model. state_dict(), PATH) Load: device = torch. 
    3. Save on CPU, Load on GPU. Save: torch. save(model. state_dict(), PATH) Load: device = torch.
      January 15, 2022 12:56 PM IST
    0
  • 461
    In PyTorch, the learnable parameters (i.e. weights and biases) of an torch.nn.Module model are contained in the model’s parameters (accessed with model.parameters()). A state_dict is simply a Python dictionary object that maps each layer to its parameter tensor. Note that only layers with learnable parameters (convolutional layers, linear layers, etc.) and registered buffers (batchnorm’s running_mean) have entries in the model’s state_dict. Optimizer objects (torch.optim) also have a state_dict, which contains information about the optimizer’s state, as well as the hyperparameters used.


    Save:

    torch.save(model.state_dict(), PATH)


    Load:

    model = TheModelClass(*args, **kwargs)
    model.load_state_dict(torch.load(PATH))
    model.eval()
      August 16, 2021 3:05 PM IST
    0