Export

The export command is used to prepare a model for fine-tuning or inference. It allows exporting the model from training checkpoints which contain additional information such as optimizer states that are not needed for fine-tuning or inference.

Python

import lightly_train

lightly_train.train(
    out="out/my_experiment",
    data="my_data_dir",
    model="torchvision/resnet50",
    method="dino",
)

lightly_train.export(
    out="my_exported_model.pth",
    checkpoint="out/my_experiment/checkpoints/last.ckpt",
    part="model",
    format="torch_state_dict",
)

Command Line

lightly-train train out="out/my_experiment" data="my_data_dir" model="torchvision/resnet50" method="dino"
lightly-train export out="my_exported_model.pth" checkpoint="out/my_experiment/checkpoints/last.ckpt" part="model" format="torch_state_dict"

The above code example trains a model and exports the last training checkpoint as a torch state dictionary.

Tip

See lightly_train.export() for a complete list of arguments.

Warning

It is recommended to always export the model after training as the training checkpoints include not only the model weights but also the model code. If modifications are made to the codebase after training, the LightlyTrain checkpoint might not be loadable anymore in the future. Exporting the model as a torch state dict ensures that the model can be loaded in the future even if the codebase changes.

Out

The out argument specifies the output file where the exported model is saved.

Checkpoint

The checkpoint argument specifies the LightlyTrain checkpoint to use for exporting the model. This is the checkpoint saved to out/my_experiment/checkpoints/last.ckpt after training.

Format

The format argument specifies the format in which the model is exported. The following formats are supported.

• torch_state_dict (Recommended)

  Only the model’s state dict is saved which can be loaded with:

  from torchvision.models import resnet50
  
  model = resnet50()
  model.load_state_dict(torch.load("my_exported_model.pth"))
  

  This is the recommended format and ensures compatibility with different LightlyTrain versions.

• torch_model

  The model is saved as a torch module which can be loaded with:

  import torch
  
  model = torch.load("my_exported_model.pth")
  

  This requires that the same LightlyTrain version is installed when the model is exported and when it is loaded again.

  • ultralytics

    The model is saved as an ultralytics model which can be loaded with:

    from ultralytics import YOLO
    
    model = YOLO("my_exported_model.pth")
    

Part

The part argument specifies which part of the model to export. The following parts are supported.

• model

  Exports the model as passed with the model argument in the train function.

• embedding_model

  Exports the embedding model. This includes the model passed with the model argument in the train function and an extra embedding layer if the embed_dim argument was set during training. This is useful if you want to use the model for embedding images.