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Pipelines

.. automodule::
   pythae.pipelines



.. toctree::
   :maxdepth: 1

   training
   generation



.. autosummary::
    ~pythae.pipelines.TrainingPipeline
    ~pythae.pipelines.GenerationPipeline
    :nosignatures:

Basic Examples

To launch a model training with the :class:`~pythae.pipelines.TrainingPipeline`, you only need to set up your :class:`~pythae.trainers.BaseTrainerConfig`, :class:`~pythae.models.BaseAEConfig` build the model and trainer accordingly and then call a :class:`~pythae.pipelines.TrainingPipeline` instance.

>>> from pythae.pipelines import TrainingPipeline
>>> from pythae.models import VAE, VAEConfig
>>> from pythae.trainers import BaseTrainerConfig

>>> # Set up the training configuration
>>> my_training_config = BaseTrainerConfig(
...     output_dir='my_model',
...     num_epochs=50,
...     learning_rate=1e-3,
...     per_device_train_batch_size=64,
...      per_device_eval_batch_size=64,
...     steps_saving=None
... )
>>> # Set up the model configuration
>>> my_vae_config = model_config = VAEConfig(
...     input_dim=(1, 28, 28),
...     latent_dim=10
... )
>>> # Build the model
>>> my_vae_model = VAE(
...     model_config=my_vae_config
... )
>>> # Build the Pipeline
>>> pipeline = TrainingPipeline(
...     training_config=my_training_config,
...     model=my_vae_model
... )
>>> # Launch the Pipeline
>>> pipeline(
...     train_data=your_train_data, # must be torch.Tensor or np.array
...     eval_data=your_eval_data # must be torch.Tensor or np.array
... )

To launch a data generation from a trained model using the :class:`~pythae.pipelines.GenerationPipeline` provided in Pythae you only need 1) a trained model, 2) the sampler's configuration and 3) to create and launch the pipeline as follows

>>> from pythae.models import AutoModel
>>> from pythae.samplers import MAFSamplerConfig
>>> from pythae.pipelines import GenerationPipeline
>>> # Retrieve the trained model
>>> my_trained_vae = AutoModel.load_from_folder(
...  'path/to/your/trained/model'
... )
>>> my_sampler_config = MAFSamplerConfig(
...  n_made_blocks: int = 2
...  n_hidden_in_made: int = 3
...  hidden_size: int = 128
... )
>>> # Build the pipeline
>>> pipe = GenerationPipeline(
...  model=my_trained_vae,
...  sampler_config=my_sampler_config
... )
>>> # Launch data generation
>>> generated_samples = pipe(
...  num_samples=args.num_samples,
...  return_gen=True, # If false returns nothing
...  train_data=train_data, # Needed to fit the sampler
...  eval_data=eval_data, # Needed to fit the sampler
...  training_config=BaseTrainerConfig(num_epochs=200) # TrainingConfig to use to fit the sampler
... )