Pipelines¶

The Pipelines module is created to facilitate the use of the library. It provides ways to perform end-to-end operation such as model training or generation. A typical Pipeline is composed by several pythae’s instances which are articulated together.

A __call__ function is defined and used to launch the Pipeline.

`TrainingPipeline`	This Pipeline provides an end to end way to train your VAE model.
`GenerationPipeline`	This Pipeline provides an end to end way to generate samples from a trained VAE model.

Basic Examples¶

To launch a model training with the TrainingPipeline, you only need to set up your BaseTrainerConfig, BaseAEConfig build the model and trainer accordingly and then call a 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 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
... )