scenic/projects/baselines/plainvit/main.py

# Copyright 2025 The Scenic Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Main file for PlainViT."""

from absl import flags
from clu import metric_writers
import jax
import jax.numpy as jnp
import ml_collections
from scenic import app
from scenic.projects.baselines.plainvit import plainvit
from scenic.projects.baselines.plainvit import trainer
from scenic.train_lib import train_utils

FLAGS = flags.FLAGS


def get_model_cls(model_name: str):
  """Get the model class for the PlainViT project."""
  if model_name == 'plainvit':
    return plainvit.PlainViT
  else:
    raise ValueError(f'Unrecognized model: {model_name}.')


def get_trainer(trainer_name):
  if trainer_name == 'plainvit_trainer':
    return trainer.train
  else:
    raise ValueError(f'Unrecognized trainer: {trainer_name}.')


def main(rng: jnp.ndarray, config: ml_collections.ConfigDict, workdir: str,
         writer: metric_writers.MetricWriter):
  """Main function for the PlainViT project."""
  # Build the loss_fn, metrics, and flax_model.
  model_cls = get_model_cls(config.model_name)
  data_rng, rng = jax.random.split(rng)
  dataset = train_utils.get_dataset(
      config, data_rng, dataset_service_address=FLAGS.dataset_service_address)
  get_trainer(config.trainer_name)(
      rng=rng,
      config=config,
      model_cls=model_cls,
      dataset=dataset,
      workdir=workdir,
      writer=writer)


if __name__ == '__main__':
  app.run(main=main)