scenic/projects/pointcloud/main_seg.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.

r"""Main file for PCT Scenic.

"""

from absl import flags
from absl import logging
from clu import metric_writers
import jax
import jax.numpy as jnp
import ml_collections
from scenic import app
from scenic.projects.pointcloud import segmentation_model
from scenic.projects.pointcloud import segmentation_trainer
from scenic.projects.pointcloud import shapenet_dataset

FLAGS = flags.FLAGS


def main(rng: jnp.ndarray, config: ml_collections.ConfigDict, workdir: str,
         writer: metric_writers.MetricWriter):
  """Main function for Scenic."""

  model_cls = segmentation_model.PointCloudTransformerSegmentationModel
  data_rng, rng = jax.random.split(rng)

  # Dataset loading
  device_count = jax.device_count()
  logging.info('device_count: %d', device_count)
  logging.info('num_hosts : %d', jax.process_count())
  logging.info('host_id : %d', jax.process_index())

  logging.info('rng: %s', rng)
  batch_size = config.batch_size
  if batch_size % device_count > 0:
    raise ValueError(f'Batch size ({batch_size}) must be divisible by the '
                     f'number of devices ({device_count})')

  eval_batch_size = config.get('eval_batch_size', batch_size)
  if eval_batch_size % device_count > 0:
    raise ValueError(f'Eval batch size ({eval_batch_size}) must be divisible '
                     f'by the number of devices ({device_count})')

  local_batch_size = batch_size // jax.process_count()
  eval_local_batch_size = eval_batch_size // jax.process_count()
  device_batch_size = batch_size // device_count
  logging.info('local_batch_size : %d', local_batch_size)
  logging.info('device_batch_size : %d', device_batch_size)

  dataset = shapenet_dataset.get_dataset(
      batch_size=local_batch_size,
      eval_batch_size=eval_local_batch_size,
      num_shards=jax.local_device_count(),
      dtype_str=config.data_dtype_str,
      shuffle_seed=0,
      rng=data_rng,
      prefetch_buffer_size=2,
      dataset_configs=None,
      dataset_service_address=FLAGS.dataset_service_address,
      seq_length=config.max_seq_len)

  segmentation_trainer.train(
      rng=rng,
      config=config,
      model_cls=model_cls,
      dataset=dataset,
      workdir=workdir,
      writer=writer)


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