official/vision/examples/starter/example_config.py

# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Example experiment configuration definition."""
import dataclasses
from typing import List

from official.core import config_definitions as cfg
from official.core import exp_factory
from official.modeling import hyperparams
from official.modeling import optimization


@dataclasses.dataclass
class ExampleDataConfig(cfg.DataConfig):
  """Input config for training. Add more fields as needed."""
  input_path: str = ''
  global_batch_size: int = 0
  is_training: bool = True
  dtype: str = 'float32'
  shuffle_buffer_size: int = 10000
  cycle_length: int = 10
  file_type: str = 'tfrecord'


@dataclasses.dataclass
class ExampleModel(hyperparams.Config):
  """The model config. Used by build_example_model function."""
  num_classes: int = 0
  input_size: List[int] = dataclasses.field(default_factory=list)


@dataclasses.dataclass
class Losses(hyperparams.Config):
  l2_weight_decay: float = 0.0


@dataclasses.dataclass
class Evaluation(hyperparams.Config):
  top_k: int = 5


@dataclasses.dataclass
class ExampleTask(cfg.TaskConfig):
  """The task config."""
  model: ExampleModel = ExampleModel()
  train_data: ExampleDataConfig = ExampleDataConfig(is_training=True)
  validation_data: ExampleDataConfig = ExampleDataConfig(is_training=False)
  losses: Losses = Losses()
  evaluation: Evaluation = Evaluation()


@exp_factory.register_config_factory('tf_vision_example_experiment')
def tf_vision_example_experiment() -> cfg.ExperimentConfig:
  """Definition of a full example experiment."""
  train_batch_size = 256
  eval_batch_size = 256
  steps_per_epoch = 10
  config = cfg.ExperimentConfig(
      task=ExampleTask(
          model=ExampleModel(num_classes=10, input_size=[128, 128, 3]),
          losses=Losses(l2_weight_decay=1e-4),
          train_data=ExampleDataConfig(
              input_path='/path/to/train*',
              is_training=True,
              global_batch_size=train_batch_size),
          validation_data=ExampleDataConfig(
              input_path='/path/to/valid*',
              is_training=False,
              global_batch_size=eval_batch_size)),
      trainer=cfg.TrainerConfig(
          steps_per_loop=steps_per_epoch,
          summary_interval=steps_per_epoch,
          checkpoint_interval=steps_per_epoch,
          train_steps=90 * steps_per_epoch,
          validation_steps=steps_per_epoch,
          validation_interval=steps_per_epoch,
          optimizer_config=optimization.OptimizationConfig({
              'optimizer': {
                  'type': 'sgd',
                  'sgd': {
                      'momentum': 0.9
                  }
              },
              'learning_rate': {
                  'type': 'cosine',
                  'cosine': {
                      'initial_learning_rate': 1.6,
                      'decay_steps': 350 * steps_per_epoch
                  }
              },
              'warmup': {
                  'type': 'linear',
                  'linear': {
                      'warmup_steps': 5 * steps_per_epoch,
                      'warmup_learning_rate': 0
                  }
              }
          })),
      restrictions=[
          'task.train_data.is_training != None',
          'task.validation_data.is_training != None'
      ])

  return config