official/legacy/image_classification/configs/configs.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.
# 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.

"""Configuration utils for image classification experiments."""

import dataclasses

from official.legacy.image_classification import dataset_factory
from official.legacy.image_classification.configs import base_configs
from official.legacy.image_classification.efficientnet import efficientnet_config
from official.legacy.image_classification.resnet import resnet_config
from official.legacy.image_classification.vgg import vgg_config


@dataclasses.dataclass
class EfficientNetImageNetConfig(base_configs.ExperimentConfig):
  """Base configuration to train efficientnet-b0 on ImageNet.

  Attributes:
    export: An `ExportConfig` instance
    runtime: A `RuntimeConfig` instance.
    dataset: A `DatasetConfig` instance.
    train: A `TrainConfig` instance.
    evaluation: An `EvalConfig` instance.
    model: A `ModelConfig` instance.
  """
  export: base_configs.ExportConfig = dataclasses.field(
      default_factory=base_configs.ExportConfig
  )
  runtime: base_configs.RuntimeConfig = dataclasses.field(
      default_factory=base_configs.RuntimeConfig
  )
  train_dataset: dataset_factory.DatasetConfig = dataclasses.field(
      default_factory=lambda: dataset_factory.ImageNetConfig(split='train')
  )
  validation_dataset: dataset_factory.DatasetConfig = dataclasses.field(
      default_factory=lambda: dataset_factory.ImageNetConfig(split='validation')
  )
  train: base_configs.TrainConfig = dataclasses.field(
      default_factory=lambda: base_configs.TrainConfig(  # pylint: disable=g-long-lambda
          resume_checkpoint=True,
          epochs=500,
          steps=None,
          callbacks=base_configs.CallbacksConfig(
              enable_checkpoint_and_export=True, enable_tensorboard=True
          ),
          metrics=['accuracy', 'top_5'],
          time_history=base_configs.TimeHistoryConfig(log_steps=100),
          tensorboard=base_configs.TensorBoardConfig(
              track_lr=True, write_model_weights=False
          ),
          set_epoch_loop=False,
      )
  )
  evaluation: base_configs.EvalConfig = dataclasses.field(
      default_factory=lambda: base_configs.EvalConfig(  # pylint: disable=g-long-lambda
          epochs_between_evals=1, steps=None
      )
  )
  model: base_configs.ModelConfig = dataclasses.field(
      default_factory=efficientnet_config.EfficientNetModelConfig
  )


@dataclasses.dataclass
class ResNetImagenetConfig(base_configs.ExperimentConfig):
  """Base configuration to train resnet-50 on ImageNet."""
  export: base_configs.ExportConfig = dataclasses.field(
      default_factory=base_configs.ExportConfig
  )
  runtime: base_configs.RuntimeConfig = dataclasses.field(
      default_factory=base_configs.RuntimeConfig
  )
  train_dataset: dataset_factory.DatasetConfig = dataclasses.field(
      default_factory=lambda: dataset_factory.ImageNetConfig(  # pylint: disable=g-long-lambda
          split='train', one_hot=False, mean_subtract=True, standardize=True
      )
  )
  validation_dataset: dataset_factory.DatasetConfig = dataclasses.field(
      default_factory=lambda: dataset_factory.ImageNetConfig(  # pylint: disable=g-long-lambda
          split='validation',
          one_hot=False,
          mean_subtract=True,
          standardize=True,
      )
  )
  train: base_configs.TrainConfig = dataclasses.field(
      default_factory=lambda: base_configs.TrainConfig(  # pylint: disable=g-long-lambda
          resume_checkpoint=True,
          epochs=90,
          steps=None,
          callbacks=base_configs.CallbacksConfig(
              enable_checkpoint_and_export=True, enable_tensorboard=True
          ),
          metrics=['accuracy', 'top_5'],
          time_history=base_configs.TimeHistoryConfig(log_steps=100),
          tensorboard=base_configs.TensorBoardConfig(
              track_lr=True, write_model_weights=False
          ),
          set_epoch_loop=False,
      )
  )
  evaluation: base_configs.EvalConfig = dataclasses.field(
      default_factory=lambda: base_configs.EvalConfig(  # pylint: disable=g-long-lambda
          epochs_between_evals=1, steps=None
      )
  )
  model: base_configs.ModelConfig = dataclasses.field(
      default_factory=resnet_config.ResNetModelConfig
  )


@dataclasses.dataclass
class VGGImagenetConfig(base_configs.ExperimentConfig):
  """Base configuration to train vgg-16 on ImageNet."""
  export: base_configs.ExportConfig = dataclasses.field(
      default_factory=base_configs.ExportConfig
  )
  runtime: base_configs.RuntimeConfig = dataclasses.field(
      default_factory=base_configs.RuntimeConfig
  )
  train_dataset: dataset_factory.DatasetConfig = dataclasses.field(
      default_factory=lambda: dataset_factory.ImageNetConfig(  # pylint: disable=g-long-lambda
          split='train', one_hot=False, mean_subtract=True, standardize=True
      )
  )
  validation_dataset: dataset_factory.DatasetConfig = dataclasses.field(
      default_factory=lambda: dataset_factory.ImageNetConfig(  # pylint: disable=g-long-lambda
          split='validation',
          one_hot=False,
          mean_subtract=True,
          standardize=True,
      )
  )
  train: base_configs.TrainConfig = dataclasses.field(
      default_factory=lambda: base_configs.TrainConfig(  # pylint: disable=g-long-lambda
          resume_checkpoint=True,
          epochs=90,
          steps=None,
          callbacks=base_configs.CallbacksConfig(
              enable_checkpoint_and_export=True, enable_tensorboard=True
          ),
          metrics=['accuracy', 'top_5'],
          time_history=base_configs.TimeHistoryConfig(log_steps=100),
          tensorboard=base_configs.TensorBoardConfig(
              track_lr=True, write_model_weights=False
          ),
          set_epoch_loop=False,
      )
  )
  evaluation: base_configs.EvalConfig = dataclasses.field(
      default_factory=lambda: base_configs.EvalConfig(  # pylint: disable=g-long-lambda
          epochs_between_evals=1, steps=None
      )
  )
  model: base_configs.ModelConfig = dataclasses.field(
      default_factory=vgg_config.VGGModelConfig
  )


def get_config(model: str, dataset: str) -> base_configs.ExperimentConfig:
  """Given model and dataset names, return the ExperimentConfig."""
  dataset_model_config_map = {
      'imagenet': {
          'efficientnet': EfficientNetImageNetConfig(),
          'resnet': ResNetImagenetConfig(),
          'vgg': VGGImagenetConfig(),
      }
  }
  try:
    return dataset_model_config_map[dataset][model]
  except KeyError:
    if dataset not in dataset_model_config_map:
      raise KeyError('Invalid dataset received. Received: {}. Supported '
                     'datasets include: {}'.format(
                         dataset, ', '.join(dataset_model_config_map.keys())))
    raise KeyError('Invalid model received. Received: {}. Supported models for'
                   '{} include: {}'.format(
                       model, dataset,
                       ', '.join(dataset_model_config_map[dataset].keys())))