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# 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())))
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