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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.
"""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
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