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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 definitions for EfficientNet losses, learning rates, and optimizers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import dataclasses
from official.legacy.image_classification.configs import base_configs
from official.modeling.hyperparams import base_config
@dataclasses.dataclass
class EfficientNetModelConfig(base_configs.ModelConfig):
"""Configuration for the EfficientNet model.
This configuration will default to settings used for training efficientnet-b0
on a v3-8 TPU on ImageNet.
Attributes:
name: The name of the model. Defaults to 'EfficientNet'.
num_classes: The number of classes in the model.
model_params: A dictionary that represents the parameters of the
EfficientNet model. These will be passed in to the "from_name" function.
loss: The configuration for loss. Defaults to a categorical cross entropy
implementation.
optimizer: The configuration for optimizations. Defaults to an RMSProp
configuration.
learning_rate: The configuration for learning rate. Defaults to an
exponential configuration.
"""
name: str = 'EfficientNet'
num_classes: int = 1000
model_params: base_config.Config = dataclasses.field(
default_factory=lambda: {
'model_name': 'efficientnet-b0',
'model_weights_path': '',
'weights_format': 'saved_model',
'overrides': {
'batch_norm': 'default',
'rescale_input': True,
'num_classes': 1000,
'activation': 'swish',
'dtype': 'float32',
}
})
loss: base_configs.LossConfig = dataclasses.field(
default_factory=lambda: base_configs.LossConfig( # pylint: disable=g-long-lambda
name='categorical_crossentropy', label_smoothing=0.1
)
)
optimizer: base_configs.OptimizerConfig = dataclasses.field(
default_factory=lambda: base_configs.OptimizerConfig( # pylint: disable=g-long-lambda
name='rmsprop',
decay=0.9,
epsilon=0.001,
momentum=0.9,
moving_average_decay=None,
)
)
learning_rate: base_configs.LearningRateConfig = dataclasses.field(
default_factory=lambda: base_configs.LearningRateConfig( # pylint: disable=g-long-lambda
name='exponential',
initial_lr=0.008,
decay_epochs=2.4,
decay_rate=0.97,
warmup_epochs=5,
scale_by_batch_size=1.0 / 128.0,
staircase=True,
)
)
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