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| # Lint as: python3 | |
| # Copyright 2019 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 | |
| from typing import Any, Mapping | |
| import dataclasses | |
| from official.modeling.hyperparams import base_config | |
| from official.vision.image_classification.configs import base_configs | |
| 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 = base_configs.LossConfig( | |
| name='categorical_crossentropy', label_smoothing=0.1) | |
| optimizer: base_configs.OptimizerConfig = base_configs.OptimizerConfig( | |
| name='rmsprop', | |
| decay=0.9, | |
| epsilon=0.001, | |
| momentum=0.9, | |
| moving_average_decay=None) | |
| learning_rate: base_configs.LearningRateConfig = base_configs.LearningRateConfig( # pylint: disable=line-too-long | |
| name='exponential', | |
| initial_lr=0.008, | |
| decay_epochs=2.4, | |
| decay_rate=0.97, | |
| warmup_epochs=5, | |
| scale_by_batch_size=1. / 128., | |
| staircase=True) | |