Dataclasses for text classifier
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Placeholder for internal Python strict library compatibility macro.
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package(
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default_visibility = ["//mediapipe:__subpackages__"],
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)
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licenses(["notice"])
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py_library(
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name = "hyperparameters",
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srcs = ["hyperparameters.py"],
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)
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mediapipe/model_maker/python/core/hyperparameters.py
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mediapipe/model_maker/python/core/hyperparameters.py
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Hyperparameters for training models. Shared across tasks."""
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import dataclasses
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import tempfile
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from typing import Optional
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# TODO: Integrate this class into ImageClassifier and other tasks.
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@dataclasses.dataclass
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class BaseHParams:
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"""Hyperparameters used for training models.
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A common set of hyperparameters shared by the training jobs of all model
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maker tasks.
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Attributes:
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learning_rate: The learning rate to use for gradient descent training.
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batch_size: Batch size for training.
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epochs: Number of training iterations over the dataset.
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steps_per_epoch: An optional integer indicate the number of training steps
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per epoch. If not set, the training pipeline calculates the default steps
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per epoch as the training dataset size devided by batch size.
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shuffle: True if the dataset is shuffled before training.
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export_dir: The location of the model checkpoint files.
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distribution_strategy: A string specifying which Distribution Strategy to
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use. Accepted values are 'off', 'one_device', 'mirrored',
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'parameter_server', 'multi_worker_mirrored', and 'tpu' -- case
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insensitive. 'off' means not to use Distribution Strategy; 'tpu' means to
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use TPUStrategy using `tpu_address`. See the tf.distribute.Strategy
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documentation for more details:
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https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy.
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num_gpus: How many GPUs to use at each worker with the
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DistributionStrategies API. The default is -1, which means utilize all
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available GPUs.
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tpu: The Cloud TPU to use for training. This should be either the name used
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when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.
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"""
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# Parameters for train configuration
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learning_rate: float
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batch_size: int
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epochs: int
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steps_per_epoch: Optional[int] = None
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# Dataset-related parameters
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shuffle: bool = False
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# Parameters for model / checkpoint files
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export_dir: str = tempfile.mkdtemp()
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# Parameters for hardware acceleration
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distribution_strategy: str = 'off'
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num_gpus: int = -1 # default value of -1 means use all available GPUs
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tpu: str = ''
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