85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
# Copyright 2022 The MediaPipe Authors.
|
|
#
|
|
# 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.
|
|
"""Hyperparameters for training models. Shared across tasks."""
|
|
|
|
import dataclasses
|
|
import tempfile
|
|
from typing import Mapping, Optional
|
|
|
|
import tensorflow as tf
|
|
|
|
from official.common import distribute_utils
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class BaseHParams:
|
|
"""Hyperparameters used for training models.
|
|
|
|
A common set of hyperparameters shared by the training jobs of all model
|
|
maker tasks.
|
|
|
|
Attributes:
|
|
learning_rate: The learning rate to use for gradient descent training.
|
|
batch_size: Batch size for training.
|
|
epochs: Number of training iterations over the dataset.
|
|
steps_per_epoch: An optional integer indicate the number of training steps
|
|
per epoch. If not set, the training pipeline calculates the default steps
|
|
per epoch as the training dataset size divided by batch size.
|
|
class_weights: An optional mapping of indices to weights for weighting the
|
|
loss function during training.
|
|
shuffle: True if the dataset is shuffled before training.
|
|
export_dir: The location of the model checkpoint files.
|
|
distribution_strategy: A string specifying which Distribution Strategy to
|
|
use. Accepted values are 'off', 'one_device', 'mirrored',
|
|
'parameter_server', 'multi_worker_mirrored', and 'tpu' -- case
|
|
insensitive. 'off' means not to use Distribution Strategy; 'tpu' means to
|
|
use TPUStrategy using `tpu_address`. See the tf.distribute.Strategy
|
|
documentation for more details:
|
|
https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy.
|
|
num_gpus: How many GPUs to use at each worker with the
|
|
DistributionStrategies API. The default is 0.
|
|
tpu: The TPU resource to be used for training. This should be either the
|
|
name used when creating the Cloud TPU, a grpc://ip.address.of.tpu:8470
|
|
url, or an empty string if using a local TPU.
|
|
"""
|
|
|
|
# Parameters for train configuration
|
|
learning_rate: float
|
|
batch_size: int
|
|
epochs: int
|
|
steps_per_epoch: Optional[int] = None
|
|
class_weights: Optional[Mapping[int, float]] = None
|
|
|
|
# Dataset-related parameters
|
|
shuffle: bool = False
|
|
|
|
# Parameters for model / checkpoint files
|
|
export_dir: str = tempfile.mkdtemp()
|
|
|
|
# Parameters for hardware acceleration
|
|
distribution_strategy: str = 'off'
|
|
num_gpus: int = 0
|
|
tpu: str = ''
|
|
_strategy: tf.distribute.Strategy = dataclasses.field(init=False)
|
|
|
|
def __post_init__(self):
|
|
self._strategy = distribute_utils.get_distribution_strategy(
|
|
distribution_strategy=self.distribution_strategy,
|
|
num_gpus=self.num_gpus,
|
|
tpu_address=self.tpu,
|
|
)
|
|
|
|
def get_strategy(self):
|
|
return self._strategy
|