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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.
"""Learning rate utilities for vision tasks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from typing import Any, Mapping, Optional
import numpy as np
import tensorflow as tf, tf_keras
BASE_LEARNING_RATE = 0.1
class WarmupDecaySchedule(tf_keras.optimizers.schedules.LearningRateSchedule):
"""A wrapper for LearningRateSchedule that includes warmup steps."""
def __init__(self,
lr_schedule: tf_keras.optimizers.schedules.LearningRateSchedule,
warmup_steps: int,
warmup_lr: Optional[float] = None):
"""Add warmup decay to a learning rate schedule.
Args:
lr_schedule: base learning rate scheduler
warmup_steps: number of warmup steps
warmup_lr: an optional field for the final warmup learning rate. This
should be provided if the base `lr_schedule` does not contain this
field.
"""
super(WarmupDecaySchedule, self).__init__()
self._lr_schedule = lr_schedule
self._warmup_steps = warmup_steps
self._warmup_lr = warmup_lr
def __call__(self, step: int):
lr = self._lr_schedule(step)
if self._warmup_steps:
if self._warmup_lr is not None:
initial_learning_rate = tf.convert_to_tensor(
self._warmup_lr, name="initial_learning_rate")
else:
initial_learning_rate = tf.convert_to_tensor(
self._lr_schedule.initial_learning_rate,
name="initial_learning_rate")
dtype = initial_learning_rate.dtype
global_step_recomp = tf.cast(step, dtype)
warmup_steps = tf.cast(self._warmup_steps, dtype)
warmup_lr = initial_learning_rate * global_step_recomp / warmup_steps
lr = tf.cond(global_step_recomp < warmup_steps, lambda: warmup_lr,
lambda: lr)
return lr
def get_config(self) -> Mapping[str, Any]:
config = self._lr_schedule.get_config()
config.update({
"warmup_steps": self._warmup_steps,
"warmup_lr": self._warmup_lr,
})
return config
class CosineDecayWithWarmup(tf_keras.optimizers.schedules.LearningRateSchedule):
"""Class to generate learning rate tensor."""
def __init__(self, batch_size: int, total_steps: int, warmup_steps: int):
"""Creates the cosine learning rate tensor with linear warmup.
Args:
batch_size: The training batch size used in the experiment.
total_steps: Total training steps.
warmup_steps: Steps for the warm up period.
"""
super(CosineDecayWithWarmup, self).__init__()
base_lr_batch_size = 256
self._total_steps = total_steps
self._init_learning_rate = BASE_LEARNING_RATE * batch_size / base_lr_batch_size
self._warmup_steps = warmup_steps
def __call__(self, global_step: int):
global_step = tf.cast(global_step, dtype=tf.float32)
warmup_steps = self._warmup_steps
init_lr = self._init_learning_rate
total_steps = self._total_steps
linear_warmup = global_step / warmup_steps * init_lr
cosine_learning_rate = init_lr * (tf.cos(np.pi *
(global_step - warmup_steps) /
(total_steps - warmup_steps)) +
1.0) / 2.0
learning_rate = tf.where(global_step < warmup_steps, linear_warmup,
cosine_learning_rate)
return learning_rate
def get_config(self):
return {
"total_steps": self._total_steps,
"warmup_learning_rate": self._warmup_learning_rate,
"warmup_steps": self._warmup_steps,
"init_learning_rate": self._init_learning_rate,
}
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