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