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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # 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 generator"""
- import math
- import numpy as np
-
- def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
- lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
- lr = float(init_lr) + lr_inc * current_step
- return lr
-
- def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch):
- """
- generate learning rate array with cosine
-
- Args:
- lr(float): base learning rate
- steps_per_epoch(int): steps size of one epoch
- warmup_epochs(int): number of warmup epochs
- max_epoch(int): total epochs of training
- Returns:
- np.array, learning rate array
- """
- base_lr = lr
- warmup_init_lr = 0
- total_steps = int(max_epoch * steps_per_epoch)
- warmup_steps = int(warmup_epochs * steps_per_epoch)
- decay_steps = total_steps - warmup_steps
-
- lr_each_step = []
- for i in range(total_steps):
- if i < warmup_steps:
- lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
- else:
- linear_decay = (total_steps - i) / decay_steps
- cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
- decayed = linear_decay * cosine_decay + 0.00001
- lr = base_lr * decayed
- lr_each_step.append(lr)
- return np.array(lr_each_step).astype(np.float32)
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