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- # Copyright 2022 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 scheduler."""
-
- from collections import Counter
- import numpy as np
-
-
- def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
- """Linear learning rate."""
- lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
- lr = float(init_lr) + lr_inc * current_step
- return lr
-
- def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
- """Warmup step learning rate."""
- base_lr = lr
- warmup_init_lr = 0
- total_steps = int(max_epoch * steps_per_epoch)
- warmup_steps = int(warmup_epochs * steps_per_epoch)
- if lr_epochs[-1] != max_epoch:
- lr_epochs = lr_epochs[:-1]
- lr_epochs.append(max_epoch)
- milestones = lr_epochs
- milestones_steps = []
- for milestone in milestones:
- milestones_step = milestone * steps_per_epoch
- milestones_steps.append(milestones_step)
-
- lr_each_step = []
- lr = base_lr
- milestones_steps_counter = Counter(milestones_steps)
- if isinstance(gamma, list):
- gamma_per_milestone = [1]
- for reduction in gamma:
- gamma_per_milestone.append(reduction * gamma_per_milestone[-1])
- current_milestone = 0
- for i in range(total_steps):
- if i < warmup_steps:
- lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
- elif isinstance(gamma, list):
- if milestones_steps_counter[i] == 1:
- current_milestone += milestones_steps_counter[i]
- lr = base_lr * gamma_per_milestone[current_milestone]
- else:
- lr = lr * gamma**milestones_steps_counter[i]
- lr_each_step.append(lr)
-
- return np.array(lr_each_step).astype(np.float32)
-
- def get_lr(cfg, steps_per_epoch):
- """generate learning rate."""
- lr = warmup_step_lr(cfg['lr'],
- cfg['lr_epochs'],
- steps_per_epoch,
- cfg['warmup'],
- cfg['total_epochs'],
- gamma=cfg['gamma'],
- )
- return lr
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