|
- # 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.
- # ============================================================================
- """
- warm up cosine annealing learning rate.
- """
- import math
- import numpy as np
-
- from .linear_warmup import linear_warmup_lr
-
-
- def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, t_max, eta_min=0):
- """warm up cosine annealing 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)
-
- lr_each_step = []
- for i in range(total_steps):
- last_epoch = i // steps_per_epoch
- if i < warmup_steps:
- lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
- else:
- lr = eta_min + (base_lr - eta_min)*(1. + math.cos(math.pi*last_epoch / t_max)) / 2
- lr_each_step.append(lr)
-
- return np.array(lr_each_step).astype(np.float32)
|
