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- # Copyright 2021 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 schedule."""
- import math
- from .config import cfg_res50
- import numpy as np
-
-
- def warmup_cosine_annealing_lr(lr5, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
- """ warmup cosine annealing lr"""
- base_lr = lr5
- 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:
- lr5 = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
- else:
- lr5 = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max)) / 2
- lr_each_step.append(lr5)
-
- return np.array(lr_each_step).astype(np.float32)
-
-
- def _linear_warmup_learning_rate(current_step, warmup_steps, base_lr, init_lr):
- lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
- learning_rate = float(init_lr) + lr_inc * current_step
- return learning_rate
-
-
- def _a_cosine_learning_rate(current_step, base_lr, warmup_steps, decay_steps):
- base = float(current_step - warmup_steps) / float(decay_steps)
- learning_rate = (1 + math.cos(base * math.pi)) / 2 * base_lr
- return learning_rate
-
-
- def _dynamic_lr(base_lr, total_steps, warmup_steps, warmup_ratio=1 / 3):
- lr = []
- for i in range(total_steps):
- if i < warmup_steps:
- lr.append(_linear_warmup_learning_rate(i, warmup_steps, base_lr, base_lr * warmup_ratio))
- else:
- lr.append(_a_cosine_learning_rate(i, base_lr, warmup_steps, total_steps))
-
- return lr
-
-
- def adjust_learning_rate(initial_lr, gamma, stepvalues, steps_pre_epoch, total_epochs, warmup_epoch=5):
- if cfg_res50['lr_type'] == 'dynamic_lr':
- return _dynamic_lr(initial_lr, total_epochs * steps_pre_epoch, warmup_epoch * steps_pre_epoch,
- warmup_ratio=1 / 3)
-
- lr_each_step = []
- for epoch in range(1, total_epochs + 1):
- for _ in range(steps_pre_epoch):
- if epoch <= warmup_epoch:
- lr = 0.1 * initial_lr * (1.5849 ** (epoch - 1))
- else:
- if stepvalues[0] <= epoch <= stepvalues[1]:
- lr = initial_lr * (gamma ** (1))
- elif epoch > stepvalues[1]:
- lr = initial_lr * (gamma ** (2))
- else:
- lr = initial_lr
- lr_each_step.append(lr)
- return lr_each_step
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