forked from mindspore-Ecosystem/mindspore
53 lines
2.0 KiB
Python
Executable File
53 lines
2.0 KiB
Python
Executable File
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""learning rate generator"""
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import math
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import numpy as np
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def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
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lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
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lr = float(init_lr) + lr_inc * current_step
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return lr
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def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch):
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"""
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generate learning rate array with cosine
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Args:
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lr(float): base learning rate
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steps_per_epoch(int): steps size of one epoch
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warmup_epochs(int): number of warmup epochs
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max_epoch(int): total epochs of training
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Returns:
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np.array, learning rate array
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"""
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base_lr = lr
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warmup_init_lr = 0
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total_steps = int(max_epoch * steps_per_epoch)
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warmup_steps = int(warmup_epochs * steps_per_epoch)
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decay_steps = total_steps - warmup_steps
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lr_each_step = []
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for i in range(total_steps):
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if i < warmup_steps:
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lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
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else:
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linear_decay = (total_steps - i) / decay_steps
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cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
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decayed = linear_decay * cosine_decay + 0.00001
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lr = base_lr * decayed
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lr_each_step.append(lr)
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return np.array(lr_each_step).astype(np.float32)
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