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91 lines
3.5 KiB
91 lines
3.5 KiB
# 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 get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
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"""
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generate learning rate array
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Args:
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global_step(int): total steps of the training
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lr_init(float): init learning rate
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lr_end(float): end learning rate
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lr_max(float): max learning rate
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warmup_epochs(int): number of warmup epochs
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total_epochs(int): total epoch of training
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steps_per_epoch(int): steps of one epoch
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lr_decay_mode(string): learning rate decay mode, including steps, poly, cosine or default
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Returns:
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np.array, learning rate array
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"""
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lr_each_step = []
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total_steps = steps_per_epoch * total_epochs
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warmup_steps = steps_per_epoch * warmup_epochs
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if lr_decay_mode == 'steps':
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decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
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for i in range(total_steps):
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if i < decay_epoch_index[0]:
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lr = lr_max
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elif i < decay_epoch_index[1]:
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lr = lr_max * 0.1
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elif i < decay_epoch_index[2]:
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lr = lr_max * 0.01
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else:
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lr = lr_max * 0.001
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lr_each_step.append(lr)
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elif lr_decay_mode == 'poly':
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if warmup_steps != 0:
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inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
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else:
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inc_each_step = 0
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for i in range(total_steps):
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if i < warmup_steps:
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lr = float(lr_init) + inc_each_step * float(i)
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else:
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base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
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lr = float(lr_max) * base * base
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if lr < 0.0:
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lr = 0.0
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lr_each_step.append(lr)
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elif lr_decay_mode == 'cosine':
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decay_steps = total_steps - warmup_steps
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for i in range(total_steps):
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if i < warmup_steps:
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lr_inc = (float(lr_max) - float(lr_init)) / float(warmup_steps)
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lr = float(lr_init) + lr_inc * (i + 1)
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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 = lr_max * decayed
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lr_each_step.append(lr)
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else:
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for i in range(total_steps):
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if i < warmup_steps:
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lr = lr_init + (lr_max - lr_init) * i / warmup_steps
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else:
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lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
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lr_each_step.append(lr)
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current_step = global_step
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lr_each_step = np.array(lr_each_step).astype(np.float32)
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learning_rate = lr_each_step[current_step:]
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return learning_rate
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