41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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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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import tensorflow as tf
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class LearningRateScheduler:
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def __init__(self, args, steps_per_epoch, optimizer):
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assert (
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args.deep_warmup_epochs <= args.num_epochs
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), "Number of warmup epochs cannot be higher than training epochs"
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self.base_lr = args.deep_learning_rate
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self.warmup_steps = args.deep_warmup_epochs * steps_per_epoch
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bound_epoch = (
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args.deep_warmup_epochs + (args.num_epochs - args.deep_warmup_epochs) / 2
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)
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self.boundaries = [bound_epoch * steps_per_epoch]
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self.values = [self.base_lr / 4, self.base_lr / 8]
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self.optimizer = optimizer
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@tf.function
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def __call__(self, step):
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if step < self.warmup_steps:
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warmup_lr = self.base_lr * step / self.warmup_steps
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self.optimizer.lr.assign(warmup_lr)
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else:
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index = tf.reduce_sum(tf.cast(step > self.boundaries, tf.int64))
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value = tf.gather(self.values, index)
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self.optimizer.lr.assign(value)
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