mindarmour/examples/common/networks/vgg/warmup_step_lr.py

# 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 step learning rate.
"""
from collections import Counter
import numpy as np

from .linear_warmup import linear_warmup_lr


def lr_steps(global_step, lr_init, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
    """Set learning rate."""
    lr_each_step = []
    total_steps = steps_per_epoch*total_epochs
    warmup_steps = steps_per_epoch*warmup_epochs
    if warmup_steps != 0:
        inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
    else:
        inc_each_step = 0
    for i in range(total_steps):
        if i < warmup_steps:
            lr_value = float(lr_init) + inc_each_step*float(i)
        else:
            base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
            lr_value = float(lr_max)*base*base
            if lr_value < 0.0:
                lr_value = 0.0
        lr_each_step.append(lr_value)

    current_step = global_step
    lr_each_step = np.array(lr_each_step).astype(np.float32)
    learning_rate = lr_each_step[current_step:]

    return learning_rate


def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
    """warmup_step_lr"""
    base_lr = lr
    warmup_init_lr = 0
    total_steps = int(max_epoch*steps_per_epoch)
    warmup_steps = int(warmup_epochs*steps_per_epoch)
    milestones = lr_epochs
    milestones_steps = []
    for milestone in milestones:
        milestones_step = milestone*steps_per_epoch
        milestones_steps.append(milestones_step)

    lr_each_step = []
    lr = base_lr
    milestones_steps_counter = Counter(milestones_steps)
    for i in range(total_steps):
        if i < warmup_steps:
            lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
        else:
            lr = lr*gamma**milestones_steps_counter[i]
        lr_each_step.append(lr)

    return np.array(lr_each_step).astype(np.float32)


def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
    return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)


def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
    lr_epochs = []
    for i in range(1, max_epoch):
        if i % epoch_size == 0:
            lr_epochs.append(i)
    return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)