utils.py

#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
@author: kaifang zhang
@license: Apache License
@time: 2020/12/01
@contact: 1115291605@qq.com
"""


import numpy as np

# 1. 计算数据集(图片)的均值和方差(RGB的3个通道)
def compute_mean_var(image):
    # image.shape: [image_num, w, h, c]
    mean = []
    var  = []
    for c in range(image.shape[-1]):
        mean.append(np.mean(image[:, :, :, c]))
        var.append(np.std(image[:, :, :, c]))
    return mean, var

# 2. 归一化图片
def norm_images(image):
    # image.shape: [image_num, w, h, c]
    image = image.astype('float32')
    mean, var = compute_mean_var(image)
    image[:, :, :, 0] = (image[:, :, :, 0] - mean[0]) / var[0]
    image[:, :, :, 1] = (image[:, :, :, 1] - mean[1]) / var[1]
    image[:, :, :, 2] = (image[:, :, :, 2] - mean[2]) / var[2]
    return image

def normalize(x, mean, std):
    # x shape: [224, 224, 3]
    # mean：shape为1；这里用到了广播机制。我们安装好右边对齐的原则，可以得到如下；
    # mean : [1, 1, 3], std: [3]        先插入1
    # mean : [224, 224, 3], std: [3]    再变为224
    x = (x - mean)/std              # 这1行代码等价19到21行的1行代码；
    return x

# 3. 学习率调整测率200epoch
def lr_schedule_200ep(epoch):
    if epoch < 60:
        return 0.1
    if epoch < 120:
        return 0.02
    if epoch < 160:
        return 0.004
    if epoch < 200:
        return 0.0008
    if epoch < 250:
        return 0.0003
    if epoch < 300:
        return 0.0001
    else:
        return 0.00006

# 4. 学习率调整测率500epoch
def lr_schedule_300ep(epoch):
    if epoch < 150:
        return 0.1
    if epoch < 225:
        return 0.01
    if epoch < 300:
        return 0.001