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基于概率的数据增强 

MindSpore提供了一系列基于概率的自动数据增强API，用户可以对各种数据增强操作进行随机选择与组合，使数据增强更加灵活。
RandomApply操作 

RandomApply 操作接收一个数据增强操作列表，以一定的概率顺序执行列表中各数据增强操作，默认概率为0.5，否则都不执行。
在下面的代码示例中，通过调用 RandomApply 接口来以0.5的概率来顺序执行 RandomCrop 和 RandomColorAdjust 操作。
import mindspore.dataset.vision as vision
from mindspore.dataset.transforms import RandomApply
transforms_list = [vision.RandomCrop(512), vision.RandomColorAdjust()]
rand_apply = RandomApply(transforms_list)
RandomChoice
RandomChoice操作接收一个数据增强操作列表transforms，从中随机选择一个数据增强操作执行。
在下面的代码示例中，通过调用RandomChoice操作等概率地在CenterCrop和RandomCrop中选择一个操作执行。
import mindspore.dataset.vision as vision
from mindspore.dataset.transforms import RandomChoice
transforms_list = [vision.CenterCrop(512), vision.RandomCrop(512)]
rand_choice = RandomChoice(transforms_list)
RandomSelectSubpolicy
RandomSelectSubpolicy操作接收一个预置策略列表，包含一系列子策略组合，每一子策略由若干个顺序执行的数据增强操作及其执行概率组成。
对各图像先等概率随机选择一种子策略，再依照子策略中的概率顺序执行各个操作。
在下面的代码示例中，预置了两条子策略：
子策略1中包含RandomRotation、RandomVerticalFlip两个操作，概率分别为0.5、1.0。
子策略2中包含RandomRotation和RandomColorAdjust两个操作，概率分别为1.0和0.2。
import mindspore.dataset.vision as vision
from mindspore.dataset.vision import RandomSelectSubpolicy
policy_list = [
    # policy 1: (transforms, probability)
    [(vision.RandomRotation((45, 45)), 0.5),
     (vision.RandomVerticalFlip(), 1.0)],
    # policy 2: (transforms, probability)
    [(vision.RandomRotation((90, 90)), 1.0),
     (vision.RandomColorAdjust(), 0.2)]
policy = RandomSelectSubpolicy(policy_list)
基于回调参数的数据增强
MindSpore的sync_wait接口支持按训练数据的batch或epoch粒度，在训练过程中动态调整数据增强策略，用户可以设定阻塞条件来触发特定的数据增强操作。
sync_wait将阻塞整个数据处理pipeline，直到sync_update触发用户预先定义的callback函数，两者需配合使用，对应说明如下：
sync_wait(condition_name, num_batch=1, callback=None)：为数据集添加一个阻塞条件condition_name，当sync_update调用时执行指定的callback函数。
sync_update(condition_name, num_batch=None, data=None)：用于释放对应condition_name的阻塞，并对data触发指定的callback函数。
下面将演示基于回调参数的自动数据增强的用法。
用户预先定义Augment类，其中preprocess为自定义的数据增强函数，update为更新数据增强策略的回调函数。
import numpy as np
class Augment:
    def __init__(self):
        self.ep_num = 0
        self.step_num = 0
    def preprocess(self, input_):
        return np.array((input_ + self.step_num ** self.ep_num - 1),)
    def update(self, data):
        self.ep_num = data['ep_num']
        self.step_num = data['step_num']
arr = list(range(1, 4))
dataset = ds.NumpySlicesDataset(arr, shuffle=False)
aug = Augment()
dataset = dataset.sync_wait(condition_name="policy", callback=aug.update)
dataset = dataset.map(operations=[aug.preprocess])
for ep_num in range(epochs):
    for data in itr:
        print("epcoh: {}, step:{}, data :{}".format(ep_num, step_num, data))
        step_num += 1
        dataset.sync_update(condition_name="policy",
                            data={'ep_num': ep_num, 'step_num': step_num})
epcoh: 0, step:0, data :[Tensor(shape=[], dtype=Int64, value= 1)]
epcoh: 0, step:1, data :[Tensor(shape=[], dtype=Int64, value= 2)]
epcoh: 0, step:2, data :[Tensor(shape=[], dtype=Int64, value= 3)]
epcoh: 1, step:3, data :[Tensor(shape=[], dtype=Int64, value= 1)]
epcoh: 1, step:4, data :[Tensor(shape=[], dtype=Int64, value= 5)]
epcoh: 1, step:5, data :[Tensor(shape=[], dtype=Int64, value= 7)]
epcoh: 2, step:6, data :[Tensor(shape=[], dtype=Int64, value= 6)]
epcoh: 2, step:7, data :[Tensor(shape=[], dtype=Int64, value= 50)]
epcoh: 2, step:8, data :[Tensor(shape=[], dtype=Int64, value= 66)]
epcoh: 3, step:9, data :[Tensor(shape=[], dtype=Int64, value= 81)]
epcoh: 3, step:10, data :[Tensor(shape=[], dtype=Int64, value= 1001)]
epcoh: 3, step:11, data :[Tensor(shape=[], dtype=Int64, value= 1333)]
epcoh: 4, step:12, data :[Tensor(shape=[], dtype=Int64, value= 1728)]
epcoh: 4, step:13, data :[Tensor(shape=[], dtype=Int64, value= 28562)]
epcoh: 4, step:14, data :[Tensor(shape=[], dtype=Int64, value= 38418)]
ImageNet自动数据增强
下面以ImageNet数据集上实现AutoAugment作为示例。
针对ImageNet数据集的数据增强策略包含25条子策略，每条子策略中包含两种变换，针对一个batch中的每张图像随机挑选一个子策略的组合，以预定的概率来决定是否执行子策略中的每种变换。
用户可以使用MindSpore中mindspore.dataset.vision模块的RandomSelectSubpolicy接口来实现AutoAugment，在ImageNet分类训练中标准的数据增强方式分以下几个步骤：
RandomCropDecodeResize：随机裁剪后进行解码。
RandomHorizontalFlip：水平方向上随机翻转。
Normalize：归一化。
HWC2CHW：图片通道变化。
定义MindSpore操作到AutoAugment操作的映射：
import mindspore.dataset.vision as vision
import mindspore.dataset.transforms as transforms
# define Auto Augmentation operations
PARAMETER_MAX = 10
def float_parameter(level, maxval):
    return float(level) * maxval /  PARAMETER_MAX
def int_parameter(level, maxval):
    return int(level * maxval / PARAMETER_MAX)
def shear_x(level):
    transforms_list = []
    v = float_parameter(level, 0.3)
    transforms_list.append(vision.RandomAffine(degrees=0, shear=(-v, -v)))
    transforms_list.append(vision.RandomAffine(degrees=0, shear=(v, v)))
    return transforms.RandomChoice(transforms_list)
def shear_y(level):
    transforms_list = []
    v = float_parameter(level, 0.3)
    transforms_list.append(vision.RandomAffine(degrees=0, shear=(0, 0, -v, -v)))
    transforms_list.append(vision.RandomAffine(degrees=0, shear=(0, 0, v, v)))
    return transforms.RandomChoice(transforms_list)
def translate_x(level):
    transforms_list = []
    v = float_parameter(level, 150 / 331)
    transforms_list.append(vision.RandomAffine(degrees=0, translate=(-v, -v)))
    transforms_list.append(vision.RandomAffine(degrees=0, translate=(v, v)))
    return transforms.RandomChoice(transforms_list)
def translate_y(level):
    transforms_list = []
    v = float_parameter(level, 150 / 331)
    transforms_list.append(vision.RandomAffine(degrees=0, translate=(0, 0, -v, -v)))
    transforms_list.append(vision.RandomAffine(degrees=0, translate=(0, 0, v, v)))
    return transforms.RandomChoice(transforms_list)
def color_impl(level):
    v = float_parameter(level, 1.8) + 0.1
    return vision.RandomColor(degrees=(v, v))
def rotate_impl(level):
    transforms_list = []
    v = int_parameter(level, 30)
    transforms_list.append(vision.RandomRotation(degrees=(-v, -v)))
    transforms_list.append(vision.RandomRotation(degrees=(v, v)))
    return transforms.RandomChoice(transforms_list)
def solarize_impl(level):
    level = int_parameter(level, 256)
    v = 256 - level
    return vision.RandomSolarize(threshold=(0, v))
def posterize_impl(level):
    level = int_parameter(level, 4)
    v = 4 - level
    return vision.RandomPosterize(bits=(v, v))
def contrast_impl(level):
    v = float_parameter(level, 1.8) + 0.1
    return vision.RandomColorAdjust(contrast=(v, v))
def autocontrast_impl(level):
    return vision.AutoContrast()
def sharpness_impl(level):
    v = float_parameter(level, 1.8) + 0.1
    return vision.RandomSharpness(degrees=(v, v))
def brightness_impl(level):
    v = float_parameter(level, 1.8) + 0.1
    return vision.RandomColorAdjust(brightness=(v, v))
# define the Auto Augmentation policy
imagenet_policy = [
    [(posterize_impl(8), 0.4), (rotate_impl(9), 0.6)],
    [(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)],
    [(vision.Equalize(), 0.8), (vision.Equalize(), 0.6)],
    [(posterize_impl(7), 0.6), (posterize_impl(6), 0.6)],
    [(vision.Equalize(), 0.4), (solarize_impl(4), 0.2)],
    [(vision.Equalize(), 0.4), (rotate_impl(8), 0.8)],
    [(solarize_impl(3), 0.6), (vision.Equalize(), 0.6)],
    [(posterize_impl(5), 0.8), (vision.Equalize(), 1.0)],
    [(rotate_impl(3), 0.2), (solarize_impl(8), 0.6)],
    [(vision.Equalize(), 0.6), (posterize_impl(6), 0.4)],
    [(rotate_impl(8), 0.8), (color_impl(0), 0.4)],
    [(rotate_impl(9), 0.4), (vision.Equalize(), 0.6)],
    [(vision.Equalize(), 0.0), (vision.Equalize(), 0.8)],
    [(vision.Invert(), 0.6), (vision.Equalize(), 1.0)],
    [(color_impl(4), 0.6), (contrast_impl(8), 1.0)],
    [(rotate_impl(8), 0.8), (color_impl(2), 1.0)],
    [(color_impl(8), 0.8), (solarize_impl(7), 0.8)],
    [(sharpness_impl(7), 0.4), (vision.Invert(), 0.6)],
    [(shear_x(5), 0.6), (vision.Equalize(), 1.0)],
    [(color_impl(0), 0.4), (vision.Equalize(), 0.6)],
    [(vision.Equalize(), 0.4), (solarize_impl(4), 0.2)],
    [(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)],
    [(vision.Invert(), 0.6), (vision.Equalize(), 1.0)],
    [(color_impl(4), 0.6), (contrast_impl(8), 1.0)],
    [(vision.Equalize(), 0.8), (vision.Equalize(), 0.6)],
def create_dataset(dataset_path, train, repeat_num=1,
                   batch_size=32, shuffle=False, num_samples=5):
    # create a train or eval imagenet2012 dataset for ResNet-50
    dataset = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8,
                                    shuffle=shuffle, decode=True)
    image_size = 224
    # define map operations
    if train:
        trans = RandomSelectSubpolicy(imagenet_policy)
    else:
        trans = [vision.Resize(256),
                 vision.CenterCrop(image_size)]
    type_cast_op = transforms.TypeCast(ms.int32)
    # map images and labes
    dataset = dataset.map(operations=[vision.Resize(256), vision.CenterCrop(image_size)], input_columns="image")
    dataset = dataset.map(operations=trans, input_columns="image")
    dataset = dataset.map(operations=type_cast_op, input_columns="label")
    # apply the batch and repeat operation
    dataset = dataset.batch(batch_size, drop_remainder=True)
    dataset = dataset.repeat(repeat_num)
    return dataset
# Define the path to image folder directory.
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/ImageNetSimilar.tar.gz"
download(url, "./", kind="tar.gz", replace=True)
dataset = create_dataset(dataset_path="ImageNetSimilar",
                         train=True,
                         batch_size=5,
                         shuffle=False)
epochs = 5
columns = 5
rows = 5
fig = plt.figure(figsize=(8, 8))
itr = dataset.create_dict_iterator()
for ep_num in range(epochs):
    step_num = 0
    for data in itr:
        for index in range(rows):
            fig.add_subplot(rows, columns, step_num * rows + index + 1)
            plt.imshow(data['image'].asnumpy()[index])
        step_num += 1
plt.show()