{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 自动数据增强\n",
"\n",
"[](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/zh_cn/dataset/mindspore_augment.ipynb) [](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/zh_cn/dataset/mindspore_augment.py) [](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/dataset/augment.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MindSpore除了可以让用户自定义数据增强的使用，还提供了一种自动数据增强方式，可以基于特定策略自动对图像进行数据增强处理。\n",
"\n",
"下面分为**基于概率**和**基于回调参数**两种不同的自动数据增强方式进行介绍。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 基于概率的数据增强\n",
"\n",
"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。\n",
"\n",
"### RandomApply操作\n",
"\n",
"`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。\n",
"\n",
"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。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import mindspore.dataset.vision as vision\n",
"from mindspore.dataset.transforms import RandomApply\n",
"\n",
"transforms_list = [vision.RandomCrop(512), vision.RandomColorAdjust()]\n",
"rand_apply = RandomApply(transforms_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RandomChoice\n",
"\n",
"`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。\n",
"\n",
"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。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import mindspore.dataset.vision as vision\n",
"from mindspore.dataset.transforms import RandomChoice\n",
"\n",
"transforms_list = [vision.CenterCrop(512), vision.RandomCrop(512)]\n",
"rand_choice = RandomChoice(transforms_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RandomSelectSubpolicy\n",
"\n",
"`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。\n",
"\n",
"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。\n",
"\n",
"在下面的代码示例中，预置了两条子策略：\n",
"\n",
"- 子策略1中包含`RandomRotation`、`RandomVerticalFlip`两个操作，概率分别为0.5、1.0。\n",
"\n",
"- 子策略2中包含`RandomRotation`和`RandomColorAdjust`两个操作，概率分别为1.0和0.2。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import mindspore.dataset.vision as vision\n",
"from mindspore.dataset.vision import RandomSelectSubpolicy\n",
"\n",
"policy_list = [\n",
" # policy 1: (transforms, probability)\n",
" [(vision.RandomRotation((45, 45)), 0.5),\n",
" (vision.RandomVerticalFlip(), 1.0)],\n",
" # policy 2: (transforms, probability)\n",
" [(vision.RandomRotation((90, 90)), 1.0),\n",
" (vision.RandomColorAdjust(), 0.2)]\n",
"]\n",
"\n",
"policy = RandomSelectSubpolicy(policy_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 基于回调参数的数据增强\n",
"\n",
"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。\n",
"\n",
"`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：\n",
"\n",
"- 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。\n",
"\n",
"- 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。\n",
"\n",
"下面将演示基于回调参数的自动数据增强的用法。\n",
"\n",
"1. 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。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"class Augment:\n",
" def __init__(self):\n",
" self.ep_num = 0\n",
" self.step_num = 0\n",
"\n",
" def preprocess(self, input_):\n",
" return np.array((input_ + self.step_num ** self.ep_num - 1),)\n",
"\n",
" def update(self, data):\n",
" self.ep_num = data['ep_num']\n",
" self.step_num = data['step_num']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. 数据处理pipeline先回调自定义的增强策略更新函数`update`，然后在`map`操作中按更新后的策略来执行`preprocess`中定义的数据增强操作。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import mindspore.dataset as ds\n",
"\n",
"arr = list(range(1, 4))\n",
"dataset = ds.NumpySlicesDataset(arr, shuffle=False)\n",
"\n",
"aug = Augment()\n",
"dataset = dataset.sync_wait(condition_name=\"policy\", callback=aug.update)\n",
"dataset = dataset.map(operations=[aug.preprocess])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. 在每个step中调用`sync_update`进行数据增强策略的更新。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epcoh: 0, step:0, data :[Tensor(shape=[], dtype=Int64, value= 1)]\n",
"epcoh: 0, step:1, data :[Tensor(shape=[], dtype=Int64, value= 2)]\n",
"epcoh: 0, step:2, data :[Tensor(shape=[], dtype=Int64, value= 3)]\n",
"epcoh: 1, step:3, data :[Tensor(shape=[], dtype=Int64, value= 1)]\n",
"epcoh: 1, step:4, data :[Tensor(shape=[], dtype=Int64, value= 5)]\n",
"epcoh: 1, step:5, data :[Tensor(shape=[], dtype=Int64, value= 7)]\n",
"epcoh: 2, step:6, data :[Tensor(shape=[], dtype=Int64, value= 6)]\n",
"epcoh: 2, step:7, data :[Tensor(shape=[], dtype=Int64, value= 50)]\n",
"epcoh: 2, step:8, data :[Tensor(shape=[], dtype=Int64, value= 66)]\n",
"epcoh: 3, step:9, data :[Tensor(shape=[], dtype=Int64, value= 81)]\n",
"epcoh: 3, step:10, data :[Tensor(shape=[], dtype=Int64, value= 1001)]\n",
"epcoh: 3, step:11, data :[Tensor(shape=[], dtype=Int64, value= 1333)]\n",
"epcoh: 4, step:12, data :[Tensor(shape=[], dtype=Int64, value= 1728)]\n",
"epcoh: 4, step:13, data :[Tensor(shape=[], dtype=Int64, value= 28562)]\n",
"epcoh: 4, step:14, data :[Tensor(shape=[], dtype=Int64, value= 38418)]\n"
]
}
],
"source": [
"epochs = 5\n",
"itr = dataset.create_tuple_iterator(num_epochs=epochs)\n",
"\n",
"step_num = 0\n",
"for ep_num in range(epochs):\n",
" for data in itr:\n",
" print(\"epcoh: {}, step:{}, data :{}\".format(ep_num, step_num, data))\n",
" step_num += 1\n",
" dataset.sync_update(condition_name=\"policy\",\n",
" data={'ep_num': ep_num, 'step_num': step_num})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ImageNet自动数据增强\n",
"\n",
"下面以ImageNet数据集上实现AutoAugment作为示例。\n",
"\n",
"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。\n",
"\n",
"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：\n",
"\n",
"- `RandomCropDecodeResize`：随机裁剪后进行解码。\n",
"- `RandomHorizontalFlip`：水平方向上随机翻转。\n",
"- `Normalize`：归一化。\n",
"- `HWC2CHW`：图片通道变化。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. 定义MindSpore操作到AutoAugment操作的映射："
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import mindspore.dataset.vision as vision\n",
"import mindspore.dataset.transforms as transforms\n",
"\n",
"# define Auto Augmentation operations\n",
"PARAMETER_MAX = 10\n",
"\n",
"def float_parameter(level, maxval):\n",
" return float(level) * maxval / PARAMETER_MAX\n",
"\n",
"def int_parameter(level, maxval):\n",
" return int(level * maxval / PARAMETER_MAX)\n",
"\n",
"def shear_x(level):\n",
" transforms_list = []\n",
" v = float_parameter(level, 0.3)\n",
"\n",
" transforms_list.append(vision.RandomAffine(degrees=0, shear=(-v, -v)))\n",
" transforms_list.append(vision.RandomAffine(degrees=0, shear=(v, v)))\n",
" return transforms.RandomChoice(transforms_list)\n",
"\n",
"def shear_y(level):\n",
" transforms_list = []\n",
" v = float_parameter(level, 0.3)\n",
"\n",
" transforms_list.append(vision.RandomAffine(degrees=0, shear=(0, 0, -v, -v)))\n",
" transforms_list.append(vision.RandomAffine(degrees=0, shear=(0, 0, v, v)))\n",
" return transforms.RandomChoice(transforms_list)\n",
"\n",
"def translate_x(level):\n",
" transforms_list = []\n",
" v = float_parameter(level, 150 / 331)\n",
"\n",
" transforms_list.append(vision.RandomAffine(degrees=0, translate=(-v, -v)))\n",
" transforms_list.append(vision.RandomAffine(degrees=0, translate=(v, v)))\n",
" return transforms.RandomChoice(transforms_list)\n",
"\n",
"def translate_y(level):\n",
" transforms_list = []\n",
" v = float_parameter(level, 150 / 331)\n",
"\n",
" transforms_list.append(vision.RandomAffine(degrees=0, translate=(0, 0, -v, -v)))\n",
" transforms_list.append(vision.RandomAffine(degrees=0, translate=(0, 0, v, v)))\n",
" return transforms.RandomChoice(transforms_list)\n",
"\n",
"def color_impl(level):\n",
" v = float_parameter(level, 1.8) + 0.1\n",
" return vision.RandomColor(degrees=(v, v))\n",
"\n",
"def rotate_impl(level):\n",
" transforms_list = []\n",
" v = int_parameter(level, 30)\n",
"\n",
" transforms_list.append(vision.RandomRotation(degrees=(-v, -v)))\n",
" transforms_list.append(vision.RandomRotation(degrees=(v, v)))\n",
" return transforms.RandomChoice(transforms_list)\n",
"\n",
"def solarize_impl(level):\n",
" level = int_parameter(level, 256)\n",
" v = 256 - level\n",
" return vision.RandomSolarize(threshold=(0, v))\n",
"\n",
"def posterize_impl(level):\n",
" level = int_parameter(level, 4)\n",
" v = 4 - level\n",
" return vision.RandomPosterize(bits=(v, v))\n",
"\n",
"def contrast_impl(level):\n",
" v = float_parameter(level, 1.8) + 0.1\n",
" return vision.RandomColorAdjust(contrast=(v, v))\n",
"\n",
"def autocontrast_impl(level):\n",
" return vision.AutoContrast()\n",
"\n",
"def sharpness_impl(level):\n",
" v = float_parameter(level, 1.8) + 0.1\n",
" return vision.RandomSharpness(degrees=(v, v))\n",
"\n",
"def brightness_impl(level):\n",
" v = float_parameter(level, 1.8) + 0.1\n",
" return vision.RandomColorAdjust(brightness=(v, v))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. 定义ImageNet数据集的AutoAugment策略："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# define the Auto Augmentation policy\n",
"imagenet_policy = [\n",
" [(posterize_impl(8), 0.4), (rotate_impl(9), 0.6)],\n",
" [(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)],\n",
" [(vision.Equalize(), 0.8), (vision.Equalize(), 0.6)],\n",
" [(posterize_impl(7), 0.6), (posterize_impl(6), 0.6)],\n",
"\n",
" [(vision.Equalize(), 0.4), (solarize_impl(4), 0.2)],\n",
" [(vision.Equalize(), 0.4), (rotate_impl(8), 0.8)],\n",
" [(solarize_impl(3), 0.6), (vision.Equalize(), 0.6)],\n",
" [(posterize_impl(5), 0.8), (vision.Equalize(), 1.0)],\n",
" [(rotate_impl(3), 0.2), (solarize_impl(8), 0.6)],\n",
" [(vision.Equalize(), 0.6), (posterize_impl(6), 0.4)],\n",
"\n",
" [(rotate_impl(8), 0.8), (color_impl(0), 0.4)],\n",
" [(rotate_impl(9), 0.4), (vision.Equalize(), 0.6)],\n",
" [(vision.Equalize(), 0.0), (vision.Equalize(), 0.8)],\n",
" [(vision.Invert(), 0.6), (vision.Equalize(), 1.0)],\n",
" [(color_impl(4), 0.6), (contrast_impl(8), 1.0)],\n",
"\n",
" [(rotate_impl(8), 0.8), (color_impl(2), 1.0)],\n",
" [(color_impl(8), 0.8), (solarize_impl(7), 0.8)],\n",
" [(sharpness_impl(7), 0.4), (vision.Invert(), 0.6)],\n",
" [(shear_x(5), 0.6), (vision.Equalize(), 1.0)],\n",
" [(color_impl(0), 0.4), (vision.Equalize(), 0.6)],\n",
"\n",
" [(vision.Equalize(), 0.4), (solarize_impl(4), 0.2)],\n",
" [(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)],\n",
" [(vision.Invert(), 0.6), (vision.Equalize(), 1.0)],\n",
" [(color_impl(4), 0.6), (contrast_impl(8), 1.0)],\n",
" [(vision.Equalize(), 0.8), (vision.Equalize(), 0.6)],\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. 在`RandomCropDecodeResize`操作后插入AutoAugment变换。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mindspore.dataset as ds\n",
"import mindspore as ms\n",
"\n",
"\n",
"def create_dataset(dataset_path, train, repeat_num=1,\n",
" batch_size=32, shuffle=False, num_samples=5):\n",
" # create a train or eval imagenet2012 dataset for ResNet-50\n",
" dataset = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8,\n",
" shuffle=shuffle, decode=True)\n",
"\n",
" image_size = 224\n",
"\n",
" # define map operations\n",
" if train:\n",
" trans = RandomSelectSubpolicy(imagenet_policy)\n",
" else:\n",
" trans = [vision.Resize(256),\n",
" vision.CenterCrop(image_size)]\n",
" type_cast_op = transforms.TypeCast(ms.int32)\n",
"\n",
" # map images and labes\n",
" dataset = dataset.map(operations=[vision.Resize(256), vision.CenterCrop(image_size)], input_columns=\"image\")\n",
" dataset = dataset.map(operations=trans, input_columns=\"image\")\n",
" dataset = dataset.map(operations=type_cast_op, input_columns=\"label\")\n",
"\n",
" # apply the batch and repeat operation\n",
" dataset = dataset.batch(batch_size, drop_remainder=True)\n",
" dataset = dataset.repeat(repeat_num)\n",
"\n",
" return dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. 验证自动数据增强效果:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from download import download\n",
"\n",
"# Define the path to image folder directory.\n",
"url = \"https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/ImageNetSimilar.tar.gz\"\n",
"download(url, \"./\", kind=\"tar.gz\", replace=True)\n",
"dataset = create_dataset(dataset_path=\"ImageNetSimilar\",\n",
" train=True,\n",
" batch_size=5,\n",
" shuffle=False)\n",
"\n",
"epochs = 5\n",
"columns = 5\n",
"rows = 5\n",
"fig = plt.figure(figsize=(8, 8))\n",
"itr = dataset.create_dict_iterator(num_epochs=epochs)\n",
"\n",
"for ep_num in range(epochs):\n",
" step_num = 0\n",
" for data in itr:\n",
" for index in range(rows):\n",
" fig.add_subplot(rows, columns, step_num * rows + index + 1)\n",
" plt.imshow(data['image'].asnumpy()[index])\n",
" step_num += 1\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 为了更好地演示效果，此处只加载5张图片，且读取时不进行`shuffle`操作，自动数据增强时也不进行`Normalize`和`HWC2CHW`操作。\n",
"\n",
"\n",
"\n",
"运行结果可以看到，batch中每张图像的增强效果，垂直方向表示1个batch的5张图像，水平方向表示5个batch。\n",
"\n",
"## 参考文献\n",
"\n",
"[1] Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le [AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/abs/1805.09501)."
]
}
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