loading_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载数据集\n",
    "\n",
    "## 概述\n",
    "\n",
    "MindSpore可以帮助你加载常见的数据集、特定数据格式的数据集或自定义的数据集。加载数据集时，需先导入所需要依赖的库`mindspore.dataset`。\n",
    "\n",
    "接下来，以加载数常用数据集（CIFAR-10数据集）、特定格式数据集以及自定义数据集为例来体验MindSpore加载数据集操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 整体流程\n",
    "\n",
    "1. 准备环节。下载本次体验流程所需的数据集。\n",
    "2. 加载常用数据集并输出结果，以CIFAR-10二进制数据集为例。\n",
    "3. 加载特定格式数据集并输出结果，以MindRecord格式数据集为例。\n",
    "4. 加载自定义数据集并输出结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备环节\n",
    "\n",
    "### 导入`mindspore.dataset`模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore.dataset as ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下载所需数据集\n",
    "\n",
    "1. 在当前`notebook`工作目录创建`./datasets/cifar-10`目录，用于存放数据集。\n",
    "2. 在当前`notebook`工作目录创建`./datasets/mindrecord`目录，用于后续存放转换后的MindRecord格式数据集文件。\n",
    "3. 下载[CIFAR-10二进制格式数据集](https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz)，并将数据集文件解压到`./datasets/cifar-10/cifar-10-batches-bin`目录下。\n",
    "4. 下载数据集[CIFAR-10 Python文件格式数据集](http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)，并将数据集文件解压到`./datasets/cifar-10/cifar-10-batches-py`目录下。\n",
    "\n",
    "    此时当前`notebook`工作目录下`datasets`目录结构为：\n",
    "\n",
    "    ```shell\n",
    "    $ tree datasets\n",
    "    datasets\n",
    "    ├── cifar-10\n",
    "    │   ├── cifar-10-batches-bin\n",
    "    │   │   ├── batches.meta.txt\n",
    "    │   │   ├── data_batch_1.bin\n",
    "    │   │   ├── data_batch_2.bin\n",
    "    │   │   ├── data_batch_3.bin\n",
    "    │   │   ├── data_batch_4.bin\n",
    "    │   │   ├── data_batch_5.bin\n",
    "    │   │   ├── readme.html\n",
    "    │   │   └── test_batch.bin\n",
    "    │   └── cifar-10-batches-py\n",
    "    │       ├── batches.meta\n",
    "    │       ├── data_batch_1\n",
    "    │       ├── data_batch_2\n",
    "    │       ├── data_batch_3\n",
    "    │       ├── data_batch_4\n",
    "    │       ├── data_batch_5\n",
    "    │       ├── readme.html\n",
    "    │       └── test_batch\n",
    "    └── mindrecord\n",
    "    ```\n",
    "\n",
    "    其中：\n",
    "    - `cifar-10-batches-bin`目录为CIFAR-10二进制格式数据集目录。\n",
    "    - `cifar-10-batches-py`目录为CIFAR-10 Python文件格式数据集目录。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载常见的数据集\n",
    "\n",
    "MindSpore可以加载常见的标准数据集。支持的数据集如下表：\n",
    "\n",
    "| 数据集:    | 简要说明 |\n",
    "| :---------: | :-------------:|\n",
    "| ImageNet  | ImageNet是根据WordNet层次结构组织的图像数据库，其中层次结构的每个节点都由成百上千个图像表示。 |\n",
    "| MNIST     | 是一个手写数字图像的大型数据库，通常用于训练各种图像处理系统。  |\n",
    "| CIFAR-10  | 常用于训练图像的采集机器学习和计算机视觉算法。CIFAR-10数据集包含10种不同类别的60,000张32x32彩色图像。 |\n",
    "| CIFAR-100 | 该数据集类似于CIFAR-10，不同之处在于它有100个类别，每个类别包含600张图像：500张训练图像和100张测试图像。|\n",
    "| PASCAL-VOC | 数据内容多样，可用于训练计算机视觉模型（分类、定位、检测、分割、动作识别等）。|\n",
    "| CelebA    | CelebA人脸数据集包含上万个名人身份的人脸图片，每张图片有40个特征标记，常用于人脸相关的训练任务。 |\n",
    "\n",
    "加载常见数据集的详细步骤如下，以创建`CIFAR-10`对象为例，用于加载支持的数据集。\n",
    "\n",
    "1. 使用二进制格式的数据集（CIFAR-10 binary version），配置数据集目录，定义需要加载的数据集实例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"./datasets/cifar-10/cifar-10-batches-bin\"\n",
    "cifar10_dataset = ds.Cifar10Dataset(DATA_DIR)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 创建迭代器，通过迭代器读取数据。此处读取前2个图像及其标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data of image 1 is below:\n",
      "[[[179 147 140]\n",
      "  [173 148 138]\n",
      "  [131 108  98]\n",
      "  ...\n",
      "  [129  90  77]\n",
      "  [167 140 124]\n",
      "  [188 172 154]]\n",
      "\n",
      " [[177 156 131]\n",
      "  [182 167 142]\n",
      "  [120 108  85]\n",
      "  ...\n",
      "  [156 142 130]\n",
      "  [199 171 159]\n",
      "  [174 126 106]]\n",
      "\n",
      " [[145 129 103]\n",
      "  [128 107  81]\n",
      "  [166 144 118]\n",
      "  ...\n",
      "  [145 129 115]\n",
      "  [138  94  72]\n",
      "  [179 108  84]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[123 135  91]\n",
      "  [134 146 101]\n",
      "  [113 123  86]\n",
      "  ...\n",
      "  [117 106  79]\n",
      "  [ 87  81  67]\n",
      "  [ 80  80  56]]\n",
      "\n",
      " [[148 159 114]\n",
      "  [135 146 103]\n",
      "  [125 135  97]\n",
      "  ...\n",
      "  [150 137  93]\n",
      "  [123 116  88]\n",
      "  [124 120  93]]\n",
      "\n",
      " [[150 162 102]\n",
      "  [160 171 115]\n",
      "  [132 141  97]\n",
      "  ...\n",
      "  [139 126  79]\n",
      "  [113 100  84]\n",
      "  [ 98  83  72]]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The label of image 1 is : 6\n",
      "The data of image 2 is below:\n",
      "[[[ 91  93 133]\n",
      "  [ 94  97 127]\n",
      "  [ 75  86 127]\n",
      "  ...\n",
      "  [ 86  89 117]\n",
      "  [ 84  86 113]\n",
      "  [ 80  80 110]]\n",
      "\n",
      " [[ 96 104 130]\n",
      "  [ 98 106 124]\n",
      "  [ 83  99 124]\n",
      "  ...\n",
      "  [102 102 111]\n",
      "  [ 99 101 110]\n",
      "  [ 75  88 106]]\n",
      "\n",
      " [[ 76  92 126]\n",
      "  [ 91 101 126]\n",
      "  [ 89 104 132]\n",
      "  ...\n",
      "  [100 104 114]\n",
      "  [102 106 115]\n",
      "  [ 88  95 116]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[127 113 132]\n",
      "  [139 125 138]\n",
      "  [147 131 142]\n",
      "  ...\n",
      "  [159 127 111]\n",
      "  [133 127 137]\n",
      "  [133 124 139]]\n",
      "\n",
      " [[132 120 135]\n",
      "  [140 129 136]\n",
      "  [142 130 138]\n",
      "  ...\n",
      "  [166 133 115]\n",
      "  [139 130 136]\n",
      "  [141 133 142]]\n",
      "\n",
      " [[118 115 143]\n",
      "  [126 121 143]\n",
      "  [115 111 134]\n",
      "  ...\n",
      "  [148 130 146]\n",
      "  [139 130 156]\n",
      "  [129 121 146]]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The label of image 2 is : 5\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "count = 0\n",
    "for data in cifar10_dataset.create_dict_iterator():\n",
    "# In CIFAR-10 dataset, each dictionary of data has keys \"image\" and \"label\".\n",
    "    image = data[\"image\"]\n",
    "    print(f\"The data of image {count+1} is below:\")\n",
    "    print(image)\n",
    "    plt.figure(count)\n",
    "    plt.imshow(image)\n",
    "    plt.title(f\"image{count+1}\")\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    print(f\"\\nThe label of image {count+1} is :\", data[\"label\"])\n",
    "    count += 1\n",
    "    if count == 2:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载特定数据格式的数据集\n",
    "\n",
    "\n",
    "### MindSpore数据格式\n",
    "\n",
    "MindSpore天然支持读取MindSpore数据格式——`MindRecord`存储的数据集，在性能和特性上有更好的支持。  \n",
    "\n",
    "> 阅读[将数据集转换为MindSpore数据格式](https://www.mindspore.cn/tutorial/zh-CN/r0.7/use/data_preparation/converting_datasets.html)，了解如何将数据集转换为MindSpore数据格式。\n",
    "\n",
    "可以通过`MindDataset`对象对数据集进行读取。详细方法如下所示："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 将CIFAR-10数据集转换为`MindRecord`数据格式。此处使用的数据集为CIFAR-10 Python文件格式数据集（`cifar-10-batches-py`）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSRStatus.SUCCESS"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mindspore.mindrecord import Cifar10ToMR\n",
    "\n",
    "\n",
    "CIFAR10_DIR = \"./datasets/cifar-10/cifar-10-batches-py\"\n",
    "MINDRECORD_FILE = \"./datasets/mindrecord/cifar10.mindrecord\"\n",
    "cifar10_transformer = Cifar10ToMR(CIFAR10_DIR, MINDRECORD_FILE)\n",
    "cifar10_transformer.transform(['label'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 使用`MindDataset`类创建数据集`data_set`，用于读取数据。其中`dataset_file`为指定MindRecord的文件或文件列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "data_set = ds.MindDataset(dataset_file=\"./datasets/mindrecord/cifar10.mindrecord\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 创建字典迭代器，通过迭代器读取数据记录。此处读取前5个数据的标签数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "6\n",
      "0\n",
      "6\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "num_iter = 0\n",
    "for data in data_set.create_dict_iterator():\n",
    "    print(data[\"label\"])\n",
    "    num_iter += 1\n",
    "    if num_iter == 5:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载自定义数据集\n",
    "\n",
    "现实场景中，数据集的种类多种多样，对于自定义数据集或者目前不支持直接加载的数据集，有两种方法可以处理。\n",
    "一种方法是将数据集转成MindRecord格式（请参考[将数据集转换为MindSpore数据格式](https://www.mindspore.cn/tutorial/zh-CN/r0.7/use/data_preparation/converting_datasets.html)章节），另一种方法是通过`GeneratorDataset`对象加载，以下将展示如何使用`GeneratorDataset`。\n",
    "\n",
    "1. 定义一个可迭代的对象，用于生成数据集。以下展示了两种示例，一种是含有`yield`返回值的自定义函数，另一种是含有`__getitem__`的自定义类。两种示例都将产生一个含有从0到9数字的数据集。\n",
    "   \n",
    "> 自定义的可迭代对象，每次返回`numpy array`的元组，作为一行数据。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp;以下一段代码创建含有`yield`返回值的自定义函数`generator_func`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # Import numpy lib.\n",
    "\n",
    "\n",
    "def generator_func(num):\n",
    "   for i in range(num):\n",
    "       yield (np.array([i]),)  # Notice, tuple of only one element needs following a comma at the end.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp;创建含有`__getitem__`的自定义类："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # Import numpy lib.\n",
    "\n",
    "\n",
    "class Generator():\n",
    "\n",
    "   def __init__(self, num):\n",
    "       self.num = num\n",
    "\n",
    "   def __getitem__(self, item):\n",
    "       return (np.array([item]),)  # Notice, tuple of only one element needs following a comma at the end.\n",
    "\n",
    "   def __len__(self):\n",
    "       return self.num\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 使用`GeneratorDataset`创建数据集，并通过给数据创建迭代器的方式，获取相应的数据。\n",
    "\n",
    "    - 将`generator_func`传入`GeneratorDataset`创建数据集`dataset1`，并设定`column`名为“data” 。\n",
    "    - 将定义的`Generator`对象传入`GeneratorDataset`创建数据集`dataset2`，并设定`column`名为“data” 。\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp;以下一段代码分别对`dataset1`和`dataset2`创建返回值为序列类型的迭代器，并打印输出数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset1:\n",
      "[array([0], dtype=int32)]\n",
      "[array([1], dtype=int32)]\n",
      "[array([2], dtype=int32)]\n",
      "[array([3], dtype=int32)]\n",
      "[array([4], dtype=int32)]\n",
      "[array([5], dtype=int32)]\n",
      "[array([6], dtype=int32)]\n",
      "[array([7], dtype=int32)]\n",
      "[array([8], dtype=int32)]\n",
      "[array([9], dtype=int32)]\n",
      "dataset2:\n",
      "[array([0], dtype=int64)]\n",
      "[array([1], dtype=int64)]\n",
      "[array([2], dtype=int64)]\n",
      "[array([3], dtype=int64)]\n",
      "[array([4], dtype=int64)]\n",
      "[array([5], dtype=int64)]\n",
      "[array([6], dtype=int64)]\n",
      "[array([7], dtype=int64)]\n",
      "[array([8], dtype=int64)]\n",
      "[array([9], dtype=int64)]\n"
     ]
    }
   ],
   "source": [
    "dataset1 = ds.GeneratorDataset(source=generator_func(10), column_names=[\"data\"], shuffle=False)\n",
    "dataset2 = ds.GeneratorDataset(source=Generator(10), column_names=[\"data\"], shuffle=False)\n",
    "\n",
    "print(\"dataset1:\") \n",
    "for data in dataset1.create_tuple_iterator():  # each data is a sequence\n",
    "    print(data)\n",
    "\n",
    "print(\"dataset2:\")\n",
    "for data in dataset2.create_tuple_iterator():  # each data is a sequence\n",
    "    print(data)\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp;以下一段代码分别对`dataset1`和`dataset2`创建迭代器，并打印输出数据果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset1:\n",
      "[0]\n",
      "[1]\n",
      "[2]\n",
      "[3]\n",
      "[4]\n",
      "[5]\n",
      "[6]\n",
      "[7]\n",
      "[8]\n",
      "[9]\n",
      "dataset2:\n",
      "[0]\n",
      "[1]\n",
      "[2]\n",
      "[3]\n",
      "[4]\n",
      "[5]\n",
      "[6]\n",
      "[7]\n",
      "[8]\n",
      "[9]\n"
     ]
    }
   ],
   "source": [
    "dataset1 = ds.GeneratorDataset(source=generator_func(10), column_names=[\"data\"], shuffle=False)\n",
    "dataset2 = ds.GeneratorDataset(source=Generator(10), column_names=[\"data\"], shuffle=False)\n",
    "\n",
    "\n",
    "print(\"dataset1:\")\n",
    "for data in dataset1.create_dict_iterator():  # each data is a dictionary\n",
    "    print(data[\"data\"])\n",
    "\n",
    "print(\"dataset2:\")\n",
    "for data in dataset2.create_dict_iterator():  # each data is a dictionary\n",
    "    print(data[\"data\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "以上便完成了MindSpore加载数据集的体验，我们通过本次体验全面了解了MindSpore加载数据集的几种方式和支持的数据集类型、如何创建自定义数据集，以及输出展示加载后的数据集结果。"
   ]
  }
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