chapter2/2.1.4-pytorch-basics-data-loader.ipynb

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   "source": [
    "# PyTorch 基础 :数据的加载和预处理\n",
    "PyTorch通过torch.utils.data对一般常用的数据加载进行了封装，可以很容易地实现多线程数据预读和批量加载。\n",
    "并且torchvision已经预先实现了常用图像数据集，包括前面使用过的CIFAR-10，ImageNet、COCO、MNIST、LSUN等数据集，可通过torchvision.datasets方便的调用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.1.post2'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 首先要引入相关的包\n",
    "import torch\n",
    "#打印一下版本\n",
    "torch.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "Dataset是一个抽象类，为了能够方便的读取，需要将要使用的数据包装为Dataset类。\n",
    "自定义的Dataset需要继承它并且实现两个成员方法：\n",
    "1. `__getitem__()` 该方法定义用索引(`0` 到 `len(self)`)获取一条数据或一个样本\n",
    "2. `__len__()` 该方法返回数据集的总长度\n",
    "\n",
    "下面我们使用kaggle上的一个竞赛[bluebook for bulldozers](https://www.kaggle.com/c/bluebook-for-bulldozers/data)自定义一个数据集，为了方便介绍，我们使用里面的数据字典来做说明（因为条数少）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#引用\n",
    "from torch.utils.data import Dataset\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义一个数据集\n",
    "class BulldozerDataset(Dataset):\n",
    "    \"\"\" 数据集演示 \"\"\"\n",
    "    def __init__(self, csv_file):\n",
    "        \"\"\"实现初始化方法，在初始化的时候将数据读载入\"\"\"\n",
    "        self.df=pd.read_csv(csv_file)\n",
    "    def __len__(self):\n",
    "        '''\n",
    "        返回df的长度\n",
    "        '''\n",
    "        return len(self.df)\n",
    "    def __getitem__(self, idx):\n",
    "        '''\n",
    "        根据 idx 返回一行数据\n",
    "        '''\n",
    "        return self.df.iloc[idx].SalePrice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "至此，我们的数据集已经定义完成了，我们可以实例化一个对象访问它"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_demo= BulldozerDataset('median_benchmark.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以直接使用如下命令查看数据集数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11573"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#实现了 __len__ 方法所以可以直接使用len获取数据总数\n",
    "len(ds_demo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24000.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用索引可以直接访问对应的数据，对应 __getitem__ 方法\n",
    "ds_demo[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自定义的数据集已经创建好了，下面我们使用官方提供的数据载入器，读取数据\n",
    "## Dataloader\n",
    "DataLoader为我们提供了对Dataset的读取操作，常用参数有：batch_size(每个batch的大小)、 shuffle(是否进行shuffle操作)、 num_workers(加载数据的时候使用几个子进程)。下面做一个简单的操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl = torch.utils.data.DataLoader(ds_demo, batch_size=10, shuffle=True, num_workers=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataLoader返回的是一个可迭代对象，我们可以使用迭代器分次获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([24000., 24000., 24000., 24000., 24000., 24000., 24000., 24000., 24000.,\n",
      "        24000.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "idata=iter(dl)\n",
    "print(next(idata))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "常见的用法是使用for循环对其进行遍历"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor([24000., 24000., 24000., 24000., 24000., 24000., 24000., 24000., 24000.,\n",
      "        24000.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "for i, data in enumerate(dl):\n",
    "    print(i,data)\n",
    "    # 为了节约空间，这里只循环一遍\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们已经可以通过dataset定义数据集，并使用Datalorder载入和遍历数据集，除了这些以外，PyTorch还提供能torcvision的计算机视觉扩展包，里面封装了\n",
    "## torchvision 包\n",
    "torchvision 是PyTorch中专门用来处理图像的库，PyTorch官网的安装教程中最后的pip install torchvision 就是安装这个包。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### torchvision.datasets\n",
    "torchvision.datasets 可以理解为PyTorch团队自定义的dataset，这些dataset帮我们提前处理好了很多的图片数据集，我们拿来就可以直接使用：\n",
    "- MNIST\n",
    "- COCO\n",
    "- Captions\n",
    "- Detection\n",
    "- LSUN\n",
    "- ImageFolder\n",
    "- Imagenet-12\n",
    "- CIFAR\n",
    "- STL10\n",
    "- SVHN\n",
    "- PhotoTour\n",
    "我们可以直接使用，示例如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.datasets as datasets\n",
    "trainset = datasets.MNIST(root='./data', # 表示 MNIST 数据的加载的目录\n",
    "                                      train=True,  # 表示是否加载数据库的训练集，false的时候加载测试集\n",
    "                                      download=True, # 表示是否自动下载 MNIST 数据集\n",
    "                                      transform=None) # 表示是否需要对数据进行预处理，none为不进行预处理\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### torchvision.models\n",
    "torchvision不仅提供了常用图片数据集，还提供了训练好的模型，可以加载之后，直接使用，或者在进行迁移学习\n",
    "torchvision.models模块的 子模块中包含以下模型结构。\n",
    "- AlexNet\n",
    "- VGG\n",
    "- ResNet\n",
    "- SqueezeNet\n",
    "- DenseNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#我们直接可以使用训练好的模型，当然这个与datasets相同，都是需要从服务器下载的\n",
    "import torchvision.models as models\n",
    "resnet18 = models.resnet18(pretrained=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### torchvision.transforms\n",
    "transforms 模块提供了一般的图像转换操作类，用作数据处理和数据增强"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms as transforms\n",
    "transform = transforms.Compose([\n",
    "    transforms.RandomCrop(32, padding=4),  #先四周填充0，在把图像随机裁剪成32*32\n",
    "    transforms.RandomHorizontalFlip(),  #图像一半的概率翻转，一半的概率不翻转\n",
    "    transforms.RandomRotation((-45,45)), #随机旋转\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.229, 0.224, 0.225)), #R,G,B每层的归一化用到的均值和方差\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "肯定有人会问：(0.485, 0.456, 0.406), (0.2023, 0.1994, 0.2010) 这几个数字是什么意思？\n",
    "\n",
    "官方的这个帖子有详细的说明:\n",
    "https://discuss.pytorch.org/t/normalization-in-the-mnist-example/457/21\n",
    "这些都是根据ImageNet训练的归一化参数，可以直接使用，我们认为这个是固定值就可以"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们已经完成了Python的基本内容的介绍，下面我们要介绍神经网络的理论基础，里面的公式等内容我们都使用PyTorch来实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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