{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Dataset类\n",
    "其实这就表示，无论使用自定义的数据集，还是官方为我们封装好的数据集，其本质都是继承了Dataset类。而在继承Dataset类时，至少需要重写以下几个方法：\n",
    "\n",
    "* \\_\\_init\\_\\_()：构造函数，可自定义数据读取方法以及进行数据预处理；\n",
    "* \\_\\_len\\_\\_()：返回数据集大小；\n",
    "* \\_\\_getitem\\_\\_()：索引数据集中的某一个数据。"
   ],
   "id": "6f460291da56d507"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T01:44:55.209362Z",
     "start_time": "2025-06-13T01:44:52.973675Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "\n",
    "class MyDataset(Dataset):\n",
    "    # 构造函数\n",
    "    def __init__(self, data_tensor, target_tensor):\n",
    "        self.data_tensor = data_tensor\n",
    "        self.target_tensor = target_tensor\n",
    "\n",
    "    # 返回数据集大小\n",
    "    def __len__(self):\n",
    "        return self.data_tensor.size(0)\n",
    "\n",
    "    # 返回索引的数据与标签\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data_tensor[idx], self.target_tensor[idx]"
   ],
   "id": "1244f4325aad0ac5",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T01:44:55.286472Z",
     "start_time": "2025-06-13T01:44:55.230898Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 生成数据\n",
    "data_tensor = torch.randn(10, 3)\n",
    "target_tensor = torch.randint(2, (10,))  # 二分类任务\n",
    "\n",
    "print(data_tensor)\n",
    "print(target_tensor)"
   ],
   "id": "e2f8ad8dc637791a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.5479,  0.1374,  1.6763],\n",
      "        [-0.5671, -0.0821, -1.8523],\n",
      "        [-0.4039, -0.5871, -0.3510],\n",
      "        [-0.2339,  0.2773, -0.0820],\n",
      "        [-0.3131,  0.5911,  0.2030],\n",
      "        [-0.7087, -0.2614, -0.2661],\n",
      "        [ 0.3220, -0.0340, -1.2429],\n",
      "        [-0.1282, -0.2188, -0.7576],\n",
      "        [ 1.1233, -0.2452,  0.4664],\n",
      "        [ 1.2570,  0.3728,  0.9745]])\n",
      "tensor([0, 1, 1, 1, 0, 0, 0, 0, 0, 0])\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T01:44:55.610922Z",
     "start_time": "2025-06-13T01:44:55.606365Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 创建数据集\n",
    "my_dataset = MyDataset(data_tensor, target_tensor)\n",
    "\n",
    "# 查看数据集大小\n",
    "print(\"dataset size：\", len(my_dataset))\n",
    "\n",
    "# 使用索引获取数据\n",
    "print(\"data:\", my_dataset[0])  # 获取第一个数据"
   ],
   "id": "cd8f86852f54b0c5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset size： 10\n",
      "data: (tensor([1.5479, 0.1374, 1.6763]), tensor(0))\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T01:48:47.876220Z",
     "start_time": "2025-06-13T01:48:47.866170Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "tensor_loader = DataLoader(dataset=my_dataset, batch_size=2, shuffle=True, num_workers=0)\n",
    "\n",
    "# 以循环的方式获取数据\n",
    "for data, target in tensor_loader:\n",
    "    print(\"data:\", data)\n",
    "    print(\"target:\", target)\n",
    "\n",
    "print(\"one batch tensor data:\", next(iter(tensor_loader)))"
   ],
   "id": "d78ddf04e79df9ae",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: tensor([[ 0.3220, -0.0340, -1.2429],\n",
      "        [-0.2339,  0.2773, -0.0820]])\n",
      "target: tensor([0, 1])\n",
      "data: tensor([[-0.5671, -0.0821, -1.8523],\n",
      "        [-0.3131,  0.5911,  0.2030]])\n",
      "target: tensor([1, 0])\n",
      "data: tensor([[-0.1282, -0.2188, -0.7576],\n",
      "        [-0.4039, -0.5871, -0.3510]])\n",
      "target: tensor([0, 1])\n",
      "data: tensor([[-0.7087, -0.2614, -0.2661],\n",
      "        [ 1.2570,  0.3728,  0.9745]])\n",
      "target: tensor([0, 0])\n",
      "data: tensor([[ 1.5479,  0.1374,  1.6763],\n",
      "        [ 1.1233, -0.2452,  0.4664]])\n",
      "target: tensor([0, 0])\n",
      "one batch tensor data: [tensor([[ 0.3220, -0.0340, -1.2429],\n",
      "        [ 1.1233, -0.2452,  0.4664]]), tensor([0, 0])]\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T01:53:08.605768Z",
     "start_time": "2025-06-13T01:53:07.616662Z"
    }
   },
   "cell_type": "code",
   "source": "!pip install torchvision pillow",
   "id": "b6845476dfd81659",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (0.17.1)\r\n",
      "Requirement already satisfied: pillow in /opt/conda/lib/python3.10/site-packages (10.2.0)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision) (1.26.3)\r\n",
      "Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from torchvision) (2.2.1)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (3.13.1)\r\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (4.9.0)\r\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (1.12)\r\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (3.1)\r\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (3.1.3)\r\n",
      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (2024.2.0)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch->torchvision) (2.1.3)\r\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->torchvision) (1.3.0)\r\n",
      "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001B[0m\u001B[33m\r\n",
      "\u001B[0m"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T02:12:21.816805Z",
     "start_time": "2025-06-13T02:12:21.421202Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 以MNIST数据集为例\n",
    "import torchvision\n",
    "\n",
    "mnist_dataset = torchvision.datasets.MNIST(root=\"./data\", train=True, transform=None, target_transform=None,\n",
    "                                           download=True)"
   ],
   "id": "f8112ef53ed2d0ce",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-13T02:17:29.130555Z",
     "start_time": "2025-06-13T02:17:28.225901Z"
    }
   },
   "cell_type": "code",
   "source": [
    "mnist_dataset_list = list(mnist_dataset)\n",
    "\n",
    "display(mnist_dataset_list[0][0])\n",
    "print(\"Image label is:\", mnist_dataset_list[0][1])"
   ],
   "id": "9b9c1d2993b37bcd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=L size=28x28>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA90lEQVR4AWNgGMyAWUhIqK5jvdSy/9/rQe5kgTlWjs3KRiAYxHsyKfDzxYMgFiOIAALDvfwQBsO/pK8Mz97fhPLAlNDtvyBwbNv3j8jCUHbAnOy/f89yM2jPwiLJwMc4628UqgQTnPvp/0eGFAQXLg5lcO/764YuhuArf3y4IAfmfoQwlBX44e/fckkMYaiA7q6/f6dJ45IViP3zdzcuSQaGn39/OkBl4WEL4euFmLIwXDuETav6lKfAIPy1DYucRNFdUPCe9MOUE3e6CpI6FogZSEKrwbFyOIATQ5v5mkcgXV9auVGlwK4NDGRguL75b88HVDla8QBFF16ADQA8sQAAAABJRU5ErkJggg==",
      "image/jpeg": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image label is: 5\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'size'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[23], line 6\u001B[0m\n\u001B[1;32m      3\u001B[0m display(mnist_dataset_list[\u001B[38;5;241m0\u001B[39m][\u001B[38;5;241m0\u001B[39m])\n\u001B[1;32m      4\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mImage label is:\u001B[39m\u001B[38;5;124m\"\u001B[39m, mnist_dataset_list[\u001B[38;5;241m0\u001B[39m][\u001B[38;5;241m1\u001B[39m])\n\u001B[0;32m----> 6\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mmnist_dataset_list\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msize\u001B[49m)\n",
      "\u001B[0;31mAttributeError\u001B[0m: 'list' object has no attribute 'size'"
     ]
    }
   ],
   "execution_count": 23
  }
 ],
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