{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试 QAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/media/pc/data/4tb/xinet/web/pytorch-book/docs\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import time\n",
    "import torch.quantization\n",
    "\n",
    "# 设置 warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\n",
    "    action='ignore',\n",
    "    category=DeprecationWarning,\n",
    "    module=r'.*'\n",
    ")\n",
    "warnings.filterwarnings(\n",
    "    action='default',\n",
    "    module=r'torch.quantization'\n",
    ")\n",
    "\n",
    "# 为可重复的结果指定随机种子\n",
    "torch.manual_seed(191009)\n",
    "\n",
    "from mod import load_mod\n",
    "load_mod()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作为最后一个主要的设置步骤，我们为训练和测试集定义了数据加载器。\n",
    "\n",
    "要使用整个 ImageNet 数据集运行本教程中的代码，请先按照 [ImageNet Data](http://www.image-net.org/download) 中的说明下载 ImageNet。将下载的文件解压缩到 `data_path` 文件夹中。\n",
    "\n",
    "下载完数据后，我们将在下面展示一些函数，[这些函数](https://github.com/pytorch/vision/blob/master/references/detection/train.py)定义了用于读取数据的数据加载器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_book.datasets.imagenet import prepare_data_loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '/media/pc/data/4tb/xinet/datasets/imagenet2'\n",
    "\n",
    "train_batch_size = 30\n",
    "eval_batch_size = 50\n",
    "\n",
    "data_loader, data_loader_test = prepare_data_loaders(data_path,\n",
    "                                                     train_batch_size,\n",
    "                                                     eval_batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{rubric} 训练模型\n",
    "```\n",
    "\n",
    "写一个通用函数 {func}`~tools.train_model` 来训练模型：\n",
    "\n",
    "-  调度学习率\n",
    "-  保存最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tools'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/test.ipynb Cell 7'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/4tb/xinet/web/pytorch-book/docs/quantization/study/test.ipynb#ch0000008vscode-remote?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtools\u001b[39;00m \u001b[39mimport\u001b[39;00m train_model\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tools'"
     ]
    }
   ],
   "source": [
    "from tools import train_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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