docs/zh_CN/source/quickstart/quickstart_in_one_minute.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "affecting-convenience",
   "metadata": {},
   "source": [
    "# 在一分钟内实现一个图形分类应用\n",
    "\n",
    "在本教程中，我们会演示使用TinyMS API构建LeNet5模型，下载数据集，训练，启动服务器和推理的过程。\n",
    "\n",
    "## 环境要求\n",
    " - Ubuntu: `18.04`\n",
    " - Python: `3.7.x`\n",
    " - Flask: `1.1.2`\n",
    " - MindSpore: `CPU-1.1.1`\n",
    " - TinyMS: `0.1.0`\n",
    " - numpy: `1.17.5`\n",
    " - Pillow: `8.1.0`\n",
    " - pip: `21.0.1`\n",
    " - requests: `2.18.4`\n",
    " \n",
    "## 介绍\n",
    "\n",
    "TinyMS是一个高级API，目的是让新手用户能够更加轻松地上手深度学习。TinyMS可以有效地减少用户在构建、训练、验证和推理一个模型过程中的操作次数。TinyMS也提供了教程和文档帮助开发者更好的上手和开发。\n",
    "\n",
    "本教程包括6部分，`构建模型`、`下载数据集`、`训练`，`定义servable json`, `启动服务器`和`推理`，其中服务器将在一个子进程中启动。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "phantom-mills",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[WARNING] ME(12716:140477914756928,MainProcess):2021-03-19-15:58:38.621.652 [mindspore/ops/operations/array_ops.py:2302] WARN_DEPRECATED: The usage of Pack is deprecated. Please use Stack.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: 'ControlDepend' is deprecated from version 1.1 and will be removed in a future version, use 'Depend' instead.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import tinyms.optimizers as opt\n",
    "\n",
    "from PIL import Image\n",
    "from tinyms import context\n",
    "from tinyms.data import MnistDataset, download_dataset\n",
    "from tinyms.vision import mnist_transform, ImageViewer\n",
    "from tinyms.model import Model, lenet5\n",
    "from tinyms.serving import start_server, predict, list_servables, shutdown, server_started\n",
    "from tinyms.metrics import Accuracy\n",
    "from tinyms.losses import SoftmaxCrossEntropyWithLogits\n",
    "from tinyms.callbacks import ModelCheckpoint, CheckpointConfig, LossMonitor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aggressive-medline",
   "metadata": {},
   "source": [
    "### 1. 构建模型\n",
    "\n",
    "TinyMS封装了MindSpore LeNet5模型中的init和construct函数，代码行数能够大大减少，原有的大量代码段行数会被极限压缩:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "matched-explanation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建网络\n",
    "net = lenet5(class_num=10)\n",
    "model = Model(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "guided-activation",
   "metadata": {},
   "source": [
    "### 2. 下载数据集\n",
    "\n",
    "如果根目录下没有创建`mnist`文件夹则MNIST数据集会被自动下载并存放到根目录，如果`mnist`文件夹已经存在于根目录 ，则此步操作会被跳过"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "laden-slovakia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************Dataset already exists.**************\n"
     ]
    }
   ],
   "source": [
    "# 下载数据集\n",
    "mnist_path = '/root/mnist'\n",
    "if not os.path.exists(mnist_path):\n",
    "    download_dataset('mnist', '/root')\n",
    "    print('************Download complete*************')\n",
    "else:\n",
    "    print('************Dataset already exists.**************')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regulated-bradford",
   "metadata": {},
   "source": [
    "### 3. 训练模型\n",
    "\n",
    "数据集中的训练集、验证集都会在此步骤中定义，同时也会定义训练参数。训练后生成的ckpt文件会保存到`/etc/tinyms/serving/lenet5`文件夹以便后续使用，训练完成后会进行验证并输出 `Accuracy`指标。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bearing-showcase",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 创建mnist路径\n",
    "ckpt_folder = '/etc/tinyms/serving/lenet5'\n",
    "ckpt_path = '/etc/tinyms/serving/lenet5/lenet5.ckpt'\n",
    "if not os.path.exists(ckpt_folder):\n",
    "    !mkdir -p  /etc/tinyms/serving/lenet5\n",
    "else:\n",
    "    print('lenet5 ckpt folder already exists')\n",
    "\n",
    "# 设置环境参数\n",
    "device_target = \"CPU\"\n",
    "context.set_context(mode=context.GRAPH_MODE, device_target=device_target)  \n",
    "dataset_sink_mode = False\n",
    "\n",
    "# 创建数据集\n",
    "train_dataset = MnistDataset(os.path.join(mnist_path, \"train\"), shuffle=True)\n",
    "train_dataset = mnist_transform.apply_ds(train_dataset)\n",
    "eval_dataset = MnistDataset(os.path.join(mnist_path, \"test\"), shuffle=True)\n",
    "eval_dataset = mnist_transform.apply_ds(eval_dataset)\n",
    "\n",
    "# 设置训练参数\n",
    "lr = 0.01\n",
    "momentum = 0.9\n",
    "epoch_size = 1\n",
    "batch_size = 32\n",
    "\n",
    "# 定义loss函数\n",
    "net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n",
    "\n",
    "# 定义optimizer\n",
    "net_opt = opt.Momentum(net.trainable_params(), lr, momentum)\n",
    "net_metrics={\"Accuracy\": Accuracy()}\n",
    "model.compile(loss_fn=net_loss, optimizer=net_opt, metrics=net_metrics)\n",
    "\n",
    "print('************************Start training*************************')\n",
    "ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\", config=CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10))\n",
    "model.train(epoch_size, train_dataset, callbacks=[ckpoint_cb, LossMonitor()],dataset_sink_mode=dataset_sink_mode)\n",
    "print('************************Finished training*************************')\n",
    "model.save_checkpoint(ckpt_path)\n",
    "\n",
    "\n",
    "model.load_checkpoint(ckpt_path)\n",
    "print('************************Start evaluation*************************')\n",
    "acc = model.eval(eval_dataset, dataset_sink_mode=dataset_sink_mode)\n",
    "print(\"============== Accuracy:{} ==============\".format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dramatic-characteristic",
   "metadata": {},
   "source": [
    "### 4. 定义servable.json\n",
    "\n",
    "定义lenet5 servable json文件，Servable json文件定义了servable名称，模型名称，模型格式和分类数量，以便后续推理使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "raising-ending",
   "metadata": {},
   "outputs": [],
   "source": [
    "servable_json = [{'name': 'lenet5', \n",
    "                  'description': 'This servable hosts a lenet5 model predicting numbers', \n",
    "                  'model': {\n",
    "                      \"name\": \"lenet5\", \n",
    "                      \"format\": \"ckpt\", \n",
    "                      \"class_num\": 10}}]\n",
    "os.chdir(\"/etc/tinyms/serving\")\n",
    "json_data = json.dumps(servable_json, indent=4)\n",
    "\n",
    "with open('servable.json', 'w') as json_file:\n",
    "    json_file.write(json_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sustained-matthew",
   "metadata": {},
   "source": [
    "### 5. 启动服务器\n",
    "\n",
    "#### 5.1 介绍\n",
    "TinyMS推理是C/S（Client/Server）架构。TinyMS使用[Flask](https://flask.palletsprojects.com/en/1.1.x/)这个轻量化的网页服务器架构作为C/S通讯的基础架构。为了能够对模型进行推理，用户必须首先启动服务器。如果成功启动，服务器会在子进程中运行并且会监听从地址127.0.0.1，端口号5000发送来的POST请求并且使用MindSpore作为后端来处理这些请求。后端会构建模型，运行推理并且返回结果给客户端\n",
    "\n",
    "#### 5.2 启动服务器\n",
    "\n",
    "运行下列代码以启动服务器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "convinced-theorem",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Server starts at host 127.0.0.1, port 5000\n"
     ]
    }
   ],
   "source": [
    "start_server()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "european-wallpaper",
   "metadata": {},
   "source": [
    "### 6. 推理\n",
    "\n",
    "#### 6.1 上传图片\n",
    "\n",
    "用户需要上传一张0~9之间的数字图片作为输入。如果使用命令行终端，可以使用'scp'或者'wget'获取图片，如果使用Jupyter，点击菜单右上方的'Upload'按钮并且选择上传的图片。本教程中使用的图片可以点击[这里](https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/tinyms-test-pics/numbers/7.png)进行下载，将图片保存在根目录下，重命名为'7.png'（或者任何用户喜欢的名字）\n",
    "\n",
    "或者运行如下的代码下载图片："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "activated-schema",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-03-19 15:59:11--  https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/tinyms-test-pics/numbers/7.png\n",
      "Resolving ascend-tutorials.obs.cn-north-4.myhuaweicloud.com (ascend-tutorials.obs.cn-north-4.myhuaweicloud.com)... 49.4.112.5, 49.4.112.90, 49.4.112.113, ...\n",
      "Connecting to ascend-tutorials.obs.cn-north-4.myhuaweicloud.com (ascend-tutorials.obs.cn-north-4.myhuaweicloud.com)|49.4.112.5|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 34970 (34K) [image/png]\n",
      "Saving to: ‘/root/7.png’\n",
      "\n",
      "7.png               100%[===================>]  34.15K  --.-KB/s    in 0.09s   \n",
      "\n",
      "2021-03-19 15:59:12 (371 KB/s) - ‘/root/7.png’ saved [34970/34970]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists('/root/7.png'):\n",
    "    !wget -P /root/ https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/tinyms-test-pics/numbers/7.png\n",
    "else:\n",
    "    print('7.png already exists')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "convertible-timer",
   "metadata": {},
   "source": [
    "#### 6.2 List servables\n",
    "\n",
    "使用`list_servables`函数检查当前后端的serving模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "satisfactory-windows",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'description': 'This servable hosts a lenet5 model predicting numbers',\n",
       "  'model': {'class_num': 10, 'format': 'ckpt', 'name': 'lenet5'},\n",
       "  'name': 'lenet5'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_servables()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optional-transsexual",
   "metadata": {},
   "source": [
    "如果输出的`description`字段显示这是一个`lenet5`的模型，则可以继续到下一步发送推理请求"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "commercial-consensus",
   "metadata": {},
   "source": [
    "#### 6.3 发送推理请求\n",
    "\n",
    "运行`predict`函数发送推理请求，第4个参数选择`TOP1_CLASS`或者`TOP5_CLASS`输出:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "breeding-economics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP1: 7, score: 0.99943381547927856445\n"
     ]
    }
   ],
   "source": [
    "# 设置图片路径和输出策略（可以在TOP1和TOP5中选择）\n",
    "image_path = \"/root/7.png\"\n",
    "strategy = \"TOP1_CLASS\"\n",
    "\n",
    "# predict(image_path, servable_name, dataset='mnist', strategy='TOP1_CLASS')\n",
    "# predict方法的四个参数分别是图片路径、servable名称，数据集名称（默认MNIST）和输出策略（默认输出TOP1，可以选择TOP5）\n",
    "if server_started() is True:\n",
    "    img_viewer = ImageViewer(Image.open(image_path), image_path)\n",
    "    img_viewer.show()\n",
    "    print(predict(image_path,'lenet5', 'mnist', strategy))\n",
    "else:\n",
    "    print(\"Server not started\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "introductory-greenhouse",
   "metadata": {},
   "source": [
    "如果用户能看到类似如下输出:  \n",
    "```\n",
    "TOP1: 7, score: 0.99934917688369750977\n",
    "```  \n",
    "那么意味着已经进行了一次成功的推理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "crucial-chick",
   "metadata": {},
   "source": [
    "## 关闭服务器\n",
    "\n",
    "运行以下代码关闭服务器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "touched-africa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Server shutting down...'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shutdown()"
   ]
  }
 ],
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