tensorflow2paddle.ipynb

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    "# X2Paddle快速上手——TensorFlow迁移至PaddlePaddle\n",
    "***X2Paddle简介***：X2Paddle支持将Caffe/TensorFlow/ONNX/PyTorch深度学习框架训练得到的模型，迁移至PaddlePaddle模型。   \n",
    "***X2Paddle代码GitHub链接***：[https://github.com/PaddlePaddle/X2Paddle](https://github.com/PaddlePaddle/X2Paddle)  \n",
    "***【注意】***前往GitHub给[X2Paddle](https://github.com/PaddlePaddle/X2Paddle)点击Star，关注项目，即可随时了解X2Paddle的最新进展。  \n",
    "本教程用于帮助用户学习将TensorFlow训练后的预测模型迁移至PaddlePaddle框架，以TensorFlow版本的[MobileNetV1](https://github.com/tensorflow/models/tree/master/research/slim)为例进行详细介绍。  \n",
    "\n",
    "## 安装及准备\n",
    "### 1. 安装X2Paddle\n",
    "***方式一：（推荐）***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "! git clone https://github.com/PaddlePaddle/X2Paddle.git\n",
    "! cd X2Paddle\n",
    "! git checkout develop\n",
    "! python setup.py install"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***方式二：***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install x2paddle==1.0.1 --index https://pypi.Python.org/simple/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 安装TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install tensorflow==1.14.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 安装PaddlePaddle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install paddlepaddle==2.0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型迁移\n",
    "### 1. 获取MobileNetV1的FrozenModel\n",
    "由于X2Paddle只支持TensorFlow中FrozenModel的转换，如果为纯checkpoint模型，需要参考参考X2Paddle官方[文档](https://github.com/PaddlePaddle/X2Paddle/blob/develop/docs/user_guides/export_tf_model.md)，将其转换为FrozenModel，本示例中提供的模型为FrozenModel，所以无需转换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! wget http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz\n",
    "! tar zxvf mobilenet_v1_0.25_128.tgz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 转换\n",
    "需要传入的参数如下：\n",
    "> --framework (-f)：源模型类型，此处设置为tensorflow。  \n",
    "> --save_dir (-s)：指定转换后的模型保存目录路径。  \n",
    "> --model (-m)：指定tensorflow的pb模型。  \n",
    "> --paddle_type (-pt)：指定转换为动态图代码(dygraph)或者静态图代码(static)，默认为dygraph。  \n",
    "\n",
    "***方式一：***生成静态图代码，并保存成静态图预测模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! x2paddle -f tensorflow -m ./mobilenet_v1_0.25_128_frozen.pb -s pd_model_static -pt static"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***方式二：***生成动态图代码，并保存成静态图预测模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! x2paddle -f tensorflow -m ./mobilenet_v1_0.25_128_frozen.pb  -s pd_model_dygraph -pt dygraph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用方式一转换得到的PaddlePaddle预测模型进行预测：  \n",
    "（1）下载ImageNet类别文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! wget https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2）预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造输入\n",
    "import cv2\n",
    "import numpy as np\n",
    "img = cv2.imread(\"dog_tf.png\").astype(\"float32\") / 255.0\n",
    "img = np.expand_dims(img, 0)\n",
    "img -= 0.5\n",
    "img *= 2.0\n",
    "    \n",
    "# 进行预测\n",
    "import paddle\n",
    "import numpy as np\n",
    "paddle.enable_static()\n",
    "exe = paddle.static.Executor(paddle.CPUPlace())\n",
    "[prog, inputs, outputs] = paddle.static.load_inference_model(path_prefix=\"pd_model_static/inference_model\", \n",
    "                                                             executor=exe, \n",
    "                                                             model_filename=\"model.pdmodel\",\n",
    "                                                             params_filename=\"model.pdiparams\")\n",
    "result = exe.run(prog, feed={inputs[0]: img}, fetch_list=outputs)\n",
    "max_index = np.argmax(result[0])\n",
    "with open('imagenet_classes.txt') as f:\n",
    "    classes = [line.strip() for line in f.readlines()]\n",
    "print(\"The category of dog.jpg is: {}\".format(classes[max_index]))"
   ]
  }
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