vgg_translate_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial : tensorflow2fluid转换VGG_16模型\n",
    "\n",
    "VGG_16是CV领域的一个经典模型，本文档以tensorflow/models下的[VGG_16](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)为例，展示如何将TensorFlow训练好的模型转换为PaddlePaddle模型。 \n",
    "### 下载预训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Download percentage 100.00%"
     ]
    }
   ],
   "source": [
    "import urllib\n",
    "import sys\n",
    "def schedule(a, b, c):\n",
    "    per = 100.0 * a * b / c\n",
    "    per = int(per)\n",
    "    sys.stderr.write(\"\\rDownload percentage %.2f%%\" % per)\n",
    "    sys.stderr.flush()\n",
    "\n",
    "url = \"http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz\"\n",
    "fetch = urllib.urlretrieve(url, \"./vgg_16.tar.gz\", schedule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 解压下载的压缩文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tarfile\n",
    "with tarfile.open(\"./vgg_16.tar.gz\", \"r:gz\") as f:\n",
    "    file_names = f.getnames()\n",
    "    for file_name in file_names:\n",
    "        f.extract(file_name, \"./\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 保存模型为checkpoint格式\n",
    "\n",
    "tensorflow2fluid目前支持checkpoint格式的模型或者是将网络结构和参数序列化的pb格式模型，上面下载的`vgg_16.ckpt`仅仅存储了模型参数，因此我们需要重新加载参数，并将网络结构和参数一起保存为checkpoint模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from vgg_16.ckpt\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.contrib.slim as slim\n",
    "from tensorflow.contrib.slim.nets import vgg\n",
    "import tensorflow as tf\n",
    "import numpy\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    inputs = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224, 3], name=\"inputs\")\n",
    "    logits, endpoint = vgg.vgg_16(inputs, num_classes=1000, is_training=False)\n",
    "    load_model = slim.assign_from_checkpoint_fn(\"vgg_16.ckpt\", slim.get_model_variables(\"vgg_16\"))\n",
    "    load_model(sess)\n",
    "    \n",
    "    numpy.random.seed(13)\n",
    "    data = numpy.random.rand(5, 224, 224, 3)\n",
    "    input_tensor = sess.graph.get_tensor_by_name(\"inputs:0\")\n",
    "    output_tensor = sess.graph.get_tensor_by_name(\"vgg_16/fc8/squeezed:0\")\n",
    "    result = sess.run([output_tensor], {input_tensor:data})\n",
    "    numpy.save(\"tensorflow.npy\", numpy.array(result))\n",
    "    \n",
    "    saver = tf.train.Saver()\n",
    "    saver.save(sess, \"./checkpoint/model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将模型转换为PaddlePaddle模型\n",
    "\n",
    "注意：部分OP在转换时，需要将参数写入文件；或者是运行tensorflow模型进行infer，获取tensor值。两种情况下均会消耗一定的时间用于IO或计算，对于后一种情况，建议转换模型时将`use_cuda`参数设为`True`，加快infer速度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loading tensorflow model...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from checkpoint/model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from checkpoint/model\n",
      "INFO:root:Tensorflow model loaded!\n",
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      "INFO:root:TotalNum:86,TraslatedNum:86,CurrentNode:vgg_16/fc8/squeezed\n",
      "INFO:root:Model translated!\n"
     ]
    }
   ],
   "source": [
    "import tf2fluid.convert as convert\n",
    "import argparse\n",
    "parser = convert._get_parser()\n",
    "parser.meta_file = \"checkpoint/model.meta\"\n",
    "parser.ckpt_dir = \"checkpoint\"\n",
    "parser.in_nodes = [\"inputs\"]\n",
    "parser.input_shape = [\"None,224,224,3\"]\n",
    "parser.output_nodes = [\"vgg_16/fc8/squeezed\"]\n",
    "parser.use_cuda = \"True\"\n",
    "parser.input_format = \"NHWC\"\n",
    "parser.save_dir = \"paddle_model\"\n",
    "\n",
    "convert.run(parser)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载转换后的PaddlePaddle模型，并进行预测\n",
    "**需要注意，转换后的PaddlePaddle CV模型输入格式为NCHW**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-14T10:18:46.124339Z",
     "start_time": "2019-03-14T10:18:40.858372Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import tf2fluid.model_loader as ml\n",
    "\n",
    "model = ml.ModelLoader(\"paddle_model\", use_cuda=True)\n",
    "\n",
    "numpy.random.seed(13)\n",
    "data = numpy.random.rand(5, 224, 224, 3).astype(\"float32\")\n",
    "# NHWC -> NCHW\n",
    "data = numpy.transpose(data, (0, 3, 1, 2))\n",
    "\n",
    "results = model.inference(feed_dict={model.inputs[0]:data})\n",
    "\n",
    "numpy.save(\"paddle.npy\", numpy.array(results))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对比转换前后模型之前的预测结果diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-14T10:20:13.611132Z",
     "start_time": "2019-03-14T10:20:13.598874Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.33786e-06\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "paddle_result = numpy.load(\"paddle.npy\")\n",
    "tensorflow_result = numpy.load(\"tensorflow.npy\")\n",
    "diff = numpy.fabs(paddle_result - tensorflow_result)\n",
    "print(numpy.max(diff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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