{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Broken] Evaluate YOLO v3 on Inferentia\n",
    "## Note: this tutorial runs on tensorflow-neuron 1.x only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "This tutorial walks through compiling and evaluating YOLO v3 model on Inferentia using the AWS Neuron SDK.\n",
    "\n",
    "\n",
    "In this tutorial we provide two main sections:\n",
    "\n",
    "1. Download Dataset and Generate Pretrained SavedModel\n",
    "\n",
    "2. Compile the YOLO v3 model.\n",
    "\n",
    "3. Deploy the same compiled model.\n",
    "\n",
    "Verify that this Jupyter notebook is running the Python kernel environment that was set up according to the [Tensorflow Installation Guide](../../../../frameworks/tensorflow/tensorflow-neuron/setup/tensorflow-install.html#install-neuron-tensorflow). You can select the Kernel from the “Kernel -> Change Kernel” option on the top of this Jupyter notebook page.\n",
    "\n",
    "Instructions of how to setup Neuron Tensorflow environment and run the tutorial as a Jupyter notebook are available in the Tutorial main page [Tensorflow-YOLO_v3 Tutorial](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/frameworks/tensorflow/tensorflow-neuron/tutorials/yolo_v3_demo/yolo_v3_demo.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demo requires the following pip packages:\n",
    "\n",
    "`pillow matplotlib pycocotools`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%pip install tensorflow_neuron==1.15.5.2.8.9.0 neuron_cc==1.13.5.0 requests pillow matplotlib pycocotools==2.0.1 numpy==1.18.2 torch~=1.5.0 --force \\\n",
    "    --extra-index-url=https://pip.repos.neuron.amazonaws.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1:  Download Dataset and Generate Pretrained SavedModel\n",
    "### Download COCO 2017 validation dataset\n",
    "\n",
    "We start by downloading the COCO validation dataset, which we will use to validate our model. The COCO 2017 dataset is widely used for object-detection, segmentation and image captioning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!curl -LO http://images.cocodataset.org/zips/val2017.zip\n",
    "!curl -LO http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
    "!unzip -q val2017.zip\n",
    "!unzip annotations_trainval2017.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Generate YOLO v3 tensorflow SavedModel (pretrained on COCO 2017 dataset)\n",
    "\n",
    "Script yolo_v3_coco_saved_model.py will generate a tensorflow SavedModel using pretrained weights from https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run yolo_v3_coco_saved_model.py ./yolo_v3_coco_saved_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tensorflow SavedModel can be loaded as a tensorflow predictor. When a JPEG format image is provided as input, the output result of the tensorflow predictor contains information for drawing bounding boxes and classification results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import tensorflow as tf\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "# launch predictor and run inference on an arbitrary image in the validation dataset\n",
    "yolo_pred_cpu = tf.contrib.predictor.from_saved_model('./yolo_v3_coco_saved_model')\n",
    "image_path = './val2017/000000581781.jpg'\n",
    "with open(image_path, 'rb') as f:\n",
    "    feeds = {'image': [f.read()]}\n",
    "results = yolo_pred_cpu(feeds)\n",
    "\n",
    "# load annotations to decode classification result\n",
    "with open('./annotations/instances_val2017.json') as f:\n",
    "    annotate_json = json.load(f)\n",
    "label_info = {idx+1: cat['name'] for idx, cat in enumerate(annotate_json['categories'])}\n",
    "\n",
    "# draw picture and bounding boxes\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "ax.imshow(Image.open(image_path).convert('RGB'))\n",
    "wanted = results['scores'][0] > 0.1\n",
    "for xyxy, label_no_bg in zip(results['boxes'][0][wanted], results['classes'][0][wanted]):\n",
    "    xywh = xyxy[0], xyxy[1], xyxy[2] - xyxy[0], xyxy[3] - xyxy[1]\n",
    "    rect = patches.Rectangle((xywh[0], xywh[1]), xywh[2], xywh[3], linewidth=1, edgecolor='g', facecolor='none')\n",
    "    ax.add_patch(rect)\n",
    "    rx, ry = rect.get_xy()\n",
    "    rx = rx + rect.get_width() / 2.0\n",
    "    ax.annotate(label_info[label_no_bg + 1], (rx, ry), color='w', backgroundcolor='g', fontsize=10,\n",
    "                ha='center', va='center', bbox=dict(boxstyle='square,pad=0.01', fc='g', ec='none', alpha=0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2:  Compile the Pretrained SavedModel for Neuron\n",
    "\n",
    "We make use of the Python compilation API `tfn.saved_model.compile` that is available in `tensorflow-neuron<2`. For the purpose of reducing Neuron runtime overhead, it is necessary to make use of arguments `no_fuse_ops` and `minimum_segment_size`.\n",
    "Compiled model is saved in ./yolo_v3_coco_saved_model_neuron."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "import tensorflow as tf\n",
    "import tensorflow.neuron as tfn\n",
    "\n",
    "\n",
    "def no_fuse_condition(op):\n",
    "    return op.name.startswith('Preprocessor') or op.name.startswith('Postprocessor')\n",
    "\n",
    "with tf.Session(graph=tf.Graph()) as sess:\n",
    "    tf.saved_model.loader.load(sess, ['serve'], './yolo_v3_coco_saved_model')\n",
    "    no_fuse_ops = [op.name for op in sess.graph.get_operations() if no_fuse_condition(op)]\n",
    "shutil.rmtree('./yolo_v3_coco_saved_model_neuron', ignore_errors=True)\n",
    "result = tfn.saved_model.compile(\n",
    "    './yolo_v3_coco_saved_model', './yolo_v3_coco_saved_model_neuron',\n",
    "    # to enforce trivial compilable subgraphs to run on CPU\n",
    "    no_fuse_ops=no_fuse_ops,\n",
    "    minimum_segment_size=100,\n",
    "    batch_size=2,\n",
    "    dynamic_batch_size=True,\n",
    ")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy the model on Inferentia\n",
    "## Part 3:Evaluate Model Quality after Compilation\n",
    "\n",
    "### Define evaluation functions\n",
    "We first define some handy helper functions for running evaluation on the COCO 2017 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import time\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from pycocotools.coco import COCO\n",
    "from pycocotools.cocoeval import COCOeval\n",
    "\n",
    "\n",
    "def cocoapi_eval(jsonfile,\n",
    "                 style,\n",
    "                 coco_gt=None,\n",
    "                 anno_file=None,\n",
    "                 max_dets=(100, 300, 1000)):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        jsonfile: Evaluation json file, eg: bbox.json, mask.json.\n",
    "        style: COCOeval style, can be `bbox` , `segm` and `proposal`.\n",
    "        coco_gt: Whether to load COCOAPI through anno_file,\n",
    "                 eg: coco_gt = COCO(anno_file)\n",
    "        anno_file: COCO annotations file.\n",
    "        max_dets: COCO evaluation maxDets.\n",
    "    \"\"\"\n",
    "    assert coco_gt is not None or anno_file is not None\n",
    "\n",
    "    if coco_gt is None:\n",
    "        coco_gt = COCO(anno_file)\n",
    "    print(\"Start evaluate...\")\n",
    "    coco_dt = coco_gt.loadRes(jsonfile)\n",
    "    if style == 'proposal':\n",
    "        coco_eval = COCOeval(coco_gt, coco_dt, 'bbox')\n",
    "        coco_eval.params.useCats = 0\n",
    "        coco_eval.params.maxDets = list(max_dets)\n",
    "    else:\n",
    "        coco_eval = COCOeval(coco_gt, coco_dt, style)\n",
    "    coco_eval.evaluate()\n",
    "    coco_eval.accumulate()\n",
    "    coco_eval.summarize()\n",
    "    return coco_eval.stats\n",
    "\n",
    "\n",
    "def bbox_eval(anno_file, bbox_list):\n",
    "    coco_gt = COCO(anno_file)\n",
    "\n",
    "    outfile = 'bbox_detections.json'\n",
    "    print('Generating json file...')\n",
    "    with open(outfile, 'w') as f:\n",
    "        json.dump(bbox_list, f)\n",
    "\n",
    "    map_stats = cocoapi_eval(outfile, 'bbox', coco_gt=coco_gt)\n",
    "    return map_stats\n",
    "\n",
    "\n",
    "def get_image_as_bytes(images, eval_pre_path):\n",
    "    batch_im_id_list = []\n",
    "    batch_im_name_list = []\n",
    "    batch_img_bytes_list = []\n",
    "    n = len(images)\n",
    "    batch_im_id = []\n",
    "    batch_im_name = []\n",
    "    batch_img_bytes = []\n",
    "    for i, im in enumerate(images):\n",
    "        im_id = im['id']\n",
    "        file_name = im['file_name']\n",
    "        if i % eval_batch_size == 0 and i != 0:\n",
    "            batch_im_id_list.append(batch_im_id)\n",
    "            batch_im_name_list.append(batch_im_name)\n",
    "            batch_img_bytes_list.append(batch_img_bytes)\n",
    "            batch_im_id = []\n",
    "            batch_im_name = []\n",
    "            batch_img_bytes = []\n",
    "        batch_im_id.append(im_id)\n",
    "        batch_im_name.append(file_name)\n",
    "\n",
    "        with open(os.path.join(eval_pre_path, file_name), 'rb') as f:\n",
    "            batch_img_bytes.append(f.read())\n",
    "    return batch_im_id_list, batch_im_name_list, batch_img_bytes_list\n",
    "\n",
    "\n",
    "def analyze_bbox(results, batch_im_id, _clsid2catid):\n",
    "    bbox_list = []\n",
    "    k = 0\n",
    "    for boxes, scores, classes in zip(results['boxes'], results['scores'], results['classes']):\n",
    "        if boxes is not None:\n",
    "            im_id = batch_im_id[k]\n",
    "            n = len(boxes)\n",
    "            for p in range(n):\n",
    "                clsid = classes[p]\n",
    "                score = scores[p]\n",
    "                xmin, ymin, xmax, ymax = boxes[p]\n",
    "                catid = (_clsid2catid[int(clsid)])\n",
    "                w = xmax - xmin + 1\n",
    "                h = ymax - ymin + 1\n",
    "\n",
    "                bbox = [xmin, ymin, w, h]\n",
    "                # Round to the nearest 10th to avoid huge file sizes, as COCO suggests\n",
    "                bbox = [round(float(x) * 10) / 10 for x in bbox]\n",
    "                bbox_res = {\n",
    "                    'image_id': im_id,\n",
    "                    'category_id': catid,\n",
    "                    'bbox': bbox,\n",
    "                    'score': float(score),\n",
    "                }\n",
    "                bbox_list.append(bbox_res)\n",
    "        k += 1\n",
    "    return bbox_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the actual evaluation loop. To fully utilize all four cores on one Inferentia, the optimal setup is to run multi-threaded inference using a `ThreadPoolExecutor`. The following cell is a multi-threaded adaptation of the evaluation routine at https://github.com/miemie2013/Keras-YOLOv4/blob/910c4c6f7265f5828fceed0f784496a0b46516bf/tools/cocotools.py#L97."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from concurrent import futures\n",
    "\n",
    "def evaluate(yolo_predictor, images, eval_pre_path, anno_file, eval_batch_size, _clsid2catid):\n",
    "    batch_im_id_list, batch_im_name_list, batch_img_bytes_list = get_image_as_bytes(images, eval_pre_path)\n",
    "\n",
    "    # warm up\n",
    "    yolo_predictor({'image': np.array(batch_img_bytes_list[0], dtype=object)})\n",
    "\n",
    "    with futures.ThreadPoolExecutor(4) as exe:\n",
    "        fut_im_list = []\n",
    "        fut_list = []\n",
    "        start_time = time.time()\n",
    "        for batch_im_id, batch_im_name, batch_img_bytes in zip(batch_im_id_list, batch_im_name_list, batch_img_bytes_list):\n",
    "            if len(batch_img_bytes) != eval_batch_size:\n",
    "                continue\n",
    "            fut = exe.submit(yolo_predictor, {'image': np.array(batch_img_bytes, dtype=object)})\n",
    "            fut_im_list.append((batch_im_id, batch_im_name))\n",
    "            fut_list.append(fut)\n",
    "        bbox_list = []\n",
    "        count = 0\n",
    "        for (batch_im_id, batch_im_name), fut in zip(fut_im_list, fut_list):\n",
    "            results = fut.result()\n",
    "            bbox_list.extend(analyze_bbox(results, batch_im_id, _clsid2catid))\n",
    "            for _ in batch_im_id:\n",
    "                count += 1\n",
    "                if count % 100 == 0:\n",
    "                    print('Test iter {}'.format(count))\n",
    "        print('==================== Performance Measurement ====================')\n",
    "        print('Finished inference on {} images in {} seconds'.format(len(images), time.time() - start_time))\n",
    "        print('=================================================================')\n",
    "    # start evaluation\n",
    "    box_ap_stats = bbox_eval(anno_file, bbox_list)\n",
    "    return box_ap_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate mean average precision (mAP) score\n",
    "Here is the code to calculate mAP scores of the YOLO v3 model. The expected mAP score is around 0.328 if we use the pretrained weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "yolo_pred = tf.contrib.predictor.from_saved_model('./yolo_v3_coco_saved_model_neuron')\n",
    "\n",
    "val_coco_root = './val2017'\n",
    "val_annotate = './annotations/instances_val2017.json'\n",
    "clsid2catid = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10, 10: 11, 11: 13, 12: 14, 13: 15, 14: 16,\n",
    "               15: 17, 16: 18, 17: 19, 18: 20, 19: 21, 20: 22, 21: 23, 22: 24, 23: 25, 24: 27, 25: 28, 26: 31,\n",
    "               27: 32, 28: 33, 29: 34, 30: 35, 31: 36, 32: 37, 33: 38, 34: 39, 35: 40, 36: 41, 37: 42, 38: 43,\n",
    "               39: 44, 40: 46, 41: 47, 42: 48, 43: 49, 44: 50, 45: 51, 46: 52, 47: 53, 48: 54, 49: 55, 50: 56,\n",
    "               51: 57, 52: 58, 53: 59, 54: 60, 55: 61, 56: 62, 57: 63, 58: 64, 59: 65, 60: 67, 61: 70, 62: 72,\n",
    "               63: 73, 64: 74, 65: 75, 66: 76, 67: 77, 68: 78, 69: 79, 70: 80, 71: 81, 72: 82, 73: 84, 74: 85,\n",
    "               75: 86, 76: 87, 77: 88, 78: 89, 79: 90}\n",
    "eval_batch_size = 8\n",
    "with open(val_annotate, 'r', encoding='utf-8') as f2:\n",
    "    for line in f2:\n",
    "        line = line.strip()\n",
    "        dataset = json.loads(line)\n",
    "        images = dataset['images']\n",
    "box_ap = evaluate(yolo_pred, images, val_coco_root, val_annotate, eval_batch_size, clsid2catid)"
   ]
  }
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