SSD_Eval_Testset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data import *\n",
    "from data import VOC_CLASSES as labelmap\n",
    "from utils.augmentations import SSDAugmentation\n",
    "from layers.modules import MultiBoxLoss\n",
    "from ssd import build_ssd\n",
    "import os\n",
    "import os.path as osp\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "import torchvision as tv\n",
    "import torch.optim as optim\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.utils.data as data\n",
    "import torchvision.datasets\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import cv2 \n",
    "import pickle\n",
    "import random\n",
    "import tarfile\n",
    "import collections\n",
    "\n",
    "from opts import *\n",
    "import xml.etree.ElementTree as ET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Timer(object):\n",
    "    \"\"\"A simple timer.\"\"\"\n",
    "    def __init__(self):\n",
    "        self.time_metrics = [0.] * 5\n",
    "\n",
    "    def tic(self):\n",
    "        self.time_metrics[0] = time.time()\n",
    "\n",
    "    def toc(self, average=True):\n",
    "        self.time_metrics[1] = time.time() - self.time_metrics[0]\n",
    "        self.time_metrics[2] += self.time_metrics[1]\n",
    "        self.time_metrics[3] += 1\n",
    "        self.time_metrics[4] = self.time_metrics[2] / self.time_metrics[3]\n",
    "        if average:\n",
    "            return self.time_metrics[4]\n",
    "        else:\n",
    "            return self.time_metrics[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_rec(filename):\n",
    "    \"\"\" Parse a PASCAL VOC xml file \"\"\"\n",
    "    tree = ET.parse(filename)\n",
    "    objects = []\n",
    "    for obj in tree.findall('object'):\n",
    "        obj_struct = {}\n",
    "        obj_struct['name'] = obj.find('name').text\n",
    "        obj_struct['pose'] = obj.find('pose').text\n",
    "        obj_struct['truncated'] = int(obj.find('truncated').text)\n",
    "        obj_struct['difficult'] = int(obj.find('difficult').text)\n",
    "        bbox = obj.find('bndbox')\n",
    "        obj_struct['bbox'] = [int(bbox.find('xmin').text) - 1,\n",
    "                              int(bbox.find('ymin').text) - 1,\n",
    "                              int(bbox.find('xmax').text) - 1,\n",
    "                              int(bbox.find('ymax').text) - 1]\n",
    "        objects.append(obj_struct)\n",
    "\n",
    "    return objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_voc_results_file_template(image_set, cls):\n",
    "    filename = 'det_' + image_set + '_%s.txt' % (cls)\n",
    "    filedir = os.path.join(devkit_path, 'results')\n",
    "    if not os.path.exists(filedir):\n",
    "        os.makedirs(filedir)\n",
    "    path = os.path.join(filedir, filename)\n",
    "    return path\n",
    "\n",
    "\n",
    "def write_voc_results_file(all_boxes, dataset):\n",
    "    for cls_ind, cls in enumerate(labelmap):\n",
    "        print('Writing {:s} VOC results file'.format(cls))\n",
    "        filename = get_voc_results_file_template(set_type, cls)\n",
    "        \n",
    "        with open(filename, 'wt') as f:\n",
    "            for im_ind, index in enumerate(dataset.ids):\n",
    "                dets = all_boxes[cls_ind+1][im_ind]\n",
    "                if dets == []:\n",
    "                    continue\n",
    "\n",
    "                for k in range(dets.shape[0]):\n",
    "                    f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\\n'.\n",
    "                            format(index[1], dets[k, -1],\n",
    "                                   dets[k, 0] + 1, dets[k, 1] + 1,\n",
    "                                   dets[k, 2] + 1, dets[k, 3] + 1))\n",
    "\n",
    "\n",
    "def do_python_eval(output_dir='output'):\n",
    "    cachedir = os.path.join(devkit_path, 'annotations_cache')\n",
    "    aps = []\n",
    "    \n",
    "    if not os.path.isdir(output_dir):\n",
    "        os.mkdir(output_dir)\n",
    "        \n",
    "    for i, cls in enumerate(labelmap):\n",
    "        filename = get_voc_results_file_template(set_type, cls)\n",
    "        rec, prec, ap = voc_eval(\n",
    "           filename, annopath, imgsetpath.format(set_type), cls, cachedir,\n",
    "           ovthresh=0.5)\n",
    "        aps += [ap]\n",
    "        print('AP for ',cls,': ', ap)\n",
    "        with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:\n",
    "            pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)\n",
    "    print('Mean Average Precision(mAP): ',np.mean(aps))\n",
    "    print('Results: ')\n",
    "    for ap in aps:\n",
    "        print(ap)\n",
    "    print(aps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def voc_ap(rec, prec):\n",
    "    \"\"\"\n",
    "    Create Metric\n",
    "    \"\"\"\n",
    "    # 11 point metric\n",
    "    ap = 0.\n",
    "    for t in np.arange(0., 1.1, 0.1):\n",
    "        if np.sum(rec >= t) == 0:\n",
    "            p = 0\n",
    "        else:\n",
    "            p = np.max(prec[rec >= t])\n",
    "        ap = ap + p / 11.\n",
    "    return ap\n",
    "\n",
    "\n",
    "def voc_eval(detpath, annopath, imagesetfile, classname, cachedir, ovthresh=0.5):\n",
    "\n",
    "    \"\"\"\n",
    "    Top level function that does the PASCAL VOC evaluation.\n",
    "    \"\"\"\n",
    "# detections: detpath\n",
    "# annotations: annopath\n",
    "# imagesetfile: each line an image's name\n",
    "# cachedir: caches the annotations\n",
    "\n",
    "    if not os.path.isdir(cachedir):\n",
    "        os.mkdir(cachedir)\n",
    "    cachefile = os.path.join(cachedir, 'annots.pkl')\n",
    "    # read list of images\n",
    "    with open(imagesetfile, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    imagenames = [x.strip() for x in lines]\n",
    "    if not os.path.isfile(cachefile):\n",
    "        # load annots\n",
    "        recs = {}\n",
    "        for i, imagename in enumerate(imagenames):\n",
    "            recs[imagename] = parse_rec(annopath % (imagename))\n",
    "            if i % 100 == 0:\n",
    "                print('Reading annotation for {:d}/{:d}'.format(\n",
    "                   i + 1, len(imagenames)))\n",
    "        # save\n",
    "        print('Saving cached annotations to {:s}'.format(cachefile))\n",
    "        with open(cachefile, 'wb') as f:\n",
    "            pickle.dump(recs, f)\n",
    "    else:\n",
    "        # load\n",
    "        with open(cachefile, 'rb') as f:\n",
    "            recs = pickle.load(f)\n",
    "\n",
    "    # extract gt objects for this class\n",
    "    class_recs = {}\n",
    "    npos = 0\n",
    "    for imagename in imagenames:\n",
    "        R = [obj for obj in recs[imagename] if obj['name'] == classname]\n",
    "        bbox = np.array([x['bbox'] for x in R])\n",
    "        difficult = np.array([x['difficult'] for x in R]).astype(np.bool)\n",
    "        det = [False] * len(R)\n",
    "        npos = npos + sum(~difficult)\n",
    "        class_recs[imagename] = {'bbox': bbox,\n",
    "                                 'difficult': difficult,\n",
    "                                 'det': det}\n",
    "\n",
    "    # read dets\n",
    "    detfile = detpath.format(classname)\n",
    "    with open(detfile, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    if any(lines) == 1:\n",
    "\n",
    "        splitlines = [x.strip().split(' ') for x in lines]\n",
    "        image_ids = [x[0] for x in splitlines]\n",
    "        confidence = np.array([float(x[1]) for x in splitlines])\n",
    "        BB = np.array([[float(z) for z in x[2:]] for x in splitlines])\n",
    "\n",
    "        # sort by confidence\n",
    "        sorted_ind = np.argsort(-confidence)\n",
    "        sorted_scores = np.sort(-confidence)\n",
    "        BB = BB[sorted_ind, :]\n",
    "        image_ids = [image_ids[x] for x in sorted_ind]\n",
    "\n",
    "        # go down dets and mark TPs and FPs\n",
    "        nd = len(image_ids)\n",
    "        tp = np.zeros(nd)\n",
    "        fp = np.zeros(nd)\n",
    "        for d in range(nd):\n",
    "            R = class_recs[image_ids[d]]\n",
    "            bb = BB[d, :].astype(float)\n",
    "            ovmax = -np.inf\n",
    "            BBGT = R['bbox'].astype(float)\n",
    "            if BBGT.size > 0:\n",
    "                # compute overlaps\n",
    "                # intersection\n",
    "                ixmin = np.maximum(BBGT[:, 0], bb[0])\n",
    "                iymin = np.maximum(BBGT[:, 1], bb[1])\n",
    "                ixmax = np.minimum(BBGT[:, 2], bb[2])\n",
    "                iymax = np.minimum(BBGT[:, 3], bb[3])\n",
    "                iw = np.maximum(ixmax - ixmin, 0.)\n",
    "                ih = np.maximum(iymax - iymin, 0.)\n",
    "                inters = iw * ih\n",
    "                uni = ((bb[2] - bb[0]) * (bb[3] - bb[1]) +\n",
    "                       (BBGT[:, 2] - BBGT[:, 0]) *\n",
    "                       (BBGT[:, 3] - BBGT[:, 1]) - inters)\n",
    "                overlaps = inters / uni\n",
    "                ovmax = np.max(overlaps)\n",
    "                jmax = np.argmax(overlaps)\n",
    "\n",
    "            if ovmax > ovthresh:\n",
    "                if not R['difficult'][jmax]:\n",
    "                    if not R['det'][jmax]:\n",
    "                        tp[d] = 1.\n",
    "                        R['det'][jmax] = 1\n",
    "                    else:\n",
    "                        fp[d] = 1.\n",
    "            else:\n",
    "                fp[d] = 1.\n",
    "\n",
    "        # precision recall\n",
    "        fp = np.cumsum(fp)\n",
    "        tp = np.cumsum(tp)\n",
    "        rec = tp / float(npos)\n",
    "        \n",
    "        # ground truth\n",
    "        prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)\n",
    "        ap = voc_ap(rec, prec)\n",
    "    else:\n",
    "        rec = -1.\n",
    "        prec = -1.\n",
    "        ap = -1.\n",
    "\n",
    "    return rec, prec, ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_net(eval_save_folder, net, cuda, dataset, transform, top_k,\n",
    "             im_size=300, thresh=0.05):\n",
    "    num_images = len(dataset)\n",
    "    all_boxes = [[[] for _ in range(num_images)]\n",
    "                 for _ in range(len(labelmap)+1)]\n",
    "\n",
    "    # timers\n",
    "    _t = {'im_detect': Timer(), 'misc': Timer()}\n",
    "    \n",
    "    output_dir = \"out/\"\n",
    "    if not os.path.isdir(output_dir):\n",
    "        os.mkdir(output_dir)\n",
    "    det_file = os.path.join(output_dir, 'detections.pkl')\n",
    "\n",
    "    for i in range(num_images):\n",
    "        im, gt, h, w = dataset.pull_item(i)\n",
    "\n",
    "        x = im.unsqueeze(0)\n",
    "        if cuda=='cuda':\n",
    "            x = x.cuda()\n",
    "        _t['im_detect'].tic()\n",
    "        detections = net(x).data\n",
    "        detect_time = _t['im_detect'].toc(average=False)\n",
    "\n",
    "        # skip j = 0, because it's the background class\n",
    "        for j in range(1, detections.size(1)):\n",
    "            dets = detections[0, j, :]\n",
    "            mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()\n",
    "            dets = torch.masked_select(dets, mask).view(-1, 5)\n",
    "            if dets.size(0) == 0:\n",
    "                continue\n",
    "            boxes = dets[:, 1:]\n",
    "            boxes[:, 0] *= w\n",
    "            boxes[:, 2] *= w\n",
    "            boxes[:, 1] *= h\n",
    "            boxes[:, 3] *= h\n",
    "            scores = dets[:, 0].cpu().numpy()\n",
    "            cls_dets = np.hstack((boxes.cpu().numpy(),\n",
    "                                  scores[:, np.newaxis])).astype(np.float32,\n",
    "                                                                 copy=False)\n",
    "            all_boxes[j][i] = cls_dets\n",
    "\n",
    "        print('image_detect: {:d}/{:d} {:.3f}s'.format(i + 1,\n",
    "                                                    num_images, detect_time))\n",
    "\n",
    "    with open(det_file, 'wb') as f:\n",
    "        pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "    print('\\nEvaluating detections')\n",
    "    evaluate_detections(all_boxes, output_dir, dataset)\n",
    "    \n",
    "def evaluate_detections(box_list, output_dir, dataset):\n",
    "    write_voc_results_file(box_list, dataset)\n",
    "    do_python_eval(output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EVALUATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Initialize pointers\r\n",
      "basenet = 'weights/vgg16_reducedfc.pth'\r\n",
      "data_set = 'VOC'\r\n",
      "dataset_root = voc_root = '//datasets/ee285f-public/PascalVOC2012/'\r\n",
      "save_folder = 'trained_weights/'\r\n",
      "#trained_model = 'ssd_pretrained.pth'\r\n",
      "eval_save_folder = 'eval/'\r\n",
      "devkit_path = 'devkit_path/'\r\n",
      "output_dir = \"out/\"\r\n",
      "\r\n",
      "#Run related metaparameters\r\n",
      "\r\n",
      "batch_size = 32\r\n",
      "resume = None\r\n",
      "\r\n",
      "#Optimization metaparameters\r\n",
      "lr = 1e-3\r\n",
      "momentum = 0.9\r\n",
      "weight_decay = 5e-4\r\n",
      "gamma = 0.1\r\n",
      "    \r\n",
      "confidence_threshold = 0.01\r\n",
      "top_k = 5\r\n",
      "cleanup = True\r\n",
      "\r\n",
      "YEAR = '2012'\r\n",
      "dataset_mean = (104, 117, 123)\r\n",
      "set_type = 'train'\r\n",
      "trained_model = 'weights/ssd_pretrained.pth'\r\n"
     ]
    }
   ],
   "source": [
    "!cat opts.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Please run the below cell twice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished loading model!\n",
      "image_detect: 1/4952 0.876s\n",
      "image_detect: 2/4952 0.132s\n",
      "image_detect: 3/4952 0.055s\n",
      "image_detect: 4/4952 0.070s\n",
      "image_detect: 5/4952 0.038s\n",
      "image_detect: 6/4952 0.037s\n",
      "image_detect: 7/4952 0.027s\n",
      "image_detect: 8/4952 0.016s\n",
      "image_detect: 9/4952 0.013s\n",
      "image_detect: 10/4952 0.101s\n",
      "image_detect: 11/4952 0.026s\n",
      "image_detect: 12/4952 0.012s\n",
      "image_detect: 13/4952 0.024s\n",
      "image_detect: 14/4952 0.046s\n",
      "image_detect: 15/4952 0.014s\n",
      "image_detect: 16/4952 0.013s\n",
      "image_detect: 17/4952 0.011s\n",
      "image_detect: 18/4952 0.025s\n",
      "image_detect: 19/4952 0.018s\n",
      "image_detect: 20/4952 0.021s\n",
      "image_detect: 21/4952 0.024s\n",
      "image_detect: 22/4952 0.027s\n",
      "image_detect: 23/4952 0.014s\n",
      "image_detect: 24/4952 0.014s\n",
      "image_detect: 25/4952 0.028s\n",
      "image_detect: 26/4952 0.048s\n",
      "image_detect: 27/4952 0.094s\n",
      "image_detect: 28/4952 0.023s\n",
      "image_detect: 29/4952 0.022s\n",
      "image_detect: 30/4952 0.058s\n",
      "image_detect: 31/4952 0.056s\n",
      "image_detect: 32/4952 0.019s\n",
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      "image_detect: 39/4952 0.017s\n",
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      "image_detect: 46/4952 0.034s\n",
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      "image_detect: 48/4952 0.055s\n",
      "image_detect: 49/4952 0.039s\n",
      "image_detect: 50/4952 0.014s\n",
      "image_detect: 51/4952 0.080s\n",
      "image_detect: 52/4952 0.019s\n",
      "image_detect: 53/4952 0.052s\n",
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      "image_detect: 56/4952 0.086s\n",
      "image_detect: 57/4952 0.056s\n",
      "image_detect: 58/4952 0.044s\n",
      "image_detect: 59/4952 0.042s\n",
      "image_detect: 60/4952 0.080s\n",
      "image_detect: 61/4952 0.024s\n",
      "image_detect: 62/4952 0.032s\n",
      "image_detect: 63/4952 0.097s\n",
      "image_detect: 64/4952 0.053s\n",
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      "image_detect: 67/4952 0.166s\n",
      "image_detect: 68/4952 0.156s\n",
      "image_detect: 69/4952 0.033s\n",
      "image_detect: 70/4952 0.061s\n",
      "image_detect: 71/4952 0.042s\n",
      "image_detect: 72/4952 0.021s\n",
      "image_detect: 73/4952 0.097s\n",
      "image_detect: 74/4952 0.020s\n",
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      "image_detect: 76/4952 0.025s\n",
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      "image_detect: 78/4952 0.097s\n",
      "image_detect: 79/4952 0.035s\n",
      "image_detect: 80/4952 0.054s\n",
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      "\n",
      "Evaluating detections\n",
      "Writing aeroplane VOC results file\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:18: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Writing bicycle VOC results file\n",
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      "Reading annotation for 2501/4952\n",
      "Reading annotation for 2601/4952\n",
      "Reading annotation for 2701/4952\n",
      "Reading annotation for 2801/4952\n",
      "Reading annotation for 2901/4952\n",
      "Reading annotation for 3001/4952\n",
      "Reading annotation for 3101/4952\n",
      "Reading annotation for 3201/4952\n",
      "Reading annotation for 3301/4952\n",
      "Reading annotation for 3401/4952\n",
      "Reading annotation for 3501/4952\n",
      "Reading annotation for 3601/4952\n",
      "Reading annotation for 3701/4952\n",
      "Reading annotation for 3801/4952\n",
      "Reading annotation for 3901/4952\n",
      "Reading annotation for 4001/4952\n",
      "Reading annotation for 4101/4952\n",
      "Reading annotation for 4201/4952\n",
      "Reading annotation for 4301/4952\n",
      "Reading annotation for 4401/4952\n",
      "Reading annotation for 4501/4952\n",
      "Reading annotation for 4601/4952\n",
      "Reading annotation for 4701/4952\n",
      "Reading annotation for 4801/4952\n",
      "Reading annotation for 4901/4952\n",
      "Saving cached annotations to devkit_path/Clean/annotations_cache/annots.pkl\n",
      "AP for  aeroplane :  0.8172067227584159\n",
      "AP for  bicycle :  0.8544087543032679\n",
      "AP for  bird :  0.7571878065529903\n",
      "AP for  boat :  0.6958081268465254\n",
      "AP for  bottle :  0.4990436632399726\n",
      "AP for  bus :  0.847963849998348\n",
      "AP for  car :  0.8571707873816133\n",
      "AP for  cat :  0.8736534193723002\n",
      "AP for  chair :  0.6142116241960403\n",
      "AP for  cow :  0.8232509665290421\n",
      "AP for  diningtable :  0.7917483402653241\n",
      "AP for  dog :  0.8558595676293885\n",
      "AP for  horse :  0.8709121152099316\n",
      "AP for  motorbike :  0.8473813290961792\n",
      "AP for  person :  0.7893046165958839\n",
      "AP for  pottedplant :  0.5000069712617657\n",
      "AP for  sheep :  0.7744717626179469\n",
      "AP for  sofa :  0.7911820451931647\n",
      "AP for  train :  0.8616088216646566\n",
      "AP for  tvmonitor :  0.7631161816188186\n",
      "Mean Average Precision(mAP):  0.7742748736165789\n",
      "Results: \n",
      "0.8172067227584159\n",
      "0.8544087543032679\n",
      "0.7571878065529903\n",
      "0.6958081268465254\n",
      "0.4990436632399726\n",
      "0.847963849998348\n",
      "0.8571707873816133\n",
      "0.8736534193723002\n",
      "0.6142116241960403\n",
      "0.8232509665290421\n",
      "0.7917483402653241\n",
      "0.8558595676293885\n",
      "0.8709121152099316\n",
      "0.8473813290961792\n",
      "0.7893046165958839\n",
      "0.5000069712617657\n",
      "0.7744717626179469\n",
      "0.7911820451931647\n",
      "0.8616088216646566\n",
      "0.7631161816188186\n",
      "[0.8172067227584159, 0.8544087543032679, 0.7571878065529903, 0.6958081268465254, 0.4990436632399726, 0.847963849998348, 0.8571707873816133, 0.8736534193723002, 0.6142116241960403, 0.8232509665290421, 0.7917483402653241, 0.8558595676293885, 0.8709121152099316, 0.8473813290961792, 0.7893046165958839, 0.5000069712617657, 0.7744717626179469, 0.7911820451931647, 0.8616088216646566, 0.7631161816188186]\n"
     ]
    }
   ],
   "source": [
    "# VOC2007 Testset was downloaded\n",
    "dataset_root = voc_root = '../../VOCdevkit/VOC2007/'\n",
    "\n",
    "annopath = os.path.join(voc_root, 'Annotations', '%s.xml')\n",
    "imgpath = os.path.join(voc_root, 'JPEGImages', '%s.jpg')\n",
    "imgsetpath = os.path.join(voc_root, 'ImageSets',\n",
    "                          'Main', '{:s}.txt')\n",
    "YEAR = '2007'\n",
    "set_type = 'test'\n",
    "\n",
    "devkit_path = 'devkit_path/Clean/'\n",
    "if not os.path.isdir(devkit_path):\n",
    "    os.mkdir(devkit_path)\n",
    "    \n",
    "dataset_mean = (104, 117, 123)\n",
    "\n",
    "num_classes = len(labelmap) + 1                      # +1 for background\n",
    "net = build_ssd('test', 300, num_classes) # initialize SSD\n",
    "\n",
    "if device=='cuda':\n",
    "    net.load_state_dict(torch.load(trained_model))\n",
    "else:\n",
    "    net.load_state_dict(torch.load(trained_model,  map_location='cpu'))\n",
    "    \n",
    "net.eval()\n",
    "print('Finished loading model!')\n",
    "    # load data\n",
    "\n",
    "dataset = VOCDetection(voc_root, [(YEAR, set_type)],\n",
    "                BaseTransform(300, dataset_mean),VOCAnnotationTransform())\n",
    "\n",
    "if device=='cuda':\n",
    "    net = net.cuda()\n",
    "    cudnn.benchmark = True\n",
    "\n",
    "# evaluation   \n",
    "test_net(save_folder, net, device, dataset,\n",
    "            BaseTransform(net.size, dataset_mean), top_k, 300,\n",
    "             thresh=confidence_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Average Precision(mAP):  0.7742748736165789"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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