vidor_eval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "# Choose to evaluate the results using commands in (1) or (2):\n",
    "\n",
    "# ------------------------------------------------------------------------------------ #\n",
    "\n",
    "### (1) Evaluating methods with GT bboxes: load validation results\n",
    "\n",
    "### Baseline (Image-based) \n",
    "result_dir = './output/BASELINE_32x2_R50_SHORT_SCRATCH_EVAL_GT'\n",
    "\n",
    "### Baseline (SlowFast)\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT'\n",
    "\n",
    "### Baseline (SlowFast) + Trajectory\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory'\n",
    "\n",
    "### Baseline (SlowFast) + Trajectory + (BN&FCs&ReLU)\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-proj_fcs-norm_feat-sep_sub_obj_fcs-bn_10xlr'\n",
    "\n",
    "### Baseline (SlowFast) + relativity feature\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_relativity-feat'\n",
    "\n",
    "### Baseline (SlowFast) + Trajectory + human pose\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-human_pose'\n",
    "\n",
    "### Baseline (SlowFast) + Trajectory + human pose + (BN&FCs&ReLU)\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-proj_fcs-norm_feat-sep_sub_obj_fcs-bn_10xlr-human_pose'\n",
    "\n",
    "### Baseline (SlowFast) + Trajectory + ToI Pooling\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-toipool'\n",
    "\n",
    "### Baseline (SlowFast) + Trajectory + Spatial Configuration Module\n",
    "# result_dir = 'output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-spa_conf'\n",
    "\n",
    "### Baseline (SlowFast) + Trajectory + ToI Pooling + Spatial Configuration Module\n",
    "# result_dir = 'output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-toipool-spa_conf'\n",
    "\n",
    "# ------------------------------------------------------------------------------------ #\n",
    "\n",
    "### (1-1) Evaluating above methods with 22,808 examples in total\n",
    "result_json_name = 'all_results_vidor_checkpoint_epoch_00020.pyth_proposal_less-168-examples.json'\n",
    "\n",
    "# ------------------------------------------------------------------------------------ #\n",
    "\n",
    "### (2) OR evaluating methods WITH DETECTED BBOXES loaded from vidvrd-mff: load validation results\n",
    "\n",
    "# Baseline (Image-based) \n",
    "# result_dir = 'output/BASELINE_32x2_R50_SHORT_SCRATCH_EVAL_NONGT'\n",
    "# result_json_name = 'all_results_vidor_checkpoint_epoch_00020.pyth_proposal.json'\n",
    "\n",
    "# Baseline (SlowFast) \n",
    "# result_dir = 'output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT'\n",
    "# result_json_name = 'all_results_vidor_checkpoint_epoch_00020.pyth_proposal.json'\n",
    "\n",
    "# Baseline (SlowFast) & trajectory\n",
    "# result_dir = 'output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT_trajectory'\n",
    "# result_json_name = 'all_results_vidor_checkpoint_epoch_00020.pyth_proposal.json'\n",
    "\n",
    "# Baseline (SlowFast) & trajectory & ToI Pooling\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT_trajectory-toipool'\n",
    "# result_json_name = 'all_results_vidor_checkpoint_epoch_00020.pyth_proposal.json'\n",
    "\n",
    "# Baseline (SlowFast) & trajectory & Spatial Configuation Module\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT_trajectory-spa_conf'\n",
    "# result_json_name = 'all_results_vidor_checkpoint_epoch_00020.pyth_proposal_less-168-examples.json'\n",
    "\n",
    "# Baseline (SlowFast) & trajectory + ToI POoling + Spatial Configuation Module\n",
    "# result_dir = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT_trajectory-toipool-spa_conf/'\n",
    "# result_json_name = 'all_results_vidor__proposal_less-168-examples.json'\n",
    "\n",
    "# ------------------------------------------------------------------------------------ #\n",
    "\n",
    "with open(os.path.join(result_dir, result_json_name), 'r') as f:\n",
    "    all_results = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Settings\n",
    "\n",
    "delete_less_than_25_instances = False # non-rare setting\n",
    "delete_more_than_25_instances = True # rare setting\n",
    "assert not (delete_less_than_25_instances and delete_more_than_25_instances)\n",
    "\n",
    "# For computing Temporal-mAP\n",
    "only_evaluate_temporal_predicates = False # should be True when visualizing correct prediction!\n",
    "\n",
    "### Settings for visualizing demo images purpose only\n",
    "is_demo = False\n",
    "is_demo_incorrect_hois = False\n",
    "is_demo_save_imgs = False\n",
    "is_demo_show_imgs = False\n",
    "is_demo_top_10 = False # may be used with incorrect hois\n",
    "\n",
    "only_demo_specific_videos = False\n",
    "only_demo_specific_frames = False\n",
    "to_demo_video_names = []\n",
    "to_demo_frame_names = []\n",
    "\n",
    "if is_demo:\n",
    "#     assert only_evaluate_temporal_predicates ^ is_demo_incorrect_hois # sanity check!\n",
    "    demo_vis_name = 'vidhoi_2D' # 'vidor_TP'\n",
    "#     if is_demo_incorrect_hois:\n",
    "#         demo_vis_name += '_wrong'\n",
    "    if only_evaluate_temporal_predicates:\n",
    "        demo_vis_name += '_onlytemp'\n",
    "    if only_demo_specific_videos:\n",
    "        to_demo_video_names = [\n",
    "            '1110/2584172238',\n",
    "        ]\n",
    "        demo_vis_name += '_specvids'\n",
    "    elif only_demo_specific_frames:\n",
    "        to_demo_frame_names = [\n",
    "#             '0080/9439876127',\n",
    "#             '1009/2975784201_000106',\n",
    "#             '1009/2975784201_000136',\n",
    "#             '1009/2975784201_000166',\n",
    "#             '1009/2975784201_000196',\n",
    "#             '1009/2975784201_000226',\n",
    "#             '1009/2975784201_000256',\n",
    "#             '1009/4488998616',\n",
    "#             '1009/4896969617_000016',\n",
    "#             '1009/4896969617_000046',\n",
    "#             '1009/4896969617_000076',\n",
    "#             '1009/4896969617_000226',\n",
    "#             '1009/4896969617_000256',\n",
    "#             '1009/4896969617_000286',\n",
    "#             '1017/4518113460_000376',\n",
    "#             '1017/4518113460_000406',\n",
    "#             '1017/4518113460_000436',\n",
    "#             '1017/4518113460_000466',\n",
    "#             '1017/4518113460_000496',\n",
    "#             '1017/2623954636_000076',\n",
    "#             '1017/2623954636_000136',\n",
    "#             '1017/2623954636_000166',\n",
    "#             '1017/2623954636_000196',\n",
    "#             '1017/2623954636_000226',\n",
    "#             '1017/2623954636_000256',\n",
    "#             '1017/2623954636_000706',\n",
    "#             '1017/2623954636_000736',\n",
    "#             '1017/2623954636_000856',\n",
    "#             '1017/2623954636_000886',\n",
    "#             '1017/2623954636_000916',\n",
    "#             '1009/7114553643_000736',\n",
    "#             '1009/7114553643_000766',\n",
    "#             '1009/7114553643_000796',\n",
    "#             '1009/7114553643_000826',\n",
    "#             '1009/7114553643_000856',\n",
    "#             '1009/7114553643_000886',\n",
    "#             '1009/7114553643_000916',\n",
    "#             '1018/3155382178',\n",
    "#             '1101/6305304857',\n",
    "#             '1101/6443512089_000676',\n",
    "#             '1101/6443512089_000706',\n",
    "#             '1101/6443512089_000736',\n",
    "#             '1101/6443512089_000766',\n",
    "#             '1101/6443512089_000796',\n",
    "#             '1101/6443512089_000826',\n",
    "#             '1101/6443512089_000856',\n",
    "            '1110/2584172238_000202',\n",
    "            '1110/2584172238_000226',\n",
    "            '1110/2584172238_000250',\n",
    "            '1110/2584172238_000274',\n",
    "            '1110/2584172238_000298',\n",
    "            '1110/2584172238_000322',\n",
    "            '1110/2584172238_000346',\n",
    "        ]\n",
    "        demo_vis_name += '_specvids'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('slowfast/datasets/vidor/idx_to_pred.pkl', 'rb') as f:\n",
    "    idx_to_pred = pickle.load(f)\n",
    "with open('slowfast/datasets/vidor/idx_to_obj.pkl', 'rb') as f:\n",
    "    idx_to_obj = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('slowfast/datasets/vidor/pred_to_idx.pkl', 'rb') as f:\n",
    "    pred_to_idx = pickle.load(f)\n",
    "# pred_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "temporal_predicates = [\n",
    "    'towards',\n",
    "    'away',\n",
    "    'pull',\n",
    "    'caress',\n",
    "    'push',\n",
    "    'press',\n",
    "    'wave',\n",
    "    'hit',\n",
    "    'lift',\n",
    "    'pat',\n",
    "    'grab',\n",
    "    'chase',\n",
    "    'release',\n",
    "    'wave_hand_to',\n",
    "    'squeeze',\n",
    "    'kick',\n",
    "    'shout_at',\n",
    "    'throw',\n",
    "    'smell',\n",
    "    'knock',\n",
    "    'lick',\n",
    "    'open',\n",
    "    'close',\n",
    "    'get_on',\n",
    "    'get_off',\n",
    "]\n",
    "\n",
    "if only_evaluate_temporal_predicates:\n",
    "    temporal_predicates_idx = [pred_to_idx[pred] for pred in temporal_predicates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['preds_score', 'preds', 'preds_bbox_pair_ids', 'proposal_scores', 'proposal_boxes', 'proposal_classes', 'gt_boxes', 'gt_action_labels', 'gt_obj_classes', 'gt_bbox_pair_ids'])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_results[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22808\n"
     ]
    }
   ],
   "source": [
    "# Visualization\n",
    "import json\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "json_file = './output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-spa_conf/all_results_vidor_checkpoint_epoch_00020.pyth_proposal_less-168-examples_demo-all.json'\n",
    "with open(json_file, 'r') as f:\n",
    "    res = json.load(f)\n",
    "print(len(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "idx: 17227\n",
      "dict_keys(['preds_score', 'preds', 'preds_bbox_pair_ids', 'proposal_scores', 'proposal_boxes', 'proposal_classes', 'gt_boxes', 'gt_action_labels', 'gt_obj_classes', 'gt_bbox_pair_ids', 'orig_video_idx'])\n",
      "1027/5042598042/5042598042_000061\n",
      "[[0.0, 112.21562194824219, 15.365625381469727, 185.7843780517578, 220.24063110351562], [0.0, 32.12812423706055, 13.037500381469727, 87.0718765258789, 191.3718719482422]]\n",
      "slowfast/datasets/vidor/frames/1027/5042598042/5042598042_000061.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "idx = random.randint(0, len(res))\n",
    "# idx = 329\n",
    "print('idx:', idx)\n",
    "print(res[idx].keys())\n",
    "print(res[idx]['orig_video_idx'][0])\n",
    "\n",
    "print(res[idx]['gt_boxes'])\n",
    "\n",
    "img_path = 'slowfast/datasets/vidor/frames/' + res[idx]['orig_video_idx'][0] + '.jpg'\n",
    "print(img_path)\n",
    "\n",
    "img = plt.imread(img_path)\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "proposal_scores [1.0, 1.0]\n",
      "proposal_scores [1.0, 1.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import math\n",
    "# from slowfast.datasets.cv2_transform import scale\n",
    "\n",
    "def scale(size, image):\n",
    "    \"\"\"\n",
    "    Scale the short side of the image to size.\n",
    "    Args:\n",
    "        size (int): size to scale the image.\n",
    "        image (array): image to perform short side scale. Dimension is\n",
    "            `height` x `width` x `channel`.\n",
    "    Returns:\n",
    "        (ndarray): the scaled image with dimension of\n",
    "            `height` x `width` x `channel`.\n",
    "    \"\"\"\n",
    "    height = image.shape[0]\n",
    "    width = image.shape[1]\n",
    "    if (width <= height and width == size) or (\n",
    "        height <= width and height == size\n",
    "    ):\n",
    "        return image\n",
    "    new_width = size\n",
    "    new_height = size\n",
    "    if width < height:\n",
    "        new_height = int(math.floor((float(height) / width) * size))\n",
    "    else:\n",
    "        new_width = int(math.floor((float(width) / height) * size))\n",
    "    img = cv2.resize(\n",
    "        image, (new_width, new_height), interpolation=cv2.INTER_LINEAR\n",
    "    )\n",
    "    return img\n",
    "\n",
    "def vis_detections_allclss(im, dets, dets_clss, vidor_clss, thresh=0.5, \n",
    "                           proposal_scores=None, sub_obj_pair=False, pred_cls=-1):\n",
    "    \"\"\"Visual debugging of detections.\"\"\"\n",
    "    for i in range(len(dets)):\n",
    "        bbox = tuple(int(np.round(x)) for x in dets[i])\n",
    "        class_name = vidor_clss[int(dets_clss[i])]\n",
    "        if sub_obj_pair: # if evaluating on HOI pair\n",
    "            class_name = class_name + '_s' if i == 0 else class_name + '_o'\n",
    "        \n",
    "        if proposal_scores is not None:\n",
    "            print('proposal_scores', proposal_scores)\n",
    "            score = proposal_scores[i]\n",
    "            if score > thresh:\n",
    "#                 print(bbox)\n",
    "                cv2.rectangle(im, bbox[0:2], bbox[2:4], (0, 204, 0), 2)\n",
    "#                 print(class_name, bbox, score)\n",
    "                cv2.putText(im, '%s: %.3f' % (class_name, score), (bbox[0], bbox[1] + 15), cv2.FONT_HERSHEY_PLAIN,\n",
    "                            1.0, (0, 0, 255), thickness=2)\n",
    "        else:\n",
    "            cv2.rectangle(im, bbox[0:2], bbox[2:4], (0, 204, 0), 2)\n",
    "#             print(class_name, bbox)\n",
    "            cv2.putText(im, '%s' % (class_name), (bbox[0], bbox[1] + 15), cv2.FONT_HERSHEY_PLAIN,\n",
    "                        1.0, (0, 0, 255), thickness=2)\n",
    "    \n",
    "    if pred_cls != -1:\n",
    "        pred_name = idx_to_pred[pred_cls]\n",
    "        box1_x1y1 = list(int(np.round(x)) for x in dets[0])[:2]\n",
    "        box2_x1y1 = list(int(np.round(x)) for x in dets[1])[:2]\n",
    "        box1_box2_mid = (np.array(box1_x1y1) + np.array(box2_x1y1)) / 2\n",
    "        box1_box2_mid = tuple(int(np.round(x)) for x in box1_box2_mid)\n",
    "\n",
    "        cv2.line(im, tuple(box1_x1y1), tuple(box2_x1y1), (255, 0, 0), 2)\n",
    "        cv2.putText(im, pred_name, box1_box2_mid, cv2.FONT_HERSHEY_PLAIN,\n",
    "                            1.0, (0, 0, 255), thickness=2)\n",
    "#         cv.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),2,cv.LINE_AA)\n",
    "\n",
    "    return im\n",
    "\n",
    "vidor_classes = list(idx_to_obj.values())\n",
    "\n",
    "# img = cv2.resize(img, (224,224))\n",
    "img_vis = scale(224, img)\n",
    "\n",
    "# visualize gt boxes\n",
    "# img_vis = vis_detections_allclss(img, res[idx]['gt_boxes'], res[idx]['gt_obj_classes'], vidor_classes)\n",
    "\n",
    "# visualize proposal boxes (same as gt boxes when in ORACLE model)\n",
    "img_vis = vis_detections_allclss(img_vis, [x[1:] for x in res[idx]['proposal_boxes']], [x[1] for x in res[idx]['proposal_classes']], vidor_classes, 0.2, proposal_scores=[x[1] for x in res[idx]['proposal_scores']])\n",
    "\n",
    "# Can use all other result file as idx remains in the same order\n",
    "# img_vis = vis_detections_allclss(img_vis, [x[1:] for x in all_results[idx]['proposal_boxes']], [x[1] for x in all_results[idx]['proposal_classes']], vidor_classes, 0.2, proposal_scores=[x[1] for x in all_results[idx]['proposal_scores']])\n",
    "\n",
    "plt.imshow(img_vis)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vis_hoi(img_idx, sub_cls, pred_cls, obj_cls, gt_sub_box, gt_obj_box):\n",
    "    # img_path = 'slowfast/datasets/vidor/frames/' + res[img_idx]['orig_video_idx'][0] + '.jpg'\n",
    "    new_idx = f\"{int(res[img_idx]['orig_video_idx'][0].split('_')[-1])-15:06d}\"\n",
    "    frame_name = f\"{res[img_idx]['orig_video_idx'][0].split('_')[0] + '_' + new_idx}\"\n",
    "    img_path = f\"slowfast/datasets/vidor/frames/{frame_name}.jpg\"\n",
    "    \n",
    "    img = plt.imread(img_path)\n",
    "    img = scale(224, img)\n",
    "    img = vis_detections_allclss(img, [gt_sub_box, gt_obj_box], [sub_cls, obj_cls], vidor_classes, sub_obj_pair=True, pred_cls=pred_cls)\n",
    "    return img, '/'.join([frame_name.split('/')[0], frame_name.split('/')[2]]) # frame_name # '/'.join(frame_name.split('/')[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of preds_score: 4\n",
      "shape of preds: 4\n",
      "shape of preds_bbox_pair_ids: 4\n",
      "shape of proposal_scores: 5\n",
      "shape of proposal_boxes: 5\n",
      "shape of proposal_classes: 5\n",
      "shape of gt_boxes: 2\n",
      "shape of gt_action_labels: 1\n",
      "shape of gt_obj_classes: 2\n",
      "shape of gt_bbox_pair_ids: 1\n"
     ]
    }
   ],
   "source": [
    "idx = 0\n",
    "for key in all_results[idx].keys():\n",
    "    print(f'shape of {key}: {len(all_results[idx][key])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bbox_iou(boxA, boxB):\n",
    "    # determine the (x, y)-coordinates of the intersection rectangle\n",
    "    xA = max(boxA[0], boxB[0])\n",
    "    yA = max(boxA[1], boxB[1])\n",
    "    xB = min(boxA[2], boxB[2])\n",
    "    yB = min(boxA[3], boxB[3])\n",
    "\n",
    "    # compute the area of intersection rectangle\n",
    "    interArea = abs(max((xB - xA, 0)) * max((yB - yA), 0))\n",
    "    if interArea == 0:\n",
    "        return 0\n",
    "    \n",
    "    # compute the area of both the prediction and ground-truth\n",
    "    # rectangles\n",
    "    boxAArea = abs((boxA[2] - boxA[0]) * (boxA[3] - boxA[1]))\n",
    "    boxBArea = abs((boxB[2] - boxB[0]) * (boxB[3] - boxB[1]))\n",
    "\n",
    "    # compute the intersection over union by taking the intersection\n",
    "    # area and dividing it by the sum of prediction + ground-truth\n",
    "    # areas - the interesection area\n",
    "    iou = interArea / float(boxAArea + boxBArea - interArea)\n",
    "\n",
    "    # return the intersection over union value\n",
    "    return iou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 21758/21758 [00:00<00:00, 39332.13it/s]\n",
      "100%|██████████| 21758/21758 [00:36<00:00, 589.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Final] correct HOI/correct detection ratio: 0.007967\n",
      "[Final] correct detection/total detection ratio: 0.094499\n",
      "[Final] at_least_one_pair_bbox_detected ratio: 10254 / 21758 = 0.471275\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "tp, fp, scores, sum_gt = {}, {}, {}, {}\n",
    "\n",
    "# Construct dictionaries of triplet class \n",
    "for result in tqdm(all_results):\n",
    "    bbox_pair_ids = result['gt_bbox_pair_ids']\n",
    "    \n",
    "    for idx, action_label in enumerate(result['gt_action_labels']):\n",
    "        # action_label != 0.0\n",
    "        action_label_idxs = [i for i in range(50) if action_label[i] == 1.0]\n",
    "        for action_label_idx in action_label_idxs:\n",
    "            if only_evaluate_temporal_predicates and action_label_idx not in temporal_predicates_idx:\n",
    "                continue\n",
    "            \n",
    "            subject_label_idx = 0 # person idx = 0\n",
    "            object_label_idx = int(result['gt_obj_classes'][bbox_pair_ids[idx][1]][1])\n",
    "            triplet_class = (subject_label_idx, action_label_idx, object_label_idx)\n",
    "            \n",
    "            if triplet_class not in tp: # should also not exist in fp, scores & sum_gt\n",
    "                tp[triplet_class] = []\n",
    "                fp[triplet_class] = []\n",
    "                scores[triplet_class] = []\n",
    "                sum_gt[triplet_class] = 0\n",
    "            sum_gt[triplet_class] += 1\n",
    "            \n",
    "# delete triplet classes that have less than 25 instances\n",
    "if delete_less_than_25_instances or delete_more_than_25_instances:\n",
    "    triplet_classes_to_delete = []\n",
    "    for triplet_class, count in sum_gt.items():\n",
    "        if delete_less_than_25_instances and count < 25 or delete_more_than_25_instances and count >= 25:\n",
    "            triplet_classes_to_delete.append(triplet_class)\n",
    "    for triplet_class in triplet_classes_to_delete:\n",
    "        del tp[triplet_class], fp[triplet_class], scores[triplet_class], sum_gt[triplet_class]\n",
    "\n",
    "# Collect true positive, false positive & scores\n",
    "import math\n",
    "correct_det_count = correct_hoi_count = total_det_count = 0\n",
    "at_least_one_pair_bbox_detected_count = total_count = 0\n",
    "\n",
    "for img_idx, result in enumerate(tqdm(all_results)): # for each keyframe\n",
    "    if is_demo:\n",
    "        new_idx = f\"{int(res[img_idx]['orig_video_idx'][0].split('_')[-1])-15:06d}\"\n",
    "        frame_name = f\"{res[img_idx]['orig_video_idx'][0].split('_')[0] + '_' + new_idx}\"\n",
    "        frame_name = '/'.join([frame_name.split('/')[0], frame_name.split('/')[2]])\n",
    "        video_name = frame_name.split('_')[0]\n",
    "        \n",
    "        if only_demo_specific_videos and video_name not in to_demo_video_names:\n",
    "            continue\n",
    "        if only_demo_specific_frames and frame_name not in to_demo_frame_names:\n",
    "            continue\n",
    "#         print(f'Demoing {idx} / {len(all_results)} frame...')\n",
    "    preds_bbox_pair_ids = result['preds_bbox_pair_ids']\n",
    "    gt_bbox_pair_ids = result['gt_bbox_pair_ids']\n",
    "    gt_bbox_pair_matched = set()\n",
    "    \n",
    "    # take only top 100 confident triplets\n",
    "#     preds_scores = [\n",
    "#         (math.log(result['preds_score'][i][j]) + \\\n",
    "#          math.log(result['proposal_scores'][preds_bbox_pair_ids[i][0]][1]) + \\\n",
    "#          math.log(result['proposal_scores'][preds_bbox_pair_ids[i][1]][1]), i, j) \n",
    "#         for i in range(len(result['preds_score'])) for j in range(len(result['preds_score'][i]))\n",
    "#     ]\n",
    "    preds_scores = [\n",
    "        (math.log(result['preds_score'][i][j] if result['preds_score'][i][j] > 0 else 1e-300) + \\\n",
    "        math.log(result['proposal_scores'][preds_bbox_pair_ids[i][0]][1] if result['proposal_scores'][preds_bbox_pair_ids[i][0]][1] > 0 else 1e-300) + \\\n",
    "        math.log(result['proposal_scores'][preds_bbox_pair_ids[i][1]][1] if result['proposal_scores'][preds_bbox_pair_ids[i][1]][1] > 0 else 1e-300), i, j) \n",
    "        for i in range(len(result['preds_score'])) for j in range(len(result['preds_score'][i]))\n",
    "    ]\n",
    "    preds_scores.sort(reverse=True)\n",
    "    preds_scores = preds_scores[:10] if is_demo_top_10 else preds_scores[:100]\n",
    "    \n",
    "    at_least_one_pair_bbox_detected = False\n",
    "    \n",
    "    for score, i, j in preds_scores: # for each HOI prediction, i-th pair and j-th action\n",
    "        pred_sub_cls = int(result['proposal_classes'][preds_bbox_pair_ids[i][0]][1])\n",
    "        pred_obj_cls = int(result['proposal_classes'][preds_bbox_pair_ids[i][1]][1])\n",
    "        pred_rel_cls = j\n",
    "        \n",
    "        triplet_class = (pred_sub_cls, pred_rel_cls, pred_obj_cls)\n",
    "        if triplet_class not in tp:\n",
    "            continue\n",
    "        \n",
    "        pred_sub_box = result['proposal_boxes'][preds_bbox_pair_ids[i][0]][1:]\n",
    "        pred_obj_box = result['proposal_boxes'][preds_bbox_pair_ids[i][1]][1:]\n",
    "        is_match = False\n",
    "        max_ov = max_gt_id = 0\n",
    "        for k, gt_bbox_pair_id in enumerate(gt_bbox_pair_ids):  # for each ground truth HOI\n",
    "            gt_sub_cls = int(result['gt_obj_classes'][gt_bbox_pair_id[0]][1])\n",
    "            gt_obj_cls = int(result['gt_obj_classes'][gt_bbox_pair_id[1]][1])\n",
    "            gt_rel_cls = result['gt_action_labels'][k][j]\n",
    "            \n",
    "            gt_sub_box = result['gt_boxes'][gt_bbox_pair_id[0]][1:]\n",
    "            gt_obj_box = result['gt_boxes'][gt_bbox_pair_id[1]][1:]\n",
    "            sub_ov = bbox_iou(gt_sub_box, pred_sub_box)\n",
    "            obj_ov = bbox_iou(gt_obj_box, pred_obj_box)\n",
    "            \n",
    "            # import pdb; pdb.set_trace()\n",
    "            \n",
    "            if gt_sub_cls == pred_sub_cls and gt_obj_cls == pred_obj_cls and sub_ov >= 0.5 and obj_ov >= 0.5:\n",
    "                correct_det_count += 1\n",
    "                if not at_least_one_pair_bbox_detected:\n",
    "                    at_least_one_pair_bbox_detected = True\n",
    "                    at_least_one_pair_bbox_detected_count += 1\n",
    "                          \n",
    "                if gt_rel_cls == 1.0:\n",
    "                    if is_demo: #  and not is_demo_incorrect_hois:\n",
    "#                         print(f'Image idx: {img_idx}. Correct tripet: {idx_to_obj[pred_sub_cls]}, {idx_to_pred[j]}, {idx_to_obj[pred_obj_cls]}')\n",
    "\n",
    "                        sub_cls = idx_to_obj[pred_sub_cls].replace('/', 'or') + str(preds_bbox_pair_ids[i][0])\n",
    "                        pred_cls = idx_to_pred[j]\n",
    "                        obj_cls = idx_to_obj[pred_obj_cls].replace('/', 'or') + str(preds_bbox_pair_ids[i][1])\n",
    "            \n",
    "#                         new_idx = f\"{int(res[img_idx]['orig_video_idx'][0].split('_')[-1])-15:06d}\"\n",
    "#                         frame_name = f\"{res[img_idx]['orig_video_idx'][0].split('_')[0] + '_' + new_idx}\"\n",
    "#                         frame_name = '/'.join([frame_name.split('/')[0], frame_name.split('/')[2]])\n",
    "                        \n",
    "                        save_dir = f'demo/{demo_vis_name}/{frame_name.split(\"/\")[0]}'\n",
    "                        save_path = f'demo/{demo_vis_name}/{frame_name}' + '_' + f'{score:.2f}_{sub_cls}-{pred_cls}-{obj_cls}.jpg'\n",
    "                        \n",
    "                        if (is_demo_save_imgs and not os.path.exists(save_path)) or is_demo_show_imgs:\n",
    "                            img_vis, _ = vis_hoi(img_idx, pred_sub_cls, j, pred_obj_cls, gt_sub_box, gt_obj_box)\n",
    "                            plt.imshow(img_vis)\n",
    "                            plt.axis('off')\n",
    "                            if is_demo_save_imgs and not os.path.exists(save_path):\n",
    "#                                 print('frame_name:', frame_name)\n",
    "#                                 print('save_dir:', save_dir)\n",
    "#                                 print('save_path:', save_path)\n",
    "\n",
    "                                if not os.path.isdir(save_dir):\n",
    "                                    os.makedirs(save_dir)\n",
    "\n",
    "                                plt.savefig(save_path, bbox_inches='tight')\n",
    "                            if is_demo_show_imgs:\n",
    "                                print('save_path:', save_path)\n",
    "                                plt.show() # (Optional) Show the figure. THIS SHOULD COME AFTER plt.savefig\n",
    "                        \n",
    "                    is_match = True\n",
    "                    correct_hoi_count += 1\n",
    "                    min_ov_cur = min(sub_ov, obj_ov)\n",
    "                    if min_ov_cur > max_ov:\n",
    "                        max_ov = min_ov_cur\n",
    "                        max_gt_id = k\n",
    "#                     print(f'a pair of bbox correctly localized and detected predicate is correct!')\n",
    "#                     print(f'Current correct HOI/correct detection ratio: {correct_hoi_count/correct_det_count:5f}')\n",
    "                else: # Wrong predicate \n",
    "                    if is_demo and is_demo_incorrect_hois: # is_demo_incorrect_hois is True\n",
    "#                         print(f'Wrong tripet: {idx_to_obj[pred_sub_cls]}, {idx_to_pred[j]}, {idx_to_obj[pred_obj_cls]}')\n",
    "                        \n",
    "                        sub_cls = idx_to_obj[pred_sub_cls].replace('/', 'or') + str(preds_bbox_pair_ids[i][0])\n",
    "                        pred_cls = idx_to_pred[j]\n",
    "                        obj_cls = idx_to_obj[pred_obj_cls].replace('/', 'or') + str(preds_bbox_pair_ids[i][1])\n",
    "                \n",
    "#                         new_idx = f\"{int(res[img_idx]['orig_video_idx'][0].split('_')[-1])-15:06d}\"\n",
    "#                         frame_name = f\"{res[img_idx]['orig_video_idx'][0].split('_')[0] + '_' + new_idx}\"\n",
    "#                         frame_name = '/'.join([frame_name.split('/')[0], frame_name.split('/')[2]])\n",
    "                        \n",
    "                        save_dir = f'demo/{demo_vis_name}_wrong/{frame_name.split(\"/\")[0]}'\n",
    "                        save_path = f'demo/{demo_vis_name}_wrong/{frame_name}' + '_' + f'{score:.2f}_{sub_cls}-{pred_cls}-{obj_cls}.jpg'\n",
    "                            \n",
    "                        if (is_demo_save_imgs and not os.path.exists(save_path)) or is_demo_show_imgs:\n",
    "                            img_vis, _ = vis_hoi(img_idx, pred_sub_cls, j, pred_obj_cls, gt_sub_box, gt_obj_box)\n",
    "                            plt.imshow(img_vis)\n",
    "                            plt.axis('off')\n",
    "                            if is_demo_save_imgs and not os.path.exists(save_path):\n",
    "                                if not os.path.isdir(save_dir):\n",
    "                                    os.makedirs(save_dir)\n",
    "\n",
    "                                plt.savefig(save_path, bbox_inches='tight')\n",
    "                            if is_demo_show_imgs:\n",
    "                                print('save_path:', save_path)\n",
    "                                plt.show() # (Optional) Show the figure. THIS SHOULD COME AFTER plt.savefig\n",
    "                            \n",
    "#                     print(f'a pair of bbox correctly localized and detected but predicate is not correct!')\n",
    "#                     print(f'Current correct HOI/correct detection ratio: {correct_hoi_count/correct_det_count:5f}')\n",
    "            total_det_count += 1\n",
    "            # print(f'Current detection/total detection ratio: {correct_det_count/total_det_count:5f}')\n",
    "        \n",
    "        if is_match and max_gt_id not in gt_bbox_pair_matched:\n",
    "            tp[triplet_class].append(1)\n",
    "            fp[triplet_class].append(0)\n",
    "            gt_bbox_pair_matched.add(max_gt_id)\n",
    "        else:\n",
    "            tp[triplet_class].append(0)\n",
    "            fp[triplet_class].append(1)\n",
    "        scores[triplet_class].append(score)\n",
    "    total_count += 1\n",
    "#     print(f'Current at_least_one_pair_bbox_detected ratio: {at_least_one_pair_bbox_detected_count} / {total_count} = {at_least_one_pair_bbox_detected_count/total_count:5f}')\n",
    "\n",
    "print(f'[Final] correct HOI/correct detection ratio: {correct_hoi_count/correct_det_count:5f}')\n",
    "print(f'[Final] correct detection/total detection ratio: {correct_det_count/total_det_count:5f}')\n",
    "print(f'[Final] at_least_one_pair_bbox_detected ratio: {at_least_one_pair_bbox_detected_count} / {total_count} = {at_least_one_pair_bbox_detected_count/total_count:5f}')\n",
    "    \n",
    "# for result in all_results:\n",
    "#     bbox_pair_ids = result['bbox_pair_ids']\n",
    "#     import pdb; pdb.set_trace()\n",
    "# #     result['preds_score']\n",
    "    \n",
    "#     for idx, score in enumerate(result['preds_score']):\n",
    "#         pass\n",
    "        \n",
    "# print('tp:', tp)\n",
    "# print('fp:', fp)\n",
    "# print('scores:', scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "312it [00:00, 3951.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "mAP: 0.02094 max recall: 0.13703\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Compute mAP\n",
    "import numpy as np\n",
    "\n",
    "def voc_ap(rec, prec):\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",
    "ap = np.zeros(len(tp))\n",
    "max_recall = np.zeros(len(tp))\n",
    "\n",
    "for i, triplet_class in tqdm(enumerate(tp.keys())):\n",
    "    sum_gt_temp = sum_gt[triplet_class]\n",
    "\n",
    "    if sum_gt_temp == 0:\n",
    "        continue\n",
    "    tp_temp = np.asarray((tp[triplet_class]).copy())\n",
    "    fp_temp = np.asarray((fp[triplet_class]).copy())\n",
    "    res_num = len(tp_temp)\n",
    "    if res_num == 0:\n",
    "        continue\n",
    "    scores_temp = np.asarray(scores[triplet_class].copy())\n",
    "    sort_inds = np.argsort(-scores_temp) # sort decreasingly\n",
    "\n",
    "    fp_temp = fp_temp[sort_inds]\n",
    "    tp_temp = tp_temp[sort_inds]\n",
    "\n",
    "    fp_temp = np.cumsum(fp_temp) \n",
    "    tp_temp = np.cumsum(tp_temp)\n",
    "\n",
    "    rec = tp_temp / sum_gt_temp # recall\n",
    "    prec = tp_temp / (fp_temp + tp_temp) # precision\n",
    "\n",
    "    ap[i] = voc_ap(rec, prec)\n",
    "    max_recall[i] = np.max(rec)\n",
    "\n",
    "mAP = np.mean(ap[:])\n",
    "m_rec = np.mean(max_recall[:])\n",
    "\n",
    "print('------------------------------------------')\n",
    "print('mAP: {:.5f} max recall: {:.5f}'.format(mAP, m_rec))\n",
    "print('------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, next_to, screen/monitor): mAP = 0.05334675206679852, count = 9116 (0.522%)\n",
      "(person, towards, screen/monitor): mAP = 0.00077416500774165, count = 2534 (0.145%)\n",
      "(person, point_to, screen/monitor): mAP = 0.0, count = 748 (0.043%)\n",
      "(person, in_front_of, person): mAP = 0.18348969155450878, count = 226650 (12.984%)\n",
      "(person, watch, person): mAP = 0.04090909090909091, count = 107092 (6.135%)\n",
      "(person, next_to, person): mAP = 0.2866096220495521, count = 237779 (13.622%)\n",
      "(person, watch, tiger): mAP = 0.0, count = 53 (0.003%)\n",
      "(person, in_front_of, tiger): mAP = 0.1515151515151515, count = 55 (0.003%)\n",
      "(person, behind, person): mAP = 0.02748526900859433, count = 120238 (6.888%)\n",
      "(person, lean_on, sofa): mAP = 0.3199503016468455, count = 5951 (0.341%)\n",
      "(person, above, sofa): mAP = 0.028224716460010575, count = 5121 (0.293%)\n",
      "(person, hug, person): mAP = 0.004248088360237893, count = 34100 (1.953%)\n",
      "(person, speak_to, person): mAP = 0.0, count = 26149 (1.498%)\n",
      "(person, hold_hand_of, person): mAP = 2.9434706462389805e-05, count = 21543 (1.234%)\n",
      "(person, pull, person): mAP = 0.0, count = 7916 (0.453%)\n",
      "(person, next_to, cup): mAP = 0.11075324675324676, count = 4571 (0.262%)\n",
      "(person, next_to, bottle): mAP = 0.1955820868255798, count = 4756 (0.272%)\n",
      "(person, next_to, table): mAP = 0.2701380172123296, count = 17485 (1.002%)\n",
      "(person, next_to, chair): mAP = 0.015527010680459249, count = 7636 (0.437%)\n",
      "(person, press, table): mAP = 0.0, count = 1486 (0.085%)\n",
      "(person, next_to, dish): mAP = 0.15592635212888378, count = 1849 (0.106%)\n",
      "(person, towards, person): mAP = 0.017482517482517484, count = 30988 (1.775%)\n",
      "(person, towards, bottle): mAP = 0.0, count = 681 (0.039%)\n",
      "(person, away, table): mAP = 0.005681818181818182, count = 2632 (0.151%)\n",
      "(person, next_to, cellphone): mAP = 0.013636363636363636, count = 548 (0.031%)\n",
      "(person, hold, cellphone): mAP = 0.0012987012987012987, count = 265 (0.015%)\n",
      "(person, use, cellphone): mAP = 0.0, count = 64 (0.004%)\n",
      "(person, watch, cellphone): mAP = 0.0, count = 302 (0.017%)\n",
      "(person, next_to, toy): mAP = 0.12077107391029544, count = 17121 (0.981%)\n",
      "(person, hold, toy): mAP = 4.63821892393321e-05, count = 6430 (0.368%)\n",
      "(person, in_front_of, sofa): mAP = 0.18038270270245454, count = 12855 (0.736%)\n",
      "(person, lean_on, baby_walker): mAP = 0.5308585746712605, count = 1510 (0.087%)\n",
      "(person, inside, baby_walker): mAP = 0.0, count = 918 (0.053%)\n",
      "(person, push, baby_walker): mAP = 0.0, count = 71 (0.004%)\n",
      "(person, pull, baby_walker): mAP = 0.0, count = 54 (0.003%)\n",
      "(person, next_to, ski): mAP = 0.014024472917090367, count = 303 (0.017%)\n",
      "(person, above, ski): mAP = 0.0606060606060606, count = 201 (0.012%)\n",
      "(person, behind, guitar): mAP = 0.053188135337822955, count = 5012 (0.287%)\n",
      "(person, play(instrument), guitar): mAP = 0.296591604176496, count = 2077 (0.119%)\n",
      "(person, watch, guitar): mAP = 0.09090909090909091, count = 4137 (0.237%)\n",
      "(person, ride, ski): mAP = 0.01515151515151515, count = 181 (0.010%)\n",
      "(person, next_to, bat): mAP = 0.028409090909090908, count = 335 (0.019%)\n",
      "(person, hold, bat): mAP = 0.0, count = 153 (0.009%)\n",
      "(person, towards, bat): mAP = 0.0, count = 184 (0.011%)\n",
      "(person, away, person): mAP = 0.09090909090909091, count = 33817 (1.937%)\n",
      "(person, away, bat): mAP = 0.0, count = 194 (0.011%)\n",
      "(person, lean_on, table): mAP = 0.0014268242967794538, count = 6272 (0.359%)\n",
      "(person, behind, table): mAP = 0.00011012609437806288, count = 6531 (0.374%)\n",
      "(person, watch, camera): mAP = 0.0, count = 179 (0.010%)\n",
      "(person, use, camera): mAP = 0.0, count = 50 (0.003%)\n",
      "(person, lean_on, baby_seat): mAP = 0.2713981655910181, count = 3038 (0.174%)\n",
      "(person, above, baby_seat): mAP = 0.0037313432835820895, count = 2671 (0.153%)\n",
      "(person, next_to, guitar): mAP = 0.2489348048689341, count = 7549 (0.432%)\n",
      "(person, away, guitar): mAP = 0.0, count = 1544 (0.088%)\n",
      "(person, towards, guitar): mAP = 0.00011478420569329661, count = 1447 (0.083%)\n",
      "(person, in_front_of, guitar): mAP = 0.026010235945748347, count = 6922 (0.397%)\n",
      "(person, in_front_of, dog): mAP = 0.14939925907976898, count = 5182 (0.297%)\n",
      "(person, above, person): mAP = 0.0002806869545284334, count = 47007 (2.693%)\n",
      "(person, carry, person): mAP = 0.0, count = 12447 (0.713%)\n",
      "(person, above, dog): mAP = 0.0010030377715366538, count = 2039 (0.117%)\n",
      "(person, watch, dog): mAP = 0.09090909090909091, count = 3348 (0.192%)\n",
      "(person, in_front_of, cellphone): mAP = 0.005367651102678325, count = 446 (0.026%)\n",
      "(person, in_front_of, sheep/goat): mAP = 0.0, count = 39 (0.002%)\n",
      "(person, watch, sheep/goat): mAP = 0.0, count = 9 (0.001%)\n",
      "(person, behind, sheep/goat): mAP = 0.0, count = 56 (0.003%)\n",
      "(person, caress, sheep/goat): mAP = 0.0, count = 1 (0.000%)\n",
      "(person, watch, toy): mAP = 0.045454545454545456, count = 10742 (0.615%)\n",
      "(person, grab, toy): mAP = 0.0, count = 959 (0.055%)\n",
      "(person, lift, toy): mAP = 0.0, count = 1082 (0.062%)\n",
      "(person, touch, dog): mAP = 0.0, count = 958 (0.055%)\n",
      "(person, inside, baby_seat): mAP = 0.00022713077054113903, count = 2115 (0.121%)\n",
      "(person, above, chair): mAP = 0.09090909090909091, count = 1373 (0.079%)\n",
      "(person, watch, ball/sports_ball): mAP = 0.0004434589800443459, count = 1820 (0.104%)\n",
      "(person, behind, chair): mAP = 0.008696352363314277, count = 4268 (0.244%)\n",
      "(person, away, car): mAP = 0.10249986742792498, count = 1018 (0.058%)\n",
      "(person, in_front_of, car): mAP = 0.1448560336362162, count = 2517 (0.144%)\n",
      "(person, next_to, ball/sports_ball): mAP = 0.18417571809768135, count = 3923 (0.225%)\n",
      "(person, towards, ball/sports_ball): mAP = 0.0009881422924901185, count = 889 (0.051%)\n",
      "(person, away, chair): mAP = 0.045454545454545456, count = 2221 (0.127%)\n",
      "(person, towards, car): mAP = 0.01884375166817915, count = 950 (0.054%)\n",
      "(person, chase, ball/sports_ball): mAP = 0.0, count = 140 (0.008%)\n",
      "(person, behind, dog): mAP = 0.06917609829510732, count = 3345 (0.192%)\n",
      "(person, next_to, car): mAP = 0.05098848160755821, count = 2377 (0.136%)\n",
      "(person, towards, dog): mAP = 0.012987012987012986, count = 1825 (0.105%)\n",
      "(person, next_to, dog): mAP = 0.2097952858190082, count = 5629 (0.322%)\n",
      "(person, hold, ball/sports_ball): mAP = 0.0, count = 1298 (0.074%)\n",
      "(person, caress, dog): mAP = 0.0005064573309698658, count = 1126 (0.065%)\n",
      "(person, lean_on, stool): mAP = 0.0, count = 339 (0.019%)\n",
      "(person, above, stool): mAP = 0.31549697195670356, count = 509 (0.029%)\n",
      "(person, pat, stool): mAP = 0.0, count = 81 (0.005%)\n",
      "(person, next_to, stool): mAP = 0.2502563226247437, count = 2195 (0.126%)\n",
      "(person, towards, stool): mAP = 0.07272727272727274, count = 316 (0.018%)\n",
      "(person, away, stool): mAP = 0.03790527654164018, count = 359 (0.021%)\n",
      "(person, next_to, electric_fan): mAP = 0.012368583797155226, count = 148 (0.008%)\n",
      "(person, push, person): mAP = 0.0, count = 10747 (0.616%)\n",
      "(person, touch, person): mAP = 0.0, count = 9808 (0.562%)\n",
      "(person, hold, person): mAP = 0.00028409090909090913, count = 32938 (1.887%)\n",
      "(person, next_to, camel): mAP = 0.0, count = 84 (0.005%)\n",
      "(person, point_to, person): mAP = 0.0, count = 5270 (0.302%)\n",
      "(person, in_front_of, chair): mAP = 0.16894815863710588, count = 7244 (0.415%)\n",
      "(person, next_to, bird): mAP = 0.4436445907034142, count = 253 (0.014%)\n",
      "(person, carry, bird): mAP = 0.0, count = 40 (0.002%)\n",
      "(person, watch, bird): mAP = 0.0, count = 246 (0.014%)\n",
      "(person, next_to, backpack): mAP = 0.09462892397024134, count = 4168 (0.239%)\n",
      "(person, carry, backpack): mAP = 0.001594896331738437, count = 653 (0.037%)\n",
      "(person, wave, person): mAP = 0.0, count = 5149 (0.295%)\n",
      "(person, in_front_of, toy): mAP = 0.037369574113036264, count = 13782 (0.790%)\n",
      "(person, wave, toy): mAP = 0.0, count = 1703 (0.098%)\n",
      "(person, hug, toy): mAP = 0.0, count = 4336 (0.248%)\n",
      "(person, away, dog): mAP = 0.022727272727272728, count = 1874 (0.107%)\n",
      "(person, beneath, toy): mAP = 0.045292785506308636, count = 2632 (0.151%)\n",
      "(person, lean_on, chair): mAP = 0.056818181818181816, count = 1630 (0.093%)\n",
      "(person, lean_on, person): mAP = 0.0016036475096602328, count = 52568 (3.011%)\n",
      "(person, pat, person): mAP = 0.0, count = 7643 (0.438%)\n",
      "(person, next_to, snowboard): mAP = 0.0, count = 132 (0.008%)\n",
      "(person, behind, toy): mAP = 0.03666884771686394, count = 10118 (0.580%)\n",
      "(person, push, toy): mAP = 0.0, count = 1546 (0.089%)\n",
      "(person, behind, car): mAP = 0.07961465737537675, count = 1820 (0.104%)\n",
      "(person, pull, toy): mAP = 0.0, count = 1182 (0.068%)\n",
      "(person, watch, frisbee): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, next_to, frisbee): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, towards, frisbee): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, watch, dish): mAP = 0.0, count = 667 (0.038%)\n",
      "(person, hold, bottle): mAP = 0.003934001420338019, count = 1823 (0.104%)\n",
      "(person, release, bottle): mAP = 0.0, count = 228 (0.013%)\n",
      "(person, push, dish): mAP = 0.0, count = 142 (0.008%)\n",
      "(person, next_to, cat): mAP = 0.052692640807905526, count = 1739 (0.100%)\n",
      "(person, watch, cat): mAP = 0.06451612903225806, count = 998 (0.057%)\n",
      "(person, next_to, handbag): mAP = 0.0010822510822510823, count = 2072 (0.119%)\n",
      "(person, in_front_of, cat): mAP = 0.19467074025512895, count = 1483 (0.085%)\n",
      "(person, next_to, cake): mAP = 0.11193576611122312, count = 1513 (0.087%)\n",
      "(person, hug, cat): mAP = 0.0015151515151515152, count = 476 (0.027%)\n",
      "(person, kiss, cat): mAP = 0.0, count = 102 (0.006%)\n",
      "(person, feed, cat): mAP = 0.0, count = 196 (0.011%)\n",
      "(person, behind, cat): mAP = 0.11947278911564627, count = 844 (0.048%)\n",
      "(person, towards, chair): mAP = 0.006493506493506493, count = 1831 (0.105%)\n",
      "(person, next_to, baby_seat): mAP = 0.005358851674641148, count = 4022 (0.230%)\n",
      "(person, lift, ball/sports_ball): mAP = 0.0, count = 200 (0.011%)\n",
      "(person, away, baby_seat): mAP = 0.0, count = 629 (0.036%)\n",
      "(person, throw, ball/sports_ball): mAP = 0.0, count = 86 (0.005%)\n",
      "(person, in_front_of, backpack): mAP = 0.09707273792371256, count = 3844 (0.220%)\n",
      "(person, behind, backpack): mAP = 0.0, count = 2484 (0.142%)\n",
      "(person, in_front_of, screen/monitor): mAP = 0.4439801085026949, count = 10520 (0.603%)\n",
      "(person, watch, screen/monitor): mAP = 0.06818181818181818, count = 5479 (0.314%)\n",
      "(person, watch, stool): mAP = 0.0008658008658008659, count = 1293 (0.074%)\n",
      "(person, touch, stool): mAP = 0.0, count = 142 (0.008%)\n",
      "(person, release, stool): mAP = 0.0, count = 40 (0.002%)\n",
      "(person, kiss, person): mAP = 0.0, count = 6404 (0.367%)\n",
      "(person, lean_on, toy): mAP = 0.21807624735823558, count = 5060 (0.290%)\n",
      "(person, inside, toy): mAP = 0.012234863552444434, count = 2440 (0.140%)\n",
      "(person, drive, toy): mAP = 0.0, count = 392 (0.022%)\n",
      "(person, chase, person): mAP = 3.545596369309318e-05, count = 4186 (0.240%)\n",
      "(person, in_front_of, refrigerator): mAP = 0.03986902927580894, count = 706 (0.040%)\n",
      "(person, watch, chair): mAP = 0.0001429388221841052, count = 4111 (0.236%)\n",
      "(person, away, refrigerator): mAP = 0.0, count = 443 (0.025%)\n",
      "(person, push, chair): mAP = 0.0, count = 511 (0.029%)\n",
      "(person, in_front_of, aircraft): mAP = 0.012477718360071303, count = 377 (0.022%)\n",
      "(person, next_to, aircraft): mAP = 0.0064714946070878274, count = 358 (0.021%)\n",
      "(person, shake_hand_with, dog): mAP = 0.0, count = 295 (0.017%)\n",
      "(person, hug, dog): mAP = 0.2727272727272727, count = 1280 (0.073%)\n",
      "(person, in_front_of, laptop): mAP = 0.37199640585186156, count = 2119 (0.121%)\n",
      "(person, above, bicycle): mAP = 0.22506413781100562, count = 2405 (0.138%)\n",
      "(person, ride, bicycle): mAP = 0.006493506493506493, count = 1463 (0.084%)\n",
      "(person, behind, camera): mAP = 0.0, count = 180 (0.010%)\n",
      "(person, towards, camera): mAP = 0.0, count = 66 (0.004%)\n",
      "(person, away, camera): mAP = 0.01515151515151515, count = 100 (0.006%)\n",
      "(person, in_front_of, camera): mAP = 0.0, count = 335 (0.019%)\n",
      "(person, next_to, camera): mAP = 0.0213903743315508, count = 330 (0.019%)\n",
      "(person, next_to, crocodile): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, carry, crocodile): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, in_front_of, handbag): mAP = 0.0, count = 1780 (0.102%)\n",
      "(person, in_front_of, crocodile): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, in_front_of, penguin): mAP = 0.6493506493506495, count = 142 (0.008%)\n",
      "(person, touch, penguin): mAP = 0.0, count = 2 (0.000%)\n",
      "(person, next_to, penguin): mAP = 0.19138755980861238, count = 139 (0.008%)\n",
      "(person, behind, penguin): mAP = 0.42132867132867136, count = 126 (0.007%)\n",
      "(person, next_to, sink): mAP = 0.09090909090909091, count = 237 (0.014%)\n",
      "(person, next_to, faucet): mAP = 0.09090909090909091, count = 368 (0.021%)\n",
      "(person, next_to, hamster/rat): mAP = 0.0008340283569641369, count = 168 (0.010%)\n",
      "(person, hold, hamster/rat): mAP = 0.0, count = 76 (0.004%)\n",
      "(person, clean, hamster/rat): mAP = 0.0, count = 10 (0.001%)\n",
      "(person, close, faucet): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, grab, hamster/rat): mAP = 0.0, count = 8 (0.000%)\n",
      "(person, release, hamster/rat): mAP = 0.0, count = 8 (0.000%)\n",
      "(person, beneath, person): mAP = 0.0007906857221925714, count = 15272 (0.875%)\n",
      "(person, hold, ski): mAP = 0.0, count = 159 (0.009%)\n",
      "(person, hold, cup): mAP = 0.022727272727272728, count = 1751 (0.100%)\n",
      "(person, above, toy): mAP = 0.05767749618795022, count = 5230 (0.300%)\n",
      "(person, towards, handbag): mAP = 0.0, count = 316 (0.018%)\n",
      "(person, carry, handbag): mAP = 0.0, count = 228 (0.013%)\n",
      "(person, watch, handbag): mAP = 0.0, count = 577 (0.033%)\n",
      "(person, away, handbag): mAP = 0.0, count = 365 (0.021%)\n",
      "(person, above, watercraft): mAP = 0.1477124183006536, count = 493 (0.028%)\n",
      "(person, ride, watercraft): mAP = 0.0303030303030303, count = 347 (0.020%)\n",
      "(person, away, watercraft): mAP = 0.0, count = 441 (0.025%)\n",
      "(person, kick, person): mAP = 0.0, count = 3003 (0.172%)\n",
      "(person, kick, ball/sports_ball): mAP = 0.0, count = 100 (0.006%)\n",
      "(person, above, elephant): mAP = 0.0, count = 13 (0.001%)\n",
      "(person, ride, elephant): mAP = 0.0, count = 12 (0.001%)\n",
      "(person, behind, elephant): mAP = 0.0, count = 26 (0.001%)\n",
      "(person, in_front_of, elephant): mAP = 0.0, count = 28 (0.002%)\n",
      "(person, in_front_of, ball/sports_ball): mAP = 0.028025219404661225, count = 3352 (0.192%)\n",
      "(person, towards, cup): mAP = 0.001443001443001443, count = 1039 (0.060%)\n",
      "(person, away, cup): mAP = 0.006993006993006993, count = 1199 (0.069%)\n",
      "(person, pull, ski): mAP = 0.0, count = 64 (0.004%)\n",
      "(person, lean_on, ball/sports_ball): mAP = 0.0, count = 513 (0.029%)\n",
      "(person, pat, ball/sports_ball): mAP = 0.0, count = 192 (0.011%)\n",
      "(person, press, ball/sports_ball): mAP = 0.0, count = 293 (0.017%)\n",
      "(person, wave, ball/sports_ball): mAP = 0.0, count = 301 (0.017%)\n",
      "(person, away, toy): mAP = 0.0018552875695732837, count = 4020 (0.230%)\n",
      "(person, towards, table): mAP = 0.0, count = 2543 (0.146%)\n",
      "(person, above, horse): mAP = 0.3525262026842465, count = 532 (0.030%)\n",
      "(person, ride, horse): mAP = 0.009090909090909092, count = 326 (0.019%)\n",
      "(person, in_front_of, baby_seat): mAP = 0.0279869676276511, count = 4022 (0.230%)\n",
      "(person, feed, person): mAP = 0.0, count = 9601 (0.550%)\n",
      "(person, press, person): mAP = 0.0, count = 7991 (0.458%)\n",
      "(person, squeeze, toy): mAP = 0.0, count = 731 (0.042%)\n",
      "(person, press, toy): mAP = 0.0, count = 2153 (0.123%)\n",
      "(person, hit, person): mAP = 0.0, count = 6270 (0.359%)\n",
      "(person, grab, handbag): mAP = 0.0, count = 103 (0.006%)\n",
      "(person, hold, handbag): mAP = 0.0, count = 447 (0.026%)\n",
      "(person, next_to, sofa): mAP = 0.016817100981122164, count = 12376 (0.709%)\n",
      "(person, inside, person): mAP = 0.0, count = 23661 (1.355%)\n",
      "(person, hold, camera): mAP = 0.09090909090909091, count = 136 (0.008%)\n",
      "(person, grab, camera): mAP = 0.0, count = 32 (0.002%)\n",
      "(person, in_front_of, horse): mAP = 0.2475237974029305, count = 1314 (0.075%)\n",
      "(person, pull, horse): mAP = 0.0, count = 179 (0.010%)\n",
      "(person, watch, horse): mAP = 0.00043497172683775554, count = 675 (0.039%)\n",
      "(person, next_to, horse): mAP = 0.018343111613769704, count = 1325 (0.076%)\n",
      "(person, towards, horse): mAP = 0.0, count = 363 (0.021%)\n",
      "(person, feed, horse): mAP = 0.0, count = 141 (0.008%)\n",
      "(person, caress, horse): mAP = 0.0, count = 169 (0.010%)\n",
      "(person, squeeze, person): mAP = 0.0, count = 3651 (0.209%)\n",
      "(person, watch, laptop): mAP = 0.0, count = 1508 (0.086%)\n",
      "(person, beneath, bottle): mAP = 0.0013080444735120995, count = 689 (0.039%)\n",
      "(person, bite, bottle): mAP = 0.0, count = 352 (0.020%)\n",
      "(person, pull, dog): mAP = 0.0, count = 808 (0.046%)\n",
      "(person, wave, bat): mAP = 0.0, count = 67 (0.004%)\n",
      "(person, in_front_of, electric_fan): mAP = 0.236646418098031, count = 173 (0.010%)\n",
      "(person, carry, ball/sports_ball): mAP = 0.0, count = 531 (0.030%)\n",
      "(person, in_front_of, table): mAP = 0.0, count = 13080 (0.749%)\n",
      "(person, behind, ball/sports_ball): mAP = 0.0, count = 1889 (0.108%)\n",
      "(person, bite, ball/sports_ball): mAP = 0.0, count = 320 (0.018%)\n",
      "(person, watch, oven): mAP = 0.0, count = 111 (0.006%)\n",
      "(person, next_to, oven): mAP = 0.0, count = 128 (0.007%)\n",
      "(person, open, oven): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, close, oven): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, watch, microwave): mAP = 0.0, count = 70 (0.004%)\n",
      "(person, next_to, microwave): mAP = 0.0, count = 172 (0.010%)\n",
      "(person, press, microwave): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, lift, person): mAP = 0.0, count = 5085 (0.291%)\n",
      "(person, shake_hand_with, person): mAP = 0.0, count = 2482 (0.142%)\n",
      "(person, caress, person): mAP = 0.0, count = 16106 (0.923%)\n",
      "(person, towards, dish): mAP = 0.0, count = 165 (0.009%)\n",
      "(person, towards, electric_fan): mAP = 0.0, count = 10 (0.001%)\n",
      "(person, away, electric_fan): mAP = 0.0, count = 10 (0.001%)\n",
      "(person, hold, dish): mAP = 0.0, count = 329 (0.019%)\n",
      "(person, wave_hand_to, person): mAP = 0.0, count = 3355 (0.192%)\n",
      "(person, inside, car): mAP = 0.0030766458124147165, count = 452 (0.026%)\n",
      "(person, drive, car): mAP = 0.0, count = 192 (0.011%)\n",
      "(person, ride, car): mAP = 0.006065577845725508, count = 413 (0.024%)\n",
      "(person, hit, car): mAP = 0.0, count = 192 (0.011%)\n",
      "(person, hit, toy): mAP = 0.0, count = 1141 (0.065%)\n",
      "(person, away, backpack): mAP = 0.0005379236148466917, count = 833 (0.048%)\n",
      "(person, towards, sofa): mAP = 0.09161199625117152, count = 2370 (0.136%)\n",
      "(person, watch, cake): mAP = 0.00016742005692281937, count = 564 (0.032%)\n",
      "(person, towards, cake): mAP = 0.0, count = 37 (0.002%)\n",
      "(person, point_to, cake): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, away, cake): mAP = 0.0, count = 46 (0.003%)\n",
      "(person, touch, cake): mAP = 0.0, count = 36 (0.002%)\n",
      "(person, release, person): mAP = 0.0, count = 3871 (0.222%)\n",
      "(person, touch, screen/monitor): mAP = 0.0, count = 999 (0.057%)\n",
      "(person, next_to, snake): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, grab, snake): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, hold, snake): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, watch, snake): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, release, snake): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, touch, snake): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, watch, cup): mAP = 0.0, count = 2100 (0.120%)\n",
      "(person, touch, dish): mAP = 0.0, count = 173 (0.010%)\n",
      "(person, towards, baby_seat): mAP = 0.0, count = 639 (0.037%)\n",
      "(person, in_front_of, watercraft): mAP = 0.8272727272727274, count = 687 (0.039%)\n",
      "(person, away, sofa): mAP = 0.0025974025974025974, count = 2332 (0.134%)\n",
      "(person, towards, ski): mAP = 0.0, count = 151 (0.009%)\n",
      "(person, away, ski): mAP = 0.004545454545454546, count = 163 (0.009%)\n",
      "(person, lift, cup): mAP = 0.0, count = 233 (0.013%)\n",
      "(person, grab, bottle): mAP = 0.0, count = 238 (0.014%)\n",
      "(person, hold, guitar): mAP = 0.012121212121212121, count = 1388 (0.080%)\n",
      "(person, next_to, bread): mAP = 0.0, count = 122 (0.007%)\n",
      "(person, bite, bread): mAP = 0.0, count = 13 (0.001%)\n",
      "(person, watch, bread): mAP = 0.0, count = 75 (0.004%)\n",
      "(person, away, ball/sports_ball): mAP = 0.001594896331738437, count = 1037 (0.059%)\n",
      "(person, get_on, sofa): mAP = 0.0, count = 202 (0.012%)\n",
      "(person, ride, train): mAP = 0.004545454545454546, count = 37 (0.002%)\n",
      "(person, inside, train): mAP = 0.0202020202020202, count = 42 (0.002%)\n",
      "(person, beneath, screen/monitor): mAP = 0.0, count = 3066 (0.176%)\n",
      "(person, away, screen/monitor): mAP = 0.0006402048655569782, count = 2866 (0.164%)\n",
      "(person, hold, screen/monitor): mAP = 0.0, count = 2243 (0.128%)\n",
      "(person, behind, baby_walker): mAP = 0.0, count = 1539 (0.088%)\n",
      "(person, watch, baby_walker): mAP = 0.0, count = 1424 (0.082%)\n",
      "(person, touch, toy): mAP = 0.0, count = 2103 (0.120%)\n",
      "(person, feed, dog): mAP = 0.0, count = 1005 (0.058%)\n",
      "(person, above, surfboard): mAP = 0.0, count = 86 (0.005%)\n",
      "(person, ride, surfboard): mAP = 0.0, count = 74 (0.004%)\n",
      "(person, towards, toy): mAP = 0.09090909090909091, count = 3946 (0.226%)\n",
      "(person, push, ball/sports_ball): mAP = 0.0, count = 323 (0.019%)\n",
      "(person, above, motorcycle): mAP = 0.30652680652680653, count = 191 (0.011%)\n",
      "(person, drive, motorcycle): mAP = 0.0, count = 67 (0.004%)\n",
      "(person, above, screen/monitor): mAP = 0.0, count = 1126 (0.065%)\n",
      "(person, grab, person): mAP = 0.0, count = 3501 (0.201%)\n",
      "(person, knock, person): mAP = 0.0, count = 2369 (0.136%)\n",
      "(person, next_to, crab): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, hold, crab): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, clean, person): mAP = 0.0, count = 4267 (0.244%)\n",
      "(person, inside, watercraft): mAP = 0.0, count = 268 (0.015%)\n",
      "(person, watch, bottle): mAP = 0.0, count = 2244 (0.129%)\n",
      "(person, behind, handbag): mAP = 0.0, count = 780 (0.045%)\n",
      "(person, release, ball/sports_ball): mAP = 0.0, count = 199 (0.011%)\n",
      "(person, grab, ball/sports_ball): mAP = 0.0, count = 194 (0.011%)\n",
      "(person, lean_on, bicycle): mAP = 9.796238244514106e-05, count = 1856 (0.106%)\n",
      "(person, watch, vegetables): mAP = 0.0, count = 88 (0.005%)\n",
      "(person, next_to, vegetables): mAP = 0.2670828415193378, count = 430 (0.025%)\n",
      "(person, hug, vegetables): mAP = 0.0, count = 27 (0.002%)\n",
      "(person, in_front_of, faucet): mAP = 0.0, count = 415 (0.024%)\n",
      "(person, in_front_of, oven): mAP = 0.0, count = 147 (0.008%)\n",
      "(person, behind, horse): mAP = 0.011080647177379088, count = 894 (0.051%)\n",
      "(person, in_front_of, baby_walker): mAP = 0.060283479960899326, count = 2699 (0.155%)\n",
      "(person, towards, baby_walker): mAP = 0.0, count = 344 (0.020%)\n",
      "(person, away, baby_walker): mAP = 0.0, count = 343 (0.020%)\n",
      "(person, hug, ball/sports_ball): mAP = 0.0, count = 615 (0.035%)\n",
      "(person, towards, cellphone): mAP = 0.0, count = 70 (0.004%)\n",
      "(person, away, cellphone): mAP = 0.0, count = 97 (0.006%)\n",
      "(person, release, bat): mAP = 0.0, count = 48 (0.003%)\n",
      "(person, grab, bat): mAP = 0.0, count = 44 (0.003%)\n",
      "(person, above, scooter): mAP = 0.0, count = 27 (0.002%)\n",
      "(person, ride, scooter): mAP = 0.0, count = 25 (0.001%)\n",
      "(person, next_to, scooter): mAP = 0.0, count = 47 (0.003%)\n",
      "(person, watch, scooter): mAP = 0.0, count = 39 (0.002%)\n",
      "(person, touch, ball/sports_ball): mAP = 0.0, count = 327 (0.019%)\n",
      "(person, push, dog): mAP = 0.0, count = 930 (0.053%)\n",
      "(person, hold, dog): mAP = 0.00013578654355353385, count = 1752 (0.100%)\n",
      "(person, release, dog): mAP = 0.0, count = 503 (0.029%)\n",
      "(person, behind, baby_seat): mAP = 0.0, count = 2826 (0.162%)\n",
      "(person, next_to, refrigerator): mAP = 0.0, count = 669 (0.038%)\n",
      "(person, towards, refrigerator): mAP = 0.0, count = 446 (0.026%)\n",
      "(person, shout_at, person): mAP = 0.0, count = 2860 (0.164%)\n",
      "(person, towards, sink): mAP = 0.0, count = 38 (0.002%)\n",
      "(person, towards, faucet): mAP = 0.0, count = 21 (0.001%)\n",
      "(person, away, sink): mAP = 0.0, count = 29 (0.002%)\n",
      "(person, cut, vegetables): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, watch, baby_seat): mAP = 0.0, count = 2750 (0.158%)\n",
      "(person, behind, suitcase): mAP = 1.0000000000000002, count = 339 (0.019%)\n",
      "(person, bite, person): mAP = 0.0, count = 5868 (0.336%)\n",
      "(person, get_off, bicycle): mAP = 0.0, count = 237 (0.014%)\n",
      "(person, away, bicycle): mAP = 0.014890868294009655, count = 1336 (0.077%)\n",
      "(person, push, bicycle): mAP = 0.0, count = 743 (0.043%)\n",
      "(person, next_to, bicycle): mAP = 0.06632113123959017, count = 4232 (0.242%)\n",
      "(person, watch, motorcycle): mAP = 0.0, count = 282 (0.016%)\n",
      "(person, in_front_of, motorcycle): mAP = 0.0, count = 420 (0.024%)\n",
      "(person, next_to, motorcycle): mAP = 0.05497132379297984, count = 556 (0.032%)\n",
      "(person, away, faucet): mAP = 0.0, count = 56 (0.003%)\n",
      "(person, release, chair): mAP = 0.0, count = 207 (0.012%)\n",
      "(person, pull, chair): mAP = 0.0, count = 355 (0.020%)\n",
      "(person, press, baby_seat): mAP = 0.0, count = 616 (0.035%)\n",
      "(person, beneath, watercraft): mAP = 0.0, count = 151 (0.009%)\n",
      "(person, point_to, toy): mAP = 0.0, count = 927 (0.053%)\n",
      "(person, release, toy): mAP = 0.0, count = 1065 (0.061%)\n",
      "(person, behind, bicycle): mAP = 0.0012812690665039658, count = 2980 (0.171%)\n",
      "(person, watch, bicycle): mAP = 0.00020521239482864764, count = 2532 (0.145%)\n",
      "(person, towards, bicycle): mAP = 0.0015408320493066256, count = 1329 (0.076%)\n",
      "(person, get_on, bicycle): mAP = 0.0, count = 240 (0.014%)\n",
      "(person, above, skateboard): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, ride, skateboard): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, in_front_of, bicycle): mAP = 0.01718340091388571, count = 3609 (0.207%)\n",
      "(person, hold, bicycle): mAP = 0.00033921302578018993, count = 1331 (0.076%)\n",
      "(person, lean_on, bus/truck): mAP = 0.0, count = 112 (0.006%)\n",
      "(person, inside, bus/truck): mAP = 0.0, count = 86 (0.005%)\n",
      "(person, next_to, chicken): mAP = 0.0, count = 86 (0.005%)\n",
      "(person, ride, baby_seat): mAP = 0.0015408320493066256, count = 850 (0.049%)\n",
      "(person, watch, fruits): mAP = 0.0, count = 64 (0.004%)\n",
      "(person, next_to, fruits): mAP = 0.0, count = 426 (0.024%)\n",
      "(person, hold, fruits): mAP = 0.0, count = 56 (0.003%)\n",
      "(person, behind, screen/monitor): mAP = 6.138358602909582e-05, count = 5504 (0.315%)\n",
      "(person, next_to, laptop): mAP = 0.0, count = 2062 (0.118%)\n",
      "(person, next_to, fish): mAP = 0.0503251837420331, count = 386 (0.022%)\n",
      "(person, watch, fish): mAP = 0.0, count = 188 (0.011%)\n",
      "(person, hold, fish): mAP = 0.0, count = 93 (0.005%)\n",
      "(person, next_to, baby_walker): mAP = 0.08145416899699917, count = 2383 (0.137%)\n",
      "(person, cut, fish): mAP = 0.0, count = 5 (0.000%)\n",
      "(person, carry, guitar): mAP = 0.0, count = 939 (0.054%)\n",
      "(person, next_to, bus/truck): mAP = 0.1484848484848485, count = 448 (0.026%)\n",
      "(person, drive, bus/truck): mAP = 0.0, count = 69 (0.004%)\n",
      "(person, ride, bus/truck): mAP = 0.0, count = 86 (0.005%)\n",
      "(person, next_to, racket): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, hold, racket): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, watch, racket): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, wave, racket): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, hit, ball/sports_ball): mAP = 0.0, count = 196 (0.011%)\n",
      "(person, away, racket): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, towards, backpack): mAP = 0.0, count = 840 (0.048%)\n",
      "(person, inside, dog): mAP = 0.0, count = 1026 (0.059%)\n",
      "(person, behind, laptop): mAP = 0.0015151515151515152, count = 1304 (0.075%)\n",
      "(person, hold, bread): mAP = 0.0, count = 63 (0.004%)\n",
      "(person, lick, bread): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, caress, hamster/rat): mAP = 0.0, count = 20 (0.001%)\n",
      "(person, behind, hamster/rat): mAP = 0.0, count = 82 (0.005%)\n",
      "(person, towards, hamster/rat): mAP = 0.0, count = 28 (0.002%)\n",
      "(person, touch, hamster/rat): mAP = 0.0, count = 23 (0.001%)\n",
      "(person, away, hamster/rat): mAP = 0.0, count = 28 (0.002%)\n",
      "(person, in_front_of, hamster/rat): mAP = 0.13295454545454546, count = 151 (0.009%)\n",
      "(person, next_to, bench): mAP = 0.0, count = 224 (0.013%)\n",
      "(person, behind, bench): mAP = 0.0, count = 74 (0.004%)\n",
      "(person, towards, bench): mAP = 0.0, count = 61 (0.003%)\n",
      "(person, away, bench): mAP = 0.0, count = 64 (0.004%)\n",
      "(person, ride, toy): mAP = 0.002560819462227913, count = 1874 (0.107%)\n",
      "(person, watch, refrigerator): mAP = 0.0, count = 528 (0.030%)\n",
      "(person, release, dish): mAP = 0.0, count = 41 (0.002%)\n",
      "(person, behind, bus/truck): mAP = 0.0, count = 325 (0.019%)\n",
      "(person, towards, aircraft): mAP = 0.0, count = 159 (0.009%)\n",
      "(person, away, aircraft): mAP = 0.0, count = 182 (0.010%)\n",
      "(person, towards, bus/truck): mAP = 0.0, count = 143 (0.008%)\n",
      "(person, in_front_of, bus/truck): mAP = 0.0, count = 485 (0.028%)\n",
      "(person, away, bus/truck): mAP = 0.0, count = 191 (0.011%)\n",
      "(person, watch, sofa): mAP = 0.0, count = 8107 (0.464%)\n",
      "(person, carry, camera): mAP = 0.0, count = 50 (0.003%)\n",
      "(person, behind, sofa): mAP = 0.0, count = 7158 (0.410%)\n",
      "(person, behind, cellphone): mAP = 0.0, count = 224 (0.013%)\n",
      "(person, use, laptop): mAP = 0.0, count = 151 (0.009%)\n",
      "(person, behind, motorcycle): mAP = 0.075849592164569, count = 383 (0.022%)\n",
      "(person, hold, motorcycle): mAP = 0.0, count = 123 (0.007%)\n",
      "(person, pull, motorcycle): mAP = 0.0, count = 70 (0.004%)\n",
      "(person, ride, motorcycle): mAP = 0.0, count = 150 (0.009%)\n",
      "(person, away, motorcycle): mAP = 0.045454545454545456, count = 182 (0.010%)\n",
      "(person, release, motorcycle): mAP = 0.0, count = 57 (0.003%)\n",
      "(person, above, baby_walker): mAP = 0.012987012987012986, count = 1276 (0.073%)\n",
      "(person, release, guitar): mAP = 0.0, count = 225 (0.013%)\n",
      "(person, bite, toy): mAP = 0.0, count = 1899 (0.109%)\n",
      "(person, grab, guitar): mAP = 0.0, count = 235 (0.013%)\n",
      "(person, touch, chair): mAP = 0.0, count = 552 (0.032%)\n",
      "(person, pat, toy): mAP = 0.0, count = 1514 (0.087%)\n",
      "(person, in_front_of, piano): mAP = 0.12247909573566051, count = 1348 (0.077%)\n",
      "(person, play(instrument), piano): mAP = 0.0, count = 84 (0.005%)\n",
      "(person, lean_on, bench): mAP = 0.0, count = 18 (0.001%)\n",
      "(person, above, bench): mAP = 0.0, count = 33 (0.002%)\n",
      "(person, towards, piano): mAP = 0.0, count = 106 (0.006%)\n",
      "(person, watch, piano): mAP = 0.0007158196134574087, count = 368 (0.021%)\n",
      "(person, away, piano): mAP = 0.0106951871657754, count = 132 (0.008%)\n",
      "(person, touch, horse): mAP = 0.0, count = 149 (0.009%)\n",
      "(person, away, horse): mAP = 0.0, count = 398 (0.023%)\n",
      "(person, pat, horse): mAP = 0.0, count = 126 (0.007%)\n",
      "(person, get_on, toy): mAP = 0.0, count = 229 (0.013%)\n",
      "(person, next_to, train): mAP = 0.03125000000000001, count = 115 (0.007%)\n",
      "(person, behind, train): mAP = 0.04812834224598932, count = 115 (0.007%)\n",
      "(person, away, train): mAP = 0.002840909090909091, count = 111 (0.006%)\n",
      "(person, away, traffic_light): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, in_front_of, traffic_light): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, towards, train): mAP = 0.0, count = 94 (0.005%)\n",
      "(person, towards, traffic_light): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, away, bottle): mAP = 0.0, count = 797 (0.046%)\n",
      "(person, get_off, toy): mAP = 0.0, count = 231 (0.013%)\n",
      "(person, lean_on, car): mAP = 0.0, count = 824 (0.047%)\n",
      "(person, watch, lion): mAP = 0.0, count = 14 (0.001%)\n",
      "(person, away, antelope): mAP = 0.0, count = 3 (0.000%)\n",
      "(person, in_front_of, antelope): mAP = 0.36363636363636365, count = 3 (0.000%)\n",
      "(person, watch, pig): mAP = 0.21936758893280633, count = 24 (0.001%)\n",
      "(person, watch, bat): mAP = 0.0, count = 215 (0.012%)\n",
      "(person, watch, bear): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, behind, piano): mAP = 0.0, count = 354 (0.020%)\n",
      "(person, next_to, piano): mAP = 0.0002990430622009569, count = 1139 (0.065%)\n",
      "(person, inside, sink): mAP = 0.0, count = 36 (0.002%)\n",
      "(person, lean_on, sink): mAP = 0.045454545454545456, count = 73 (0.004%)\n",
      "(person, above, table): mAP = 0.0, count = 5834 (0.334%)\n",
      "(person, touch, vegetables): mAP = 0.0, count = 8 (0.000%)\n",
      "(person, watch, backpack): mAP = 0.0, count = 2198 (0.126%)\n",
      "(person, above, backpack): mAP = 0.0, count = 1193 (0.068%)\n",
      "(person, in_front_of, fish): mAP = 0.11465677179962895, count = 346 (0.020%)\n",
      "(person, press, sofa): mAP = 0.0, count = 1300 (0.074%)\n",
      "(person, next_to, stingray): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, towards, fruits): mAP = 0.0, count = 12 (0.001%)\n",
      "(person, pat, guitar): mAP = 0.0, count = 310 (0.018%)\n",
      "(person, grab, cup): mAP = 0.0, count = 359 (0.021%)\n",
      "(person, release, cup): mAP = 0.0, count = 391 (0.022%)\n",
      "(person, watch, train): mAP = 0.0, count = 105 (0.006%)\n",
      "(person, next_to, suitcase): mAP = 0.0, count = 562 (0.032%)\n",
      "(person, pull, suitcase): mAP = 0.0, count = 1 (0.000%)\n",
      "(person, lift, bicycle): mAP = 0.0, count = 338 (0.019%)\n",
      "(person, clean, dog): mAP = 0.0, count = 444 (0.025%)\n",
      "(person, pat, dog): mAP = 0.0, count = 718 (0.041%)\n",
      "(person, bite, fruits): mAP = 0.0, count = 4 (0.000%)\n",
      "(person, grab, fruits): mAP = 0.0, count = 5 (0.000%)\n",
      "(person, open, refrigerator): mAP = 0.0, count = 10 (0.001%)\n",
      "(person, in_front_of, microwave): mAP = 0.0, count = 196 (0.011%)\n",
      "(person, towards, stop_sign): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, beneath, stop_sign): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, away, stop_sign): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, chase, dog): mAP = 0.0, count = 467 (0.027%)\n",
      "(person, towards, cat): mAP = 0.0, count = 273 (0.016%)\n",
      "(person, caress, cat): mAP = 0.0, count = 287 (0.016%)\n",
      "(person, above, cat): mAP = 0.012211668928086838, count = 654 (0.037%)\n",
      "(person, wave_hand_to, cat): mAP = 0.0, count = 60 (0.003%)\n",
      "(person, away, cat): mAP = 0.0, count = 280 (0.016%)\n",
      "(person, point_to, ball/sports_ball): mAP = 0.0, count = 161 (0.009%)\n",
      "(person, clean, horse): mAP = 0.0, count = 72 (0.004%)\n",
      "(person, throw, toy): mAP = 0.0, count = 217 (0.012%)\n",
      "(person, watch, table): mAP = 0.0, count = 7338 (0.420%)\n",
      "(person, behind, ski): mAP = 0.0, count = 229 (0.013%)\n",
      "(person, watch, faucet): mAP = 0.0, count = 322 (0.018%)\n",
      "(person, press, faucet): mAP = 0.0, count = 5 (0.000%)\n",
      "(person, inside, cup): mAP = 0.0, count = 140 (0.008%)\n",
      "(person, grab, cat): mAP = 0.0, count = 57 (0.003%)\n",
      "(person, hold, cat): mAP = 0.0, count = 535 (0.031%)\n",
      "(person, release, cat): mAP = 0.0, count = 63 (0.004%)\n",
      "(person, touch, cat): mAP = 0.0, count = 186 (0.011%)\n",
      "(person, wave_hand_to, toy): mAP = 0.0, count = 405 (0.023%)\n",
      "(person, hit, dog): mAP = 0.0, count = 763 (0.044%)\n",
      "(person, speak_to, dog): mAP = 0.0, count = 1265 (0.072%)\n",
      "(person, press, screen/monitor): mAP = 0.0, count = 696 (0.040%)\n",
      "(person, lift, bottle): mAP = 0.0, count = 93 (0.005%)\n",
      "(person, ride, snowboard): mAP = 0.0, count = 68 (0.004%)\n",
      "(person, above, snowboard): mAP = 0.0, count = 84 (0.005%)\n",
      "(person, away, snowboard): mAP = 0.0, count = 32 (0.002%)\n",
      "(person, pat, baby_seat): mAP = 0.0, count = 534 (0.031%)\n",
      "(person, watch, duck): mAP = 0.0, count = 2 (0.000%)\n",
      "(person, lift, camera): mAP = 0.0, count = 28 (0.002%)\n",
      "(person, carry, bottle): mAP = 0.0, count = 403 (0.023%)\n",
      "(person, release, cellphone): mAP = 0.0, count = 17 (0.001%)\n",
      "(person, press, laptop): mAP = 0.0, count = 294 (0.017%)\n",
      "(person, watch, car): mAP = 0.01562794128544768, count = 1637 (0.094%)\n",
      "(person, lick, person): mAP = 0.0, count = 2346 (0.134%)\n",
      "(person, beneath, guitar): mAP = 0.0, count = 660 (0.038%)\n",
      "(person, pull, dish): mAP = 0.0, count = 93 (0.005%)\n",
      "(person, hug, guitar): mAP = 0.0, count = 836 (0.048%)\n",
      "(person, press, cellphone): mAP = 0.0, count = 38 (0.002%)\n",
      "(person, push, baby_seat): mAP = 0.0, count = 468 (0.027%)\n",
      "(person, in_front_of, cup): mAP = 0.007177033492822965, count = 3365 (0.193%)\n",
      "(person, watch, aircraft): mAP = 0.0, count = 265 (0.015%)\n",
      "(person, behind, aircraft): mAP = 0.0, count = 280 (0.016%)\n",
      "(person, press, aircraft): mAP = 0.0, count = 66 (0.004%)\n",
      "(person, lift, cat): mAP = 0.0, count = 91 (0.005%)\n",
      "(person, watch, cattle/cow): mAP = 0.0, count = 8 (0.000%)\n",
      "(person, in_front_of, cattle/cow): mAP = 0.0, count = 8 (0.000%)\n",
      "(person, above, cattle/cow): mAP = 0.0, count = 5 (0.000%)\n",
      "(person, ride, cattle/cow): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, wave_hand_to, cattle/cow): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, behind, cattle/cow): mAP = 0.0, count = 8 (0.000%)\n",
      "(person, towards, cattle/cow): mAP = 0.0, count = 2 (0.000%)\n",
      "(person, chase, cattle/cow): mAP = 0.0, count = 0 (0.000%)\n",
      "(person, next_to, cattle/cow): mAP = 0.0, count = 8 (0.000%)\n",
      "(person, away, cattle/cow): mAP = 0.0, count = 2 (0.000%)\n",
      "(person, beneath, electric_fan): mAP = 0.0, count = 13 (0.001%)\n",
      "(person, beneath, cellphone): mAP = 0.0, count = 75 (0.004%)\n",
      "(person, hold, vegetables): mAP = 0.0, count = 33 (0.002%)\n",
      "(person, away, vegetables): mAP = 0.0, count = 20 (0.001%)\n",
      "(person, away, fruits): mAP = 0.0, count = 14 (0.001%)\n",
      "(person, towards, vegetables): mAP = 0.0, count = 17 (0.001%)\n",
      "(person, release, handbag): mAP = 0.0, count = 100 (0.006%)\n",
      "(person, in_front_of, bench): mAP = 0.0, count = 137 (0.008%)\n"
     ]
    }
   ],
   "source": [
    "# tp_counts = [len(value) for value in tp.values()]\n",
    "tp_counts = []\n",
    "total_count = 0\n",
    "for value in tp.values():\n",
    "    total_count += len(value)\n",
    "    tp_counts.append(len(value))\n",
    "tp_count_ratio = [count/total_count for count in tp_counts]\n",
    "\n",
    "tp_keys = list(tp.keys())\n",
    "unsorted_maps = []\n",
    "for idx, classAP in enumerate(ap):\n",
    "    s, p, o = tp_keys[idx]\n",
    "    count = tp_counts[idx]\n",
    "    count_ratio = tp_count_ratio[idx]\n",
    "    unsorted_maps.append((classAP, (s, p, o), count, count_ratio))\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, behind, suitcase): mAP = 1.00000, count = 339 (0.019%)\n",
      "(person, in_front_of, watercraft): mAP = 0.82727, count = 687 (0.039%)\n",
      "(person, in_front_of, penguin): mAP = 0.64935, count = 142 (0.008%)\n",
      "(person, lean_on, baby_walker): mAP = 0.53086, count = 1510 (0.087%)\n",
      "(person, in_front_of, screen/monitor): mAP = 0.44398, count = 10520 (0.603%)\n",
      "(person, next_to, bird): mAP = 0.44364, count = 253 (0.014%)\n",
      "(person, behind, penguin): mAP = 0.42133, count = 126 (0.007%)\n",
      "(person, in_front_of, laptop): mAP = 0.37200, count = 2119 (0.121%)\n",
      "(person, in_front_of, antelope): mAP = 0.36364, count = 3 (0.000%)\n",
      "(person, above, horse): mAP = 0.35253, count = 532 (0.030%)\n",
      "(person, lean_on, sofa): mAP = 0.31995, count = 5951 (0.341%)\n",
      "(person, above, stool): mAP = 0.31550, count = 509 (0.029%)\n",
      "(person, above, motorcycle): mAP = 0.30653, count = 191 (0.011%)\n",
      "(person, play(instrument), guitar): mAP = 0.29659, count = 2077 (0.119%)\n",
      "(person, next_to, person): mAP = 0.28661, count = 237779 (13.622%)\n",
      "(person, hug, dog): mAP = 0.27273, count = 1280 (0.073%)\n",
      "(person, lean_on, baby_seat): mAP = 0.27140, count = 3038 (0.174%)\n",
      "(person, next_to, table): mAP = 0.27014, count = 17485 (1.002%)\n",
      "(person, next_to, vegetables): mAP = 0.26708, count = 430 (0.025%)\n",
      "(person, next_to, stool): mAP = 0.25026, count = 2195 (0.126%)\n",
      "(person, next_to, guitar): mAP = 0.24893, count = 7549 (0.432%)\n",
      "(person, in_front_of, horse): mAP = 0.24752, count = 1314 (0.075%)\n",
      "(person, in_front_of, electric_fan): mAP = 0.23665, count = 173 (0.010%)\n",
      "(person, above, bicycle): mAP = 0.22506, count = 2405 (0.138%)\n",
      "(person, watch, pig): mAP = 0.21937, count = 24 (0.001%)\n",
      "(person, lean_on, toy): mAP = 0.21808, count = 5060 (0.290%)\n",
      "(person, next_to, dog): mAP = 0.20980, count = 5629 (0.322%)\n",
      "(person, next_to, bottle): mAP = 0.19558, count = 4756 (0.272%)\n",
      "(person, in_front_of, cat): mAP = 0.19467, count = 1483 (0.085%)\n",
      "(person, next_to, penguin): mAP = 0.19139, count = 139 (0.008%)\n",
      "--\n",
      "(person, next_to, ball/sports_ball): mAP = 0.18418, count = 3923 (0.225%)\n",
      "(person, in_front_of, person): mAP = 0.18349, count = 226650 (12.984%)\n",
      "(person, in_front_of, sofa): mAP = 0.18038, count = 12855 (0.736%)\n",
      "(person, in_front_of, chair): mAP = 0.16895, count = 7244 (0.415%)\n",
      "(person, next_to, dish): mAP = 0.15593, count = 1849 (0.106%)\n",
      "(person, in_front_of, tiger): mAP = 0.15152, count = 55 (0.003%)\n",
      "(person, in_front_of, dog): mAP = 0.14940, count = 5182 (0.297%)\n",
      "(person, next_to, bus/truck): mAP = 0.14848, count = 448 (0.026%)\n",
      "(person, above, watercraft): mAP = 0.14771, count = 493 (0.028%)\n",
      "(person, in_front_of, car): mAP = 0.14486, count = 2517 (0.144%)\n",
      "(person, in_front_of, hamster/rat): mAP = 0.13295, count = 151 (0.009%)\n",
      "(person, in_front_of, piano): mAP = 0.12248, count = 1348 (0.077%)\n",
      "(person, next_to, toy): mAP = 0.12077, count = 17121 (0.981%)\n",
      "(person, behind, cat): mAP = 0.11947, count = 844 (0.048%)\n",
      "(person, in_front_of, fish): mAP = 0.11466, count = 346 (0.020%)\n",
      "(person, next_to, cake): mAP = 0.11194, count = 1513 (0.087%)\n",
      "(person, next_to, cup): mAP = 0.11075, count = 4571 (0.262%)\n",
      "(person, away, car): mAP = 0.10250, count = 1018 (0.058%)\n",
      "(person, in_front_of, backpack): mAP = 0.09707, count = 3844 (0.220%)\n",
      "(person, next_to, backpack): mAP = 0.09463, count = 4168 (0.239%)\n",
      "(person, towards, sofa): mAP = 0.09161, count = 2370 (0.136%)\n",
      "(person, hold, camera): mAP = 0.09091, count = 136 (0.008%)\n",
      "(person, towards, toy): mAP = 0.09091, count = 3946 (0.226%)\n",
      "(person, away, person): mAP = 0.09091, count = 33817 (1.937%)\n",
      "(person, next_to, sink): mAP = 0.09091, count = 237 (0.014%)\n",
      "(person, next_to, faucet): mAP = 0.09091, count = 368 (0.021%)\n",
      "(person, above, chair): mAP = 0.09091, count = 1373 (0.079%)\n",
      "(person, watch, dog): mAP = 0.09091, count = 3348 (0.192%)\n",
      "(person, watch, guitar): mAP = 0.09091, count = 4137 (0.237%)\n",
      "(person, next_to, baby_walker): mAP = 0.08145, count = 2383 (0.137%)\n",
      "(person, behind, car): mAP = 0.07961, count = 1820 (0.104%)\n",
      "(person, behind, motorcycle): mAP = 0.07585, count = 383 (0.022%)\n",
      "(person, towards, stool): mAP = 0.07273, count = 316 (0.018%)\n",
      "(person, behind, dog): mAP = 0.06918, count = 3345 (0.192%)\n",
      "(person, watch, screen/monitor): mAP = 0.06818, count = 5479 (0.314%)\n",
      "(person, next_to, bicycle): mAP = 0.06632, count = 4232 (0.242%)\n",
      "(person, watch, cat): mAP = 0.06452, count = 998 (0.057%)\n",
      "(person, above, ski): mAP = 0.06061, count = 201 (0.012%)\n",
      "(person, in_front_of, baby_walker): mAP = 0.06028, count = 2699 (0.155%)\n",
      "(person, above, toy): mAP = 0.05768, count = 5230 (0.300%)\n",
      "(person, lean_on, chair): mAP = 0.05682, count = 1630 (0.093%)\n",
      "(person, next_to, motorcycle): mAP = 0.05497, count = 556 (0.032%)\n",
      "(person, next_to, screen/monitor): mAP = 0.05335, count = 9116 (0.522%)\n",
      "(person, behind, guitar): mAP = 0.05319, count = 5012 (0.287%)\n",
      "(person, next_to, cat): mAP = 0.05269, count = 1739 (0.100%)\n",
      "(person, next_to, car): mAP = 0.05099, count = 2377 (0.136%)\n",
      "(person, next_to, fish): mAP = 0.05033, count = 386 (0.022%)\n",
      "(person, behind, train): mAP = 0.04813, count = 115 (0.007%)\n",
      "(person, away, motorcycle): mAP = 0.04545, count = 182 (0.010%)\n",
      "(person, away, chair): mAP = 0.04545, count = 2221 (0.127%)\n",
      "(person, watch, toy): mAP = 0.04545, count = 10742 (0.615%)\n",
      "(person, lean_on, sink): mAP = 0.04545, count = 73 (0.004%)\n",
      "(person, beneath, toy): mAP = 0.04529, count = 2632 (0.151%)\n",
      "(person, watch, person): mAP = 0.04091, count = 107092 (6.135%)\n",
      "(person, in_front_of, refrigerator): mAP = 0.03987, count = 706 (0.040%)\n",
      "(person, away, stool): mAP = 0.03791, count = 359 (0.021%)\n",
      "(person, in_front_of, toy): mAP = 0.03737, count = 13782 (0.790%)\n",
      "(person, behind, toy): mAP = 0.03667, count = 10118 (0.580%)\n",
      "(person, next_to, train): mAP = 0.03125, count = 115 (0.007%)\n",
      "(person, ride, watercraft): mAP = 0.03030, count = 347 (0.020%)\n",
      "(person, next_to, bat): mAP = 0.02841, count = 335 (0.019%)\n",
      "(person, above, sofa): mAP = 0.02822, count = 5121 (0.293%)\n",
      "(person, in_front_of, ball/sports_ball): mAP = 0.02803, count = 3352 (0.192%)\n",
      "(person, in_front_of, baby_seat): mAP = 0.02799, count = 4022 (0.230%)\n",
      "(person, behind, person): mAP = 0.02749, count = 120238 (6.888%)\n",
      "(person, in_front_of, guitar): mAP = 0.02601, count = 6922 (0.397%)\n",
      "(person, hold, cup): mAP = 0.02273, count = 1751 (0.100%)\n",
      "(person, away, dog): mAP = 0.02273, count = 1874 (0.107%)\n",
      "(person, next_to, camera): mAP = 0.02139, count = 330 (0.019%)\n",
      "(person, inside, train): mAP = 0.02020, count = 42 (0.002%)\n",
      "(person, towards, car): mAP = 0.01884, count = 950 (0.054%)\n",
      "(person, next_to, horse): mAP = 0.01834, count = 1325 (0.076%)\n",
      "(person, towards, person): mAP = 0.01748, count = 30988 (1.775%)\n",
      "(person, in_front_of, bicycle): mAP = 0.01718, count = 3609 (0.207%)\n",
      "(person, next_to, sofa): mAP = 0.01682, count = 12376 (0.709%)\n",
      "(person, watch, car): mAP = 0.01563, count = 1637 (0.094%)\n",
      "(person, next_to, chair): mAP = 0.01553, count = 7636 (0.437%)\n",
      "(person, ride, ski): mAP = 0.01515, count = 181 (0.010%)\n",
      "(person, away, camera): mAP = 0.01515, count = 100 (0.006%)\n",
      "(person, away, bicycle): mAP = 0.01489, count = 1336 (0.077%)\n",
      "(person, next_to, ski): mAP = 0.01402, count = 303 (0.017%)\n",
      "(person, next_to, cellphone): mAP = 0.01364, count = 548 (0.031%)\n",
      "(person, towards, dog): mAP = 0.01299, count = 1825 (0.105%)\n",
      "(person, above, baby_walker): mAP = 0.01299, count = 1276 (0.073%)\n",
      "(person, in_front_of, aircraft): mAP = 0.01248, count = 377 (0.022%)\n",
      "(person, next_to, electric_fan): mAP = 0.01237, count = 148 (0.008%)\n",
      "(person, inside, toy): mAP = 0.01223, count = 2440 (0.140%)\n",
      "(person, above, cat): mAP = 0.01221, count = 654 (0.037%)\n",
      "(person, hold, guitar): mAP = 0.01212, count = 1388 (0.080%)\n",
      "(person, behind, horse): mAP = 0.01108, count = 894 (0.051%)\n",
      "(person, away, piano): mAP = 0.01070, count = 132 (0.008%)\n",
      "(person, ride, horse): mAP = 0.00909, count = 326 (0.019%)\n",
      "(person, behind, chair): mAP = 0.00870, count = 4268 (0.244%)\n",
      "(person, in_front_of, cup): mAP = 0.00718, count = 3365 (0.193%)\n",
      "(person, away, cup): mAP = 0.00699, count = 1199 (0.069%)\n",
      "(person, ride, bicycle): mAP = 0.00649, count = 1463 (0.084%)\n",
      "(person, towards, chair): mAP = 0.00649, count = 1831 (0.105%)\n",
      "(person, next_to, aircraft): mAP = 0.00647, count = 358 (0.021%)\n",
      "(person, ride, car): mAP = 0.00607, count = 413 (0.024%)\n",
      "(person, away, table): mAP = 0.00568, count = 2632 (0.151%)\n",
      "(person, in_front_of, cellphone): mAP = 0.00537, count = 446 (0.026%)\n",
      "(person, next_to, baby_seat): mAP = 0.00536, count = 4022 (0.230%)\n",
      "(person, ride, train): mAP = 0.00455, count = 37 (0.002%)\n",
      "(person, away, ski): mAP = 0.00455, count = 163 (0.009%)\n",
      "(person, hug, person): mAP = 0.00425, count = 34100 (1.953%)\n",
      "(person, hold, bottle): mAP = 0.00393, count = 1823 (0.104%)\n",
      "(person, above, baby_seat): mAP = 0.00373, count = 2671 (0.153%)\n",
      "(person, inside, car): mAP = 0.00308, count = 452 (0.026%)\n",
      "(person, away, train): mAP = 0.00284, count = 111 (0.006%)\n",
      "(person, away, sofa): mAP = 0.00260, count = 2332 (0.134%)\n",
      "(person, ride, toy): mAP = 0.00256, count = 1874 (0.107%)\n",
      "(person, away, toy): mAP = 0.00186, count = 4020 (0.230%)\n",
      "(person, lean_on, person): mAP = 0.00160, count = 52568 (3.011%)\n",
      "(person, carry, backpack): mAP = 0.00159, count = 653 (0.037%)\n",
      "(person, away, ball/sports_ball): mAP = 0.00159, count = 1037 (0.059%)\n",
      "(person, ride, baby_seat): mAP = 0.00154, count = 850 (0.049%)\n",
      "(person, towards, bicycle): mAP = 0.00154, count = 1329 (0.076%)\n",
      "(person, hug, cat): mAP = 0.00152, count = 476 (0.027%)\n",
      "(person, behind, laptop): mAP = 0.00152, count = 1304 (0.075%)\n",
      "(person, towards, cup): mAP = 0.00144, count = 1039 (0.060%)\n",
      "(person, lean_on, table): mAP = 0.00143, count = 6272 (0.359%)\n",
      "(person, beneath, bottle): mAP = 0.00131, count = 689 (0.039%)\n",
      "(person, hold, cellphone): mAP = 0.00130, count = 265 (0.015%)\n",
      "(person, behind, bicycle): mAP = 0.00128, count = 2980 (0.171%)\n",
      "(person, next_to, handbag): mAP = 0.00108, count = 2072 (0.119%)\n",
      "(person, above, dog): mAP = 0.00100, count = 2039 (0.117%)\n",
      "(person, towards, ball/sports_ball): mAP = 0.00099, count = 889 (0.051%)\n",
      "(person, watch, stool): mAP = 0.00087, count = 1293 (0.074%)\n",
      "(person, next_to, hamster/rat): mAP = 0.00083, count = 168 (0.010%)\n",
      "(person, beneath, person): mAP = 0.00079, count = 15272 (0.875%)\n",
      "(person, towards, screen/monitor): mAP = 0.00077, count = 2534 (0.145%)\n",
      "(person, watch, piano): mAP = 0.00072, count = 368 (0.021%)\n",
      "(person, away, screen/monitor): mAP = 0.00064, count = 2866 (0.164%)\n",
      "(person, away, backpack): mAP = 0.00054, count = 833 (0.048%)\n",
      "(person, caress, dog): mAP = 0.00051, count = 1126 (0.065%)\n",
      "(person, watch, ball/sports_ball): mAP = 0.00044, count = 1820 (0.104%)\n",
      "(person, watch, horse): mAP = 0.00043, count = 675 (0.039%)\n",
      "(person, hold, bicycle): mAP = 0.00034, count = 1331 (0.076%)\n",
      "(person, next_to, piano): mAP = 0.00030, count = 1139 (0.065%)\n",
      "(person, hold, person): mAP = 0.00028, count = 32938 (1.887%)\n",
      "(person, above, person): mAP = 0.00028, count = 47007 (2.693%)\n",
      "(person, inside, baby_seat): mAP = 0.00023, count = 2115 (0.121%)\n",
      "(person, watch, bicycle): mAP = 0.00021, count = 2532 (0.145%)\n",
      "(person, watch, cake): mAP = 0.00017, count = 564 (0.032%)\n",
      "(person, watch, chair): mAP = 0.00014, count = 4111 (0.236%)\n",
      "(person, hold, dog): mAP = 0.00014, count = 1752 (0.100%)\n",
      "(person, towards, guitar): mAP = 0.00011, count = 1447 (0.083%)\n",
      "(person, behind, table): mAP = 0.00011, count = 6531 (0.374%)\n",
      "(person, lean_on, bicycle): mAP = 0.00010, count = 1856 (0.106%)\n",
      "(person, behind, screen/monitor): mAP = 0.00006, count = 5504 (0.315%)\n",
      "(person, hold, toy): mAP = 0.00005, count = 6430 (0.368%)\n",
      "(person, chase, person): mAP = 0.00004, count = 4186 (0.240%)\n",
      "(person, hold_hand_of, person): mAP = 0.00003, count = 21543 (1.234%)\n",
      "(person, shout_at, person): mAP = 0.00000, count = 2860 (0.164%)\n",
      "(person, knock, person): mAP = 0.00000, count = 2369 (0.136%)\n",
      "(person, shake_hand_with, dog): mAP = 0.00000, count = 295 (0.017%)\n",
      "(person, shake_hand_with, person): mAP = 0.00000, count = 2482 (0.142%)\n",
      "(person, squeeze, toy): mAP = 0.00000, count = 731 (0.042%)\n",
      "(person, squeeze, person): mAP = 0.00000, count = 3651 (0.209%)\n",
      "(person, lick, bread): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, lick, person): mAP = 0.00000, count = 2346 (0.134%)\n",
      "(person, clean, hamster/rat): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, clean, horse): mAP = 0.00000, count = 72 (0.004%)\n",
      "(person, clean, dog): mAP = 0.00000, count = 444 (0.025%)\n",
      "(person, clean, person): mAP = 0.00000, count = 4267 (0.244%)\n",
      "(person, beneath, stop_sign): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, beneath, electric_fan): mAP = 0.00000, count = 13 (0.001%)\n",
      "(person, beneath, watercraft): mAP = 0.00000, count = 151 (0.009%)\n",
      "(person, beneath, cellphone): mAP = 0.00000, count = 75 (0.004%)\n",
      "(person, beneath, screen/monitor): mAP = 0.00000, count = 3066 (0.176%)\n",
      "(person, beneath, guitar): mAP = 0.00000, count = 660 (0.038%)\n",
      "(person, speak_to, dog): mAP = 0.00000, count = 1265 (0.072%)\n",
      "(person, speak_to, person): mAP = 0.00000, count = 26149 (1.498%)\n",
      "(person, kiss, cat): mAP = 0.00000, count = 102 (0.006%)\n",
      "(person, kiss, person): mAP = 0.00000, count = 6404 (0.367%)\n",
      "(person, chase, cattle/cow): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, chase, dog): mAP = 0.00000, count = 467 (0.027%)\n",
      "(person, chase, ball/sports_ball): mAP = 0.00000, count = 140 (0.008%)\n",
      "(person, feed, horse): mAP = 0.00000, count = 141 (0.008%)\n",
      "(person, feed, dog): mAP = 0.00000, count = 1005 (0.058%)\n",
      "(person, feed, cat): mAP = 0.00000, count = 196 (0.011%)\n",
      "(person, feed, person): mAP = 0.00000, count = 9601 (0.550%)\n",
      "(person, wave_hand_to, cattle/cow): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, wave_hand_to, cat): mAP = 0.00000, count = 60 (0.003%)\n",
      "(person, wave_hand_to, toy): mAP = 0.00000, count = 405 (0.023%)\n",
      "(person, wave_hand_to, person): mAP = 0.00000, count = 3355 (0.192%)\n",
      "(person, push, dish): mAP = 0.00000, count = 142 (0.008%)\n",
      "(person, push, baby_walker): mAP = 0.00000, count = 71 (0.004%)\n",
      "(person, push, dog): mAP = 0.00000, count = 930 (0.053%)\n",
      "(person, push, bicycle): mAP = 0.00000, count = 743 (0.043%)\n",
      "(person, push, ball/sports_ball): mAP = 0.00000, count = 323 (0.019%)\n",
      "(person, push, baby_seat): mAP = 0.00000, count = 468 (0.027%)\n",
      "(person, push, toy): mAP = 0.00000, count = 1546 (0.089%)\n",
      "(person, push, chair): mAP = 0.00000, count = 511 (0.029%)\n",
      "(person, push, person): mAP = 0.00000, count = 10747 (0.616%)\n",
      "(person, get_off, bicycle): mAP = 0.00000, count = 237 (0.014%)\n",
      "(person, get_off, toy): mAP = 0.00000, count = 231 (0.013%)\n",
      "(person, drive, bus/truck): mAP = 0.00000, count = 69 (0.004%)\n",
      "(person, drive, motorcycle): mAP = 0.00000, count = 67 (0.004%)\n",
      "(person, drive, toy): mAP = 0.00000, count = 392 (0.022%)\n",
      "(person, drive, car): mAP = 0.00000, count = 192 (0.011%)\n",
      "(person, kick, ball/sports_ball): mAP = 0.00000, count = 100 (0.006%)\n",
      "(person, kick, person): mAP = 0.00000, count = 3003 (0.172%)\n",
      "(person, throw, ball/sports_ball): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, throw, toy): mAP = 0.00000, count = 217 (0.012%)\n",
      "(person, close, faucet): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, close, oven): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, open, oven): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, open, refrigerator): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, bite, bread): mAP = 0.00000, count = 13 (0.001%)\n",
      "(person, bite, fruits): mAP = 0.00000, count = 4 (0.000%)\n",
      "(person, bite, ball/sports_ball): mAP = 0.00000, count = 320 (0.018%)\n",
      "(person, bite, bottle): mAP = 0.00000, count = 352 (0.020%)\n",
      "(person, bite, toy): mAP = 0.00000, count = 1899 (0.109%)\n",
      "(person, bite, person): mAP = 0.00000, count = 5868 (0.336%)\n",
      "(person, hug, vegetables): mAP = 0.00000, count = 27 (0.002%)\n",
      "(person, hug, ball/sports_ball): mAP = 0.00000, count = 615 (0.035%)\n",
      "(person, hug, toy): mAP = 0.00000, count = 4336 (0.248%)\n",
      "(person, hug, guitar): mAP = 0.00000, count = 836 (0.048%)\n",
      "(person, cut, fish): mAP = 0.00000, count = 5 (0.000%)\n",
      "(person, cut, vegetables): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, get_on, bicycle): mAP = 0.00000, count = 240 (0.014%)\n",
      "(person, get_on, sofa): mAP = 0.00000, count = 202 (0.012%)\n",
      "(person, get_on, toy): mAP = 0.00000, count = 229 (0.013%)\n",
      "(person, pull, horse): mAP = 0.00000, count = 179 (0.010%)\n",
      "(person, pull, dish): mAP = 0.00000, count = 93 (0.005%)\n",
      "(person, pull, baby_walker): mAP = 0.00000, count = 54 (0.003%)\n",
      "(person, pull, suitcase): mAP = 0.00000, count = 1 (0.000%)\n",
      "(person, pull, ski): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, pull, dog): mAP = 0.00000, count = 808 (0.046%)\n",
      "(person, pull, motorcycle): mAP = 0.00000, count = 70 (0.004%)\n",
      "(person, pull, toy): mAP = 0.00000, count = 1182 (0.068%)\n",
      "(person, pull, chair): mAP = 0.00000, count = 355 (0.020%)\n",
      "(person, pull, person): mAP = 0.00000, count = 7916 (0.453%)\n",
      "(person, caress, hamster/rat): mAP = 0.00000, count = 20 (0.001%)\n",
      "(person, caress, sheep/goat): mAP = 0.00000, count = 1 (0.000%)\n",
      "(person, caress, horse): mAP = 0.00000, count = 169 (0.010%)\n",
      "(person, caress, cat): mAP = 0.00000, count = 287 (0.016%)\n",
      "(person, caress, person): mAP = 0.00000, count = 16106 (0.923%)\n",
      "(person, inside, sink): mAP = 0.00000, count = 36 (0.002%)\n",
      "(person, inside, watercraft): mAP = 0.00000, count = 268 (0.015%)\n",
      "(person, inside, baby_walker): mAP = 0.00000, count = 918 (0.053%)\n",
      "(person, inside, bus/truck): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, inside, cup): mAP = 0.00000, count = 140 (0.008%)\n",
      "(person, inside, dog): mAP = 0.00000, count = 1026 (0.059%)\n",
      "(person, inside, person): mAP = 0.00000, count = 23661 (1.355%)\n",
      "(person, press, microwave): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, press, faucet): mAP = 0.00000, count = 5 (0.000%)\n",
      "(person, press, aircraft): mAP = 0.00000, count = 66 (0.004%)\n",
      "(person, press, cellphone): mAP = 0.00000, count = 38 (0.002%)\n",
      "(person, press, screen/monitor): mAP = 0.00000, count = 696 (0.040%)\n",
      "(person, press, sofa): mAP = 0.00000, count = 1300 (0.074%)\n",
      "(person, press, table): mAP = 0.00000, count = 1486 (0.085%)\n",
      "(person, press, laptop): mAP = 0.00000, count = 294 (0.017%)\n",
      "(person, press, ball/sports_ball): mAP = 0.00000, count = 293 (0.017%)\n",
      "(person, press, baby_seat): mAP = 0.00000, count = 616 (0.035%)\n",
      "(person, press, toy): mAP = 0.00000, count = 2153 (0.123%)\n",
      "(person, press, person): mAP = 0.00000, count = 7991 (0.458%)\n",
      "(person, use, cellphone): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, use, camera): mAP = 0.00000, count = 50 (0.003%)\n",
      "(person, use, laptop): mAP = 0.00000, count = 151 (0.009%)\n",
      "(person, lift, cup): mAP = 0.00000, count = 233 (0.013%)\n",
      "(person, lift, bicycle): mAP = 0.00000, count = 338 (0.019%)\n",
      "(person, lift, camera): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, lift, ball/sports_ball): mAP = 0.00000, count = 200 (0.011%)\n",
      "(person, lift, bottle): mAP = 0.00000, count = 93 (0.005%)\n",
      "(person, lift, cat): mAP = 0.00000, count = 91 (0.005%)\n",
      "(person, lift, toy): mAP = 0.00000, count = 1082 (0.062%)\n",
      "(person, lift, person): mAP = 0.00000, count = 5085 (0.291%)\n",
      "(person, grab, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, grab, hamster/rat): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, grab, bat): mAP = 0.00000, count = 44 (0.003%)\n",
      "(person, grab, cup): mAP = 0.00000, count = 359 (0.021%)\n",
      "(person, grab, fruits): mAP = 0.00000, count = 5 (0.000%)\n",
      "(person, grab, camera): mAP = 0.00000, count = 32 (0.002%)\n",
      "(person, grab, ball/sports_ball): mAP = 0.00000, count = 194 (0.011%)\n",
      "(person, grab, bottle): mAP = 0.00000, count = 238 (0.014%)\n",
      "(person, grab, cat): mAP = 0.00000, count = 57 (0.003%)\n",
      "(person, grab, toy): mAP = 0.00000, count = 959 (0.055%)\n",
      "(person, grab, handbag): mAP = 0.00000, count = 103 (0.006%)\n",
      "(person, grab, guitar): mAP = 0.00000, count = 235 (0.013%)\n",
      "(person, grab, person): mAP = 0.00000, count = 3501 (0.201%)\n",
      "(person, ride, cattle/cow): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, ride, scooter): mAP = 0.00000, count = 25 (0.001%)\n",
      "(person, ride, elephant): mAP = 0.00000, count = 12 (0.001%)\n",
      "(person, ride, bus/truck): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, ride, skateboard): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, ride, snowboard): mAP = 0.00000, count = 68 (0.004%)\n",
      "(person, ride, surfboard): mAP = 0.00000, count = 74 (0.004%)\n",
      "(person, ride, motorcycle): mAP = 0.00000, count = 150 (0.009%)\n",
      "(person, release, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, release, hamster/rat): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, release, dish): mAP = 0.00000, count = 41 (0.002%)\n",
      "(person, release, bat): mAP = 0.00000, count = 48 (0.003%)\n",
      "(person, release, stool): mAP = 0.00000, count = 40 (0.002%)\n",
      "(person, release, cup): mAP = 0.00000, count = 391 (0.022%)\n",
      "(person, release, cellphone): mAP = 0.00000, count = 17 (0.001%)\n",
      "(person, release, dog): mAP = 0.00000, count = 503 (0.029%)\n",
      "(person, release, ball/sports_ball): mAP = 0.00000, count = 199 (0.011%)\n",
      "(person, release, motorcycle): mAP = 0.00000, count = 57 (0.003%)\n",
      "(person, release, bottle): mAP = 0.00000, count = 228 (0.013%)\n",
      "(person, release, cat): mAP = 0.00000, count = 63 (0.004%)\n",
      "(person, release, toy): mAP = 0.00000, count = 1065 (0.061%)\n",
      "(person, release, handbag): mAP = 0.00000, count = 100 (0.006%)\n",
      "(person, release, chair): mAP = 0.00000, count = 207 (0.012%)\n",
      "(person, release, guitar): mAP = 0.00000, count = 225 (0.013%)\n",
      "(person, release, person): mAP = 0.00000, count = 3871 (0.222%)\n",
      "(person, play(instrument), piano): mAP = 0.00000, count = 84 (0.005%)\n",
      "(person, touch, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, touch, hamster/rat): mAP = 0.00000, count = 23 (0.001%)\n",
      "(person, touch, penguin): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, touch, horse): mAP = 0.00000, count = 149 (0.009%)\n",
      "(person, touch, dish): mAP = 0.00000, count = 173 (0.010%)\n",
      "(person, touch, stool): mAP = 0.00000, count = 142 (0.008%)\n",
      "(person, touch, cake): mAP = 0.00000, count = 36 (0.002%)\n",
      "(person, touch, dog): mAP = 0.00000, count = 958 (0.055%)\n",
      "(person, touch, vegetables): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, touch, screen/monitor): mAP = 0.00000, count = 999 (0.057%)\n",
      "(person, touch, ball/sports_ball): mAP = 0.00000, count = 327 (0.019%)\n",
      "(person, touch, cat): mAP = 0.00000, count = 186 (0.011%)\n",
      "(person, touch, toy): mAP = 0.00000, count = 2103 (0.120%)\n",
      "(person, touch, chair): mAP = 0.00000, count = 552 (0.032%)\n",
      "(person, touch, person): mAP = 0.00000, count = 9808 (0.562%)\n",
      "(person, point_to, cake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, point_to, screen/monitor): mAP = 0.00000, count = 748 (0.043%)\n",
      "(person, point_to, ball/sports_ball): mAP = 0.00000, count = 161 (0.009%)\n",
      "(person, point_to, toy): mAP = 0.00000, count = 927 (0.053%)\n",
      "(person, point_to, person): mAP = 0.00000, count = 5270 (0.302%)\n",
      "(person, carry, crocodile): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, carry, bird): mAP = 0.00000, count = 40 (0.002%)\n",
      "(person, carry, camera): mAP = 0.00000, count = 50 (0.003%)\n",
      "(person, carry, ball/sports_ball): mAP = 0.00000, count = 531 (0.030%)\n",
      "(person, carry, bottle): mAP = 0.00000, count = 403 (0.023%)\n",
      "(person, carry, handbag): mAP = 0.00000, count = 228 (0.013%)\n",
      "(person, carry, guitar): mAP = 0.00000, count = 939 (0.054%)\n",
      "(person, carry, person): mAP = 0.00000, count = 12447 (0.713%)\n",
      "(person, pat, horse): mAP = 0.00000, count = 126 (0.007%)\n",
      "(person, pat, stool): mAP = 0.00000, count = 81 (0.005%)\n",
      "(person, pat, dog): mAP = 0.00000, count = 718 (0.041%)\n",
      "(person, pat, ball/sports_ball): mAP = 0.00000, count = 192 (0.011%)\n",
      "(person, pat, baby_seat): mAP = 0.00000, count = 534 (0.031%)\n",
      "(person, pat, toy): mAP = 0.00000, count = 1514 (0.087%)\n",
      "(person, pat, guitar): mAP = 0.00000, count = 310 (0.018%)\n",
      "(person, pat, person): mAP = 0.00000, count = 7643 (0.438%)\n",
      "(person, wave, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, wave, bat): mAP = 0.00000, count = 67 (0.004%)\n",
      "(person, wave, ball/sports_ball): mAP = 0.00000, count = 301 (0.017%)\n",
      "(person, wave, toy): mAP = 0.00000, count = 1703 (0.098%)\n",
      "(person, wave, person): mAP = 0.00000, count = 5149 (0.295%)\n",
      "(person, hold, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, hold, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, hold, hamster/rat): mAP = 0.00000, count = 76 (0.004%)\n",
      "(person, hold, crab): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, hold, dish): mAP = 0.00000, count = 329 (0.019%)\n",
      "(person, hold, fish): mAP = 0.00000, count = 93 (0.005%)\n",
      "(person, hold, bat): mAP = 0.00000, count = 153 (0.009%)\n",
      "(person, hold, ski): mAP = 0.00000, count = 159 (0.009%)\n",
      "(person, hold, bread): mAP = 0.00000, count = 63 (0.004%)\n",
      "(person, hold, fruits): mAP = 0.00000, count = 56 (0.003%)\n",
      "(person, hold, vegetables): mAP = 0.00000, count = 33 (0.002%)\n",
      "(person, hold, screen/monitor): mAP = 0.00000, count = 2243 (0.128%)\n",
      "(person, hold, ball/sports_ball): mAP = 0.00000, count = 1298 (0.074%)\n",
      "(person, hold, motorcycle): mAP = 0.00000, count = 123 (0.007%)\n",
      "(person, hold, cat): mAP = 0.00000, count = 535 (0.031%)\n",
      "(person, hold, handbag): mAP = 0.00000, count = 447 (0.026%)\n",
      "(person, hit, dog): mAP = 0.00000, count = 763 (0.044%)\n",
      "(person, hit, ball/sports_ball): mAP = 0.00000, count = 196 (0.011%)\n",
      "(person, hit, toy): mAP = 0.00000, count = 1141 (0.065%)\n",
      "(person, hit, car): mAP = 0.00000, count = 192 (0.011%)\n",
      "(person, hit, person): mAP = 0.00000, count = 6270 (0.359%)\n",
      "(person, in_front_of, traffic_light): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, in_front_of, microwave): mAP = 0.00000, count = 196 (0.011%)\n",
      "(person, in_front_of, crocodile): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, in_front_of, faucet): mAP = 0.00000, count = 415 (0.024%)\n",
      "(person, in_front_of, oven): mAP = 0.00000, count = 147 (0.008%)\n",
      "(person, in_front_of, sheep/goat): mAP = 0.00000, count = 39 (0.002%)\n",
      "(person, in_front_of, cattle/cow): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, in_front_of, elephant): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, in_front_of, bus/truck): mAP = 0.00000, count = 485 (0.028%)\n",
      "(person, in_front_of, bench): mAP = 0.00000, count = 137 (0.008%)\n",
      "(person, in_front_of, camera): mAP = 0.00000, count = 335 (0.019%)\n",
      "(person, in_front_of, table): mAP = 0.00000, count = 13080 (0.749%)\n",
      "(person, in_front_of, motorcycle): mAP = 0.00000, count = 420 (0.024%)\n",
      "(person, in_front_of, handbag): mAP = 0.00000, count = 1780 (0.102%)\n",
      "(person, towards, traffic_light): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, towards, sink): mAP = 0.00000, count = 38 (0.002%)\n",
      "(person, towards, stop_sign): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, towards, faucet): mAP = 0.00000, count = 21 (0.001%)\n",
      "(person, towards, aircraft): mAP = 0.00000, count = 159 (0.009%)\n",
      "(person, towards, frisbee): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, towards, hamster/rat): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, towards, train): mAP = 0.00000, count = 94 (0.005%)\n",
      "(person, towards, electric_fan): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, towards, cattle/cow): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, towards, piano): mAP = 0.00000, count = 106 (0.006%)\n",
      "(person, towards, horse): mAP = 0.00000, count = 363 (0.021%)\n",
      "(person, towards, refrigerator): mAP = 0.00000, count = 446 (0.026%)\n",
      "(person, towards, dish): mAP = 0.00000, count = 165 (0.009%)\n",
      "(person, towards, baby_walker): mAP = 0.00000, count = 344 (0.020%)\n",
      "(person, towards, bat): mAP = 0.00000, count = 184 (0.011%)\n",
      "(person, towards, ski): mAP = 0.00000, count = 151 (0.009%)\n",
      "(person, towards, bus/truck): mAP = 0.00000, count = 143 (0.008%)\n",
      "(person, towards, bench): mAP = 0.00000, count = 61 (0.003%)\n",
      "(person, towards, cellphone): mAP = 0.00000, count = 70 (0.004%)\n",
      "(person, towards, cake): mAP = 0.00000, count = 37 (0.002%)\n",
      "(person, towards, fruits): mAP = 0.00000, count = 12 (0.001%)\n",
      "(person, towards, vegetables): mAP = 0.00000, count = 17 (0.001%)\n",
      "(person, towards, camera): mAP = 0.00000, count = 66 (0.004%)\n",
      "(person, towards, table): mAP = 0.00000, count = 2543 (0.146%)\n",
      "(person, towards, backpack): mAP = 0.00000, count = 840 (0.048%)\n",
      "(person, towards, bottle): mAP = 0.00000, count = 681 (0.039%)\n",
      "(person, towards, cat): mAP = 0.00000, count = 273 (0.016%)\n",
      "(person, towards, baby_seat): mAP = 0.00000, count = 639 (0.037%)\n",
      "(person, towards, handbag): mAP = 0.00000, count = 316 (0.018%)\n",
      "(person, away, traffic_light): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, away, sink): mAP = 0.00000, count = 29 (0.002%)\n",
      "(person, away, stop_sign): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, away, antelope): mAP = 0.00000, count = 3 (0.000%)\n",
      "(person, away, faucet): mAP = 0.00000, count = 56 (0.003%)\n",
      "(person, away, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, away, aircraft): mAP = 0.00000, count = 182 (0.010%)\n",
      "(person, away, hamster/rat): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, away, electric_fan): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, away, cattle/cow): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, away, horse): mAP = 0.00000, count = 398 (0.023%)\n",
      "(person, away, refrigerator): mAP = 0.00000, count = 443 (0.025%)\n",
      "(person, away, watercraft): mAP = 0.00000, count = 441 (0.025%)\n",
      "(person, away, baby_walker): mAP = 0.00000, count = 343 (0.020%)\n",
      "(person, away, bat): mAP = 0.00000, count = 194 (0.011%)\n",
      "(person, away, bus/truck): mAP = 0.00000, count = 191 (0.011%)\n",
      "(person, away, snowboard): mAP = 0.00000, count = 32 (0.002%)\n",
      "(person, away, bench): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, away, cellphone): mAP = 0.00000, count = 97 (0.006%)\n",
      "(person, away, cake): mAP = 0.00000, count = 46 (0.003%)\n",
      "(person, away, fruits): mAP = 0.00000, count = 14 (0.001%)\n",
      "(person, away, vegetables): mAP = 0.00000, count = 20 (0.001%)\n",
      "(person, away, bottle): mAP = 0.00000, count = 797 (0.046%)\n",
      "(person, away, cat): mAP = 0.00000, count = 280 (0.016%)\n",
      "(person, away, baby_seat): mAP = 0.00000, count = 629 (0.036%)\n",
      "(person, away, handbag): mAP = 0.00000, count = 365 (0.021%)\n",
      "(person, away, guitar): mAP = 0.00000, count = 1544 (0.088%)\n",
      "(person, behind, aircraft): mAP = 0.00000, count = 280 (0.016%)\n",
      "(person, behind, hamster/rat): mAP = 0.00000, count = 82 (0.005%)\n",
      "(person, behind, sheep/goat): mAP = 0.00000, count = 56 (0.003%)\n",
      "(person, behind, cattle/cow): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, behind, piano): mAP = 0.00000, count = 354 (0.020%)\n",
      "(person, behind, baby_walker): mAP = 0.00000, count = 1539 (0.088%)\n",
      "(person, behind, elephant): mAP = 0.00000, count = 26 (0.001%)\n",
      "(person, behind, ski): mAP = 0.00000, count = 229 (0.013%)\n",
      "(person, behind, bus/truck): mAP = 0.00000, count = 325 (0.019%)\n",
      "(person, behind, bench): mAP = 0.00000, count = 74 (0.004%)\n",
      "(person, behind, cellphone): mAP = 0.00000, count = 224 (0.013%)\n",
      "(person, behind, sofa): mAP = 0.00000, count = 7158 (0.410%)\n",
      "(person, behind, camera): mAP = 0.00000, count = 180 (0.010%)\n",
      "(person, behind, ball/sports_ball): mAP = 0.00000, count = 1889 (0.108%)\n",
      "(person, behind, backpack): mAP = 0.00000, count = 2484 (0.142%)\n",
      "(person, behind, baby_seat): mAP = 0.00000, count = 2826 (0.162%)\n",
      "(person, behind, handbag): mAP = 0.00000, count = 780 (0.045%)\n",
      "(person, next_to, microwave): mAP = 0.00000, count = 172 (0.010%)\n",
      "(person, next_to, crocodile): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, stingray): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, oven): mAP = 0.00000, count = 128 (0.007%)\n",
      "(person, next_to, frisbee): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, camel): mAP = 0.00000, count = 84 (0.005%)\n",
      "(person, next_to, chicken): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, next_to, cattle/cow): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, next_to, crab): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, refrigerator): mAP = 0.00000, count = 669 (0.038%)\n",
      "(person, next_to, scooter): mAP = 0.00000, count = 47 (0.003%)\n",
      "(person, next_to, suitcase): mAP = 0.00000, count = 562 (0.032%)\n",
      "(person, next_to, bread): mAP = 0.00000, count = 122 (0.007%)\n",
      "(person, next_to, snowboard): mAP = 0.00000, count = 132 (0.008%)\n",
      "(person, next_to, bench): mAP = 0.00000, count = 224 (0.013%)\n",
      "(person, next_to, fruits): mAP = 0.00000, count = 426 (0.024%)\n",
      "(person, next_to, laptop): mAP = 0.00000, count = 2062 (0.118%)\n",
      "(person, above, cattle/cow): mAP = 0.00000, count = 5 (0.000%)\n",
      "(person, above, scooter): mAP = 0.00000, count = 27 (0.002%)\n",
      "(person, above, elephant): mAP = 0.00000, count = 13 (0.001%)\n",
      "(person, above, skateboard): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, above, snowboard): mAP = 0.00000, count = 84 (0.005%)\n",
      "(person, above, bench): mAP = 0.00000, count = 33 (0.002%)\n",
      "(person, above, screen/monitor): mAP = 0.00000, count = 1126 (0.065%)\n",
      "(person, above, surfboard): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, above, table): mAP = 0.00000, count = 5834 (0.334%)\n",
      "(person, above, backpack): mAP = 0.00000, count = 1193 (0.068%)\n",
      "(person, watch, microwave): mAP = 0.00000, count = 70 (0.004%)\n",
      "(person, watch, bear): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, watch, tiger): mAP = 0.00000, count = 53 (0.003%)\n",
      "(person, watch, duck): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, watch, faucet): mAP = 0.00000, count = 322 (0.018%)\n",
      "(person, watch, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, watch, oven): mAP = 0.00000, count = 111 (0.006%)\n",
      "(person, watch, aircraft): mAP = 0.00000, count = 265 (0.015%)\n",
      "(person, watch, frisbee): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, watch, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, watch, train): mAP = 0.00000, count = 105 (0.006%)\n",
      "(person, watch, sheep/goat): mAP = 0.00000, count = 9 (0.001%)\n",
      "(person, watch, lion): mAP = 0.00000, count = 14 (0.001%)\n",
      "(person, watch, cattle/cow): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, watch, bird): mAP = 0.00000, count = 246 (0.014%)\n",
      "(person, watch, refrigerator): mAP = 0.00000, count = 528 (0.030%)\n",
      "(person, watch, scooter): mAP = 0.00000, count = 39 (0.002%)\n",
      "(person, watch, dish): mAP = 0.00000, count = 667 (0.038%)\n",
      "(person, watch, baby_walker): mAP = 0.00000, count = 1424 (0.082%)\n",
      "(person, watch, fish): mAP = 0.00000, count = 188 (0.011%)\n",
      "(person, watch, bat): mAP = 0.00000, count = 215 (0.012%)\n",
      "(person, watch, bread): mAP = 0.00000, count = 75 (0.004%)\n",
      "(person, watch, cup): mAP = 0.00000, count = 2100 (0.120%)\n",
      "(person, watch, cellphone): mAP = 0.00000, count = 302 (0.017%)\n",
      "(person, watch, fruits): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, watch, vegetables): mAP = 0.00000, count = 88 (0.005%)\n",
      "(person, watch, sofa): mAP = 0.00000, count = 8107 (0.464%)\n",
      "(person, watch, camera): mAP = 0.00000, count = 179 (0.010%)\n",
      "(person, watch, table): mAP = 0.00000, count = 7338 (0.420%)\n",
      "(person, watch, laptop): mAP = 0.00000, count = 1508 (0.086%)\n",
      "(person, watch, motorcycle): mAP = 0.00000, count = 282 (0.016%)\n",
      "(person, watch, backpack): mAP = 0.00000, count = 2198 (0.126%)\n",
      "(person, watch, bottle): mAP = 0.00000, count = 2244 (0.129%)\n",
      "(person, watch, baby_seat): mAP = 0.00000, count = 2750 (0.158%)\n",
      "(person, watch, handbag): mAP = 0.00000, count = 577 (0.033%)\n",
      "(person, lean_on, stool): mAP = 0.00000, count = 339 (0.019%)\n",
      "(person, lean_on, bus/truck): mAP = 0.00000, count = 112 (0.006%)\n",
      "(person, lean_on, bench): mAP = 0.00000, count = 18 (0.001%)\n",
      "(person, lean_on, ball/sports_ball): mAP = 0.00000, count = 513 (0.029%)\n",
      "(person, lean_on, car): mAP = 0.00000, count = 824 (0.047%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP\n",
    "sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx == 30:\n",
    "        print('--')\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, hug, dog): mAP = 0.27273, count = 1280 (0.073%)\n",
      "(person, hug, person): mAP = 0.00425, count = 34100 (1.953%)\n",
      "(person, hug, cat): mAP = 0.00152, count = 476 (0.027%)\n",
      "(person, hug, vegetables): mAP = 0.00000, count = 27 (0.002%)\n",
      "(person, hug, ball/sports_ball): mAP = 0.00000, count = 615 (0.035%)\n",
      "(person, hug, toy): mAP = 0.00000, count = 4336 (0.248%)\n",
      "(person, hug, guitar): mAP = 0.00000, count = 836 (0.048%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP and view only \"hug\"\n",
    "# sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] != 'hug':\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, lean_on, baby_walker): mAP = 0.53086, count = 1510 (0.087%)\n",
      "(person, lean_on, sofa): mAP = 0.31995, count = 5951 (0.341%)\n",
      "(person, lean_on, baby_seat): mAP = 0.27140, count = 3038 (0.174%)\n",
      "(person, lean_on, toy): mAP = 0.21808, count = 5060 (0.290%)\n",
      "(person, lean_on, chair): mAP = 0.05682, count = 1630 (0.093%)\n",
      "(person, lean_on, sink): mAP = 0.04545, count = 73 (0.004%)\n",
      "(person, lean_on, person): mAP = 0.00160, count = 52568 (3.011%)\n",
      "(person, lean_on, table): mAP = 0.00143, count = 6272 (0.359%)\n",
      "(person, lean_on, bicycle): mAP = 0.00010, count = 1856 (0.106%)\n",
      "(person, lean_on, stool): mAP = 0.00000, count = 339 (0.019%)\n",
      "(person, lean_on, bus/truck): mAP = 0.00000, count = 112 (0.006%)\n",
      "(person, lean_on, bench): mAP = 0.00000, count = 18 (0.001%)\n",
      "(person, lean_on, ball/sports_ball): mAP = 0.00000, count = 513 (0.029%)\n",
      "(person, lean_on, car): mAP = 0.00000, count = 824 (0.047%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP and view only \"lean_on\"\n",
    "# sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] != 'lean_on':\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, ride, watercraft): mAP = 0.03030, count = 347 (0.020%)\n",
      "(person, ride, ski): mAP = 0.01515, count = 181 (0.010%)\n",
      "(person, ride, horse): mAP = 0.00909, count = 326 (0.019%)\n",
      "(person, ride, bicycle): mAP = 0.00649, count = 1463 (0.084%)\n",
      "(person, ride, car): mAP = 0.00607, count = 413 (0.024%)\n",
      "(person, ride, train): mAP = 0.00455, count = 37 (0.002%)\n",
      "(person, ride, toy): mAP = 0.00256, count = 1874 (0.107%)\n",
      "(person, ride, baby_seat): mAP = 0.00154, count = 850 (0.049%)\n",
      "(person, ride, cattle/cow): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, ride, scooter): mAP = 0.00000, count = 25 (0.001%)\n",
      "(person, ride, elephant): mAP = 0.00000, count = 12 (0.001%)\n",
      "(person, ride, bus/truck): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, ride, skateboard): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, ride, snowboard): mAP = 0.00000, count = 68 (0.004%)\n",
      "(person, ride, surfboard): mAP = 0.00000, count = 74 (0.004%)\n",
      "(person, ride, motorcycle): mAP = 0.00000, count = 150 (0.009%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP and view only \"ride\"\n",
    "# sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] != 'ride':\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, towards, sofa): mAP = 0.09161, count = 2370 (0.136%)\n",
      "(person, towards, toy): mAP = 0.09091, count = 3946 (0.226%)\n",
      "(person, towards, stool): mAP = 0.07273, count = 316 (0.018%)\n",
      "(person, towards, car): mAP = 0.01884, count = 950 (0.054%)\n",
      "(person, towards, person): mAP = 0.01748, count = 30988 (1.775%)\n",
      "(person, towards, dog): mAP = 0.01299, count = 1825 (0.105%)\n",
      "(person, towards, chair): mAP = 0.00649, count = 1831 (0.105%)\n",
      "(person, towards, bicycle): mAP = 0.00154, count = 1329 (0.076%)\n",
      "(person, towards, cup): mAP = 0.00144, count = 1039 (0.060%)\n",
      "(person, towards, ball/sports_ball): mAP = 0.00099, count = 889 (0.051%)\n",
      "(person, towards, screen/monitor): mAP = 0.00077, count = 2534 (0.145%)\n",
      "(person, towards, guitar): mAP = 0.00011, count = 1447 (0.083%)\n",
      "(person, towards, traffic_light): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, towards, sink): mAP = 0.00000, count = 38 (0.002%)\n",
      "(person, towards, stop_sign): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, towards, faucet): mAP = 0.00000, count = 21 (0.001%)\n",
      "(person, towards, aircraft): mAP = 0.00000, count = 159 (0.009%)\n",
      "(person, towards, frisbee): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, towards, hamster/rat): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, towards, train): mAP = 0.00000, count = 94 (0.005%)\n",
      "(person, towards, electric_fan): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, towards, cattle/cow): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, towards, piano): mAP = 0.00000, count = 106 (0.006%)\n",
      "(person, towards, horse): mAP = 0.00000, count = 363 (0.021%)\n",
      "(person, towards, refrigerator): mAP = 0.00000, count = 446 (0.026%)\n",
      "(person, towards, dish): mAP = 0.00000, count = 165 (0.009%)\n",
      "(person, towards, baby_walker): mAP = 0.00000, count = 344 (0.020%)\n",
      "(person, towards, bat): mAP = 0.00000, count = 184 (0.011%)\n",
      "(person, towards, ski): mAP = 0.00000, count = 151 (0.009%)\n",
      "(person, towards, bus/truck): mAP = 0.00000, count = 143 (0.008%)\n",
      "(person, towards, bench): mAP = 0.00000, count = 61 (0.003%)\n",
      "(person, towards, cellphone): mAP = 0.00000, count = 70 (0.004%)\n",
      "(person, towards, cake): mAP = 0.00000, count = 37 (0.002%)\n",
      "(person, towards, fruits): mAP = 0.00000, count = 12 (0.001%)\n",
      "(person, towards, vegetables): mAP = 0.00000, count = 17 (0.001%)\n",
      "(person, towards, camera): mAP = 0.00000, count = 66 (0.004%)\n",
      "(person, towards, table): mAP = 0.00000, count = 2543 (0.146%)\n",
      "(person, towards, backpack): mAP = 0.00000, count = 840 (0.048%)\n",
      "(person, towards, bottle): mAP = 0.00000, count = 681 (0.039%)\n",
      "(person, towards, cat): mAP = 0.00000, count = 273 (0.016%)\n",
      "(person, towards, baby_seat): mAP = 0.00000, count = 639 (0.037%)\n",
      "(person, towards, handbag): mAP = 0.00000, count = 316 (0.018%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP and view only \"towards\"\n",
    "# sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] != 'towards':\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, hug, dog): mAP = 0.27273, count = 1280 (0.073%)\n",
      "(person, hug, person): mAP = 0.00425, count = 34100 (1.953%)\n",
      "(person, hug, cat): mAP = 0.00152, count = 476 (0.027%)\n",
      "(person, hug, vegetables): mAP = 0.00000, count = 27 (0.002%)\n",
      "(person, hug, ball/sports_ball): mAP = 0.00000, count = 615 (0.035%)\n",
      "(person, hug, toy): mAP = 0.00000, count = 4336 (0.248%)\n",
      "(person, hug, guitar): mAP = 0.00000, count = 836 (0.048%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP and view only \"hug\"\n",
    "# sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] != 'hug':\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, away, car): mAP = 0.10250, count = 1018 (0.058%)\n",
      "(person, away, person): mAP = 0.09091, count = 33817 (1.937%)\n",
      "(person, away, motorcycle): mAP = 0.04545, count = 182 (0.010%)\n",
      "(person, away, chair): mAP = 0.04545, count = 2221 (0.127%)\n",
      "(person, away, stool): mAP = 0.03791, count = 359 (0.021%)\n",
      "(person, away, dog): mAP = 0.02273, count = 1874 (0.107%)\n",
      "(person, away, camera): mAP = 0.01515, count = 100 (0.006%)\n",
      "(person, away, bicycle): mAP = 0.01489, count = 1336 (0.077%)\n",
      "(person, away, piano): mAP = 0.01070, count = 132 (0.008%)\n",
      "(person, away, cup): mAP = 0.00699, count = 1199 (0.069%)\n",
      "(person, away, table): mAP = 0.00568, count = 2632 (0.151%)\n",
      "(person, away, ski): mAP = 0.00455, count = 163 (0.009%)\n",
      "(person, away, train): mAP = 0.00284, count = 111 (0.006%)\n",
      "(person, away, sofa): mAP = 0.00260, count = 2332 (0.134%)\n",
      "(person, away, toy): mAP = 0.00186, count = 4020 (0.230%)\n",
      "(person, away, ball/sports_ball): mAP = 0.00159, count = 1037 (0.059%)\n",
      "(person, away, screen/monitor): mAP = 0.00064, count = 2866 (0.164%)\n",
      "(person, away, backpack): mAP = 0.00054, count = 833 (0.048%)\n",
      "(person, away, traffic_light): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, away, sink): mAP = 0.00000, count = 29 (0.002%)\n",
      "(person, away, stop_sign): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, away, antelope): mAP = 0.00000, count = 3 (0.000%)\n",
      "(person, away, faucet): mAP = 0.00000, count = 56 (0.003%)\n",
      "(person, away, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, away, aircraft): mAP = 0.00000, count = 182 (0.010%)\n",
      "(person, away, hamster/rat): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, away, electric_fan): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, away, cattle/cow): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, away, horse): mAP = 0.00000, count = 398 (0.023%)\n",
      "(person, away, refrigerator): mAP = 0.00000, count = 443 (0.025%)\n",
      "(person, away, watercraft): mAP = 0.00000, count = 441 (0.025%)\n",
      "(person, away, baby_walker): mAP = 0.00000, count = 343 (0.020%)\n",
      "(person, away, bat): mAP = 0.00000, count = 194 (0.011%)\n",
      "(person, away, bus/truck): mAP = 0.00000, count = 191 (0.011%)\n",
      "(person, away, snowboard): mAP = 0.00000, count = 32 (0.002%)\n",
      "(person, away, bench): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, away, cellphone): mAP = 0.00000, count = 97 (0.006%)\n",
      "(person, away, cake): mAP = 0.00000, count = 46 (0.003%)\n",
      "(person, away, fruits): mAP = 0.00000, count = 14 (0.001%)\n",
      "(person, away, vegetables): mAP = 0.00000, count = 20 (0.001%)\n",
      "(person, away, bottle): mAP = 0.00000, count = 797 (0.046%)\n",
      "(person, away, cat): mAP = 0.00000, count = 280 (0.016%)\n",
      "(person, away, baby_seat): mAP = 0.00000, count = 629 (0.036%)\n",
      "(person, away, handbag): mAP = 0.00000, count = 365 (0.021%)\n",
      "(person, away, guitar): mAP = 0.00000, count = 1544 (0.088%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP and view only \"away\"\n",
    "# sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] != 'away':\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, play(instrument), guitar): mAP = 0.29659, count = 2077 (0.119%)\n",
      "(person, play(instrument), piano): mAP = 0.00000, count = 84 (0.005%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP and view only \"play(instrument)\"\n",
    "# sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] != 'play(instrument)':\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, push, dish): mAP = 0.00000, count = 142 (0.008%)\n",
      "(person, push, baby_walker): mAP = 0.00000, count = 71 (0.004%)\n",
      "(person, push, dog): mAP = 0.00000, count = 930 (0.053%)\n",
      "(person, push, bicycle): mAP = 0.00000, count = 743 (0.043%)\n",
      "(person, push, ball/sports_ball): mAP = 0.00000, count = 323 (0.019%)\n",
      "(person, push, baby_seat): mAP = 0.00000, count = 468 (0.027%)\n",
      "(person, push, toy): mAP = 0.00000, count = 1546 (0.089%)\n",
      "(person, push, chair): mAP = 0.00000, count = 511 (0.029%)\n",
      "(person, push, person): mAP = 0.00000, count = 10747 (0.616%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP and view only \"push\"\n",
    "# sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] != 'push':\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, pull, horse): mAP = 0.00000, count = 179 (0.010%)\n",
      "(person, pull, dish): mAP = 0.00000, count = 93 (0.005%)\n",
      "(person, pull, baby_walker): mAP = 0.00000, count = 54 (0.003%)\n",
      "(person, pull, suitcase): mAP = 0.00000, count = 1 (0.000%)\n",
      "(person, pull, ski): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, pull, dog): mAP = 0.00000, count = 808 (0.046%)\n",
      "(person, pull, motorcycle): mAP = 0.00000, count = 70 (0.004%)\n",
      "(person, pull, toy): mAP = 0.00000, count = 1182 (0.068%)\n",
      "(person, pull, chair): mAP = 0.00000, count = 355 (0.020%)\n",
      "(person, pull, person): mAP = 0.00000, count = 7916 (0.453%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by mAP and view only \"pull\"\n",
    "# sorted_maps = sorted(unsorted_maps, reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] != 'pull':\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, next_to, person): mAP = 0.28661, count = 237779 (13.622%)\n",
      "(person, in_front_of, person): mAP = 0.18349, count = 226650 (12.984%)\n",
      "(person, behind, person): mAP = 0.02749, count = 120238 (6.888%)\n",
      "(person, watch, person): mAP = 0.04091, count = 107092 (6.135%)\n",
      "(person, lean_on, person): mAP = 0.00160, count = 52568 (3.011%)\n",
      "(person, above, person): mAP = 0.00028, count = 47007 (2.693%)\n",
      "(person, hug, person): mAP = 0.00425, count = 34100 (1.953%)\n",
      "(person, away, person): mAP = 0.09091, count = 33817 (1.937%)\n",
      "(person, hold, person): mAP = 0.00028, count = 32938 (1.887%)\n",
      "(person, towards, person): mAP = 0.01748, count = 30988 (1.775%)\n",
      "(person, speak_to, person): mAP = 0.00000, count = 26149 (1.498%)\n",
      "(person, inside, person): mAP = 0.00000, count = 23661 (1.355%)\n",
      "(person, hold_hand_of, person): mAP = 0.00003, count = 21543 (1.234%)\n",
      "(person, next_to, table): mAP = 0.27014, count = 17485 (1.002%)\n",
      "(person, next_to, toy): mAP = 0.12077, count = 17121 (0.981%)\n",
      "(person, caress, person): mAP = 0.00000, count = 16106 (0.923%)\n",
      "(person, beneath, person): mAP = 0.00079, count = 15272 (0.875%)\n",
      "(person, in_front_of, toy): mAP = 0.03737, count = 13782 (0.790%)\n",
      "(person, in_front_of, table): mAP = 0.00000, count = 13080 (0.749%)\n",
      "(person, in_front_of, sofa): mAP = 0.18038, count = 12855 (0.736%)\n",
      "(person, carry, person): mAP = 0.00000, count = 12447 (0.713%)\n",
      "(person, next_to, sofa): mAP = 0.01682, count = 12376 (0.709%)\n",
      "(person, push, person): mAP = 0.00000, count = 10747 (0.616%)\n",
      "(person, watch, toy): mAP = 0.04545, count = 10742 (0.615%)\n",
      "(person, in_front_of, screen/monitor): mAP = 0.44398, count = 10520 (0.603%)\n",
      "(person, behind, toy): mAP = 0.03667, count = 10118 (0.580%)\n",
      "(person, touch, person): mAP = 0.00000, count = 9808 (0.562%)\n",
      "(person, feed, person): mAP = 0.00000, count = 9601 (0.550%)\n",
      "(person, next_to, screen/monitor): mAP = 0.05335, count = 9116 (0.522%)\n",
      "(person, watch, sofa): mAP = 0.00000, count = 8107 (0.464%)\n",
      "--\n",
      "(person, press, person): mAP = 0.00000, count = 7991 (0.458%)\n",
      "(person, pull, person): mAP = 0.00000, count = 7916 (0.453%)\n",
      "(person, pat, person): mAP = 0.00000, count = 7643 (0.438%)\n",
      "(person, next_to, chair): mAP = 0.01553, count = 7636 (0.437%)\n",
      "(person, next_to, guitar): mAP = 0.24893, count = 7549 (0.432%)\n",
      "(person, watch, table): mAP = 0.00000, count = 7338 (0.420%)\n",
      "(person, in_front_of, chair): mAP = 0.16895, count = 7244 (0.415%)\n",
      "(person, behind, sofa): mAP = 0.00000, count = 7158 (0.410%)\n",
      "(person, in_front_of, guitar): mAP = 0.02601, count = 6922 (0.397%)\n",
      "(person, behind, table): mAP = 0.00011, count = 6531 (0.374%)\n",
      "(person, hold, toy): mAP = 0.00005, count = 6430 (0.368%)\n",
      "(person, kiss, person): mAP = 0.00000, count = 6404 (0.367%)\n",
      "(person, lean_on, table): mAP = 0.00143, count = 6272 (0.359%)\n",
      "(person, hit, person): mAP = 0.00000, count = 6270 (0.359%)\n",
      "(person, lean_on, sofa): mAP = 0.31995, count = 5951 (0.341%)\n",
      "(person, bite, person): mAP = 0.00000, count = 5868 (0.336%)\n",
      "(person, above, table): mAP = 0.00000, count = 5834 (0.334%)\n",
      "(person, next_to, dog): mAP = 0.20980, count = 5629 (0.322%)\n",
      "(person, behind, screen/monitor): mAP = 0.00006, count = 5504 (0.315%)\n",
      "(person, watch, screen/monitor): mAP = 0.06818, count = 5479 (0.314%)\n",
      "(person, point_to, person): mAP = 0.00000, count = 5270 (0.302%)\n",
      "(person, above, toy): mAP = 0.05768, count = 5230 (0.300%)\n",
      "(person, in_front_of, dog): mAP = 0.14940, count = 5182 (0.297%)\n",
      "(person, wave, person): mAP = 0.00000, count = 5149 (0.295%)\n",
      "(person, above, sofa): mAP = 0.02822, count = 5121 (0.293%)\n",
      "(person, lift, person): mAP = 0.00000, count = 5085 (0.291%)\n",
      "(person, lean_on, toy): mAP = 0.21808, count = 5060 (0.290%)\n",
      "(person, behind, guitar): mAP = 0.05319, count = 5012 (0.287%)\n",
      "(person, next_to, bottle): mAP = 0.19558, count = 4756 (0.272%)\n",
      "(person, next_to, cup): mAP = 0.11075, count = 4571 (0.262%)\n",
      "(person, hug, toy): mAP = 0.00000, count = 4336 (0.248%)\n",
      "(person, behind, chair): mAP = 0.00870, count = 4268 (0.244%)\n",
      "(person, clean, person): mAP = 0.00000, count = 4267 (0.244%)\n",
      "(person, next_to, bicycle): mAP = 0.06632, count = 4232 (0.242%)\n",
      "(person, chase, person): mAP = 0.00004, count = 4186 (0.240%)\n",
      "(person, next_to, backpack): mAP = 0.09463, count = 4168 (0.239%)\n",
      "(person, watch, guitar): mAP = 0.09091, count = 4137 (0.237%)\n",
      "(person, watch, chair): mAP = 0.00014, count = 4111 (0.236%)\n",
      "(person, next_to, baby_seat): mAP = 0.00536, count = 4022 (0.230%)\n",
      "(person, in_front_of, baby_seat): mAP = 0.02799, count = 4022 (0.230%)\n",
      "(person, away, toy): mAP = 0.00186, count = 4020 (0.230%)\n",
      "(person, towards, toy): mAP = 0.09091, count = 3946 (0.226%)\n",
      "(person, next_to, ball/sports_ball): mAP = 0.18418, count = 3923 (0.225%)\n",
      "(person, release, person): mAP = 0.00000, count = 3871 (0.222%)\n",
      "(person, in_front_of, backpack): mAP = 0.09707, count = 3844 (0.220%)\n",
      "(person, squeeze, person): mAP = 0.00000, count = 3651 (0.209%)\n",
      "(person, in_front_of, bicycle): mAP = 0.01718, count = 3609 (0.207%)\n",
      "(person, grab, person): mAP = 0.00000, count = 3501 (0.201%)\n",
      "(person, in_front_of, cup): mAP = 0.00718, count = 3365 (0.193%)\n",
      "(person, wave_hand_to, person): mAP = 0.00000, count = 3355 (0.192%)\n",
      "(person, in_front_of, ball/sports_ball): mAP = 0.02803, count = 3352 (0.192%)\n",
      "(person, watch, dog): mAP = 0.09091, count = 3348 (0.192%)\n",
      "(person, behind, dog): mAP = 0.06918, count = 3345 (0.192%)\n",
      "(person, beneath, screen/monitor): mAP = 0.00000, count = 3066 (0.176%)\n",
      "(person, lean_on, baby_seat): mAP = 0.27140, count = 3038 (0.174%)\n",
      "(person, kick, person): mAP = 0.00000, count = 3003 (0.172%)\n",
      "(person, behind, bicycle): mAP = 0.00128, count = 2980 (0.171%)\n",
      "(person, away, screen/monitor): mAP = 0.00064, count = 2866 (0.164%)\n",
      "(person, shout_at, person): mAP = 0.00000, count = 2860 (0.164%)\n",
      "(person, behind, baby_seat): mAP = 0.00000, count = 2826 (0.162%)\n",
      "(person, watch, baby_seat): mAP = 0.00000, count = 2750 (0.158%)\n",
      "(person, in_front_of, baby_walker): mAP = 0.06028, count = 2699 (0.155%)\n",
      "(person, above, baby_seat): mAP = 0.00373, count = 2671 (0.153%)\n",
      "(person, away, table): mAP = 0.00568, count = 2632 (0.151%)\n",
      "(person, beneath, toy): mAP = 0.04529, count = 2632 (0.151%)\n",
      "(person, towards, table): mAP = 0.00000, count = 2543 (0.146%)\n",
      "(person, towards, screen/monitor): mAP = 0.00077, count = 2534 (0.145%)\n",
      "(person, watch, bicycle): mAP = 0.00021, count = 2532 (0.145%)\n",
      "(person, in_front_of, car): mAP = 0.14486, count = 2517 (0.144%)\n",
      "(person, behind, backpack): mAP = 0.00000, count = 2484 (0.142%)\n",
      "(person, shake_hand_with, person): mAP = 0.00000, count = 2482 (0.142%)\n",
      "(person, inside, toy): mAP = 0.01223, count = 2440 (0.140%)\n",
      "(person, above, bicycle): mAP = 0.22506, count = 2405 (0.138%)\n",
      "(person, next_to, baby_walker): mAP = 0.08145, count = 2383 (0.137%)\n",
      "(person, next_to, car): mAP = 0.05099, count = 2377 (0.136%)\n",
      "(person, towards, sofa): mAP = 0.09161, count = 2370 (0.136%)\n",
      "(person, knock, person): mAP = 0.00000, count = 2369 (0.136%)\n",
      "(person, lick, person): mAP = 0.00000, count = 2346 (0.134%)\n",
      "(person, away, sofa): mAP = 0.00260, count = 2332 (0.134%)\n",
      "(person, watch, bottle): mAP = 0.00000, count = 2244 (0.129%)\n",
      "(person, hold, screen/monitor): mAP = 0.00000, count = 2243 (0.128%)\n",
      "(person, away, chair): mAP = 0.04545, count = 2221 (0.127%)\n",
      "(person, watch, backpack): mAP = 0.00000, count = 2198 (0.126%)\n",
      "(person, next_to, stool): mAP = 0.25026, count = 2195 (0.126%)\n",
      "(person, press, toy): mAP = 0.00000, count = 2153 (0.123%)\n",
      "(person, in_front_of, laptop): mAP = 0.37200, count = 2119 (0.121%)\n",
      "(person, inside, baby_seat): mAP = 0.00023, count = 2115 (0.121%)\n",
      "(person, touch, toy): mAP = 0.00000, count = 2103 (0.120%)\n",
      "(person, watch, cup): mAP = 0.00000, count = 2100 (0.120%)\n",
      "(person, play(instrument), guitar): mAP = 0.29659, count = 2077 (0.119%)\n",
      "(person, next_to, handbag): mAP = 0.00108, count = 2072 (0.119%)\n",
      "(person, next_to, laptop): mAP = 0.00000, count = 2062 (0.118%)\n",
      "(person, above, dog): mAP = 0.00100, count = 2039 (0.117%)\n",
      "(person, bite, toy): mAP = 0.00000, count = 1899 (0.109%)\n",
      "(person, behind, ball/sports_ball): mAP = 0.00000, count = 1889 (0.108%)\n",
      "(person, away, dog): mAP = 0.02273, count = 1874 (0.107%)\n",
      "(person, ride, toy): mAP = 0.00256, count = 1874 (0.107%)\n",
      "(person, lean_on, bicycle): mAP = 0.00010, count = 1856 (0.106%)\n",
      "(person, next_to, dish): mAP = 0.15593, count = 1849 (0.106%)\n",
      "(person, towards, chair): mAP = 0.00649, count = 1831 (0.105%)\n",
      "(person, towards, dog): mAP = 0.01299, count = 1825 (0.105%)\n",
      "(person, hold, bottle): mAP = 0.00393, count = 1823 (0.104%)\n",
      "(person, watch, ball/sports_ball): mAP = 0.00044, count = 1820 (0.104%)\n",
      "(person, behind, car): mAP = 0.07961, count = 1820 (0.104%)\n",
      "(person, in_front_of, handbag): mAP = 0.00000, count = 1780 (0.102%)\n",
      "(person, hold, dog): mAP = 0.00014, count = 1752 (0.100%)\n",
      "(person, hold, cup): mAP = 0.02273, count = 1751 (0.100%)\n",
      "(person, next_to, cat): mAP = 0.05269, count = 1739 (0.100%)\n",
      "(person, wave, toy): mAP = 0.00000, count = 1703 (0.098%)\n",
      "(person, watch, car): mAP = 0.01563, count = 1637 (0.094%)\n",
      "(person, lean_on, chair): mAP = 0.05682, count = 1630 (0.093%)\n",
      "(person, push, toy): mAP = 0.00000, count = 1546 (0.089%)\n",
      "(person, away, guitar): mAP = 0.00000, count = 1544 (0.088%)\n",
      "(person, behind, baby_walker): mAP = 0.00000, count = 1539 (0.088%)\n",
      "(person, pat, toy): mAP = 0.00000, count = 1514 (0.087%)\n",
      "(person, next_to, cake): mAP = 0.11194, count = 1513 (0.087%)\n",
      "(person, lean_on, baby_walker): mAP = 0.53086, count = 1510 (0.087%)\n",
      "(person, watch, laptop): mAP = 0.00000, count = 1508 (0.086%)\n",
      "(person, press, table): mAP = 0.00000, count = 1486 (0.085%)\n",
      "(person, in_front_of, cat): mAP = 0.19467, count = 1483 (0.085%)\n",
      "(person, ride, bicycle): mAP = 0.00649, count = 1463 (0.084%)\n",
      "(person, towards, guitar): mAP = 0.00011, count = 1447 (0.083%)\n",
      "(person, watch, baby_walker): mAP = 0.00000, count = 1424 (0.082%)\n",
      "(person, hold, guitar): mAP = 0.01212, count = 1388 (0.080%)\n",
      "(person, above, chair): mAP = 0.09091, count = 1373 (0.079%)\n",
      "(person, in_front_of, piano): mAP = 0.12248, count = 1348 (0.077%)\n",
      "(person, away, bicycle): mAP = 0.01489, count = 1336 (0.077%)\n",
      "(person, hold, bicycle): mAP = 0.00034, count = 1331 (0.076%)\n",
      "(person, towards, bicycle): mAP = 0.00154, count = 1329 (0.076%)\n",
      "(person, next_to, horse): mAP = 0.01834, count = 1325 (0.076%)\n",
      "(person, in_front_of, horse): mAP = 0.24752, count = 1314 (0.075%)\n",
      "(person, behind, laptop): mAP = 0.00152, count = 1304 (0.075%)\n",
      "(person, press, sofa): mAP = 0.00000, count = 1300 (0.074%)\n",
      "(person, hold, ball/sports_ball): mAP = 0.00000, count = 1298 (0.074%)\n",
      "(person, watch, stool): mAP = 0.00087, count = 1293 (0.074%)\n",
      "(person, hug, dog): mAP = 0.27273, count = 1280 (0.073%)\n",
      "(person, above, baby_walker): mAP = 0.01299, count = 1276 (0.073%)\n",
      "(person, speak_to, dog): mAP = 0.00000, count = 1265 (0.072%)\n",
      "(person, away, cup): mAP = 0.00699, count = 1199 (0.069%)\n",
      "(person, above, backpack): mAP = 0.00000, count = 1193 (0.068%)\n",
      "(person, pull, toy): mAP = 0.00000, count = 1182 (0.068%)\n",
      "(person, hit, toy): mAP = 0.00000, count = 1141 (0.065%)\n",
      "(person, next_to, piano): mAP = 0.00030, count = 1139 (0.065%)\n",
      "(person, caress, dog): mAP = 0.00051, count = 1126 (0.065%)\n",
      "(person, above, screen/monitor): mAP = 0.00000, count = 1126 (0.065%)\n",
      "(person, lift, toy): mAP = 0.00000, count = 1082 (0.062%)\n",
      "(person, release, toy): mAP = 0.00000, count = 1065 (0.061%)\n",
      "(person, towards, cup): mAP = 0.00144, count = 1039 (0.060%)\n",
      "(person, away, ball/sports_ball): mAP = 0.00159, count = 1037 (0.059%)\n",
      "(person, inside, dog): mAP = 0.00000, count = 1026 (0.059%)\n",
      "(person, away, car): mAP = 0.10250, count = 1018 (0.058%)\n",
      "(person, feed, dog): mAP = 0.00000, count = 1005 (0.058%)\n",
      "(person, touch, screen/monitor): mAP = 0.00000, count = 999 (0.057%)\n",
      "(person, watch, cat): mAP = 0.06452, count = 998 (0.057%)\n",
      "(person, grab, toy): mAP = 0.00000, count = 959 (0.055%)\n",
      "(person, touch, dog): mAP = 0.00000, count = 958 (0.055%)\n",
      "(person, towards, car): mAP = 0.01884, count = 950 (0.054%)\n",
      "(person, carry, guitar): mAP = 0.00000, count = 939 (0.054%)\n",
      "(person, push, dog): mAP = 0.00000, count = 930 (0.053%)\n",
      "(person, point_to, toy): mAP = 0.00000, count = 927 (0.053%)\n",
      "(person, inside, baby_walker): mAP = 0.00000, count = 918 (0.053%)\n",
      "(person, behind, horse): mAP = 0.01108, count = 894 (0.051%)\n",
      "(person, towards, ball/sports_ball): mAP = 0.00099, count = 889 (0.051%)\n",
      "(person, ride, baby_seat): mAP = 0.00154, count = 850 (0.049%)\n",
      "(person, behind, cat): mAP = 0.11947, count = 844 (0.048%)\n",
      "(person, towards, backpack): mAP = 0.00000, count = 840 (0.048%)\n",
      "(person, hug, guitar): mAP = 0.00000, count = 836 (0.048%)\n",
      "(person, away, backpack): mAP = 0.00054, count = 833 (0.048%)\n",
      "(person, lean_on, car): mAP = 0.00000, count = 824 (0.047%)\n",
      "(person, pull, dog): mAP = 0.00000, count = 808 (0.046%)\n",
      "(person, away, bottle): mAP = 0.00000, count = 797 (0.046%)\n",
      "(person, behind, handbag): mAP = 0.00000, count = 780 (0.045%)\n",
      "(person, hit, dog): mAP = 0.00000, count = 763 (0.044%)\n",
      "(person, point_to, screen/monitor): mAP = 0.00000, count = 748 (0.043%)\n",
      "(person, push, bicycle): mAP = 0.00000, count = 743 (0.043%)\n",
      "(person, squeeze, toy): mAP = 0.00000, count = 731 (0.042%)\n",
      "(person, pat, dog): mAP = 0.00000, count = 718 (0.041%)\n",
      "(person, in_front_of, refrigerator): mAP = 0.03987, count = 706 (0.040%)\n",
      "(person, press, screen/monitor): mAP = 0.00000, count = 696 (0.040%)\n",
      "(person, beneath, bottle): mAP = 0.00131, count = 689 (0.039%)\n",
      "(person, in_front_of, watercraft): mAP = 0.82727, count = 687 (0.039%)\n",
      "(person, towards, bottle): mAP = 0.00000, count = 681 (0.039%)\n",
      "(person, watch, horse): mAP = 0.00043, count = 675 (0.039%)\n",
      "(person, next_to, refrigerator): mAP = 0.00000, count = 669 (0.038%)\n",
      "(person, watch, dish): mAP = 0.00000, count = 667 (0.038%)\n",
      "(person, beneath, guitar): mAP = 0.00000, count = 660 (0.038%)\n",
      "(person, above, cat): mAP = 0.01221, count = 654 (0.037%)\n",
      "(person, carry, backpack): mAP = 0.00159, count = 653 (0.037%)\n",
      "(person, towards, baby_seat): mAP = 0.00000, count = 639 (0.037%)\n",
      "(person, away, baby_seat): mAP = 0.00000, count = 629 (0.036%)\n",
      "(person, press, baby_seat): mAP = 0.00000, count = 616 (0.035%)\n",
      "(person, hug, ball/sports_ball): mAP = 0.00000, count = 615 (0.035%)\n",
      "(person, watch, handbag): mAP = 0.00000, count = 577 (0.033%)\n",
      "(person, watch, cake): mAP = 0.00017, count = 564 (0.032%)\n",
      "(person, next_to, suitcase): mAP = 0.00000, count = 562 (0.032%)\n",
      "(person, next_to, motorcycle): mAP = 0.05497, count = 556 (0.032%)\n",
      "(person, touch, chair): mAP = 0.00000, count = 552 (0.032%)\n",
      "(person, next_to, cellphone): mAP = 0.01364, count = 548 (0.031%)\n",
      "(person, hold, cat): mAP = 0.00000, count = 535 (0.031%)\n",
      "(person, pat, baby_seat): mAP = 0.00000, count = 534 (0.031%)\n",
      "(person, above, horse): mAP = 0.35253, count = 532 (0.030%)\n",
      "(person, carry, ball/sports_ball): mAP = 0.00000, count = 531 (0.030%)\n",
      "(person, watch, refrigerator): mAP = 0.00000, count = 528 (0.030%)\n",
      "(person, lean_on, ball/sports_ball): mAP = 0.00000, count = 513 (0.029%)\n",
      "(person, push, chair): mAP = 0.00000, count = 511 (0.029%)\n",
      "(person, above, stool): mAP = 0.31550, count = 509 (0.029%)\n",
      "(person, release, dog): mAP = 0.00000, count = 503 (0.029%)\n",
      "(person, above, watercraft): mAP = 0.14771, count = 493 (0.028%)\n",
      "(person, in_front_of, bus/truck): mAP = 0.00000, count = 485 (0.028%)\n",
      "(person, hug, cat): mAP = 0.00152, count = 476 (0.027%)\n",
      "(person, push, baby_seat): mAP = 0.00000, count = 468 (0.027%)\n",
      "(person, chase, dog): mAP = 0.00000, count = 467 (0.027%)\n",
      "(person, inside, car): mAP = 0.00308, count = 452 (0.026%)\n",
      "(person, next_to, bus/truck): mAP = 0.14848, count = 448 (0.026%)\n",
      "(person, hold, handbag): mAP = 0.00000, count = 447 (0.026%)\n",
      "(person, in_front_of, cellphone): mAP = 0.00537, count = 446 (0.026%)\n",
      "(person, towards, refrigerator): mAP = 0.00000, count = 446 (0.026%)\n",
      "(person, clean, dog): mAP = 0.00000, count = 444 (0.025%)\n",
      "(person, away, refrigerator): mAP = 0.00000, count = 443 (0.025%)\n",
      "(person, away, watercraft): mAP = 0.00000, count = 441 (0.025%)\n",
      "(person, next_to, vegetables): mAP = 0.26708, count = 430 (0.025%)\n",
      "(person, next_to, fruits): mAP = 0.00000, count = 426 (0.024%)\n",
      "(person, in_front_of, motorcycle): mAP = 0.00000, count = 420 (0.024%)\n",
      "(person, in_front_of, faucet): mAP = 0.00000, count = 415 (0.024%)\n",
      "(person, ride, car): mAP = 0.00607, count = 413 (0.024%)\n",
      "(person, wave_hand_to, toy): mAP = 0.00000, count = 405 (0.023%)\n",
      "(person, carry, bottle): mAP = 0.00000, count = 403 (0.023%)\n",
      "(person, away, horse): mAP = 0.00000, count = 398 (0.023%)\n",
      "(person, drive, toy): mAP = 0.00000, count = 392 (0.022%)\n",
      "(person, release, cup): mAP = 0.00000, count = 391 (0.022%)\n",
      "(person, next_to, fish): mAP = 0.05033, count = 386 (0.022%)\n",
      "(person, behind, motorcycle): mAP = 0.07585, count = 383 (0.022%)\n",
      "(person, in_front_of, aircraft): mAP = 0.01248, count = 377 (0.022%)\n",
      "(person, next_to, faucet): mAP = 0.09091, count = 368 (0.021%)\n",
      "(person, watch, piano): mAP = 0.00072, count = 368 (0.021%)\n",
      "(person, away, handbag): mAP = 0.00000, count = 365 (0.021%)\n",
      "(person, towards, horse): mAP = 0.00000, count = 363 (0.021%)\n",
      "(person, away, stool): mAP = 0.03791, count = 359 (0.021%)\n",
      "(person, grab, cup): mAP = 0.00000, count = 359 (0.021%)\n",
      "(person, next_to, aircraft): mAP = 0.00647, count = 358 (0.021%)\n",
      "(person, pull, chair): mAP = 0.00000, count = 355 (0.020%)\n",
      "(person, behind, piano): mAP = 0.00000, count = 354 (0.020%)\n",
      "(person, bite, bottle): mAP = 0.00000, count = 352 (0.020%)\n",
      "(person, ride, watercraft): mAP = 0.03030, count = 347 (0.020%)\n",
      "(person, in_front_of, fish): mAP = 0.11466, count = 346 (0.020%)\n",
      "(person, towards, baby_walker): mAP = 0.00000, count = 344 (0.020%)\n",
      "(person, away, baby_walker): mAP = 0.00000, count = 343 (0.020%)\n",
      "(person, lean_on, stool): mAP = 0.00000, count = 339 (0.019%)\n",
      "(person, behind, suitcase): mAP = 1.00000, count = 339 (0.019%)\n",
      "(person, lift, bicycle): mAP = 0.00000, count = 338 (0.019%)\n",
      "(person, next_to, bat): mAP = 0.02841, count = 335 (0.019%)\n",
      "(person, in_front_of, camera): mAP = 0.00000, count = 335 (0.019%)\n",
      "(person, next_to, camera): mAP = 0.02139, count = 330 (0.019%)\n",
      "(person, hold, dish): mAP = 0.00000, count = 329 (0.019%)\n",
      "(person, touch, ball/sports_ball): mAP = 0.00000, count = 327 (0.019%)\n",
      "(person, ride, horse): mAP = 0.00909, count = 326 (0.019%)\n",
      "(person, behind, bus/truck): mAP = 0.00000, count = 325 (0.019%)\n",
      "(person, push, ball/sports_ball): mAP = 0.00000, count = 323 (0.019%)\n",
      "(person, watch, faucet): mAP = 0.00000, count = 322 (0.018%)\n",
      "(person, bite, ball/sports_ball): mAP = 0.00000, count = 320 (0.018%)\n",
      "(person, towards, stool): mAP = 0.07273, count = 316 (0.018%)\n",
      "(person, towards, handbag): mAP = 0.00000, count = 316 (0.018%)\n",
      "(person, pat, guitar): mAP = 0.00000, count = 310 (0.018%)\n",
      "(person, next_to, ski): mAP = 0.01402, count = 303 (0.017%)\n",
      "(person, watch, cellphone): mAP = 0.00000, count = 302 (0.017%)\n",
      "(person, wave, ball/sports_ball): mAP = 0.00000, count = 301 (0.017%)\n",
      "(person, shake_hand_with, dog): mAP = 0.00000, count = 295 (0.017%)\n",
      "(person, press, laptop): mAP = 0.00000, count = 294 (0.017%)\n",
      "(person, press, ball/sports_ball): mAP = 0.00000, count = 293 (0.017%)\n",
      "(person, caress, cat): mAP = 0.00000, count = 287 (0.016%)\n",
      "(person, watch, motorcycle): mAP = 0.00000, count = 282 (0.016%)\n",
      "(person, away, cat): mAP = 0.00000, count = 280 (0.016%)\n",
      "(person, behind, aircraft): mAP = 0.00000, count = 280 (0.016%)\n",
      "(person, towards, cat): mAP = 0.00000, count = 273 (0.016%)\n",
      "(person, inside, watercraft): mAP = 0.00000, count = 268 (0.015%)\n",
      "(person, hold, cellphone): mAP = 0.00130, count = 265 (0.015%)\n",
      "(person, watch, aircraft): mAP = 0.00000, count = 265 (0.015%)\n",
      "(person, next_to, bird): mAP = 0.44364, count = 253 (0.014%)\n",
      "(person, watch, bird): mAP = 0.00000, count = 246 (0.014%)\n",
      "(person, get_on, bicycle): mAP = 0.00000, count = 240 (0.014%)\n",
      "(person, grab, bottle): mAP = 0.00000, count = 238 (0.014%)\n",
      "(person, next_to, sink): mAP = 0.09091, count = 237 (0.014%)\n",
      "(person, get_off, bicycle): mAP = 0.00000, count = 237 (0.014%)\n",
      "(person, grab, guitar): mAP = 0.00000, count = 235 (0.013%)\n",
      "(person, lift, cup): mAP = 0.00000, count = 233 (0.013%)\n",
      "(person, get_off, toy): mAP = 0.00000, count = 231 (0.013%)\n",
      "(person, get_on, toy): mAP = 0.00000, count = 229 (0.013%)\n",
      "(person, behind, ski): mAP = 0.00000, count = 229 (0.013%)\n",
      "(person, release, bottle): mAP = 0.00000, count = 228 (0.013%)\n",
      "(person, carry, handbag): mAP = 0.00000, count = 228 (0.013%)\n",
      "(person, release, guitar): mAP = 0.00000, count = 225 (0.013%)\n",
      "(person, next_to, bench): mAP = 0.00000, count = 224 (0.013%)\n",
      "(person, behind, cellphone): mAP = 0.00000, count = 224 (0.013%)\n",
      "(person, throw, toy): mAP = 0.00000, count = 217 (0.012%)\n",
      "(person, watch, bat): mAP = 0.00000, count = 215 (0.012%)\n",
      "(person, release, chair): mAP = 0.00000, count = 207 (0.012%)\n",
      "(person, get_on, sofa): mAP = 0.00000, count = 202 (0.012%)\n",
      "(person, above, ski): mAP = 0.06061, count = 201 (0.012%)\n",
      "(person, lift, ball/sports_ball): mAP = 0.00000, count = 200 (0.011%)\n",
      "(person, release, ball/sports_ball): mAP = 0.00000, count = 199 (0.011%)\n",
      "(person, feed, cat): mAP = 0.00000, count = 196 (0.011%)\n",
      "(person, hit, ball/sports_ball): mAP = 0.00000, count = 196 (0.011%)\n",
      "(person, in_front_of, microwave): mAP = 0.00000, count = 196 (0.011%)\n",
      "(person, away, bat): mAP = 0.00000, count = 194 (0.011%)\n",
      "(person, grab, ball/sports_ball): mAP = 0.00000, count = 194 (0.011%)\n",
      "(person, pat, ball/sports_ball): mAP = 0.00000, count = 192 (0.011%)\n",
      "(person, drive, car): mAP = 0.00000, count = 192 (0.011%)\n",
      "(person, hit, car): mAP = 0.00000, count = 192 (0.011%)\n",
      "(person, above, motorcycle): mAP = 0.30653, count = 191 (0.011%)\n",
      "(person, away, bus/truck): mAP = 0.00000, count = 191 (0.011%)\n",
      "(person, watch, fish): mAP = 0.00000, count = 188 (0.011%)\n",
      "(person, touch, cat): mAP = 0.00000, count = 186 (0.011%)\n",
      "(person, towards, bat): mAP = 0.00000, count = 184 (0.011%)\n",
      "(person, away, aircraft): mAP = 0.00000, count = 182 (0.010%)\n",
      "(person, away, motorcycle): mAP = 0.04545, count = 182 (0.010%)\n",
      "(person, ride, ski): mAP = 0.01515, count = 181 (0.010%)\n",
      "(person, behind, camera): mAP = 0.00000, count = 180 (0.010%)\n",
      "(person, watch, camera): mAP = 0.00000, count = 179 (0.010%)\n",
      "(person, pull, horse): mAP = 0.00000, count = 179 (0.010%)\n",
      "(person, in_front_of, electric_fan): mAP = 0.23665, count = 173 (0.010%)\n",
      "(person, touch, dish): mAP = 0.00000, count = 173 (0.010%)\n",
      "(person, next_to, microwave): mAP = 0.00000, count = 172 (0.010%)\n",
      "(person, caress, horse): mAP = 0.00000, count = 169 (0.010%)\n",
      "(person, next_to, hamster/rat): mAP = 0.00083, count = 168 (0.010%)\n",
      "(person, towards, dish): mAP = 0.00000, count = 165 (0.009%)\n",
      "(person, away, ski): mAP = 0.00455, count = 163 (0.009%)\n",
      "(person, point_to, ball/sports_ball): mAP = 0.00000, count = 161 (0.009%)\n",
      "(person, hold, ski): mAP = 0.00000, count = 159 (0.009%)\n",
      "(person, towards, aircraft): mAP = 0.00000, count = 159 (0.009%)\n",
      "(person, hold, bat): mAP = 0.00000, count = 153 (0.009%)\n",
      "(person, towards, ski): mAP = 0.00000, count = 151 (0.009%)\n",
      "(person, beneath, watercraft): mAP = 0.00000, count = 151 (0.009%)\n",
      "(person, in_front_of, hamster/rat): mAP = 0.13295, count = 151 (0.009%)\n",
      "(person, use, laptop): mAP = 0.00000, count = 151 (0.009%)\n",
      "(person, ride, motorcycle): mAP = 0.00000, count = 150 (0.009%)\n",
      "(person, touch, horse): mAP = 0.00000, count = 149 (0.009%)\n",
      "(person, next_to, electric_fan): mAP = 0.01237, count = 148 (0.008%)\n",
      "(person, in_front_of, oven): mAP = 0.00000, count = 147 (0.008%)\n",
      "(person, towards, bus/truck): mAP = 0.00000, count = 143 (0.008%)\n",
      "(person, push, dish): mAP = 0.00000, count = 142 (0.008%)\n",
      "(person, touch, stool): mAP = 0.00000, count = 142 (0.008%)\n",
      "(person, in_front_of, penguin): mAP = 0.64935, count = 142 (0.008%)\n",
      "(person, feed, horse): mAP = 0.00000, count = 141 (0.008%)\n",
      "(person, chase, ball/sports_ball): mAP = 0.00000, count = 140 (0.008%)\n",
      "(person, inside, cup): mAP = 0.00000, count = 140 (0.008%)\n",
      "(person, next_to, penguin): mAP = 0.19139, count = 139 (0.008%)\n",
      "(person, in_front_of, bench): mAP = 0.00000, count = 137 (0.008%)\n",
      "(person, hold, camera): mAP = 0.09091, count = 136 (0.008%)\n",
      "(person, next_to, snowboard): mAP = 0.00000, count = 132 (0.008%)\n",
      "(person, away, piano): mAP = 0.01070, count = 132 (0.008%)\n",
      "(person, next_to, oven): mAP = 0.00000, count = 128 (0.007%)\n",
      "(person, behind, penguin): mAP = 0.42133, count = 126 (0.007%)\n",
      "(person, pat, horse): mAP = 0.00000, count = 126 (0.007%)\n",
      "(person, hold, motorcycle): mAP = 0.00000, count = 123 (0.007%)\n",
      "(person, next_to, bread): mAP = 0.00000, count = 122 (0.007%)\n",
      "(person, next_to, train): mAP = 0.03125, count = 115 (0.007%)\n",
      "(person, behind, train): mAP = 0.04813, count = 115 (0.007%)\n",
      "(person, lean_on, bus/truck): mAP = 0.00000, count = 112 (0.006%)\n",
      "(person, watch, oven): mAP = 0.00000, count = 111 (0.006%)\n",
      "(person, away, train): mAP = 0.00284, count = 111 (0.006%)\n",
      "(person, towards, piano): mAP = 0.00000, count = 106 (0.006%)\n",
      "(person, watch, train): mAP = 0.00000, count = 105 (0.006%)\n",
      "(person, grab, handbag): mAP = 0.00000, count = 103 (0.006%)\n",
      "(person, kiss, cat): mAP = 0.00000, count = 102 (0.006%)\n",
      "(person, away, camera): mAP = 0.01515, count = 100 (0.006%)\n",
      "(person, kick, ball/sports_ball): mAP = 0.00000, count = 100 (0.006%)\n",
      "(person, release, handbag): mAP = 0.00000, count = 100 (0.006%)\n",
      "(person, away, cellphone): mAP = 0.00000, count = 97 (0.006%)\n",
      "(person, towards, train): mAP = 0.00000, count = 94 (0.005%)\n",
      "(person, hold, fish): mAP = 0.00000, count = 93 (0.005%)\n",
      "(person, lift, bottle): mAP = 0.00000, count = 93 (0.005%)\n",
      "(person, pull, dish): mAP = 0.00000, count = 93 (0.005%)\n",
      "(person, lift, cat): mAP = 0.00000, count = 91 (0.005%)\n",
      "(person, watch, vegetables): mAP = 0.00000, count = 88 (0.005%)\n",
      "(person, throw, ball/sports_ball): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, above, surfboard): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, inside, bus/truck): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, next_to, chicken): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, ride, bus/truck): mAP = 0.00000, count = 86 (0.005%)\n",
      "(person, next_to, camel): mAP = 0.00000, count = 84 (0.005%)\n",
      "(person, play(instrument), piano): mAP = 0.00000, count = 84 (0.005%)\n",
      "(person, above, snowboard): mAP = 0.00000, count = 84 (0.005%)\n",
      "(person, behind, hamster/rat): mAP = 0.00000, count = 82 (0.005%)\n",
      "(person, pat, stool): mAP = 0.00000, count = 81 (0.005%)\n",
      "(person, hold, hamster/rat): mAP = 0.00000, count = 76 (0.004%)\n",
      "(person, watch, bread): mAP = 0.00000, count = 75 (0.004%)\n",
      "(person, beneath, cellphone): mAP = 0.00000, count = 75 (0.004%)\n",
      "(person, ride, surfboard): mAP = 0.00000, count = 74 (0.004%)\n",
      "(person, behind, bench): mAP = 0.00000, count = 74 (0.004%)\n",
      "(person, lean_on, sink): mAP = 0.04545, count = 73 (0.004%)\n",
      "(person, clean, horse): mAP = 0.00000, count = 72 (0.004%)\n",
      "(person, push, baby_walker): mAP = 0.00000, count = 71 (0.004%)\n",
      "(person, watch, microwave): mAP = 0.00000, count = 70 (0.004%)\n",
      "(person, towards, cellphone): mAP = 0.00000, count = 70 (0.004%)\n",
      "(person, pull, motorcycle): mAP = 0.00000, count = 70 (0.004%)\n",
      "(person, drive, bus/truck): mAP = 0.00000, count = 69 (0.004%)\n",
      "(person, ride, snowboard): mAP = 0.00000, count = 68 (0.004%)\n",
      "(person, wave, bat): mAP = 0.00000, count = 67 (0.004%)\n",
      "(person, drive, motorcycle): mAP = 0.00000, count = 67 (0.004%)\n",
      "(person, towards, camera): mAP = 0.00000, count = 66 (0.004%)\n",
      "(person, press, aircraft): mAP = 0.00000, count = 66 (0.004%)\n",
      "(person, use, cellphone): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, pull, ski): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, watch, fruits): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, away, bench): mAP = 0.00000, count = 64 (0.004%)\n",
      "(person, hold, bread): mAP = 0.00000, count = 63 (0.004%)\n",
      "(person, release, cat): mAP = 0.00000, count = 63 (0.004%)\n",
      "(person, towards, bench): mAP = 0.00000, count = 61 (0.003%)\n",
      "(person, wave_hand_to, cat): mAP = 0.00000, count = 60 (0.003%)\n",
      "(person, release, motorcycle): mAP = 0.00000, count = 57 (0.003%)\n",
      "(person, grab, cat): mAP = 0.00000, count = 57 (0.003%)\n",
      "(person, behind, sheep/goat): mAP = 0.00000, count = 56 (0.003%)\n",
      "(person, away, faucet): mAP = 0.00000, count = 56 (0.003%)\n",
      "(person, hold, fruits): mAP = 0.00000, count = 56 (0.003%)\n",
      "(person, in_front_of, tiger): mAP = 0.15152, count = 55 (0.003%)\n",
      "(person, pull, baby_walker): mAP = 0.00000, count = 54 (0.003%)\n",
      "(person, watch, tiger): mAP = 0.00000, count = 53 (0.003%)\n",
      "(person, use, camera): mAP = 0.00000, count = 50 (0.003%)\n",
      "(person, carry, camera): mAP = 0.00000, count = 50 (0.003%)\n",
      "(person, release, bat): mAP = 0.00000, count = 48 (0.003%)\n",
      "(person, next_to, scooter): mAP = 0.00000, count = 47 (0.003%)\n",
      "(person, away, cake): mAP = 0.00000, count = 46 (0.003%)\n",
      "(person, grab, bat): mAP = 0.00000, count = 44 (0.003%)\n",
      "(person, inside, train): mAP = 0.02020, count = 42 (0.002%)\n",
      "(person, release, dish): mAP = 0.00000, count = 41 (0.002%)\n",
      "(person, carry, bird): mAP = 0.00000, count = 40 (0.002%)\n",
      "(person, release, stool): mAP = 0.00000, count = 40 (0.002%)\n",
      "(person, in_front_of, sheep/goat): mAP = 0.00000, count = 39 (0.002%)\n",
      "(person, watch, scooter): mAP = 0.00000, count = 39 (0.002%)\n",
      "(person, towards, sink): mAP = 0.00000, count = 38 (0.002%)\n",
      "(person, press, cellphone): mAP = 0.00000, count = 38 (0.002%)\n",
      "(person, towards, cake): mAP = 0.00000, count = 37 (0.002%)\n",
      "(person, ride, train): mAP = 0.00455, count = 37 (0.002%)\n",
      "(person, touch, cake): mAP = 0.00000, count = 36 (0.002%)\n",
      "(person, inside, sink): mAP = 0.00000, count = 36 (0.002%)\n",
      "(person, above, bench): mAP = 0.00000, count = 33 (0.002%)\n",
      "(person, hold, vegetables): mAP = 0.00000, count = 33 (0.002%)\n",
      "(person, grab, camera): mAP = 0.00000, count = 32 (0.002%)\n",
      "(person, away, snowboard): mAP = 0.00000, count = 32 (0.002%)\n",
      "(person, away, sink): mAP = 0.00000, count = 29 (0.002%)\n",
      "(person, in_front_of, elephant): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, towards, hamster/rat): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, away, hamster/rat): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, lift, camera): mAP = 0.00000, count = 28 (0.002%)\n",
      "(person, hug, vegetables): mAP = 0.00000, count = 27 (0.002%)\n",
      "(person, above, scooter): mAP = 0.00000, count = 27 (0.002%)\n",
      "(person, behind, elephant): mAP = 0.00000, count = 26 (0.001%)\n",
      "(person, ride, scooter): mAP = 0.00000, count = 25 (0.001%)\n",
      "(person, watch, pig): mAP = 0.21937, count = 24 (0.001%)\n",
      "(person, touch, hamster/rat): mAP = 0.00000, count = 23 (0.001%)\n",
      "(person, towards, faucet): mAP = 0.00000, count = 21 (0.001%)\n",
      "(person, caress, hamster/rat): mAP = 0.00000, count = 20 (0.001%)\n",
      "(person, away, vegetables): mAP = 0.00000, count = 20 (0.001%)\n",
      "(person, lean_on, bench): mAP = 0.00000, count = 18 (0.001%)\n",
      "(person, release, cellphone): mAP = 0.00000, count = 17 (0.001%)\n",
      "(person, towards, vegetables): mAP = 0.00000, count = 17 (0.001%)\n",
      "(person, watch, lion): mAP = 0.00000, count = 14 (0.001%)\n",
      "(person, away, fruits): mAP = 0.00000, count = 14 (0.001%)\n",
      "(person, above, elephant): mAP = 0.00000, count = 13 (0.001%)\n",
      "(person, bite, bread): mAP = 0.00000, count = 13 (0.001%)\n",
      "(person, beneath, electric_fan): mAP = 0.00000, count = 13 (0.001%)\n",
      "(person, ride, elephant): mAP = 0.00000, count = 12 (0.001%)\n",
      "(person, towards, fruits): mAP = 0.00000, count = 12 (0.001%)\n",
      "(person, clean, hamster/rat): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, towards, electric_fan): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, away, electric_fan): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, open, refrigerator): mAP = 0.00000, count = 10 (0.001%)\n",
      "(person, watch, sheep/goat): mAP = 0.00000, count = 9 (0.001%)\n",
      "(person, grab, hamster/rat): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, release, hamster/rat): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, touch, vegetables): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, watch, cattle/cow): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, in_front_of, cattle/cow): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, behind, cattle/cow): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, next_to, cattle/cow): mAP = 0.00000, count = 8 (0.000%)\n",
      "(person, cut, fish): mAP = 0.00000, count = 5 (0.000%)\n",
      "(person, grab, fruits): mAP = 0.00000, count = 5 (0.000%)\n",
      "(person, press, faucet): mAP = 0.00000, count = 5 (0.000%)\n",
      "(person, above, cattle/cow): mAP = 0.00000, count = 5 (0.000%)\n",
      "(person, bite, fruits): mAP = 0.00000, count = 4 (0.000%)\n",
      "(person, away, antelope): mAP = 0.00000, count = 3 (0.000%)\n",
      "(person, in_front_of, antelope): mAP = 0.36364, count = 3 (0.000%)\n",
      "(person, touch, penguin): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, watch, duck): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, towards, cattle/cow): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, away, cattle/cow): mAP = 0.00000, count = 2 (0.000%)\n",
      "(person, caress, sheep/goat): mAP = 0.00000, count = 1 (0.000%)\n",
      "(person, pull, suitcase): mAP = 0.00000, count = 1 (0.000%)\n",
      "(person, watch, frisbee): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, frisbee): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, towards, frisbee): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, crocodile): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, carry, crocodile): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, in_front_of, crocodile): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, close, faucet): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, open, oven): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, close, oven): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, press, microwave): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, point_to, cake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, grab, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, hold, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, watch, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, release, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, touch, snake): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, crab): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, hold, crab): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, cut, vegetables): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, above, skateboard): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, ride, skateboard): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, hold, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, watch, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, wave, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, away, racket): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, lick, bread): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, away, traffic_light): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, in_front_of, traffic_light): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, towards, traffic_light): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, watch, bear): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, next_to, stingray): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, towards, stop_sign): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, beneath, stop_sign): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, away, stop_sign): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, ride, cattle/cow): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, wave_hand_to, cattle/cow): mAP = 0.00000, count = 0 (0.000%)\n",
      "(person, chase, cattle/cow): mAP = 0.00000, count = 0 (0.000%)\n"
     ]
    }
   ],
   "source": [
    "# Sorted by nb of occurrence\n",
    "sorted_maps_by_count = sorted(unsorted_maps, key=lambda val:val[3], reverse=True)\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps_by_count):\n",
    "    if idx == 30:\n",
    "        print('--')\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- Predicate-wise mean AP --\n",
      "next_to: 0.608925 (10.621504%)\n",
      "towards: 0.073444 (5.989976%)\n",
      "point_to: 0.018741 (1.339396%)\n",
      "in_front_of: 0.400805 (10.430193%)\n",
      "watch: 0.131691 (8.228991%)\n",
      "behind: 0.202858 (8.764025%)\n",
      "lean_on: 0.276777 (3.037799%)\n",
      "above: 0.459127 (3.381015%)\n",
      "hug: 0.083723 (1.963965%)\n",
      "speak_to: 0.000000 (2.263934%)\n",
      "hold_hand_of: 0.000082 (1.730711%)\n",
      "pull: 0.069446 (1.738679%)\n",
      "press: 0.000174 (1.812495%)\n",
      "away: 0.118901 (5.955495%)\n",
      "hold: 0.022500 (3.028020%)\n",
      "use: 0.007499 (0.061066%)\n",
      "inside: 0.139079 (1.371269%)\n",
      "push: 0.002252 (1.919052%)\n",
      "play(instrument): 0.323125 (0.202612%)\n",
      "ride: 0.193999 (0.470563%)\n",
      "carry: 0.011869 (1.455878%)\n",
      "caress: 0.015169 (1.329979%)\n",
      "grab: 0.000003 (1.259206%)\n",
      "lift: 0.005693 (1.261814%)\n",
      "touch: 0.024523 (1.892685%)\n",
      "chase: 0.000035 (0.943807%)\n",
      "pat: 0.000016 (1.480507%)\n",
      "wave: 0.000000 (1.245153%)\n",
      "beneath: 0.156812 (1.924413%)\n",
      "release: 0.000000 (1.409444%)\n",
      "kiss: 0.000000 (0.882741%)\n",
      "feed: 0.000000 (1.330703%)\n",
      "throw: 0.026605 (0.207103%)\n",
      "drive: 0.000874 (0.186241%)\n",
      "shake_hand_with: 0.000000 (0.623699%)\n",
      "clean: 0.000054 (0.875859%)\n",
      "close: 0.000000 (0.003332%)\n",
      "kick: 0.000000 (0.661005%)\n",
      "squeeze: 0.000000 (0.881292%)\n",
      "hit: 0.000000 (1.563160%)\n",
      "bite: 0.000000 (1.160327%)\n",
      "open: 0.000000 (0.007099%)\n",
      "wave_hand_to: 0.000000 (0.998136%)\n",
      "get_on: 0.000000 (0.231515%)\n",
      "knock: 0.000000 (0.536628%)\n",
      "shout_at: 0.000000 (0.609936%)\n",
      "cut: 0.000000 (0.009562%)\n",
      "get_off: 0.000000 (0.182184%)\n",
      "lick: 0.000000 (0.535831%)\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "pred_APs = defaultdict(list)\n",
    "pred_count = defaultdict(int)\n",
    "total_count = 0\n",
    "for classAP, (s, p, o), count, count_ratio in unsorted_maps:\n",
    "    pred_APs[p].append(classAP)\n",
    "    pred_count[p] += count\n",
    "    total_count += count\n",
    "    \n",
    "print('-- Predicate-wise mean AP --')\n",
    "pred_mean_APs = {}\n",
    "for idx, (p, APs) in enumerate(pred_APs.items()):\n",
    "#     if idx == 17:\n",
    "#         print('--')\n",
    "    print(f'{idx_to_pred[p]}: {sum(APs)/len(APs):5f} ({pred_count[p]*100/total_count:5f}%)')\n",
    "    pred_mean_APs[idx_to_pred[p]] = sum(APs)/len(APs)\n",
    "    \n",
    "print(len(pred_mean_APs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(person, pull, baby_walker): mAP = 0.69445, count = 185 (0.013%)\n",
      "(person, lean_on, person): mAP = 0.20742, count = 25376 (1.838%)\n",
      "(person, hug, person): mAP = 0.12892, count = 19153 (1.387%)\n",
      "(person, away, person): mAP = 0.04717, count = 46135 (3.342%)\n",
      "(person, lift, ball/sports_ball): mAP = 0.04545, count = 585 (0.042%)\n",
      "(person, towards, person): mAP = 0.04393, count = 46230 (3.349%)\n",
      "(person, push, baby_walker): mAP = 0.02024, count = 249 (0.018%)\n"
     ]
    }
   ],
   "source": [
    "# (person, away, person)\n",
    "# (person, towards, person)\n",
    "# (person, lift, ball/sports_ball)\n",
    "# (person, push, baby_walker)\n",
    "# (person, pull, baby_walker)\n",
    "# (person, hug, person)\n",
    "# (person, lean_on, person)\n",
    "\n",
    "for idx, (classAP, (s, p, o), count, count_ratio) in enumerate(sorted_maps):\n",
    "    if idx_to_pred[p] == 'away' and idx_to_obj[o] == 'person':\n",
    "        person_away_person = classAP\n",
    "    elif idx_to_pred[p] == 'towards' and idx_to_obj[o] == 'person':\n",
    "        person_towards_person = classAP\n",
    "    elif idx_to_pred[p] == 'lift' and idx_to_obj[o] == 'ball/sports_ball':\n",
    "        person_lift_ball_sports_ball = classAP\n",
    "    elif idx_to_pred[p] == 'push' and idx_to_obj[o] == 'baby_walker':\n",
    "        person_push_baby_walker = classAP\n",
    "    elif idx_to_pred[p] == 'pull' and idx_to_obj[o] == 'baby_walker':\n",
    "        person_pull_baby_walker = classAP\n",
    "    elif idx_to_pred[p] == 'hug' and idx_to_obj[o] == 'person':\n",
    "        person_hug_person = classAP\n",
    "    elif idx_to_pred[p] == 'lean_on' and idx_to_obj[o] == 'person':\n",
    "        person_lean_on_person = classAP\n",
    "    else:\n",
    "        continue\n",
    "    print(f'({idx_to_obj[s]}, {idx_to_pred[p]}, {idx_to_obj[o]}): mAP = {classAP:.5f}, count = {count} ({count_ratio*100:.3f}%)')\n",
    "\n",
    "example_mAPs = [\n",
    "    person_away_person, \n",
    "    person_towards_person, \n",
    "    person_lift_ball_sports_ball, \n",
    "    person_push_baby_walker, \n",
    "    person_pull_baby_walker, \n",
    "    person_hug_person,\n",
    "    person_lean_on_person,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# image_baseline_example_mAPs = example_mAPs\n",
    "# slowfast_example_mAPs = example_mAPs\n",
    "# slowfast_trajectory_example_mAPs = example_mAPs\n",
    "# slowfast_trajectory_toipool_example_mAPs = example_mAPs\n",
    "# slowfast_trajectory_spatial_example_mAPs = example_mAPs\n",
    "slowfast_trajectory_toipool_spatial_example_mAPs = example_mAPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nwith open('slowfast/datasets/vidor/bar_chart_examples.json', 'r') as f:\\n    bar_chart_examples = json.load(f)\\nimage_baseline_example_mAPs = bar_chart_examples['image_baseline_example_mAPs']\\nslowfast_example_mAPs = bar_chart_examples['slowfast_example_mAPs']\\nslowfast_trajectory_example_mAPs = bar_chart_examples['slowfast_trajectory_example_mAPs']\\nslowfast_trajectory_toipool_example_mAPs = bar_chart_examples['slowfast_trajectory_toipool_example_mAPs']\\nslowfast_trajectory_spatial_example_mAPs = bar_chart_examples['slowfast_trajectory_spatial_example_mAPs']\\nslowfast_trajectory_toipool_spatial_example_mAPs = bar_chart_examples['slowfast_trajectory_toipool_spatial_example_mAPs']\\n\""
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "## save essential results for draw the below bar chart ###\n",
    "\n",
    "bar_chart_examples = {\n",
    "    \"image_baseline_example_mAPs\": image_baseline_example_mAPs,\n",
    "    \"slowfast_example_mAPs\": slowfast_example_mAPs,\n",
    "    \"slowfast_trajectory_example_mAPs\": slowfast_trajectory_example_mAPs,\n",
    "    \"slowfast_trajectory_toipool_example_mAPs\": slowfast_trajectory_toipool_example_mAPs,\n",
    "    \"slowfast_trajectory_spatial_example_mAPs\": slowfast_trajectory_spatial_example_mAPs,\n",
    "    \"slowfast_trajectory_toipool_spatial_example_mAPs\": slowfast_trajectory_toipool_spatial_example_mAPs,\n",
    "}\n",
    "with open('slowfast/datasets/vidor/bar_chart_examples.json', 'w') as f:\n",
    "    json.dump(bar_chart_examples, f)\n",
    "\n",
    "'''\n",
    "with open('slowfast/datasets/vidor/bar_chart_examples.json', 'r') as f:\n",
    "    bar_chart_examples = json.load(f)\n",
    "image_baseline_example_mAPs = bar_chart_examples['image_baseline_example_mAPs']\n",
    "slowfast_example_mAPs = bar_chart_examples['slowfast_example_mAPs']\n",
    "slowfast_trajectory_example_mAPs = bar_chart_examples['slowfast_trajectory_example_mAPs']\n",
    "slowfast_trajectory_toipool_example_mAPs = bar_chart_examples['slowfast_trajectory_toipool_example_mAPs']\n",
    "slowfast_trajectory_spatial_example_mAPs = bar_chart_examples['slowfast_trajectory_spatial_example_mAPs']\n",
    "slowfast_trajectory_toipool_spatial_example_mAPs = bar_chart_examples['slowfast_trajectory_toipool_spatial_example_mAPs']\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "\n",
    "# use_thresh = True # check this!\n",
    "# if use_thresh:\n",
    "#     keys = sorted_predicates_threshed\n",
    "# else:\n",
    "#     only_show_top = 50 # only show top 30 frequent predicates\n",
    "#     keys = sorted_predicates[:only_show_top]\n",
    "keys = [\n",
    "    '(person, away, person)',\n",
    "    '(person, towards, person)',\n",
    "    '(person, lift, ball/sports_ball)',\n",
    "    '(person, push, baby_walker)',\n",
    "    '(person, pull, baby_walker)',\n",
    "    '(person, hug, person)',\n",
    "    '(person, lean_on, person)',\n",
    "]\n",
    "key_idxs_to_be_excluded = [0, 3]\n",
    "\n",
    "### Display Options ###\n",
    "normalize_to_baseline = False\n",
    "normalize_to_max = True\n",
    "assert not (normalize_to_baseline and normalize_to_max) # only can turn on one normalization\n",
    "\n",
    "if normalize_to_baseline:\n",
    "    slowfast_example_mAPs_new = [slowfast_example_mAPs[i] / image_baseline_example_mAPs[i] for i in range(len(keys))]\n",
    "    slowfast_trajectory_example_mAPs_new = [slowfast_trajectory_example_mAPs[i] / image_baseline_example_mAPs[i] for i in range(len(keys))]\n",
    "    slowfast_trajectory_toipool_example_mAPs_new = [slowfast_trajectory_toipool_example_mAPs[i] / image_baseline_example_mAPs[i] for i in range(len(keys))]\n",
    "    slowfast_trajectory_spatial_example_mAPs_new = [slowfast_trajectory_spatial_example_mAPs[i] / image_baseline_example_mAPs[i] for i in range(len(keys))]\n",
    "    slowfast_trajectory_toipool_spatial_example_mAPs_new = [slowfast_trajectory_toipool_spatial_example_mAPs[i] / image_baseline_example_mAPs[i] for i in range(len(keys))]    \n",
    "elif normalize_to_max:\n",
    "    max_list = [\n",
    "        max([\n",
    "            image_baseline_example_mAPs[i],\n",
    "            slowfast_example_mAPs[i], \n",
    "            slowfast_trajectory_example_mAPs[i],\n",
    "            slowfast_trajectory_toipool_example_mAPs[i],\n",
    "            slowfast_trajectory_spatial_example_mAPs[i],\n",
    "            slowfast_trajectory_toipool_spatial_example_mAPs[i],\n",
    "        ]) for i in range(len(keys))\n",
    "    ]\n",
    "    slowfast_example_mAPs_new = [slowfast_example_mAPs[i] / max_list[i] for i in range(len(keys))]\n",
    "    slowfast_trajectory_example_mAPs_new = [slowfast_trajectory_example_mAPs[i] / max_list[i] for i in range(len(keys))]\n",
    "    slowfast_trajectory_toipool_example_mAPs_new = [slowfast_trajectory_toipool_example_mAPs[i] / max_list[i] for i in range(len(keys))]\n",
    "    slowfast_trajectory_spatial_example_mAPs_new = [slowfast_trajectory_spatial_example_mAPs[i] / max_list[i] for i in range(len(keys))]\n",
    "    slowfast_trajectory_toipool_spatial_example_mAPs_new = [slowfast_trajectory_toipool_spatial_example_mAPs[i] / max_list[i] for i in range(len(keys))]\n",
    "else:\n",
    "    slowfast_example_mAPs_new = slowfast_example_mAPs\n",
    "    slowfast_trajectory_example_mAPs_new = slowfast_trajectory_example_mAPs\n",
    "    slowfast_trajectory_toipool_example_mAPs_new = slowfast_trajectory_toipool_example_mAPs\n",
    "    slowfast_trajectory_spatial_example_mAPs_new = slowfast_trajectory_spatial_example_mAPs\n",
    "    slowfast_trajectory_toipool_spatial_example_mAPs_new = slowfast_trajectory_toipool_spatial_example_mAPs\n",
    "    \n",
    "log_scale = False # NOTE: Need to be used along with normalize_to_baseline or normalize_to_max!\n",
    "if log_scale:\n",
    "    slowfast_example_mAPs_new = [math.log10(slowfast_example_mAPs_new[i]*100) if slowfast_example_mAPs_new[i]*100 >= 10 else 0.0 for i in range(len(keys))]\n",
    "    slowfast_trajectory_example_mAPs_new = [math.log10(slowfast_trajectory_example_mAPs_new[i]*100) if slowfast_example_mAPs_new[i]*100 >= 10 else 0.0 for i in range(len(keys))]\n",
    "    slowfast_trajectory_toipool_example_mAPs_new = [math.log10(slowfast_trajectory_toipool_example_mAPs_new[i]*100) if slowfast_example_mAPs_new[i]*100 >= 10 else 0.0 for i in range(len(keys))]\n",
    "    slowfast_trajectory_spatial_example_mAPs_new = [math.log10(slowfast_trajectory_spatial_example_mAPs_new[i]*100) if slowfast_example_mAPs_new[i]*100 >- 10 else 0.0 for i in range(len(keys))]\n",
    "    slowfast_trajectory_toipool_spatial_example_mAPs_new = [math.log10(slowfast_trajectory_toipool_spatial_example_mAPs_new[i]*100) if slowfast_example_mAPs_new[i]*100 >- 10 else 0.0 for i in range(len(keys))]\n",
    "\n",
    "percent_view = True\n",
    "image_baseline_example_mAPs_new = image_baseline_example_mAPs\n",
    "if percent_view:\n",
    "    image_baseline_example_mAPs_new =[image_baseline_example_mAPs_new[i]*100 for i in range(len(keys))]\n",
    "    slowfast_example_mAPs_new = [slowfast_example_mAPs_new[i]*100 for i in range(len(keys))]\n",
    "    slowfast_trajectory_example_mAPs_new = [slowfast_trajectory_example_mAPs_new[i]*100 for i in range(len(keys))]\n",
    "    slowfast_trajectory_toipool_example_mAPs_new = [slowfast_trajectory_toipool_example_mAPs_new[i]*100 for i in range(len(keys))]\n",
    "    slowfast_trajectory_spatial_example_mAPs_new = [slowfast_trajectory_spatial_example_mAPs_new[i]*100 for i in range(len(keys))]\n",
    "    slowfast_trajectory_toipool_spatial_example_mAPs_new = [slowfast_trajectory_toipool_spatial_example_mAPs_new[i]*100 for i in range(len(keys))]\n",
    "### Display Options END ###\n",
    "\n",
    "data = {\n",
    "    \"2D model\": image_baseline_example_mAPs_new,\n",
    "    \"3D model\": slowfast_example_mAPs_new,\n",
    "    \"Ours-T\": slowfast_trajectory_example_mAPs_new,\n",
    "    \"Ours-T+V\": slowfast_trajectory_toipool_example_mAPs_new,\n",
    "    \"Ours-T+P\": slowfast_trajectory_spatial_example_mAPs_new,\n",
    "    \"Ours-T+V+P\": slowfast_trajectory_toipool_spatial_example_mAPs_new,\n",
    "}\n",
    "\n",
    "if key_idxs_to_be_excluded:\n",
    "    for idx in key_idxs_to_be_excluded[::-1]:\n",
    "        keys.pop(idx)\n",
    "        for key in data.keys():\n",
    "            data[key].pop(idx)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bar_plot(ax, data, total_width=.8, single_width=.9)\n",
    "fig.set_size_inches(10, 6)\n",
    "plt.xticks(range(len(keys)), keys)\n",
    "plt.xticks(rotation=10,fontsize=12)\n",
    "# plt.title('Temporal-aware HOIs mAPs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.05997278880762845,\n",
       " 0.003367003367003367,\n",
       " 0.05295780774249673,\n",
       " 0.0,\n",
       " 0.013175230566534914]"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slowfast_example_mAPs_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicates = list(pred_mean_APs.keys())\n",
    "\n",
    "# image_baseline_pred_mean_APs = pred_mean_APs\n",
    "# slowfast_pred_mean_APs = pred_mean_APs\n",
    "# slowfast_trajectory_pred_mean_APs = pred_mean_APs\n",
    "# slowfast_trajectory_spatial_pred_mean_APs = pred_mean_APs\n",
    "# slowfast_trajectory_toipool_pred_mean_APs = pred_mean_APs\n",
    "slowfast_trajectory_toipool_spatial_pred_mean_APs = pred_mean_APs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "### save essential results for draw the below bar chart ###\n",
    "data = {\n",
    "    \"image_baseline_pred_mean_APs\": image_baseline_pred_mean_APs,\n",
    "    \"slowfast_pred_mean_APs\": slowfast_pred_mean_APs,\n",
    "    \"slowfast_trajectory_pred_mean_APs\": slowfast_trajectory_pred_mean_APs,\n",
    "    \"slowfast_trajectory_toipool_pred_mean_APs\": slowfast_trajectory_toipool_pred_mean_APs,\n",
    "    \"slowfast_trajectory_spatial_pred_mean_APs\": slowfast_trajectory_spatial_pred_mean_APs,\n",
    "    \"slowfast_trajectory_toipool_spatial_pred_mean_APs\": slowfast_trajectory_toipool_spatial_pred_mean_APs,\n",
    "}\n",
    "with open('slowfast/datasets/vidor/bar_chart_data.json', 'w') as f:\n",
    "    json.dump(data, f)\n",
    "\n",
    "# with open('slowfast/datasets/vidor/bar_chart_data.json', 'r') as f:\n",
    "#     data = json.load(f)\n",
    "# image_baseline_pred_mean_APs = data['image_baseline_pred_mean_APs']\n",
    "# slowfast_pred_mean_APs = data['slowfast_pred_mean_APs']\n",
    "# slowfast_trajectory_pred_mean_APs = data['slowfast_trajectory_pred_mean_APs']\n",
    "# slowfast_trajectory_toipool_pred_mean_APs = data['slowfast_trajectory_toipool_pred_mean_APs']\n",
    "# slowfast_trajectory_spatial_pred_mean_APs = data['slowfast_trajectory_spatial_pred_mean_APs']\n",
    "# slowfast_trajectory_toipool_spatial_pred_mean_APs = data['slowfast_trajectory_toipool_spatial_pred_mean_APs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert image_baseline_pred_mean_APs.keys() == slowfast_pred_mean_APs.keys() == slowfast_trajectory_pred_mean_APs.keys() == slowfast_trajectory_spatial_pred_mean_APs.keys() == slowfast_trajectory_toipool_pred_mean_APs.keys() == slowfast_trajectory_toipool_spatial_pred_mean_APs.keys()\n",
    "# print(image_baseline_pred_mean_APs.keys())\n",
    "# print(slowfast_pred_mean_APs.keys())\n",
    "# print(slowfast_trajectory_pred_mean_APs.keys())\n",
    "# print(slowfast_trajectory_spatial_pred_mean_APs.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "sorted_predicates_dict = {idx_to_pred[k]: v for k, v in sorted(pred_count.items(), key=lambda item: item[1], reverse=True)}\n",
    "sorted_predicates = list(sorted_predicates_dict.keys())\n",
    "\n",
    "thresh = 0.001\n",
    "sorted_predicates_dict_threshed = {key:pred_mean_APs[key] for key in sorted_predicates_dict.keys() if pred_mean_APs[key] >= thresh}\n",
    "sorted_predicates_threshed = list(sorted_predicates_dict_threshed.keys())\n",
    "\n",
    "data_list = {\n",
    "    'sorted_predicates': sorted_predicates,\n",
    "    'sorted_predicates_threshed': sorted_predicates_threshed,\n",
    "}\n",
    "\n",
    "with open('slowfast/datasets/vidor/bar_chart_data_list.json', 'w') as f:\n",
    "    json.dump(data_list, f)\n",
    "\n",
    "# with open('slowfast/datasets/vidor/bar_chart_data_list.json', 'r') as f:\n",
    "#     data_list = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "def bar_plot(ax, data, colors=None, total_width=0.8, single_width=1, legend=True):\n",
    "    \"\"\"Draws a bar plot with multiple bars per data point.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax : matplotlib.pyplot.axis\n",
    "        The axis we want to draw our plot on.\n",
    "\n",
    "    data: dictionary\n",
    "        A dictionary containing the data we want to plot. Keys are the names of the\n",
    "        data, the items is a list of the values.\n",
    "\n",
    "        Example:\n",
    "        data = {\n",
    "            \"x\":[1,2,3],\n",
    "            \"y\":[1,2,3],\n",
    "            \"z\":[1,2,3],\n",
    "        }\n",
    "\n",
    "    colors : array-like, optional\n",
    "        A list of colors which are used for the bars. If None, the colors\n",
    "        will be the standard matplotlib color cyle. (default: None)\n",
    "\n",
    "    total_width : float, optional, default: 0.8\n",
    "        The width of a bar group. 0.8 means that 80% of the x-axis is covered\n",
    "        by bars and 20% will be spaces between the bars.\n",
    "\n",
    "    single_width: float, optional, default: 1\n",
    "        The relative width of a single bar within a group. 1 means the bars\n",
    "        will touch eachother within a group, values less than 1 will make\n",
    "        these bars thinner.\n",
    "\n",
    "    legend: bool, optional, default: True\n",
    "        If this is set to true, a legend will be added to the axis.\n",
    "    \"\"\"\n",
    "\n",
    "    # Check if colors where provided, otherwhise use the default color cycle\n",
    "    if colors is None:\n",
    "        colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "\n",
    "    # Number of bars per group\n",
    "    n_bars = len(data)\n",
    "\n",
    "    # The width of a single bar\n",
    "    bar_width = total_width / n_bars\n",
    "\n",
    "    # List containing handles for the drawn bars, used for the legend\n",
    "    bars = []\n",
    "\n",
    "    # Iterate over all data\n",
    "    for i, (name, values) in enumerate(data.items()):\n",
    "        # The offset in x direction of that bar\n",
    "        x_offset = (i - n_bars / 2) * bar_width + bar_width / 2\n",
    "\n",
    "        # Draw a bar for every value of that type\n",
    "        for x, y in enumerate(values):\n",
    "            bar = ax.bar(x + x_offset, y, width=bar_width * single_width, color=colors[i % len(colors)])\n",
    "\n",
    "        # Add a handle to the last drawn bar, which we'll need for the legend\n",
    "        bars.append(bar[0])\n",
    "\n",
    "    # Draw legend if we need\n",
    "    if legend:\n",
    "        ax.legend(bars, data.keys(), prop=dict(size=10))\n",
    "#         ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "use_thresh = True # check this!\n",
    "if use_thresh:\n",
    "    keys = sorted_predicates_threshed\n",
    "else:\n",
    "    only_show_top = 50 # only show top 30 frequent predicates\n",
    "    keys = sorted_predicates[:only_show_top]\n",
    "\n",
    "precent_view = True\n",
    "data = {\n",
    "    \"2D model\": [image_baseline_pred_mean_APs[key]*100 if percent_view else image_baseline_pred_mean_APs[key] for key in keys], # list(image_baseline_pred_mean_APs.values())[:only_show_top],\n",
    "    \"3D model\": [slowfast_pred_mean_APs[key]*100 if percent_view else slowfast_pred_mean_APs[key] for key in keys], # list(slowfast_pred_mean_APs.values())[:only_show_top],\n",
    "    \"Ours-T\": [slowfast_trajectory_pred_mean_APs[key]*100 if percent_view else slowfast_trajectory_pred_mean_APs[key] for key in keys], # list(slowfast_trajectory_pred_mean_APs.values())[:only_show_top],\n",
    "    \"Ours-T+V\": [slowfast_trajectory_toipool_pred_mean_APs[key]*100 if percent_view else slowfast_trajectory_toipool_pred_mean_APs[key] for key in keys], # list(slowfast_trajectory_toipool_pred_mean_APs.values())[:only_show_top],\n",
    "    \"Ours-T+P\": [slowfast_trajectory_spatial_pred_mean_APs[key]*100 if percent_view else slowfast_trajectory_spatial_pred_mean_APs[key] for key in keys], # list(slowfast_trajectory_spatial_pred_mean_APs.values())[:only_show_top],\n",
    "    \"Ours-T+V+P\": [slowfast_trajectory_toipool_spatial_pred_mean_APs[key]*100 if percent_view else slowfast_trajectory_toipool_spatial_pred_mean_APs[key] for key in keys], # list(slowfast_trajectory_spatial_pred_mean_APs.values())[:only_show_top],\n",
    "}\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bar_plot(ax, data, total_width=.8, single_width=.9)\n",
    "fig.set_size_inches(20, 5)\n",
    "#     plt.xticks(range(len(predicates[:only_show_top])), predicates[:only_show_top])\n",
    "plt.xticks(range(len(keys)), keys)\n",
    "#     ax.set_xticklabels(predicates)\n",
    "# plt.title('Predicate mean AP')\n",
    "plt.xticks(rotation=45,fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "slowfast",
   "language": "python",
   "name": "slowfast"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}