task3_pennfudan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "738475d4-eee4-44d0-9ca6-c45773c9ab88",
   "metadata": {},
   "source": [
    "# Project 3\n",
    "## Task 3 - PennFudan\n",
    "\n",
    "This is mostly a copy-and-paste job from the tutorial once again, but I organized the code differently to make for a cleaner notebook. I also show some output of my exploration of the dataset here. There is a lot going on behind the scenes, especially since the tutorial uses a lot of pre-written code from the `torchvision` Github repo, which I had to download (imported here from `detection`). It didn't quite work as advertised, so I had to do a bit of modification of the source code.\n",
    "\n",
    "First, I download the tutorial data and looked at it using a mix of functions suggested by the tutorial and my own code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b347cd74-0b37-4044-a3f1-f34fb9b44d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "\n",
    "import detection.utils as d_utils\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from task3_functions_pt2 import (PennFudanDataset, get_transform,\n",
    "                                 get_model_instance_segmentation,\n",
    "                                 train_and_evaluate)\n",
    "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e21bf9c-c0f3-41fc-8477-7125c3f11674",
   "metadata": {},
   "outputs": [],
   "source": [
    "explore_dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=False))\n",
    "explore_data_loader = torch.utils.data.DataLoader(\n",
    " explore_dataset, batch_size=1, shuffle=True, num_workers=4,\n",
    " collate_fn=d_utils.collate_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "326c6d4c-d46b-45a7-a8c8-b6074084d90b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image index: 68\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": [
    "# Rerun this to look at various images and bounding-box targets\n",
    "images,targets = next(iter(explore_data_loader))\n",
    "im_uint8 = torchvision.transforms.functional.convert_image_dtype(images[0], dtype=torch.uint8)\n",
    "im_box = torchvision.utils.draw_bounding_boxes(im_uint8, targets[0]['boxes'])\n",
    "plt.imshow(torch.permute(im_box, (1,2,0)));\n",
    "print(f'Image index: {int(targets[0][\"image_id\"])}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a690f7b1-a3d4-47ea-ba7e-7181337f63cc",
   "metadata": {},
   "source": [
    "Next I followed their tutorial to finetune a pretrained model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3665909-225f-4592-b1f7-ff13e48e167f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True))\n",
    "dataset_test = PennFudanDataset('data/PennFudanPed', get_transform(train=False))\n",
    "# grab torchvision.models.detection.maskrcnn_resnet50_fpn\n",
    "# then modify the mask predictor to output only two classes\n",
    "# foreground (i.e. person) vs background (i.e. not person)\n",
    "model = get_model_instance_segmentation(num_classes=2)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2063be30-59da-4505-8ceb-cd0d78ce4941",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: [0]  [ 0/60]  eta: 0:04:19  lr: 0.000090  loss: 5.2017 (5.2017)  loss_classifier: 0.6096 (0.6096)  loss_box_reg: 0.3836 (0.3836)  loss_mask: 4.1916 (4.1916)  loss_objectness: 0.0024 (0.0024)  loss_rpn_box_reg: 0.0145 (0.0145)  time: 4.3230  data: 1.8284  max mem: 2522\n",
      "Epoch: [0]  [10/60]  eta: 0:00:37  lr: 0.000936  loss: 3.1199 (3.5915)  loss_classifier: 0.3728 (0.4059)  loss_box_reg: 0.1670 (0.2036)  loss_mask: 2.5907 (2.8866)  loss_objectness: 0.0363 (0.0774)  loss_rpn_box_reg: 0.0116 (0.0181)  time: 0.7574  data: 0.1676  max mem: 3870\n",
      "Epoch: [0]  [20/60]  eta: 0:00:22  lr: 0.001783  loss: 1.1753 (2.9295)  loss_classifier: 0.1825 (0.2671)  loss_box_reg: 0.1383 (0.1819)  loss_mask: 0.7405 (1.8869)  loss_objectness: 0.0454 (0.2816)  loss_rpn_box_reg: 0.0131 (0.3121)  time: 0.3754  data: 0.0016  max mem: 3870\n",
      "Epoch: [0]  [30/60]  eta: 0:00:14  lr: 0.002629  loss: 1.0173 (2.2889)  loss_classifier: 0.1046 (0.2163)  loss_box_reg: 0.1291 (0.1822)  loss_mask: 0.5878 (1.4664)  loss_objectness: 0.0492 (0.2078)  loss_rpn_box_reg: 0.0144 (0.2162)  time: 0.3568  data: 0.0018  max mem: 3870\n",
      "Epoch: [0]  [40/60]  eta: 0:00:09  lr: 0.003476  loss: 0.7737 (1.9068)  loss_classifier: 0.0918 (0.1856)  loss_box_reg: 0.1346 (0.1720)  loss_mask: 0.5277 (1.2174)  loss_objectness: 0.0443 (0.1649)  loss_rpn_box_reg: 0.0122 (0.1668)  time: 0.3620  data: 0.0017  max mem: 3870\n",
      "Epoch: [0]  [50/60]  eta: 0:00:04  lr: 0.004323  loss: 0.6650 (1.8352)  loss_classifier: 0.0819 (0.1656)  loss_box_reg: 0.1148 (0.1612)  loss_mask: 0.4008 (1.0599)  loss_objectness: 0.0443 (0.1830)  loss_rpn_box_reg: 0.0113 (0.2654)  time: 0.3546  data: 0.0018  max mem: 3870\n",
      "Epoch: [0]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.6672 (1.6750)  loss_classifier: 0.0640 (0.1528)  loss_box_reg: 0.1024 (0.1568)  loss_mask: 0.4297 (0.9740)  loss_objectness: 0.0447 (0.1624)  loss_rpn_box_reg: 0.0108 (0.2291)  time: 0.3427  data: 0.0019  max mem: 3870\n",
      "Epoch: [0] Total time: 0:00:25 (0.4303 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:37  model_time: 0.1210 (0.1210)  evaluator_time: 0.0350 (0.0350)  time: 1.9444  data: 1.7874  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.1050 (0.0985)  evaluator_time: 0.0360 (0.0293)  time: 0.1404  data: 0.0008  max mem: 3870\n",
      "Test: Total time: 0:00:08 (0.1712 s / it)\n",
      "Averaged stats: model_time: 0.1050 (0.0985)  evaluator_time: 0.0360 (0.0293)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.292\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.709\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.131\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.245\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.337\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.172\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.406\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.483\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.523\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
      "Epoch: [1]  [ 0/60]  eta: 0:02:14  lr: 0.005000  loss: 0.4797 (0.4797)  loss_classifier: 0.1369 (0.1369)  loss_box_reg: 0.1597 (0.1597)  loss_mask: 0.1258 (0.1258)  loss_objectness: 0.0331 (0.0331)  loss_rpn_box_reg: 0.0242 (0.0242)  time: 2.2355  data: 1.8284  max mem: 3870\n",
      "Epoch: [1]  [10/60]  eta: 0:00:27  lr: 0.005000  loss: 0.5957 (1.5312)  loss_classifier: 0.1079 (0.1032)  loss_box_reg: 0.1501 (0.1356)  loss_mask: 0.3756 (0.4487)  loss_objectness: 0.0331 (0.2341)  loss_rpn_box_reg: 0.0175 (0.6096)  time: 0.5436  data: 0.1677  max mem: 3870\n",
      "Epoch: [1]  [20/60]  eta: 0:00:17  lr: 0.005000  loss: 0.6255 (1.1395)  loss_classifier: 0.0808 (0.0995)  loss_box_reg: 0.1380 (0.1509)  loss_mask: 0.3883 (0.4250)  loss_objectness: 0.0364 (0.1401)  loss_rpn_box_reg: 0.0133 (0.3240)  time: 0.3597  data: 0.0015  max mem: 3870\n",
      "Epoch: [1]  [30/60]  eta: 0:00:12  lr: 0.005000  loss: 0.7173 (1.0065)  loss_classifier: 0.0820 (0.0994)  loss_box_reg: 0.1700 (0.1749)  loss_mask: 0.3815 (0.4048)  loss_objectness: 0.0325 (0.1027)  loss_rpn_box_reg: 0.0084 (0.2248)  time: 0.3561  data: 0.0017  max mem: 3870\n",
      "Epoch: [1]  [40/60]  eta: 0:00:08  lr: 0.005000  loss: 0.7550 (0.9657)  loss_classifier: 0.0971 (0.1020)  loss_box_reg: 0.2282 (0.1832)  loss_mask: 0.3818 (0.4240)  loss_objectness: 0.0142 (0.0828)  loss_rpn_box_reg: 0.0110 (0.1736)  time: 0.3710  data: 0.0019  max mem: 3870\n",
      "Epoch: [1]  [50/60]  eta: 0:00:03  lr: 0.005000  loss: 0.7089 (1.0521)  loss_classifier: 0.1020 (0.1040)  loss_box_reg: 0.1681 (0.1807)  loss_mask: 0.3858 (0.4140)  loss_objectness: 0.0113 (0.1159)  loss_rpn_box_reg: 0.0095 (0.2374)  time: 0.3642  data: 0.0017  max mem: 3870\n",
      "Epoch: [1]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.6073 (0.9838)  loss_classifier: 0.0977 (0.1007)  loss_box_reg: 0.1486 (0.1741)  loss_mask: 0.3106 (0.4039)  loss_objectness: 0.0142 (0.1019)  loss_rpn_box_reg: 0.0080 (0.2031)  time: 0.3554  data: 0.0016  max mem: 3870\n",
      "Epoch: [1] Total time: 0:00:23 (0.3978 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:36  model_time: 0.1100 (0.1100)  evaluator_time: 0.0240 (0.0240)  time: 1.9214  data: 1.7864  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.0930 (0.0902)  evaluator_time: 0.0230 (0.0211)  time: 0.1209  data: 0.0009  max mem: 3870\n",
      "Test: Total time: 0:00:07 (0.1544 s / it)\n",
      "Averaged stats: model_time: 0.0930 (0.0902)  evaluator_time: 0.0230 (0.0211)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.665\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.934\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.804\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.204\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.639\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.709\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.290\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.729\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.732\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.240\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.706\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.758\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.526\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.924\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.580\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.197\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.379\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.561\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.247\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.614\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.624\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.320\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.619\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.638\n",
      "Epoch: [2]  [ 0/60]  eta: 0:02:11  lr: 0.005000  loss: 1.4090 (1.4090)  loss_classifier: 0.1749 (0.1749)  loss_box_reg: 0.1779 (0.1779)  loss_mask: 0.9730 (0.9730)  loss_objectness: 0.0660 (0.0660)  loss_rpn_box_reg: 0.0171 (0.0171)  time: 2.1895  data: 1.8054  max mem: 3870\n",
      "Epoch: [2]  [10/60]  eta: 0:00:27  lr: 0.005000  loss: 0.5421 (0.6671)  loss_classifier: 0.0759 (0.0972)  loss_box_reg: 0.1314 (0.1417)  loss_mask: 0.3153 (0.3796)  loss_objectness: 0.0322 (0.0378)  loss_rpn_box_reg: 0.0106 (0.0108)  time: 0.5485  data: 0.1654  max mem: 3870\n",
      "Epoch: [2]  [20/60]  eta: 0:00:18  lr: 0.005000  loss: 0.5356 (0.9330)  loss_classifier: 0.0759 (0.0897)  loss_box_reg: 0.1246 (0.1230)  loss_mask: 0.3032 (0.3469)  loss_objectness: 0.0275 (0.1025)  loss_rpn_box_reg: 0.0070 (0.2708)  time: 0.3889  data: 0.0016  max mem: 3870\n",
      "Epoch: [2]  [30/60]  eta: 0:00:13  lr: 0.005000  loss: 0.5827 (0.8708)  loss_classifier: 0.0928 (0.1064)  loss_box_reg: 0.1266 (0.1460)  loss_mask: 0.3315 (0.3548)  loss_objectness: 0.0187 (0.0766)  loss_rpn_box_reg: 0.0062 (0.1870)  time: 0.3884  data: 0.0019  max mem: 3870\n",
      "Epoch: [2]  [40/60]  eta: 0:00:08  lr: 0.005000  loss: 0.7535 (0.8441)  loss_classifier: 0.1463 (0.1274)  loss_box_reg: 0.2098 (0.1696)  loss_mask: 0.3315 (0.3408)  loss_objectness: 0.0201 (0.0622)  loss_rpn_box_reg: 0.0067 (0.1442)  time: 0.3859  data: 0.0019  max mem: 3870\n",
      "Epoch: [2]  [50/60]  eta: 0:00:04  lr: 0.005000  loss: 0.6697 (0.8305)  loss_classifier: 0.1855 (0.1464)  loss_box_reg: 0.2261 (0.1837)  loss_mask: 0.2652 (0.3281)  loss_objectness: 0.0131 (0.0533)  loss_rpn_box_reg: 0.0080 (0.1190)  time: 0.3734  data: 0.0019  max mem: 3870\n",
      "Epoch: [2]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.6970 (0.8194)  loss_classifier: 0.2013 (0.1545)  loss_box_reg: 0.2319 (0.1937)  loss_mask: 0.2578 (0.3211)  loss_objectness: 0.0114 (0.0469)  loss_rpn_box_reg: 0.0122 (0.1032)  time: 0.3603  data: 0.0017  max mem: 3870\n",
      "Epoch: [2] Total time: 0:00:24 (0.4125 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:35  model_time: 0.0990 (0.0990)  evaluator_time: 0.0190 (0.0190)  time: 1.9114  data: 1.7924  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.0960 (0.0895)  evaluator_time: 0.0230 (0.0209)  time: 0.1178  data: 0.0010  max mem: 3870\n",
      "Test: Total time: 0:00:07 (0.1534 s / it)\n",
      "Averaged stats: model_time: 0.0960 (0.0895)  evaluator_time: 0.0230 (0.0209)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.510\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.940\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.444\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.146\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.411\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.541\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.229\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.572\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.580\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.588\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.598\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.607\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.940\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.746\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.009\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.651\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.262\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.670\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.689\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.694\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713\n",
      "Epoch: [3]  [ 0/60]  eta: 0:02:08  lr: 0.000500  loss: 0.5550 (0.5550)  loss_classifier: 0.1301 (0.1301)  loss_box_reg: 0.1789 (0.1789)  loss_mask: 0.2292 (0.2292)  loss_objectness: 0.0113 (0.0113)  loss_rpn_box_reg: 0.0055 (0.0055)  time: 2.1415  data: 1.8194  max mem: 3870\n",
      "Epoch: [3]  [10/60]  eta: 0:00:28  lr: 0.000500  loss: 0.6296 (1.4208)  loss_classifier: 0.1513 (0.1681)  loss_box_reg: 0.2106 (0.2309)  loss_mask: 0.2145 (0.2405)  loss_objectness: 0.0103 (0.2134)  loss_rpn_box_reg: 0.0067 (0.5678)  time: 0.5657  data: 0.1669  max mem: 3870\n",
      "Epoch: [3]  [20/60]  eta: 0:00:19  lr: 0.000500  loss: 0.6296 (1.0711)  loss_classifier: 0.1708 (0.1736)  loss_box_reg: 0.2195 (0.2279)  loss_mask: 0.1915 (0.2508)  loss_objectness: 0.0088 (0.1169)  loss_rpn_box_reg: 0.0067 (0.3019)  time: 0.3981  data: 0.0017  max mem: 3870\n",
      "Epoch: [3]  [30/60]  eta: 0:00:13  lr: 0.000500  loss: 0.5895 (0.9131)  loss_classifier: 0.1540 (0.1673)  loss_box_reg: 0.2112 (0.2081)  loss_mask: 0.1898 (0.2486)  loss_objectness: 0.0087 (0.0829)  loss_rpn_box_reg: 0.0047 (0.2063)  time: 0.3790  data: 0.0018  max mem: 3870\n",
      "Epoch: [3]  [40/60]  eta: 0:00:08  lr: 0.000500  loss: 0.5538 (0.8477)  loss_classifier: 0.1400 (0.1612)  loss_box_reg: 0.1540 (0.1997)  loss_mask: 0.2292 (0.2615)  loss_objectness: 0.0088 (0.0659)  loss_rpn_box_reg: 0.0049 (0.1594)  time: 0.3781  data: 0.0019  max mem: 3870\n",
      "Epoch: [3]  [50/60]  eta: 0:00:04  lr: 0.000500  loss: 0.5374 (0.7893)  loss_classifier: 0.1121 (0.1561)  loss_box_reg: 0.1243 (0.1884)  loss_mask: 0.2329 (0.2606)  loss_objectness: 0.0090 (0.0549)  loss_rpn_box_reg: 0.0056 (0.1293)  time: 0.3856  data: 0.0017  max mem: 3870\n",
      "Epoch: [3]  [59/60]  eta: 0:00:00  lr: 0.000500  loss: 0.5374 (0.7636)  loss_classifier: 0.0948 (0.1556)  loss_box_reg: 0.1243 (0.1874)  loss_mask: 0.1971 (0.2605)  loss_objectness: 0.0090 (0.0486)  loss_rpn_box_reg: 0.0056 (0.1115)  time: 0.3770  data: 0.0017  max mem: 3870\n",
      "Epoch: [3] Total time: 0:00:25 (0.4188 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:35  model_time: 0.1000 (0.1000)  evaluator_time: 0.0130 (0.0130)  time: 1.9114  data: 1.7974  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.0920 (0.0857)  evaluator_time: 0.0150 (0.0149)  time: 0.1082  data: 0.0009  max mem: 3870\n",
      "Test: Total time: 0:00:07 (0.1434 s / it)\n",
      "Averaged stats: model_time: 0.0920 (0.0857)  evaluator_time: 0.0150 (0.0149)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.732\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.945\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.908\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.607\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.779\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.308\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.786\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.791\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.725\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.695\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.945\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.860\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.045\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.445\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.738\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.293\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.747\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.752\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.180\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.731\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.780\n",
      "Epoch: [4]  [ 0/60]  eta: 0:02:22  lr: 0.000500  loss: 1.2910 (1.2910)  loss_classifier: 0.2758 (0.2758)  loss_box_reg: 0.2675 (0.2675)  loss_mask: 0.7124 (0.7124)  loss_objectness: 0.0190 (0.0190)  loss_rpn_box_reg: 0.0163 (0.0163)  time: 2.3745  data: 1.8294  max mem: 3870\n",
      "Epoch: [4]  [10/60]  eta: 0:00:29  lr: 0.000500  loss: 0.5344 (0.6440)  loss_classifier: 0.1385 (0.1711)  loss_box_reg: 0.1769 (0.1836)  loss_mask: 0.2048 (0.2608)  loss_objectness: 0.0087 (0.0138)  loss_rpn_box_reg: 0.0097 (0.0147)  time: 0.5802  data: 0.1676  max mem: 3870\n",
      "Epoch: [4]  [20/60]  eta: 0:00:19  lr: 0.000500  loss: 0.5034 (0.6169)  loss_classifier: 0.1326 (0.1608)  loss_box_reg: 0.1576 (0.1813)  loss_mask: 0.2048 (0.2527)  loss_objectness: 0.0076 (0.0111)  loss_rpn_box_reg: 0.0061 (0.0110)  time: 0.3984  data: 0.0015  max mem: 3870\n",
      "Epoch: [4]  [30/60]  eta: 0:00:13  lr: 0.000500  loss: 0.5225 (0.6123)  loss_classifier: 0.1304 (0.1535)  loss_box_reg: 0.1579 (0.1743)  loss_mask: 0.2246 (0.2628)  loss_objectness: 0.0087 (0.0119)  loss_rpn_box_reg: 0.0043 (0.0098)  time: 0.3853  data: 0.0017  max mem: 3870\n",
      "Epoch: [4]  [40/60]  eta: 0:00:08  lr: 0.000500  loss: 0.6359 (0.6166)  loss_classifier: 0.1627 (0.1603)  loss_box_reg: 0.1831 (0.1835)  loss_mask: 0.2101 (0.2520)  loss_objectness: 0.0098 (0.0113)  loss_rpn_box_reg: 0.0073 (0.0095)  time: 0.3879  data: 0.0017  max mem: 3870\n",
      "Epoch: [4]  [50/60]  eta: 0:00:04  lr: 0.000500  loss: 0.5146 (0.5917)  loss_classifier: 0.1437 (0.1529)  loss_box_reg: 0.1384 (0.1721)  loss_mask: 0.1911 (0.2474)  loss_objectness: 0.0076 (0.0105)  loss_rpn_box_reg: 0.0047 (0.0087)  time: 0.3923  data: 0.0017  max mem: 3870\n",
      "Epoch: [4]  [59/60]  eta: 0:00:00  lr: 0.000500  loss: 0.4916 (0.5870)  loss_classifier: 0.1315 (0.1504)  loss_box_reg: 0.1146 (0.1711)  loss_mask: 0.1856 (0.2460)  loss_objectness: 0.0081 (0.0107)  loss_rpn_box_reg: 0.0041 (0.0088)  time: 0.3973  data: 0.0017  max mem: 3870\n",
      "Epoch: [4] Total time: 0:00:25 (0.4288 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:35  model_time: 0.0940 (0.0940)  evaluator_time: 0.0130 (0.0130)  time: 1.9164  data: 1.8084  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.0860 (0.0826)  evaluator_time: 0.0130 (0.0126)  time: 0.1027  data: 0.0010  max mem: 3870\n",
      "Test: Total time: 0:00:06 (0.1383 s / it)\n",
      "Averaged stats: model_time: 0.0860 (0.0826)  evaluator_time: 0.0130 (0.0126)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.743\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.945\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.900\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.568\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.792\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.316\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.786\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.790\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.725\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.828\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.685\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.945\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.866\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.020\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.730\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.292\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.735\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.741\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.180\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.694\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.772\n",
      "Epoch: [5]  [ 0/60]  eta: 0:02:12  lr: 0.000500  loss: 0.8754 (0.8754)  loss_classifier: 0.1393 (0.1393)  loss_box_reg: 0.1647 (0.1647)  loss_mask: 0.5099 (0.5099)  loss_objectness: 0.0252 (0.0252)  loss_rpn_box_reg: 0.0364 (0.0364)  time: 2.2045  data: 1.8284  max mem: 3870\n",
      "Epoch: [5]  [10/60]  eta: 0:00:28  lr: 0.000500  loss: 0.7560 (0.7316)  loss_classifier: 0.1393 (0.1812)  loss_box_reg: 0.1920 (0.2213)  loss_mask: 0.2379 (0.2985)  loss_objectness: 0.0104 (0.0150)  loss_rpn_box_reg: 0.0099 (0.0155)  time: 0.5779  data: 0.1679  max mem: 3870\n",
      "Epoch: [5]  [20/60]  eta: 0:00:19  lr: 0.000500  loss: 0.6713 (0.6839)  loss_classifier: 0.1561 (0.1761)  loss_box_reg: 0.1914 (0.2122)  loss_mask: 0.2286 (0.2722)  loss_objectness: 0.0082 (0.0123)  loss_rpn_box_reg: 0.0043 (0.0111)  time: 0.3913  data: 0.0017  max mem: 3870\n",
      "Epoch: [5]  [30/60]  eta: 0:00:13  lr: 0.000500  loss: 0.5347 (0.6641)  loss_classifier: 0.1454 (0.1738)  loss_box_reg: 0.1534 (0.1990)  loss_mask: 0.2275 (0.2690)  loss_objectness: 0.0077 (0.0120)  loss_rpn_box_reg: 0.0039 (0.0103)  time: 0.3865  data: 0.0018  max mem: 3870\n",
      "Epoch: [5]  [40/60]  eta: 0:00:08  lr: 0.000500  loss: 0.4998 (0.6196)  loss_classifier: 0.1308 (0.1630)  loss_box_reg: 0.1404 (0.1847)  loss_mask: 0.2156 (0.2526)  loss_objectness: 0.0061 (0.0105)  loss_rpn_box_reg: 0.0045 (0.0089)  time: 0.3937  data: 0.0019  max mem: 3870\n",
      "Epoch: [5]  [50/60]  eta: 0:00:04  lr: 0.000500  loss: 0.4029 (0.5820)  loss_classifier: 0.1011 (0.1524)  loss_box_reg: 0.0926 (0.1682)  loss_mask: 0.1698 (0.2418)  loss_objectness: 0.0058 (0.0105)  loss_rpn_box_reg: 0.0035 (0.0091)  time: 0.3797  data: 0.0018  max mem: 3870\n",
      "Epoch: [5]  [59/60]  eta: 0:00:00  lr: 0.000500  loss: 0.4858 (0.5940)  loss_classifier: 0.1304 (0.1563)  loss_box_reg: 0.1477 (0.1721)  loss_mask: 0.1943 (0.2462)  loss_objectness: 0.0060 (0.0103)  loss_rpn_box_reg: 0.0053 (0.0092)  time: 0.3899  data: 0.0018  max mem: 3870\n",
      "Epoch: [5] Total time: 0:00:25 (0.4244 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:35  model_time: 0.0980 (0.0980)  evaluator_time: 0.0120 (0.0120)  time: 1.9174  data: 1.8064  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.0850 (0.0834)  evaluator_time: 0.0110 (0.0124)  time: 0.1019  data: 0.0008  max mem: 3870\n",
      "Test: Total time: 0:00:06 (0.1388 s / it)\n",
      "Averaged stats: model_time: 0.0850 (0.0834)  evaluator_time: 0.0110 (0.0124)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.759\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.944\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.898\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.630\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.807\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.318\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.807\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.812\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.848\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.700\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.944\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.894\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.415\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.746\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.298\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.747\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.752\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.783\n",
      "Epoch: [6]  [ 0/60]  eta: 0:02:09  lr: 0.000050  loss: 0.4137 (0.4137)  loss_classifier: 0.0975 (0.0975)  loss_box_reg: 0.0861 (0.0861)  loss_mask: 0.1866 (0.1866)  loss_objectness: 0.0200 (0.0200)  loss_rpn_box_reg: 0.0235 (0.0235)  time: 2.1615  data: 1.8114  max mem: 3870\n",
      "Epoch: [6]  [10/60]  eta: 0:00:27  lr: 0.000050  loss: 0.6821 (0.6478)  loss_classifier: 0.2130 (0.1955)  loss_box_reg: 0.2431 (0.2109)  loss_mask: 0.2024 (0.2211)  loss_objectness: 0.0068 (0.0096)  loss_rpn_box_reg: 0.0058 (0.0107)  time: 0.5576  data: 0.1660  max mem: 3870\n",
      "Epoch: [6]  [20/60]  eta: 0:00:19  lr: 0.000050  loss: 0.5602 (0.5745)  loss_classifier: 0.1502 (0.1623)  loss_box_reg: 0.1630 (0.1729)  loss_mask: 0.1990 (0.2231)  loss_objectness: 0.0063 (0.0081)  loss_rpn_box_reg: 0.0052 (0.0081)  time: 0.3975  data: 0.0015  max mem: 3870\n",
      "Epoch: [6]  [30/60]  eta: 0:00:13  lr: 0.000050  loss: 0.4981 (0.8301)  loss_classifier: 0.1442 (0.1698)  loss_box_reg: 0.1630 (0.1740)  loss_mask: 0.1990 (0.2187)  loss_objectness: 0.0064 (0.0762)  loss_rpn_box_reg: 0.0050 (0.1915)  time: 0.4053  data: 0.0017  max mem: 3870\n",
      "Epoch: [6]  [40/60]  eta: 0:00:08  lr: 0.000050  loss: 0.5675 (0.7632)  loss_classifier: 0.1499 (0.1650)  loss_box_reg: 0.1758 (0.1726)  loss_mask: 0.2171 (0.2193)  loss_objectness: 0.0072 (0.0599)  loss_rpn_box_reg: 0.0059 (0.1465)  time: 0.4007  data: 0.0019  max mem: 3870\n",
      "Epoch: [6]  [50/60]  eta: 0:00:04  lr: 0.000050  loss: 0.5675 (0.8705)  loss_classifier: 0.1513 (0.1650)  loss_box_reg: 0.1550 (0.1736)  loss_mask: 0.2031 (0.2262)  loss_objectness: 0.0072 (0.0900)  loss_rpn_box_reg: 0.0068 (0.2157)  time: 0.3993  data: 0.0018  max mem: 3870\n",
      "Epoch: [6]  [59/60]  eta: 0:00:00  lr: 0.000050  loss: 0.4560 (0.8105)  loss_classifier: 0.1401 (0.1596)  loss_box_reg: 0.1376 (0.1678)  loss_mask: 0.1792 (0.2213)  loss_objectness: 0.0080 (0.0776)  loss_rpn_box_reg: 0.0053 (0.1842)  time: 0.3844  data: 0.0017  max mem: 3870\n",
      "Epoch: [6] Total time: 0:00:25 (0.4281 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:35  model_time: 0.0990 (0.0990)  evaluator_time: 0.0120 (0.0120)  time: 1.9034  data: 1.7914  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.0870 (0.0827)  evaluator_time: 0.0120 (0.0128)  time: 0.1025  data: 0.0010  max mem: 3870\n",
      "Test: Total time: 0:00:06 (0.1380 s / it)\n",
      "Averaged stats: model_time: 0.0870 (0.0827)  evaluator_time: 0.0120 (0.0128)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.767\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.947\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.906\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.623\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.815\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.812\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.817\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.750\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.855\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.698\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.947\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.881\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.411\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.742\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.298\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.745\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.752\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.783\n",
      "Epoch: [7]  [ 0/60]  eta: 0:02:20  lr: 0.000050  loss: 0.5339 (0.5339)  loss_classifier: 0.1331 (0.1331)  loss_box_reg: 0.1712 (0.1712)  loss_mask: 0.2167 (0.2167)  loss_objectness: 0.0098 (0.0098)  loss_rpn_box_reg: 0.0031 (0.0031)  time: 2.3415  data: 1.8364  max mem: 3870\n",
      "Epoch: [7]  [10/60]  eta: 0:00:28  lr: 0.000050  loss: 0.5339 (0.6565)  loss_classifier: 0.1563 (0.1727)  loss_box_reg: 0.1712 (0.1874)  loss_mask: 0.2167 (0.2766)  loss_objectness: 0.0078 (0.0099)  loss_rpn_box_reg: 0.0060 (0.0099)  time: 0.5709  data: 0.1685  max mem: 3870\n",
      "Epoch: [7]  [20/60]  eta: 0:00:19  lr: 0.000050  loss: 0.4642 (0.5691)  loss_classifier: 0.1467 (0.1545)  loss_box_reg: 0.1406 (0.1687)  loss_mask: 0.1866 (0.2287)  loss_objectness: 0.0072 (0.0095)  loss_rpn_box_reg: 0.0054 (0.0078)  time: 0.3983  data: 0.0016  max mem: 3870\n",
      "Epoch: [7]  [30/60]  eta: 0:00:13  lr: 0.000050  loss: 0.4807 (0.6054)  loss_classifier: 0.1445 (0.1640)  loss_box_reg: 0.1510 (0.1823)  loss_mask: 0.1866 (0.2425)  loss_objectness: 0.0068 (0.0090)  loss_rpn_box_reg: 0.0051 (0.0076)  time: 0.3947  data: 0.0016  max mem: 3870\n",
      "Epoch: [7]  [40/60]  eta: 0:00:08  lr: 0.000050  loss: 0.5375 (0.7789)  loss_classifier: 0.1445 (0.1615)  loss_box_reg: 0.1510 (0.1793)  loss_mask: 0.1968 (0.2436)  loss_objectness: 0.0059 (0.0564)  loss_rpn_box_reg: 0.0050 (0.1380)  time: 0.3956  data: 0.0017  max mem: 3870\n",
      "Epoch: [7]  [50/60]  eta: 0:00:04  lr: 0.000050  loss: 0.4719 (0.7302)  loss_classifier: 0.1161 (0.1585)  loss_box_reg: 0.1403 (0.1759)  loss_mask: 0.1857 (0.2366)  loss_objectness: 0.0072 (0.0469)  loss_rpn_box_reg: 0.0050 (0.1123)  time: 0.3983  data: 0.0019  max mem: 3870\n",
      "Epoch: [7]  [59/60]  eta: 0:00:00  lr: 0.000050  loss: 0.5289 (0.7205)  loss_classifier: 0.1363 (0.1599)  loss_box_reg: 0.1525 (0.1810)  loss_mask: 0.2013 (0.2417)  loss_objectness: 0.0072 (0.0411)  loss_rpn_box_reg: 0.0054 (0.0968)  time: 0.3895  data: 0.0019  max mem: 3870\n",
      "Epoch: [7] Total time: 0:00:25 (0.4315 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:35  model_time: 0.1030 (0.1030)  evaluator_time: 0.0120 (0.0120)  time: 1.9184  data: 1.8024  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.0850 (0.0831)  evaluator_time: 0.0120 (0.0126)  time: 0.1024  data: 0.0010  max mem: 3870\n",
      "Test: Total time: 0:00:06 (0.1387 s / it)\n",
      "Averaged stats: model_time: 0.0850 (0.0831)  evaluator_time: 0.0120 (0.0126)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.766\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.947\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.915\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.628\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.813\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.319\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.810\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.815\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.852\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.694\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.947\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.881\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.030\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.398\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.738\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.295\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.742\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.749\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.180\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.725\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.778\n",
      "Epoch: [8]  [ 0/60]  eta: 0:02:11  lr: 0.000050  loss: 0.8643 (0.8643)  loss_classifier: 0.1462 (0.1462)  loss_box_reg: 0.2252 (0.2252)  loss_mask: 0.4856 (0.4856)  loss_objectness: 0.0037 (0.0037)  loss_rpn_box_reg: 0.0035 (0.0035)  time: 2.1985  data: 1.8124  max mem: 3870\n",
      "Epoch: [8]  [10/60]  eta: 0:00:28  lr: 0.000050  loss: 0.4223 (0.5554)  loss_classifier: 0.1329 (0.1478)  loss_box_reg: 0.1127 (0.1526)  loss_mask: 0.1879 (0.2322)  loss_objectness: 0.0058 (0.0114)  loss_rpn_box_reg: 0.0062 (0.0115)  time: 0.5769  data: 0.1662  max mem: 3870\n",
      "Epoch: [8]  [20/60]  eta: 0:00:19  lr: 0.000050  loss: 0.4223 (0.5383)  loss_classifier: 0.1264 (0.1445)  loss_box_reg: 0.1147 (0.1544)  loss_mask: 0.1737 (0.2194)  loss_objectness: 0.0059 (0.0101)  loss_rpn_box_reg: 0.0062 (0.0099)  time: 0.3957  data: 0.0017  max mem: 3870\n",
      "Epoch: [8]  [30/60]  eta: 0:00:13  lr: 0.000050  loss: 0.4007 (0.5153)  loss_classifier: 0.1084 (0.1422)  loss_box_reg: 0.1153 (0.1515)  loss_mask: 0.1733 (0.2042)  loss_objectness: 0.0078 (0.0091)  loss_rpn_box_reg: 0.0037 (0.0083)  time: 0.3761  data: 0.0019  max mem: 3870\n",
      "Epoch: [8]  [40/60]  eta: 0:00:08  lr: 0.000050  loss: 0.3648 (0.4952)  loss_classifier: 0.0897 (0.1355)  loss_box_reg: 0.0845 (0.1402)  loss_mask: 0.1745 (0.2040)  loss_objectness: 0.0066 (0.0084)  loss_rpn_box_reg: 0.0032 (0.0071)  time: 0.3793  data: 0.0018  max mem: 3870\n",
      "Epoch: [8]  [50/60]  eta: 0:00:04  lr: 0.000050  loss: 0.4880 (0.5491)  loss_classifier: 0.1006 (0.1483)  loss_box_reg: 0.1149 (0.1579)  loss_mask: 0.1918 (0.2250)  loss_objectness: 0.0077 (0.0099)  loss_rpn_box_reg: 0.0051 (0.0080)  time: 0.3923  data: 0.0019  max mem: 3870\n",
      "Epoch: [8]  [59/60]  eta: 0:00:00  lr: 0.000050  loss: 0.6851 (0.5618)  loss_classifier: 0.1499 (0.1521)  loss_box_reg: 0.1908 (0.1650)  loss_mask: 0.2329 (0.2267)  loss_objectness: 0.0094 (0.0099)  loss_rpn_box_reg: 0.0068 (0.0082)  time: 0.4126  data: 0.0019  max mem: 3870\n",
      "Epoch: [8] Total time: 0:00:25 (0.4295 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:35  model_time: 0.0960 (0.0960)  evaluator_time: 0.0110 (0.0110)  time: 1.9174  data: 1.8094  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.0850 (0.0830)  evaluator_time: 0.0120 (0.0128)  time: 0.1025  data: 0.0007  max mem: 3870\n",
      "Test: Total time: 0:00:06 (0.1388 s / it)\n",
      "Averaged stats: model_time: 0.0850 (0.0830)  evaluator_time: 0.0120 (0.0128)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.768\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.947\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.915\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.630\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.816\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.812\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.817\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.854\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.699\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.947\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.881\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.030\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.401\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.743\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.297\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.744\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.751\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.180\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.781\n",
      "Epoch: [9]  [ 0/60]  eta: 0:02:19  lr: 0.000005  loss: 8.1709 (8.1709)  loss_classifier: 0.2524 (0.2524)  loss_box_reg: 0.3074 (0.3074)  loss_mask: 0.2308 (0.2308)  loss_objectness: 2.0162 (2.0162)  loss_rpn_box_reg: 5.3641 (5.3641)  time: 2.3295  data: 1.8524  max mem: 3870\n",
      "Epoch: [9]  [10/60]  eta: 0:00:26  lr: 0.000005  loss: 0.4918 (1.3157)  loss_classifier: 0.1825 (0.1828)  loss_box_reg: 0.1733 (0.2086)  loss_mask: 0.1651 (0.2429)  loss_objectness: 0.0063 (0.1890)  loss_rpn_box_reg: 0.0028 (0.4924)  time: 0.5388  data: 0.1699  max mem: 3870\n",
      "Epoch: [9]  [20/60]  eta: 0:00:18  lr: 0.000005  loss: 0.4918 (0.9870)  loss_classifier: 0.1440 (0.1646)  loss_box_reg: 0.1565 (0.1908)  loss_mask: 0.2299 (0.2647)  loss_objectness: 0.0068 (0.1043)  loss_rpn_box_reg: 0.0062 (0.2626)  time: 0.3754  data: 0.0016  max mem: 3870\n",
      "Epoch: [9]  [30/60]  eta: 0:00:13  lr: 0.000005  loss: 0.6980 (0.9194)  loss_classifier: 0.1676 (0.1780)  loss_box_reg: 0.1984 (0.2105)  loss_mask: 0.2513 (0.2725)  loss_objectness: 0.0097 (0.0756)  loss_rpn_box_reg: 0.0084 (0.1828)  time: 0.3993  data: 0.0017  max mem: 3870\n",
      "Epoch: [9]  [40/60]  eta: 0:00:08  lr: 0.000005  loss: 0.6178 (0.8267)  loss_classifier: 0.1536 (0.1697)  loss_box_reg: 0.1709 (0.1950)  loss_mask: 0.2088 (0.2617)  loss_objectness: 0.0089 (0.0599)  loss_rpn_box_reg: 0.0071 (0.1404)  time: 0.4032  data: 0.0017  max mem: 3870\n",
      "Epoch: [9]  [50/60]  eta: 0:00:04  lr: 0.000005  loss: 0.4135 (0.7603)  loss_classifier: 0.1316 (0.1670)  loss_box_reg: 0.1081 (0.1843)  loss_mask: 0.1562 (0.2434)  loss_objectness: 0.0089 (0.0507)  loss_rpn_box_reg: 0.0062 (0.1150)  time: 0.4021  data: 0.0019  max mem: 3870\n",
      "Epoch: [9]  [59/60]  eta: 0:00:00  lr: 0.000005  loss: 0.3830 (0.7280)  loss_classifier: 0.1140 (0.1634)  loss_box_reg: 0.0868 (0.1799)  loss_mask: 0.1562 (0.2410)  loss_objectness: 0.0089 (0.0447)  loss_rpn_box_reg: 0.0047 (0.0990)  time: 0.3942  data: 0.0017  max mem: 3870\n",
      "Epoch: [9] Total time: 0:00:25 (0.4272 s / it)\n",
      "creating index...\n",
      "index created!\n",
      "Test:  [ 0/50]  eta: 0:01:35  model_time: 0.1030 (0.1030)  evaluator_time: 0.0130 (0.0130)  time: 1.9196  data: 1.8026  max mem: 3870\n",
      "Test:  [49/50]  eta: 0:00:00  model_time: 0.0850 (0.0832)  evaluator_time: 0.0120 (0.0129)  time: 0.1026  data: 0.0008  max mem: 3870\n",
      "Test: Total time: 0:00:06 (0.1389 s / it)\n",
      "Averaged stats: model_time: 0.0850 (0.0832)  evaluator_time: 0.0120 (0.0129)\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.02s).\n",
      "IoU metric: bbox\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.769\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.947\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.915\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.166\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.630\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.816\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.320\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.813\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.818\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.855\n",
      "IoU metric: segm\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.697\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.947\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.881\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.030\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.401\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.741\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.297\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.744\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.750\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.180\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.780\n",
      "That's it!\n"
     ]
    }
   ],
   "source": [
    "train_and_evaluate(dataset, dataset_test, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff50645-4b91-4d0e-ad63-39527fefda67",
   "metadata": {},
   "source": [
    "And now to test the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5315efdf-ae24-407f-b763-8f893fe44a69",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "img, _ = dataset_test[10]\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    prediction = model([img.to('cuda')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "88b1fae6-a37f-40a1-9834-d55ce96a5cbe",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "img_uint8 = torchvision.transforms.functional.convert_image_dtype(img, dtype=torch.uint8)\n",
    "img_box = torchvision.utils.draw_bounding_boxes(img_uint8, prediction[0]['boxes'])\n",
    "plt.imshow(torch.permute(img_box, (1,2,0)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "554a3b54-1133-4000-b1f3-af2990c0ca7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a66b57f880>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.imshow(prediction[0]['masks'][0, 0, ...].cpu())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbeeecca-2be7-4fdc-90b8-d93d005c5075",
   "metadata": {},
   "source": [
    "## Appendix\n",
    "\n",
    "### task3_functions_pt2.py\n",
    "\n",
    "```python\n",
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "import detection.transforms as T\n",
    "import detection.utils as utils\n",
    "\n",
    "from detection.engine import train_one_epoch, evaluate\n",
    "from PIL import Image\n",
    "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
    "from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor\n",
    "\n",
    "class PennFudanDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, root, transforms):\n",
    "        self.root = root\n",
    "        self.transforms = transforms\n",
    "        # load all image files, sorting them to\n",
    "        # ensure that they are aligned\n",
    "        self.imgs = list(sorted(os.listdir(os.path.join(root, \"PNGImages\"))))\n",
    "        self.masks = list(sorted(os.listdir(os.path.join(root, \"PedMasks\"))))\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        # load images and masks\n",
    "        img_path = os.path.join(self.root, \"PNGImages\", self.imgs[idx])\n",
    "        mask_path = os.path.join(self.root, \"PedMasks\", self.masks[idx])\n",
    "        img = Image.open(img_path).convert(\"RGB\")\n",
    "        # note that we haven't converted the mask to RGB,\n",
    "        # because each color corresponds to a different instance\n",
    "        # with 0 being background\n",
    "        mask = Image.open(mask_path)\n",
    "        # convert the PIL Image into a numpy array\n",
    "        mask = np.array(mask)\n",
    "        # instances are encoded as different colors\n",
    "        obj_ids = np.unique(mask)\n",
    "        # first id is the background, so remove it\n",
    "        obj_ids = obj_ids[1:]\n",
    "\n",
    "        # split the color-encoded mask into a set\n",
    "        # of binary masks\n",
    "        masks = mask == obj_ids[:, None, None]\n",
    "\n",
    "        # get bounding box coordinates for each mask\n",
    "        num_objs = len(obj_ids)\n",
    "        boxes = []\n",
    "        for i in range(num_objs):\n",
    "            pos = np.where(masks[i])\n",
    "            xmin = np.min(pos[1])\n",
    "            xmax = np.max(pos[1])\n",
    "            ymin = np.min(pos[0])\n",
    "            ymax = np.max(pos[0])\n",
    "            boxes.append([xmin, ymin, xmax, ymax])\n",
    "\n",
    "        # convert everything into a torch.Tensor\n",
    "        boxes = torch.as_tensor(boxes, dtype=torch.float32)\n",
    "        # there is only one class\n",
    "        labels = torch.ones((num_objs,), dtype=torch.int64)\n",
    "        masks = torch.as_tensor(masks, dtype=torch.uint8)\n",
    "\n",
    "        image_id = torch.tensor([idx])\n",
    "        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n",
    "        # suppose all instances are not crowd\n",
    "        iscrowd = torch.zeros((num_objs,), dtype=torch.int64)\n",
    "\n",
    "        target = {}\n",
    "        target[\"boxes\"] = boxes\n",
    "        target[\"labels\"] = labels\n",
    "        target[\"masks\"] = masks\n",
    "        target[\"image_id\"] = image_id\n",
    "        target[\"area\"] = area\n",
    "        target[\"iscrowd\"] = iscrowd\n",
    "\n",
    "        if self.transforms is not None:\n",
    "            img, target = self.transforms(img, target)\n",
    "\n",
    "        return img, target\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.imgs)\n",
    "\n",
    "    \n",
    "def get_model_instance_segmentation(num_classes):\n",
    "    # load an instance segmentation model pre-trained on COCO\n",
    "    model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)\n",
    "\n",
    "    # get number of input features for the classifier\n",
    "    in_features = model.roi_heads.box_predictor.cls_score.in_features\n",
    "    # replace the pre-trained head with a new one\n",
    "    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n",
    "\n",
    "    # now get the number of input features for the mask classifier\n",
    "    in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels\n",
    "    hidden_layer = 256\n",
    "    # and replace the mask predictor with a new one\n",
    "    model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,\n",
    "                                                       hidden_layer,\n",
    "                                                       num_classes)\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "def train_and_evaluate(dataset, dataset_test, model):\n",
    "        # train on the GPU or on the CPU, if a GPU is not available\n",
    "    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "\n",
    "    # split the dataset in train and test set\n",
    "    indices = torch.randperm(len(dataset)).tolist()\n",
    "    dataset = torch.utils.data.Subset(dataset, indices[:-50])\n",
    "    dataset_test = torch.utils.data.Subset(dataset_test, indices[-50:])\n",
    "\n",
    "    # define training and validation data loaders\n",
    "    data_loader = torch.utils.data.DataLoader(\n",
    "        dataset, batch_size=2, shuffle=True, num_workers=4,\n",
    "        collate_fn=utils.collate_fn)\n",
    "\n",
    "    data_loader_test = torch.utils.data.DataLoader(\n",
    "        dataset_test, batch_size=1, shuffle=False, num_workers=4,\n",
    "        collate_fn=utils.collate_fn)\n",
    "\n",
    "    # move model to the right device\n",
    "    model.to(device)\n",
    "\n",
    "    # construct an optimizer\n",
    "    params = [p for p in model.parameters() if p.requires_grad]\n",
    "    optimizer = torch.optim.SGD(params, lr=0.005,\n",
    "                                momentum=0.9, weight_decay=0.0005)\n",
    "    # and a learning rate scheduler\n",
    "    lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n",
    "                                                   step_size=3,\n",
    "                                                   gamma=0.1)\n",
    "\n",
    "    # let's train it for 10 epochs\n",
    "    num_epochs = 10\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        # train for one epoch, printing every 10 iterations\n",
    "        train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)\n",
    "        # update the learning rate\n",
    "        lr_scheduler.step()\n",
    "        # evaluate on the test dataset\n",
    "        evaluate(model, data_loader_test, device=device)\n",
    "\n",
    "    print(\"That's it!\")\n",
    "\n",
    "    \n",
    "def get_transform(train):\n",
    "    transforms = []\n",
    "    transforms.append(T.PILToTensor())\n",
    "    if train:\n",
    "        transforms.append(T.RandomHorizontalFlip(0.5))\n",
    "    return T.Compose(transforms)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9347460-5755-4cf4-b61f-4cf552495482",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}