test-cityscapes-style-eval.py

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "import abstract_cityscapes_style_eval\n",
    "import evalInstanceLevelSemanticLabeling as evalInst\n",
    "import maskrcnn_benchmark.data.datasets.debugdataset as debugdataset\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_of_inst = 3\n",
    "dataset = debugdataset.DebugDataset(\n",
    "    dataset_size=1, \n",
    "    min_num_instances=num_of_inst, \n",
    "    max_num_instances=num_of_inst\n",
    ")\n",
    "\n",
    "predictions = []\n",
    "for img, target, idx in dataset:\n",
    "    target.add_field(\"scores\", torch.ones(len(target))) \n",
    "    predictions.append(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'labelID': 99, 'instID': 0, 'pixelCount': 2500, 'matchedPred': []}\n",
      "{'labelID': 99, 'instID': 1, 'pixelCount': 2500, 'matchedPred': []}\n",
      "{'labelID': 99, 'instID': 2, 'pixelCount': 2500, 'matchedPred': []}\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": [
    "perImgGtInstances, gtMasks = evalInst.prepareGtImage(dataset, 0)\n",
    "for inst, m in zip(perImgGtInstances, gtMasks):\n",
    "    print(inst)\n",
    "    plt.imshow(m)\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'imgName': 0, 'predID': 0, 'labelID': 99, 'pixelCount': 2500, 'confidence': 1.0, 'matchedGt': []}\n",
      "{'imgName': 0, 'predID': 1, 'labelID': 99, 'pixelCount': 2500, 'confidence': 1.0, 'matchedGt': []}\n",
      "{'imgName': 0, 'predID': 2, 'labelID': 99, 'pixelCount': 2500, 'confidence': 1.0, 'matchedGt': []}\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": [
    "perImgPredInstances, predMasks = evalInst.preparePredImage(predictions, idx)\n",
    "for inst, m in zip(perImgPredInstances, predMasks):\n",
    "    print(inst)\n",
    "    plt.imshow(m)\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matching GT with Preds: 100%|██████████| 1/1 [00:00<00:00, 56.83it/s]\n"
     ]
    }
   ],
   "source": [
    "matches = evalInst.matchGtWithPreds(dataset, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'box': [{'labelID': 99,\n",
       "   'instID': 0,\n",
       "   'pixelCount': 2500,\n",
       "   'matchedPred': [{'imgName': 0,\n",
       "     'predID': 0,\n",
       "     'labelID': 99,\n",
       "     'pixelCount': 2500,\n",
       "     'confidence': 1.0,\n",
       "     'intersection': 2500}]},\n",
       "  {'labelID': 99,\n",
       "   'instID': 1,\n",
       "   'pixelCount': 2500,\n",
       "   'matchedPred': [{'imgName': 0,\n",
       "     'predID': 1,\n",
       "     'labelID': 99,\n",
       "     'pixelCount': 2500,\n",
       "     'confidence': 1.0,\n",
       "     'intersection': 2500}]},\n",
       "  {'labelID': 99,\n",
       "   'instID': 2,\n",
       "   'pixelCount': 2500,\n",
       "   'matchedPred': [{'imgName': 0,\n",
       "     'predID': 2,\n",
       "     'labelID': 99,\n",
       "     'pixelCount': 2500,\n",
       "     'confidence': 1.0,\n",
       "     'intersection': 2500}]}]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches[0]['groundTruth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import evalInstanceLevelSemanticLabeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import evalInstanceLevelSemanticLabeling\n",
    "args = evalInstanceLevelSemanticLabeling.args\n",
    "args.instLabels = list(dataset.classid_to_name.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "###########################################################################\n",
       "    exportFile : evaluationResults/resultInstanceLevelSemanticLabeling.json\n",
       "      overlaps : [0.5  0.55 0.6  0.65 0.7  0.75 0.8  0.85 0.9  0.95]\n",
       "minRegionSizes : [100 200 400]\n",
       "    JSONOutput : True\n",
       "         quiet : False\n",
       "           csv : False\n",
       "     colorized : True\n",
       "    instLabels : ['box']\n",
       "###########################################################################"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['exportFile', 'overlaps', 'minRegionSizes', 'JSONOutput', 'quiet', 'csv', 'colorized', 'instLabels'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args.__dict__.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "#################################################################\n",
      "\u001b[1mwhat           :             AP         AP_50%         AP_75%\u001b[0m\n",
      "#################################################################\n",
      "box            :\u001b[32;1m          1.000\u001b[32;1m          1.000\u001b[32;1m          1.000\u001b[0m\n",
      "-----------------------------------------------------------------\n",
      "average        :\u001b[32;1m          1.000\u001b[32;1m          1.000\u001b[32;1m          1.000\u001b[0m\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# evaluate matches\n",
    "apScores = evalInstanceLevelSemanticLabeling.evaluateMatches(matches, args)\n",
    "# averages\n",
    "avgDict = evalInstanceLevelSemanticLabeling.computeAverages(apScores,args)\n",
    "# Print results\n",
    "print(evalInstanceLevelSemanticLabeling.printResults(avgDict, args))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}