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botcs/test-abstract-cityscapes.ipynb

Created March 3, 2019 07:05
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test-abstract-cityscapes.ipynb
{
"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 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 = 2\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"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'labelID': 99, 'instID': 1, '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 = abstract_cityscapes_style_eval.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"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'imgName': 0, 'predID': 1, 'labelID': 99, 'pixelCount': 2500, 'confidence': 1.0, 'matchedGt': []}\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAD8CAYAAADzEfagAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADbRJREFUeJzt3V2MXOV9x/Hvr35NIMG8yXJtqwYFNeKiBcviRURRhEULNApckAgUFStyZamlEhGVUtNKrSL1IvQiJEgVqRXTOlWaQElaLERFiSGqehGDCe+4hIWCbBdwQ4DQRqGQ/Hsxj+nENew+9u6eWfr9SKs95zlnZ/6Lhq/nzOxqU1VIkmbml4YeQJIWEqMpSR2MpiR1MJqS1MFoSlIHoylJHeYkmkkuTvJUkqkkW+fiPiRpCJntn9NMsgj4AXARsB94ALiqqp6c1TuSpAHMxTPNc4Cpqnq2qv4b+CZw2RzcjyTNu8VzcJurgX1j+/uBc9/tC5ZmWS3nuDkYRZJm5nVe+WFVnTrdeXMRzRlJsgXYArCc93NuNg41iiTxnbr9+ZmcNxeX5weAtWP7a9raL6iqbVW1oao2LGHZHIwhSbNvLqL5AHBGktOSLAWuBHbOwf1I0ryb9cvzqnorye8DdwOLgFuq6onZvh9JGsKcvKZZVXcBd83FbUvSkPyNIEnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDkZTkjoYTUnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDkZTkjoYTUnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDkZTkjpMG80ktyQ5mOTxsbWTktyT5On2+cS2niQ3JZlK8miS9XM5vCTNt5k80/xr4OLD1rYCu6rqDGBX2we4BDijfWwBbp6dMSVpMkwbzar6Z+BHhy1fBuxo2zuAy8fWv1Yj3wNWJFk1W8NK0tCO9jXNlVX1Qtt+EVjZtlcD+8bO29/WJOk94ZjfCKqqAqr365JsSbInyZ43eeNYx5CkeXG00Xzp0GV3+3ywrR8A1o6dt6at/R9Vta2qNlTVhiUsO8oxJGl+HW00dwKb2vYm4I6x9avbu+jnAa+NXcZL0oK3eLoTknwD+BhwSpL9wJ8CXwBuS7IZeB74VDv9LuBSYAr4CfCZOZhZkgYzbTSr6qp3OLTxCOcWcM2xDiVJk8rfCJKkDkZTkjoYTUnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDkZTkjoYTUnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6jDt3z2XNLvu/veH5+R2f/OXz5qT29Uv8pmmJHUwmpLUwWhKUgejKUkdjKYkdTCaktTBaEpSB6MpSR2MpiR1MJqS1MFoSlIHoylJHYymJHWYNppJ1ia5L8mTSZ5Icm1bPynJPUmebp9PbOtJclOSqSSPJlk/19+EJM2XmTzTfAv4g6o6EzgPuCbJmcBWYFdVnQHsavsAlwBntI8twM2zPrUkDWTaaFbVC1X1/bb9OrAXWA1cBuxop+0ALm/blwFfq5HvASuSrJr1ySVpAF2vaSZZB5wN7AZWVtUL7dCLwMq2vRrYN/Zl+9va4be1JcmeJHve5I3OsSVpGDOOZpLjgW8Bn62qH48fq6oCqueOq2pbVW2oqg1LWNbzpZI0mBlFM8kSRsH8elV9uy2/dOiyu30+2NYPAGvHvnxNW5OkBW8m754H2A7sraovjh3aCWxq25uAO8bWr27vop8HvDZ2GS9JC9pM/rDaBcBvA48lOfQXof4I+AJwW5LNwPPAp9qxu4BLgSngJ8BnZnViSRrQtNGsqn8B8g6HNx7h/AKuOca5JGki+Sd8pXnmn9pd2Pw1SknqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDkZTkjoYTUnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDkZTkjoYTUnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDtNGM8nyJPcneSTJE0k+39ZPS7I7yVSSW5MsbevL2v5UO75ubr8FSZo/M3mm+QZwYVX9OnAWcHGS84AbgBur6kPAK8Dmdv5m4JW2fmM7T5LeE6aNZo38Z9td0j4KuBC4va3vAC5v25e1fdrxjUkyaxNL0oBm9JpmkkVJHgYOAvcAzwCvVtVb7ZT9wOq2vRrYB9COvwacfITb3JJkT5I9b/LGsX0XkjRPZhTNqvpZVZ0FrAHOAT58rHdcVduqakNVbVjCsmO9OUmaF13vnlfVq8B9wPnAiiSL26E1wIG2fQBYC9COnwC8PCvTStLAZvLu+alJVrTt9wEXAXsZxfOKdtom4I62vbPt047fW1U1m0NL0lAWT38Kq4AdSRYxiuxtVXVnkieBbyb5M+AhYHs7fzvwN0mmgB8BV87B3JI0iGmjWVWPAmcfYf1ZRq9vHr7+U+CTszKdJE0YfyNIkjoYTUnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDkZTkjoYTUnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDkZTkjoYTUnqYDQlqYPRlKQORlOSOhhNSepgNCWpg9GUpA5GU5I6GE1J6mA0JamD0ZSkDjOOZpJFSR5KcmfbPy3J7iRTSW5NsrStL2v7U+34urkZXZLmX88zzWuBvWP7NwA3VtWHgFeAzW19M/BKW7+xnSdJ7wkzimaSNcBvAV9t+wEuBG5vp+wALm/bl7V92vGN7XxJWvBm+kzzS8DngJ+3/ZOBV6vqrba/H1jdtlcD+wDa8dfa+ZK04E0bzSQfBw5W1YOzecdJtiTZk2TPm7wxmzctSXNm8QzOuQD4RJJLgeXAB4EvAyuSLG7PJtcAB9r5B4C1wP4ki4ETgJcPv9Gq2gZsA/hgTqpj/UYkaT5M+0yzqq6vqjVVtQ64Eri3qj4N3Adc0U7bBNzRtne2fdrxe6vKKEp6TziWn9P8Q+C6JFOMXrPc3ta3Aye39euArcc2oiRNjplcnr+tqr4LfLdtPwucc4Rzfgp8chZmk6SJ428ESVIHoylJHYymJHUwmpLUwWhKUgejKUkdjKYkdTCaktTBaEpSB6MpSR2MpiR1MJqS1MFoSlIHoylJHYymJHUwmpLUwWhKUgejKUkdjKYkdTCaktTBaEpSB6MpSR2MpiR1MJqS1MFoSlIHoylJHYymJHUwmpLUwWhKUgejKUkdjKYkdUhVDT0DSV4Hnhp6jqNwCvDDoYfo5MzzZyHO/f955l+pqlOnO2nxLNzRbHiqqjYMPUSvJHsW2tzOPH8W4tzOPD0vzyWpg9GUpA6TEs1tQw9wlBbi3M48fxbi3M48jYl4I0iSFopJeaYpSQvC4NFMcnGSp5JMJdk69DyHJLklycEkj4+tnZTkniRPt88ntvUkual9D48mWT/QzGuT3JfkySRPJLl2gcy9PMn9SR5pc3++rZ+WZHeb79YkS9v6srY/1Y6vG2LuNsuiJA8luXMhzJzkuSSPJXk4yZ62NumPjxVJbk/yr0n2Jjl/0JmrarAPYBHwDHA6sBR4BDhzyJnGZvsosB54fGztz4GtbXsrcEPbvhT4RyDAecDugWZeBaxv2x8AfgCcuQDmDnB8214C7G7z3AZc2da/Avxu2/494Ctt+0rg1gEfJ9cBfwvc2fYnembgOeCUw9Ym/fGxA/idtr0UWDHkzIM80Mb+Y5wP3D22fz1w/ZAzHTbfusOi+RSwqm2vYvTzpQB/CVx1pPMGnv8O4KKFNDfwfuD7wLmMfmB58eGPFeBu4Py2vbidlwFmXQPsAi4E7mz/o076zEeK5sQ+PoATgH87/L/VkDMPfXm+Gtg3tr+/rU2qlVX1Qtt+EVjZtifu+2iXf2czetY28XO3y9yHgYPAPYyuQF6tqreOMNvbc7fjrwEnz+/EAHwJ+Bzw87Z/MpM/cwH/lOTBJFva2iQ/Pk4D/gP4q/YyyFeTHMeAMw8dzQWrRv+MTeSPHiQ5HvgW8Nmq+vH4sUmdu6p+VlVnMXr2dg7w4YFHeldJPg4crKoHh56l00eqaj1wCXBNko+OH5zAx8diRi+T3VxVZwP/xehy/G3zPfPQ0TwArB3bX9PWJtVLSVYBtM8H2/rEfB9JljAK5ter6ttteeLnPqSqXgXuY3RpuyLJoV/1HZ/t7bnb8ROAl+d51AuATyR5Dvgmo0v0LzPZM1NVB9rng8DfM/oHapIfH/uB/VW1u+3fziiig808dDQfAM5o7zguZfQC+c6BZ3o3O4FNbXsTo9cMD61f3d65Ow94bezSYd4kCbAd2FtVXxw7NOlzn5pkRdt+H6PXYfcyiucV7bTD5z70/VwB3Nuebcybqrq+qtZU1TpGj9t7q+rTTPDMSY5L8oFD28BvAI8zwY+PqnoR2JfkV9vSRuDJQWeezxd13+GF3ksZvcv7DPDHQ88zNtc3gBeANxn9a7eZ0WtQu4Cnge8AJ7VzA/xF+x4eAzYMNPNHGF2mPAo83D4uXQBz/xrwUJv7ceBP2vrpwP3AFPB3wLK2vrztT7Xjpw/8WPkY//vu+cTO3GZ7pH08cej/twXw+DgL2NMeH/8AnDjkzP5GkCR1GPryXJIWFKMpSR2MpiR1MJqS1MFoSlIHoylJHYymJHUwmpLU4X8Aph+MS4LPHQQAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"perImgPredInstances, predMasks = abstract_cityscapes_style_eval.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, 8.57it/s]\n"
]
}
],
"source": [
"matches = abstract_cityscapes_style_eval.matchGtWithPreds(dataset, predictions)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: {'groundTruth': {'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",
" 'prediction': {'box': [{'imgName': 0,\n",
" 'predID': 0,\n",
" 'labelID': 99,\n",
" 'pixelCount': 2500,\n",
" 'confidence': 1.0,\n",
" 'matchedGt': [{'labelID': 99,\n",
" 'instID': 0,\n",
" 'pixelCount': 2500,\n",
" 'intersection': 2500}]},\n",
" {'imgName': 0,\n",
" 'predID': 1,\n",
" 'labelID': 99,\n",
" 'pixelCount': 2500,\n",
" 'confidence': 1.0,\n",
" 'matchedGt': [{'labelID': 99,\n",
" 'instID': 1,\n",
" 'pixelCount': 2500,\n",
" 'intersection': 2500}]}]}}}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"matches"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import evalInstanceLevelSemanticLabeling\n",
"args = evalInstanceLevelSemanticLabeling.args\n",
"args.instLabels = list(dataset.classid_to_name.values())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"##################################################\n",
"\u001b[1mwhat : AP AP_50%\u001b[0m\n",
"##################################################\n",
"box :\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[0m\n",
"\n"
]
}
],
"source": [
"# evaluate matches\n",
"apScores = evalInstanceLevelSemanticLabeling.evaluateMatches(matches, args)\n",
"# averages\n",
"avgDict = evalInstanceLevelSemanticLabeling.computeAverages(apScores,args)\n",
"# Print results\n",
"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
}
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