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nerfstudio/scripts/benchmarking/create_tables.ipynb
Ethan Weber ca6c8efbe2 some edits
2023-01-24 09:19:35 +00:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import copy\n",
"import pprint\n",
"from benchmark_nerfstudio_paper import ablations_table_rows\n",
"from collections import defaultdict\n",
"\n",
"# methods sorted in the same order we want in the paper tables\n",
"methods = [row[0] for row in ablations_table_rows]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"def csv_filename_to_results(csv_filename, num_decimals=2, num_rows_to_consider=None):\n",
" # code to read the csv file\n",
" # and return a dictionary\n",
"\n",
" df = pd.read_csv(csv_filename)\n",
"\n",
" column_titles = list(df.columns.values)\n",
"\n",
" # iterate as long as the value is not None\n",
" if num_rows_to_consider is None:\n",
" rows = list(df.values)\n",
" else:\n",
" rows = list(df.values)[:num_rows_to_consider]\n",
" valid_row = list(df.values[-1])\n",
" for row in rows:\n",
" for i in range(len(row)):\n",
" value = row[i]\n",
" if not pd.isnull(value):\n",
" valid_row[i] = value\n",
" last_row = valid_row\n",
" # last_row will be the row that's valid\n",
"\n",
" from collections import defaultdict\n",
" results = defaultdict(dict)\n",
" for column_title, last_row_value in zip(column_titles, last_row):\n",
" if column_title == \"Step\" or \"__MAX\" in column_title or \"__MIN\" in column_title:\n",
" continue\n",
" name = column_title.split(\" - \")[0]\n",
" capture_name, method = name.split(\"_\")\n",
" metric = float(last_row_value)\n",
" # format to have 2 decimal places\n",
" if num_decimals == 2:\n",
" metric = f\"{metric:.2f}\"\n",
" elif num_decimals == 3:\n",
" metric = f\"{metric:.3f}\"\n",
" else:\n",
" metric = f\"{metric}\"\n",
" results[capture_name][method] = metric\n",
"\n",
" # import pprint\n",
" # pprint.pprint(results)\n",
" # print(len(results))\n",
"\n",
" results = dict(results)\n",
" return results"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# the csv filenames\n",
"# these are generated from exporting CSVs from wandb graphs\n",
"csv_filename_psrn = \"/home/ethanweber/nerfactory/wandb-psnr.csv\"\n",
"csv_filename_ssim = \"/home/ethanweber/nerfactory/wandb-ssim.csv\"\n",
"csv_filename_lpips = \"/home/ethanweber/nerfactory/wandb-lpips.csv\"\n",
"results_psrn = csv_filename_to_results(csv_filename_psrn, num_decimals=None)\n",
"results_ssim = csv_filename_to_results(csv_filename_ssim, num_decimals=None)\n",
"results_lpips = csv_filename_to_results(csv_filename_lpips, num_decimals=None)\n",
"\n",
"\n",
"csv_filename_psrn = \"/home/ethanweber/nerfactory/wandb-jan9-psnr.csv\"\n",
"csv_filename_ssim = \"/home/ethanweber/nerfactory/wandb-jan9-ssim.csv\"\n",
"csv_filename_lpips = \"/home/ethanweber/nerfactory/wandb-jan9-lpips.csv\"\n",
"results_psrn_2 = csv_filename_to_results(csv_filename_psrn, num_decimals=None)\n",
"results_ssim_2 = csv_filename_to_results(csv_filename_ssim, num_decimals=None)\n",
"results_lpips_2 = csv_filename_to_results(csv_filename_lpips, num_decimals=None)\n",
"\n",
"# merge the results\n",
"for capture_name, results in results_psrn_2.items():\n",
" results_psrn[capture_name].update(results)\n",
"for capture_name, results in results_ssim_2.items():\n",
" results_ssim[capture_name].update(results)\n",
"for capture_name, results in results_lpips_2.items():\n",
" results_lpips[capture_name].update(results)\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"def get_averages_from_results(results):\n",
" averages = defaultdict(list)\n",
" for capture_name, methods in results.items():\n",
" for method, metric in methods.items():\n",
" averages[method].append(float(metric))\n",
" for method, metrics in averages.items():\n",
" val = sum(metrics) / len(metrics)\n",
" averages[method] = float(f\"{val:.4f}\")\n",
" return averages"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"defaultdict(<class 'list'>,\n",
" {'1-prop-network': 0.0668,\n",
" 'l2-contraction': 0.0598,\n",
" 'nerfacto': 0.0601,\n",
" 'no-contraction': 0.1308,\n",
" 'random-background-color': 0.0599,\n",
" 'shared-prop-network': 0.0605,\n",
" 'synthetic-on-real': 0.114,\n",
" 'w/o-app': 0.0622,\n",
" 'w/o-pose': 0.0614,\n",
" 'w/o-pose-app': 0.0636})\n"
]
}
],
"source": [
"pprint.pprint(get_averages_from_results(results_lpips))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"def generate_row(method, capture_names):\n",
" # remove spaces from method\n",
" method_text = method.replace(\"-\", \" \")\n",
" string = f\"{method_text}\"\n",
" for idx, capture_name in enumerate(capture_names):\n",
" psnr = results_psrn[capture_name][method]\n",
" ssim = results_ssim[capture_name][method]\n",
" lpips = results_lpips[capture_name][method]\n",
" if idx == len(capture_names) - 1:\n",
" string += \" & \\\\multicolumn{1}{r|}{\" + f\"{psnr} / {ssim} / {lpips}\" + \"}\"\n",
" else:\n",
" string += \" & \\\\multicolumn{1}{r}{\" + f\"{psnr} / {ssim} / {lpips}\" + \"}\"\n",
" return string"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Egypt', 'person', 'kitchen', 'plane', 'dozer']\n",
"nerfacto & \\multicolumn{1}{r}{22.714982986450195 / 0.8457087874412537 / 0.0387668460607528} & \\multicolumn{1}{r}{26.58124351501465 / 0.8588576912879944 / 0.0272847861051559} & \\multicolumn{1}{r}{21.02389335632324 / 0.8730602860450745 / 0.0538232773542404} & \\multicolumn{1}{r}{23.30121612548828 / 0.8020055890083313 / 0.0444588512182235} & \\multicolumn{1}{r|}{21.23827743530273 / 0.8753665089607239 / 0.0552346743643283} \\\\ \n",
"w/o pose & \\multicolumn{1}{r}{22.709941864013672 / 0.8510463237762451 / 0.0384077467024326} & \\multicolumn{1}{r}{26.875864028930664 / 0.875615656375885 / 0.0267722699791193} & \\multicolumn{1}{r}{20.83899688720703 / 0.8627068996429443 / 0.0561660341918468} & \\multicolumn{1}{r}{23.130054473876957 / 0.8018836975097656 / 0.0470539852976799} & \\multicolumn{1}{r|}{21.35797691345215 / 0.8832465410232544 / 0.0533054806292057} \\\\ \n",
"w/o app & \\multicolumn{1}{r}{23.50516891479492 / 0.8450330495834351 / 0.0371274687349796} & \\multicolumn{1}{r}{29.19672203063965 / 0.8823416829109192 / 0.0238535590469837} & \\multicolumn{1}{r}{23.997222900390625 / 0.8697791695594788 / 0.0548945926129817} & \\multicolumn{1}{r}{23.31549835205078 / 0.7850948572158813 / 0.0470350570976734} & \\multicolumn{1}{r|}{24.386877059936523 / 0.8825743198394775 / 0.0454414412379264} \\\\ \n",
"w/o pose app & \\multicolumn{1}{r}{23.166139602661133 / 0.837310791015625 / 0.0366224125027656} & \\multicolumn{1}{r}{29.16166877746582 / 0.8803766369819641 / 0.023823982104659} & \\multicolumn{1}{r}{23.605825424194336 / 0.8618716597557068 / 0.0610234178602695} & \\multicolumn{1}{r}{22.98196029663086 / 0.7747009992599487 / 0.0486842803657054} & \\multicolumn{1}{r|}{24.1285343170166 / 0.8762974143028259 / 0.0484496317803859} \\\\ \n",
"1 prop network & \\multicolumn{1}{r}{22.29441261291504 / 0.8302732706069946 / 0.0457336418330669} & \\multicolumn{1}{r}{26.79549789428711 / 0.8694913983345032 / 0.0297231022268533} & \\multicolumn{1}{r}{20.88402557373047 / 0.8669102787971497 / 0.0568834841251373} & \\multicolumn{1}{r}{23.38462257385254 / 0.8012978434562683 / 0.0477019511163234} & \\multicolumn{1}{r|}{21.26655387878418 / 0.8781195878982544 / 0.0575154237449169} \\\\ \n",
"l2 contraction & \\multicolumn{1}{r}{22.735132217407227 / 0.8507159352302551 / 0.0394952110946178} & \\multicolumn{1}{r}{26.3044490814209 / 0.8442776203155518 / 0.0297974403947591} & \\multicolumn{1}{r}{20.831445693969727 / 0.870667040348053 / 0.0538116246461868} & \\multicolumn{1}{r}{23.17695617675781 / 0.8045668601989746 / 0.0425091050565242} & \\multicolumn{1}{r|}{21.17496681213379 / 0.8793404698371887 / 0.0562350191175937} \\\\ \n",
"shared prop network & \\multicolumn{1}{r}{22.72172737121582 / 0.8462676405906677 / 0.036957062780857} & \\multicolumn{1}{r}{26.2338924407959 / 0.8363291621208191 / 0.030552914366126} & \\multicolumn{1}{r}{20.846466064453125 / 0.8672903180122375 / 0.0565056912600994} & \\multicolumn{1}{r}{23.31382369995117 / 0.8025220036506653 / 0.0451882556080818} & \\multicolumn{1}{r|}{21.209640502929688 / 0.8794581890106201 / 0.0534685812890529} \\\\ \n",
"random background color & \\multicolumn{1}{r}{22.50684356689453 / 0.8338311910629272 / 0.0386323854327201} & \\multicolumn{1}{r}{26.413747787475582 / 0.8475838899612427 / 0.0302980989217758} & \\multicolumn{1}{r}{20.803401947021484 / 0.8670746684074402 / 0.0552690662443637} & \\multicolumn{1}{r}{23.181974411010746 / 0.7954545021057129 / 0.0450066737830638} & \\multicolumn{1}{r|}{21.30900382995605 / 0.8826950192451477 / 0.0531600639224052} \\\\ \n",
"no contraction & \\multicolumn{1}{r}{18.0621395111084 / 0.5198838710784912 / 0.1507363319396972} & \\multicolumn{1}{r}{21.95572662353516 / 0.511165201663971 / 0.1250636577606201} & \\multicolumn{1}{r}{19.351024627685547 / 0.764460563659668 / 0.0784180983901023} & \\multicolumn{1}{r}{18.450420379638672 / 0.5193044543266296 / 0.1613750755786895} & \\multicolumn{1}{r|}{19.532262802124023 / 0.7856844067573547 / 0.0809869393706321} \\\\ \n",
"synthetic on real & \\multicolumn{1}{r}{18.51077651977539 / 0.5463988184928894 / 0.1192879229784011} & \\multicolumn{1}{r}{24.6822566986084 / 0.6670649647712708 / 0.0987291559576988} & \\multicolumn{1}{r}{20.980388641357425 / 0.745147705078125 / 0.0741989538073539} & \\multicolumn{1}{r}{19.319589614868164 / 0.5777738690376282 / 0.1021366342902183} & \\multicolumn{1}{r|}{21.40807342529297 / 0.7580105066299438 / 0.0801835656166076} \\\\ \\hline\n",
"\n",
"['floating-tree', 'aspen', 'stump', 'sculpture', 'Giannini-Hall']\n",
"nerfacto & \\multicolumn{1}{r}{20.39972686767578 / 0.854109525680542 / 0.0524107255041599} & \\multicolumn{1}{r}{18.57237243652344 / 0.6354746222496033 / 0.1751366555690765} & \\multicolumn{1}{r}{21.928449630737305 / 0.7976410388946533 / 0.0387384481728076} & \\multicolumn{1}{r}{22.58584213256836 / 0.7889150381088257 / 0.0407779291272163} & \\multicolumn{1}{r|}{19.99639129638672 / 0.7731102108955383 / 0.0745075196027755} \\\\ \n",
"w/o pose & \\multicolumn{1}{r}{20.298728942871094 / 0.8402881622314453 / 0.0546704977750778} & \\multicolumn{1}{r}{18.61675453186035 / 0.6382865905761719 / 0.1753666847944259} & \\multicolumn{1}{r}{21.633838653564453 / 0.7653179168701172 / 0.0428420193493366} & \\multicolumn{1}{r}{22.506526947021484 / 0.7845871448516846 / 0.0455470941960811} & \\multicolumn{1}{r|}{19.97289276123047 / 0.7722745537757874 / 0.0739260166883468} \\\\ \n",
"w/o app & \\multicolumn{1}{r}{22.46498680114746 / 0.8535845279693604 / 0.0616657771170139} & \\multicolumn{1}{r}{18.27329635620117 / 0.6129483580589294 / 0.1905485093593597} & \\multicolumn{1}{r}{25.68264579772949 / 0.8736064434051514 / 0.0317401029169559} & \\multicolumn{1}{r}{23.268043518066406 / 0.7738019227981567 / 0.0487339384853839} & \\multicolumn{1}{r|}{21.44996833801269 / 0.7652416825294495 / 0.0814294144511222} \\\\ \n",
"w/o pose app & \\multicolumn{1}{r}{22.154300689697266 / 0.8340064287185669 / 0.0661337822675705} & \\multicolumn{1}{r}{18.28131675720215 / 0.611172080039978 / 0.1861454993486404} & \\multicolumn{1}{r}{25.562631607055664 / 0.8677808046340942 / 0.0340047031641006} & \\multicolumn{1}{r}{22.98941802978516 / 0.76247638463974 / 0.0532486699521541} & \\multicolumn{1}{r|}{21.344554901123047 / 0.7619099020957947 / 0.0781947895884513} \\\\ \n",
"1 prop network & \\multicolumn{1}{r}{20.28233528137207 / 0.8498139977455139 / 0.0570690296590328} & \\multicolumn{1}{r}{18.41587829589844 / 0.6070924401283264 / 0.2094678878784179} & \\multicolumn{1}{r}{22.85092544555664 / 0.8734478950500488 / 0.0329417251050472} & \\multicolumn{1}{r}{22.60772132873535 / 0.8046638369560242 / 0.0431603491306304} & \\multicolumn{1}{r|}{19.89984703063965 / 0.759652853012085 / 0.0880114957690239} \\\\ \n",
"l2 contraction & \\multicolumn{1}{r}{20.49176979064941 / 0.8501678109169006 / 0.0527788065373897} & \\multicolumn{1}{r}{18.66337013244629 / 0.6445528268814087 / 0.1680227816104889} & \\multicolumn{1}{r}{22.26547622680664 / 0.8150904178619385 / 0.0378103740513324} & \\multicolumn{1}{r}{22.462072372436523 / 0.7815755009651184 / 0.0426461324095726} & \\multicolumn{1}{r|}{19.985918045043945 / 0.7727018594741821 / 0.0751706212759018} \\\\ \n",
"shared prop network & \\multicolumn{1}{r}{20.333086013793945 / 0.8504053950309753 / 0.0537901557981967} & \\multicolumn{1}{r}{18.6508731842041 / 0.6390713453292847 / 0.1719826906919479} & \\multicolumn{1}{r}{22.207321166992188 / 0.805411696434021 / 0.0403469167649745} & \\multicolumn{1}{r}{22.50098991394043 / 0.7922176718711853 / 0.0416620261967182} & \\multicolumn{1}{r|}{19.98356437683105 / 0.7727880477905273 / 0.0745216161012649} \\\\ \n",
"random background color & \\multicolumn{1}{r}{20.243900299072266 / 0.8465315699577332 / 0.0529390051960945} & \\multicolumn{1}{r}{18.56953430175781 / 0.636640191078186 / 0.1718863695859909} & \\multicolumn{1}{r}{22.578887939453125 / 0.8352341055870056 / 0.0351601541042327} & \\multicolumn{1}{r}{22.478111267089844 / 0.7781183123588562 / 0.0417613536119461} & \\multicolumn{1}{r|}{20.105976104736328 / 0.7749199867248535 / 0.0752039030194282} \\\\ \n",
"no contraction & \\multicolumn{1}{r}{18.98202896118164 / 0.6787521839141846 / 0.0879055261611938} & \\multicolumn{1}{r}{15.803391456604004 / 0.3301353752613067 / 0.3417288959026336} & \\multicolumn{1}{r}{20.686363220214844 / 0.7041175365447998 / 0.0748037695884704} & \\multicolumn{1}{r}{21.432249069213867 / 0.7197808623313904 / 0.0730623304843902} & \\multicolumn{1}{r|}{17.751110076904297 / 0.5955697298049927 / 0.1335323601961136} \\\\ \n",
"synthetic on real & \\multicolumn{1}{r}{21.130878448486328 / 0.7259055376052856 / 0.0717189684510231} & \\multicolumn{1}{r}{15.527592658996582 / 0.3011459112167358 / 0.3317984342575073} & \\multicolumn{1}{r}{23.858999252319336 / 0.7855907678604126 / 0.0529582537710666} & \\multicolumn{1}{r}{22.620250701904297 / 0.7056115865707397 / 0.0756969302892685} & \\multicolumn{1}{r|}{18.407384872436523 / 0.5756094455718994 / 0.1331061422824859} \\\\ \\hline\n"
]
}
],
"source": [
"capture_names_row1 = [\n",
" \"Egypt\",\n",
" \"person\",\n",
" \"kitchen\",\n",
" \"plane\",\n",
" \"dozer\"\n",
"]\n",
"capture_names_row2 = [\n",
" \"floating-tree\",\n",
" \"aspen\",\n",
" \"stump\",\n",
" \"sculpture\",\n",
" \"Giannini-Hall\"\n",
"]\n",
"\n",
"print(capture_names_row1)\n",
"for method in methods[:-1]:\n",
" # print(generate_row(method, capture_names_row1) + ' \\\\\\ \\cline{1-1}')\n",
" print(generate_row(method, capture_names_row1) + ' \\\\\\ ')\n",
"print(generate_row(methods[-1], capture_names_row1) + ' \\\\\\ \\hline')\n",
"\n",
"print()\n",
"\n",
"print(capture_names_row2)\n",
"for method in methods[:-1]:\n",
" # print(generate_row(method, capture_names_row2) + ' \\\\\\ \\cline{1-1}')\n",
" print(generate_row(method, capture_names_row2) + ' \\\\\\ ')\n",
"print(generate_row(methods[-1], capture_names_row2) + ' \\\\\\ \\hline')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# mipnerf360 experiments\n",
"\n",
"csv_filename_psrn = \"/home/ethanweber/nerfactory/mipnerf360-nerfacto-psnr.csv\"\n",
"csv_filename_ssim = \"/home/ethanweber/nerfactory/mipnerf360-nerfacto-ssim.csv\"\n",
"csv_filename_lpips = \"/home/ethanweber/nerfactory/mipnerf360-nerfacto-lpips.csv\"\n",
"\n",
"csv_filename_psrn = \"/home/ethanweber/nerfactory/mipnerf360-instant-ngp-psnr.csv\"\n",
"csv_filename_ssim = \"/home/ethanweber/nerfactory/mipnerf360-instant-ngp-ssim.csv\"\n",
"csv_filename_lpips = \"/home/ethanweber/nerfactory/mipnerf360-instant-ngp-lpips.csv\"\n",
"\n",
"results_psrn = csv_filename_to_results(csv_filename_psrn, num_rows_to_consider=1)\n",
"results_ssim = csv_filename_to_results(csv_filename_ssim, num_decimals=3, num_rows_to_consider=1)\n",
"results_lpips = csv_filename_to_results(csv_filename_lpips, num_decimals=3, num_rows_to_consider=1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"psnr\n",
"[('bicycle', {'instant-ngp-w/o-pose-app': '19.92'}),\n",
" ('garden', {'instant-ngp-w/o-pose-app': '22.68'}),\n",
" ('stump', {'instant-ngp-w/o-pose-app': '20.25'}),\n",
" ('room', {'instant-ngp-w/o-pose-app': '28.04'}),\n",
" ('counter', {'instant-ngp-w/o-pose-app': '23.59'}),\n",
" ('kitchen', {'instant-ngp-w/o-pose-app': '24.61'}),\n",
" ('bonsai', {'instant-ngp-w/o-pose-app': '25.67'})]\n",
"\n",
"ssim\n",
"[('bicycle', {'instant-ngp-w/o-pose-app': '0.448'}),\n",
" ('garden', {'instant-ngp-w/o-pose-app': '0.542'}),\n",
" ('stump', {'instant-ngp-w/o-pose-app': '0.465'}),\n",
" ('room', {'instant-ngp-w/o-pose-app': '0.828'}),\n",
" ('counter', {'instant-ngp-w/o-pose-app': '0.701'}),\n",
" ('kitchen', {'instant-ngp-w/o-pose-app': '0.642'}),\n",
" ('bonsai', {'instant-ngp-w/o-pose-app': '0.792'})]\n",
"\n",
"lpips\n",
"[('bicycle', {'instant-ngp-w/o-pose-app': '0.449'}),\n",
" ('garden', {'instant-ngp-w/o-pose-app': '0.402'}),\n",
" ('stump', {'instant-ngp-w/o-pose-app': '0.440'}),\n",
" ('room', {'instant-ngp-w/o-pose-app': '0.170'}),\n",
" ('counter', {'instant-ngp-w/o-pose-app': '0.271'}),\n",
" ('kitchen', {'instant-ngp-w/o-pose-app': '0.281'}),\n",
" ('bonsai', {'instant-ngp-w/o-pose-app': '0.186'})]\n",
"\n"
]
}
],
"source": [
"mipnerf360_nerfacto_capture_names = [\"bicycle\", \"garden\", \"stump\", \"room\", \"counter\", \"kitchen\", \"bonsai\"]\n",
"\n",
"print(\"psnr\")\n",
"s = [(key, results_psrn[key]) for key in mipnerf360_nerfacto_capture_names]\n",
"pprint.pprint(s)\n",
"print()\n",
"\n",
"print(\"ssim\")\n",
"s = [(key, results_ssim[key]) for key in mipnerf360_nerfacto_capture_names]\n",
"pprint.pprint(s)\n",
"print()\n",
"\n",
"print(\"lpips\")\n",
"s = [(key, results_lpips[key]) for key in mipnerf360_nerfacto_capture_names]\n",
"pprint.pprint(s)\n",
"print()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"psnr\n",
"[('bicycle', {'instant-ngp-w/o-pose-app': '18.78'}),\n",
" ('garden', {'instant-ngp-w/o-pose-app': '20.82'}),\n",
" ('stump', {'instant-ngp-w/o-pose-app': '19.73'}),\n",
" ('room', {'instant-ngp-w/o-pose-app': '25.26'}),\n",
" ('counter', {'instant-ngp-w/o-pose-app': '22.09'}),\n",
" ('kitchen', {'instant-ngp-w/o-pose-app': '22.82'}),\n",
" ('bonsai', {'instant-ngp-w/o-pose-app': '23.86'})]\n",
"\n",
"ssim\n",
"[('bicycle', {'instant-ngp-w/o-pose-app': '0.389'}),\n",
" ('garden', {'instant-ngp-w/o-pose-app': '0.390'}),\n",
" ('stump', {'instant-ngp-w/o-pose-app': '0.408'}),\n",
" ('room', {'instant-ngp-w/o-pose-app': '0.749'}),\n",
" ('counter', {'instant-ngp-w/o-pose-app': '0.621'}),\n",
" ('kitchen', {'instant-ngp-w/o-pose-app': '0.530'}),\n",
" ('bonsai', {'instant-ngp-w/o-pose-app': '0.665'})]\n",
"\n",
"lpips\n",
"[('bicycle', {'instant-ngp-w/o-pose-app': '0.620'}),\n",
" ('garden', {'instant-ngp-w/o-pose-app': '0.619'}),\n",
" ('stump', {'instant-ngp-w/o-pose-app': '0.607'}),\n",
" ('room', {'instant-ngp-w/o-pose-app': '0.311'}),\n",
" ('counter', {'instant-ngp-w/o-pose-app': '0.417'}),\n",
" ('kitchen', {'instant-ngp-w/o-pose-app': '0.427'}),\n",
" ('bonsai', {'instant-ngp-w/o-pose-app': '0.382'})]\n",
"\n"
]
}
],
"source": [
"mipnerf360_nerfacto_capture_names = [\"bicycle\", \"garden\", \"stump\", \"room\", \"counter\", \"kitchen\", \"bonsai\"]\n",
"\n",
"print(\"psnr\")\n",
"s = [(key, results_psrn[key]) for key in mipnerf360_nerfacto_capture_names]\n",
"pprint.pprint(s)\n",
"print()\n",
"\n",
"print(\"ssim\")\n",
"s = [(key, results_ssim[key]) for key in mipnerf360_nerfacto_capture_names]\n",
"pprint.pprint(s)\n",
"print()\n",
"\n",
"print(\"lpips\")\n",
"s = [(key, results_lpips[key]) for key in mipnerf360_nerfacto_capture_names]\n",
"pprint.pprint(s)\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "nerfactory",
"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": "3.10.6"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "b7b86c17c0dfd6b313529972d424f2e075cc198a46cda005757126afe8133c7b"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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