diff --git a/scripts/benchmarking/create_tables.ipynb b/scripts/benchmarking/create_tables.ipynb deleted file mode 100644 index 563d62f98..000000000 --- a/scripts/benchmarking/create_tables.ipynb +++ /dev/null @@ -1,495 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "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": 5, - "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", - "\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)" - ] - }, - { - "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(,\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 = [\"Egypt\", \"person\", \"kitchen\", \"plane\", \"dozer\"]\n", - "capture_names_row2 = [\"floating-tree\", \"aspen\", \"stump\", \"sculpture\", \"Giannini-Hall\"]\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\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# mipnerf360 experiments\n", - "\n", - "csv_filename_psrn = \"results/mipnerf360-nerfacto-psnr.csv\"\n", - "csv_filename_ssim = \"results/mipnerf360-nerfacto-ssim.csv\"\n", - "csv_filename_lpips = \"results/mipnerf360-nerfacto-lpips.csv\"\n", - "\n", - "results_psrn_5K = csv_filename_to_results(csv_filename_psrn, num_decimals=2, num_rows_to_consider=1)\n", - "results_ssim_5K = csv_filename_to_results(csv_filename_ssim, num_decimals=3, num_rows_to_consider=1)\n", - "results_lpips_5K = csv_filename_to_results(csv_filename_lpips, num_decimals=3, num_rows_to_consider=1)\n", - "\n", - "results_psrn_30K = csv_filename_to_results(csv_filename_psrn, num_decimals=2)\n", - "results_ssim_30K = csv_filename_to_results(csv_filename_ssim, num_decimals=3)\n", - "results_lpips_30K = csv_filename_to_results(csv_filename_lpips, num_decimals=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "psnr\n", - "[('bicycle', {'nerfacto-w/o-pose-app': '22.36'}),\n", - " ('garden', {'nerfacto-w/o-pose-app': '24.05'}),\n", - " ('stump', {'nerfacto-w/o-pose-app': '18.94'}),\n", - " ('room', {'nerfacto-w/o-pose-app': '29.36'}),\n", - " ('counter', {'nerfacto-w/o-pose-app': '25.92'}),\n", - " ('kitchen', {'nerfacto-w/o-pose-app': '28.17'}),\n", - " ('bonsai', {'nerfacto-w/o-pose-app': '28.98'})]\n", - "\n", - "ssim\n", - "[('bicycle', {'nerfacto-w/o-pose-app': '0.474'}),\n", - " ('garden', {'nerfacto-w/o-pose-app': '0.617'}),\n", - " ('stump', {'nerfacto-w/o-pose-app': '0.364'}),\n", - " ('room', {'nerfacto-w/o-pose-app': '0.866'}),\n", - " ('counter', {'nerfacto-w/o-pose-app': '0.776'}),\n", - " ('kitchen', {'nerfacto-w/o-pose-app': '0.838'}),\n", - " ('bonsai', {'nerfacto-w/o-pose-app': '0.880'})]\n", - "\n", - "lpips\n", - "[('bicycle', {'nerfacto-w/o-pose-app': '0.551'}),\n", - " ('garden', {'nerfacto-w/o-pose-app': '0.385'}),\n", - " ('stump', {'nerfacto-w/o-pose-app': '0.669'}),\n", - " ('room', {'nerfacto-w/o-pose-app': '0.302'}),\n", - " ('counter', {'nerfacto-w/o-pose-app': '0.346'}),\n", - " ('kitchen', {'nerfacto-w/o-pose-app': '0.223'}),\n", - " ('bonsai', {'nerfacto-w/o-pose-app': '0.252'})]\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": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "psnr\n", - "\\begin{tabular}[c]{@{}c@{}}Nerfacto (ours) w/o pose \\& app\\end{tabular} & \\multicolumn{1}{c|}{23.44 / 22.36} & \\multicolumn{1}{c|}{25.58 / 24.05} & \\multicolumn{1}{c|}{19.02 / 18.94} & \\multicolumn{1}{c|}{30.74 / 29.36} & \\multicolumn{1}{c|}{27.09 / 25.92} & \\multicolumn{1}{c|}{30.09 / 28.17} & \\multicolumn{1}{c|}{31.31 / 28.98}\\\\ \\hline\n", - "mean 30K psnr\n", - "mean 5K psnr\n", - "26.752857142857142 / 25.397142857142857\n", - "ssim\n", - "\\begin{tabular}[c]{@{}c@{}}Nerfacto (ours) w/o pose \\& app\\end{tabular} & \\multicolumn{1}{c|}{0.563 / 0.474} & \\multicolumn{1}{c|}{0.732 / 0.617} & \\multicolumn{1}{c|}{0.387 / 0.364} & \\multicolumn{1}{c|}{0.901 / 0.866} & \\multicolumn{1}{c|}{0.832 / 0.776} & \\multicolumn{1}{c|}{0.895 / 0.838} & \\multicolumn{1}{c|}{0.927 / 0.880}\\\\ \\hline\n", - "mean 30K ssim\n", - "mean 5K ssim\n", - "0.7481428571428571 / 0.687857142857143\n", - "lpips\n", - "\\begin{tabular}[c]{@{}c@{}}Nerfacto (ours) w/o pose \\& app\\end{tabular} & \\multicolumn{1}{c|}{0.457 / 0.551} & \\multicolumn{1}{c|}{0.279 / 0.385} & \\multicolumn{1}{c|}{0.630 / 0.669} & \\multicolumn{1}{c|}{0.218 / 0.302} & \\multicolumn{1}{c|}{0.262 / 0.346} & \\multicolumn{1}{c|}{0.149 / 0.223} & \\multicolumn{1}{c|}{0.155 / 0.252}\\\\ \\hline\n", - "mean 30K lpips\n", - "mean 5K lpips\n", - "0.3071428571428571 / 0.3897142857142857\n" - ] - } - ], - "source": [ - "mipnerf360_nerfacto_capture_names = [\"bicycle\", \"garden\", \"stump\", \"room\", \"counter\", \"kitchen\", \"bonsai\"]\n", - "method = \"nerfacto-w/o-pose-app\"\n", - "import numpy as np\n", - "\n", - "# create the rows for a metrics\n", - "def generate_row(results_30K, results_5K):\n", - " string = \"\"\"\\\\begin{tabular}[c]{@{}c@{}}Nerfacto (ours) w/o pose \\& app\\end{tabular} & \"\"\"\n", - "\n", - " # \\multicolumn{1}{c|}{0.602 / 0.504} & \\multicolumn{1}{c|}{0.778 / 0.614} & \\multicolumn{1}{c|}{0.436 / 0.395} & \\multicolumn{1}{c|}{0.912 / 0.865} & \\multicolumn{1}{c|}{0.846 / 0.774} & \\multicolumn{1}{c|}{0.907 / 0.842} & 0.940 / 0.884\n", - "\n", - " for idx, capture_name in enumerate(mipnerf360_nerfacto_capture_names):\n", - " psnr_30K = results_30K[capture_name][method]\n", - " psnr_5K = results_5K[capture_name][method]\n", - " string += \"\"\"\\multicolumn{1}{c|}{\"\"\" + f\"{psnr_30K} / {psnr_5K}\" + \"}\"\n", - " if idx != len(mipnerf360_nerfacto_capture_names) - 1:\n", - " string += \" & \"\n", - "\n", - " string += \"\"\"\\\\\\ \\hline\"\"\"\n", - " return string\n", - "\n", - "\n", - "print(\"psnr\")\n", - "string = generate_row(results_psrn_30K, results_psrn_5K)\n", - "print(string)\n", - "print(\"mean 30K psnr\")\n", - "psnr_30K = np.mean(\n", - " [float(results_psrn_30K[capture_name][method]) for capture_name in mipnerf360_nerfacto_capture_names]\n", - ")\n", - "print(\"mean 5K psnr\")\n", - "psnr_5K = np.mean([float(results_psrn_5K[capture_name][method]) for capture_name in mipnerf360_nerfacto_capture_names])\n", - "print(f\"{psnr_30K} / {psnr_5K}\")\n", - "\n", - "\n", - "print(\"ssim\")\n", - "string = generate_row(results_ssim_30K, results_ssim_5K)\n", - "print(string)\n", - "print(\"mean 30K ssim\")\n", - "ssim_30K = np.mean(\n", - " [float(results_ssim_30K[capture_name][method]) for capture_name in mipnerf360_nerfacto_capture_names]\n", - ")\n", - "print(\"mean 5K ssim\")\n", - "ssim_5K = np.mean([float(results_ssim_5K[capture_name][method]) for capture_name in mipnerf360_nerfacto_capture_names])\n", - "print(f\"{ssim_30K} / {ssim_5K}\")\n", - "\n", - "print(\"lpips\")\n", - "string = generate_row(results_lpips_30K, results_lpips_5K)\n", - "print(string)\n", - "print(\"mean 30K lpips\")\n", - "lpips_30K = np.mean(\n", - " [float(results_lpips_30K[capture_name][method]) for capture_name in mipnerf360_nerfacto_capture_names]\n", - ")\n", - "print(\"mean 5K lpips\")\n", - "lpips_5K = np.mean(\n", - " [float(results_lpips_5K[capture_name][method]) for capture_name in mipnerf360_nerfacto_capture_names]\n", - ")\n", - "print(f\"{lpips_30K} / {lpips_5K}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['23.44', '25.58', '19.02', '30.74', '27.09', '30.09', '31.31']" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[results_psrn_30K[capture_name][method] for capture_name in mipnerf360_nerfacto_capture_names]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[23.44, 25.58, 19.02, 30.74, 27.09, 30.09, 31.31]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[float(results_psrn_30K[capture_name][method]) for capture_name in mipnerf360_nerfacto_capture_names]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "27.464285714285715" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = [23.44, 25.58, 24, 30.74, 27.09, 30.09, 31.31]\n", - "np.mean(a)" - ] - }, - { - "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 (main, Oct 7 2022, 20:19:58) [GCC 11.2.0]" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "b7b86c17c0dfd6b313529972d424f2e075cc198a46cda005757126afe8133c7b" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/scripts/benchmarking/results/mipnerf360-nerfacto-lpips.csv b/scripts/benchmarking/results/mipnerf360-nerfacto-lpips.csv deleted file mode 100644 index 3e57fe607..000000000 --- a/scripts/benchmarking/results/mipnerf360-nerfacto-lpips.csv +++ /dev/null @@ -1,7 +0,0 @@ -"Step","bonsai_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips","bonsai_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MIN","bonsai_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MAX","kitchen_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips","kitchen_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MIN","kitchen_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MAX","counter_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips","counter_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MIN","counter_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MAX","room_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips","room_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MIN","room_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MAX","stump_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips","stump_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MIN","stump_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MAX","garden_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips","garden_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MIN","garden_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MAX","bicycle_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips","bicycle_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MIN","bicycle_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/lpips__MAX" -"5000","0.2519197463989258","0.2519197463989258","0.2519197463989258","0.22290439903736115","0.22290439903736115","0.22290439903736115","0.3463352918624878","0.3463352918624878","0.3463352918624878","0.30166396498680115","0.30166396498680115","0.30166396498680115","0.6690490245819092","0.6690490245819092","0.6690490245819092","0.38469502329826355","0.38469502329826355","0.38469502329826355","0.5510148406028748","0.5510148406028748","0.5510148406028748" -"10000","0.20537590980529785","0.20537590980529785","0.20537590980529785","0.1862574815750122","0.1862574815750122","0.1862574815750122","0.3060450553894043","0.3060450553894043","0.3060450553894043","0.264473021030426","0.264473021030426","0.264473021030426","0.6549886465072632","0.6549886465072632","0.6549886465072632","0.3379896581172943","0.3379896581172943","0.3379896581172943","0.5165897607803345","0.5165897607803345","0.5165897607803345" -"15000","0.18232251703739166","0.18232251703739166","0.18232251703739166","0.1694253832101822","0.1694253832101822","0.1694253832101822","0.28753527998924255","0.28753527998924255","0.28753527998924255","0.24498172104358673","0.24498172104358673","0.24498172104358673","0.6469297409057617","0.6469297409057617","0.6469297409057617","0.3143976628780365","0.3143976628780365","0.3143976628780365","0.49204298853874207","0.49204298853874207","0.49204298853874207" -"20000","0.16992567479610443","0.16992567479610443","0.16992567479610443","0.1603987067937851","0.1603987067937851","0.1603987067937851","0.2758496105670929","0.2758496105670929","0.2758496105670929","0.2337770015001297","0.2337770015001297","0.2337770015001297","0.6356087923049927","0.6356087923049927","0.6356087923049927","0.2970008850097656","0.2970008850097656","0.2970008850097656","0.477325439453125","0.477325439453125","0.477325439453125" -"25000","0.16045209765434265","0.16045209765434265","0.16045209765434265","0.15450946986675262","0.15450946986675262","0.15450946986675262","0.2683956027030945","0.2683956027030945","0.2683956027030945","0.2252463847398758","0.2252463847398758","0.2252463847398758","0.6341891884803772","0.6341891884803772","0.6341891884803772","0.28854474425315857","0.28854474425315857","0.28854474425315857","0.4702630937099457","0.4702630937099457","0.4702630937099457" -"30000","0.1552095264196396","0.1552095264196396","0.1552095264196396","0.14859946072101593","0.14859946072101593","0.14859946072101593","0.26168495416641235","0.26168495416641235","0.26168495416641235","0.21848224103450775","0.21848224103450775","0.21848224103450775","0.6303073167800903","0.6303073167800903","0.6303073167800903","0.27927762269973755","0.27927762269973755","0.27927762269973755","0.45694661140441895","0.45694661140441895","0.45694661140441895" \ No newline at end of file diff --git a/scripts/benchmarking/results/mipnerf360-nerfacto-psnr.csv b/scripts/benchmarking/results/mipnerf360-nerfacto-psnr.csv deleted file mode 100644 index 1bff3b40f..000000000 --- a/scripts/benchmarking/results/mipnerf360-nerfacto-psnr.csv +++ /dev/null @@ -1,7 +0,0 @@ -"Step","bonsai_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr","bonsai_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MIN","bonsai_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MAX","kitchen_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr","kitchen_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MIN","kitchen_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MAX","counter_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr","counter_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MIN","counter_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MAX","room_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr","room_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MIN","room_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MAX","stump_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr","stump_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MIN","stump_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MAX","garden_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr","garden_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MIN","garden_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MAX","bicycle_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr","bicycle_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MIN","bicycle_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/psnr__MAX" -"5000","28.984577178955078","28.984577178955078","28.984577178955078","28.172439575195312","28.172439575195312","28.172439575195312","25.917695999145508","25.917695999145508","25.917695999145508","29.36033821105957","29.36033821105957","29.36033821105957","18.943880081176758","18.943880081176758","18.943880081176758","24.045259475708008","24.045259475708008","24.045259475708008","22.35852813720703","22.35852813720703","22.35852813720703" -"10000","30.010135650634766","30.010135650634766","30.010135650634766","29.070581436157227","29.070581436157227","29.070581436157227","26.49431610107422","26.49431610107422","26.49431610107422","29.885282516479492","29.885282516479492","29.885282516479492","19.130979537963867","19.130979537963867","19.130979537963867","24.74232292175293","24.74232292175293","24.74232292175293","22.801597595214844","22.801597595214844","22.801597595214844" -"15000","30.6047306060791","30.6047306060791","30.6047306060791","29.542308807373047","29.542308807373047","29.542308807373047","26.782276153564453","26.782276153564453","26.782276153564453","30.297269821166992","30.297269821166992","30.297269821166992","19.193822860717773","19.193822860717773","19.193822860717773","25.023618698120117","25.023618698120117","25.023618698120117","23.096738815307617","23.096738815307617","23.096738815307617" -"20000","30.892518997192383","30.892518997192383","30.892518997192383","29.758039474487305","29.758039474487305","29.758039474487305","26.81525993347168","26.81525993347168","26.81525993347168","30.452899932861328","30.452899932861328","30.452899932861328","19.138933181762695","19.138933181762695","19.138933181762695","25.29572868347168","25.29572868347168","25.29572868347168","23.274446487426758","23.274446487426758","23.274446487426758" -"25000","30.987964630126953","30.987964630126953","30.987964630126953","29.747432708740234","29.747432708740234","29.747432708740234","26.988618850708008","26.988618850708008","26.988618850708008","30.530561447143555","30.530561447143555","30.530561447143555","19.102785110473633","19.102785110473633","19.102785110473633","25.39276695251465","25.39276695251465","25.39276695251465","23.29694175720215","23.29694175720215","23.29694175720215" -"30000","31.31144142150879","31.31144142150879","31.31144142150879","30.09200668334961","30.09200668334961","30.09200668334961","27.089460372924805","27.089460372924805","27.089460372924805","30.7379093170166","30.7379093170166","30.7379093170166","19.015052795410156","19.015052795410156","19.015052795410156","25.584802627563477","25.584802627563477","25.584802627563477","23.43606948852539","23.43606948852539","23.43606948852539" \ No newline at end of file diff --git a/scripts/benchmarking/results/mipnerf360-nerfacto-ssim.csv b/scripts/benchmarking/results/mipnerf360-nerfacto-ssim.csv deleted file mode 100644 index 7fbb39a8b..000000000 --- a/scripts/benchmarking/results/mipnerf360-nerfacto-ssim.csv +++ /dev/null @@ -1,7 +0,0 @@ -"Step","bonsai_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim","bonsai_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MIN","bonsai_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MAX","kitchen_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim","kitchen_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MIN","kitchen_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MAX","counter_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim","counter_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MIN","counter_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MAX","room_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim","room_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MIN","room_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MAX","stump_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim","stump_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MIN","stump_nerfacto-w/o-pose-app - Eval Images Metrics Dict (all images)/ssim__MAX","garden_nerfacto-w/o-pose-app - 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\ No newline at end of file diff --git a/scripts/benchmarking/results/wandb-jan9-psnr.csv b/scripts/benchmarking/results/wandb-jan9-psnr.csv deleted file mode 100644 index c61841b3e..000000000 --- a/scripts/benchmarking/results/wandb-jan9-psnr.csv +++ /dev/null @@ -1,7 +0,0 @@ -"Step","Giannini-Hall_synthetic-on-real - Eval Images Metrics Dict (all images)/psnr","Giannini-Hall_synthetic-on-real - Eval Images Metrics Dict (all images)/psnr__MIN","Giannini-Hall_synthetic-on-real - Eval Images Metrics Dict (all images)/psnr__MAX","Giannini-Hall_no-contraction - Eval Images Metrics Dict (all images)/psnr","Giannini-Hall_no-contraction - Eval Images Metrics Dict (all images)/psnr__MIN","Giannini-Hall_no-contraction - Eval Images Metrics Dict (all images)/psnr__MAX","sculpture_synthetic-on-real - Eval Images Metrics Dict (all images)/psnr","sculpture_synthetic-on-real - Eval Images Metrics Dict (all images)/psnr__MIN","sculpture_synthetic-on-real - Eval Images Metrics Dict (all 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\ No newline at end of file diff --git a/scripts/benchmarking/results/wandb-jan9-ssim.csv b/scripts/benchmarking/results/wandb-jan9-ssim.csv deleted file mode 100644 index a6589593b..000000000 --- a/scripts/benchmarking/results/wandb-jan9-ssim.csv +++ /dev/null @@ -1,7 +0,0 @@ -"Step","Giannini-Hall_synthetic-on-real - Eval Images Metrics Dict (all images)/ssim","Giannini-Hall_synthetic-on-real - Eval Images Metrics Dict (all images)/ssim__MIN","Giannini-Hall_synthetic-on-real - Eval Images Metrics Dict (all images)/ssim__MAX","Giannini-Hall_no-contraction - Eval Images Metrics Dict (all images)/ssim","Giannini-Hall_no-contraction - Eval Images Metrics Dict (all images)/ssim__MIN","Giannini-Hall_no-contraction - Eval Images Metrics Dict (all images)/ssim__MAX","sculpture_synthetic-on-real - Eval Images Metrics Dict (all images)/ssim","sculpture_synthetic-on-real - Eval Images Metrics Dict (all images)/ssim__MIN","sculpture_synthetic-on-real - Eval Images Metrics Dict (all 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\ No newline at end of file diff --git a/scripts/benchmarking/results/wandb-lpips.csv b/scripts/benchmarking/results/wandb-lpips.csv deleted file mode 100644 index d9b734815..000000000 --- a/scripts/benchmarking/results/wandb-lpips.csv +++ /dev/null @@ -1,7 +0,0 @@ -"Step","Giannini-Hall_random-background-color - Eval Images Metrics Dict (all images)/lpips","Giannini-Hall_random-background-color - Eval Images Metrics Dict (all images)/lpips__MIN","Giannini-Hall_random-background-color - Eval Images Metrics Dict (all images)/lpips__MAX","Giannini-Hall_shared-prop-network - Eval Images Metrics Dict (all images)/lpips","Giannini-Hall_shared-prop-network - Eval Images Metrics Dict (all images)/lpips__MIN","Giannini-Hall_shared-prop-network - Eval Images Metrics Dict (all images)/lpips__MAX","Giannini-Hall_l2-contraction - Eval Images Metrics Dict (all images)/lpips","Giannini-Hall_l2-contraction - Eval Images Metrics Dict (all images)/lpips__MIN","Giannini-Hall_l2-contraction - Eval Images 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