Last active
March 10, 2023 03:04
-
-
Save acmiyaguchi/36e5df4f0fd989878acbd8defdc6e34b to your computer and use it in GitHub Desktop.
Revisions
-
acmiyaguchi revised this gist
Mar 10, 2023 . 1 changed file with 3 additions and 1 deletion.There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,4 +1,6 @@ Place the logs for the experiements at the root. Place the notebook in a `notebooks` directory or modify the path in the notebook. Download link: https://f004.backblazeb2.com/file/acm-ivalab/GraspKpNet/logs/exp-2023-03-08.zip See https://github.com/ivalab/GraspKpNet/pull/3 -
Mar 10, 2023 .There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,4 @@ Place the logs for the experiements at the root. Place the notebook in a `notebooks` directory or modify the path in the notebook. Download link: https://f004.backblazeb2.com/file/acm-ivalab/GraspKpNet/logs/exp-2023-03-08.zip This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,285 @@ { "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# pretrained model results\n", "\n", "2023-03-09 " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>task</th>\n", " <th>success</th>\n", " <th>total</th>\n", " <th>wall_time</th>\n", " <th>exp_id</th>\n", " <th>dataset</th>\n", " <th>accuracy</th>\n", " <th>fps</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>dbmctdet</td>\n", " <td>9108</td>\n", " <td>9354</td>\n", " <td>113.399522</td>\n", " <td>model_alexnet_ajd</td>\n", " <td>jac_coco_36</td>\n", " <td>0.973701</td>\n", " <td>82.487120</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>dbmctdet</td>\n", " <td>9203</td>\n", " <td>9354</td>\n", " <td>124.360462</td>\n", " <td>model_dla34_ajd</td>\n", " <td>jac_coco_36</td>\n", " <td>0.983857</td>\n", " <td>75.216832</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>dbmctdet</td>\n", " <td>9162</td>\n", " <td>9354</td>\n", " <td>124.072763</td>\n", " <td>model_resnet18_ajd</td>\n", " <td>jac_coco_36</td>\n", " <td>0.979474</td>\n", " <td>75.391244</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>dbmctdet</td>\n", " <td>9189</td>\n", " <td>9354</td>\n", " <td>122.886355</td>\n", " <td>model_resnet50_ajd</td>\n", " <td>jac_coco_36</td>\n", " <td>0.982360</td>\n", " <td>76.119110</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>dbmctdet</td>\n", " <td>9201</td>\n", " <td>9354</td>\n", " <td>114.280679</td>\n", " <td>model_vgg16_ajd</td>\n", " <td>jac_coco_36</td>\n", " <td>0.983643</td>\n", " <td>81.851106</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>dbmctdet_cornell</td>\n", " <td>20930</td>\n", " <td>22110</td>\n", " <td>78.452531</td>\n", " <td>model_alexnet_cornell</td>\n", " <td>cornell</td>\n", " <td>0.946630</td>\n", " <td>281.826472</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>dbmctdet_cornell</td>\n", " <td>21399</td>\n", " <td>22110</td>\n", " <td>141.670923</td>\n", " <td>model_dla34_cornell</td>\n", " <td>cornell</td>\n", " <td>0.967843</td>\n", " <td>156.065899</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>dbmctdet_cornell</td>\n", " <td>21162</td>\n", " <td>22110</td>\n", " <td>77.845522</td>\n", " <td>model_resnet18_cornell</td>\n", " <td>cornell</td>\n", " <td>0.957123</td>\n", " <td>284.024046</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>dbmctdet_cornell</td>\n", " <td>21260</td>\n", " <td>22110</td>\n", " <td>78.786531</td>\n", " <td>model_resnet50_cornell</td>\n", " <td>cornell</td>\n", " <td>0.961556</td>\n", " <td>280.631723</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>dbmctdet_cornell</td>\n", " <td>21319</td>\n", " <td>22110</td>\n", " <td>78.759574</td>\n", " <td>model_vgg16_cornell</td>\n", " <td>cornell</td>\n", " <td>0.964224</td>\n", " <td>280.727776</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " task success total wall_time exp_id \\\n", "0 dbmctdet 9108 9354 113.399522 model_alexnet_ajd \n", "1 dbmctdet 9203 9354 124.360462 model_dla34_ajd \n", "2 dbmctdet 9162 9354 124.072763 model_resnet18_ajd \n", "3 dbmctdet 9189 9354 122.886355 model_resnet50_ajd \n", "4 dbmctdet 9201 9354 114.280679 model_vgg16_ajd \n", "5 dbmctdet_cornell 20930 22110 78.452531 model_alexnet_cornell \n", "6 dbmctdet_cornell 21399 22110 141.670923 model_dla34_cornell \n", "7 dbmctdet_cornell 21162 22110 77.845522 model_resnet18_cornell \n", "8 dbmctdet_cornell 21260 22110 78.786531 model_resnet50_cornell \n", "9 dbmctdet_cornell 21319 22110 78.759574 model_vgg16_cornell \n", "\n", " dataset accuracy fps \n", "0 jac_coco_36 0.973701 82.487120 \n", "1 jac_coco_36 0.983857 75.216832 \n", "2 jac_coco_36 0.979474 75.391244 \n", "3 jac_coco_36 0.982360 76.119110 \n", "4 jac_coco_36 0.983643 81.851106 \n", "5 cornell 0.946630 281.826472 \n", "6 cornell 0.967843 156.065899 \n", "7 cornell 0.957123 284.024046 \n", "8 cornell 0.961556 280.631723 \n", "9 cornell 0.964224 280.727776 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "import json\n", "import pandas as pd\n", "\n", "def parse_log_line(line):\n", " data = line.split(\":\")[1:]\n", " return json.loads(\":\".join(data))\n", "\n", "paths = Path(\"../exp\").glob(\"**/log.txt\")\n", "results = []\n", "for path in paths:\n", " text = path.read_text()\n", " # first line is options, second line is timing, third line is results\n", " lines = text.strip().split(\"\\n\")\n", " if len(lines) < 3:\n", " print(f\"skipping {path}\")\n", " continue\n", " # each line is a timestamp: json object\n", " opts = parse_log_line(lines[0])\n", " result = parse_log_line(lines[2])\n", " result[\"task\"] = opts[\"task\"]\n", " result[\"exp_id\"] = opts[\"exp_id\"]\n", " result[\"dataset\"] = opts[\"dataset\"]\n", " results.append(result)\n", "\n", "df = pd.DataFrame(results)\n", "df[\"accuracy\"] = df.success / df.total\n", "df[\"fps\"] = df.total / df.wall_time\n", "df" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| exp_id | dataset | accuracy | fps |\n", "|:-----------------------|:------------|-----------:|---------:|\n", "| model_alexnet_ajd | jac_coco_36 | 0.973701 | 82.4871 |\n", "| model_dla34_ajd | jac_coco_36 | 0.983857 | 75.2168 |\n", "| model_resnet18_ajd | jac_coco_36 | 0.979474 | 75.3912 |\n", "| model_resnet50_ajd | jac_coco_36 | 0.98236 | 76.1191 |\n", "| model_vgg16_ajd | jac_coco_36 | 0.983643 | 81.8511 |\n", "| model_alexnet_cornell | cornell | 0.94663 | 281.826 |\n", "| model_dla34_cornell | cornell | 0.967843 | 156.066 |\n", "| model_resnet18_cornell | cornell | 0.957123 | 284.024 |\n", "| model_resnet50_cornell | cornell | 0.961556 | 280.632 |\n", "| model_vgg16_cornell | cornell | 0.964224 | 280.728 |\n" ] } ], "source": [ "# get the results as a markdown table\n", "res = df[[\"exp_id\", \"dataset\", \"accuracy\", \"fps\"]]\n", "print(res.to_markdown(index=False))" ] } ], "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": "3.10.5" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "5fba4eb1ddfb0b5f2c0f3e2fd25cac3f968691247a9de4806ec9bb18fee8fadd" } } }, "nbformat": 4, "nbformat_minor": 2 }