File size: 4,906 Bytes
ff66cf3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
{
"cells": [
{
"cell_type": "markdown",
"id": "8f4d6efb",
"metadata": {},
"source": [
"# Results\n",
"\n",
"This notebook gathers results from evaluation JSON files and prints them as a list. \n",
"\n",
"### Setup\n",
"\n",
"- Set the root folder environment variable with `export CLIPORT_ROOT=<cliport_root>`\n",
"- Train and evaluate agents by following the [README guide](https://github.com/cliport/cliport#single-task-training--evaluation)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d072ae18",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"pybullet build time: Aug 16 2021 17:58:31\n"
]
}
],
"source": [
"import os\n",
"import sys\n",
"import json\n",
"\n",
"from cliport import agents\n",
"from cliport import tasks"
]
},
{
"cell_type": "markdown",
"id": "e2ee3b65",
"metadata": {},
"source": [
"### Settings"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "95c14026",
"metadata": {},
"outputs": [],
"source": [
"root_folder = os.environ['CLIPORT_ROOT']\n",
"exp_folder = os.path.join(root_folder, 'cliport_quickstart') # replace 'cliport_quickstart' with your exps folder"
]
},
{
"cell_type": "markdown",
"id": "2627285a",
"metadata": {},
"source": [
"### Gather JSON Results"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6f5186e1",
"metadata": {},
"outputs": [],
"source": [
"tasks_list = list(tasks.names.keys())\n",
"agents_list = list(agents.names.keys())\n",
"demos_list = [1, 10, 100, 1000]\n",
"\n",
"results = {}\n",
"for t in tasks_list:\n",
" for a in agents_list:\n",
" for d in demos_list:\n",
" task_folder = f'{t}-{a}-n{d}-train'\n",
" task_folder_path = os.path.join(exp_folder, task_folder, 'checkpoints')\n",
"\n",
" if os.path.exists(task_folder_path):\n",
" jsons = [f for f in os.listdir(task_folder_path) if '.json' in f]\n",
" for j in jsons:\n",
" model_type = 'multi' if 'multi' in j else 'single'\n",
" eval_type = 'val' if 'val' in j else 'test'\n",
" \n",
" with open(os.path.join(task_folder_path, j)) as f:\n",
" res = json.load(f)\n",
" \n",
" results[f'{t}-{a}-n{d}-{model_type}-{eval_type}'] = res"
]
},
{
"cell_type": "markdown",
"id": "0b6fcfa9",
"metadata": {},
"source": [
"### Print Results"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2554998c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Experiments folder: /home/mshr/cliport/cliport_quickstart\n",
"\n",
"----- VAL -----\n",
"\n",
"stack-block-pyramid-seq-seen-colors | Train Demos: 1000\n",
"\t97.3 : cliport | multi\n",
"\n",
"----- TEST -----\n",
"\n",
"stack-block-pyramid-seq-seen-colors | Train Demos: 1000\n",
"\t96.5 : cliport | multi\n",
"\n"
]
}
],
"source": [
"print(f'Experiments folder: {exp_folder}\\n')\n",
"\n",
"for eval_type in ['val', 'test']:\n",
" print(f'----- {eval_type.upper()} -----\\n')\n",
" for t in tasks_list:\n",
" for a in agents_list:\n",
" for d in demos_list:\n",
" for model_type in ['single', 'multi']:\n",
" eval_key = f'{t}-{a}-n{d}-{model_type}-{eval_type}'\n",
" \n",
" if eval_key in results: \n",
" print(f'{t} | Train Demos: {d}')\n",
" \n",
" res = results[eval_key]\n",
" best_score, best_ckpt = max(zip([v['mean_reward'] for v in list(res.values())], \n",
" res.keys())) # TODO: test that this works for full results folder\n",
" \n",
" print(f'\\t{best_score*100:1.1f} : {a} | {model_type}\\n')\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|