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{
"cells": [
{
"cell_type": "markdown",
"id": "9985af01-9a57-451f-8c7d-842f9066414c",
"metadata": {},
"source": [
"# Crafter Human Dataset\n",
"\n",
"This dataset was created using the data available at https://archive.org/details/crafter_human_dataset. We only made it available to users using the following script. \n",
"\n",
"## Using this dataset\n",
"To use this dataset, be sure to check our [IL-Datasets](https://github.com/NathanGavenski/IL-Datasets) toolkit.\n",
"\n",
"## Original citation\n",
"Please, if using this dataset, don't forget to cite Hafner's original work:\n",
"```\n",
"@article{hafner2021crafter,\n",
" title={Benchmarking the Spectrum of Agent Capabilities},\n",
" author={Danijar Hafner},\n",
" year={2021},\n",
" journal={arXiv preprint arXiv:2109.06780},\n",
"}\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a07ce4e2",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"from PIL import Image\n",
"from glob import glob"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c48dd39c",
"metadata": {},
"outputs": [],
"source": [
"files = glob(\"./files/*.npz\")\n",
"print(f\"Files found: {len(files)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3342b01a",
"metadata": {},
"outputs": [],
"source": [
"f = np.load(files[0])\n",
"for _file in files:\n",
" f = np.load(_file)\n",
" achieve_goal = 0\n",
" for k in [k for k in f.keys() if \"achivement\" in k]:\n",
" achieve_goal += f[k].sum()\n",
"\n",
" if achieve_goal == 0:\n",
" print(_file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52c8ca21-bddc-43e0-bd0d-994f9f2bd048",
"metadata": {},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"from tqdm import tqdm\n",
"\n",
"count = 0\n",
"\n",
"os.makedirs(\"./images/\", exist_ok=True)\n",
"\n",
"dataset = defaultdict(list)\n",
"for _file in tqdm(files):\n",
" f = np.load(_file)\n",
"\n",
" for idx, image in enumerate(f['image']):\n",
" Image.fromarray(image).save(f\"images/{count}.png\")\n",
" dataset['obs'].append(f\"images/{count}.png\")\n",
" dataset['actions'].append(f['action'][idx].item())\n",
" dataset['rewards'].append(f['reward'][idx].item())\n",
" dataset['episode_starts'].append(f['done'][idx].item())\n",
" count += 1\n",
" dataset['episode_returns'].append(sum(dataset['rewards']))\n",
"\n",
"for k, v in dataset.items():\n",
" dataset[k] = np.array(dataset[k])\n",
"\n",
"np.savez(\"crafter\", **dataset)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a0eee2d-9cab-4a0c-b221-b4d1f0155cc7",
"metadata": {},
"outputs": [],
"source": [
"from imitation_datasets.dataset.huggingface import baseline_to_huggingface\n",
"\n",
"baseline_to_huggingface(\"./crafter.npz\", \"./crafter.jsonl\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a506b25-760e-4ffe-afa2-6d6d73058bf7",
"metadata": {},
"outputs": [],
"source": [
"import tarfile\n",
"\n",
"with tarfile.open(\"dataset.tar.gz\", \"w:gz\") as tar_file:\n",
" tar_file.add(\"./crafter.jsonl\", \"crafter.jsonl\")\n",
"\n",
"with tarfile.open(\"images.tar.gz\", \"w:gz\") as tar_file:\n",
" tar_file.add(\"images/\", \"images/\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "30224224-fd9c-453c-b202-7fad802026f5",
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"\n",
"shutil.rmtree(\"images/\")\n",
"os.remove(\"crafter.npz\")\n",
"os.remove(\"crafter.jsonl\")"
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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