will33am commited on
Commit
8b4081b
1 Parent(s): 68ca385

[update] images-sample

Browse files
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+ {
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+ "cell_type": "markdown",
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+ "id": "a25ac442",
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+ "metadata": {},
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+ "source": [
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+ "## Performance HuggingFace Dataset vs Dataset Loader"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "3da127dc",
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+ "metadata": {},
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+ "source": [
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+ "## Hugging Face Dataset"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 34,
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+ "id": "aef315bf",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "from datasets import load_dataset\n",
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+ "import torch\n",
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+ "import glob"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 63,
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+ "id": "c0ed6498",
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "Found cached dataset ava (/home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/723cc8bd5959ef1cd88b7d51648a8bc7fd98c9d8ddb768cb8c8ebaade1b82306)\n"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "CPU times: user 211 ms, sys: 11.8 ms, total: 223 ms\n",
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+ "Wall time: 852 ms\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "%%time\n",
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+ "ds = load_dataset(\"will33am/AVA\",split = 'train')\n",
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+ "ds = ds.remove_columns([\"rating_counts\",\"text_tag_0\",\"text_tag_1\"])"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 66,
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+ "id": "c51e48dd",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "CPU times: user 7.3 ms, sys: 644 µs, total: 7.94 ms\n",
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+ "Wall time: 6.24 ms\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "%%time\n",
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+ "image = ds[0]['image']"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "25b4bd86",
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+ "metadata": {},
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+ "source": [
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+ "## Pytorch Dataset"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 30,
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+ "id": "07480450",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "from PIL import Image\n",
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+ "class Datasets(torch.utils.data.Dataset):\n",
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+ " def __init__(self,files):\n",
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+ " self.files = files\n",
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+ " def __getitem__(self,idx):\n",
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+ " return Image.open(self.files[idx])\n",
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+ " \n",
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+ " def __len__(self):\n",
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+ " return len(self.files)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 31,
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+ "id": "7dcb29a3",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "files = glob.glob(\"../../AVA_src/images/images/*.jpg\")\n",
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+ "dataset = Datasets(files)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 53,
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+ "id": "43c33afc",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "CPU times: user 1.21 ms, sys: 0 ns, total: 1.21 ms\n",
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+ "Wall time: 656 µs\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "%%time\n",
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+ "image = dataset[0]"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 58,
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+ "id": "771fe113",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "10.731707317073171"
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+ ]
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+ "execution_count": 58,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "# 10 veces mas rapido acceder a un elemento\n",
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+ "(7.04e-3)/(656 * 1e-6 )"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "huggingface",
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+ "language": "python",
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+ "name": "huggingface"
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- "execution_count": 1,
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- "from datasets import load_dataset"
 
 
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- "Downloading builder script: 0%| | 0.00/2.17k [00:00<?, ?B/s]"
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- "Downloading and preparing dataset ava/default to /home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/7f76b3807b4156161ed44f936b3e89ecbab31823986cc11c2a46b584b358197b...\n"
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- "Computing checksums of downloaded files. They can be used for integrity verification. You can disable this by passing ignore_verifications=True to load_dataset\n"
 
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- "text/plain": [
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- "Generating train split: 0 examples [00:00, ? examples/s]"
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- },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
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- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1587\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1578\u001b[0m writer \u001b[38;5;241m=\u001b[39m writer_class(\n\u001b[1;32m 1579\u001b[0m features\u001b[38;5;241m=\u001b[39mwriter\u001b[38;5;241m.\u001b[39m_features,\n\u001b[1;32m 1580\u001b[0m path\u001b[38;5;241m=\u001b[39mfpath\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSSSSS\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mshard_id\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m05d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJJJJJ\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjob_id\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m05d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1585\u001b[0m embed_local_files\u001b[38;5;241m=\u001b[39membed_local_files,\n\u001b[1;32m 1586\u001b[0m )\n\u001b[0;32m-> 1587\u001b[0m example \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrecord\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mfeatures \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m record\n\u001b[1;32m 1588\u001b[0m writer\u001b[38;5;241m.\u001b[39mwrite(example, key)\n",
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- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/features/features.py:1800\u001b[0m, in \u001b[0;36mFeatures.encode_example\u001b[0;34m(self, example)\u001b[0m\n\u001b[1;32m 1799\u001b[0m example \u001b[38;5;241m=\u001b[39m cast_to_python_objects(example)\n\u001b[0;32m-> 1800\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mencode_nested_example\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample\u001b[49m\u001b[43m)\u001b[49m\n",
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- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/features/features.py:1202\u001b[0m, in \u001b[0;36mencode_nested_example\u001b[0;34m(schema, obj, level)\u001b[0m\n\u001b[1;32m 1200\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot None but expected a dictionary instead\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1201\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m-> 1202\u001b[0m {\n\u001b[1;32m 1203\u001b[0m k: encode_nested_example(sub_schema, sub_obj, level\u001b[38;5;241m=\u001b[39mlevel \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 1204\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, (sub_schema, sub_obj) \u001b[38;5;129;01min\u001b[39;00m zip_dict(schema, obj)\n\u001b[1;32m 1205\u001b[0m }\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1207\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1208\u001b[0m )\n\u001b[1;32m 1210\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(schema, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n",
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- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/features/features.py:1202\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1200\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot None but expected a dictionary instead\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1201\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[0;32m-> 1202\u001b[0m {\n\u001b[1;32m 1203\u001b[0m k: encode_nested_example(sub_schema, sub_obj, level\u001b[38;5;241m=\u001b[39mlevel \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 1204\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, (sub_schema, sub_obj) \u001b[38;5;129;01min\u001b[39;00m zip_dict(schema, obj)\n\u001b[1;32m 1205\u001b[0m }\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1207\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1208\u001b[0m )\n\u001b[1;32m 1210\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(schema, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n",
108
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/utils/py_utils.py:302\u001b[0m, in \u001b[0;36mzip_dict\u001b[0;34m(*dicts)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m unique_values(itertools\u001b[38;5;241m.\u001b[39mchain(\u001b[38;5;241m*\u001b[39mdicts)): \u001b[38;5;66;03m# set merge all keys\u001b[39;00m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;66;03m# Will raise KeyError if the dict don't have the same keys\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m key, \u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdicts\u001b[49m\u001b[43m)\u001b[49m\n",
109
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/utils/py_utils.py:302\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m unique_values(itertools\u001b[38;5;241m.\u001b[39mchain(\u001b[38;5;241m*\u001b[39mdicts)): \u001b[38;5;66;03m# set merge all keys\u001b[39;00m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;66;03m# Will raise KeyError if the dict don't have the same keys\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m key, \u001b[38;5;28mtuple\u001b[39m(\u001b[43md\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m dicts)\n",
110
- "\u001b[0;31mKeyError\u001b[0m: 'text_tag_0'",
111
- "\nThe above exception was the direct cause of the following exception:\n",
112
- "\u001b[0;31mDatasetGenerationError\u001b[0m Traceback (most recent call last)",
113
- "File \u001b[0;32m<timed exec>:1\u001b[0m\n",
114
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/load.py:1757\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)\u001b[0m\n\u001b[1;32m 1754\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[1;32m 1756\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m \u001b[43mbuilder_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1758\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1759\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1760\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_verifications\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_verifications\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1761\u001b[0m \u001b[43m \u001b[49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1762\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1763\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1765\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m 1766\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1767\u001b[0m keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[1;32m 1768\u001b[0m )\n",
115
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:860\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 859\u001b[0m prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[0;32m--> 860\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 864\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 866\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
116
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1611\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m 1610\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verify_infos, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_splits_kwargs):\n\u001b[0;32m-> 1611\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1612\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_splits_kwargs\u001b[49m\n\u001b[1;32m 1613\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
117
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:953\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m 949\u001b[0m split_dict\u001b[38;5;241m.\u001b[39madd(split_generator\u001b[38;5;241m.\u001b[39msplit_info)\n\u001b[1;32m 951\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 952\u001b[0m \u001b[38;5;66;03m# Prepare split will record examples associated to the split\u001b[39;00m\n\u001b[0;32m--> 953\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_split\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 954\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m 956\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot find data file. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 957\u001b[0m \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_download_instructions \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 958\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mOriginal error:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 959\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[1;32m 960\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
118
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1449\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split\u001b[0;34m(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\u001b[0m\n\u001b[1;32m 1447\u001b[0m gen_kwargs \u001b[38;5;241m=\u001b[39m split_generator\u001b[38;5;241m.\u001b[39mgen_kwargs\n\u001b[1;32m 1448\u001b[0m job_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m-> 1449\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m job_id, done, content \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_split_single(\n\u001b[1;32m 1450\u001b[0m gen_kwargs\u001b[38;5;241m=\u001b[39mgen_kwargs, job_id\u001b[38;5;241m=\u001b[39mjob_id, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_prepare_split_args\n\u001b[1;32m 1451\u001b[0m ):\n\u001b[1;32m 1452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m done:\n\u001b[1;32m 1453\u001b[0m result \u001b[38;5;241m=\u001b[39m content\n",
119
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1606\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1604\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, SchemaInferenceError) \u001b[38;5;129;01mand\u001b[39;00m e\u001b[38;5;241m.\u001b[39m__context__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1605\u001b[0m e \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m__context__\n\u001b[0;32m-> 1606\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DatasetGenerationError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred while generating the dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m job_id, \u001b[38;5;28;01mTrue\u001b[39;00m, (total_num_examples, total_num_bytes, writer\u001b[38;5;241m.\u001b[39m_features, num_shards, shard_lengths)\n",
120
- "\u001b[0;31mDatasetGenerationError\u001b[0m: An error occurred while generating the dataset"
121
  ]
122
  }
123
  ],
124
  "source": [
125
  "%%time\n",
126
- "ds = load_dataset(\"will33am/AVA\")"
127
  ]
128
  },
129
  {
130
  "cell_type": "code",
131
- "execution_count": null,
132
- "id": "72f30241",
133
  "metadata": {},
134
- "outputs": [],
135
- "source": []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
136
  }
137
  ],
138
  "metadata": {
 
1
  {
2
  "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "id": "a25ac442",
6
+ "metadata": {},
7
+ "source": [
8
+ "## Performance HuggingFace Dataset vs Dataset Loader"
9
+ ]
10
+ },
11
+ {
12
+ "cell_type": "markdown",
13
+ "id": "3da127dc",
14
+ "metadata": {},
15
+ "source": [
16
+ "## Hugging Face Dataset"
17
+ ]
18
+ },
19
  {
20
  "cell_type": "code",
21
+ "execution_count": 34,
22
  "id": "aef315bf",
23
  "metadata": {},
24
  "outputs": [],
25
  "source": [
26
+ "from datasets import load_dataset\n",
27
+ "import torch\n",
28
+ "import glob"
29
  ]
30
  },
31
  {
32
  "cell_type": "code",
33
+ "execution_count": 63,
34
  "id": "c0ed6498",
35
  "metadata": {},
36
  "outputs": [
37
  {
38
+ "name": "stderr",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
39
  "output_type": "stream",
40
  "text": [
41
+ "Found cached dataset ava (/home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/723cc8bd5959ef1cd88b7d51648a8bc7fd98c9d8ddb768cb8c8ebaade1b82306)\n"
42
  ]
43
  },
44
  {
45
+ "name": "stdout",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
46
  "output_type": "stream",
47
  "text": [
48
+ "CPU times: user 211 ms, sys: 11.8 ms, total: 223 ms\n",
49
+ "Wall time: 852 ms\n"
50
  ]
51
+ }
52
+ ],
53
+ "source": [
54
+ "%%time\n",
55
+ "ds = load_dataset(\"will33am/AVA\",split = 'train')\n",
56
+ "ds = ds.remove_columns([\"rating_counts\",\"text_tag_0\",\"text_tag_1\"])"
57
+ ]
58
+ },
59
+ {
60
+ "cell_type": "code",
61
+ "execution_count": 66,
62
+ "id": "c51e48dd",
63
+ "metadata": {},
64
+ "outputs": [
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
65
  {
66
+ "name": "stdout",
67
  "output_type": "stream",
68
  "text": [
69
+ "CPU times: user 7.3 ms, sys: 644 µs, total: 7.94 ms\n",
70
+ "Wall time: 6.24 ms\n"
71
  ]
72
+ }
73
+ ],
74
+ "source": [
75
+ "%%time\n",
76
+ "image = ds[0]['image']"
77
+ ]
78
+ },
79
+ {
80
+ "cell_type": "markdown",
81
+ "id": "25b4bd86",
82
+ "metadata": {},
83
+ "source": [
84
+ "## Pytorch Dataset"
85
+ ]
86
+ },
87
+ {
88
+ "cell_type": "code",
89
+ "execution_count": 30,
90
+ "id": "07480450",
91
+ "metadata": {},
92
+ "outputs": [],
93
+ "source": [
94
+ "from PIL import Image\n",
95
+ "class Datasets(torch.utils.data.Dataset):\n",
96
+ " def __init__(self,files):\n",
97
+ " self.files = files\n",
98
+ " def __getitem__(self,idx):\n",
99
+ " return Image.open(self.files[idx])\n",
100
+ " \n",
101
+ " def __len__(self):\n",
102
+ " return len(self.files)"
103
+ ]
104
+ },
105
+ {
106
+ "cell_type": "code",
107
+ "execution_count": 31,
108
+ "id": "7dcb29a3",
109
+ "metadata": {},
110
+ "outputs": [],
111
+ "source": [
112
+ "files = glob.glob(\"../../AVA_src/images/images/*.jpg\")\n",
113
+ "dataset = Datasets(files)"
114
+ ]
115
+ },
116
+ {
117
+ "cell_type": "code",
118
+ "execution_count": 53,
119
+ "id": "43c33afc",
120
+ "metadata": {},
121
+ "outputs": [
122
  {
123
+ "name": "stdout",
124
+ "output_type": "stream",
125
+ "text": [
126
+ "CPU times: user 1.21 ms, sys: 0 ns, total: 1.21 ms\n",
127
+ "Wall time: 656 µs\n"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
128
  ]
129
  }
130
  ],
131
  "source": [
132
  "%%time\n",
133
+ "image = dataset[0]"
134
  ]
135
  },
136
  {
137
  "cell_type": "code",
138
+ "execution_count": 58,
139
+ "id": "771fe113",
140
  "metadata": {},
141
+ "outputs": [
142
+ {
143
+ "data": {
144
+ "text/plain": [
145
+ "10.731707317073171"
146
+ ]
147
+ },
148
+ "execution_count": 58,
149
+ "metadata": {},
150
+ "output_type": "execute_result"
151
+ }
152
+ ],
153
+ "source": [
154
+ "# 10 veces mas rapido acceder a un elemento\n",
155
+ "(7.04e-3)/(656 * 1e-6 )"
156
+ ]
157
  }
158
  ],
159
  "metadata": {
scripts/.ipynb_checkpoints/create_sample_dataset-checkpoint.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from os import makedirs
2
+ from os import listdir
3
+ from os.path import join
4
+ from shutil import copy
5
+ from concurrent.futures import ThreadPoolExecutor
6
+
7
+ NUM_FILES = 5000
8
+
9
+
10
+ # +
11
+
12
+ # copy a file from source to destination
13
+ def copy_file(src_path, dest_dir):
14
+ # copy source file to dest file
15
+ dest_path = copy(src_path, dest_dir)
16
+ # report progress
17
+ print(f'.copied {src_path} to {dest_path}')
18
+
19
+ def main(src='../../AVA_src/images/images/', dest='../../AVA_src/images_5k/'):
20
+ # create the destination directory if needed
21
+ makedirs(dest, exist_ok=True)
22
+ # create full paths for all files we wish to copy
23
+ files = [join(src,name) for name in listdir(src)][:NUM_FILES]
24
+ print("Files: ",files)
25
+ # create the thread pool
26
+ with ThreadPoolExecutor(10) as exe:
27
+ # submit all copy tasks
28
+ _ = [exe.submit(copy_file, path, dest) for path in files]
29
+
30
+ if __name__ == '__main__':
31
+ main()
scripts/create_sample_dataset.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from os import makedirs
2
+ from os import listdir
3
+ from os.path import join
4
+ from shutil import copy
5
+ from concurrent.futures import ThreadPoolExecutor
6
+
7
+ NUM_FILES = 5000
8
+
9
+
10
+ # +
11
+
12
+ # copy a file from source to destination
13
+ def copy_file(src_path, dest_dir):
14
+ # copy source file to dest file
15
+ dest_path = copy(src_path, dest_dir)
16
+ # report progress
17
+ print(f'.copied {src_path} to {dest_path}')
18
+
19
+ def main(src='../../AVA_src/images/images/', dest='../../AVA_src/images_5k/'):
20
+ # create the destination directory if needed
21
+ makedirs(dest, exist_ok=True)
22
+ # create full paths for all files we wish to copy
23
+ files = [join(src,name) for name in listdir(src)][:NUM_FILES]
24
+ print("Files: ",files)
25
+ # create the thread pool
26
+ with ThreadPoolExecutor(10) as exe:
27
+ # submit all copy tasks
28
+ _ = [exe.submit(copy_file, path, dest) for path in files]
29
+
30
+ if __name__ == '__main__':
31
+ main()