[update] images-sample

data/images_5k.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34abe889037a8b8640821de4e444ee0313448af8fb2d709e68bb30e5c373e547
+size 656457088

notebooks/.ipynb_checkpoints/Test-checkpoint.ipynb

@@ -1,6 +1,209 @@
 {
- "cells": [],
- "metadata": {},
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a25ac442",
+   "metadata": {},
+   "source": [
+    "## Performance HuggingFace Dataset vs Dataset Loader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3da127dc",
+   "metadata": {},
+   "source": [
+    "## Hugging Face Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "aef315bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "import torch\n",
+    "import glob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "c0ed6498",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset ava (/home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/723cc8bd5959ef1cd88b7d51648a8bc7fd98c9d8ddb768cb8c8ebaade1b82306)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 211 ms, sys: 11.8 ms, total: 223 ms\n",
+      "Wall time: 852 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = load_dataset(\"will33am/AVA\",split = 'train')\n",
+    "ds = ds.remove_columns([\"rating_counts\",\"text_tag_0\",\"text_tag_1\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "c51e48dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.3 ms, sys: 644 µs, total: 7.94 ms\n",
+      "Wall time: 6.24 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "image = ds[0]['image']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25b4bd86",
+   "metadata": {},
+   "source": [
+    "## Pytorch Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "07480450",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "class Datasets(torch.utils.data.Dataset):\n",
+    "    def __init__(self,files):\n",
+    "        self.files = files\n",
+    "    def __getitem__(self,idx):\n",
+    "        return Image.open(self.files[idx])\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return len(self.files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7dcb29a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = glob.glob(\"../../AVA_src/images/images/*.jpg\")\n",
+    "dataset = Datasets(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "43c33afc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.21 ms, sys: 0 ns, total: 1.21 ms\n",
+      "Wall time: 656 µs\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "image = dataset[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "771fe113",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.731707317073171"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 10 veces mas rapido acceder a un elemento\n",
+    "(7.04e-3)/(656 * 1e-6 )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "huggingface",
+   "language": "python",
+   "name": "huggingface"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

notebooks/Test.ipynb

@@ -1,138 +1,159 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a25ac442",
+   "metadata": {},
+   "source": [
+    "## Performance HuggingFace Dataset vs Dataset Loader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3da127dc",
+   "metadata": {},
+   "source": [
+    "## Hugging Face Dataset"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 34,
    "id": "aef315bf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from datasets import load_dataset"
+    "from datasets import load_dataset\n",
+    "import torch\n",
+    "import glob"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 63,
    "id": "c0ed6498",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd5f00716a7c42bd9962e71e5585e952",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Downloading builder script:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading and preparing dataset ava/default to /home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/7f76b3807b4156161ed44f936b3e89ecbab31823986cc11c2a46b584b358197b...\n"
+      "Found cached dataset ava (/home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/723cc8bd5959ef1cd88b7d51648a8bc7fd98c9d8ddb768cb8c8ebaade1b82306)\n"
      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "40ff2d92e7a24fe4a1c169bb467e5dbc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "CPU times: user 211 ms, sys: 11.8 ms, total: 223 ms\n",
+      "Wall time: 852 ms\n"
      ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "706a2a08eb364f0790c22a1a3ae053fe",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Computing checksums: 100%|##########| 1/1 [01:33<00:00, 93.75s/it]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5133a8f32d5a44829a7e2e1df96b3216",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generating train split: 0 examples [00:00, ? examples/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = load_dataset(\"will33am/AVA\",split = 'train')\n",
+    "ds = ds.remove_columns([\"rating_counts\",\"text_tag_0\",\"text_tag_1\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "c51e48dd",
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "0it [00:00, ?it/s]\u001b[A"
+      "CPU times: user 7.3 ms, sys: 644 µs, total: 7.94 ms\n",
+      "Wall time: 6.24 ms\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "image = ds[0]['image']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25b4bd86",
+   "metadata": {},
+   "source": [
+    "## Pytorch Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "07480450",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "class Datasets(torch.utils.data.Dataset):\n",
+    "    def __init__(self,files):\n",
+    "        self.files = files\n",
+    "    def __getitem__(self,idx):\n",
+    "        return Image.open(self.files[idx])\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return len(self.files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7dcb29a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = glob.glob(\"../../AVA_src/images/images/*.jpg\")\n",
+    "dataset = Datasets(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "43c33afc",
+   "metadata": {},
+   "outputs": [
     {
-     "ename": "DatasetGenerationError",
-     "evalue": "An error occurred while generating the dataset",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "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",
-      "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",
-      "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",
-      "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",
-      "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",
-      "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",
-      "\u001b[0;31mKeyError\u001b[0m: 'text_tag_0'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mDatasetGenerationError\u001b[0m                    Traceback (most recent call last)",
-      "File \u001b[0;32m<timed exec>:1\u001b[0m\n",
-      "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",
-      "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",
-      "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",
-      "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",
-      "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",
-      "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",
-      "\u001b[0;31mDatasetGenerationError\u001b[0m: An error occurred while generating the dataset"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.21 ms, sys: 0 ns, total: 1.21 ms\n",
+      "Wall time: 656 µs\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
-    "ds = load_dataset(\"will33am/AVA\")"
+    "image = dataset[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "72f30241",
+   "execution_count": 58,
+   "id": "771fe113",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.731707317073171"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 10 veces mas rapido acceder a un elemento\n",
+    "(7.04e-3)/(656 * 1e-6 )"
+   ]
   }
  ],
  "metadata": {

scripts/.ipynb_checkpoints/create_sample_dataset-checkpoint.py

@@ -0,0 +1,31 @@
+from os import makedirs
+from os import listdir
+from os.path import join
+from shutil import copy
+from concurrent.futures import ThreadPoolExecutor
+
+NUM_FILES = 5000
+
+
+# +
+
+ # copy a file from source to destination
+def copy_file(src_path, dest_dir):
+    # copy source file to dest file
+    dest_path = copy(src_path, dest_dir)
+    # report progress
+    print(f'.copied {src_path} to {dest_path}')
+    
+def main(src='../../AVA_src/images/images/', dest='../../AVA_src/images_5k/'):
+    # create the destination directory if needed
+    makedirs(dest, exist_ok=True)
+    # create full paths for all files we wish to copy
+    files = [join(src,name) for name in listdir(src)][:NUM_FILES]
+    print("Files: ",files)
+    # create the thread pool
+    with ThreadPoolExecutor(10) as exe:
+        # submit all copy tasks
+        _ = [exe.submit(copy_file, path, dest) for path in files]
+
+if __name__ == '__main__':
+    main()

scripts/create_sample_dataset.py

@@ -0,0 +1,31 @@
+from os import makedirs
+from os import listdir
+from os.path import join
+from shutil import copy
+from concurrent.futures import ThreadPoolExecutor
+
+NUM_FILES = 5000
+
+
+# +
+
+ # copy a file from source to destination
+def copy_file(src_path, dest_dir):
+    # copy source file to dest file
+    dest_path = copy(src_path, dest_dir)
+    # report progress
+    print(f'.copied {src_path} to {dest_path}')
+    
+def main(src='../../AVA_src/images/images/', dest='../../AVA_src/images_5k/'):
+    # create the destination directory if needed
+    makedirs(dest, exist_ok=True)
+    # create full paths for all files we wish to copy
+    files = [join(src,name) for name in listdir(src)][:NUM_FILES]
+    print("Files: ",files)
+    # create the thread pool
+    with ThreadPoolExecutor(10) as exe:
+        # submit all copy tasks
+        _ = [exe.submit(copy_file, path, dest) for path in files]
+
+if __name__ == '__main__':
+    main()