Updated build script to include more example data

Makefile

@@ -1,15 +1,14 @@
-.PHONY: prepare webdataset parquet clean
+.PHONY: prepare build clean
+
+all: prepare build
 
 prepare:
 	mkdir -p downloads
-	python scripts/prepare.py downloads data manifest.csv
+	python scripts/prepare.py manifest.csv downloads data 
 
-webdataset:
+build:
 	mkdir -p shards
-	python scripts/build_webdataset.py data shards
-
-parquet:
-	python scripts/build_parquet.py
+	python scripts/build.py manifest.csv data shards
 
 clean:
-	rm -vr downloads
+	rm -vr downloads shards

README.md

@@ -50,26 +50,25 @@ data
             000000.depth.tiff
             000000.vehicle.json
             000000.timestamp.txt
-            000000.camera.json
             000001.image.png
             000001.panoptic.png
             000001.depth.tiff
             000001.vehicle.json
             000001.timestamp.txt
-            000001.camera.json
-            ...
+        000000.camera.json
         000001
             ...
+        000001.camera.json
+        ...
     val
         000000
             ...
-        000001
-            ....
+        000000.camera.json
+        ...
     test
         000000
             ...
-        000001
-            ....
+        000000.camera.json
 
 ```
 
@@ -86,7 +85,7 @@ git clone https://huggingface.co/datasets/khwstolle/csvps && cd csvps
 
 1. Install the [Cityscapes developer kit](https://github.com/mcordts/cityscapesScripts) using `pip`.
 ```bash
-python -m pip install cityscapesscripts
+python -m pip install -r requirements.txt
 ```
 
 2. Run the preparation script provided in this repository. 
@@ -102,7 +101,6 @@ make clean
 
 ## Usage
 
-Due to the structure, the dataset can be easily loaded using the `webdataset` library.
 To convert the `train`, `val` and `test` directories into a `tar` archive, run the following command:
 
 ```bash

examples.ipynb

@@ -14,44 +14,340 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "000000/000000: image.png timestamp.txt vehicle.json \n",
-      "000000/000001: image.png timestamp.txt vehicle.json \n",
-      "000000/000002: image.png timestamp.txt vehicle.json \n",
-      "000000/000003: image.png timestamp.txt vehicle.json \n",
-      "000000/000004: depth.tiff image.png panoptic.png timestamp.txt vehicle.json \n",
-      "000000/000005: image.png timestamp.txt vehicle.json \n",
-      "000000/000006: image.png timestamp.txt vehicle.json \n",
-      "000000/000007: image.png timestamp.txt vehicle.json \n",
-      "000000/000008: image.png timestamp.txt vehicle.json \n",
-      "000000/000009: depth.tiff image.png panoptic.png timestamp.txt vehicle.json \n",
-      "000000/000010: image.png timestamp.txt vehicle.json \n"
+      "Sample csvps-val/000030\n",
+      "{'frames': {'is_annotated': [False, False, False, False, True, False, False,\n",
+      "                             False, False, True, False, False, False, False,\n",
+      "                             True, False, False, False, False, True, False,\n",
+      "                             False, False, False, True, False, False, False,\n",
+      "                             False, True],\n",
+      "            'number': ['000000', '000001', '000002', '000003', '000004',\n",
+      "                       '000005', '000006', '000007', '000008', '000009',\n",
+      "                       '000010', '000011', '000012', '000013', '000014',\n",
+      "                       '000015', '000016', '000017', '000018', '000019',\n",
+      "                       '000020', '000021', '000022', '000023', '000024',\n",
+      "                       '000025', '000026', '000027', '000028', '000029'],\n",
+      "            'primary_key': ['lindau_000037_000000', 'lindau_000037_000001',\n",
+      "                            'lindau_000037_000002', 'lindau_000037_000003',\n",
+      "                            'lindau_000037_000004', 'lindau_000037_000005',\n",
+      "                            'lindau_000037_000006', 'lindau_000037_000007',\n",
+      "                            'lindau_000037_000008', 'lindau_000037_000009',\n",
+      "                            'lindau_000037_000010', 'lindau_000037_000011',\n",
+      "                            'lindau_000037_000012', 'lindau_000037_000013',\n",
+      "                            'lindau_000037_000014', 'lindau_000037_000015',\n",
+      "                            'lindau_000037_000016', 'lindau_000037_000017',\n",
+      "                            'lindau_000037_000018', 'lindau_000037_000019',\n",
+      "                            'lindau_000037_000020', 'lindau_000037_000021',\n",
+      "                            'lindau_000037_000022', 'lindau_000037_000023',\n",
+      "                            'lindau_000037_000024', 'lindau_000037_000025',\n",
+      "                            'lindau_000037_000026', 'lindau_000037_000027',\n",
+      "                            'lindau_000037_000028', 'lindau_000037_000029'],\n",
+      "            'timestamps': [0, 58925424, 117850824, 176776224, 235701592,\n",
+      "                           294627064, 353552488, 412478000, 471403416,\n",
+      "                           530328888, 589254288, 648179664, 707105072,\n",
+      "                           766030408, 824955840, 883881184, 942806640,\n",
+      "                           1001732056, 1060657528, 1119582984, 1178508320,\n",
+      "                           1237433744, 1296359088, 1355284520, 1414209856,\n",
+      "                           1473135304, 1532060720, 1590986120, 1649911576,\n",
+      "                           1708836952]}}\n",
+      "Sample csvps-val/000031\n",
+      "{'frames': {'is_annotated': [False, False, False, False, True, False, False,\n",
+      "                             False, False, True, False, False, False, False,\n",
+      "                             True, False, False, False, False, True, False,\n",
+      "                             False, False, False, True, False, False, False,\n",
+      "                             False, True],\n",
+      "            'number': ['000000', '000001', '000002', '000003', '000004',\n",
+      "                       '000005', '000006', '000007', '000008', '000009',\n",
+      "                       '000010', '000011', '000012', '000013', '000014',\n",
+      "                       '000015', '000016', '000017', '000018', '000019',\n",
+      "                       '000020', '000021', '000022', '000023', '000024',\n",
+      "                       '000025', '000026', '000027', '000028', '000029'],\n",
+      "            'primary_key': ['lindau_000047_000000', 'lindau_000047_000001',\n",
+      "                            'lindau_000047_000002', 'lindau_000047_000003',\n",
+      "                            'lindau_000047_000004', 'lindau_000047_000005',\n",
+      "                            'lindau_000047_000006', 'lindau_000047_000007',\n",
+      "                            'lindau_000047_000008', 'lindau_000047_000009',\n",
+      "                            'lindau_000047_000010', 'lindau_000047_000011',\n",
+      "                            'lindau_000047_000012', 'lindau_000047_000013',\n",
+      "                            'lindau_000047_000014', 'lindau_000047_000015',\n",
+      "                            'lindau_000047_000016', 'lindau_000047_000017',\n",
+      "                            'lindau_000047_000018', 'lindau_000047_000019',\n",
+      "                            'lindau_000047_000020', 'lindau_000047_000021',\n",
+      "                            'lindau_000047_000022', 'lindau_000047_000023',\n",
+      "                            'lindau_000047_000024', 'lindau_000047_000025',\n",
+      "                            'lindau_000047_000026', 'lindau_000047_000027',\n",
+      "                            'lindau_000047_000028', 'lindau_000047_000029'],\n",
+      "            'timestamps': [0, 58925416, 117850784, 176776216, 235701560,\n",
+      "                           294627000, 353552352, 412477776, 471403168,\n",
+      "                           530328552, 589253984, 648179336, 707104784,\n",
+      "                           766030160, 824955608, 883880992, 942806400,\n",
+      "                           1001731832, 1060657200, 1119582648, 1178508008,\n",
+      "                           1237433456, 1296358824, 1355284248, 1414209648,\n",
+      "                           1473135024, 1532060464, 1590985824, 1649911272,\n",
+      "                           1708836624]}}\n",
+      "Sample csvps-val/000032\n",
+      "{'frames': {'is_annotated': [False, False, False, False, True, False, False,\n",
+      "                             False, False, True, False, False, False, False,\n",
+      "                             True, False, False, False, False, True, False,\n",
+      "                             False, False, False, True, False, False, False,\n",
+      "                             False, True],\n",
+      "            'number': ['000000', '000001', '000002', '000003', '000004',\n",
+      "                       '000005', '000006', '000007', '000008', '000009',\n",
+      "                       '000010', '000011', '000012', '000013', '000014',\n",
+      "                       '000015', '000016', '000017', '000018', '000019',\n",
+      "                       '000020', '000021', '000022', '000023', '000024',\n",
+      "                       '000025', '000026', '000027', '000028', '000029'],\n",
+      "            'primary_key': ['lindau_000057_000000', 'lindau_000057_000001',\n",
+      "                            'lindau_000057_000002', 'lindau_000057_000003',\n",
+      "                            'lindau_000057_000004', 'lindau_000057_000005',\n",
+      "                            'lindau_000057_000006', 'lindau_000057_000007',\n",
+      "                            'lindau_000057_000008', 'lindau_000057_000009',\n",
+      "                            'lindau_000057_000010', 'lindau_000057_000011',\n",
+      "                            'lindau_000057_000012', 'lindau_000057_000013',\n",
+      "                            'lindau_000057_000014', 'lindau_000057_000015',\n",
+      "                            'lindau_000057_000016', 'lindau_000057_000017',\n",
+      "                            'lindau_000057_000018', 'lindau_000057_000019',\n",
+      "                            'lindau_000057_000020', 'lindau_000057_000021',\n",
+      "                            'lindau_000057_000022', 'lindau_000057_000023',\n",
+      "                            'lindau_000057_000024', 'lindau_000057_000025',\n",
+      "                            'lindau_000057_000026', 'lindau_000057_000027',\n",
+      "                            'lindau_000057_000028', 'lindau_000057_000029'],\n",
+      "            'timestamps': [0, 58925896, 117851752, 176777576, 235703424,\n",
+      "                           294629232, 353555104, 412480888, 471406760,\n",
+      "                           530332656, 589258512, 648184360, 707110176,\n",
+      "                           766036096, 824961936, 883887864, 942813704,\n",
+      "                           1001739592, 1060665480, 1119591288, 1178517152,\n",
+      "                           1237442936, 1296368816, 1355294616, 1414220496,\n",
+      "                           1473146352, 1532072200, 1590998064, 1649923840,\n",
+      "                           1708849704]}}\n"
      ]
     }
    ],
    "source": [
     "import webdataset as wds\n",
+    "import json\n",
+    "\n",
+    "from pprint import pformat\n",
+    "import os\n",
     "\n",
     "# Create iterable dataset\n",
-    "ds = wds.WebDataset(\"shards/csvps-val.tar\", shardshuffle=False, verbose=True)\n",
+    "shard_dir = \"shards/val\"\n",
+    "ds = wds.WebDataset([os.path.join(shard_dir, shard_file) for shard_file in os.listdir(shard_dir)], shardshuffle=False, verbose=True)\n",
     "\n",
     "# Iterate over the dataset and print the keys and the first few samples\n",
     "for i, sample in enumerate(ds):\n",
-    "    if i > 10:  \n",
+    "    if i > 2:  \n",
     "        break\n",
-    "    print(sample[\"__key__\"], end=\": \")\n",
-    "    for k in sample:\n",
-    "        if not k.startswith(\"__\"):\n",
-    "            print(k, end=\" \")\n",
-    "    print(flush=True)\n",
+    "    meta_data = json.loads(sample[\"json\"].decode())\n",
+    "    print(\"Sample \" + sample[\"__key__\"])\n",
+    "    print(pformat(meta_data, compact=True))\n",
     "    "
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample frames\n",
+    "\n",
+    "We can use `webdataset`'s compose helper to split the sequences into individual (pairs of) frames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__key__ (<class 'str'>) csvps-val/000030/000000:000001\n",
+      "camera.json (<class 'bytes'>) ...\n",
+      "json (<class 'bytes'>) ...\n",
+      "panoptic.png (<class 'list'>) 2\n",
+      "depth.tiff (<class 'list'>) 2\n",
+      "image.png (<class 'list'>) 2\n",
+      "vehicle.json (<class 'list'>) 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import collections\n",
+    "import itertools\n",
+    "\n",
+    "def find_frame_keys(keys):\n",
+    "    r\"\"\"\n",
+    "    Returns a mapping from frame number to the keys of the sample that correspond to \n",
+    "    that frame.\n",
+    "    \"\"\"\n",
+    "    meta_keys = set()\n",
+    "    frame_keys = collections.defaultdict(list)\n",
+    "    for key in keys:\n",
+    "        if key.startswith(\"__\"):\n",
+    "            continue\n",
+    "        if \".\" not in key:\n",
+    "            meta_keys.add(key)\n",
+    "            continue \n",
+    "        stem, other = key.split(\".\", 1)\n",
+    "        if stem.isdigit():\n",
+    "            frame_keys[stem].append(other)\n",
+    "        else:\n",
+    "            meta_keys.add(key)\n",
+    "    return dict(frame_keys), meta_keys\n",
+    "\n",
+    "\n",
+    "def generate_range(src, length: int = 2, *, missing_ok: bool =True):\n",
+    "    for sample in src:\n",
+    "        key = sample[\"__key__\"]\n",
+    "        frame_keys, meta_keys = find_frame_keys(sample.keys()) \n",
+    "        \n",
+    "        pair_keys = set(itertools.chain.from_iterable(frame_keys.values()))\n",
+    "        meta_data = {key: sample[key] for key in meta_keys}\n",
+    "\n",
+    "        frame_ids = list(frame_keys.keys())\n",
+    "\n",
+    "        for i in range(0, len(frame_keys) - length):\n",
+    "            ids = frame_ids[i:i + length]\n",
+    "\n",
+    "            pair_data = {\n",
+    "                \"__key__\": f\"{key}/{ids[0]}:{ids[-1]}\" if len(ids) > 1 else f\"{key}/{ids[0]}\",\n",
+    "                **meta_data,\n",
+    "                **{\n",
+    "                    source_key: [sample.get(f\"{frame}.{source_key}\", None) for frame in ids]\n",
+    "                    for source_key in pair_keys\n",
+    "                }\n",
+    "            }\n",
+    "\n",
+    "            yield pair_data\n",
+    "\n",
+    "ds_per_frame = ds.compose(generate_range)\n",
+    "\n",
+    "sample = next(iter(ds_per_frame))\n",
+    "\n",
+    "for key, value in sample.items():\n",
+    "    print(f\"{key} ({type(value)})\", end=\" \")\n",
+    "    if isinstance(value, list):\n",
+    "        print(len(value))\n",
+    "    elif isinstance(value, bytes):\n",
+    "        print(\"...\")\n",
+    "    else:\n",
+    "        print(value)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hugging Face Datasets\n",
+    "\n",
+    "The WebDataset can be used directly in Hugging Face Datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86f83a11f89e4f37b4acc752f5316585",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Resolving data files:   0%|          | 0/40 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2914d517b4e436388dbf345bbb856c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0/40 [00:00<?, ?files/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d2ca2ef894e49beae715e904168e870",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "DatasetGenerationError",
+     "evalue": "An error occurred while generating the dataset",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/builder.py:1625\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   1624\u001b[0m example \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mfeatures\u001b[38;5;241m.\u001b[39mencode_example(record) \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[0;32m-> 1625\u001b[0m \u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexample\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1626\u001b[0m num_examples_progress_update \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/arrow_writer.py:537\u001b[0m, in \u001b[0;36mArrowWriter.write\u001b[0;34m(self, example, key, writer_batch_size)\u001b[0m\n\u001b[1;32m    535\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhkey_record \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 537\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_examples_on_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/arrow_writer.py:495\u001b[0m, in \u001b[0;36mArrowWriter.write_examples_on_file\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    491\u001b[0m         batch_examples[col] \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    492\u001b[0m             row[\u001b[38;5;241m0\u001b[39m][col]\u001b[38;5;241m.\u001b[39mto_pylist()[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(row[\u001b[38;5;241m0\u001b[39m][col], (pa\u001b[38;5;241m.\u001b[39mArray, pa\u001b[38;5;241m.\u001b[39mChunkedArray)) \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;241m0\u001b[39m][col]\n\u001b[1;32m    493\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcurrent_examples\n\u001b[1;32m    494\u001b[0m         ]\n\u001b[0;32m--> 495\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_examples\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcurrent_examples \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/arrow_writer.py:609\u001b[0m, in \u001b[0;36mArrowWriter.write_batch\u001b[0;34m(self, batch_examples, writer_batch_size)\u001b[0m\n\u001b[1;32m    608\u001b[0m pa_table \u001b[38;5;241m=\u001b[39m pa\u001b[38;5;241m.\u001b[39mTable\u001b[38;5;241m.\u001b[39mfrom_arrays(arrays, schema\u001b[38;5;241m=\u001b[39mschema)\n\u001b[0;32m--> 609\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpa_table\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter_batch_size\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/arrow_writer.py:627\u001b[0m, in \u001b[0;36mArrowWriter.write_table\u001b[0;34m(self, pa_table, writer_batch_size)\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_examples \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m pa_table\u001b[38;5;241m.\u001b[39mnum_rows\n\u001b[0;32m--> 627\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpa_writer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpa_table\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter_batch_size\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/pyarrow/ipc.pxi:529\u001b[0m, in \u001b[0;36mpyarrow.lib._CRecordBatchWriter.write_table\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/pyarrow/error.pxi:89\u001b[0m, in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/fsspec/implementations/local.py:422\u001b[0m, in \u001b[0;36mLocalFileOpener.write\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    421\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrite\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 422\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mOSError\u001b[0m: [Errno 122] Disk quota exceeded",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/builder.py:1634\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   1633\u001b[0m num_shards \u001b[38;5;241m=\u001b[39m shard_id \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m-> 1634\u001b[0m num_examples, num_bytes \u001b[38;5;241m=\u001b[39m \u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfinalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1635\u001b[0m writer\u001b[38;5;241m.\u001b[39mclose()\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/arrow_writer.py:636\u001b[0m, in \u001b[0;36mArrowWriter.finalize\u001b[0;34m(self, close_stream)\u001b[0m\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhkey_record \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 636\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_examples_on_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;66;03m# If schema is known, infer features even if no examples were written\u001b[39;00m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/arrow_writer.py:495\u001b[0m, in \u001b[0;36mArrowWriter.write_examples_on_file\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    491\u001b[0m         batch_examples[col] \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    492\u001b[0m             row[\u001b[38;5;241m0\u001b[39m][col]\u001b[38;5;241m.\u001b[39mto_pylist()[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(row[\u001b[38;5;241m0\u001b[39m][col], (pa\u001b[38;5;241m.\u001b[39mArray, pa\u001b[38;5;241m.\u001b[39mChunkedArray)) \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;241m0\u001b[39m][col]\n\u001b[1;32m    493\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcurrent_examples\n\u001b[1;32m    494\u001b[0m         ]\n\u001b[0;32m--> 495\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_examples\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcurrent_examples \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/arrow_writer.py:609\u001b[0m, in \u001b[0;36mArrowWriter.write_batch\u001b[0;34m(self, batch_examples, writer_batch_size)\u001b[0m\n\u001b[1;32m    608\u001b[0m pa_table \u001b[38;5;241m=\u001b[39m pa\u001b[38;5;241m.\u001b[39mTable\u001b[38;5;241m.\u001b[39mfrom_arrays(arrays, schema\u001b[38;5;241m=\u001b[39mschema)\n\u001b[0;32m--> 609\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpa_table\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter_batch_size\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/arrow_writer.py:627\u001b[0m, in \u001b[0;36mArrowWriter.write_table\u001b[0;34m(self, pa_table, writer_batch_size)\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_examples \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m pa_table\u001b[38;5;241m.\u001b[39mnum_rows\n\u001b[0;32m--> 627\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpa_writer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpa_table\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter_batch_size\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/pyarrow/ipc.pxi:529\u001b[0m, in \u001b[0;36mpyarrow.lib._CRecordBatchWriter.write_table\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/pyarrow/error.pxi:89\u001b[0m, in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/fsspec/implementations/local.py:422\u001b[0m, in \u001b[0;36mLocalFileOpener.write\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    421\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrite\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 422\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mOSError\u001b[0m: [Errno 122] Disk quota exceeded",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mDatasetGenerationError\u001b[0m                    Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mdatasets\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mdatasets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwebdataset\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshards\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(dataset)\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/load.py:2151\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m   2148\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m builder_instance\u001b[38;5;241m.\u001b[39mas_streaming_dataset(split\u001b[38;5;241m=\u001b[39msplit)\n\u001b[1;32m   2150\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 2151\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   2152\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   2153\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   2154\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2155\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   2156\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2157\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2159\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m   2160\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m   2161\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   2162\u001b[0m )\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/builder.py:924\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m    922\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    923\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--> 924\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    925\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    926\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    927\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    928\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    929\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    930\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m    931\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/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/builder.py:1648\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m   1647\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, verification_mode, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_splits_kwargs):\n\u001b[0;32m-> 1648\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   1649\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1650\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1651\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBASIC_CHECKS\u001b[49m\n\u001b[1;32m   1652\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mALL_CHECKS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1653\u001b[0m \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\u001b[43m,\u001b[49m\n\u001b[1;32m   1654\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/builder.py:1000\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m    996\u001b[0m split_dict\u001b[38;5;241m.\u001b[39madd(split_generator\u001b[38;5;241m.\u001b[39msplit_info)\n\u001b[1;32m    998\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    999\u001b[0m     \u001b[38;5;66;03m# Prepare split will record examples associated to the split\u001b[39;00m\n\u001b[0;32m-> 1000\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   1001\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   1002\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m   1003\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   1004\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   1005\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   1006\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[1;32m   1007\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/builder.py:1486\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   1484\u001b[0m job_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1485\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pbar:\n\u001b[0;32m-> 1486\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mjob_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdone\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_split_single\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgen_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgen_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjob_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjob_id\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[43m_prepare_split_args\u001b[49m\n\u001b[1;32m   1488\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m   1489\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdone\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m   1490\u001b[0m \u001b[43m            \u001b[49m\u001b[43mresult\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\n",
+      "File \u001b[0;32m/gpfs/home3/kstolle/.local/opt/miniconda3/envs/multidvps-py312/lib/python3.12/site-packages/datasets/builder.py:1643\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   1641\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   1642\u001b[0m         e \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m__context__\n\u001b[0;32m-> 1643\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   1645\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"
+     ]
+    }
+   ],
+   "source": [
+    "import datasets\n",
+    "\n",
+    "dataset = datasets.load_dataset(\"webdataset\", data_dir=\"shards\", split=\"train\")\n",
+    "print(dataset)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {

scripts/build.py

@@ -0,0 +1,363 @@
+#!/usr/bin/env python
+r"""
+Builds a WebDataset from the Cityscapes Video dataset.
+
+Adapted from the `WebDataset documentation<https://github.com/webdataset/webdataset/>`_.
+"""
+
+import itertools
+import collections
+import typing as T
+from pprint import pformat
+import argparse
+import multiprocessing as mp
+import tarfile
+import pandas as pd
+from io import BytesIO
+import json
+
+from pathlib import Path
+from tqdm import tqdm
+
+
+def parse_args():
+    ap = argparse.ArgumentParser(
+        description="Build a WebDataset from the Cityscapes Video dataset."
+    )
+
+    # Flags and optional
+    ap.add_argument(
+        "--shard-size",
+        "-s",
+        type=int,
+        default=10,
+        help=("Number of sequences per shard."),
+    )
+    ap.add_argument(
+        "--name",
+        "-n",
+        type=str,
+        default="csvps",
+        help=(
+            "Name of the dataset. This will be used as the prefix for the tar files."
+        ),
+    )
+    ap.add_argument(
+        "--variant",
+        type=str,
+        default="",
+        help=(
+            "When passing different manifest variants, this will be used to postfix "
+            "each split such that the resulting dataset name is unique."
+        ),
+    )
+    ap.add_argument(
+        "--force", "-f", action="store_true", help="Overwrite existing data."
+    )
+    ap.add_argument(
+        "--splits", nargs="+", default=["train", "val", "test"], help="Splits to build."
+    )
+
+    # Positional
+    ap.add_argument("manifest", type=Path, help="Path to the manifest CSV file.")
+    ap.add_argument("data", type=Path, help="Path to the Cityscapes Video dataset.")
+    ap.add_argument("output", type=Path, help="Path to the output directory.")
+
+    rt = ap.parse_args()
+
+    # Validation
+    if rt.shard_size < 1:
+        ap.error("Shard size must be a positive integer.")
+    if rt.name == "":
+        ap.error("Name must be a non-empty string.")
+    if not rt.name.isalnum() and not rt.name.islower():
+        ap.error("Name must be a lowercase alpha-numeric string.")
+    if rt.variant != "" and not rt.variant.isalnum() and not rt.variant.islower():
+        ap.error("Variant must be a lowercase alpha-numeric string.")
+    if not rt.manifest.exists():
+        ap.error(f"Manifest file not found: {rt.manifest}")
+    if not rt.data.exists():
+        ap.error(f"Data directory not found: {rt.data}")
+    if not rt.output.exists():
+        rt.output.mkdir(parents=True)
+        print(f"Created output directory: {rt.output}")
+
+    return rt
+
+
+PAD_TO: T.Final[int] = 6  # 06-padding is given by the dataset and should not be changed
+
+
+def pad_number(n: int) -> str:
+    r"""
+    For sorting, numbers are padded with zeros to a fixed width.
+    """
+    if not isinstance(n, int):
+        msg = f"Expected an integer, got {n} of type {type(n)}"
+        raise TypeError(msg)
+    return f"{n:0{PAD_TO}d}"
+
+
+def read_timestamp(path: Path) -> int:
+    with path.open("r") as f:
+        ts = f.read().strip()
+    if not ts.isdigit():
+        msg = f"Expected a timestamp, got {ts} from {path}"
+        raise ValueError(msg)
+    return int(ts)
+
+
+def write_bytes(tar: tarfile.TarFile, bt: bytes, arc: str):
+    r""" "
+    Simple utility to write the bytes (e.g. metadata json) directly from memory to
+    the tarfile, since these do not exist as a file.
+    """
+    with BytesIO() as buf:
+        buf.write(bt)
+
+        # The TarInfo object must be created manually since the meta-data
+        # JSON is written to a buffer (BytesIO) and not a file.
+        tar_info = tarfile.TarInfo(arc)
+        tar_info.size = buf.tell()  # number of bytes written
+
+        # Reset the buffer to the beginning before adding it to the tarfile
+        buf.seek(0)
+
+        tar.addfile(tar_info, buf)
+
+
+def find_sequence_files(
+    seq: int,
+    group: pd.DataFrame,
+    *,
+    data_dir: Path,
+    dataset_name: str,
+    missing_ok: bool = False,
+    frame_inputs: T.Sequence[str] = ("image.png", "vehicle.json"),
+    frame_annotations: T.Sequence[str] = ("panoptic.png", "depth.tiff"),
+    sequence_data: T.Sequence[str] = ("camera.json",),
+    separator: str = "/",
+) -> T.Iterator[tuple[Path | bytes, str]]:
+    seq_pad = pad_number(seq)
+    seq_dir = data_dir / seq_pad
+
+    group = group.sort_values("frame")
+
+    # Add frame-wise data
+    primary_keys = group.index.tolist()
+    frame_numbers = list(map(pad_number, group["frame"].tolist()))
+
+    for i, meta in enumerate(
+        group.drop(columns=["sequence", "frame", "split"]).to_dict(
+            orient="records", index=True
+        )
+    ):
+        frame_06 = frame_numbers[i]
+        is_ann = meta["is_annotated"]
+
+        # Write primary key
+        meta["primary_key"] = primary_keys[i]
+
+        # Add files to the tarfile
+        for var in frame_inputs + frame_annotations:
+            path_file = seq_dir / f"{frame_06}.{var}"
+            if not path_file.exists():
+                if missing_ok or (var in frame_annotations and not is_ann):
+                    continue  # missing annotation OK
+                msg = f"File not found: {path_file}"
+                raise FileNotFoundError(msg)
+
+            yield (
+                path_file,
+                separator.join(
+                    (
+                        dataset_name,
+                        # {seq}.{frame}.{var}.{ext}
+                        path_file.relative_to(data_dir).as_posix().replace("/", "."),
+                    )
+                ),
+            )
+
+        # Add the timestamp to the meta-data if it exists
+        path_ts = seq_dir / f"{frame_06}.timestamp.txt"
+        if not path_ts.exists():
+            if not missing_ok:
+                msg = f"Timestamp file not found: {path_ts}"
+                raise FileNotFoundError(msg)
+            meta["timestamp"] = None
+        else:
+            meta["timestamp"] = read_timestamp(path_ts)
+
+        # Write frame metadata
+        yield (
+            json.dumps(meta).encode("utf-8"),
+            f"{dataset_name}/{seq_pad}.{frame_06}.metadata.json",
+        )
+
+    # Add sequence-wise files {seq}.{var}.{ext}, e.g. 000000.camera.json
+    for var in sequence_data:
+        path_file = seq_dir.with_suffix("." + var)
+        if not path_file.exists():
+            if missing_ok:
+                continue
+            msg = f"File not found: {path_file}"
+            raise FileNotFoundError(msg)
+
+        yield (
+            path_file,
+            separator.join(
+                (
+                    dataset_name,
+                    # {seq}.{var}.{ext}
+                    path_file.relative_to(data_dir).as_posix(),
+                )
+            ),
+        )
+
+    # Write frames array
+    yield (
+        json.dumps(frame_numbers).encode("utf-8"),
+        f"{dataset_name}/{seq_pad}.frames.json",
+    )
+
+
+def run_collector(
+    seq: int, group: pd.DataFrame, kwargs: dict
+) -> tuple[int, list[tuple[Path | bytes, str]]]:
+    r"""
+    Worker that collects the files for a single sequence.
+    """
+    return (seq, list(find_sequence_files(seq, group, **kwargs)))
+
+
+def run_writer(
+    tar_path: Path, items: list[list[tuple[Path | bytes, str]]], compression: str = "gz"
+) -> None:
+    r"""
+    Worker that writes the files to a tar archive.
+    """
+    if compression != "":
+        tar_path = tar_path.with_suffix(f".tar.{compression}")
+        write_mode = f"w:{compression}"
+    else:
+        tar_path.with_suffix(".tar")
+        write_mode = "w"
+
+    with tarfile.open(tar_path, write_mode) as tar:
+        for item in itertools.chain.from_iterable(items):
+            try:
+                path, arc = item
+            except ValueError:
+                msg = f"Expected a tuple of length 2, got {item}"
+                raise ValueError(msg)
+
+            if isinstance(path, Path):
+                tar.add(path, arcname=arc)
+            else:
+                write_bytes(tar, path, arc)
+
+
+def build_shard(
+    mfst: pd.DataFrame,
+    *,
+    tar_dir: Path,
+    shard_size: int,
+    **kwargs,
+):
+    # Make dirs
+    tar_dir.mkdir(exist_ok=True, parents=True)
+
+    write_log = collections.defaultdict(list)
+
+    # Create a list of all sequences
+    # groups = [(seq, group) for seq, group in mfst.groupby("sequence")]
+    # shards = [groups[i : i + shard_size] for i in range(0, len(groups), shard_size)
+    n_groups = len(mfst["sequence"].unique())
+    n_shards = n_groups // shard_size
+
+    targets = [None] * n_groups
+
+    # Start a multiprocessing pool
+    n_proc = min(mp.cpu_count(), 16)
+    with mp.Pool(n_proc) as pool:
+        write_jobs: list[mp.AsyncResult] = []
+
+        # Data collection
+        with tqdm(total=n_groups, desc="Collecting data") as pbar_group:
+            for seq, files in pool.starmap(
+                run_collector,
+                [(seq, group, kwargs) for seq, group in mfst.groupby("sequence")],
+                chunksize=min(8, shard_size),
+            ):
+                assert targets[seq] is None, f"Duplicate sequence: {seq}"
+
+                pbar_group.update()
+
+                # Write to the file specs list
+                targets[seq] = files
+
+                # Get a view of only the current shards's files
+                shard_index = seq // shard_size
+                shard_offset = shard_index * shard_size
+                shard_specs = targets[shard_offset : shard_offset + shard_size]
+
+                # Pad the shard index
+                shard_06 = pad_number(shard_index)
+
+                write_log[shard_06].append(pad_number(seq))
+
+                # If the shard is fully populated, write it to a tar file in another process
+                if all(s is not None for s in shard_specs):
+                    tar_path = tar_dir / shard_06
+
+                    write_jobs.append(
+                        pool.apply_async(
+                            run_writer,
+                            (tar_path, shard_specs, ""),
+                        )
+                    )
+
+        # Wait for write-workers to finish generating the TAR files
+        with tqdm(total=n_shards, desc="Writing shards") as pbar_shard:
+            for j in write_jobs:
+                j.get()
+                pbar_shard.update()
+
+        pool.close()
+        pool.join()
+
+    print("Created shard files:\n" + pformat(dict(write_log)))
+
+
+def main():
+    args = parse_args()
+    manifest = pd.read_csv(args.manifest, index_col="primary_key")
+
+    # For each split, build a tar archive containing the sorted files
+    for split in args.splits:
+        split_out = "-".join([s for s in (split, args.variant) if len(s) > 0])
+        tar_dir = args.output / split_out
+
+        if tar_dir.exists():
+            if args.force:
+                print(f"Removing existing dataset: {tar_dir}")
+                for f in tar_dir.glob("*.tar"):
+                    f.unlink()
+            else:
+                msg = f"Dataset already exists: {tar_dir}"
+                raise FileExistsError(msg)
+
+        print(f"Generating {split_out} split...")
+
+        build_shard(
+            manifest[manifest["split"] == split],
+            tar_dir=tar_dir,
+            data_dir=args.data / split,
+            shard_size=args.shard_size,
+            dataset_name=f"{args.name}-{split_out}",
+            missing_ok=True,
+        )
+
+
+if __name__ == "__main__":
+    main()

scripts/build_parquet.py

@@ -1,2 +0,0 @@
-print("Building a Parquet dataset is not (yet) implemented. Use WebDataset instead.")
-exit(1)

scripts/build_webdataset.py

@@ -1,49 +0,0 @@
-#!/usr/bin/env python
-r"""
-Builds a WebDataset from the Cityscapes Video dataset.
-"""
-
-import argparse
-import tarfile
-
-from pathlib import Path
-from tqdm import tqdm
-
-
-def parse_args():
-    p = argparse.ArgumentParser(
-        description="Build a WebDataset from the Cityscapes Video dataset."
-    )
-    p.add_argument("--prefix", default="csvps", help="Prefix for the tar files.")
-    p.add_argument("data", type=Path, help="Path to the Cityscapes Video dataset.")
-    p.add_argument("output", type=Path, help="Path to the output directory.")
-
-    return p.parse_args()
-
-
-def build_dataset(split: str, data_dir: Path, out_dir: Path, *, prefix: str = ""):
-    data_dir = data_dir / split
-    name = f"{split}.tar"
-    if prefix and prefix != "":
-        name = f"{prefix}-{name}"
-    tar_path = out_dir / name
-
-    if tar_path.exists():
-        print(f"Error: Tar archive already exists: {tar_path}")
-
-    with tarfile.open(tar_path, "w") as tar:
-        # Add the files to the tar archive
-        for file in tqdm(sorted(data_dir.glob("**/*")), desc=f"Building {tar_path}"):
-            tar.add(file, arcname=file.relative_to(data_dir))
-
-
-def main():
-    args = parse_args()
-
-    # For each split, build a tar archive containing the sorted files
-    for split in ("train", "val", "test"):
-        build_dataset(split, args.data, args.output, prefix=args.prefix)
-
-
-if __name__ == "__main__":
-    main()

scripts/prepare.py

@@ -28,7 +28,11 @@ def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         description="Download and extract the Cityscapes dataset."
     )
-
+    parser.add_argument(
+        "manifest_file",
+        type=Path,
+        help="Path to the manifest file (e.g., 'manifest.csv').",
+    )
     parser.add_argument(
         "downloads_dir",
         type=Path,
@@ -39,11 +43,6 @@ def parse_args() -> argparse.Namespace:
         type=Path,
         help="Path to the directory where extracted files should be moved (e.g., 'data').",
     )
-    parser.add_argument(
-        "manifest_file",
-        type=Path,
-        help="Path to the manifest file (e.g., 'manifest.csv').",
-    )
     return parser.parse_args()
 
 
@@ -110,13 +109,22 @@ def unzip_and_move(
             primary_key = re_name.sub(r"\1", res_path.stem)
             sample = manifest.loc[primary_key]
 
-            # New name is: split/<sequence>/<frame>.<type>.<ext>
-            new_path = (
-                data_dir
-                / sample["split"]
-                / "{:06d}".format(sample["sequence"])
-                / "{:06d}.{:s}{:s}".format(sample["frame"], pkg_type, res_path.suffix)
-            )
+            # Build the new path
+            new_path = data_dir / sample["split"]
+
+            if pkg_type in {"camera"}:
+                # New name is: split/<sequence>.<type>.<ext>
+                new_path /= "{:06d}.{:s}{:s}".format(
+                    sample["sequence"], pkg_type, res_path.suffix
+                )
+            else:
+                # New name is: split/<sequence>/<frame>.<type>.<ext>
+                new_path /= "{:06d}".format(sample["sequence"])
+                new_path /= "{:06d}.{:s}{:s}".format(
+                    sample["frame"], pkg_type, res_path.suffix
+                )
+            if new_path.is_file():
+                continue
             new_path.parent.mkdir(parents=True, exist_ok=True)
 
             shutil.move(res_path, new_path)