Upload folder using huggingface_hub

README.md

@@ -0,0 +1,19 @@
+# Empty solution example for the S23DR competition
+
+This repo provides a minimalistic example of a valid, but empty submission to S23DR competition.
+We recommend to take a look at the [another example](https://huggingface.co/usm3d/handcrafted_baseline_submission),
+which implement some primitive algorithm and provides useful I/O and visualization functions.
+
+This one, though, containt the minimal code, which succeeds at reading the dataset and producing a solution, which consists of two vertices at the origin and edge of zero length connecting them.
+
+
+The repo consistst of the following parts:
+
+- `script.py` - the main file, which is run by the competition space. It should produce `submission.parquet` as the result of the run.
+- `hoho.py` - the file for parsing the dataset at the inference time. Do NOT change it.
+
+
+---
+license: apache-2.0
+---
+

example_notebook.ipynb

@@ -0,0 +1,1010 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "Mq5iNIZ9xWxt",
+      "metadata": {
+        "id": "Mq5iNIZ9xWxt"
+      },
+      "source": [
+        "# Empty Submission Example for S23DR Challenge\n",
+        "\n",
+        "### Helpful Links\n",
+        "[Challenge Page](https://huggingface.co/spaces/usm3d/S23DR)  \n",
+        "[Workshop Page](usm3d.github.io)  \n",
+        "\n",
+        "[HoHo Train Set](https://huggingface.co/datasets/usm3d/hoho-train-set)  \n",
+        "[Handcrafted Baseline Solution](https://huggingface.co/usm3d/handcrafted_baseline_submission) \n",
+        " "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "dua8UJOoxiDi",
+      "metadata": {
+        "id": "dua8UJOoxiDi"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "We'll start by checking if we are running to Google Colab (and if we are setting `IN_COLAB = True` and installing the [hoho tools](https://huggingface.co/usm3d/tools))."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "ItDDqoXop8bb",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ItDDqoXop8bb",
+        "outputId": "0c9d26a7-bf79-4452-c772-d5579a9cb2a9"
+      },
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "  import google.colab\n",
+        "  IN_COLAB = True\n",
+        "except:\n",
+        "  IN_COLAB = False\n",
+        "\n",
+        "if IN_COLAB:\n",
+        "    !pip install git+http://hf.co/usm3d/tools.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2tHX74Z-x1cU",
+      "metadata": {
+        "id": "2tHX74Z-x1cU"
+      },
+      "source": [
+        "We need to be logged into HF for this to work because the training dataset is gated. If you haven't already please go to the [dastaset page](https://huggingface.co/datasets/usm3d/hoho-train-set) to agree to our terms and request access to the dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "zq_ljluLqzzv",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zq_ljluLqzzv",
+        "outputId": "b66806f1-b88a-47e0-8194-79515b73fa23"
+      },
+      "outputs": [],
+      "source": [
+        "if IN_COLAB:\n",
+        "    !huggingface-cli login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "Xf2PY79fywa5",
+      "metadata": {
+        "id": "Xf2PY79fywa5"
+      },
+      "source": [
+        "## Data Download, Analysis, and Visualization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "e171b1ec-e861-4349-98fd-2eac4d080ff5",
+      "metadata": {
+        "id": "e171b1ec-e861-4349-98fd-2eac4d080ff5"
+      },
+      "outputs": [],
+      "source": [
+        "import hoho\n",
+        "from hoho import *\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pathlib import Path\n",
+        "from collections import Counter\n",
+        "import itertools\n",
+        "import datasets\n",
+        "import trimesh\n",
+        "from tqdm.notebook import tqdm\n",
+        "import webdataset as wds\n",
+        "import sys"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "83649a4c-fde7-4051-ba71-e596d382e76a",
+      "metadata": {
+        "id": "83649a4c-fde7-4051-ba71-e596d382e76a"
+      },
+      "source": [
+        "### Load the hoho package and point to the data folder\n",
+        "\n",
+        "We download only one shard of the data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "ffffc234",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ffffc234",
+        "outputId": "e969db58-e88e-457a-eee2-14e65c8117fb"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/Users/jack/dev/USM3D/comp/tools/hoho/hoho.py:309: UserWarning: streaming isn't using with 'all': changing `split` to 'train'\n",
+            "  warnings.warn('streaming isn\\'t using with \\'all\\': changing `split` to \\'train\\'')\n",
+            "/Users/jack/dev/USM3D/comp/tools/hoho/hoho.py:310: UserWarning: no tarfiles found in data/usm-training-data/data/val.\n",
+            "  warnings.warn(msg)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/Users/jack/dev/USM3D/comp/empty_submission\n",
+            "total 104\n",
+            "-rw-r--r--   1 jack  staff   1.5K Apr 26 12:51 .gitattributes\n",
+            "-rw-r--r--   1 jack  staff   855B Apr 26 12:51 README.md\n",
+            "drwxr-xr-x  10 jack  staff   320B Apr 26 12:55 \u001b[34m..\u001b[m\u001b[m\n",
+            "drwxr-xr-x   3 jack  staff    96B Apr 26 15:06 \u001b[34mdata\u001b[m\u001b[m\n",
+            "-rw-r--r--   1 jack  staff   5.8K Apr 26 15:42 submission.parquet\n",
+            "-rw-r--r--   1 jack  staff   2.3K Apr 26 15:50 script.py\n",
+            "drwxr-xr-x  15 jack  staff   480B Apr 26 15:50 \u001b[34m.git\u001b[m\u001b[m\n",
+            "-rw-r--r--   1 jack  staff    32K Apr 26 18:26 example_notebook.ipynb\n",
+            "drwxr-xr-x   9 jack  staff   288B Apr 28 10:32 \u001b[34m.\u001b[m\u001b[m\n",
+            "Using data/usm-training-data/data as the data directory (we are running locally)\n",
+            "------------ Loading dataset------------ \n",
+            "params.json not found (this means we probably aren't in the test env). Using example params.\n",
+            "{'competition_id': 'usm3d/S23DR', 'competition_type': 'script', 'metric': 'custom', 'token': 'hf_**********************************', 'team_id': 'local-test-team_id', 'submission_id': 'local-test-submission_id', 'submission_id_col': '__key__', 'submission_cols': ['__key__', 'wf_edges', 'wf_vertices', 'edge_semantics'], 'submission_rows': 180, 'output_path': '.', 'submission_repo': '<THE HF MODEL ID of THIS REPO', 'time_limit': 7200, 'dataset': 'usm3d/usm-test-data-x', 'submission_filenames': ['submission.parquet']}\n",
+            "------------ Now you can do your solution ---------------\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "0it [00:00, ?it/s]"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2it [00:34, 17.31s/it]\n"
+          ]
+        },
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "Cell \u001b[0;32mIn[4], line 47\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m------------ Now you can do your solution ---------------\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     46\u001b[0m solution \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, sample \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tqdm(dataset)):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;66;03m# replace this with your solution\u001b[39;00m\n\u001b[1;32m     49\u001b[0m     pred_vertices, pred_edges \u001b[38;5;241m=\u001b[39m empty_solution(sample)\n\u001b[1;32m     51\u001b[0m     solution\u001b[38;5;241m.\u001b[39mappend({\n\u001b[1;32m     52\u001b[0m                     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__key__\u001b[39m\u001b[38;5;124m'\u001b[39m: sample[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__key__\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[1;32m     53\u001b[0m                     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwf_vertices\u001b[39m\u001b[38;5;124m'\u001b[39m: pred_vertices\u001b[38;5;241m.\u001b[39mtolist(),\n\u001b[1;32m     54\u001b[0m                     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwf_edges\u001b[39m\u001b[38;5;124m'\u001b[39m: pred_edges\n\u001b[1;32m     55\u001b[0m                 })\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1182\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1183\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1184\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/pipeline.py:70\u001b[0m, in \u001b[0;36mDataPipeline.iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Create an iterator through the entire dataset, using the given number of repetitions.\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrepetitions):\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterator1()\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/filters.py:302\u001b[0m, in \u001b[0;36m_map\u001b[0;34m(data, f, handler)\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_map\u001b[39m(data, f, handler\u001b[38;5;241m=\u001b[39mreraise_exception):\n\u001b[1;32m    301\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Map samples.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m    303\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    304\u001b[0m             result \u001b[38;5;241m=\u001b[39m f(sample)\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/filters.py:302\u001b[0m, in \u001b[0;36m_map\u001b[0;34m(data, f, handler)\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_map\u001b[39m(data, f, handler\u001b[38;5;241m=\u001b[39mreraise_exception):\n\u001b[1;32m    301\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Map samples.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m    303\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    304\u001b[0m             result \u001b[38;5;241m=\u001b[39m f(sample)\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/tariterators.py:219\u001b[0m, in \u001b[0;36mgroup_by_keys\u001b[0;34m(data, keys, lcase, suffixes, handler)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Group tarfile contents by keys and yield samples.\u001b[39;00m\n\u001b[1;32m    204\u001b[0m \n\u001b[1;32m    205\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;124;03m    iterator over samples.\u001b[39;00m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    218\u001b[0m current_sample \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 219\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m filesample \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    221\u001b[0m         \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filesample, \u001b[38;5;28mdict\u001b[39m)\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/tariterators.py:177\u001b[0m, in \u001b[0;36mtar_file_expander\u001b[0;34m(data, handler, select_files, rename_files)\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(source, \u001b[38;5;28mdict\u001b[39m)\n\u001b[1;32m    176\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m source\n\u001b[0;32m--> 177\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m tar_file_iterator(\n\u001b[1;32m    178\u001b[0m     source[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    179\u001b[0m     handler\u001b[38;5;241m=\u001b[39mhandler,\n\u001b[1;32m    180\u001b[0m     select_files\u001b[38;5;241m=\u001b[39mselect_files,\n\u001b[1;32m    181\u001b[0m     rename_files\u001b[38;5;241m=\u001b[39mrename_files,\n\u001b[1;32m    182\u001b[0m ):\n\u001b[1;32m    183\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m    184\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(sample, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m sample \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfname\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m sample\n\u001b[1;32m    185\u001b[0m     )\n\u001b[1;32m    186\u001b[0m     sample[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__url__\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m url\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/tariterators.py:142\u001b[0m, in \u001b[0;36mtar_file_iterator\u001b[0;34m(fileobj, skip_meta, handler, select_files, rename_files)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m select_files \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;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m select_files(fname):\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 142\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mstream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextractfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarinfo\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    143\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(fname\u001b[38;5;241m=\u001b[39mfname, data\u001b[38;5;241m=\u001b[39mdata)\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m result\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/tarfile.py:689\u001b[0m, in \u001b[0;36m_FileInFile.read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m    687\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data:\n\u001b[1;32m    688\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfileobj\u001b[38;5;241m.\u001b[39mseek(offset \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposition \u001b[38;5;241m-\u001b[39m start))\n\u001b[0;32m--> 689\u001b[0m     b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfileobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlength\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    690\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(b) \u001b[38;5;241m!=\u001b[39m length:\n\u001b[1;32m    691\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ReadError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munexpected end of data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/tarfile.py:526\u001b[0m, in \u001b[0;36m_Stream.read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m    524\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the next size number of bytes from the stream.\"\"\"\u001b[39;00m\n\u001b[1;32m    525\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m size \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[0;32m--> 526\u001b[0m buf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    527\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpos \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(buf)\n\u001b[1;32m    528\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m buf\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/tarfile.py:544\u001b[0m, in \u001b[0;36m_Stream._read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m    542\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuf \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    543\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 544\u001b[0m     buf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfileobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbufsize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    545\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m buf:\n\u001b[1;32m    546\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+            "File \u001b[0;32m~/miniconda3/envs/d2/lib/python3.10/site-packages/webdataset/gopen.py:87\u001b[0m, in \u001b[0;36mPipe.read\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\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[39mkw):\n\u001b[1;32m     86\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Wrap stream.read and checks status.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\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[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_status()\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": [
+        "# %load script.py\n",
+        "### This is example of the script that will be run in the test environment.\n",
+        "### Some parts of the code are compulsory and you should NOT CHANGE THEM.\n",
+        "### They are between '''---compulsory---''' comments.\n",
+        "### You can change the rest of the code to define and test your solution.\n",
+        "### However, you should not change the signature of the provided function.\n",
+        "### The script would save \"submission.parquet\" file in the current directory.\n",
+        "### You can use any additional files and subdirectories to organize your code.\n",
+        "\n",
+        "'''---compulsory---'''\n",
+        "import hoho; hoho.setup() # YOU MUST CALL hoho.setup() BEFORE ANYTHING ELSE\n",
+        "'''---compulsory---'''\n",
+        "\n",
+        "from pathlib import Path\n",
+        "from tqdm import tqdm\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "\n",
+        "def empty_solution(sample):\n",
+        "    '''Return a minimal valid solution, i.e. 2 vertices and 1 edge.'''\n",
+        "    return np.zeros((2,3)), [(0, 1)]\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    print (\"------------ Loading dataset------------ \")\n",
+        "    params = hoho.get_params()\n",
+        "    \n",
+        "    # by default it is usually better to use `get_dataset()` like this\n",
+        "    # \n",
+        "    # dataset = hoho.get_dataset(split='all')\n",
+        "    # \n",
+        "    # but in this case (because we don't do anything with the sample \n",
+        "    # anyway) we set `decode=None`. We can set the `split` argument \n",
+        "    # to 'train' or 'val' ('all' defaults back to 'train') if we are \n",
+        "    # testing ourselves locally. \n",
+        "    # \n",
+        "    # dataset = hoho.get_dataset(split='val', decode=None)\n",
+        "    #\n",
+        "    # On the test server *`split` must be set to 'all'* \n",
+        "    # to compute both the public and private leaderboards.\n",
+        "    # \n",
+        "    dataset = hoho.get_dataset(split='all', decode=None)\n",
+        "    \n",
+        "    print('------------ Now you can do your solution ---------------')\n",
+        "    solution = []\n",
+        "    for i, sample in enumerate(tqdm(dataset)):\n",
+        "        # replace this with your solution\n",
+        "        pred_vertices, pred_edges = empty_solution(sample)\n",
+        "        \n",
+        "        solution.append({\n",
+        "                        '__key__': sample['__key__'], \n",
+        "                        'wf_vertices': pred_vertices.tolist(),\n",
+        "                        'wf_edges': pred_edges\n",
+        "                    })\n",
+        "    print('------------ Saving results ---------------')\n",
+        "    sub = pd.DataFrame(solution, columns=[\"__key__\", \"wf_vertices\", \"wf_edges\"])\n",
+        "    sub.to_parquet(Path(params['output_path']) / \"submission.parquet\")\n",
+        "    print(\"------------ Done ------------ \")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f077cbd7",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b65ed78e",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a4584502",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.13"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "00dd3ba2c97a4a5f9e20e4c90ad4bcf8": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_e11a7d4a03954577aa59223fbe24780d",
+            "placeholder": "​",
+            "style": "IPY_MODEL_4d456e625cc248c18a6ee6171919a462",
+            "value": ""
+          }
+        },
+        "025d370c61d84fcba1a866731a16ee27": {
+          "model_module": "@jupyter-widgets/base",
+          "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "1bee0e2f81104a22bad736cea9725dcb": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "2509efdc0c6a4dd6b1ade16d30e8209d": {
+          "model_module": "@jupyter-widgets/base",
+          "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "2ac27da2aa014ec782d2e7073d153f99": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "452e42ee4a734b7a98b5945cffff9dac": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_2509efdc0c6a4dd6b1ade16d30e8209d",
+            "placeholder": "​",
+            "style": "IPY_MODEL_2ac27da2aa014ec782d2e7073d153f99",
+            "value": "Map: 100%"
+          }
+        },
+        "453f4cacb5bf490cb90f10c9cdccca30": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HBoxModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_452e42ee4a734b7a98b5945cffff9dac",
+              "IPY_MODEL_f4f69c0ea946405a8c1b88e68cfe6e38",
+              "IPY_MODEL_8e41e4539ee54d708b593c5d44699855"
+            ],
+            "layout": "IPY_MODEL_9a3ccc55920d4f9588e89e4d5579b0b0"
+          }
+        },
+        "4d456e625cc248c18a6ee6171919a462": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "8d39835b987249799b1d000f9960e6f9": {
+          "model_module": "@jupyter-widgets/base",
+          "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "8e41e4539ee54d708b593c5d44699855": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_ccfd6307c2cd4ba6b49e10ddb4a70d66",
+            "placeholder": "​",
+            "style": "IPY_MODEL_1bee0e2f81104a22bad736cea9725dcb",
+            "value": " 150/150 [00:02&lt;00:00, 55.18 examples/s]"
+          }
+        },
+        "9a3ccc55920d4f9588e89e4d5579b0b0": {
+          "model_module": "@jupyter-widgets/base",
+          "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "9bac4c96e2b741b782da7fc36b79ead1": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "FloatProgressModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "success",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_f0190337a99243e4b022dcf565dc7f5c",
+            "max": 1,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_a9355a1d675c4ac4a2f68f4663a1d390",
+            "value": 1
+          }
+        },
+        "a9355a1d675c4ac4a2f68f4663a1d390": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "ProgressStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "a9ef94f8656c483ebb8ee36c33c21a15": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HBoxModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_00dd3ba2c97a4a5f9e20e4c90ad4bcf8",
+              "IPY_MODEL_9bac4c96e2b741b782da7fc36b79ead1",
+              "IPY_MODEL_d4f9d577a48e46d39a86be7ad18f96d9"
+            ],
+            "layout": "IPY_MODEL_e47db715aee544a38f1286b2861e378f"
+          }
+        },
+        "b6d2b0649d794440aa5e5c77fdba4c34": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "ccfd6307c2cd4ba6b49e10ddb4a70d66": {
+          "model_module": "@jupyter-widgets/base",
+          "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "d354d88c25b04ca18c0d23d61f4e9cba": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "ProgressStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "d4f9d577a48e46d39a86be7ad18f96d9": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_025d370c61d84fcba1a866731a16ee27",
+            "placeholder": "​",
+            "style": "IPY_MODEL_b6d2b0649d794440aa5e5c77fdba4c34",
+            "value": " 191/? [00:17&lt;00:00, 22.28it/s]"
+          }
+        },
+        "e11a7d4a03954577aa59223fbe24780d": {
+          "model_module": "@jupyter-widgets/base",
+          "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "e47db715aee544a38f1286b2861e378f": {
+          "model_module": "@jupyter-widgets/base",
+          "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "f0190337a99243e4b022dcf565dc7f5c": {
+          "model_module": "@jupyter-widgets/base",
+          "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": "20px"
+          }
+        },
+        "f4f69c0ea946405a8c1b88e68cfe6e38": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "FloatProgressModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "success",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_8d39835b987249799b1d000f9960e6f9",
+            "max": 150,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_d354d88c25b04ca18c0d23d61f4e9cba",
+            "value": 150
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

script.py

@@ -0,0 +1,58 @@
+### This is example of the script that will be run in the test environment.
+### Some parts of the code are compulsory and you should NOT CHANGE THEM.
+### They are between '''---compulsory---''' comments.
+### You can change the rest of the code to define and test your solution.
+### However, you should not change the signature of the provided function.
+### The script would save "submission.parquet" file in the current directory.
+### You can use any additional files and subdirectories to organize your code.
+
+'''---compulsory---'''
+import hoho; hoho.setup() # YOU MUST CALL hoho.setup() BEFORE ANYTHING ELSE
+'''---compulsory---'''
+
+from pathlib import Path
+from tqdm import tqdm
+import pandas as pd
+import numpy as np
+
+
+def empty_solution(sample):
+    '''Return a minimal valid solution, i.e. 2 vertices and 1 edge.'''
+    return np.zeros((2,3)), [(0, 1)]
+
+
+if __name__ == "__main__":
+    print ("------------ Loading dataset------------ ")
+    params = hoho.get_params()
+    
+    # by default it is usually better to use `get_dataset()` like this
+    # 
+    # dataset = hoho.get_dataset(split='all')
+    # 
+    # but in this case (because we don't do anything with the sample 
+    # anyway) we set `decode=None`. We can set the `split` argument 
+    # to 'train' or 'val' ('all' defaults back to 'train') if we are 
+    # testing ourselves locally. 
+    # 
+    # dataset = hoho.get_dataset(split='val', decode=None)
+    #
+    # On the test server *`split` must be set to 'all'* 
+    # to compute both the public and private leaderboards.
+    # 
+    dataset = hoho.get_dataset(split='all', decode=None)
+    
+    print('------------ Now you can do your solution ---------------')
+    solution = []
+    for i, sample in enumerate(tqdm(dataset)):
+        # replace this with your solution
+        pred_vertices, pred_edges = empty_solution(sample)
+        
+        solution.append({
+                        '__key__': sample['__key__'], 
+                        'wf_vertices': pred_vertices.tolist(),
+                        'wf_edges': pred_edges
+                    })
+    print('------------ Saving results ---------------')
+    sub = pd.DataFrame(solution, columns=["__key__", "wf_vertices", "wf_edges"])
+    sub.to_parquet(Path(params['output_path']) / "submission.parquet")
+    print("------------ Done ------------ ")