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Merge pull request #120 from EvolvingLMMs-Lab/pufanyi/hf_dataset_docs
Add docs for datasets upload to HF
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Contribute Your Models and Datasets\n", | ||
| "\n", | ||
| "[](https://colab.research.google.com/github/EvolvingLMMs-Lab/lmms-eval/blob/main/tools/make_image_hf_dataset.ipynb)\n", | ||
| "\n", | ||
| "This notebook will guide you to make correct format of Huggingface dataset, in proper parquet format and visualizable in Huggingface dataset hub.\n", | ||
| "\n", | ||
| "We will take the example of the dataset [`lmms-lab/VQAv2_TOY`](https://huggingface.co/datasets/lmms-lab/VQAv2_TOY) and convert it to the proper format." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Preparation\n", | ||
| "\n", | ||
| "We need to install `datasets` library to create the dataset and `Pillow` to handle images." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "vscode": { | ||
| "languageId": "shellscript" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!pip install datasets Pillow" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "And we need to login into Hugging Face to upload the dataset. You should goto the [Hugging Face website](https://huggingface.co/settings/tokens) to get your API token." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "vscode": { | ||
| "languageId": "shellscript" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!huggingface-cli login --token hf_YOUR_HF_TOKEN # replace hf_YOUR_HF_TOKEN to your own Hugging Face token." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "vscode": { | ||
| "languageId": "plaintext" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "## Download Dataset\n", | ||
| "\n", | ||
| "We have uploaded the zip file of the dataset to [Hugging Face](https://huggingface.co/datasets/pufanyi/VQAv2_TOY/tree/main/source_data) for download. This dataset is a subset of the [VQAv2](https://visualqa.org/) dataset, with $20$ entries each from the `val`, `test`, and `test-dev` splits, for easier downloading." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "vscode": { | ||
| "languageId": "shellscript" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!wget https://huggingface.co/datasets/lmms-lab/VQAv2_TOY/resolve/main/source_data/sample_data.zip -P data\n", | ||
| "!unzip data/sample_data.zip -d data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We can open `data/questions` to take a view of the dataset organization. We found that the toy-`VQAv2` dataset is organized as follows:\n", | ||
| "\n", | ||
| "```json\n", | ||
| "{\n", | ||
| " \"info\": { /* some infomation */ },\n", | ||
| " \"task_type\": \"TASK_TYPE\", \"data_type\": \"mscoco\",\n", | ||
| " \"license\": { /* some license */ },\n", | ||
| " \"questions\": [\n", | ||
| " {\n", | ||
| " \"image_id\": 262144, // integer id of the image\n", | ||
| " \"question\": \"Is the ball flying towards the batter?\",\n", | ||
| " \"question_id\": 262144000\n", | ||
| " },\n", | ||
| " /* ... */\n", | ||
| " ]\n", | ||
| "}\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Define Dataset Features _(Optional<sup>*</sup>)_\n", | ||
| "\n", | ||
| "You can define the features of the dataset. For more details, please refer to the [official documentation](https://huggingface.co/docs/datasets/en/about_dataset_features).\n", | ||
| "\n", | ||
| "<sup>*</sup> _Note that if the dataset features are consistent and all entries in your dataset table are non-null **for all splits of data**, you can skip this step._" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import datasets\n", | ||
| "\n", | ||
| "features = datasets.Features(\n", | ||
| " {\n", | ||
| " \"question_id\": datasets.Value(\"int64\"),\n", | ||
| " \"question\": datasets.Value(\"string\"),\n", | ||
| " \"image_id\": datasets.Value(\"string\"),\n", | ||
| " \"image\": datasets.Image(),\n", | ||
| " }\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Define Data Generator\n", | ||
| "\n", | ||
| "We use [`datasets.Dataset.from_generator`](https://huggingface.co/docs/datasets/v2.20.0/en/package_reference/main_classes#datasets.Dataset.from_generator) to create the dataset.\n", | ||
| "\n", | ||
| "The generator function should `yield` dictionaries with the keys corresponding to the dataset features. This can save memory when loading large datasets.\n", | ||
| "\n", | ||
| "For the image data, we can convert the image to [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html) object.\n", | ||
| "\n", | ||
| "Note that if some columns are missing in some splits of the dataset (for example, the `answer` column is usually missing in the `test` split), we need to set these columns to null to ensure that all splits have the same features." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "import json\n", | ||
| "from PIL import Image\n", | ||
| "\n", | ||
| "\n", | ||
| "def generator(qa_file, image_folder, image_prefix):\n", | ||
| " with open(qa_file, \"r\") as f:\n", | ||
| " data = json.load(f)\n", | ||
| " qa = data[\"questions\"]\n", | ||
| "\n", | ||
| " for q in qa:\n", | ||
| " image_id = q[\"image_id\"]\n", | ||
| " image_path = os.path.join(image_folder, f\"{image_prefix}_{image_id:012}.jpg\")\n", | ||
| " q[\"image\"] = Image.open(image_path)\n", | ||
| " yield q" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Generate Dataset\n", | ||
| "\n", | ||
| "We generate the dataset using the generator function.\n", | ||
| "\n", | ||
| "Note that if you skip the step of defining dataset features, there is no need to pass the `features` argument. The dataset infer the features from the dataset automatically." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "NUM_PROC = 32 # number of processes to use for multiprocessing, set to 1 for no multiprocessing\n", | ||
| "\n", | ||
| "data_val = datasets.Dataset.from_generator(\n", | ||
| " generator,\n", | ||
| " gen_kwargs={\n", | ||
| " \"qa_file\": \"data/questions/vqav2_toy_questions_val2014.json\",\n", | ||
| " \"image_folder\": \"data/images\",\n", | ||
| " \"image_prefix\": \"COCO_val2014\",\n", | ||
| " },\n", | ||
| " # For this dataset, there is no need to specify the features, as all cells are non-null and all splits have the same schema\n", | ||
| " # features=features,\n", | ||
| " num_proc=NUM_PROC,\n", | ||
| ")\n", | ||
| "\n", | ||
| "data_test = datasets.Dataset.from_generator(\n", | ||
| " generator,\n", | ||
| " gen_kwargs={\n", | ||
| " \"qa_file\": \"data/questions/vqav2_toy_questions_test2015.json\",\n", | ||
| " \"image_folder\": \"data/images\",\n", | ||
| " \"image_prefix\": \"COCO_test2015\",\n", | ||
| " },\n", | ||
| " # features=features,\n", | ||
| " num_proc=NUM_PROC,\n", | ||
| ")\n", | ||
| "\n", | ||
| "data_test_dev = datasets.Dataset.from_generator(\n", | ||
| " generator,\n", | ||
| " gen_kwargs={\n", | ||
| " \"qa_file\": \"data/questions/vqav2_toy_questions_test-dev2015.json\",\n", | ||
| " \"image_folder\": \"data/images\",\n", | ||
| " \"image_prefix\": \"COCO_test2015\",\n", | ||
| " },\n", | ||
| " # features=features,\n", | ||
| " num_proc=NUM_PROC,\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Dataset Upload\n", | ||
| "\n", | ||
| "Finally, we group the dataset with different splits and upload it to the Huggingface dataset hub." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "data = datasets.DatasetDict({\"val\": data_val, \"test\": data_test, \"test_dev\": data_test_dev})" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "data.push_to_hub(\"lmms-lab/VQAv2_TOY\") # replace lmms-lab to your username" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Now, you can check the dataset on the [Hugging Face dataset hub](https://huggingface.co/datasets/lmms-lab/VQAv2_TOY)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "lmms-eval", | ||
| "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": "3.11.9" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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