119 lines
3.7 KiB
Plaintext
119 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Open-flamingo Captioning\n",
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"This notebook is an implementation of [OpenFlamingo](https://github.com/mlfoundations/open_flamingo) for image captioning. \n",
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"\n",
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"This will require HIGH RAM shape on Google Colab, but T4 16gb is enough to run the 3B model. 9B model requires 24GB GPU or better.\n",
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"\n",
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"1. Read [Docs](doc/CAPTION.md) for basic usage guide. \n",
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"2. Open in [Google Colab](https://colab.research.google.com/github/victorchall/EveryDream2trainer/blob/main/CaptionFL.ipynb) **OR** Runpod/Vast using the EveryDream2trainer docker container/template and open this notebook.\n",
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"3. Run the cells below to install dependencies.\n",
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"4. Place your images in \"input\" folder or change the data_root to point to a Gdrive folder."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# install dependencies\n",
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"!pip install open-flamingo==2.0.0\n",
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"!pip install huggingface-hub==0.15.1\n",
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"!pip install transformers==4.30.2\n",
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"!pip install pynvml\n",
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"!pip install colorama"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Colab only setup (do NOT run for docker/runpod/vast)\n",
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"!git clone https://github.com/victorchall/EveryDream2trainer\n",
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"%cd EveryDream2trainer\n",
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"%mkdir -p /content/EveryDream2trainer/input"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%cd /content/EveryDream2trainer\n",
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"#@markdown Optional: Extract all TAR and ZIP files in the input folder (so you can just upload a large TAR/ZIP)\n",
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"import os\n",
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"import zipfile\n",
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"import tarfile\n",
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"\n",
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"# Directory containing the input files\n",
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"input_folder = \"input\"\n",
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"\n",
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"# Extract ZIP files\n",
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"for file in os.listdir(input_folder):\n",
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" if file.endswith(\".zip\"):\n",
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" file_path = os.path.join(input_folder, file)\n",
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" with zipfile.ZipFile(file_path, 'r') as zip_ref:\n",
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" zip_ref.extractall(input_folder)\n",
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"\n",
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"# Extract TAR files\n",
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"for file in os.listdir(input_folder):\n",
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" if file.endswith(\".tar\"):\n",
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" file_path = os.path.join(input_folder, file)\n",
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" with tarfile.open(file_path, 'r') as tar_ref:\n",
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" tar_ref.extractall(input_folder)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Run captions.\n",
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"\n",
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"Place your images in \"input\" folder, or you can change the data_root to point to a Gdrive folder.\n",
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"\n",
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"Run either the 24GB or 16GB model or adjust settings on your own."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 24GB GPU, 9b model\n",
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"%cd /content/EveryDream2trainer\n",
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"%run caption_fl.py --data_root \"input\" --min_new_tokens 20 --max_new_tokens 30 --num_beams 3 --model \"openflamingo/OpenFlamingo-9B-vitl-mpt7b\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 16GB GPU, 3b model\n",
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"%cd /content/EveryDream2trainer\n",
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"%run caption_fl.py --data_root \"input\" --min_new_tokens 20 --max_new_tokens 30 --num_beams 8 --model \"openflamingo/OpenFlamingo-3B-vitl-mpt1b\""
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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