Welcome to the world of OpenFlamingo, an open-source implementation of DeepMind’s revolutionary visual-language model! Here, we provide you with a step-by-step guide to install, use, and train OpenFlamingo models using PyTorch.
Table of Contents
Installation
To start using OpenFlamingo, follow these installation steps:
- To install the package in an existing environment, run:
pip install open-flamingo
conda env create -f environment.yml
pip install open-flamingo[training]
pip install open-flamingo[eval]
pip install open-flamingo[all]
Approach
OpenFlamingo is a multimodal language model designed to perform various tasks, leveraging a large multimodal dataset for training. This allows generative tasks like image captioning and question-answering based on images and text together.
Usage
Now, let’s dive into how you can utilize OpenFlamingo effectively.
Initializing an OpenFlamingo Model
OpenFlamingo supports pretrained vision encoders from the OpenCLIP package and pretrained language models from the transformers package. Here’s how you can initialize it:
from open_flamingo import create_model_and_transforms
model, image_processor, tokenizer = create_model_and_transforms(
clip_vision_encoder_path="ViT-L-14",
clip_vision_encoder_pretrained="openai",
lang_encoder_path="anas-awadallampt-1b-redpajama-200b",
tokenizer_path="anas-awadallampt-1b-redpajama-200b",
cross_attn_every_n_layers=1,
cache_dir="PATHTOCACHEDIR" # Defaults to ~.cache
)
You can think of initializing the model akin to setting up a state-of-the-art multi-tool. Just as a Swiss army knife contains various tools for different tasks, initializing the OpenFlamingo model equips you with various functionalities to generate and process text and images seamlessly.
Generating Text
Below is an example workflow for generating text based on images.
from PIL import Image
import requests
import torch
# Load images
demo_image_one = Image.open(requests.get("http://images.cocodataset.org/val2017/00000039769.jpg", stream=True).raw)
demo_image_two = Image.open(requests.get("http://images.cocodataset.org/test-stuff/000000028137.jpg", stream=True).raw)
query_image = Image.open(requests.get("http://images.cocodataset.org/test-stuff/000000028352.jpg", stream=True).raw)
# Preprocess images
vision_x = [image_processor(demo_image_one).unsqueeze(0), image_processor(demo_image_two).unsqueeze(0), image_processor(query_image).unsqueeze(0)]
vision_x = torch.cat(vision_x, dim=0).unsqueeze(1).unsqueeze(0)
# Preprocess text
tokenizer.padding_side = "left" # Ensure padding is on the left for generation
lang_x = tokenizer(["imageAn image of two cats.endofchunkimageAn image of a bathroom sink.endofchunkimageAn image of"], return_tensors='pt')
# Generate text
generated_text = model.generate(
vision_x=vision_x,
lang_x=lang_x['input_ids'],
attention_mask=lang_x['attention_mask'],
max_new_tokens=20,
num_beams=3,
)
print("Generated text:", tokenizer.decode(generated_text[0]))
This example illustrates how OpenFlamingo acts like an artist, using its canvas (the input images) to generate meaningful captions or responses!
Training
To train your own OpenFlamingo model, you can utilize the training scripts provided in the repository. A sample command is shown below:
torchrun --nnodes=1 --nproc_per_node=4 open_flamingotrain/train.py --lm_path anas-awadallampt-1b-redpajama-200b --tokenizer_path anas-awadallampt-1b-redpajama-200b --cross_attn_every_n_layers 1 --dataset_resampled --batch_size_mmc4 32 --batch_size_laion 64 --train_num_samples_mmc4 125000 --train_num_samples_laion 250000 --loss_multiplier_laion 0.2 --workers=4 --run_name OpenFlamingo-3B-vitl-mpt1b --num_epochs 480 --warmup_steps 1875
Evaluation
To evaluate your model, an example evaluation script is included in the repository. Keep an eye on the evaluation README for more details.
Future Plans
- Add support for video input
Team
OpenFlamingo is a collaborative effort from a diverse team of researchers hailing from renowned institutions. Their expertise propels this project toward exciting advancements in AI.
Acknowledgments
We appreciate the foundational work from various representatives in the AI community that made OpenFlamingo possible.
Troubleshooting
If you encounter any issues during installation or usage, try the following:
- Ensure you have
pip
andconda
updated to the latest version. - If an issue arises with package installation, check the
requirements.txt
files for the necessary dependencies. - For version conflicts, create a new virtual environment.
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