nnn-retrieval 0.1

Simple and efficient training-free methods for correcting errors in contrastive image-text retrieval!

Nearest Neighbor Normalization (EMNLP 2024)

Nearest Neighbor Normalization (NNN) is a simple and efficient training-free method for correcting errors in contrastive embedding-based retrieval!

Installation

You can install NNN directly with pip using

pip install -e .

For Faiss support (which significantly speeds up retrieval and retrieval dataset normalization calculations), follow the installation instructions here. NNN is compatible with both the CPU and GPU versions of Faiss.

For development, you can clone this repo locally, then install the package using:

pip install -e .[dev]

Example usage

Here's a demonstration of how to rerank CLIP embeddings using NNN. This is basic usage; for deployment, consider using a Faiss-based retriever for better performance (e.g. FaissGPURetriever).

To run this example, you'll need to install transformers, pillow, and requests.

import numpy as np
from nnn import NNNRetriever, NNNRanker
from transformers import CLIPProcessor, CLIPModel
import torch
from PIL import Image
import requests
from io import BytesIO

device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the CLIP model
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# Example images as PyTorch tensors (replace with your images)
image_urls = [
    "https://upload.wikimedia.org/wikipedia/commons/a/a8/Tour_Eiffel_Wikimedia_Commons.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/0/00/St_Louis_night_expblend_cropped.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/d/da/The_Parthenon_in_Athens.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/4/48/Alabamahills.jpg"
]

images = [Image.open(BytesIO(requests.get(url, headers={'User-Agent': 'curl/7.64.1'}).content)) for url in image_urls]
image_inputs = processor(images=images, return_tensors="pt").to(device)

# Embed the images using CLIP
with torch.no_grad():
    image_embeddings = model.get_image_features(**image_inputs).cpu().numpy()  # move back to CPU for NNN

# Embed the caption text (used as an input for retrieval)
caption = "A description of the images you want to match."
text_inputs = processor(text=[caption], return_tensors="pt").to(device)
with torch.no_grad():
    text_embedding = model.get_text_features(**text_inputs).cpu().numpy()

# Create reference embeddings from in-distribution captions
reference_captions = [f"Reference caption {i}" for i in range(1, 11)]
reference_inputs = processor(text=reference_captions, return_tensors="pt", padding=True, truncation=True).to(device)
with torch.no_grad():
    reference_embeddings = model.get_text_features(**reference_inputs).cpu().numpy()

# Perform ranking using NNN
if device == "cuda":
    nnn_retriever = NNNRetriever(image_embeddings.shape[1], use_gpu=True, gpu_id=0)
    nnn_ranker = NNNRanker(nnn_retriever, image_embeddings, reference_embeddings, alternate_ks=8, batch_size=8, use_gpu=True, gpu_id=0)
else:
    nnn_retriever = NNNRetriever(image_embeddings.shape[1])
    nnn_ranker = NNNRanker(nnn_retriever, image_embeddings, reference_embeddings, alternate_ks=8, batch_size=8)

_, indices = nnn_ranker.search(text_embedding, 5)
print("Ranked image indices:", indices)
# Ranked image indices: [[0 4 2 3 1]]