Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS
Authored by: Merve Noyan
Embeddings are semantically meaningful compressions of information. They can be used to do similarity search, zero-shot classification or simply train a new model. Use cases for similarity search include searching for similar products in e-commerce, content search in social media and more. This notebook walks you through using 🤗transformers, 🤗datasets and FAISS to create and index embeddings from a feature extraction model to later use them for similarity search. Let’s install necessary libraries.
!pip install -q datasets faiss-gpu transformers sentencepiece
For this tutorial, we will use CLIP model to extract the features. CLIP is a revolutionary model that introduced joint training of a text encoder and an image encoder to connect two modalities.
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModel, AutoTokenizer
import faiss
import numpy as np
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModel.from_pretrained("openai/clip-vit-base-patch16").to(device)
processor = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch16")
tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch16")Load the dataset. To keep this notebook light, we will use a small captioning dataset, jmhessel/newyorker_caption_contest.
from datasets import load_dataset
ds = load_dataset("jmhessel/newyorker_caption_contest", "explanation")See an example.
>>> ds["train"][0]["image"]
ds["train"][0]["image_description"]We don’t have to write any function to embed examples or create an index. 🤗 datasets library’s FAISS integration abstracts these processes. We can simply use map method of the dataset to create a new column with the embeddings for each example like below. Let’s create one for text features on the prompt column.
dataset = ds["train"]
ds_with_embeddings = dataset.map(
lambda example: {
"embeddings": model.get_text_features(
**tokenizer([example["image_description"]], truncation=True, return_tensors="pt").to("cuda")
)[0]
.detach()
.cpu()
.numpy()
}
)We can do the same and get the image embeddings.
ds_with_embeddings = ds_with_embeddings.map(
lambda example: {
"image_embeddings": model.get_image_features(**processor([example["image"]], return_tensors="pt").to("cuda"))[
0
]
.detach()
.cpu()
.numpy()
}
)Now, we create an index for each column.
# create FAISS index for text embeddings
ds_with_embeddings.add_faiss_index(column="embeddings")# create FAISS index for image embeddings
ds_with_embeddings.add_faiss_index(column="image_embeddings")Querying the data with text prompts
We can now query the dataset with text or image to get similar items from it.
prmt = "a snowy day"
prmt_embedding = (
model.get_text_features(**tokenizer([prmt], return_tensors="pt", truncation=True).to("cuda"))[0]
.detach()
.cpu()
.numpy()
)
scores, retrieved_examples = ds_with_embeddings.get_nearest_examples("embeddings", prmt_embedding, k=1)>>> def downscale_images(image):
... width = 200
... ratio = width / float(image.size[0])
... height = int((float(image.size[1]) * float(ratio)))
... img = image.resize((width, height), Image.Resampling.LANCZOS)
... return img
>>> images = [downscale_images(image) for image in retrieved_examples["image"]]
>>> # see the closest text and image
>>> print(retrieved_examples["image_description"])
>>> display(images[0])['A man is in the snow. A boy with a huge snow shovel is there too. They are outside a house.']
Querying the data with image prompts
Image similarity inference is similar, where you just call get_image_features.
>>> import requests
>>> # image of a beaver
>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/beaver.png"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> display(downscale_images(image))
Search for the similar image.
img_embedding = (
model.get_image_features(**processor([image], return_tensors="pt", truncation=True).to("cuda"))[0]
.detach()
.cpu()
.numpy()
)
scores, retrieved_examples = ds_with_embeddings.get_nearest_examples("image_embeddings", img_embedding, k=1)Display the most similar image to the beaver image.
>>> images = [downscale_images(image) for image in retrieved_examples["image"]]
>>> # see the closest text and image
>>> print(retrieved_examples["image_description"])
>>> display(images[0])['Salmon swim upstream but they see a grizzly bear and are in shock. The bear has a smug look on his face when he sees the salmon.']
Saving, pushing and loading the embeddings
We can save the dataset with embeddings with save_faiss_index.
ds_with_embeddings.save_faiss_index("embeddings", "embeddings/embeddings.faiss")ds_with_embeddings.save_faiss_index("image_embeddings", "embeddings/image_embeddings.faiss")It’s a good practice to store the embeddings in a dataset repository, so we will create one and push our embeddings there to pull later.
We will login to Hugging Face Hub, create a dataset repository there and push our indexes there and load using snapshot_download.
from huggingface_hub import HfApi, notebook_login, snapshot_download
notebook_login()from huggingface_hub import HfApi
api = HfApi()
api.create_repo("merve/faiss_embeddings", repo_type="dataset")
api.upload_folder(
folder_path="./embeddings",
repo_id="merve/faiss_embeddings",
repo_type="dataset",
)snapshot_download(repo_id="merve/faiss_embeddings", repo_type="dataset", local_dir="downloaded_embeddings")We can load the embeddings to the dataset with no embeddings using load_faiss_index.
ds = ds["train"]
ds.load_faiss_index("embeddings", "./downloaded_embeddings/embeddings.faiss")
# infer again
prmt = "people under the rain"prmt_embedding = (
model.get_text_features(**tokenizer([prmt], return_tensors="pt", truncation=True).to("cuda"))[0]
.detach()
.cpu()
.numpy()
)
scores, retrieved_examples = ds.get_nearest_examples("embeddings", prmt_embedding, k=1)>>> display(retrieved_examples["image"][0])
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