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LinWeizheDragon / FLMR

The huggingface implementation of Fine-grained Late-interaction Multi-modal Retriever.
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FLMR

The huggingface-transformers implementation of Fine-grained Late-interaction Multi-modal Retriever.

The official implementation is at here.

The details of the model and checkpoints can be found here.

The details for reproducing the datasets and evaluation in the paper can be found here.

Updates

Table of Contents

Models and Benchmark Results

Model WIT Recall@10 IGLUE Recall@1 KVQA Recall@5 MSMARCO Recall@5 OVEN Recall@5 LLaVA Recall@1 EVQA Recall@5 EVQA Pseudo Recall@5 OKVQA Recall@5 OKVQA Pseudo Recall@5 Infoseek Recall@5 Infoseek Pseudo Recall@5
LinWeizheDragon/PreFLMR_ViT-G🤗 0.619 0.718 0.419 0.783 0.643 0.726 0.625 0.721 0.302 0.674 0.392 0.577
LinWeizheDragon/PreFLMR_ViT-L🤗 0.605 0.699 0.440 0.779 0.608 0.729 0.609 0.708 0.314 0.690 0.374 0.578
LinWeizheDragon/PreFLMR_ViT-B🤗 0.427 0.574 0.294 0.786 0.468 0.673 0.550 0.663 0.272 0.658 0.260 0.496

Note: We converted the checkpoints from PyTorch to Huggingface-transformers, whose benchmark results differ from the numbers reported in the original paper slightly. You can reproduce the results in the above paper by referring to the instructions in this document.

How to use this package

Environment

Create virtualenv:

conda create -n FLMR python=3.10 -y
conda activate FLMR

Install Pytorch:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Install faiss

conda install -c pytorch -c nvidia faiss-gpu=1.7.4 mkl=2021 blas=1.0=mkl

Test if faiss generate error

python -c "import faiss"

Install FLMR

git clone https://github.com/LinWeizheDragon/FLMR.git
cd FLMR
pip install -e .

Install ColBERT engine

cd third_party/ColBERT
pip install -e .

Install other dependencies

pip install ujson gitpython easydict ninja datasets transformers

Index a custom document collection

  1. Load pre-trained models

    import os
import torch
import pandas as pd
import numpy as np
from torchvision.transforms import ToPILImage
from transformers import AutoImageProcessor

from flmr import index_custom_collection
from flmr import FLMRQueryEncoderTokenizer, FLMRContextEncoderTokenizer, FLMRModelForRetrieval

# load models
checkpoint_path = "LinWeizheDragon/PreFLMR_ViT-G"
image_processor_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"

query_tokenizer = FLMRQueryEncoderTokenizer.from_pretrained(checkpoint_path, subfolder="query_tokenizer")
context_tokenizer = FLMRContextEncoderTokenizer.from_pretrained(
    checkpoint_path, subfolder="context_tokenizer"
)

model = FLMRModelForRetrieval.from_pretrained(
    checkpoint_path,
    query_tokenizer=query_tokenizer,
    context_tokenizer=context_tokenizer,
)
image_processor = AutoImageProcessor.from_pretrained(image_processor_name)

  2. Create document collections

    num_items = 100
feature_dim = 1664
passage_contents = [f"This is test sentence {i}" for i in range(num_items)]
# Option 1. text-only documents
custom_collection = passage_contents
# Option 2. multi-modal documents with pre-extracted image features
# passage_image_features = np.random.rand(num_items, feature_dim)
# custom_collection = [
#     (passage_content, passage_image_feature, None) for passage_content, passage_image_feature in zip(passage_contents, passage_image_features)
# ]
# Option 3. multi-modal documents with images
# random_images = torch.randn(num_items, 3, 224, 224)
# to_img = ToPILImage()
# if not os.path.exists("./test_images"):
#     os.makedirs("./test_images")
# for i, image in enumerate(random_images):
#     image = to_img(image)
#     image.save(os.path.join("./test_images", "{}.jpg".format(i)))

# image_paths = [os.path.join("./test_images", "{}.jpg".format(i)) for i in range(num_items)]

# custom_collection = [
#     (passage_content, None, image_path)
#     for passage_content, image_path in zip(passage_contents, image_paths)
# ]

  3. Run indexing on the custom collection

    index_custom_collection(
    custom_collection=custom_collection,
    model=model,
    index_root_path=".",
    index_experiment_name="test_experiment",
    index_name="test_index",
    nbits=8, # number of bits in compression
    doc_maxlen=512, # maximum allowed document length
    overwrite=True, # whether to overwrite existing indices
    use_gpu=False, # whether to enable GPU indexing
    indexing_batch_size=64,
    model_temp_folder="tmp",
    nranks=1, # number of GPUs used in indexing
)

    Search a custom document collection

  4. Create toy query data

    num_queries = 2

query_instructions = [f"instruction {i}" for i in range(num_queries)]
query_texts = [f"{query_instructions[i]} : query {i}" for i in range(num_queries)]
query_images = torch.zeros(num_queries, 3, 224, 224)
query_encoding = query_tokenizer(query_texts)
query_pixel_values = image_processor(query_images, return_tensors="pt")['pixel_values']

  5. Obtain query embeddings with model

    inputs = dict(
    input_ids=query_encoding['input_ids'],
    attention_mask=query_encoding['attention_mask'],
    pixel_values=query_pixel_values,
)

# Run model query encoding
res = model.query(**inputs)

queries = {i: query_texts[i] for i in range(num_queries)}
query_embeddings = res.late_interaction_output

  6. Search the collection

    from flmr import search_custom_collection, create_searcher

# initiate a searcher
searcher = create_searcher(
    index_root_path=".",
    index_experiment_name="test_experiment",
    index_name="test_index",
    nbits=8, # number of bits in compression
    use_gpu=True, # whether to enable GPU searching
)
# Search the custom collection
ranking = search_custom_collection(
    searcher=searcher,
    queries=queries,
    query_embeddings=query_embeddings,
    num_document_to_retrieve=5, # how many documents to retrieve for each query
)

# Analyse retrieved documents
ranking_dict = ranking.todict()
for i in range(num_queries):
    print(f"Query {i} retrieved documents:")
    retrieved_docs = ranking_dict[i]
    retrieved_docs_indices = [doc[0] for doc in retrieved_docs]
    retrieved_doc_scores = [doc[2] for doc in retrieved_docs]
    retrieved_doc_texts = [passage_contents[doc_idx] for doc_idx in retrieved_docs_indices]

    data = {
        "Confidence": retrieved_doc_scores,
        "Content": retrieved_doc_texts,
    }

    df = pd.DataFrame.from_dict(data)

    print(df)

Training with contrastive learning

import torch
from flmr import FLMRQueryEncoderTokenizer, FLMRContextEncoderTokenizer, FLMRModelForRetrieval

checkpoint_path = "LinWeizheDragon/PreFLMR_ViT-L"
image_processor_name = "openai/clip-vit-large-patch14"
query_tokenizer = FLMRQueryEncoderTokenizer.from_pretrained(checkpoint_path, subfolder="query_tokenizer")
context_tokenizer = FLMRContextEncoderTokenizer.from_pretrained(checkpoint_path, subfolder="context_tokenizer")

model = FLMRModelForRetrieval.from_pretrained(checkpoint_path,
                                                query_tokenizer=query_tokenizer,
                                                context_tokenizer=context_tokenizer,
                                                )

Q_encoding = query_tokenizer(["Using the provided image, obtain documents that address the subsequent question: What is the capital of France?", "Extract documents linked to the question provided in conjunction with the image: What is the capital of China?"])
D_encoding = context_tokenizer(["Paris is the capital of France.", "Beijing is the capital of China.",
                            "Paris is the capital of France.", "Beijing is the capital of China."])
Q_pixel_values = torch.zeros(2, 3, 224, 224)
inputs = dict(
    query_input_ids=Q_encoding['input_ids'],
    query_attention_mask=Q_encoding['attention_mask'],
    query_pixel_values=Q_pixel_values,
    context_input_ids=D_encoding['input_ids'],
    context_attention_mask=D_encoding['attention_mask'],
    use_in_batch_negatives=True,
)

res = model.forward(**inputs)
print(res)

Note that the examples in this code block are only for demonstration purposes. They show that the pre-trained model gives higher scores to correct documents. In real training, you always need to pass in the documents in the order "positive doc for query1, negative doc1 for query1, negative doc2 for query1, ..., positive doc for query2, negative doc1 for query2, negative doc2 for query2, ...". You may want to read the later section which provides an example finetuning script.

Alternative: use transformers.AutoModel to load pre-trained models

pip install transformers
from transformers import AutoConfig, AutoModel, AutoImageProcessor, AutoTokenizer
import torch

checkpoint_path = "LinWeizheDragon/PreFLMR_ViT-L"
image_processor_name = "openai/clip-vit-large-patch14"
query_tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, subfolder="query_tokenizer", trust_remote_code=True)
context_tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, subfolder="context_tokenizer", trust_remote_code=True)

model = AutoModel.from_pretrained(checkpoint_path,
                                query_tokenizer=query_tokenizer,
                                context_tokenizer=context_tokenizer,
                                trust_remote_code=True,
                                )
image_processor = AutoImageProcessor.from_pretrained(image_processor_name)

Use example scripts

We provide two scripts to show how the pretrained models can be used in evaluation:

  1. examples/example_use_flmr.py: an example script to evaluate FLMR (with 10 ROIs) on OK-VQA.
  2. examples/example_use_preflmr.py: an example script to evaluate PreFLMR on E-VQA.

Use FLMR

cd examples/

Download KBVQA_data from here and unzip the image folders. The ROI/captioning/object detection results have been included.

Run the following command (remove --run_indexing if you have already run indexing once):

python example_use_flmr.py \
            --use_gpu --run_indexing \
            --index_root_path "." \
            --index_name OKVQA_GS\
            --experiment_name OKVQA_GS \
            --indexing_batch_size 64 \
            --image_root_dir /path/to/KBVQA_data/ok-vqa/ \
            --dataset_path BByrneLab/OKVQA_FLMR_preprocessed_data \
            --passage_dataset_path BByrneLab/OKVQA_FLMR_preprocessed_GoogleSearch_passages \
            --use_split test \
            --nbits 8 \
            --Ks 1 5 10 20 50 100 \
            --checkpoint_path LinWeizheDragon/FLMR \
            --image_processor_name openai/clip-vit-base-patch32 \
            --query_batch_size 8 \
            --num_ROIs 9 \

[NEW!] Use PreFLMR

You can download the E-VQA images from https://github.com/google-research/google-research/tree/master/encyclopedic_vqa. We will add a dataset link here soon.

cd examples/

Run the following command (remove --run_indexing if you have already run indexing once):

python example_use_preflmr.py \
            --use_gpu --run_indexing \
            --index_root_path "." \
            --index_name EVQA_PreFLMR_ViT-G \
            --experiment_name EVQA \
            --indexing_batch_size 64 \
            --image_root_dir /rds/project/rds-hirYTW1FQIw/shared_space/vqa_data/KBVQA_data/EVQA/eval_image/ \
            --dataset_hf_path BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR \
            --dataset EVQA \
            --use_split test \
            --nbits 8 \
            --Ks 1 5 10 20 50 100 500 \
            --checkpoint_path LinWeizheDragon/PreFLMR_ViT-G \
            --image_processor_name laion/CLIP-ViT-bigG-14-laion2B-39B-b160k \
            --query_batch_size 8 \
            --compute_pseudo_recall \

Here, we upload all the M2KR datasets into one HF dataset BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR with different datasets as subset. To reproduce results of the other datasets in the paper, you can change the --dataset to OKVQA, KVQA, LLaVA, OVEN, Infoseek, WIT, IGLUE and EVQA.

Updates:

[NEW!] Evaluate the PreFLMR models on all M2KR benchmarks

Change the image root paths in examples/evaluate_all.sh and execute:

cd examples
bash evaluate_all.sh

Obtain the report by:

python report.py

[NEW!] Finetune the PreFLMR model on downstream datasets

You will need to install pytorch-lightning:

pip install pytorch-lightning==2.1.0

Run finetuning

python example_finetune_preflmr.py \
    --image_root_dir /path/to/EVQA/images/ \
    --dataset_hf_path BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR \
    --dataset EVQA \
    --freeze_vit \
    --log_with_wandb \
    --model_save_path saved_models \
    --checkpoint_path LinWeizheDragon/PreFLMR_ViT-G \
    --image_processor_name laion/CLIP-ViT-bigG-14-laion2B-39B-b160k \
    --batch_size 8 \
    --accumulate_grad_batches 8 \
    --valid_batch_size 16 \
    --test_batch_size 64 \
    --mode train \
    --max_epochs 99999999 \
    --learning_rate 0.000005 \
    --warmup_steps 100 \
    --accelerator auto \
    --devices auto \
    --strategy ddp_find_unused_parameters_true \
    --num_sanity_val_steps 2 \
    --precision bf16 \
    --val_check_interval 2000 \
    --save_top_k -1 \

Run Testing

python example_use_preflmr.py \
    --use_gpu --run_indexing \
    --index_root_path "." \
    --index_name EVQA_PreFLMR_ViT-G_finetuned_model_step_10156 \
    --experiment_name EVQA \
    --indexing_batch_size 64 \
    --image_root_dir /path/to/EVQA/images/ \
    --dataset_hf_path BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR \
    --dataset EVQA \
    --use_split test \
    --nbits 8 \
    --num_gpus 1 \
    --Ks 1 5 10 20 50 100 500 \
    --checkpoint_path saved_models/model_step_10156 \
    --image_processor_name laion/CLIP-ViT-bigG-14-laion2B-39B-b160k \
    --query_batch_size 8 \

Example finetuning results

By running the above script, we are able to obtain the following finetuning performance:

Step Pseudo Recall@5 on EVQA
2500 73.6
10000 73.55
12000 74.21
14000 73.73

(Checkpoints with low validation losses were picked and tested, run on 2 A100 GPUs)

Screenshot 2024-06-05 171340

Note

The FLMR model is implemented following the documentation style of transformers. You can find detailed documentation in the modeling files.

Citation

If our work helped your research, please kindly cite our paper for FLMR and PreFLMR. 

@inproceedings{
    lin2023finegrained,
    title={Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering},
    author={Weizhe Lin and Jinghong Chen and Jingbiao Mei and Alexandru Coca and Bill Byrne},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023},
    url={https://openreview.net/forum?id=IWWWulAX7g}
        }

@article{Lin_Mei_Chen_Byrne_2024, 
        title={PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers}, 
        url={http://arxiv.org/abs/2402.08327}, 
        number={arXiv:2402.08327}, 
        publisher={arXiv}, 
        author={Lin, Weizhe and Mei, Jingbiao and Chen, Jinghong and Byrne, Bill}, 
        year={2024}}