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import sys
import os
import torch
import typer
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoProcessor
from PIL import Image
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
from colpali_engine.models.paligemma_colbert_architecture import ColPali
from colpali_engine.trainer.retrieval_evaluator import CustomEvaluator
from colpali_engine.utils.colpali_processing_utils import process_images, process_queries
from colpali_engine.utils.image_from_page_utils import load_from_dataset
def main() -> None:
"""Example script to run inference with ColPali"""
# Load model
model_name = "vidore/colpali"
model = ColPali.from_pretrained("google/paligemma-3b-mix-448", torch_dtype=torch.bfloat16, device_map="cpu").eval()
model.load_adapter(model_name)
processor = AutoProcessor.from_pretrained(model_name)
# select images -> load_from_pdf(<pdf_path>), load_from_image_urls(["<url_1>"]), load_from_dataset(<path>)
images = load_from_dataset("vidore/docvqa_test_subsampled")
queries = ["From which university does James V. Fiorca come ?", "Who is the japanese prime minister?"]
# run inference - docs
dataloader = DataLoader(
images,
batch_size=4,
shuffle=False,
collate_fn=lambda x: process_images(processor, x),
)
ds = []
for batch_doc in tqdm(dataloader):
with torch.no_grad():
batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}
embeddings_doc = model(**batch_doc)
ds.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
# run inference - queries
dataloader = DataLoader(
queries,
batch_size=4,
shuffle=False,
collate_fn=lambda x: process_queries(processor, x, Image.new("RGB", (448, 448), (255, 255, 255))),
)
qs = []
for batch_query in dataloader:
with torch.no_grad():
batch_query = {k: v.to(model.device) for k, v in batch_query.items()}
embeddings_query = model(**batch_query)
qs.extend(list(torch.unbind(embeddings_query.to("cpu"))))
# run evaluation
retriever_evaluator = CustomEvaluator(is_multi_vector=True)
scores = retriever_evaluator.evaluate(qs, ds)
print(scores.argmax(axis=1))
if __name__ == "__main__":
typer.run(main)
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