groundingdino/util/predict.py

from typing import Tuple, List

import torch

from groundingdino.util.utils import get_phrases_from_posmap


def preprocess_caption(caption: str) -> str:
    result = caption.lower().strip()
    if result.endswith("."):
        return result
    return result + "."

def predict(
        model,
        image: torch.Tensor,
        caption: str,
        box_threshold: float,
        text_threshold: float,
        device: str = "cuda"
) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
    caption = preprocess_caption(caption=caption)

    model = model.to(device)
    image = image.to(device)

    with torch.no_grad():
        outputs = model(image[None], captions=[caption])

    prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)
    prediction_boxes = outputs["pred_boxes"].cpu()[0]  # prediction_boxes.shape = (nq, 4)

    mask = prediction_logits.max(dim=1)[0] > box_threshold
    logits = prediction_logits[mask]  # logits.shape = (n, 256)
    boxes = prediction_boxes[mask]  # boxes.shape = (n, 4)

    tokenizer = model.tokenizer
    tokenized = tokenizer(caption)
    print(tokenized)
    phrases = [
        get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')
        for logit
        in logits
    ]

    return boxes, logits.max(dim=1)[0], phrases