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louisoutin avatar louisoutin commented on August 23, 2024

I do it like this:
from images files, I read the bytes and add each bytes array to a dict containing the batches for the network to predict, ex:

def get_predictions(raw_frames_path: List[Path],
                    batch_size: int
                    ):
    batch = {}
    cpt = 1
    predictions = []
    images_paths = [p.as_posix() for p in raw_frames_path]
    logging.warning(f"images_paths {str(images_paths)}")
    for frame in sorted(images_paths):
        key = "img" + str(cpt)
        cpt += 1
        batch[key] = open(frame, 'rb')
        if len(batch.keys()) >= batch_size:
            # call torchserve predictions
            predictions += _get_prediction(batch)
            batch = {}  # reset batch
            cpt = 1  # reset image idx
    if len(batch.keys()) > 0:
        predictions += _get_prediction(batch)
    return predictions

from yolov5_torchserve.

kellansteele avatar kellansteele commented on August 23, 2024

What do you mean by List[Path] ?

from yolov5_torchserve.

louisoutin avatar louisoutin commented on August 23, 2024

My function accepts a list of pathlib.Path objects. But you can do the same functions with strings instead, it will work fine

from yolov5_torchserve.

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