Trainer Predict execution time

[ntdgo]

Hi,

I have a model and dataloader with 1 image.

The main function is updating the image by looping then predicting the image.

import time
# load model and weight
model = ...
if __name__ == "__main__":
    while(True):
        # load image
        start_time = time.time()
        dataloader = process_image_to_dataloader
        trainer.predict(model=model, dataloaders=[dataloader], return_predictions=True)
        print("--- %s seconds ---" % (time.time() - start_time))

As my experiment, the execution time is around 12s for 1 image prediction.
Another experiment that predicts 10 images, the execution time is also around 12s.
→ That means the number of images is not counting for execution time. (dataloader is not take long time ~0s)

Is there any efficient way to predict 1 image in loop that minimizes the execution time?

Thank you!