Comments (3)
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.
What do you mean by List[Path]
?
from yolov5_torchserve.
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.
Related Issues (14)
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from yolov5_torchserve.