infer.py

import argparse
import cv2
import os 
import torch
from PIL import Image
import numpy as np
import axengine as axe
from datasets import VOCSegmentation, Cityscapes, cityscapes

def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--img",
        type=str,
        required=True,
        help="Path to input image.",
    )
    parser.add_argument(
        "--model",
        type=str,
        required=True,
        help="Path to axmodel model.",
    )
    
    return parser.parse_args()


def infer(img: str, model: str, viz: bool = False):
    img_raw = cv2.imread(img)
    image = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB) 
    image = cv2.resize(image, (513,513))
    image = image[None]

    session = axe.InferenceSession(model)
    
    pred = session.run(None, {"input": image})[0]
    pred = torch.from_numpy(pred)
    pred = pred.max(1)[1].cpu().numpy()[0] # HW
    
    decode_fn = VOCSegmentation.decode_target
    colorized_preds = decode_fn(pred).astype('uint8')
    colorized_preds = Image.fromarray(colorized_preds)
    colorized_preds.save("output-ax.png")




if __name__ == "__main__":
    args = parse_args()
    infer(**vars(args))