predict.py

import argparse

import cv2
import paddle

from GeoTr import GeoTr
from utils import to_image, to_tensor


def run(args):
    image_path = args.image
    model_path = args.model
    output_path = args.output

    checkpoint = paddle.load(model_path)
    state_dict = checkpoint["model"]
    model = GeoTr()
    model.set_state_dict(state_dict)
    model.eval()

    img_org = cv2.imread(image_path)
    img = cv2.resize(img_org, (288, 288))
    x = to_tensor(img)
    y = to_tensor(img_org)
    bm = model(x)
    bm = paddle.nn.functional.interpolate(
        bm, y.shape[2:], mode="bilinear", align_corners=False
    )
    bm_nhwc = bm.transpose([0, 2, 3, 1])
    out = paddle.nn.functional.grid_sample(y, (bm_nhwc / 288 - 0.5) * 2)
    out_image = to_image(out)
    cv2.imwrite(output_path, out_image)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="predict")

    parser.add_argument(
        "--image",
        "-i",
        nargs="?",
        type=str,
        default="",
        help="The path of image",
    )

    parser.add_argument(
        "--model",
        "-m",
        nargs="?",
        type=str,
        default="",
        help="The path of model",
    )

    parser.add_argument(
        "--output",
        "-o",
        nargs="?",
        type=str,
        default="",
        help="The path of output",
    )

    args = parser.parse_args()

    print(args)

    run(args)