import argparse
import torch
import torch.onnx
from basic.archs.rrdbnet_arch import RRDBNet


def main(args):
    # An instance of the model
    model = RRDBNet(
        num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4
    )
    if args.params:
        keyname = "params"
    else:
        keyname = "params_ema"
    model.load_state_dict(torch.load(args.input)[keyname])
    # set the train mode to false since we will only run the forward pass.
    model.train(False)
    model.cpu().eval()

    # An example input
    x = torch.rand(1, 3, 64, 64)
    # Export the model
    with torch.no_grad():
        torch_out = torch.onnx._export(
            model, x, args.output, opset_version=11, export_params=True
        )
    print(torch_out.shape)


if __name__ == "__main__":
    """Convert pytorch model to onnx models"""
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--input",
        type=str,
        default="experiments/pretrained_models/RealESRGAN_x4plus.pth",
        help="Input model path",
    )
    parser.add_argument(
        "--output", type=str, default="realesrgan-x4.onnx", help="Output onnx path"
    )
    parser.add_argument(
        "--params", action="store_false", help="Use params instead of params_ema"
    )
    args = parser.parse_args()

    main(args)
