# Copyright (c) OpenMMLab. All rights reserved.

import argparse

from mmcv import Config

from mmcv.cnn import get_model_complexity_info

from mmseg.models import build_segmentor

def parse_args():

    parser = argparse.ArgumentParser(description='Train a segmentor')

    parser.add_argument('config', help='train config file path')

    parser.add_argument(

        '--shape',

        type=int,

        nargs='+',

        default=[2048, 1024],

        help='input image size')

    args = parser.parse_args()

    return args

def main():

    args = parse_args()

    if len(args.shape) == 1:

        input_shape = (3, args.shape[0], args.shape[0])

    elif len(args.shape) == 2:

        input_shape = (3, ) + tuple(args.shape)

    else:

        raise ValueError('invalid input shape')

    cfg = Config.fromfile(args.config)

    cfg.model.pretrained = None

    model = build_segmentor(

        cfg.model,

        train_cfg=cfg.get('train_cfg'),

        test_cfg=cfg.get('test_cfg')).cuda()

    model.eval()

    if hasattr(model, 'forward_dummy'):

        model.forward = model.forward_dummy

    else:

        raise NotImplementedError(

            'FLOPs counter is currently not currently supported with {}'.

            format(model.__class__.__name__))

    flops, params = get_model_complexity_info(model, input_shape)

    split_line = '=' * 30

    print('{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}'.format(

        split_line, input_shape, flops, params))

    print('!!!Please be cautious if you use the results in papers. '

          'You may need to check if all ops are supported and verify that the '

          'flops computation is correct.')

if __name__ == '__main__':

    main()