get_flops.py

import argparse
import torch
from mmcv import Config
from mmcv.cnn import get_model_complexity_info
from mmseg.models import build_segmentor
from mmseg.datasets import build_dataset
from fvcore.nn import FlopCountAnalysis
import models


def calc_flops(model, img_size=224):
    with torch.no_grad():
        x = torch.randn(1, 3, img_size, img_size).cuda()
        fca1 = FlopCountAnalysis(model, x)
        print('backbone:', fca1.total(module_name="backbone") / 1e9)
        try:
            print('text_encoder:',
                  fca1.total(module_name="text_encoder") / 1e9)
            print('context_decoder:',
                  fca1.total(module_name="context_decoder") / 1e9)
        except:
            pass

        try:
            print('neck:', fca1.total(module_name="neck") / 1e9)
        except:
            pass
        print('decode_head:', fca1.total(module_name="decode_head") / 1e9)
        flops1 = fca1.total()
        print("#### GFLOPs: {:.1f}".format(flops1 / 1e9))
    return flops1 / 1e9


def parse_args():
    parser = argparse.ArgumentParser(description='Train a segmentor')
    parser.add_argument('config', help='train config file path')
    parser.add_argument('--fvcore', action='store_true', default=False)
    parser.add_argument('--shape',
                        type=int,
                        nargs='+',
                        default=[1024, 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

    datasets = [build_dataset(cfg.data.train)]

    if 'CLIP' in cfg.model.type:
        cfg.model.class_names = list(datasets[0].CLASSES)

    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__))

    if args.fvcore:
        flops = calc_flops(model, input_shape[1])

        n_parameters = sum(
            p.numel() for p in model.parameters() if p.requires_grad) / 1e6
        print('number of params:', f'{n_parameters:.1f}')
        if hasattr(model, 'text_encoder'):
            n_parameters_text = sum(p.numel()
                                    for p in model.text_encoder.parameters()
                                    if p.requires_grad) / 1e6
            print('param without text encoder:',
                  n_parameters - n_parameters_text)
        if hasattr(model, 'context_decoder'):
            n_parameters_text = sum(
                p.numel() for p in model.context_decoder.parameters()
                if p.requires_grad) / 1e6
            print('param context:', n_parameters_text)
    else:
        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()