downstream/mmsegmentation/tools/benchmark.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import time

import mmcv
import numpy as np
import torch
from mmcv import Config
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model

from mmseg.datasets import build_dataloader, build_dataset
from mmseg.models import build_segmentor


def parse_args():
    parser = argparse.ArgumentParser(description='MMSeg benchmark a model')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', help='checkpoint file')
    parser.add_argument(
        '--log-interval', type=int, default=50, help='interval of logging')
    parser.add_argument(
        '--work-dir',
        help=('if specified, the results will be dumped '
              'into the directory as json'))
    parser.add_argument('--repeat-times', type=int, default=1)
    args = parser.parse_args()
    return args


def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
    if args.work_dir is not None:
        mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
        json_file = osp.join(args.work_dir, f'fps_{timestamp}.json')
    else:
        # use config filename as default work_dir if cfg.work_dir is None
        work_dir = osp.join('./work_dirs',
                            osp.splitext(osp.basename(args.config))[0])
        mmcv.mkdir_or_exist(osp.abspath(work_dir))
        json_file = osp.join(work_dir, f'fps_{timestamp}.json')

    repeat_times = args.repeat_times
    # set cudnn_benchmark
    torch.backends.cudnn.benchmark = False
    cfg.model.pretrained = None
    cfg.data.test.test_mode = True

    benchmark_dict = dict(config=args.config, unit='img / s')
    overall_fps_list = []
    for time_index in range(repeat_times):
        print(f'Run {time_index + 1}:')
        # build the dataloader
        # TODO: support multiple images per gpu (only minor changes are needed)
        dataset = build_dataset(cfg.data.test)
        data_loader = build_dataloader(
            dataset,
            samples_per_gpu=1,
            workers_per_gpu=cfg.data.workers_per_gpu,
            dist=False,
            shuffle=False)

        # build the model and load checkpoint
        cfg.model.train_cfg = None
        model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
        fp16_cfg = cfg.get('fp16', None)
        if fp16_cfg is not None:
            wrap_fp16_model(model)
        if 'checkpoint' in args and osp.exists(args.checkpoint):
            load_checkpoint(model, args.checkpoint, map_location='cpu')

        model = MMDataParallel(model, device_ids=[0])

        model.eval()

        # the first several iterations may be very slow so skip them
        num_warmup = 5
        pure_inf_time = 0
        total_iters = 200

        # benchmark with 200 image and take the average
        for i, data in enumerate(data_loader):

            torch.cuda.synchronize()
            start_time = time.perf_counter()

            with torch.no_grad():
                model(return_loss=False, rescale=True, **data)

            torch.cuda.synchronize()
            elapsed = time.perf_counter() - start_time

            if i >= num_warmup:
                pure_inf_time += elapsed
                if (i + 1) % args.log_interval == 0:
                    fps = (i + 1 - num_warmup) / pure_inf_time
                    print(f'Done image [{i + 1:<3}/ {total_iters}], '
                          f'fps: {fps:.2f} img / s')

            if (i + 1) == total_iters:
                fps = (i + 1 - num_warmup) / pure_inf_time
                print(f'Overall fps: {fps:.2f} img / s\n')
                benchmark_dict[f'overall_fps_{time_index + 1}'] = round(fps, 2)
                overall_fps_list.append(fps)
                break
    benchmark_dict['average_fps'] = round(np.mean(overall_fps_list), 2)
    benchmark_dict['fps_variance'] = round(np.var(overall_fps_list), 4)
    print(f'Average fps of {repeat_times} evaluations: '
          f'{benchmark_dict["average_fps"]}')
    print(f'The variance of {repeat_times} evaluations: '
          f'{benchmark_dict["fps_variance"]}')
    mmcv.dump(benchmark_dict, json_file, indent=4)


if __name__ == '__main__':
    main()