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- # 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()
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