content: import argparse
  import time
  
  import torch
  from mmcv import Config
  from mmcv.cnn import fuse_conv_bn
  from mmcv.parallel import MMDataParallel
  from mmcv.runner.fp16_utils import wrap_fp16_model
  
  from mmaction.datasets import build_dataloader, build_dataset
  from mmaction.models import build_model
  
  
  def parse_args():
      parser = argparse.ArgumentParser(
          description='MMAction2 benchmark a recognizer')
      parser.add_argument('config', help='test config file path')
      parser.add_argument(
          '--log-interval', default=10, help='interval of logging')
      parser.add_argument(
          '--fuse-conv-bn',
          action='store_true',
          help='Whether to fuse conv and bn, this will slightly increase'
          'the inference speed')
      args = parser.parse_args()
      return args
  
  
  def main():
      args = parse_args()
  
      cfg = Config.fromfile(args.config)
      # set cudnn_benchmark
      if cfg.get('cudnn_benchmark', False):
          torch.backends.cudnn.benchmark = True
      cfg.model.backbone.pretrained = None
      cfg.data.test.test_mode = True
  
      # build the dataloader
      dataset = build_dataset(cfg.data.test, dict(test_mode=True))
      data_loader = build_dataloader(
          dataset,
          videos_per_gpu=1,
          workers_per_gpu=cfg.data.workers_per_gpu,
          dist=False,
          shuffle=False)
  
      # build the model and load checkpoint
      model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
      fp16_cfg = cfg.get('fp16', None)
      if fp16_cfg is not None:
          wrap_fp16_model(model)
      if args.fuse_conv_bn:
          model = fuse_conv_bn(model)
  
      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
  
      # benchmark with 2000 video 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, **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 video [{i + 1:<3}/ 2000], fps: {fps:.1f} video / s')
  
          if (i + 1) == 200:
              pure_inf_time += elapsed
              fps = (i + 1 - num_warmup) / pure_inf_time
              print(f'Overall fps: {fps:.1f} video / s')
              break
  
  
  if __name__ == '__main__':
      main()
  