text: # Copyright (c) OpenMMLab. All rights reserved.
  import argparse
  import time
  import torch
  from mmcv import Config
  from mmcv.parallel import MMDataParallel
  from mmcv.runner import load_checkpoint, wrap_fp16_model
  from mmcv.runner import get_dist_info, init_dist
  
  from mmdet3d.datasets import build_dataloader, build_dataset
  from mmdet3d.models import build_detector
  from tools.misc.fuse_conv_bn import fuse_module
  
  
  def parse_args():
      parser = argparse.ArgumentParser(description='MMDet benchmark a model')
      parser.add_argument('config', help='test config file path')
      parser.add_argument('checkpoint', help='checkpoint file')
      parser.add_argument('--samples', default=2000, help='samples to benchmark')
      parser.add_argument(
          '--log-interval', default=50, 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')
      parser.add_argument("--local_rank", type=int, default=0)
      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.pretrained = None
      cfg.data.test.test_mode = True
  
      init_dist('pytorch', **cfg.dist_params)
      # re-set gpu_ids with distributed training mode
      _, world_size = get_dist_info()
      cfg.gpu_ids = range(world_size)
  
      # 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_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
      fp16_cfg = cfg.get('fp16', None)
      print(fp16_cfg)
      if fp16_cfg is not None:
          wrap_fp16_model(model)
      load_checkpoint(model, args.checkpoint, map_location='cpu')
      if args.fuse_conv_bn:
          model = fuse_module(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 several samples 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}/ {args.samples}], '
                        f'fps: {fps:.1f} img / s')
  
          if (i + 1) == args.samples:
              pure_inf_time += elapsed
              fps = (i + 1 - num_warmup) / pure_inf_time
              print(f'Overall fps: {fps:.1f} img / s')
              break
  
  
  if __name__ == '__main__':
      main()
  