def parse()

in utils_cv/tracking/references/fairmot/opts.py [0:0]

• 38 lines of code
• 16 McCabe index (conditional complexity)

  def parse(self, args=''):
    if args == '':
      opt = self.parser.parse_args()
    else:
      opt = self.parser.parse_args(args)

    opt.gpus_str = opt.gpus
    opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')]
    opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1]
    opt.lr_step = [int(i) for i in opt.lr_step.split(',')]

    opt.fix_res = not opt.keep_res
    print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.')
    opt.reg_offset = not opt.not_reg_offset

    if opt.head_conv == -1: # init default head_conv
      opt.head_conv = 256 if 'dla' in opt.arch else 256
    opt.pad = 31
    opt.num_stacks = 1

    if opt.trainval:
      opt.val_intervals = 100000000

    if opt.master_batch_size == -1:
      opt.master_batch_size = opt.batch_size // len(opt.gpus)
    rest_batch_size = (opt.batch_size - opt.master_batch_size)
    opt.chunk_sizes = [opt.master_batch_size]
    for i in range(len(opt.gpus) - 1):
      slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1)
      if i < rest_batch_size % (len(opt.gpus) - 1):
        slave_chunk_size += 1
      opt.chunk_sizes.append(slave_chunk_size)
    print('training chunk_sizes:', opt.chunk_sizes)

    opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..')
    opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task)
    opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id)
    opt.debug_dir = os.path.join(opt.save_dir, 'debug')
    print('The output will be saved to ', opt.save_dir)
    
    if opt.resume and opt.load_model == '':
      model_path = opt.save_dir[:-4] if opt.save_dir.endswith('TEST') \
                  else opt.save_dir
      opt.load_model = os.path.join(model_path, 'model_last.pth')
    return opt