code: from utils.misc import read_hdf5
  from utils.misc import extract_deps_from_weights_file
  import sys
  import numpy as np
  
  wf = sys.argv[1]
  deps = extract_deps_from_weights_file(wf)
  di = read_hdf5(wf)
  num_kernel_params = 0
  
  conv_kernel_cnt = 0
  matrix_param_cnt = 0
  vec_param_cnt = 0
  
  bias_cnt = 0
  beta_cnt = 0
  gamma_cnt = 0
  mu_cnt = 0
  var_cnt = 0
  
  for name, array in di.items():
      if array.ndim in [2, 4]:
          num_kernel_params += array.size
  
      if 'base_mask' in name:
          print(name, array)
  
      print(name, array.shape, np.mean(array), np.std(array),
            ' positive {}, negative {}, zeros {}, near-zero {}'.format(np.sum(array > 0), np.sum(array < 0), np.sum(array == 0),
                                                                       np.sum(np.abs(array) <= 1e-5)))
  
      if array.ndim == 2:
          matrix_param_cnt += array.size
      elif array.ndim == 1:
          vec_param_cnt += array.size
      elif array.ndim == 4:
          conv_kernel_cnt += array.size
      if 'running_mean' in name or 'moving_mean' in name:
          mu_cnt += array.size
      elif 'running_var' in name or 'moving_var' in name:
          var_cnt += array.size
      elif ('weight' in name and 'bn' in name.lower()) or 'gamma' in name:
          gamma_cnt += array.size
      elif ('bias' in name and 'bn' in name.lower()) or 'beta' in name:
          beta_cnt += array.size
      elif 'bias' in name:
          bias_cnt += array.size
      elif 'spatial_mask' in name:
          print(array)
          print(np.sum(array))
  
  print('number of kernel params: ', num_kernel_params)
  print('vec {}, matrix {}, conv {}, total {}'.format(vec_param_cnt, matrix_param_cnt, conv_kernel_cnt,
                                                      vec_param_cnt + matrix_param_cnt + conv_kernel_cnt))
  print('mu {}, var {}, gamma {}, beta {}, bias {}'.format(mu_cnt, var_cnt, gamma_cnt, beta_cnt, bias_cnt))
  
  print('Model deps: {}'.format(deps))
  
apis: numpy.std numpy.sum numpy.abs numpy.mean