text: #! /usr/bin/env python
  """
  Reads Darknet config and weights and creates Keras model with TF backend.
  
  """
  
  import argparse
  import configparser
  import io
  import os
  from collections import defaultdict
  
  import numpy as np
  import tensorflow as tf
  from tensorflow.keras import backend as K
  from tensorflow.keras.layers import (Conv2D, Input, ZeroPadding2D, Add,
                            UpSampling2D, MaxPooling2D, Concatenate)
  from tensorflow.keras.layers import LeakyReLU
  #from tensorflow.keras.layers.normalization import BatchNormalization
  from tensorflow.keras.models import Model
  from tensorflow.keras.regularizers import l2
  from tensorflow.keras.utils import plot_model as plot
  
  
  parser = argparse.ArgumentParser(description='Darknet To Keras Converter.')
  parser.add_argument('config_path', help='Path to Darknet cfg file.')
  parser.add_argument('weights_path', help='Path to Darknet weights file.')
  parser.add_argument('output_path', help='Path to output Keras model file.')
  parser.add_argument(
      '-p',
      '--plot_model',
      help='Plot generated Keras model and save as image.',
      action='store_true')
  parser.add_argument(
      '-w',
      '--weights_only',
      help='Save as Keras weights file instead of model file.',
      action='store_true')
  
  def unique_config_sections(config_file):
      """Convert all config sections to have unique names.
  
      Adds unique suffixes to config sections for compability with configparser.
      """
      section_counters = defaultdict(int)
      output_stream = io.StringIO()
      with open(config_file) as fin:
          for line in fin:
              if line.startswith('['):
                  section = line.strip().strip('[]')
                  _section = section + '_' + str(section_counters[section])
                  section_counters[section] += 1
                  line = line.replace(section, _section)
              output_stream.write(line)
      output_stream.seek(0)
      return output_stream
  
  # %%
  def _main(args):
      config_path = os.path.expanduser(args.config_path)
      weights_path = os.path.expanduser(args.weights_path)
      assert config_path.endswith('.cfg'), '{} is not a .cfg file'.format(
          config_path)
      assert weights_path.endswith(
          '.weights'), '{} is not a .weights file'.format(weights_path)
  
      output_path = os.path.expanduser(args.output_path)
      assert output_path.endswith(
          '.h5'), 'output path {} is not a .h5 file'.format(output_path)
      output_root = os.path.splitext(output_path)[0]
  
      # Load weights and config.
      print('Loading weights.')
      weights_file = open(weights_path, 'rb')
      major, minor, revision = np.ndarray(
          shape=(3, ), dtype='int32', buffer=weights_file.read(12))
      if (major*10+minor)>=2 and major<1000 and minor<1000:
          seen = np.ndarray(shape=(1,), dtype='int64', buffer=weights_file.read(8))
      else:
          seen = np.ndarray(shape=(1,), dtype='int32', buffer=weights_file.read(4))
      print('Weights Header: ', major, minor, revision, seen)
  
      print('Parsing Darknet config.')
      unique_config_file = unique_config_sections(config_path)
      cfg_parser = configparser.ConfigParser()
      cfg_parser.read_file(unique_config_file)
  
      print('Creating Keras model.')
      input_layer = Input(shape=(None, None, 3))
      prev_layer = input_layer
      all_layers = []
  
      weight_decay = float(cfg_parser['net_0']['decay']
                           ) if 'net_0' in cfg_parser.sections() else 5e-4
      count = 0
      out_index = []
      for section in cfg_parser.sections():
          print('Parsing section {}'.format(section))
          if section.startswith('convolutional'):
              filters = int(cfg_parser[section]['filters'])
              size = int(cfg_parser[section]['size'])
              stride = int(cfg_parser[section]['stride'])
              pad = int(cfg_parser[section]['pad'])
              activation = cfg_parser[section]['activation']
              batch_normalize = 'batch_normalize' in cfg_parser[section]
  
              padding = 'same' if pad == 1 and stride == 1 else 'valid'
  
              # Setting weights.
              # Darknet serializes convolutional weights as:
              # [bias/beta, [gamma, mean, variance], conv_weights]
              prev_layer_shape = K.int_shape(prev_layer)
  
              weights_shape = (size, size, prev_layer_shape[-1], filters)
              darknet_w_shape = (filters, weights_shape[2], size, size)
              weights_size = np.product(weights_shape)
  
              print('conv2d', 'bn'
                    if batch_normalize else '  ', activation, weights_shape)
  
              conv_bias = np.ndarray(
                  shape=(filters, ),
                  dtype='float32',
                  buffer=weights_file.read(filters * 4))
              count += filters
  
              if batch_normalize:
                  bn_weights = np.ndarray(
                      shape=(3, filters),
                      dtype='float32',
                      buffer=weights_file.read(filters * 12))
                  count += 3 * filters
  
                  bn_weight_list = [
                      bn_weights[0],  # scale gamma
                      conv_bias,  # shift beta
                      bn_weights[1],  # running mean
                      bn_weights[2]  # running var
                  ]
  
              conv_weights = np.ndarray(
                  shape=darknet_w_shape,
                  dtype='float32',
                  buffer=weights_file.read(weights_size * 4))
              count += weights_size
  
              # DarkNet conv_weights are serialized Caffe-style:
              # (out_dim, in_dim, height, width)
              # We would like to set these to Tensorflow order:
              # (height, width, in_dim, out_dim)
              conv_weights = np.transpose(conv_weights, [2, 3, 1, 0])
              conv_weights = [conv_weights] if batch_normalize else [
                  conv_weights, conv_bias
              ]
  
              # Handle activation.
              act_fn = None
              if activation == 'leaky':
                  pass  # Add advanced activation later.
              elif activation != 'linear':
                  raise ValueError(
                      'Unknown activation function `{}` in section {}'.format(
                          activation, section))
  
              # Create Conv2D layer
              if stride>1:
                  # Darknet uses left and top padding instead of 'same' mode
                  prev_layer = ZeroPadding2D(((1,0),(1,0)))(prev_layer)
              conv_layer = (Conv2D(
                  filters, (size, size),
                  strides=(stride, stride),
                  kernel_regularizer=l2(weight_decay),
                  use_bias=not batch_normalize,
                  weights=conv_weights,
                  activation=act_fn,
                  padding=padding))(prev_layer)
  
              if batch_normalize:
                  conv_layer = (tf.keras.layers.BatchNormalization(
                      weights=bn_weight_list))(conv_layer)
              prev_layer = conv_layer
  
              if activation == 'linear':
                  all_layers.append(prev_layer)
              elif activation == 'leaky':
                  act_layer = LeakyReLU(alpha=0.1)(prev_layer)
                  prev_layer = act_layer
                  all_layers.append(act_layer)
  
          elif section.startswith('route'):
              ids = [int(i) for i in cfg_parser[section]['layers'].split(',')]
              layers = [all_layers[i] for i in ids]
              if len(layers) > 1:
                  print('Concatenating route layers:', layers)
                  concatenate_layer = Concatenate()(layers)
                  all_layers.append(concatenate_layer)
                  prev_layer = concatenate_layer
              else:
                  skip_layer = layers[0]  # only one layer to route
                  all_layers.append(skip_layer)
                  prev_layer = skip_layer
  
          elif section.startswith('maxpool'):
              size = int(cfg_parser[section]['size'])
              stride = int(cfg_parser[section]['stride'])
              all_layers.append(
                  MaxPooling2D(
                      pool_size=(size, size),
                      strides=(stride, stride),
                      padding='same')(prev_layer))
              prev_layer = all_layers[-1]
  
          elif section.startswith('shortcut'):
              index = int(cfg_parser[section]['from'])
              activation = cfg_parser[section]['activation']
              assert activation == 'linear', 'Only linear activation supported.'
              all_layers.append(Add()([all_layers[index], prev_layer]))
              prev_layer = all_layers[-1]
  
          elif section.startswith('upsample'):
              stride = int(cfg_parser[section]['stride'])
              assert stride == 2, 'Only stride=2 supported.'
              all_layers.append(UpSampling2D(stride)(prev_layer))
              prev_layer = all_layers[-1]
  
          elif section.startswith('yolo'):
              out_index.append(len(all_layers)-1)
              all_layers.append(None)
              prev_layer = all_layers[-1]
  
          elif section.startswith('net'):
              pass
  
          else:
              raise ValueError(
                  'Unsupported section header type: {}'.format(section))
  
      # Create and save model.
      if len(out_index)==0: out_index.append(len(all_layers)-1)
      model = Model(inputs=input_layer, outputs=[all_layers[i] for i in out_index])
      print(model.summary())
      if args.weights_only:
          model.save_weights('{}'.format(output_path))
          print('Saved Keras weights to {}'.format(output_path))
      else:
          model.save('{}'.format(output_path))
          print('Saved Keras model to {}'.format(output_path))
  
      # Check to see if all weights have been read.
      remaining_weights = len(weights_file.read()) / 4
      weights_file.close()
      print('Read {} of {} from Darknet weights.'.format(count, count +
                                                         remaining_weights))
      if remaining_weights > 0:
          print('Warning: {} unused weights'.format(remaining_weights))
  
      if args.plot_model:
          plot(model, to_file='{}.png'.format(output_root), show_shapes=True)
          print('Saved model plot to {}.png'.format(output_root))
  
  
  if __name__ == '__main__':
      _main(parser.parse_args())
  