def __create_context(self, main_root):
		context_dir = os.path.join(main_root, 'context')
		safe_mkdir(context_dir)

		action_default = {}
		for unit, param in self.__task.data['workflow'].items():
			action_default[unit] = self.__action_mgr.default_config(param['type'])

		navigators = self.__config_mgr.navigator()
		context_format = self.__navigator_mgr.context_format(navigators)

		self.__context_mgr.create_context_file(self.__task.data, action_default, context_format, context_dir)
def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format,
                         name):
  """Strided 2-D convolution with explicit padding."""
  # The padding is consistent and is based only on `kernel_size`, not on the
  # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
  if strides > 1:
    inputs = fixed_padding(inputs, kernel_size, data_format)

  return tf.compat.v1.layers.conv2d(
      inputs=inputs,
      filters=filters,
      kernel_size=kernel_size,
      strides=strides,
      padding=('SAME' if strides == 1 else 'VALID'),
      use_bias=False,
      reuse=tf.AUTO_REUSE,
      kernel_initializer=tf.compat.v1.variance_scaling_initializer(),
      data_format=data_format,
      name=name)