tf.contrib.lite.toco_convert

tf.contrib.lite.toco_convert(
    input_data,
    input_tensors,
    output_tensors,
    inference_type=FLOAT,
    input_format=TENSORFLOW_GRAPHDEF,
    output_format=TFLITE,
    quantized_input_stats=None,
    drop_control_dependency=True
)

Defined in tensorflow/contrib/lite/python/lite.py.

Convert a model using TOCO from input_format to output_format.

Typically this is to convert from TensorFlow GraphDef to TFLite, in which case the default input_format and output_format are sufficient.

Args:

• input_data: Input data (i.e. often sess.graph_def).
• input_tensors: List of input tensors. Type and shape are computed using foo.get_shape() and foo.dtype.
• output_tensors: List of output tensors (only .name is used from this).
• inference_type: Currently must be {FLOAT, QUANTIZED_UINT8}.
• input_format: Type of data to read (currently must be TENSORFLOW_GRAPHDEF).
• output_format: Type of data to write (currently must be TFLITE or GRAPHVIZ_DOT)
• quantized_input_stats: For each member of input_tensors the mean and std deviation of training data. Only needed if inference_type is QUANTIZED_UINT8.
• drop_control_dependency: Drops control dependencies silently. This is due to tf lite not supporting control dependencies.

Returns:

The converted data. For example if tflite was the destination, then this will be a tflite flatbuffer in a bytes array.

Raises:

• ValueError: If the input tensor type is unknown
• RuntimeError: If TOCO fails to convert (in which case the runtime error's error text will contain the TOCO error log)