Convert a model's graph def into a tflite model. NOTE: this currently shells out to the toco binary, but we would like convert to Python API tooling in the future. Args: options: An Options instance. graph_def: A GraphDef object. input_tensors: List of input tensor tuples `(name,
(options, graph_def, input_tensors, output_tensors, **kwargs)
| 75 | |
| 76 | |
| 77 | def toco_convert(options, graph_def, input_tensors, output_tensors, **kwargs): |
| 78 | """Convert a model's graph def into a tflite model. |
| 79 | |
| 80 | NOTE: this currently shells out to the toco binary, but we would like |
| 81 | convert to Python API tooling in the future. |
| 82 | |
| 83 | Args: |
| 84 | options: An Options instance. |
| 85 | graph_def: A GraphDef object. |
| 86 | input_tensors: List of input tensor tuples `(name, shape, type)`. |
| 87 | output_tensors: List of output tensors (names). |
| 88 | **kwargs: Extra options to be passed. |
| 89 | |
| 90 | Returns: |
| 91 | output tflite model, log_txt from conversion |
| 92 | or None, log_txt if it did not convert properly. |
| 93 | """ |
| 94 | # Convert ophint ops if presented. |
| 95 | graph_def = tf.lite.experimental.convert_op_hints_to_stubs( |
| 96 | graph_def=graph_def) |
| 97 | graph_def_str = graph_def.SerializeToString() |
| 98 | |
| 99 | extra_toco_options = kwargs.get( |
| 100 | "extra_toco_options", generate_examples_lib.ExtraTocoOptions()) |
| 101 | test_params = kwargs.get("test_params", {}) |
| 102 | input_arrays = [x[0] for x in input_tensors] |
| 103 | data_types = [ |
| 104 | generate_examples_lib.TF_TYPE_INFO[x[2]][1] for x in input_tensors] |
| 105 | |
| 106 | if test_params.get("fully_quantize", False): |
| 107 | with tempfile.NamedTemporaryFile() as graphdef_file: |
| 108 | graphdef_file.write(graph_def_str) |
| 109 | graphdef_file.flush() |
| 110 | |
| 111 | input_shapes = generate_examples_lib.get_input_shapes_map(input_tensors) |
| 112 | converter = tf.lite.TocoConverter.from_frozen_graph( |
| 113 | graphdef_file.name, input_arrays, output_tensors, input_shapes) |
| 114 | |
| 115 | def representative_dataset(input_tensors): |
| 116 | calibration_inputs = [] |
| 117 | for _, shape, _ in input_tensors: |
| 118 | if shape: |
| 119 | dims = [dim.value for dim in shape.dims] |
| 120 | calibration_inputs.append( |
| 121 | np.random.uniform(-1, 1, tuple(dims)).astype(np.float32)) |
| 122 | return calibration_inputs |
| 123 | |
| 124 | def representative_dataset_gen(): |
| 125 | for _ in range(100): |
| 126 | yield representative_dataset(input_tensors) |
| 127 | |
| 128 | converter.target_spec.supported_ops = [ |
| 129 | tf.lite.OpsSet.TFLITE_BUILTINS_INT8 |
| 130 | ] |
| 131 | converter.representative_dataset = representative_dataset_gen |
| 132 | if extra_toco_options.inference_input_type: |
| 133 | converter.inference_input_type = ( |
| 134 | extra_toco_options.inference_input_type) |
nothing calls this directly
no test coverage detected