Constructor for TFLiteConverter. Args: graph_def: Frozen TensorFlow GraphDef. input_tensors: List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. output_tensors: List of output tensors (only .name is used from this). input_arrays_
(self,
graph_def,
input_tensors,
output_tensors,
input_arrays_with_shape=None,
output_arrays=None,
experimental_debug_info_func=None)
| 558 | """ |
| 559 | |
| 560 | def __init__(self, |
| 561 | graph_def, |
| 562 | input_tensors, |
| 563 | output_tensors, |
| 564 | input_arrays_with_shape=None, |
| 565 | output_arrays=None, |
| 566 | experimental_debug_info_func=None): |
| 567 | """Constructor for TFLiteConverter. |
| 568 | |
| 569 | Args: |
| 570 | graph_def: Frozen TensorFlow GraphDef. |
| 571 | input_tensors: List of input tensors. Type and shape are computed using |
| 572 | `foo.shape` and `foo.dtype`. |
| 573 | output_tensors: List of output tensors (only .name is used from this). |
| 574 | input_arrays_with_shape: Tuple of strings representing input tensor names |
| 575 | and list of integers representing input shapes |
| 576 | (e.g., [("foo" : [1, 16, 16, 3])]). Use only when graph cannot be loaded |
| 577 | into TensorFlow and when `input_tensors` and `output_tensors` are |
| 578 | None. (default None) |
| 579 | output_arrays: List of output tensors to freeze graph with. Use only when |
| 580 | graph cannot be loaded into TensorFlow and when `input_tensors` and |
| 581 | `output_tensors` are None. (default None) |
| 582 | experimental_debug_info_func: An experimental function to retrieve the |
| 583 | graph debug info for a set of nodes from the `graph_def`. |
| 584 | |
| 585 | Raises: |
| 586 | ValueError: Invalid arguments. |
| 587 | """ |
| 588 | super(TFLiteConverter, self).__init__() |
| 589 | self._graph_def = graph_def |
| 590 | self._input_tensors = input_tensors |
| 591 | self._output_tensors = output_tensors |
| 592 | self.inference_type = constants.FLOAT |
| 593 | self.inference_input_type = None |
| 594 | self.inference_output_type = None |
| 595 | self.output_format = constants.TFLITE |
| 596 | self.quantized_input_stats = {} |
| 597 | self.default_ranges_stats = None |
| 598 | self.drop_control_dependency = True |
| 599 | self.reorder_across_fake_quant = False |
| 600 | self.change_concat_input_ranges = False |
| 601 | self._post_training_quantize = False |
| 602 | self.dump_graphviz_dir = None |
| 603 | self.dump_graphviz_video = False |
| 604 | self._debug_info_func = experimental_debug_info_func |
| 605 | |
| 606 | # Attributes are used by models that cannot be loaded into TensorFlow. |
| 607 | if not self._has_valid_tensors(): |
| 608 | if not input_arrays_with_shape or not output_arrays: |
| 609 | raise ValueError( |
| 610 | "If input_tensors and output_tensors are None, both " |
| 611 | "input_arrays_with_shape and output_arrays must be defined.") |
| 612 | self._input_arrays_with_shape = input_arrays_with_shape |
| 613 | self._output_arrays = output_arrays |
| 614 | |
| 615 | @classmethod |
| 616 | def from_session(cls, sess, input_tensors, output_tensors): |
nothing calls this directly
no test coverage detected