def _export_tflite(self,
tflite_filepath,
label_filepath,
quantized=False,
quantization_steps=None,
representative_data=None,
inference_input_type=tf.float32,
inference_output_type=tf.float32,
with_metadata=False,
export_metadata_json_file=False):
"""Converts the retrained model to tflite format and saves it.
Args:
tflite_filepath: File path to save tflite model.
label_filepath: File path to save labels.
quantized: boolean, if True, save quantized model.
quantization_steps: Number of post-training quantization calibration steps
to run. Used only if `quantized` is True.
representative_data: Representative data used for post-training
quantization. Used only if `quantized` is True.
inference_input_type: Target data type of real-number input arrays. Allows
for a different type for input arrays. Defaults to tf.float32. Must be
be `{tf.float32, tf.uint8, tf.int8}`
inference_output_type: Target data type of real-number output arrays.
Allows for a different type for output arrays. Defaults to tf.float32.
Must be `{tf.float32, tf.uint8, tf.int8}`
with_metadata: Whether the output tflite model contains metadata.
export_metadata_json_file: Whether to export metadata in json file. If
True, export the metadata in the same directory as tflite model.Used
only if `with_metadata` is True.
"""
super(ImageClassifier,
self)._export_tflite(tflite_filepath, quantized, quantization_steps,
representative_data, inference_input_type,
inference_output_type)
if with_metadata:
if not metadata.TFLITE_SUPPORT_TOOLS_INSTALLED:
tf.compat.v1.logging.warning('Needs to install tflite-support package.')
return
if label_filepath is None:
tf.compat.v1.logging.warning(
'Label filepath is needed when exporting TFLite with metadata.')
return
model_info = metadata.get_model_info(self.model_spec, quantized=quantized)
populator = metadata.MetadataPopulatorForImageClassifier(
tflite_filepath, model_info, label_filepath)
populator.populate()
if export_metadata_json_file:
metadata.export_metadata_json_file(tflite_filepath)
def export(self,
tflite_filename,
label_filename,
quantized=False,
quantization_steps=None,
representative_data=None,
with_metadata=False,
export_metadata_json_file=False):
"""Converts the retrained model based on `model_export_format`.
Args:
tflite_filename: File name to save tflite model.
label_filename: File name to save labels.
quantized: boolean, if True, save quantized model.
quantization_steps: Number of post-training quantization calibration steps
to run. Used only if `quantized` is True.
representative_data: Representative data used for post-training
quantization. Used only if `quantized` is True.
with_metadata: Whether the output tflite model contains metadata.
export_metadata_json_file: Whether to export metadata in json file. If
True, export the metadata in the same directory as tflite model.Used
only if `with_metadata` is True.
"""
if self.model_export_format != mef.ModelExportFormat.TFLITE:
raise ValueError(
'Model Export Format %s is not supported currently.' %
self.model_export_format)
self._export_tflite(tflite_filename, label_filename, quantized,
quantization_steps, representative_data)
if with_metadata:
if not metadata.TFLITE_SUPPORT_TOOLS_INSTALLED:
tf.compat.v1.logging.warning(
'Needs to install tflite-support package.')
return
model_info = metadata.get_model_info(self.model_spec,
quantized=quantized)
populator = metadata.MetadataPopulatorForImageClassifier(
tflite_filename, model_info, label_filename)
populator.populate()
if export_metadata_json_file:
metadata.export_metadata_json_file(tflite_filename)