def _export_tflite(self,
tflite_filepath,
quantization_config=None,
with_metadata=True,
export_metadata_json_file=False):
"""Converts the retrained model to tflite format and saves it.
Args:
tflite_filepath: File path to save tflite model.
quantization_config: Configuration for post-training quantization.
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.
"""
self.model_spec.export_tflite(tflite_filepath, quantization_config)
if with_metadata:
with tempfile.TemporaryDirectory() as temp_dir:
tf.compat.v1.logging.info(
'Label file is inside the TFLite model with metadata.')
label_filepath = os.path.join(temp_dir, 'labelmap.txt')
self._export_labels(label_filepath)
model_info = _get_model_info(self.model_spec,
quantization_config)
export_dir = os.path.dirname(tflite_filepath)
populator = metadata_writer.MetadataPopulatorForObjectDetector(
tflite_filepath, export_dir, model_info, label_filepath)
populator.populate(export_metadata_json_file)
def _export_tflite(
self,
tflite_filepath: str,
quantization_type: QuantizationType = QuantizationType.INT8,
representative_data: Optional[
object_detector_dataloader.DataLoader] = None,
quantization_config: Optional[configs.QuantizationConfig] = None,
with_metadata: bool = True,
export_metadata_json_file: bool = False) -> None:
"""Converts the retrained model to tflite format and saves it.
Args:
tflite_filepath: File path to save tflite model.
quantization_type: Enum, type of post-training quantization. Accepted
values are `INT8`, `FP16`, `FP32`, `DYNAMIC`. `FP16` means float16
quantization with 2x smaller, optimized for GPU. `INT8` means full
integer quantization with 4x smaller, 3x+ speedup, optimized for Edge
TPU. 'DYNAMIC' means dynamic range quantization with 4x smaller, 2x-3x
speedup. `FP32` mean exporting float model without quantization. Please
refer to
https://www.tensorflow.org/lite/performance/post_training_quantization
for more detailed about different techniques for post-training
quantization.
representative_data: Representative dataset for full integer
quantization. Used when `quantization_type=INT8`.
quantization_config: Configuration for post-training quantization.
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 quantization_type and quantization_config:
raise ValueError(
'At most one of the paramaters `quantization_type` and '
'`quantization_config` can be set.')
if quantization_type == QuantizationType.INT8 and \
representative_data is None:
raise ValueError('`representative_data` must be set when '
'`quantization_type=QuantizationType.INT8.')
ds, _, _ = self._get_dataset_and_steps(representative_data,
batch_size=1,
is_training=False)
self.model_spec.export_tflite(tflite_filepath, quantization_type, ds,
quantization_config)
if with_metadata:
with tempfile.TemporaryDirectory() as temp_dir:
tf.compat.v1.logging.info(
'Label file is inside the TFLite model with metadata.')
label_filepath = os.path.join(temp_dir, 'labelmap.txt')
self._export_labels(label_filepath)
model_info = _get_model_info(self.model_spec,
quantization_type,
quantization_config)
export_dir = os.path.dirname(tflite_filepath)
populator = metadata_writer.MetadataPopulatorForObjectDetector(
tflite_filepath, export_dir, model_info, label_filepath)
populator.populate(export_metadata_json_file)
def _export_tflite(
self,
tflite_filepath: str,
quantization_config: configs.QuantizationConfigType = 'default',
with_metadata: bool = True,
export_metadata_json_file: bool = False) -> None:
"""Converts the retrained model to tflite format and saves it.
Args:
tflite_filepath: File path to save tflite model.
quantization_config: Configuration for post-training quantization. If
'default', sets the `quantization_config` by default according to
`self.model_spec`. If None, exports the float tflite model without
quantization.
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 quantization_config == 'default':
quantization_config = self.model_spec.get_default_quantization_config(
self.representative_data)
self.model_spec.export_tflite(self.model, tflite_filepath,
quantization_config)
if with_metadata:
with tempfile.TemporaryDirectory() as temp_dir:
tf.compat.v1.logging.info(
'Label file is inside the TFLite model with metadata.')
label_filepath = os.path.join(temp_dir, 'labelmap.txt')
self._export_labels(label_filepath)
model_info = _get_model_info(self.model_spec,
quantization_config)
export_dir = os.path.dirname(tflite_filepath)
populator = metadata_writer.MetadataPopulatorForObjectDetector(
tflite_filepath, export_dir, model_info, label_filepath)
populator.populate(export_metadata_json_file)
