def _export_tflite(self,
tflite_filepath,
quantization_config=None,
with_metadata=True):
"""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. If True,
Exports metadata in json file as well.
"""
# Sets batch size from None to 1 when converting to tflite.
model_util.set_batch_size(self.model, batch_size=1)
model_util.export_tflite(self.model, tflite_filepath,
quantization_config, self._gen_dataset,
self.model_spec.convert_from_saved_model_tf2)
# Sets batch size back to None to support retraining later.
model_util.set_batch_size(self.model, batch_size=None)
if with_metadata:
with tempfile.TemporaryDirectory() as temp_dir:
tf.compat.v1.logging.info(
'Vocab file is inside the TFLite model with metadata.')
vocab_filepath = os.path.join(temp_dir, 'vocab.txt')
self.model_spec.save_vocab(vocab_filepath)
model_info = _get_model_info(self.model_spec, vocab_filepath)
export_dir = os.path.dirname(tflite_filepath)
populator = metadata_writer.MetadataPopulatorForBertQuestionAndAnswer(
tflite_filepath, export_dir, model_info)
populator.populate()
def export_tflite(self, model, tflite_filepath, quantization_config=None):
"""Converts the retrained model to tflite format and saves it.
This method overrides the default `CustomModel._export_tflite` method, and
include the pre-processing in the exported TFLite library since support
library can't handle audio tasks yet.
Args:
model: An instance of the keras classification model to be exported.
tflite_filepath: File path to save tflite model.
quantization_config: Configuration for post-training quantization.
"""
combined = tf.keras.Sequential()
combined.add(self._preprocess_model)
combined.add(model)
# Build the model.
combined.build([None, self.expected_waveform_len])
# Sets batch size from None to 1 when converting to tflite.
model_util.set_batch_size(model, batch_size=1)
model_util.export_tflite(combined,
tflite_filepath,
quantization_config,
supported_ops=(tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS))
# Sets batch size back to None to support retraining later.
model_util.set_batch_size(model, batch_size=None)
def export_tflite(self,
model,
tflite_filepath,
with_metadata=True,
export_metadata_json_file=True,
index_to_label=None):
"""Converts the retrained model to tflite format and saves it.
This method overrides the default `CustomModel._export_tflite` method, and
include the pre-processing in the exported TFLite library since support
library can't handle audio tasks yet.
Args:
model: An instance of the keras classification model to be exported.
tflite_filepath: File path to save tflite model.
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.
index_to_label: A list that map from index to label class name.
"""
del with_metadata, export_metadata_json_file, index_to_label
combined = self.create_serving_model(model)
# Sets batch size from None to 1 when converting to tflite.
model_util.set_batch_size(model, batch_size=1)
model_util.export_tflite(combined,
tflite_filepath,
quantization_config=None,
supported_ops=(tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS))
# Sets batch size back to None to support retraining later.
model_util.set_batch_size(model, batch_size=None)
def _export_tflite(self,
tflite_filepath,
quantized=False,
quantization_steps=None,
representative_data=None,
inference_input_type=tf.float32,
inference_output_type=tf.float32):
"""Converts the retrained model to tflite format and saves it.
Args:
tflite_filepath: File path to save tflite model.
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}`
"""
# Sets batch size from None to 1 when converting to tflite.
model_util.set_batch_size(self.model, batch_size=1)
super(QuestionAnswer,
self)._export_tflite(tflite_filepath, quantized,
quantization_steps, representative_data,
inference_input_type, inference_output_type)
# Sets batch size back to None to support retraining later.
model_util.set_batch_size(self.model, batch_size=None)
def _export_tflite(self,
tflite_filepath,
quantization_config='default',
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. 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(
)
# Sets batch size from None to 1 when converting to tflite.
model_util.set_batch_size(self.model, batch_size=1)
model_util.export_tflite(self.model, tflite_filepath,
quantization_config,
self.model_spec.convert_from_saved_model_tf2)
# Sets batch size back to None to support retraining later.
model_util.set_batch_size(self.model, batch_size=None)
if with_metadata:
with tempfile.TemporaryDirectory() as temp_dir:
tf.compat.v1.logging.info(
'Vocab file and label file are inside the '
'TFLite model with metadata.')
vocab_filepath = os.path.join(temp_dir, 'vocab.txt')
self.model_spec.save_vocab(vocab_filepath)
label_filepath = os.path.join(temp_dir, 'labels.txt')
self._export_labels(label_filepath)
export_dir = os.path.dirname(tflite_filepath)
if isinstance(self.model_spec, ms.BertClassifierModelSpec):
model_info = _get_bert_model_info(self.model_spec,
vocab_filepath,
label_filepath)
populator = bert_metadata_writer.MetadataPopulatorForBertTextClassifier(
tflite_filepath, export_dir, model_info)
elif isinstance(self.model_spec, ms.AverageWordVecModelSpec):
model_info = _get_model_info(self.model_spec.name)
populator = metadata_writer.MetadataPopulatorForTextClassifier(
tflite_filepath, export_dir, model_info,
label_filepath, vocab_filepath)
else:
raise ValueError(
'Model Specification is not supported to writing '
'metadata into TFLite. Please set '
'`with_metadata=False` or write metadata by '
'yourself.')
populator.populate(export_metadata_json_file)
def _export_tflite(self, tflite_filepath, quantization_config=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.
"""
# Sets batch size from None to 1 when converting to tflite.
model_util.set_batch_size(self.model, batch_size=1)
model_util.export_tflite(self.model, tflite_filepath,
quantization_config, self._gen_dataset,
self.model_spec.convert_from_saved_model_tf2)
# Sets batch size back to None to support retraining later.
model_util.set_batch_size(self.model, batch_size=None)
def _export_tflite(self, tflite_filepath, quantization_config=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.
"""
# TODO(b/151761399): Removes these lines.
if hasattr(self.model_spec, 'uri') and 'mobilebert' in self.model_spec.uri:
raise ValueError('Couldn\'t convert MobileBert to TFLite for now.')
# Sets batch size from None to 1 when converting to tflite.
model_util.set_batch_size(self.model, batch_size=1)
model_util.export_tflite(self.model, tflite_filepath, quantization_config,
self._gen_dataset,
self.model_spec.convert_from_saved_model_tf2)
# Sets batch size back to None to support retraining later.
model_util.set_batch_size(self.model, batch_size=None)
def export_tflite(self,
model,
tflite_filepath,
with_metadata=True,
export_metadata_json_file=True,
index_to_label=None,
quantization_config=None):
"""Converts the retrained model to tflite format and saves it.
This method overrides the default `CustomModel._export_tflite` method, and
include the pre-processing in the exported TFLite library since support
library can't handle audio tasks yet.
Args:
model: An instance of the keras classification model to be exported.
tflite_filepath: File path to save tflite model.
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.
index_to_label: A list that map from index to label class name.
quantization_config: Configuration for post-training quantization.
"""
combined = self.create_serving_model(model)
# Sets batch size from None to 1 when converting to tflite.
model_util.set_batch_size(model, batch_size=1)
model_util.export_tflite(combined,
tflite_filepath,
quantization_config=quantization_config)
# Sets batch size back to None to support retraining later.
model_util.set_batch_size(model, batch_size=None)
if with_metadata:
if not ENABLE_METADATA:
print(
'Writing Metadata is not support in the installed tflite-support '
'version. Please use tflite-support >= 0.2.*')
else:
self._export_metadata(tflite_filepath, index_to_label,
export_metadata_json_file)
def _export_tflite(self,
tflite_filepath,
quantization_config='default',
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. 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(
)
# Sets batch size from None to 1 when converting to tflite.
model_util.set_batch_size(self.model, batch_size=1)
model_util.export_tflite(self.model, tflite_filepath,
quantization_config,
self.model_spec.convert_from_saved_model_tf2)
# Sets batch size back to None to support retraining later.
model_util.set_batch_size(self.model, batch_size=None)
if with_metadata:
with tempfile.TemporaryDirectory() as temp_dir:
tf.compat.v1.logging.info(
'Vocab file is inside the TFLite model with metadata.')
vocab_filepath = os.path.join(temp_dir, 'vocab.txt')
self.model_spec.save_vocab(vocab_filepath)
model_info = _get_model_info(self.model_spec, vocab_filepath)
export_dir = os.path.dirname(tflite_filepath)
populator = metadata_writer.MetadataPopulatorForBertQuestionAndAnswer(
tflite_filepath, export_dir, model_info)
populator.populate(export_metadata_json_file)
