def convert(self):
"""Converts a TensorFlow GraphDef based on instance variables.
Returns:
The converted data in serialized format. Either a TFLite Flatbuffer or a
Graphviz graph depending on value in `output_format`.
Raises:
ValueError:
Input shape is not specified.
None value for dimension in input_tensor.
"""
# Checks dimensions in input tensor.
for tensor in self._input_tensors:
if not tensor.get_shape():
raise ValueError("Provide an input shape for input array '{0}'.".format(
tensor_name(tensor)))
shape = tensor.get_shape().as_list()
if None in shape[1:]:
raise ValueError(
"None is only supported in the 1st dimension. Tensor '{0}' has "
"invalid shape '{1}'.".format(tensor_name(tensor), shape))
elif shape[0] is None:
self._set_batch_size(batch_size=1)
# Get quantization stats. Ensures there is one stat per name if the stats
# are specified.
if self.quantized_input_stats:
quantized_stats = []
invalid_stats = []
for tensor in self._input_tensors:
name = tensor_name(tensor)
if name in self.quantized_input_stats:
quantized_stats.append(self.quantized_input_stats[name])
else:
invalid_stats.append(name)
if invalid_stats:
raise ValueError("Quantization input stats are not available for input "
"tensors '{0}'.".format(",".join(invalid_stats)))
else:
quantized_stats = None
# Converts model.
result = toco_convert(
input_data=self._graph_def,
input_tensors=self._input_tensors,
output_tensors=self._output_tensors,
inference_type=self.inference_type,
inference_input_type=self.inference_input_type,
input_format=constants.TENSORFLOW_GRAPHDEF,
output_format=self.output_format,
quantized_input_stats=quantized_stats,
default_ranges_stats=self.default_ranges_stats,
drop_control_dependency=self.drop_control_dependency,
reorder_across_fake_quant=self.reorder_across_fake_quant,
change_concat_input_ranges=self.change_concat_input_ranges,
allow_custom_ops=self.allow_custom_ops,
quantize_weights=self.quantize_weights)
return result
def convert(self):
"""Converts a TensorFlow GraphDef based on instance variables.
Returns:
The converted data in serialized format. Either a TFLite Flatbuffer or a
Graphviz graph depending on value in `output_format`.
Raises:
ValueError:
None value for dimension in input_tensor.
"""
# Checks dimensions in input tensor.
for tensor in self._input_tensors:
shape = tensor.get_shape().as_list()
if None in shape[1:]:
raise ValueError(
"None is only supported in the 1st dimension. Tensor '{0}' has "
"invalid shape '{1}'.".format(tensor.name, shape))
elif shape[0] is None:
self._set_batch_size(batch_size=1)
# Converts model.
result = toco_convert(
input_data=self._graph_def,
input_tensors=self._input_tensors,
output_tensors=self._output_tensors,
inference_type=self.inference_type,
input_format=constants.TENSORFLOW_GRAPHDEF,
output_format=self.output_format,
quantized_input_stats=self.quantized_input_stats,
drop_control_dependency=self.drop_control_dependency)
return result
def testBasic(self):
in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3],
dtype=dtypes.float32)
out_tensor = in_tensor + in_tensor
sess = session.Session()
# Try running on valid graph
tflite_model = convert.toco_convert(sess.graph_def, [in_tensor],
[out_tensor])
self.assertTrue(tflite_model)
def testQuantization(self):
in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3],
dtype=dtypes.float32)
out_tensor = array_ops.fake_quant_with_min_max_args(in_tensor + in_tensor,
min=0., max=1.)
sess = session.Session()
tflite_model = convert.toco_convert(
sess.graph_def, [in_tensor], [out_tensor],
inference_type=lite_constants.QUANTIZED_UINT8,
quantized_input_stats=[(0., 1.)])
self.assertTrue(tflite_model)
def testQuantization(self):
in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3],
dtype=dtypes.float32)
out_tensor = array_ops.fake_quant_with_min_max_args(in_tensor + in_tensor,
min=0., max=1.)
sess = session.Session()
tflite_model = convert.toco_convert(
sess.graph_def, [in_tensor], [out_tensor],
inference_type=lite_constants.QUANTIZED_UINT8,
quantized_input_stats=[(0., 1.)])
self.assertTrue(tflite_model)
def convert(self):
"""Converts a TensorFlow GraphDef based on instance variables.
Returns:
The converted data in serialized format. Either a TFLite Flatbuffer or a
Graphviz graph depending on value in `output_format`.
Raises:
ValueError:
Input shape is not specified.
None value for dimension in input_tensor.
"""
# Checks dimensions in input tensor.
for tensor in self._input_tensors:
if not tensor.get_shape():
raise ValueError(
"Provide an input shape for input array '{0}'.".format(
tensor_name(tensor)))
shape = tensor.get_shape().as_list()
if None in shape[1:]:
raise ValueError(
"None is only supported in the 1st dimension. Tensor '{0}' has "
"invalid shape '{1}'.".format(tensor_name(tensor), shape))
elif shape[0] is None:
self._set_batch_size(batch_size=1)
# Get quantization stats. Ensures there is one stat per name if the stats
# are specified.
if self.quantized_input_stats:
quantized_stats = []
invalid_stats = []
for tensor in self._input_tensors:
name = tensor_name(tensor)
if name in self.quantized_input_stats:
quantized_stats.append(self.quantized_input_stats[name])
else:
invalid_stats.append(name)
if invalid_stats:
raise ValueError(
"Quantization input stats are not available for input "
"tensors '{0}'.".format(",".join(invalid_stats)))
else:
quantized_stats = None
# Converts model.
result = toco_convert(
input_data=self._graph_def,
input_tensors=self._input_tensors,
output_tensors=self._output_tensors,
inference_type=self.inference_type,
inference_input_type=self.inference_input_type,
input_format=constants.TENSORFLOW_GRAPHDEF,
output_format=self.output_format,
quantized_input_stats=quantized_stats,
default_ranges_stats=self.default_ranges_stats,
drop_control_dependency=self.drop_control_dependency,
reorder_across_fake_quant=self.reorder_across_fake_quant,
change_concat_input_ranges=self.change_concat_input_ranges,
allow_custom_ops=self.allow_custom_ops)
return result
def tflite_from_saved_model(
saved_model_dir,
output_file=None,
input_arrays=None,
input_shapes=None,
output_arrays=None,
tag_set=None,
signature_key=signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY,
batch_size=1,
inference_type=lite_constants.FLOAT,
input_format=lite_constants.TENSORFLOW_GRAPHDEF,
output_format=lite_constants.TFLITE,
quantized_input_stats=None,
drop_control_dependency=True):
"""Converts a SavedModel to TFLite FlatBuffer.
Args:
saved_model_dir: SavedModel directory to convert.
output_file: File path to write result TFLite FlatBuffer.
input_arrays: List of input tensors to freeze graph with. Uses input arrays
from SignatureDef when none are provided. (default None)
input_shapes: Map of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo": : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" : None}).
(default None)
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided. (default None)
tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to
analyze. All tags in the tag set must be present. (default "serve")
signature_key: Key identifying SignatureDef containing inputs and outputs.
batch_size: Batch size for the model. Replaces the first dimension of an
input size array if undefined. (default 1)
inference_type: Currently must be `{FLOAT, QUANTIZED_UINT8}`.
input_format: Type of data to read (currently must be TENSORFLOW_GRAPHDEF).
output_format: Type of data to write (currently must be TFLITE or
GRAPHVIZ_DOT)
quantized_input_stats: For each member of input_tensors the mean and
std deviation of training data. Only needed if `inference_type` is
`QUANTIZED_UINT8`.
drop_control_dependency: Drops control dependencies silently. This is due
to tf lite not supporting control dependencies.
Returns:
The converted data. For example if tflite was the destination, then
this will be a tflite flatbuffer in a bytes array.
Raises:
ValueError: Unable to convert to frozen graph.
"""
frozen_graph_def, in_tensors, out_tensors = _freeze_saved_model(
saved_model_dir, input_arrays, input_shapes, output_arrays, tag_set,
signature_key, batch_size)
result = convert.toco_convert(
input_data=frozen_graph_def,
input_tensors=in_tensors,
output_tensors=out_tensors,
inference_type=inference_type,
input_format=input_format,
output_format=output_format,
quantized_input_stats=quantized_input_stats,
drop_control_dependency=drop_control_dependency)
if output_file is not None:
with gfile.Open(output_file, "wb") as f:
f.write(result)
logging.info("Successfully converted to: %s", output_file)
return result
def tflite_from_saved_model(
saved_model_dir,
output_file=None,
input_arrays=None,
input_shapes=None,
output_arrays=None,
tag_set=None,
signature_key=signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY,
batch_size=1,
inference_type=lite_constants.FLOAT,
input_format=lite_constants.TENSORFLOW_GRAPHDEF,
output_format=lite_constants.TFLITE,
quantized_input_stats=None,
drop_control_dependency=True):
"""Converts a SavedModel to TFLite FlatBuffer.
Args:
saved_model_dir: SavedModel directory to convert.
output_file: File path to write result TFLite FlatBuffer.
input_arrays: List of input tensors to freeze graph with. Uses input arrays
from SignatureDef when none are provided. (default None)
input_shapes: Map of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo": : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" : None}).
(default None)
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided. (default None)
tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to
analyze. All tags in the tag set must be present. (default "serve")
signature_key: Key identifying SignatureDef containing inputs and outputs.
batch_size: Batch size for the model. Replaces the first dimension of an
input size array if undefined. (default 1)
inference_type: Currently must be `{FLOAT, QUANTIZED_UINT8}`.
input_format: Type of data to read (currently must be TENSORFLOW_GRAPHDEF).
output_format: Type of data to write (currently must be TFLITE or
GRAPHVIZ_DOT)
quantized_input_stats: For each member of input_tensors the mean and
std deviation of training data. Only needed if `inference_type` is
`QUANTIZED_UINT8`.
drop_control_dependency: Drops control dependencies silently. This is due
to tf lite not supporting control dependencies.
Returns:
The converted data. For example if tflite was the destination, then
this will be a tflite flatbuffer in a bytes array.
Raises:
ValueError: Unable to convert to frozen graph.
"""
frozen_graph_def, in_tensors, out_tensors = _freeze_saved_model(
saved_model_dir, input_arrays, input_shapes, output_arrays, tag_set,
signature_key, batch_size)
result = convert.toco_convert(
input_data=frozen_graph_def,
input_tensors=in_tensors,
output_tensors=out_tensors,
inference_type=inference_type,
input_format=input_format,
output_format=output_format,
quantized_input_stats=quantized_input_stats,
drop_control_dependency=drop_control_dependency)
if output_file is not None:
with gfile.Open(output_file, "wb") as f:
f.write(result)
logging.info("Successfully converted to: %s", output_file)
return result
