def evaluate_frozen_graph(filename, input_arrays, output_arrays):
"""Returns a function that evaluates the frozen graph on input data.
Args:
filename: Full filepath of file containing frozen GraphDef.
input_arrays: List of input tensors to freeze graph with.
output_arrays: List of output tensors to freeze graph with.
Returns:
Lambda function ([np.ndarray data] : [np.ndarray result]).
"""
with _session.Session().as_default() as sess:
with _file_io.FileIO(filename, "rb") as f:
file_content = f.read()
graph_def = _graph_pb2.GraphDef()
try:
graph_def.ParseFromString(file_content)
except (_text_format.ParseError, DecodeError):
if not isinstance(file_content, str):
if PY3:
file_content = file_content.decode("utf-8")
else:
file_content = file_content.encode("utf-8")
_text_format.Merge(file_content, graph_def)
_import_graph_def(graph_def, name="")
inputs = _util.get_tensors_from_tensor_names(sess.graph, input_arrays)
outputs = _util.get_tensors_from_tensor_names(sess.graph, output_arrays)
return lambda input_data: sess.run(outputs, dict(zip(inputs, input_data)))
def evaluate_frozen_graph(filename, input_arrays, output_arrays):
"""Returns a function that evaluates the frozen graph on input data.
Args:
filename: Full filepath of file containing frozen GraphDef.
input_arrays: List of input tensors to freeze graph with.
output_arrays: List of output tensors to freeze graph with.
Returns:
Lambda function ([np.ndarray data] : [np.ndarray result]).
"""
with _session.Session().as_default() as sess:
with _file_io.FileIO(filename, "rb") as f:
file_content = f.read()
graph_def = _graph_pb2.GraphDef()
graph_def.ParseFromString(file_content)
_import_graph_def(graph_def, name="")
inputs = _convert_saved_model.get_tensors_from_tensor_names(
sess.graph, input_arrays)
outputs = _convert_saved_model.get_tensors_from_tensor_names(
sess.graph, output_arrays)
return lambda input_data: sess.run(outputs,
dict(zip(inputs, input_data)))
def evaluate_frozen_graph(filename, input_arrays, output_arrays):
"""Returns a function that evaluates the frozen graph on input data.
Args:
filename: Full filepath of file containing frozen GraphDef.
input_arrays: List of input tensors to freeze graph with.
output_arrays: List of output tensors to freeze graph with.
Returns:
Lambda function ([np.ndarray data] : [np.ndarray result]).
"""
with _session.Session().as_default() as sess:
with _file_io.FileIO(filename, "rb") as f:
file_content = f.read()
graph_def = _graph_pb2.GraphDef()
graph_def.ParseFromString(file_content)
_import_graph_def(graph_def, name="")
inputs = _convert_saved_model.get_tensors_from_tensor_names(
sess.graph, input_arrays)
outputs = _convert_saved_model.get_tensors_from_tensor_names(
sess.graph, output_arrays)
return lambda input_data: sess.run(outputs, dict(zip(inputs, input_data)))
def from_frozen_graph(cls,
graph_def_file,
input_arrays,
output_arrays,
input_shapes=None):
"""Creates a TocoConverter class from a file containing a frozen GraphDef.
Args:
graph_def_file: Full filepath of file containing frozen GraphDef.
input_arrays: List of input tensors to freeze graph with.
output_arrays: List of output tensors to freeze graph with.
input_shapes: Dict 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)
Returns:
TocoConverter class.
Raises:
ValueError:
Unable to parse input file.
The graph is not frozen.
input_arrays or output_arrays contains an invalid tensor name.
"""
with _ops.Graph().as_default():
with _session.Session() as sess:
# Read GraphDef from file.
graph_def = _graph_pb2.GraphDef()
with open(graph_def_file, "rb") as f:
file_content = f.read()
try:
graph_def.ParseFromString(file_content)
except (_text_format.ParseError, DecodeError):
try:
print("Ignore 'tcmalloc: large alloc' warnings.")
if not isinstance(file_content, str):
if PY3:
file_content = file_content.decode("utf-8")
else:
file_content = file_content.encode("utf-8")
_text_format.Merge(file_content, graph_def)
except (_text_format.ParseError, DecodeError):
raise ValueError(
"Unable to parse input file '{}'.".format(graph_def_file))
_import_graph_def(graph_def, name="")
# Get input and output tensors.
input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays)
output_tensors = _get_tensors_from_tensor_names(sess.graph,
output_arrays)
_set_tensor_shapes(input_tensors, input_shapes)
# Check if graph is frozen.
if not _is_frozen_graph(sess):
raise ValueError("Please freeze the graph using freeze_graph.py.")
# Create TocoConverter class.
return cls(sess.graph_def, input_tensors, output_tensors)
def from_frozen_graph(cls,
graph_def_file,
input_arrays,
output_arrays,
input_shapes=None):
"""Creates a TFLiteConverter class from a file containing a frozen GraphDef.
Args:
graph_def_file: Full filepath of file containing frozen GraphDef.
input_arrays: List of input tensors to freeze graph with.
output_arrays: List of output tensors to freeze graph with.
input_shapes: Dict 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)
Returns:
TFLiteConverter class.
Raises:
IOError:
File not found.
Unable to parse input file.
ValueError:
The graph is not frozen.
input_arrays or output_arrays contains an invalid tensor name.
input_shapes is not correctly defined when required
"""
with _ops.Graph().as_default():
with _session.Session() as sess:
# Read GraphDef from file.
if not _file_io.file_exists(graph_def_file):
raise IOError(
"File '{0}' does not exist.".format(graph_def_file))
with _file_io.FileIO(graph_def_file, "rb") as f:
file_content = f.read()
try:
graph_def = _graph_pb2.GraphDef()
graph_def.ParseFromString(file_content)
except (_text_format.ParseError, DecodeError):
try:
print("Ignore 'tcmalloc: large alloc' warnings.")
if not isinstance(file_content, str):
if PY3:
file_content = file_content.decode("utf-8")
else:
file_content = file_content.encode("utf-8")
graph_def = _graph_pb2.GraphDef()
_text_format.Merge(file_content, graph_def)
except (_text_format.ParseError, DecodeError):
raise IOError(
"Unable to parse input file '{}'.".format(
graph_def_file))
# Handles models with custom TFLite ops that cannot be resolved in
# TensorFlow.
load_model_in_session = True
try:
_import_graph_def(graph_def, name="")
except _NotFoundError:
load_model_in_session = False
if load_model_in_session:
# Check if graph is frozen.
if not _is_frozen_graph(sess):
raise ValueError(
"Please freeze the graph using freeze_graph.py.")
# Get input and output tensors.
input_tensors = _get_tensors_from_tensor_names(
sess.graph, input_arrays)
output_tensors = _get_tensors_from_tensor_names(
sess.graph, output_arrays)
_set_tensor_shapes(input_tensors, input_shapes)
return cls(sess.graph_def, input_tensors, output_tensors)
else:
if not input_shapes:
raise ValueError(
"input_shapes must be defined for this model.")
if set(input_arrays) != set(input_shapes.keys()):
raise ValueError(
"input_shapes must contain a value for each item "
"in input_array.")
input_arrays_with_shape = [(name, input_shapes[name])
for name in input_arrays]
return cls(graph_def,
input_tensors=None,
output_tensors=None,
input_arrays_with_shape=input_arrays_with_shape,
output_arrays=output_arrays)
def from_frozen_graph(cls,
graph_def_file,
input_arrays,
output_arrays,
input_shapes=None):
"""Creates a TocoConverter class from a file containing a frozen GraphDef.
Args:
graph_def_file: Full filepath of file containing frozen GraphDef.
input_arrays: List of input tensors to freeze graph with.
output_arrays: List of output tensors to freeze graph with.
input_shapes: Dict 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)
Returns:
TocoConverter class.
Raises:
ValueError:
Unable to parse input file.
The graph is not frozen.
input_arrays or output_arrays contains an invalid tensor name.
input_shapes is not correctly defined when required
"""
with _ops.Graph().as_default():
with _session.Session() as sess:
# Read GraphDef from file.
graph_def = _graph_pb2.GraphDef()
with open(graph_def_file, "rb") as f:
file_content = f.read()
try:
graph_def.ParseFromString(file_content)
except (_text_format.ParseError, DecodeError):
try:
print("Ignore 'tcmalloc: large alloc' warnings.")
if not isinstance(file_content, str):
if PY3:
file_content = file_content.decode("utf-8")
else:
file_content = file_content.encode("utf-8")
_text_format.Merge(file_content, graph_def)
except (_text_format.ParseError, DecodeError):
raise ValueError(
"Unable to parse input file '{}'.".format(graph_def_file))
# Handles models with custom TFLite ops that cannot be resolved in
# TensorFlow.
load_model_in_session = True
try:
_import_graph_def(graph_def, name="")
except _NotFoundError:
load_model_in_session = False
if load_model_in_session:
# Check if graph is frozen.
if not _is_frozen_graph(sess):
raise ValueError("Please freeze the graph using freeze_graph.py.")
# Get input and output tensors.
input_tensors = _get_tensors_from_tensor_names(
sess.graph, input_arrays)
output_tensors = _get_tensors_from_tensor_names(
sess.graph, output_arrays)
_set_tensor_shapes(input_tensors, input_shapes)
return cls(sess.graph_def, input_tensors, output_tensors)
else:
if not input_shapes:
raise ValueError("input_shapes must be defined for this model.")
if set(input_arrays) != set(input_shapes.keys()):
raise ValueError("input_shapes must contain a value for each item "
"in input_array.")
input_arrays_with_shape = [
(name, input_shapes[name]) for name in input_arrays
]
return cls(
graph_def,
input_tensors=None,
output_tensors=None,
input_arrays_with_shape=input_arrays_with_shape,
output_arrays=output_arrays)
def from_frozen_graph(cls,
graph_def_file,
input_arrays,
output_arrays,
input_shapes=None):
"""Creates a TocoConverter class from a file containing a frozen GraphDef.
Args:
graph_def_file: Full filepath of file containing frozen GraphDef.
input_arrays: List of input tensors to freeze graph with.
output_arrays: List of output tensors to freeze graph with.
input_shapes: Dict 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)
Returns:
TocoConverter class.
Raises:
ValueError:
Unable to parse input file.
The graph is not frozen.
input_arrays or output_arrays contains an invalid tensor name.
"""
with _session.Session() as sess:
# Read GraphDef from file.
graph_def = _graph_pb2.GraphDef()
with open(graph_def_file, "rb") as f:
file_content = f.read()
try:
graph_def.ParseFromString(file_content)
except (_text_format.ParseError, DecodeError):
try:
print("Ignore 'tcmalloc: large alloc' warnings.")
if not isinstance(file_content, str):
if PY3:
file_content = file_content.decode('utf-8')
else:
file_content = file_content.encode('utf-8')
_text_format.Merge(file_content, graph_def)
except (_text_format.ParseError, DecodeError):
raise ValueError("Unable to parse input file '{}'.".format(
graph_def_file))
sess.graph.as_default()
_import_graph_def(graph_def, name="")
# Get input and output tensors.
input_tensors = _get_tensors_from_tensor_names(
sess.graph, input_arrays)
output_tensors = _get_tensors_from_tensor_names(
sess.graph, output_arrays)
_set_tensor_shapes(input_tensors, input_shapes)
# Check if graph is frozen.
if not _is_frozen_graph(sess):
raise ValueError(
"Please freeze the graph using freeze_graph.py.")
# Create TocoConverter class.
return cls(sess.graph_def, input_tensors, output_tensors)
