def execute(self):
if self._tf_format == 'TF_PB':
graph_def = graph_pb2.GraphDef()
with tf.io.gfile.GFile(self._tf_file, 'rb') as f:
graph_def.ParseFromString(f.read())
inputs, outputs = find_out_terminal_node(graph_def, postfix=True)
graph_def, inputs, outputs = tf_loader.from_graphdef(
self._tf_file, inputs, outputs)
elif self._tf_format == 'SAVED_MODEL':
graph_def, inputs, outputs = tf_loader.from_saved_model(
self._tf_file, None, None)
else:
if self._outputs is None:
raise ImportError("Missing '--outputs' parameter.")
if self._inputs is None:
raise ImportError("Missing '--inputs' parameter.")
inputs = [i + ":0" for i in self._inputs.split(",")]
outputs = [i + ":0" for i in self._outputs.split(",")]
if self._tf_format == 'TF_CKPT_V1':
graph_def = self.load_checkpoint_v1()
elif self._tf_format == 'TF_CKPT_V2':
graph_def, inputs, outputs = tf_loader.from_checkpoint(
self._tf_file, inputs, outputs)
onnx_model = self.convert_to_onnx(graph_def, inputs, outputs)
onnx_importer = OnnxImporter()
onnx_importer.import_from_onnx_model(onnx_model)
return onnx_importer.execute()
def convert(input_ops_dict, output_ops, input_model, output_model):
'''Convert keras h5 to tensorflow pb
Args:
input_ops_dict: input ops dict including names and shapes
output_ops: output op names
input_model: input keras h5 model name
output_model: output pb model name
'''
onnx_name = ".tmp.onnx"
pb_name = ".tmp.pb"
# keras --> onnx --> pb --> onnx --> pb
# keras --> onnx
model = tf.keras.models.load_model(input_model)
onnx_model = keras2onnx.convert_keras(model, model.name, target_opset=8)
keras2onnx.save_model(onnx_model, onnx_name)
# onnx --> tf
onnx_model = onnx.load(onnx_name)
tf_rep = prepare(onnx_model, input_shape_dict=input_ops_dict)
tf_rep.export_graph(pb_name)
# tf --> onnx (fold constants)
inputs = input_ops_dict.keys()
inputs = [i + ":0" for i in inputs]
outputs = output_ops
graph_def, inputs, outputs = tf_loader.from_graphdef(
pb_name, inputs, outputs)
with tf.Graph().as_default() as tf_graph:
tf.import_graph_def(graph_def, name="")
g = tf2onnx.tfonnx.process_tf_graph(tf_graph,
opset=8,
input_names=inputs,
output_names=outputs)
onnx_graph = optimizer.optimize_graph(g)
model_proto = onnx_graph.make_model("converted from %s" % pb_name)
utils.save_protobuf(onnx_name, model_proto)
# onnx --> tf
onnx_model = onnx.load(onnx_name)
tf_rep = prepare(onnx_model, input_shape_dict=input_ops_dict)
tf_rep.export_graph(output_model)
# remove tmp files
if os.path.exists(onnx_name):
os.remove(onnx_name)
if os.path.exists(pb_name):
os.remove(pb_name)
def main():
args = get_args()
logging.basicConfig(level=logging.get_verbosity_level(args.verbose))
if args.debug:
utils.set_debug_mode(True)
logger = logging.getLogger(constants.TF2ONNX_PACKAGE_NAME)
extra_opset = args.extra_opset or []
custom_ops = {}
if args.custom_ops:
# default custom ops for tensorflow-onnx are in the "tf" namespace
custom_ops = {
op: (default_custom_op_handler, [])
for op in args.custom_ops.split(",")
}
extra_opset.append(constants.TENSORFLOW_OPSET)
# get the frozen tensorflow model from graphdef, checkpoint or saved_model.
if args.graphdef:
graph_def, inputs, outputs = tf_loader.from_graphdef(
args.graphdef, args.inputs, args.outputs)
model_path = args.graphdef
if args.checkpoint:
graph_def, inputs, outputs = tf_loader.from_checkpoint(
args.checkpoint, args.inputs, args.outputs)
model_path = args.checkpoint
if args.saved_model:
graph_def, inputs, outputs = tf_loader.from_saved_model(
args.saved_model, args.inputs, args.outputs, args.signature_def)
model_path = args.saved_model
if args.keras:
graph_def, inputs, outputs = tf_loader.from_keras(
args.keras, args.inputs, args.outputs)
model_path = args.keras
if args.verbose:
logger.info("inputs: %s", inputs)
logger.info("outputs: %s", outputs)
with tf.Graph().as_default() as tf_graph:
tf.import_graph_def(graph_def, name='')
with tf_loader.tf_session(graph=tf_graph):
g = process_tf_graph(tf_graph,
continue_on_error=args.continue_on_error,
target=args.target,
opset=args.opset,
custom_op_handlers=custom_ops,
extra_opset=extra_opset,
shape_override=args.shape_override,
input_names=inputs,
output_names=outputs,
inputs_as_nchw=args.inputs_as_nchw)
onnx_graph = optimizer.optimize_graph(g)
model_proto = onnx_graph.make_model("converted from {}".format(model_path))
# write onnx graph
logger.info("")
logger.info("Successfully converted TensorFlow model %s to ONNX",
model_path)
if args.output:
utils.save_protobuf(args.output, model_proto)
logger.info("ONNX model is saved at %s", args.output)
else:
logger.info(
"To export ONNX model to file, please run with `--output` option")
def run_test(self,
name,
backend="caffe2",
onnx_file=None,
opset=None,
extra_opset=None,
perf=None,
fold_const=None):
"""Run complete test against backend."""
self.perf = perf
# get the model
if self.url:
_, dir_name = self.download_model()
logger.info("Downloaded to %s", dir_name)
model_path = os.path.join(
dir_name, self.local) if self.local != "." else dir_name
else:
model_path = self.local
logger.info("Load model from %s", model_path)
input_names = list(self.input_names.keys())
outputs = self.output_names
if self.model_type in ["checkpoint"]:
graph_def, input_names, outputs = tf_loader.from_checkpoint(
model_path, input_names, outputs)
elif self.model_type in ["saved_model"]:
loaded = tf_loader.from_saved_model(
model_path,
input_names,
outputs,
self.tag,
self.signatures,
self.concrete_function,
self.large_model,
return_concrete_func=self.large_model)
if self.large_model:
# Must maintain ref to imported since concrete_func uses weak refs
# pylint: disable=unused-variable
graph_def, input_names, outputs, concrete_func, imported = loaded
else:
graph_def, input_names, outputs = loaded
elif self.model_type in ["keras"]:
graph_def, input_names, outputs = tf_loader.from_keras(
model_path, input_names, outputs)
else:
graph_def, input_names, outputs = tf_loader.from_graphdef(
model_path, input_names, outputs)
if utils.is_debug_mode():
utils.save_protobuf(
os.path.join(TEMP_DIR, name + "_after_tf_optimize.pb"),
graph_def)
if self.large_model:
inputs = {}
for k in input_names:
v = self.input_names[k]
inputs[k.split(":")[0]] = tf.constant(self.make_input(v))
tf_func = tf.function(concrete_func)
logger.info("Running TF")
tf_results_d = tf_func(**inputs)
if self.structured_outputs is None:
tf_results = list(tf_results_d.values())
else:
tf_results = [
tf_results_d[output] for output in self.structured_outputs
]
if self.perf:
logger.info("Running TF perf")
start = time.time()
for _ in range(PERFITER):
_ = concrete_func(**inputs)
self.tf_runtime = time.time() - start
logger.info("TensorFlow OK")
inputs = {}
shape_override = {}
tf_reset_default_graph()
from tf2onnx.tf_utils import compress_graph_def
const_node_values = None
with tf.Graph().as_default() as tf_graph:
if self.large_model:
const_node_values = compress_graph_def(graph_def)
tf.import_graph_def(graph_def, name='')
with tf_session(graph=tf_graph) as sess:
# create the input data
for k in input_names:
v = self.input_names[k]
t = sess.graph.get_tensor_by_name(k)
expected_dtype = tf.as_dtype(t.dtype).name
if isinstance(v, six.text_type) and v.startswith("np."):
np_value = eval(v) # pylint: disable=eval-used
if expected_dtype != np_value.dtype:
logger.warning(
"dtype mismatch for input %s: expected=%s, actual=%s",
k, expected_dtype, np_value.dtype)
inputs[k] = np_value.astype(expected_dtype)
else:
inputs[k] = self.make_input(v).astype(expected_dtype)
if self.force_input_shape:
for k, v in inputs.items():
shape_override[k] = list(v.shape)
# run the model with tensorflow
if self.skip_tensorflow:
logger.info("TensorFlow SKIPPED")
elif not self.large_model:
tf_results = self.run_tensorflow(sess, inputs)
logger.info("TensorFlow OK")
model_proto = None
if self.skip_conversion:
if self.large_model:
external_tensor_storage = ExternalTensorStorage()
model_proto = utils.model_proto_from_zip(
self.converted_model, external_tensor_storage)
else:
external_tensor_storage = None
model_proto = utils.model_proto_from_file(self.converted_model)
logger.info("ONNX loaded from file")
else:
try:
# convert model to onnx
onnx_graph = self.to_onnx(sess.graph,
opset=opset,
extra_opset=extra_opset,
shape_override=shape_override,
input_names=inputs.keys(),
const_node_values=const_node_values)
onnx_graph = optimizer.optimize_graph(onnx_graph)
print("ONNX", onnx_graph.dump_node_statistics())
external_tensor_storage = ExternalTensorStorage(
) if self.large_model else None
model_proto = onnx_graph.make_model(
"converted from tf2onnx",
external_tensor_storage=external_tensor_storage)
logger.info("To_ONNX, OK")
if onnx_file:
self.create_onnx_file(name, model_proto, inputs, onnx_file,
external_tensor_storage)
if self.converted_model:
if self.large_model:
utils.save_onnx_zip(self.converted_model, model_proto,
external_tensor_storage)
else:
utils.save_protobuf(self.converted_model, model_proto)
logger.info("Created %s", self.converted_model)
except Exception:
logger.error("To_ONNX FAIL", exc_info=1)
return False
try:
onnx_results = None
if backend == "caffe2":
onnx_results = self.run_caffe2(name, model_proto, inputs)
elif backend == "onnxruntime":
onnx_results = self.run_onnxruntime(name, model_proto, inputs,
external_tensor_storage)
else:
raise ValueError("unknown backend")
logger.info("Run_ONNX OK")
try:
if self.skip_tensorflow:
logger.info("Results: skipped tensorflow")
else:
if self.check_only_shape:
for tf_res, onnx_res in zip(tf_results, onnx_results):
np.testing.assert_array_equal(
tf_res.shape, onnx_res.shape)
else:
for tf_res, onnx_res in zip(tf_results, onnx_results):
np.testing.assert_allclose(tf_res,
onnx_res,
rtol=self.rtol,
atol=self.atol)
logger.info("Results: OK")
return True
except Exception:
logger.error("Results", exc_info=1)
except Exception:
logger.error("Run_ONNX FAIL", exc_info=1)
return False
def run_test(self,
name,
backend="onnxruntime",
onnx_file=None,
opset=None,
extra_opset=None,
perf=None):
"""Run complete test against backend."""
self.perf = perf
# get the model
if self.url:
_, dir_name = self.download_model()
logger.info("Downloaded to %s", dir_name)
model_path = os.path.join(
dir_name, self.local) if self.local != "." else dir_name
else:
model_path = self.local
logger.info("Load model from %s", model_path)
input_names = list(self.input_names.keys())
initialized_tables = {}
outputs = self.output_names
tflite_path = None
to_rename = None
if self.model_type in ["checkpoint"]:
graph_def, input_names, outputs = tf_loader.from_checkpoint(
model_path, input_names, outputs)
elif self.model_type in ["saved_model"]:
loaded = tf_loader.from_saved_model(
model_path,
None,
None,
self.tag,
self.signatures,
self.concrete_function,
self.large_model,
return_concrete_func=not self.run_tf_frozen,
return_initialized_tables=True,
return_tensors_to_rename=True)
if not self.run_tf_frozen:
# Must maintain ref to imported since concrete_func uses weak refs
# pylint: disable=unused-variable
graph_def, input_names, outputs, concrete_func, imported, initialized_tables, to_rename = loaded
else:
graph_def, input_names, outputs, initialized_tables, to_rename = loaded
elif self.model_type in ["keras"]:
graph_def, input_names, outputs = tf_loader.from_keras(
model_path, input_names, outputs)
elif self.model_type in ["tflite"]:
tflite_path = model_path
graph_def = None
else:
graph_def, input_names, outputs = tf_loader.from_graphdef(
model_path, input_names, outputs)
if utils.is_debug_mode():
utils.save_protobuf(
os.path.join(TEMP_DIR, name + "_after_tf_optimize.pb"),
graph_def)
if tflite_path is not None:
inputs = {}
for k in input_names:
v = self.input_names[k]
inputs[k] = self.make_input(v)
interpreter = tf.lite.Interpreter(tflite_path)
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_name_to_index = {
n['name'].split(':')[0]: n['index']
for n in input_details
}
for k, v in inputs.items():
interpreter.resize_tensor_input(input_name_to_index[k],
v.shape)
interpreter.allocate_tensors()
def run_tflite():
for k, v in inputs.items():
interpreter.set_tensor(input_name_to_index[k], v)
interpreter.invoke()
result = [
interpreter.get_tensor(output['index'])
for output in output_details
]
return result
tf_results = run_tflite()
if self.perf:
logger.info("Running TFLite perf")
n = 0
start = time.time()
stop = start + PERF_TIME
while time.time() < stop:
for _ in range(PERF_STEP):
_ = run_tflite()
n += PERF_STEP
self.tf_runtime = 1000 * (time.time() - start) / n
logger.info("TFLite perf {:.2f}ms/inference, n={}".format(
self.tf_runtime, n))
logger.info("TFLite OK")
if not self.run_tf_frozen:
inputs = {}
for k in input_names:
v = self.input_names[k]
inputs[k.split(":")[0]] = tf.constant(self.make_input(v))
tf_func = tf.function(concrete_func)
logger.info("Running TF")
tf_results_d = tf_func(**inputs)
# If there is only a single output a dict might not be returned
if isinstance(tf_results_d, tf.Tensor):
tf_results = [tf_results_d]
else:
tf_results = [
tf_results_d[k] for k in sorted(tf_results_d.keys())
]
tf_results = [tf_res.numpy() for tf_res in tf_results]
if self.perf:
logger.info("Running TF perf")
n = 0
start = time.time()
stop = start + PERF_TIME
if self.tf_profile is not None:
tf.profiler.experimental.start(self.tf_profile)
while time.time() < stop:
for _ in range(PERF_STEP):
_ = concrete_func(**inputs)
n += PERF_STEP
if self.tf_profile is not None:
tf.profiler.experimental.stop()
self.tf_runtime = 1000 * (time.time() - start) / n
logger.info("TF perf {:.2f}ms/inference, n={}".format(
self.tf_runtime, n))
logger.info("TensorFlow OK")
shape_override = {}
const_node_values = None
tf_graph = None
if graph_def is not None:
inputs = {}
tf_reset_default_graph()
with tf.Graph().as_default() as tf_graph:
from tf2onnx.tf_utils import compress_graph_def
if self.large_model:
const_node_values = compress_graph_def(graph_def)
tf.import_graph_def(graph_def, name='')
with tf_session(graph=tf_graph) as sess:
# create the input data
for k in input_names:
v = self.input_names[k]
t = sess.graph.get_tensor_by_name(k)
expected_dtype = tf.as_dtype(t.dtype).name
if isinstance(v, six.text_type) and v.startswith("np."):
np_value = eval(v) # pylint: disable=eval-used
if expected_dtype != np_value.dtype:
logger.warning(
"dtype mismatch for input %s: expected=%s, actual=%s",
k, expected_dtype, np_value.dtype)
inputs[k] = np_value.astype(expected_dtype)
else:
if expected_dtype == "string":
inputs[k] = self.make_input(v).astype(
np.str).astype(np.object)
else:
inputs[k] = self.make_input(v).astype(
expected_dtype)
if self.force_input_shape:
for k, v in inputs.items():
shape_override[k] = list(v.shape)
# run the model with tensorflow
if self.skip_tensorflow:
logger.info("TensorFlow SKIPPED")
elif self.run_tf_frozen:
if self.tf_profile is not None:
tf.profiler.experimental.start(self.tf_profile)
tf_results = self.run_tensorflow(sess, inputs)
if self.tf_profile is not None:
tf.profiler.experimental.stop()
logger.info("TensorFlow OK")
tf_graph = sess.graph
model_proto = None
if self.skip_conversion:
if self.large_model:
external_tensor_storage = ExternalTensorStorage()
model_proto = utils.model_proto_from_zip(
self.converted_model, external_tensor_storage)
else:
external_tensor_storage = None
model_proto = utils.model_proto_from_file(self.converted_model)
logger.info("ONNX loaded from file")
else:
try:
# convert model to onnx
onnx_graph = self.to_onnx(
tf_graph,
opset=opset,
extra_opset=extra_opset,
shape_override=shape_override,
input_names=inputs.keys(),
const_node_values=const_node_values,
initialized_tables=initialized_tables,
tflite_path=tflite_path,
tensors_to_rename=to_rename)
onnx_graph = optimizer.optimize_graph(onnx_graph)
print("ONNX", onnx_graph.dump_node_statistics())
external_tensor_storage = ExternalTensorStorage(
) if self.large_model else None
model_proto = onnx_graph.make_model(
"converted from tf2onnx",
external_tensor_storage=external_tensor_storage)
logger.info("To_ONNX, OK")
if onnx_file:
self.create_onnx_file(name, model_proto, inputs, onnx_file,
external_tensor_storage)
if self.converted_model:
if self.large_model:
utils.save_onnx_zip(self.converted_model, model_proto,
external_tensor_storage)
else:
utils.save_protobuf(self.converted_model, model_proto)
logger.info("Created %s", self.converted_model)
except Exception:
logger.error("To_ONNX FAIL", exc_info=1)
return False
try:
onnx_results = None
if backend == "onnxruntime":
if to_rename is None:
struc_outputs = self.output_names
else:
struc_outputs = [
to_rename.get(k, k) for k in self.output_names
]
onnx_results = self.run_onnxruntime(name, model_proto, inputs,
struc_outputs,
external_tensor_storage)
else:
raise ValueError("unknown backend")
logger.info("Run_ONNX OK")
try:
if self.skip_tensorflow:
logger.info("Results: skipped tensorflow")
else:
if self.check_only_shape:
for tf_res, onnx_res in zip(tf_results, onnx_results):
np.testing.assert_array_equal(
tf_res.shape, onnx_res.shape)
else:
for tf_res, onnx_res in zip(tf_results, onnx_results):
good_cnt = np.count_nonzero(
np.isclose(tf_res,
onnx_res,
rtol=self.rtol,
atol=self.atol))
bad_cnt = tf_res.size - good_cnt
if bad_cnt > self.ptol / 100 * tf_res.size:
# Prints a nice error message with stats
np.testing.assert_allclose(tf_res,
onnx_res,
rtol=self.rtol,
atol=self.atol)
logger.info("Results: OK")
return True
except Exception:
logger.error("Results", exc_info=1)
except Exception:
logger.error("Run_ONNX FAIL", exc_info=1)
return False
def run_test(self,
name,
backend="caffe2",
onnx_file=None,
opset=None,
extra_opset=None,
perf=None,
fold_const=None):
"""Run complete test against backend."""
self.perf = perf
# get the model
if self.url:
_, dir_name = self.download_model()
logger.info("Downloaded to %s", dir_name)
model_path = os.path.join(dir_name, self.local)
else:
model_path = self.local
logger.info("Load model from %s", model_path)
input_names = list(self.input_names.keys())
outputs = self.output_names
if self.model_type in ["checkpoint"]:
graph_def, input_names, outputs = tf_loader.from_checkpoint(
model_path, input_names, outputs)
elif self.model_type in ["saved_model"]:
graph_def, input_names, outputs = tf_loader.from_saved_model(
model_path, input_names, outputs)
else:
graph_def, input_names, outputs = tf_loader.from_graphdef(
model_path, input_names, outputs)
# remove unused input names
input_names = list(
set(input_names).intersection(self.input_names.keys()))
graph_def = tf2onnx.tfonnx.tf_optimize(input_names, self.output_names,
graph_def, fold_const)
if utils.is_debug_mode():
utils.save_protobuf(
os.path.join(TEMP_DIR, name + "_after_tf_optimize.pb"),
graph_def)
inputs = {}
shape_override = {}
g = tf.import_graph_def(graph_def, name='')
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True),
graph=g) as sess:
# create the input data
for k in input_names:
v = self.input_names[k]
t = sess.graph.get_tensor_by_name(k)
expected_dtype = tf.as_dtype(t.dtype).name
if isinstance(v, six.text_type) and v.startswith("np."):
np_value = eval(v) # pylint: disable=eval-used
if expected_dtype != np_value.dtype:
logger.warning(
"dtype mismatch for input %s: expected=%s, actual=%s",
k, expected_dtype, np_value.dtype)
inputs[k] = np_value.astype(expected_dtype)
else:
inputs[k] = self.make_input(v).astype(expected_dtype)
if self.force_input_shape:
for k, v in inputs.items():
shape_override[k] = list(v.shape)
# run the model with tensorflow
if self.skip_tensorflow:
logger.info("TensorFlow SKIPPED")
else:
tf_results = self.run_tensorflow(sess, inputs)
logger.info("TensorFlow OK")
model_proto = None
try:
# convert model to onnx
onnx_graph = self.to_onnx(sess.graph,
opset=opset,
extra_opset=extra_opset,
shape_override=shape_override,
input_names=inputs.keys())
onnx_graph = optimizer.optimize_graph(onnx_graph)
model_proto = onnx_graph.make_model("converted from tf2onnx")
logger.info("To_ONNX, OK")
if onnx_file:
self.create_onnx_file(name, model_proto, inputs, onnx_file)
except Exception:
logger.error("To_ONNX FAIL", exc_info=1)
return False
try:
onnx_results = None
if backend == "caffe2":
onnx_results = self.run_caffe2(name, model_proto, inputs)
elif backend == "onnxruntime":
onnx_results = self.run_onnxruntime(name, model_proto, inputs)
else:
raise ValueError("unknown backend")
logger.info("Run_ONNX OK")
try:
if self.skip_tensorflow:
logger.info("Results: skipped tensorflow")
else:
if self.check_only_shape:
for tf_res, onnx_res in zip(tf_results, onnx_results):
np.testing.assert_array_equal(
tf_res.shape, onnx_res.shape)
else:
for tf_res, onnx_res in zip(tf_results, onnx_results):
np.testing.assert_allclose(tf_res,
onnx_res,
rtol=self.rtol,
atol=self.atol)
logger.info("Results: OK")
return True
except Exception:
logger.error("Results", exc_info=1)
except Exception:
logger.error("Run_ONNX FAIL", exc_info=1)
return False
def main():
args = get_args()
logging.basicConfig(level=logging.get_verbosity_level(args.verbose))
if args.debug:
utils.set_debug_mode(True)
logger = logging.getLogger(constants.TF2ONNX_PACKAGE_NAME)
extra_opset = args.extra_opset or []
tflite_path = None
custom_ops = {}
initialized_tables = None
tensors_to_rename = {}
if args.custom_ops:
using_tf_opset = False
for op in args.custom_ops.split(","):
if ":" in op:
op, domain = op.split(":")
else:
# default custom ops for tensorflow-onnx are in the "tf" namespace
using_tf_opset = True
domain = constants.TENSORFLOW_OPSET.domain
custom_ops[op] = (make_default_custom_op_handler(domain), [])
if using_tf_opset:
extra_opset.append(constants.TENSORFLOW_OPSET)
if any(opset.domain == constants.CONTRIB_OPS_DOMAIN
for opset in extra_opset):
try:
import tensorflow_text # pylint: disable=import-outside-toplevel
except ModuleNotFoundError:
logger.warning(
"tensorflow_text not installed. Model will fail to load if tensorflow_text ops are used."
)
# get the frozen tensorflow model from graphdef, checkpoint or saved_model.
graph_def = None
inputs = None
outputs = None
model_path = None
if args.graphdef:
graph_def, inputs, outputs = tf_loader.from_graphdef(
args.graphdef, args.inputs, args.outputs)
model_path = args.graphdef
if args.checkpoint:
graph_def, inputs, outputs = tf_loader.from_checkpoint(
args.checkpoint, args.inputs, args.outputs)
model_path = args.checkpoint
if args.saved_model:
graph_def, inputs, outputs, initialized_tables, tensors_to_rename = tf_loader.from_saved_model(
args.saved_model,
args.inputs,
args.outputs,
args.tag,
args.signature_def,
args.concrete_function,
args.large_model,
return_initialized_tables=True,
return_tensors_to_rename=True)
model_path = args.saved_model
if args.keras:
graph_def, inputs, outputs = tf_loader.from_keras(
args.keras, args.inputs, args.outputs)
model_path = args.keras
if args.tflite:
# Optional, but used to cut graph if provided.
inputs = args.inputs
outputs = args.outputs
tflite_path = args.tflite
model_path = tflite_path
if args.verbose:
logger.info("inputs: %s", inputs)
logger.info("outputs: %s", outputs)
if args.rename_inputs:
tensors_to_rename.update(zip(inputs, args.rename_inputs))
if args.rename_outputs:
tensors_to_rename.update(zip(outputs, args.rename_outputs))
with tf.device("/cpu:0"):
model_proto, _ = _convert_common(
graph_def,
name=model_path,
continue_on_error=args.continue_on_error,
target=args.target,
opset=args.opset,
custom_op_handlers=custom_ops,
extra_opset=extra_opset,
shape_override=args.shape_override,
input_names=inputs,
output_names=outputs,
inputs_as_nchw=args.inputs_as_nchw,
large_model=args.large_model,
tensors_to_rename=tensors_to_rename,
ignore_default=args.ignore_default,
use_default=args.use_default,
tflite_path=tflite_path,
dequantize=args.dequantize,
initialized_tables=initialized_tables,
output_frozen_graph=args.output_frozen_graph,
output_path=args.output)
# write onnx graph
logger.info("")
logger.info("Successfully converted TensorFlow model %s to ONNX",
model_path)
logger.info("Model inputs: %s", [n.name for n in model_proto.graph.input])
logger.info("Model outputs: %s",
[n.name for n in model_proto.graph.output])
if args.output:
if args.large_model:
logger.info(
"Zipped ONNX model is saved at %s. Unzip before opening in onnxruntime.",
args.output)
else:
logger.info("ONNX model is saved at %s", args.output)
else:
logger.info(
"To export ONNX model to file, please run with `--output` option")
def main():
args = get_args()
logging.basicConfig(level=logging.get_verbosity_level(args.verbose))
if args.debug:
utils.set_debug_mode(True)
logger = logging.getLogger(constants.TF2ONNX_PACKAGE_NAME)
extra_opset = args.extra_opset or []
custom_ops = {}
initialized_tables = None
if args.custom_ops:
using_tf_opset = False
for op in args.custom_ops.split(","):
if ":" in op:
op, domain = op.split(":")
else:
# default custom ops for tensorflow-onnx are in the "tf" namespace
using_tf_opset = True
domain = constants.TENSORFLOW_OPSET.domain
custom_ops[op] = (make_default_custom_op_handler(domain), [])
if using_tf_opset:
extra_opset.append(constants.TENSORFLOW_OPSET)
# get the frozen tensorflow model from graphdef, checkpoint or saved_model.
if args.graphdef:
graph_def, inputs, outputs = tf_loader.from_graphdef(
args.graphdef, args.inputs, args.outputs)
model_path = args.graphdef
if args.checkpoint:
graph_def, inputs, outputs = tf_loader.from_checkpoint(
args.checkpoint, args.inputs, args.outputs)
model_path = args.checkpoint
if args.saved_model:
graph_def, inputs, outputs, initialized_tables = tf_loader.from_saved_model(
args.saved_model,
args.inputs,
args.outputs,
args.tag,
args.signature_def,
args.concrete_function,
args.large_model,
return_initialized_tables=True)
model_path = args.saved_model
if args.keras:
graph_def, inputs, outputs = tf_loader.from_keras(
args.keras, args.inputs, args.outputs)
model_path = args.keras
if args.verbose:
logger.info("inputs: %s", inputs)
logger.info("outputs: %s", outputs)
with tf.Graph().as_default() as tf_graph:
const_node_values = None
if args.large_model:
const_node_values = compress_graph_def(graph_def)
if args.output_frozen_graph:
utils.save_protobuf(args.output_frozen_graph, graph_def)
tf.import_graph_def(graph_def, name='')
with tf_loader.tf_session(graph=tf_graph):
g = process_tf_graph(tf_graph,
continue_on_error=args.continue_on_error,
target=args.target,
opset=args.opset,
custom_op_handlers=custom_ops,
extra_opset=extra_opset,
shape_override=args.shape_override,
input_names=inputs,
output_names=outputs,
inputs_as_nchw=args.inputs_as_nchw,
ignore_default=args.ignore_default,
use_default=args.use_default,
const_node_values=const_node_values,
initialized_tables=initialized_tables)
onnx_graph = optimizer.optimize_graph(g)
tensor_storage = ExternalTensorStorage() if args.large_model else None
model_proto = onnx_graph.make_model("converted from {}".format(model_path),
external_tensor_storage=tensor_storage)
# write onnx graph
logger.info("")
logger.info("Successfully converted TensorFlow model %s to ONNX",
model_path)
if args.output:
if args.large_model:
utils.save_onnx_zip(args.output, model_proto, tensor_storage)
logger.info(
"Zipped ONNX model is saved at %s. Unzip before opening in onnxruntime.",
args.output)
else:
utils.save_protobuf(args.output, model_proto)
logger.info("ONNX model is saved at %s", args.output)
else:
logger.info(
"To export ONNX model to file, please run with `--output` option")
def pb_to_onnx(
inputs: List[Union[str, tf_compat.Tensor]],
outputs: List[Union[str, tf_compat.Tensor]],
pb_path: str,
onnx_path: str,
opset: int = default_onnx_opset(),
custom_op_handlers=None,
extra_opset=None,
shape_override: Dict[str, List] = None,
):
"""
Export an ONNX format for the graph from PB format.
Should not be called within an active graph or session.
:param inputs: the inputs the graph should be created for,
can be either a list of names or a list of tensors
:param outputs: the outputs the graph should be created for,
can be either a list of names or a list of tensors
:param pb_path: path to the existing PB file
:param onnx_path: path to the output ONNX file
:param opset: ONNX opset
:param custom_op_handlers: dictionary of custom op handlers
:param extra_opset: list of extra opset's
:param shape_override: new shape to override
"""
try:
from tf2onnx import constants, optimizer, utils
from tf2onnx.tfonnx import process_tf_graph, tf_optimize
except ModuleNotFoundError:
raise ModuleNotFoundError(
"tf2onnx must be installed on the system before using export_onnx"
)
try:
from tf2onnx import tf_loader as loader
except Exception:
from tf2onnx import loader
pb_path = clean_path(pb_path)
if not os.path.exists(pb_path):
raise FileNotFoundError(
("no pb file for the model found at {}").format(pb_path)
)
inputs = [inp if isinstance(inp, str) else inp.name for inp in inputs]
outputs = [out if isinstance(out, str) else out.name for out in outputs]
graph_def, inputs, outputs = loader.from_graphdef(pb_path, inputs, outputs)
graph_def = tf_optimize(inputs, outputs, graph_def, fold_constant=True)
with tf_compat.Graph().as_default() as tf_graph:
tf_compat.import_graph_def(graph_def, name="")
with tf_compat.Session(graph=tf_graph):
graph = process_tf_graph(
tf_graph,
continue_on_error=False,
target=",".join(constants.DEFAULT_TARGET),
opset=opset,
custom_op_handlers=custom_op_handlers,
extra_opset=extra_opset,
shape_override=shape_override,
input_names=inputs,
output_names=outputs,
)
onnx_graph = optimizer.optimize_graph(graph)
model_proto = onnx_graph.make_model("converted from {}".format(pb_path))
onnx_path = clean_path(onnx_path)
create_parent_dirs(onnx_path)
utils.save_protobuf(onnx_path, model_proto)
for n in onnx_nodes:
if n.input and n.input[0] not in nodes_outputs:
continue
if len(n.output) == 0:
n.output.append(n.name + ':0')
graph_output_names.add(n.output[0])
else:
output_name = n.output[0]
if (output_name not in nodes_inputs) and (0 < len(n.input)):
graph_output_names.add(output_name)
logging.info('Model Inputs: %s', str(list(graph_input_names)))
logging.info('Model Outputs: %s', str(list(graph_output_names)))
graph_def, inputs, outputs = tf_loader.from_graphdef(
model_path=args.in_file,
input_names=list(graph_input_names),
output_names=list(graph_output_names))
with tf.Graph().as_default() as tf_graph:
tf.import_graph_def(graph_def, name='')
if 160 <= TF2ONNX_VERSION:
with tf_loader.tf_session(graph=tf_graph):
onnx_graph = tf2onnx.tfonnx.process_tf_graph(tf_graph=tf_graph,
input_names=inputs,
output_names=outputs,
opset=9)
else:
with tf.Session(graph=tf_graph):
onnx_graph = tf2onnx.tfonnx.process_tf_graph(tf_graph=tf_graph,
input_names=inputs,
def tf2onnx_flow(pb_path: str, test_mode =False) -> onnx.ModelProto:
"""Convert frozen graph pb file into onnx
Args:
pb_path (str): input pb file path
test_mode (bool, optional): test mode. Defaults to False.
Raises:
Exception: invalid input file
Returns:
onnx.ModelProto: converted onnx
"""
TF2ONNX_VERSION = int(tf2onnx.version.version.replace('.', ''))
if 160 <= TF2ONNX_VERSION:
from tf2onnx import tf_loader
else:
from tf2onnx import loader as tf_loader
if pb_path[-3:] == '.pb':
model_name = pb_path.split('/')[-1][:-3]
# always reset tensorflow session at begin
tf.reset_default_graph()
with tf.Session() as sess:
with gfile.FastGFile(pb_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
if 160 <= int(tf2onnx.version.version.replace('.', '')):
onnx_nodes, op_cnt, attr_cnt, output_shapes, dtypes, functions = tf2onnx.tf_utils.tflist_to_onnx(
sess.graph,
{})
else:
onnx_nodes, op_cnt, attr_cnt, output_shapes, dtypes = tf2onnx.tfonnx.tflist_to_onnx(
sess.graph.get_operations(),
{})
for n in onnx_nodes:
if len(n.output) == 0:
onnx_nodes.remove(n)
# find inputs and outputs of graph
nodes_inputs = set()
nodes_outputs = set()
for n in onnx_nodes:
if n.op_type == 'Placeholder':
continue
for input in n.input:
nodes_inputs.add(input)
for output in n.output:
nodes_outputs.add(output)
graph_input_names = set()
for input_name in nodes_inputs:
if input_name not in nodes_outputs:
graph_input_names.add(input_name)
graph_output_names = set()
for n in onnx_nodes:
if n.input and n.input[0] not in nodes_outputs:
continue
if len(n.output) == 0:
n.output.append(n.name + ':0')
graph_output_names.add(n.output[0])
else:
output_name = n.output[0]
if (output_name not in nodes_inputs) and (0 < len(n.input)):
graph_output_names.add(output_name)
logging.info('Model Inputs: %s', str(list(graph_input_names)))
logging.info('Model Outputs: %s', str(list(graph_output_names)))
graph_def, inputs, outputs = tf_loader.from_graphdef(model_path=pb_path,
input_names=list(graph_input_names),
output_names=list(graph_output_names))
with tf.Graph().as_default() as tf_graph:
tf.import_graph_def(graph_def, name='')
if 160 <= TF2ONNX_VERSION:
with tf_loader.tf_session(graph=tf_graph):
onnx_graph = tf2onnx.tfonnx.process_tf_graph(tf_graph=tf_graph,
input_names=inputs,
output_names=outputs,
opset=11)
else:
with tf.Session(graph=tf_graph):
onnx_graph = tf2onnx.tfonnx.process_tf_graph(tf_graph=tf_graph,
input_names=inputs,
output_names=outputs,
opset=11)
# Optimize with tf2onnx.optimizer
onnx_graph = tf2onnx.optimizer.optimize_graph(onnx_graph)
model_proto = onnx_graph.make_model(model_name)
# Make tf2onnx output compatible with the spec. of onnx.utils.polish_model
replacing.replace_initializer_with_Constant(model_proto.graph)
model_proto = onnx.utils.polish_model(model_proto)
else:
raise Exception('expect .pb file as input, but got "' + str(pb_path) + '"')
# rename
m = model_proto
m = combo.preprocess(m)
m = combo.common_optimization(m)
m = combo.tensorflow_optimization(m)
m = combo.postprocess(m)
if not test_mode:
g = m.graph
eliminating.eliminate_shape_changing_after_input(g)
m = onnx.utils.polish_model(m)
return m
