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 = loader.from_graphdef(args.graphdef, args.inputs, args.outputs)
model_path = args.graphdef
if args.checkpoint:
graph_def, inputs, outputs = loader.from_checkpoint(args.checkpoint, args.inputs, args.outputs)
model_path = args.checkpoint
if args.saved_model:
graph_def, inputs, outputs = loader.from_saved_model(
args.saved_model, args.inputs, args.outputs, args.signature_def)
model_path = args.saved_model
if args.verbose:
logger.info("inputs: %s", inputs)
logger.info("outputs: %s", outputs)
# todo: consider to enable const folding by default?
graph_def = tf_optimize(inputs, outputs, graph_def, args.fold_const)
with tf.Graph().as_default() as tf_graph:
tf.import_graph_def(graph_def, name='')
with 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 main():
args = get_args()
logging.basicConfig(level=logging.get_verbosity_level(args.verbose))
if args.debug:
utils.set_debug_mode(True)
Test.cache_dir = args.cache
Test.target = args.target
tests = load_tests_from_yaml(args.config)
if args.list:
logger.info(sorted(tests.keys()))
return 0
if args.tests:
test_keys = args.tests.split(",")
else:
test_keys = list(tests.keys())
failed = 0
count = 0
for test in test_keys:
logger.info("===================================")
t = tests[test]
if args.tests is None:
if t.disabled and not args.include_disabled:
logger.info("Skip %s: disabled", test)
continue
condition, reason = t.check_opset_constraints(args.opset, args.extra_opset)
if not condition:
logger.info("Skip %s: %s", test, reason)
continue
count += 1
try:
logger.info("Running %s", test)
ret = t.run_test(test, backend=args.backend, onnx_file=args.onnx_file,
opset=args.opset, extra_opset=args.extra_opset, perf=args.perf,
fold_const=args.fold_const)
except Exception:
logger.error("Failed to run %s", test, exc_info=1)
ret = None
finally:
if not utils.is_debug_mode():
utils.delete_directory(TEMP_DIR)
if not ret:
failed += 1
logger.info("===================================")
logger.info("RESULT: %s failed of %s, backend=%s", failed, count, args.backend)
if args.perf:
with open(args.perf, "w") as f:
f.write("test,tensorflow,onnx\n")
for test in test_keys:
t = tests[test]
if t.perf:
f.write("{},{},{}\n".format(test, t.tf_runtime, t.onnx_runtime))
return failed
def main():
args = get_args()
logging.basicConfig(level=logging.get_verbosity_level(args.verbose))
if args.debug:
utils.set_debug_mode(True)
Test.cache_dir = args.cache
Test.target = args.target
tests = tests_from_yaml(args.config)
if args.list:
print(sorted(tests.keys()))
return 0
if args.tests:
test_keys = args.tests.split(",")
else:
test_keys = list(tests.keys())
failed = 0
count = 0
for test in test_keys:
t = tests[test]
if args.tests is None and t.disabled and not args.include_disabled:
continue
count += 1
try:
ret = t.run_test(test,
backend=args.backend,
onnx_file=args.onnx_file,
opset=args.opset,
extra_opset=args.extra_opset,
perf=args.perf,
fold_const=args.fold_const)
except Exception as ex:
ret = None
tb = traceback.format_exc()
print(ex, tb)
finally:
if not utils.is_debug_mode():
utils.delete_directory(TEMP_DIR)
if not ret:
failed += 1
print("=== RESULT: {} failed of {}, backend={}".format(
failed, count, args.backend))
if args.perf:
with open(args.perf, "w") as f:
f.write("test,tensorflow,onnx\n")
for test in test_keys:
t = tests[test]
if t.perf:
f.write("{},{},{}\n".format(test, t.tf_runtime,
t.onnx_runtime))
return failed
def convert_onnx(sess, graph_def, input_path, inputs_op, outputs_op):
graphdef = input_path
if inputs_op:
inputs_op, shape_override = utils.split_nodename_and_shape(inputs_op)
if outputs_op:
outputs_op = outputs_op.split(",")
logging.basicConfig(level=logging.get_verbosity_level(True))
utils.set_debug_mode(True)
logger = logging.getLogger(constants.TF2ONNX_PACKAGE_NAME)
graph_def, inputs_op, outputs_op = from_graphdef(sess, graph_def, graphdef,
inputs_op, outputs_op)
model_path = graphdef
graph_def = tf_optimize(inputs_op, outputs_op, graph_def, True)
with tf.Graph().as_default() as tf_graph:
tf.import_graph_def(graph_def, name='')
with tf.Session(graph=tf_graph):
g = process_tf_graph(tf_graph,
continue_on_error=False,
target=",".join(constants.DEFAULT_TARGET),
opset=10,
custom_op_handlers=None,
extra_opset=None,
shape_override=None,
input_names=inputs_op,
output_names=outputs_op,
inputs_as_nchw=None)
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:
output_path = input_path.replace(".pb", ".onnx")
utils.save_protobuf(output_path, model_proto)
logger.info("ONNX model is saved at %s", output_path)
def unittest_main():
config = get_test_config()
logging.basicConfig(level=config.log_level)
with logging.set_scope_level(logging.INFO) as logger:
logger.info(config)
unittest.main()
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:
# 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, 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,
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 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 pytest_configure():
config = get_test_config()
logging.basicConfig(level=config.log_level)
with logging.set_scope_level(logging.INFO) as logger:
logger.info(config)
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
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.
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.tflite:
graph_def = None
inputs = None
outputs = None
tflite_path = args.tflite
model_path = tflite_path
if args.verbose:
logger.info("inputs: %s", inputs)
logger.info("outputs: %s", outputs)
tf_graph = None
const_node_values = None
if graph_def is not None:
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,
tflite_path=tflite_path,
dequantize=args.dequantize)
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")