def get_args():
"""Parse commandline."""
parser = argparse.ArgumentParser()
parser.add_argument("--cache", default="/tmp/pre-trained", help="pre-trained models cache dir")
parser.add_argument("--config", default="tests/run_pretrained_models.yaml", help="yaml config to use")
parser.add_argument("--tests", help="tests to run")
parser.add_argument("--target", default="", help="target platform")
parser.add_argument("--backend", default="onnxruntime",
choices=["caffe2", "onnxmsrtnext", "onnxruntime"], help="backend to use")
parser.add_argument("--opset", type=int, default=None, help="opset to use")
parser.add_argument("--extra_opset", default=None,
help="extra opset with format like domain:version, e.g. com.microsoft:1")
parser.add_argument("--verbose", "-v", help="verbose output, option is additive", action="count")
parser.add_argument("--debug", help="debug mode", action="store_true")
parser.add_argument("--list", help="list tests", action="store_true")
parser.add_argument("--onnx-file", help="create onnx file in directory")
parser.add_argument("--perf", help="capture performance numbers")
parser.add_argument("--fold_const", help="enable tf constant_folding transformation before conversion",
action="store_true")
parser.add_argument("--include-disabled", help="include disabled tests", action="store_true")
args = parser.parse_args()
args.target = args.target.split(",")
if args.extra_opset:
tokens = args.extra_opset.split(':')
if len(tokens) != 2:
raise ValueError("invalid extra_opset argument")
args.extra_opset = [utils.make_opsetid(tokens[0], int(tokens[1]))]
return args
def to_onnx(self,
tf_graph,
opset=None,
extra_opset=None,
shape_override=None,
input_names=None,
const_node_values=None,
initialized_tables=None,
tflite_path=None):
"""Convert graph to tensorflow."""
if extra_opset is None:
extra_opset = []
if self.use_custom_ops:
extra_opset.append(
utils.make_opsetid(constants.CONTRIB_OPS_DOMAIN, 1))
return process_tf_graph(tf_graph,
continue_on_error=False,
opset=opset,
extra_opset=extra_opset,
target=Test.target,
shape_override=shape_override,
input_names=input_names,
output_names=self.output_names,
const_node_values=const_node_values,
initialized_tables=initialized_tables,
tflite_path=tflite_path,
dequantize=self.dequantize)
def get_args():
"""Parse commandline."""
parser = argparse.ArgumentParser(description="Convert tensorflow graphs to ONNX.",
formatter_class=argparse.RawDescriptionHelpFormatter, epilog=_HELP_TEXT)
parser.add_argument("--input", help="input from graphdef")
parser.add_argument("--graphdef", help="input from graphdef")
parser.add_argument("--saved-model", help="input from saved model")
parser.add_argument("--tag", help="tag to use for saved_model")
parser.add_argument("--signature_def", help="signature_def from saved_model to use")
parser.add_argument("--concrete_function", type=int, default=None,
help="For TF2.x saved_model, index of func signature in __call__ (--signature_def is ignored)")
parser.add_argument("--checkpoint", help="input from checkpoint")
parser.add_argument("--keras", help="input from keras model")
parser.add_argument("--large_model", help="use the large model format (for models > 2GB)", action="store_true")
parser.add_argument("--output", help="output model file")
parser.add_argument("--inputs", help="model input_names")
parser.add_argument("--outputs", help="model output_names")
parser.add_argument("--opset", type=int, default=None, help="opset version to use for onnx domain")
parser.add_argument("--custom-ops", help="comma-separated map of custom ops to domains in format OpName:domain")
parser.add_argument("--extra_opset", default=None,
help="extra opset with format like domain:version, e.g. com.microsoft:1")
parser.add_argument("--target", default=",".join(constants.DEFAULT_TARGET), choices=constants.POSSIBLE_TARGETS,
help="target platform")
parser.add_argument("--continue_on_error", help="continue_on_error", action="store_true")
parser.add_argument("--verbose", "-v", help="verbose output, option is additive", action="count")
parser.add_argument("--debug", help="debug mode", action="store_true")
parser.add_argument("--output_frozen_graph", help="output frozen tf graph to file")
parser.add_argument("--fold_const", help="Deprecated. Constant folding is always enabled.",
action="store_true")
# experimental
parser.add_argument("--inputs-as-nchw", help="transpose inputs as from nhwc to nchw")
args = parser.parse_args()
args.shape_override = None
if args.input:
# for backward compativility
args.graphdef = args.input
if args.graphdef or args.checkpoint:
if not args.input and not args.outputs:
parser.error("graphdef and checkpoint models need to provide inputs and outputs")
if not any([args.graphdef, args.checkpoint, args.saved_model, args.keras]):
parser.print_help()
sys.exit(1)
if args.inputs:
args.inputs, args.shape_override = utils.split_nodename_and_shape(args.inputs)
if args.outputs:
args.outputs = args.outputs.split(",")
if args.inputs_as_nchw:
args.inputs_as_nchw = args.inputs_as_nchw.split(",")
if args.target:
args.target = args.target.split(",")
if args.signature_def:
args.signature_def = [args.signature_def]
if args.extra_opset:
tokens = args.extra_opset.split(':')
if len(tokens) != 2:
parser.error("invalid extra_opset argument")
args.extra_opset = [utils.make_opsetid(tokens[0], int(tokens[1]))]
return args
def _run_test_case(self, func, output_names_with_port, feed_dict,
**kwargs):
extra_opset = [utils.make_opsetid(constants.CONTRIB_OPS_DOMAIN, 1)]
process_args = {"extra_opset": extra_opset}
return self.run_test_case(func,
feed_dict, [],
output_names_with_port,
process_args=process_args,
**kwargs)
def get_args():
"""Parse commandline."""
parser = argparse.ArgumentParser()
parser.add_argument("--cache",
default=os.path.join(tempfile.gettempdir(),
'pre-trained'),
help="pre-trained models cache dir")
parser.add_argument("--config",
default="tests/run_pretrained_models.yaml",
help="yaml config to use")
parser.add_argument("--tests", help="tests to run")
parser.add_argument("--target", default="", help="target platform")
parser.add_argument("--backend",
default="onnxruntime",
choices=["onnxruntime"],
help="backend to use")
parser.add_argument("--opset", type=int, default=None, help="opset to use")
parser.add_argument(
"--extra_opset",
default=None,
help="extra opset with format like domain:version, e.g. com.microsoft:1"
)
parser.add_argument("--skip_tf_tests",
help="skip non-tflite tests",
default="False")
parser.add_argument("--skip_tflite_tests",
help="skip tflite tests",
default="False")
parser.add_argument("--skip_tfjs_tests",
help="skip tfjs tests",
default="False")
parser.add_argument("--verbose",
"-v",
help="verbose output, option is additive",
action="count")
parser.add_argument("--debug", help="debug mode", action="store_true")
parser.add_argument("--list", help="list tests", action="store_true")
parser.add_argument("--onnx-file", help="create onnx file in directory")
parser.add_argument("--perf", help="capture performance numbers")
parser.add_argument("--include-disabled",
help="include disabled tests",
action="store_true")
args = parser.parse_args()
args.target = args.target.split(",")
args.skip_tf_tests = args.skip_tf_tests.upper() == "TRUE"
args.skip_tflite_tests = args.skip_tflite_tests.upper() == "TRUE"
args.skip_tfjs_tests = args.skip_tfjs_tests.upper() == "TRUE"
if args.extra_opset:
tokens = args.extra_opset.split(':')
if len(tokens) != 2:
raise ValueError("invalid extra_opset argument")
args.extra_opset = [utils.make_opsetid(tokens[0], int(tokens[1]))]
return args
def test_extra_opset(self):
extra_opset = [
utils.make_opsetid(constants.MICROSOFT_DOMAIN, 1),
utils.make_opsetid("my.domain", 1024),
]
with tf.Session() as sess:
x = tf.placeholder(tf.float32, [2, 3], name="input1")
x_ = tf.add(x, x)
_ = tf.identity(x_, name="output")
g = process_tf_graph(sess.graph,
opset=self.config.opset,
extra_opset=extra_opset)
self.assertEqual(g.opset, self.config.opset)
self.assertEqual(g.extra_opset, extra_opset)
# convert between graph and model proto, make sure extra opset is preserved
model_proto = g.make_model("test")
model_proto = GraphUtil.optimize_model_proto(model_proto)
g = GraphUtil.create_graph_from_onnx_model(model_proto)
self.assertEqual(g.opset, self.config.opset)
self.assertEqual(g.extra_opset, extra_opset)
def test_ms_domain(versions=None):
""" Parameterize test case to apply ms opset(s) as extra_opset. """
def _custom_name_func(testcase_func, param_num, param):
del param_num
arg = param.args[0]
return "%s_%s" % (testcase_func.__name__, arg.version)
# Test all opset versions in ms domain if versions is not specified
if versions is None:
versions = list(range(1, _MAX_MS_OPSET_VERSION + 1))
opsets = []
for version in versions:
opsets.append(
[utils.make_opsetid(constants.MICROSOFT_DOMAIN, version)])
return parameterized.expand(opsets, testcase_func_name=_custom_name_func)
def get_args():
"""Parse commandline."""
parser = argparse.ArgumentParser()
parser.add_argument("--input", help="input from graphdef")
parser.add_argument("--graphdef", help="input from graphdef")
parser.add_argument("--saved-model", help="input from saved model")
parser.add_argument("--checkpoint", help="input from checkpoint")
parser.add_argument("--output", help="output model file")
parser.add_argument("--inputs", help="model input_names")
parser.add_argument("--outputs", help="model output_names")
parser.add_argument("--opset",
type=int,
default=None,
help="opset version to use for onnx domain")
parser.add_argument("--custom-ops", help="list of custom ops")
parser.add_argument(
"--extra_opset",
default=None,
help="extra opset with format like domain:version, e.g. com.microsoft:1"
)
parser.add_argument("--target",
default=",".join(constants.DEFAULT_TARGET),
choices=constants.POSSIBLE_TARGETS,
help="target platform")
parser.add_argument("--continue_on_error",
help="continue_on_error",
action="store_true")
parser.add_argument("--verbose",
help="verbose output",
action="store_true")
parser.add_argument(
"--fold_const",
help="enable tf constant_folding transformation before conversion",
action="store_true")
# experimental
parser.add_argument("--inputs-as-nchw",
help="transpose inputs as from nhwc to nchw")
# depreciated, going to be removed some time in the future
parser.add_argument("--unknown-dim",
type=int,
default=-1,
help="default for unknown dimensions")
args = parser.parse_args()
args.shape_override = None
if args.input:
# for backward compativility
args.graphdef = args.input
if args.graphdef or args.checkpoint:
if not args.input and not args.outputs:
raise ValueError(
"graphdef and checkpoint models need to provide inputs and outputs"
)
if not any([args.graphdef, args.checkpoint, args.saved_model]):
raise ValueError("need input as graphdef, checkpoint or saved_model")
if args.inputs:
args.inputs, args.shape_override = utils.split_nodename_and_shape(
args.inputs)
if args.outputs:
args.outputs = args.outputs.split(",")
if args.inputs_as_nchw:
args.inputs_as_nchw = args.inputs_as_nchw.split(",")
if args.target:
args.target = args.target.split(",")
if args.extra_opset:
tokens = args.extra_opset.split(':')
if len(tokens) != 2:
raise ValueError("invalid extra_opset argument")
args.extra_opset = [utils.make_opsetid(tokens[0], int(tokens[1]))]
return args
def get_args():
"""Parse commandline."""
parser = argparse.ArgumentParser(description="Convert tensorflow graphs to ONNX.",
formatter_class=argparse.RawDescriptionHelpFormatter, epilog=_HELP_TEXT)
parser.add_argument("--input", help="input from graphdef")
parser.add_argument("--graphdef", help="input from graphdef")
parser.add_argument("--saved-model", help="input from saved model")
parser.add_argument("--tag", help="tag to use for saved_model")
parser.add_argument("--signature_def", help="signature_def from saved_model to use")
parser.add_argument("--concrete_function", type=int, default=None,
help="For TF2.x saved_model, index of func signature in __call__ (--signature_def is ignored)")
parser.add_argument("--checkpoint", help="input from checkpoint")
parser.add_argument("--keras", help="input from keras model")
parser.add_argument("--tflite", help="input from tflite model")
parser.add_argument("--tfjs", help="input from tfjs model")
parser.add_argument("--large_model", help="use the large model format (for models > 2GB)", action="store_true")
parser.add_argument("--output", help="output model file")
parser.add_argument("--inputs", help="model input_names (optional for saved_model, keras, and tflite)")
parser.add_argument("--outputs", help="model output_names (optional for saved_model, keras, and tflite)")
parser.add_argument("--ignore_default", help="comma-separated list of names of PlaceholderWithDefault "
"ops to change into Placeholder ops")
parser.add_argument("--use_default", help="comma-separated list of names of PlaceholderWithDefault ops to "
"change into Identity ops using their default value")
parser.add_argument("--rename-inputs", help="input names to use in final model (optional)")
parser.add_argument("--rename-outputs", help="output names to use in final model (optional)")
parser.add_argument("--use-graph-names", help="(saved model only) skip renaming io using signature names",
action="store_true")
parser.add_argument("--opset", type=int, default=None, help="opset version to use for onnx domain")
parser.add_argument("--dequantize", help="Remove quantization from model. Only supported for tflite currently.",
action="store_true")
parser.add_argument("--custom-ops", help="Comma-separated map of custom ops to domains in format OpName:domain. "
"Domain 'ai.onnx.converters.tensorflow' is used by default.")
parser.add_argument("--extra_opset", default=None,
help="extra opset with format like domain:version, e.g. com.microsoft:1")
parser.add_argument("--load_op_libraries",
help="comma-separated list of tf op library paths to register before loading model")
parser.add_argument("--target", default=",".join(constants.DEFAULT_TARGET), choices=constants.POSSIBLE_TARGETS,
help="target platform")
parser.add_argument("--continue_on_error", help="continue_on_error", action="store_true")
parser.add_argument("--verbose", "-v", help="verbose output, option is additive", action="count")
parser.add_argument("--debug", help="debug mode", action="store_true")
parser.add_argument("--output_frozen_graph", help="output frozen tf graph to file")
parser.add_argument("--fold_const", help="Deprecated. Constant folding is always enabled.",
action="store_true")
# experimental
parser.add_argument("--inputs-as-nchw", help="transpose inputs as from nhwc to nchw")
args = parser.parse_args()
args.shape_override = None
if args.input:
# for backward compativility
args.graphdef = args.input
if args.graphdef or args.checkpoint:
if not args.inputs or not args.outputs:
parser.error("graphdef and checkpoint models need to provide inputs and outputs")
if not any([args.graphdef, args.checkpoint, args.saved_model, args.keras, args.tflite, args.tfjs]):
parser.print_help()
sys.exit(1)
if args.inputs:
args.inputs, args.shape_override = utils.split_nodename_and_shape(args.inputs)
if args.outputs:
args.outputs = args.outputs.split(",")
if args.ignore_default:
args.ignore_default = args.ignore_default.split(",")
if args.use_default:
args.use_default = args.use_default.split(",")
if args.rename_outputs:
args.rename_outputs = args.rename_outputs.split(",")
if args.rename_inputs:
args.rename_inputs = args.rename_inputs.split(",")
if args.inputs_as_nchw:
args.inputs_as_nchw = args.inputs_as_nchw.split(",")
if args.target:
args.target = args.target.split(",")
if args.signature_def:
args.signature_def = [args.signature_def]
if args.dequantize:
if not args.tflite:
parser.error("dequantize flag is currently only supported for tflite")
if args.extra_opset:
tokens = args.extra_opset.split(':')
if len(tokens) != 2:
parser.error("invalid extra_opset argument")
args.extra_opset = [utils.make_opsetid(tokens[0], int(tokens[1]))]
if args.load_op_libraries:
args.load_op_libraries = args.load_op_libraries.split(",")
return args
