def test_cmdarg_parse(self):
arg = "input/V-1_2:0,input/X:0[1,2,3],Y:1[4,5],Z:3,A:1,B"
expected_inputs = ['input/V-1_2:0', 'input/X:0', 'Y:1', 'Z:3', 'A:1', 'B']
expected_shape = {'Y:1': [4, 5], 'input/X:0': [1, 2, 3]}
inputs, shape_override = utils.split_nodename_and_shape(arg)
self.assertEqual(expected_inputs, inputs)
self.assertEqual(expected_shape, shape_override)
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 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 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="onnx opset to use")
parser.add_argument("--custom-ops", help="list of custom ops")
parser.add_argument("--target",
default=",".join(DEFAULT_TARGET),
choices=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(",")
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
