documentation

Signed-off-by: xavier dupré <[email protected]>

Author: sdpython

README.md

@@ -293,6 +293,9 @@ model_proto, external_tensor_storage = tf2onnx.convert.from_keras(model,
         model: the tf.keras model we want to convert
         input_signature: a tf.TensorSpec or a numpy array defining the shape/dtype of the input
         opset: the opset to be used for the ONNX model, default is the latest
+        custom_ops: if a model contains ops not recognized by onnx runtime,
+            you can tag these ops with a custom op domain so that the
+            runtime can still open the model. Type is a dictionary `{op name: domain}`.
         target: list of workarounds applied to help certain platforms
         custom_op_handlers: dictionary of custom ops handlers
         custom_rewriter: list of custom graph rewriters
@@ -322,6 +325,9 @@ model_proto, external_tensor_storage = tf2onnx.convert.from_function(function,
         function: the tf.function we want to convert
         input_signature: a tf.TensorSpec or a numpy array defining the shape/dtype of the input
         opset: the opset to be used for the ONNX model, default is the latest
+        custom_ops: if a model contains ops not recognized by onnx runtime,
+            you can tag these ops with a custom op domain so that the
+            runtime can still open the model. Type is a dictionary `{op name: domain}`.
         target: list of workarounds applied to help certain platforms
         custom_op_handlers: dictionary of custom ops handlers
         custom_rewriter: list of custom graph rewriters

tf2onnx/convert.py

@@ -332,6 +332,9 @@ def from_keras(model, input_signature=None, opset=None, custom_ops=None, custom_
         model: the tf.keras model we want to convert
         input_signature: a tf.TensorSpec or a numpy array defining the shape/dtype of the input
         opset: the opset to be used for the ONNX model, default is the latest
+        custom_ops: if a model contains ops not recognized by onnx runtime,
+            you can tag these ops with a custom op domain so that the
+            runtime can still open the model. Type is a dictionary `{op name: domain}`.
         target: list of workarounds applied to help certain platforms
         custom_op_handlers: dictionary of custom ops handlers
         custom_rewriter: list of custom graph rewriters
@@ -417,6 +420,9 @@ def from_function(function, input_signature=None, opset=None, custom_ops=None, c
         function: the tf.function we want to convert
         input_signature: a tf.TensorSpec or a numpy array defining the shape/dtype of the input
         opset: the opset to be used for the ONNX model, default is the latest
+        custom_ops: if a model contains ops not recognized by onnx runtime,
+            you can tag these ops with a custom op domain so that the
+            runtime can still open the model. Type is a dictionary `{op name: domain}`.
         target: list of workarounds applied to help certain platforms
         custom_op_handlers: dictionary of custom ops handlers
         custom_rewriter: list of custom graph rewriters
@@ -478,6 +484,9 @@ def from_graph_def(graph_def, name=None, input_names=None, output_names=None, op
         output_names: list of output names
         name: A name for the graph
         opset: the opset to be used for the ONNX model, default is the latest
+        custom_ops: if a model contains ops not recognized by onnx runtime,
+            you can tag these ops with a custom op domain so that the
+            runtime can still open the model. Type is a dictionary `{op name: domain}`.
         target: list of workarounds applied to help certain platforms
         custom_op_handlers: dictionary of custom ops handlers
         custom_rewriter: list of custom graph rewriters