Use structured inputs by default from CLI

examples/benchmark_tfmodel_ort.py

@@ -38,7 +38,7 @@ def measure_time(fct, imgs):
 # Download model from https://tfhub.dev/captain-pool/esrgan-tf2/1
 # python -m tf2onnx.convert --saved-model esrgan --output "esrgan-tf2.onnx" --opset 12
 ort = ort.InferenceSession('esrgan-tf2.onnx')
-fct_ort = lambda img: ort.run(None, {'input_0:0': img})
+fct_ort = lambda img: ort.run(None, {'input_0': img})
 results_ort, duration_ort = measure_time(fct_ort, imgs)
 print(len(imgs), duration_ort)
 

examples/end2end_tfhub.py

@@ -62,7 +62,7 @@
 ########################################
 # Runs onnxruntime.
 session = InferenceSession("efficientnetb0clas.onnx")
-got = session.run(None, {'input_1:0': input})
+got = session.run(None, {'input_1': input})
 print(got[0])
 
 ########################################
@@ -73,5 +73,5 @@
 # Measures processing time.
 print('tf:', timeit.timeit('model.predict(input)',
                            number=10, globals=globals()))
-print('ort:', timeit.timeit("session.run(None, {'input_1:0': input})",
+print('ort:', timeit.timeit("session.run(None, {'input_1': input})",
                             number=10, globals=globals()))

examples/end2end_tfkeras.py

@@ -57,7 +57,7 @@
 ########################################
 # Runs onnxruntime.
 session = InferenceSession("simple_rnn.onnx")
-got = session.run(None, {'input_1:0': input})
+got = session.run(None, {'input_1': input})
 print(got[0])
 
 ########################################
@@ -68,5 +68,5 @@
 # Measures processing time.
 print('tf:', timeit.timeit('model.predict(input)',
                            number=100, globals=globals()))
-print('ort:', timeit.timeit("session.run(None, {'input_1:0': input})",
+print('ort:', timeit.timeit("session.run(None, {'input_1': input})",
                             number=100, globals=globals()))

examples/getting_started.py

@@ -58,7 +58,7 @@ def f(a, b):
 
 print("ORT result")
 sess = ort.InferenceSession("model.onnx")
-res = sess.run(None, {'dense_input:0': x_val})
+res = sess.run(None, {'dense_input': x_val})
 print(res[0])
 
 print("Conversion succeeded")

tests/run_pretrained_models.py

@@ -375,7 +375,7 @@ def run_test(self, name, backend="onnxruntime", onnx_file=None, opset=None, extr
         initialized_tables = {}
         outputs = self.output_names
         tflite_path = None
-        to_rename = None
+        to_rename = {}
         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"]:
@@ -438,7 +438,7 @@ def run_tflite():
             inputs = {}
             for k in input_names:
                 v = self.input_names[k]
-                inputs[k.split(":")[0]] = tf.constant(self.make_input(v))
+                inputs[to_rename.get(k, k)] = tf.constant(self.make_input(v))
             tf_func = tf.function(concrete_func)
             logger.info("Running TF")
             tf_results_d = tf_func(**inputs)
@@ -553,11 +553,10 @@ def run_tflite():
         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)
+                struc_outputs = [to_rename.get(k, k) for k in self.output_names]
+                struc_inputs = {to_rename.get(k, k): v for k, v in inputs.items()}
+                onnx_results = self.run_onnxruntime(
+                    name, model_proto, struc_inputs, struc_outputs, external_tensor_storage)
             else:
                 raise ValueError("unknown backend")
             logger.info("Run_ONNX OK")

tf2onnx/convert.py

@@ -65,6 +65,8 @@ def get_args():
                                               "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")
@@ -212,7 +214,8 @@ def main():
     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)
+            args.large_model, return_initialized_tables=True, return_tensors_to_rename=True,
+            use_graph_names=args.use_graph_names)
         model_path = args.saved_model
     if args.keras:
         graph_def, inputs, outputs = tf_loader.from_keras(

tf2onnx/tf_loader.py

@@ -310,7 +310,7 @@ def from_checkpoint(model_path, input_names, output_names):
     return frozen_graph, input_names, output_names
 
 
-def _from_saved_model_v1(sess, model_path, input_names, output_names, tag, signature_names):
+def _from_saved_model_v1(sess, model_path, input_names, output_names, tag, signature_names, use_graph_names):
     """Load tensorflow graph from saved_model."""
 
     wrn_no_tag = "'--tag' not specified for saved_model. Using --tag serve"
@@ -345,22 +345,25 @@ def _from_saved_model_v1(sess, model_path, input_names, output_names, tag, signa
         # TF1.12 changed the api
         get_signature_def = lambda meta_graph_def, k: meta_graph_def.signature_def[k]
 
+    tensors_to_rename = {}
     if input_names is None:
         input_names = []
         for k in signatures:
             inputs_tensor_info = get_signature_def(imported, k).inputs
-            for _, input_tensor in inputs_tensor_info.items():
+            for structured_name, input_tensor in inputs_tensor_info.items():
                 if input_tensor.name not in input_names:
                     input_names.append(input_tensor.name)
-    tensors_to_rename = {}
+                    if not use_graph_names:
+                        tensors_to_rename[input_tensor.name] = structured_name
     if output_names is None:
         output_names = []
         for k in signatures:
             outputs_tensor_info = get_signature_def(imported, k).outputs
             for structured_name, output_tensor in outputs_tensor_info.items():
                 if output_tensor.name not in output_names:
                     output_names.append(output_tensor.name)
-                    tensors_to_rename[output_tensor.name] = structured_name
+                    if not use_graph_names:
+                        tensors_to_rename[output_tensor.name] = structured_name
     frozen_graph, initialized_tables = \
         freeze_session(sess, input_names=input_names, output_names=output_names, get_tables=True)
     return frozen_graph, input_names, output_names, initialized_tables, tensors_to_rename
@@ -447,7 +450,7 @@ def _restore_captured_resources(concrete_func, graph_captures_copy, func_capture
 
 
 def _from_saved_model_v2(model_path, input_names, output_names, tag, signature_def,
-                         concrete_function_index, large_model):
+                         concrete_function_index, large_model, use_graph_names):
     """Load tensorflow graph from saved_model."""
 
     wrn_no_tag = "'--tag' not specified for saved_model. Using --tag serve"
@@ -495,18 +498,16 @@ def _from_saved_model_v2(model_path, input_names, output_names, tag, signature_d
         graph_captures = concrete_func.graph._captures  # pylint: disable=protected-access
         captured_inputs = [t_name.name for _, t_name in graph_captures.values()]
         inputs = [inp for inp in inputs if inp not in captured_inputs]
-        if concrete_func.structured_input_signature is not None:
-            args, kwargs = concrete_func.structured_input_signature
-            structured_inputs = [t.name for t in args if isinstance(t, tf.TensorSpec)] + sorted(kwargs.keys())
-            structured_inputs = set(inp + ":0" for inp in structured_inputs)
-            if any(inp in structured_inputs for inp in inputs):
-                inputs = [inp for inp in inputs if inp in structured_inputs]
+        if concrete_func.structured_input_signature is not None and not use_graph_names:
+            flat_structured_inp = tf.nest.flatten(concrete_func.structured_input_signature)
+            structured_inputs = [t.name for t in flat_structured_inp if isinstance(t, tf.TensorSpec)]
+            tensors_to_rename.update(zip(inputs, structured_inputs))
     else:
         inputs = input_names
 
     if output_names is None:
         outputs = [tensor.name for tensor in concrete_func.outputs if tensor.dtype != tf.dtypes.resource]
-        if isinstance(concrete_func.structured_outputs, dict):
+        if isinstance(concrete_func.structured_outputs, dict) and not use_graph_names:
             # outputs are sorted, sort structured_outputs the same way
             structured_outputs = sorted(concrete_func.structured_outputs.keys())
             tensors_to_rename.update(zip(outputs, structured_outputs))
@@ -515,7 +516,6 @@ def _from_saved_model_v2(model_path, input_names, output_names, tag, signature_d
             logger.info("Output names: %r", outputs)
     else:
         outputs = output_names
-        logger.info("Outputs not left as None; will use provided names not structured output names.")
 
     frozen_graph, initialized_tables = from_trackable(imported, concrete_func, inputs, outputs, large_model)
 
@@ -524,7 +524,8 @@ def _from_saved_model_v2(model_path, input_names, output_names, tag, signature_d
 
 def from_saved_model(model_path, input_names, output_names, tag=None,
                      signatures=None, concrete_function=None, large_model=False,
-                     return_concrete_func=False, return_initialized_tables=False, return_tensors_to_rename=False):
+                     return_concrete_func=False, return_initialized_tables=False,
+                     return_tensors_to_rename=False, use_graph_names=False):
     """Load tensorflow graph from saved_model."""
     if signatures is None:
         signatures = []
@@ -533,7 +534,7 @@ def from_saved_model(model_path, input_names, output_names, tag=None,
         if is_tf2():
             frozen_graph, input_names, output_names, concrete_func, imported, initialized_tables, tensors_to_rename = \
                 _from_saved_model_v2(model_path, input_names, output_names,
-                                     tag, signatures, concrete_function, large_model)
+                                     tag, signatures, concrete_function, large_model, use_graph_names)
             result = [frozen_graph, input_names, output_names]
             if return_concrete_func:
                 result += [concrete_func, imported]
@@ -544,7 +545,7 @@ def from_saved_model(model_path, input_names, output_names, tag=None,
         else:
             with tf_session() as sess:
                 frozen_graph, input_names, output_names, initialized_tables, tensors_to_rename = \
-                    _from_saved_model_v1(sess, model_path, input_names, output_names, tag, signatures)
+                    _from_saved_model_v1(sess, model_path, input_names, output_names, tag, signatures, use_graph_names)
                 result = [frozen_graph, input_names, output_names]
                 if return_initialized_tables:
                     result += [initialized_tables]