Update tutorials

Signed-off-by: Tom Wildenhain <[email protected]>

Author: TomWildenhain-Microsoft

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"]:
@@ -400,6 +400,7 @@ def run_test(self, name, backend="onnxruntime", onnx_file=None, opset=None, extr
         if utils.is_debug_mode():
             utils.save_protobuf(os.path.join(TEMP_DIR, name + "_after_tf_optimize.pb"), graph_def)
 
+        logger.info("Input names %s", input_names)
         if tflite_path is not None:
             inputs = {}
             for k in input_names:
@@ -438,7 +439,7 @@ def run_tflite():
             inputs = {}
             for k in input_names:
                 v = self.input_names[k]
-                inputs[to_rename[k]] = 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)
@@ -507,6 +508,7 @@ def run_tflite():
                 elif self.run_tf_frozen:
                     if self.tf_profile is not None:
                         tf.profiler.experimental.start(self.tf_profile)
+                    logger.info("TF inputs %s", list(inputs.keys()))
                     tf_results = self.run_tensorflow(sess, inputs)
                     if self.tf_profile is not None:
                         tf.profiler.experimental.stop()
@@ -553,11 +555,9 @@ 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]
+                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()}
+                logger.info("ORT inputs %s", list(struc_inputs.keys()))
                 onnx_results = self.run_onnxruntime(
                     name, model_proto, struc_inputs, struc_outputs, external_tensor_storage)
             else: