add one more example

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

Author: sdpython

examples/end2end_tfhub.py

@@ -0,0 +1,110 @@
+"""
+This example retrieves a model from tensorflowhub.
+It is converted into ONNX. Predictions are compared to
+the predictions from tensorflow to check there is no
+discrepencies. Inferencing time is also compared between
+*onnxruntime*, *tensorflow* and *tensorflow.lite*.
+"""
+from onnxruntime import InferenceSession
+import os
+import subprocess
+import timeit
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers, Input
+from tensorflow.python.saved_model import tag_constants
+import tensorflow_hub as tfhub
+
+########################################
+# Downloads the model.
+hub_layer = tfhub.KerasLayer(
+    "https://tfhub.dev/google/efficientnet/b0/classification/1")
+model = keras.Sequential()
+model.add(tf.keras.Input(shape=(224, 224, 3), dtype=tf.float32))
+model.add(hub_layer)
+print(model.summary())
+
+########################################
+# Saves the model.
+if not os.path.exists("efficientnetb0clas"):
+    os.mkdir("efficientnetb0clas")
+tf.keras.models.save_model(model, "efficientnetb0clas")
+
+input_names = [n.name for n in model.inputs]
+output_names = [n.name for n in model.outputs]
+print('inputs:', input_names)
+print('outputs:', output_names)
+
+########################################
+# Testing the model.
+input = np.random.randn(2, 224, 224, 3).astype(np.float32)
+expected = model.predict(input)
+print(expected)
+
+########################################
+# Run the command line.
+proc = subprocess.run(
+    'python -m tf2onnx.convert --saved-model efficientnetb0clas '
+    '--output efficientnetb0clas.onnx --opset 12'.split(),
+    capture_output=True)
+print(proc.returncode)
+print(proc.stdout.decode('ascii'))
+print(proc.stderr.decode('ascii'))
+
+########################################
+# Runs onnxruntime.
+session = InferenceSession("efficientnetb0clas.onnx")
+got = session.run(None, {'input_1:0': input})
+print(got[0])
+
+########################################
+# Measures the differences.
+print(np.abs(got[0] - expected).max())
+
+########################################
+# 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})",
+                            number=10, globals=globals()))
+
+########################################
+# Freezes the graph with tensorflow.lite.
+converter = tf.lite.TFLiteConverter.from_saved_model("efficientnetb0clas")
+tflite_model = converter.convert()
+with open("efficientnetb0clas.tflite", "wb") as f:
+    f.write(tflite_model)
+
+# Builds an interpreter.
+interpreter = tf.lite.Interpreter(model_path='efficientnetb0clas.tflite')
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+print("input_details", input_details)
+print("output_details", output_details)
+index = input_details[0]['index']
+
+
+def tflite_predict(input, interpreter=interpreter, index=index):
+    res = []
+    for i in range(input.shape[0]):
+        interpreter.set_tensor(index, input[i:i + 1])
+        interpreter.invoke()
+        res.append(interpreter.get_tensor(output_details[0]['index']))
+    return np.vstack(res)
+
+
+print(input[0:1].shape, "----", input_details[0]['shape'])
+output_data = tflite_predict(input, interpreter, index)
+print(output_data)
+
+########################################
+# Measures processing time again.
+
+print('tf:', timeit.timeit('model.predict(input)',
+                           number=10, globals=globals()))
+print('ort:', timeit.timeit("session.run(None, {'input_1:0': input})",
+                            number=10, globals=globals()))
+print('tflite:', timeit.timeit('tflite_predict(input)',
+                               number=10, globals=globals()))

examples/end2end_tfkeras.py

@@ -15,12 +15,10 @@
 from tensorflow.keras import layers, Input
 from tensorflow.python.saved_model import tag_constants
 from tensorflow.python.tools import freeze_graph
-from tf2onnx.tf_loader import tf_reset_default_graph, tf_session, freeze_session
 
 ########################################
 # Creates the model.
 model = keras.Sequential()
-#model.add(layers.Embedding(input_dim=10, output_dim=4))
 model.add(Input((4, 4)))
 model.add(layers.SimpleRNN(8))
 model.add(layers.Dense(2))
@@ -74,13 +72,13 @@
                             number=100, globals=globals()))
 
 ########################################
-# Freezes the graph with tensorflow.lite
+# Freezes the graph with tensorflow.lite.
 converter = tf.lite.TFLiteConverter.from_saved_model("simple_rnn")
 tflite_model = converter.convert()
 with open("simple_rnn.tflite", "wb") as f:
     f.write(tflite_model)
 
-# Builds an interpreter
+# Builds an interpreter.
 interpreter = tf.lite.Interpreter(model_path='simple_rnn.tflite')
 interpreter.allocate_tensors()
 input_details = interpreter.get_input_details()

tests/test_example.py

@@ -39,6 +39,16 @@ def test_end2end_tfkeras(self):
                       "Optimizing ONNX model",
                       "Using opset <onnx, 12>"])
 
+    @check_tf_min_version("2.3", "use tf.keras")
+    @check_opset_min_version(12)
+    @check_opset_max_version(13)
+    def test_end2end_tfhub(self):
+        self.run_example(
+            "end2end_tfhub.py",
+            expected=["ONNX model is saved at efficientnetb0clas.onnx",
+                      "Optimizing ONNX model",
+                      "Using opset <onnx, 12>"])
+
 
 if __name__ == '__main__':
     unittest.main()