getting_started.py

# SPDX-License-Identifier: Apache-2.0

"""
This example shows how to convert tf functions and keras models using the Python API.
It also demonstrates converting saved_models from the command line.
"""

importtensorflowastf
importtf2onnx
importnumpyasnp
importonnxruntimeasort
importos

##################### tf function #####################

@tf.function
deff(a, b):
returna+b

input_signature= [tf.TensorSpec([2, 3], tf.float32), tf.TensorSpec([2, 3], tf.float32)]
onnx_model, _=tf2onnx.convert.from_function(f, input_signature, opset=13)

a_val=np.ones([2, 3], np.float32)
b_val=np.zeros([2, 3], np.float32)

print("Tensorflow result")
print(f(a_val, b_val).numpy())

print("ORT result")
sess=ort.InferenceSession(onnx_model.SerializeToString())
res=sess.run(None, {'a': a_val, 'b': b_val})
print(res[0])


##################### Keras Model #####################

model=tf.keras.Sequential()
model.add(tf.keras.layers.Dense(4, activation="relu"))

input_signature= [tf.TensorSpec([3, 3], tf.float32, name='x')]
onnx_model, _=tf2onnx.convert.from_keras(model, input_signature, opset=13)

x_val=np.ones((3, 3), np.float32)

print("Keras result")
print(model(x_val).numpy())

print("ORT result")
sess=ort.InferenceSession(onnx_model.SerializeToString())
res=sess.run(None, {'x': x_val})
print(res[0])


##################### Saved Model #####################

model.save("savedmodel")
os.system("python -m tf2onnx.convert --saved-model savedmodel --output model.onnx --opset 13")

print("ORT result")
sess=ort.InferenceSession("model.onnx")
res=sess.run(None, {'dense_input': x_val})
print(res[0])

print("Conversion succeeded")