Adding basic onnx inference on sklearn example

sklearn_ex/README.md

@@ -1,6 +1,6 @@
 # sklearn example
 
-This is a simple demo showing the training of Randmon Forest Classifier, serializing it as a pickle file, converting it to an Onnx model, then making inferences using the Onnx model.
+This is a simple demo showing the training of Random Forest Classifier, serializing it as a pickle file, converting it to an Onnx model, then making inferences using the Onnx model.
 
 ## Usage
 
@@ -13,7 +13,7 @@ python train.py
 Convert to Onnx
 
 ```
-
+python convert_to_onnx.py ./output/model.pkl
 ```
 
 Make inferences

sklearn_ex/onnx_infer.py

@@ -0,0 +1,52 @@
+import argparse
+import pathlib
+
+import numpy as np
+import onnxruntime as ort
+
+# See https://scikit-learn.org/stable/datasets/toy_dataset.html#iris-dataset
+
+# ['sepal length in cm', 'sepal width in cm', 'petal length in cm', 'petal width in cm']
+
+DESCRIPTION = """
+Makes Iris predictions using Onnx classier.
+
+Inputs:
+
+0: 'sepal length in cm'
+1: 'sepal width in cm'
+2: 'petal length in cm'
+3: 'petal width in cm'
+
+Pedicts:
+
+Class
+    - Iris-Setosa
+    - Iris-Versicolour
+    - Iris-Virginica
+"""
+
+EXAMPLE_DATA = np.array(
+    [[5.4, 3.9, 1.7, 0.4], [6.1, 2.6, 5.6, 1.4], [5.2, 2.7, 3.9, 1.4]]
+)
+
+
+def main(args):
+    inference_session = ort.InferenceSession(str(args.model_path))
+    input_name = inference_session.get_inputs()[0].name
+    label_name = inference_session.get_outputs()[0].name
+    prediction = inference_session.run(
+        [label_name], {input_name: EXAMPLE_DATA.astype(np.float32)}
+    )[0]
+    print(prediction)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=DESCRIPTION
+    )
+    parser.add_argument(
+        "model_path", type=pathlib.Path, help="Path to onnx classifier model."
+    )
+    args = parser.parse_args()
+    main(args)