update python examples (#1935)

docs/python/examples/plot_backend.py

@@ -16,6 +16,7 @@ of a simple logistic regression model.
 """
 import numpy as np
 from onnxruntime import datasets
+from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
 import onnxruntime.backend as backend
 from onnx import load
 
@@ -24,9 +25,12 @@ model = load(name)
 
 rep = backend.prepare(model, 'CPU')
 x = np.array([[-1.0, -2.0]], dtype=np.float32)
-label, proba = rep.run(x)
-print("label={}".format(label))
-print("probabilities={}".format(proba))
+try:
+    label, proba = rep.run(x)
+    print("label={}".format(label))
+    print("probabilities={}".format(proba))
+except (RuntimeError, InvalidArgument) as e:
+    print(e)
 
 ########################################
 # The device depends on how the package was compiled,
@@ -40,9 +44,12 @@ print(get_device())
 
 rep = backend.prepare(name, 'CPU')
 x = np.array([[-1.0, -2.0]], dtype=np.float32)
-label, proba = rep.run(x)
-print("label={}".format(label))
-print("probabilities={}".format(proba))
+try:
+    label, proba = rep.run(x)
+    print("label={}".format(label))
+    print("probabilities={}".format(proba))
+except (RuntimeError, InvalidArgument) as e:
+    print(e)
 
 #######################################
 # The backend API is implemented by other frameworks

docs/python/examples/plot_common_errors.py

@@ -16,6 +16,7 @@ trained on *Iris* datasets. The model takes
 a vector of dimension 2 and returns a class among three.
 """
 import onnxruntime as rt
+from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
 import numpy
 from onnxruntime.datasets import get_example
 
@@ -53,9 +54,12 @@ except Exception as e:
 # and *onnxruntime* will then return all the outputs.
 
 x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
-res = sess.run(None, {input_name: x})
-print("All outputs")
-print(res)
+try:
+    res = sess.run(None, {input_name: x})
+    print("All outputs")
+    print(res)
+except (RuntimeError, InvalidArgument) as e:
+    print(e)
 
 #########################
 # The same goes if the input name is misspelled.
@@ -78,8 +82,11 @@ for x in [
         numpy.array([1.0, 2.0, 3.0], dtype=numpy.float32),
         numpy.array([[1.0, 2.0, 3.0]], dtype=numpy.float32),
         ]:
-    r = sess.run([output_name], {input_name: x})
-    print("Shape={0} and predicted labels={1}".format(x.shape, r))
+    try:
+        r = sess.run([output_name], {input_name: x})
+        print("Shape={0} and predicted labels={1}".format(x.shape, r))
+    except (RuntimeError, InvalidArgument) as e:
+        print("ERROR with Shape={0} - {1}".format(x.shape, e))
 
 for x in [
         numpy.array([1.0, 2.0, 3.0, 4.0], dtype=numpy.float32),
@@ -88,8 +95,11 @@ for x in [
         numpy.array([1.0, 2.0, 3.0], dtype=numpy.float32),
         numpy.array([[1.0, 2.0, 3.0]], dtype=numpy.float32),
         ]:
-    r = sess.run(None, {input_name: x})
-    print("Shape={0} and predicted probabilities={1}".format(x.shape, r[1]))
+    try:
+        r = sess.run(None, {input_name: x})
+        print("Shape={0} and predicted probabilities={1}".format(x.shape, r[1]))
+    except (RuntimeError, InvalidArgument) as e:
+        print("ERROR with Shape={0} - {1}".format(x.shape, e))
 
 #########################
 # It does not fail either if the number of dimension
@@ -100,5 +110,8 @@ for x in [
         numpy.array([[[1.0, 2.0, 3.0]]], dtype=numpy.float32),
         numpy.array([[[1.0, 2.0]], [[3.0, 4.0]]], dtype=numpy.float32),
         ]:
-    r = sess.run([output_name], {input_name: x})
-    print("Shape={0} and predicted labels={1}".format(x.shape, r))
+    try:
+        r = sess.run([output_name], {input_name: x})
+        print("Shape={0} and predicted labels={1}".format(x.shape, r))
+    except (RuntimeError, InvalidArgument) as e:
+        print("ERROR with Shape={0} - {1}".format(x.shape, e))

docs/python/examples/plot_convert_pipeline_vectorizer.py

@@ -70,6 +70,8 @@ with open("pipeline_vectorize.onnx", "wb") as f:
 # We load the model with ONNX Runtime and look at
 # its input and output.
 import onnxruntime as rt
+from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
+
 sess = rt.InferenceSession("pipeline_vectorize.onnx")
 
 import numpy
@@ -83,7 +85,7 @@ print("output name='{}' and shape={} and type={}".format(out.name, out.shape, ou
 
 try:
     pred_onx = sess.run([out.name], {inp.name: X_test_dict})[0]
-except RuntimeError as e:
+except (RuntimeError, InvalidArgument) as e:
     print(e)
 
 #############################

docs/python/examples/plot_dl_keras.py

@@ -43,40 +43,52 @@ plt.axis('off')
 #############################################
 # Let's load the model with onnxruntime.
 import onnxruntime as rt
+from onnxruntime.capi.onnxruntime_pybind11_state import InvalidGraph
 
-sess = rt.InferenceSession('dense121.onnx')
+try:
+    sess = rt.InferenceSession('dense121.onnx')
+    ok = True
+except (InvalidGraph, TypeError, RuntimeError) as e:
+    # Probably a mismatch between onnxruntime and onnx version.
+    print(e)
+    ok = False
 
-print("The model expects input shape:", sess.get_inputs()[0].shape)
-print("image shape:", ximg.shape)
+if ok:
+    print("The model expects input shape:", sess.get_inputs()[0].shape)
+    print("image shape:", ximg.shape)
 
 #######################################
 # Let's resize the image.
-from skimage.transform import resize
-import numpy
 
-ximg224 = resize(ximg / 255, (224, 224, 3), anti_aliasing=True)
-ximg = ximg224[numpy.newaxis, :, :, :]
-ximg = ximg.astype(numpy.float32)
+if ok:
+    from skimage.transform import resize
+    import numpy
 
-print("new shape:", ximg.shape)
+    ximg224 = resize(ximg / 255, (224, 224, 3), anti_aliasing=True)
+    ximg = ximg224[numpy.newaxis, :, :, :]
+    ximg = ximg.astype(numpy.float32)
+
+    print("new shape:", ximg.shape)
 
 ##################################
 # Let's compute the output.
 
-input_name = sess.get_inputs()[0].name
-res = sess.run(None, {input_name: ximg})
-prob = res[0]
-print(prob.ravel()[:10])  # Too big to be displayed.
+if ok:
+    input_name = sess.get_inputs()[0].name
+    res = sess.run(None, {input_name: ximg})
+    prob = res[0]
+    print(prob.ravel()[:10])  # Too big to be displayed.
 
 
 ##################################
 # Let's get more comprehensive results.
 
-from keras.applications.densenet import decode_predictions
-decoded = decode_predictions(prob)
+if ok:
+    from keras.applications.densenet import decode_predictions
+    decoded = decode_predictions(prob)
 
-import pandas
-df = pandas.DataFrame(decoded[0], columns=["class_id", "name", "P"])
-print(df)
+    import pandas
+    df = pandas.DataFrame(decoded[0], columns=["class_id", "name", "P"])
+    print(df)
 
 

docs/python/examples/plot_train_convert_predict.py

@@ -54,7 +54,7 @@ print(confusion_matrix(y_test, pred))
 from skl2onnx import convert_sklearn
 from skl2onnx.common.data_types import FloatTensorType
 
-initial_type = [('float_input', FloatTensorType([1, 4]))]
+initial_type = [('float_input', FloatTensorType([None, 4]))]
 onx = convert_sklearn(clr, initial_types=initial_type)
 with open("logreg_iris.onnx", "wb") as f:
     f.write(onx.SerializeToString())
@@ -66,8 +66,10 @@ with open("logreg_iris.onnx", "wb") as f:
 import onnxruntime as rt
 sess = rt.InferenceSession("logreg_iris.onnx")
 
-print("input name='{}' and shape={}".format(sess.get_inputs()[0].name, sess.get_inputs()[0].shape))
-print("output name='{}' and shape={}".format(sess.get_outputs()[0].name, sess.get_outputs()[0].shape))
+print("input name='{}' and shape={}".format(
+    sess.get_inputs()[0].name, sess.get_inputs()[0].shape))
+print("output name='{}' and shape={}".format(
+    sess.get_outputs()[0].name, sess.get_outputs()[0].shape))
 
 ##################################
 # We compute the predictions.