Make opset 15 default (#749)

* Make opset 15 default
* fix missing target opset in polynomial features

.azure-pipelines/linux-CI-nightly.yml

@@ -37,18 +37,6 @@ jobs:
         onnx.version: '==1.6.0'
         onnxrt.version: '-i https://test.pypi.org/simple/ ort-nightly'
         sklearn.version: '==0.22.1'
-      Py37-Onnx160-Sk0213:
-        python.version: '3.7'
-        numpy.version: '==1.18.1'
-        onnx.version: '==1.6.0'
-        onnxrt.version: '-i https://test.pypi.org/simple/ ort-nightly'
-        sklearn.version: '==0.21.3'
-      Py37-Onnx150:
-        python.version: '3.7'
-        numpy.version: '==1.18.1'
-        onnx.version: '==1.6.0'
-        onnxrt.version: '-i https://test.pypi.org/simple/ ort-nightly'
-        sklearn.version: '==0.21.3'
     maxParallel: 3
 
   steps:

.azure-pipelines/linux-conda-CI.yml

@@ -42,7 +42,7 @@ jobs:
         onnxrt.version: 'onnxruntime==1.8.1'  # '-i https://test.pypi.org/simple/ ort-nightly'
         sklearn.version: '>=0.24.2'
         onnxcc.version: '>=1.8.1'  # git
-        run.example: '1'
+        run.example: '0'
       Py39-Onnx190-Rt180-Skl0242:
         do.bench: '0'
         python.version: '3.9'

docs/tutorial/plot_bbegin_measure_time.py

@@ -87,7 +87,8 @@
 # The same is done with the two ONNX runtime
 # available.
 
-onx = to_onnx(ereg, X_train[:1].astype(numpy.float32))
+onx = to_onnx(ereg, X_train[:1].astype(numpy.float32),
+              target_opset=14)
 sess = InferenceSession(onx.SerializeToString())
 oinf = OnnxInference(onx, runtime="python_compiled")
 

docs/tutorial/plot_mcustom_parser.py

@@ -157,7 +157,8 @@ def decorrelate_transformer_parser(
 #############################################
 # And conversion.
 
-onx = to_onnx(dec, X.astype(numpy.float32))
+onx = to_onnx(dec, X.astype(numpy.float32),
+              target_opset=14)
 
 sess = InferenceSession(onx.SerializeToString())
 

docs/tutorial/plot_wext_pyod_forest.py

@@ -171,7 +171,8 @@ def pyod_iforest_converter(scope, operator, container):
 # And the conversion.
 
 if IForest is not None:
-    onx = to_onnx(model1, initial_types=initial_type)
+    onx = to_onnx(model1, initial_types=initial_type,
+                  target_opset=14)
 
 ###############################################
 # Checking discrepencies

skl2onnx/__init__.py

@@ -3,13 +3,13 @@
 """
 Main entry point to the converter from the *scikit-learn* to *onnx*.
 """
-__version__ = "1.9.4.dev"
+__version__ = "1.10.0"
 __author__ = "Microsoft"
 __producer__ = "skl2onnx"
 __producer_version__ = __version__
 __domain__ = "ai.onnx"
 __model_version__ = 0
-__max_supported_opset__ = 14  # Converters are tested up to this version.
+__max_supported_opset__ = 15  # Converters are tested up to this version.
 
 
 from .convert import convert_sklearn, to_onnx, wrap_as_onnx_mixin  # noqa

skl2onnx/common/_topology.py

@@ -48,11 +48,11 @@ def _default_OPSET_TO_IR_VERSION():
 
 try:
     from onnxconverter_common.topology import OPSET_TO_IR_VERSION
-    assert OPSET_TO_IR_VERSION[14] is not None
+    assert OPSET_TO_IR_VERSION[15] is not None
 except (ImportError, KeyError):
     OPSET_TO_IR_VERSION = _default_OPSET_TO_IR_VERSION()
 
-OPSET_ML_TO_OPSET = {1: 11, 2: 14}
+OPSET_ML_TO_OPSET = {1: 11, 2: 15}
 
 logger = getLogger('skl2onnx')
 

tests/test_algebra_complex.py

@@ -29,7 +29,7 @@ def test_complex(self):
             X = np.array([[1-2j, -12j],
                           [-1-2j, 1+2j]]).astype(dt)
 
-            for opv in (10, 11, 12, 13, TARGET_OPSET):
+            for opv in range(10, 20):
                 if opv > TARGET_OPSET:
                     continue
                 with self.subTest(dt=dt, opset=opv):

tests/test_algebra_onnx_operators.py

@@ -205,11 +205,11 @@ def test_constant(self):
         self.assertEqual(cst.value, "a")
 
     def test_constant_of_shape(self):
-        for opset in [TARGET_OPSET, 14, 13, 12, 11, 10, 9]:
+        for opset in range(20, 8, -1):
+            if opset > TARGET_OPSET:
+                continue
             for value in [np.array([5], dtype=np.float32),
                           np.array(5, dtype=np.float32)]:
-                if opset > TARGET_OPSET:
-                    continue
                 with self.subTest(opset=opset, value=value):
                     tensor_value = onnx.helper.make_tensor(
                         "value", onnx.TensorProto.FLOAT,

tests/test_opset13.py

@@ -18,7 +18,7 @@ class TestOpset13(unittest.TestCase):
     def test_reduce_sum(self):
         X = numpy.array([[2, 1], [0, 1]], dtype=numpy.float32)
 
-        for opset in (10, 11, 12, 13):
+        for opset in range(10, 20):
             if opset > TARGET_OPSET:
                 continue
             with self.subTest(opset=opset):
@@ -62,7 +62,7 @@ def test_split(self):
     def test_squeeze(self):
         x = numpy.random.randn(20, 1).astype(numpy.float32)
         y = numpy.squeeze(x)
-        for opset in (10, 11, 12, 13):
+        for opset in range(10, 20):
             if opset > TARGET_OPSET:
                 continue
             with self.subTest(opset=opset):

tests/test_sklearn_double_tensor_type_tr.py

@@ -395,7 +395,8 @@ def test_binarizer(self):
         model = Binarizer(threshold=0.5)
         model_onnx = convert_sklearn(
             model, "scikit-learn binarizer",
-            [("input", DoubleTensorType(data.shape))])
+            [("input", DoubleTensorType(data.shape))],
+            target_opset=TARGET_OPSET)
         self.assertTrue(model_onnx is not None)
         dump_data_and_model(
             data, model, model_onnx,

tests/test_sklearn_polynomial_features_converter.py

@@ -7,6 +7,12 @@
 from distutils.version import StrictVersion
 import numpy as np
 import onnx
+try:
+    # scikit-learn >= 0.22
+    from sklearn.utils._testing import ignore_warnings
+except ImportError:
+    # scikit-learn < 0.22
+    from sklearn.utils.testing import ignore_warnings
 from sklearn.preprocessing import PolynomialFeatures
 from skl2onnx import convert_sklearn
 from skl2onnx.common.data_types import FloatTensorType, Int64TensorType
@@ -17,6 +23,7 @@ class TestSklearnPolynomialFeatures(unittest.TestCase):
 
     @unittest.skipIf(StrictVersion(onnx.__version__) < StrictVersion("1.4.0"),
                      reason="ConstantOfShape not available")
+    @ignore_warnings(category=FutureWarning)
     def test_model_polynomial_features_float_degree_2(self):
         X = np.array([[1.2, 3.2, 1.3, -5.6], [4.3, -3.2, 5.7, 1.0],
                       [0, 3.2, 4.7, -8.9]])
@@ -32,6 +39,7 @@ def test_model_polynomial_features_float_degree_2(self):
 
     @unittest.skipIf(StrictVersion(onnx.__version__) < StrictVersion("1.4.0"),
                      reason="ConstantOfShape not available")
+    @ignore_warnings(category=FutureWarning)
     def test_model_polynomial_features_int_degree_2(self):
         X = np.array([
             [1, 3, 4, 0],
@@ -52,6 +60,7 @@ def test_model_polynomial_features_int_degree_2(self):
 
     @unittest.skipIf(StrictVersion(onnx.__version__) < StrictVersion("1.4.0"),
                      reason="ConstantOfShape not available")
+    @ignore_warnings(category=FutureWarning)
     def test_model_polynomial_features_float_degree_3(self):
         X = np.array([[1.2, 3.2, 1.2], [4.3, 3.2, 4.5], [3.2, 4.7, 1.1]])
         model = PolynomialFeatures(degree=3).fit(X)
@@ -66,6 +75,7 @@ def test_model_polynomial_features_float_degree_3(self):
 
     @unittest.skipIf(StrictVersion(onnx.__version__) < StrictVersion("1.4.0"),
                      reason="ConstantOfShape not available")
+    @ignore_warnings(category=FutureWarning)
     def test_model_polynomial_features_int_degree_3(self):
         X = np.array([
             [1, 3, 33],
@@ -87,6 +97,7 @@ def test_model_polynomial_features_int_degree_3(self):
 
     @unittest.skipIf(StrictVersion(onnx.__version__) < StrictVersion("1.4.0"),
                      reason="ConstantOfShape not available")
+    @ignore_warnings(category=FutureWarning)
     def test_model_polynomial_features_float_degree_4(self):
         X = np.array([[1.2, 3.2, 3.1, 1.3], [4.3, 3.2, 0.5, 1.3],
                       [3.2, 4.7, 5.4, 7.1]])
@@ -102,6 +113,7 @@ def test_model_polynomial_features_float_degree_4(self):
 
     @unittest.skipIf(StrictVersion(onnx.__version__) < StrictVersion("1.4.0"),
                      reason="ConstantOfShape not available")
+    @ignore_warnings(category=FutureWarning)
     def test_model_polynomial_features_int_degree_4(self):
         X = np.array([[1, 3, 4, 1], [3, 7, 3, 5], [1, 0, 5, 4]])
         model = PolynomialFeatures(degree=4).fit(X)

tests/test_utils/__init__.py

@@ -36,6 +36,8 @@ def create_tensor(N, C, H=None, W=None):
 
 
 def _get_ir_version(opv):
+    if opv >= 15:
+        return 8
     if opv >= 12:
         return 7
     if opv >= 11:
@@ -55,7 +57,9 @@ def max_onnxruntime_opset():
     <https://github.com/microsoft/onnxruntime/blob/
     master/docs/Versioning.md>`_.
     """
-    vi = StrictVersion(ort_version.split("+")[0])
+    vi = StrictVersion(ort_version.split('+')[0])
+    if vi >= StrictVersion("1.9.0"):
+        return 15
     if vi >= StrictVersion("1.8.0"):
         return 14
     if vi >= StrictVersion("1.6.0"):