Support for opset 15 and update version to 1.10.0 (#505)

* Support for opset 15
* add target_opset to all unit tests

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

@@ -14,10 +14,10 @@ jobs:
     vmImage: 'ubuntu-latest'
   strategy:
     matrix:
-      Python39-1101-RT181-xgb11:
+      Python39-1101-RT190:
         python.version: '3.9'
         ONNX_PATH: onnx==1.10.1  # '-i https://test.pypi.org/simple/ onnx==1.9.101'
-        ONNXRT_PATH: onnxruntime==1.8.1
+        ONNXRT_PATH: onnxruntime==1.9.0
         COREML_PATH: git+https://github.com/apple/coremltools@3.1
         xgboost.version: '>=1.2'
       Python39-190-RT180-xgb11:
@@ -105,8 +105,9 @@ jobs:
 
   - script: |
       export PYTHONPATH=.
-      python -c "import onnxconverter_common;print(onnxconverter_common.__version__)"
-      python -c "import onnxruntime;print(onnxruntime.__version__)"
+      python -c "import onnxruntime;print('onnx:',onnx.__version__)"
+      python -c "import onnxconverter_common;print('cc:',onnxconverter_common.__version__)"
+      python -c "import onnxruntime;print('ort:',onnxruntime.__version__)"
     displayName: 'version'
 
   - script: |
@@ -119,11 +120,6 @@ jobs:
       pytest tests/catboost --durations=0
     displayName: 'pytest - catboost'
 
-  - script: |
-      export PYTHONPATH=.
-      pytest tests/coreml --durations=0
-    displayName: 'pytest - coreml'
-
   - script: |
       export PYTHONPATH=.
       pytest tests/lightgbm --durations=0
@@ -165,6 +161,11 @@ jobs:
       pytest tests/hummingbirdml --durations=0
     displayName: 'pytest - hummingbirdml'
 
+  - script: |
+      export PYTHONPATH=.
+      pytest tests/coreml --durations=0
+    displayName: 'pytest - coreml'
+
   - task: PublishTestResults@2
     inputs:
       testResultsFiles: '**/test-results.xml'

.azure-pipelines/win32-conda-CI.yml

@@ -14,10 +14,10 @@ jobs:
     vmImage: 'windows-latest'
   strategy:
     matrix:
-      Python39-1101-RT181:
+      Python39-1101-RT190:
         python.version: '3.9'
         ONNX_PATH: 'onnx==1.10.1'  # '-i https://test.pypi.org/simple/ onnx==1.9.101'
-        ONNXRT_PATH: onnxruntime==1.8.1
+        ONNXRT_PATH: onnxruntime==1.9.0
         COREML_PATH: git+https://github.com/apple/coremltools@3.1
 
       Python39-190-RT181:

onnxmltools/__init__.py

@@ -5,7 +5,7 @@
 This framework converts any machine learned model into onnx format
 which is a common language to describe any machine learned model.
 """
-__version__ = "1.9.1"
+__version__ = "1.10.0"
 __author__ = "Microsoft"
 __producer__ = "OnnxMLTools"
 __producer_version__ = __version__

onnxmltools/convert/common/onnx_ex.py

@@ -0,0 +1,10 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from onnx.defs import onnx_opset_version
+from onnxconverter_common.onnx_ex import (
+    get_maximum_opset_supported as _get_maximum_opset_supported)
+
+
+def get_maximum_opset_supported():
+    return min(onnx_opset_version(),
+               max(15, _get_maximum_opset_supported()))

onnxmltools/convert/coreml/convert.py

@@ -2,10 +2,10 @@
 
 import coremltools
 from uuid import uuid4
-from onnxconverter_common.onnx_ex import get_maximum_opset_supported
 import onnx
 from ...proto import onnx_proto
 from ..common._topology import convert_topology
+from ..common.onnx_ex import get_maximum_opset_supported
 from ._parse import parse_coreml
 
 # Import modules to invoke function registrations

onnxmltools/convert/h2o/convert.py

@@ -5,8 +5,8 @@
 import tempfile
 import h2o
 
-from onnxconverter_common.onnx_ex import get_maximum_opset_supported
 import onnx
+from ..common.onnx_ex import get_maximum_opset_supported
 from ..common._topology import convert_topology
 from ..common.data_types import FloatTensorType
 from ._parse import parse_h2o

onnxmltools/convert/libsvm/convert.py

@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from uuid import uuid4
-from onnxconverter_common.onnx_ex import get_maximum_opset_supported
 import onnx
+from ..common.onnx_ex import get_maximum_opset_supported
 from ..common._topology import convert_topology
 from ._parse import parse_libsvm
 

onnxmltools/convert/lightgbm/convert.py

@@ -3,7 +3,7 @@
 from uuid import uuid4
 import onnx
 import lightgbm
-from onnxconverter_common.onnx_ex import get_maximum_opset_supported
+from ..common.onnx_ex import get_maximum_opset_supported
 from ..common._topology import convert_topology
 from ..common.utils import hummingbird_installed
 from ._parse import parse_lightgbm, WrappedBooster

onnxmltools/convert/sparkml/convert.py

@@ -2,7 +2,7 @@
 
 from uuid import uuid4
 import onnx
-from onnxconverter_common.onnx_ex import get_maximum_opset_supported
+from ..common.onnx_ex import get_maximum_opset_supported
 from ..common._topology import convert_topology
 from ._parse import parse_sparkml
 from . import operator_converters

onnxmltools/convert/xgboost/convert.py

@@ -2,8 +2,8 @@
 
 from uuid import uuid4
 import xgboost
-from onnxconverter_common.onnx_ex import get_maximum_opset_supported
 import onnx
+from ..common.onnx_ex import get_maximum_opset_supported
 from ..common._topology import convert_topology
 from ._parse import parse_xgboost, WrappedBooster
 

onnxmltools/utils/tests_helper.py

@@ -1,13 +1,18 @@
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy
 import pickle
 import os
 import warnings
+import numpy
+from onnx.defs import onnx_opset_version
+from onnxconverter_common.onnx_ex import DEFAULT_OPSET_NUMBER
 from ..convert.common.data_types import FloatTensorType
 from .utils_backend import compare_backend, extract_options, evaluate_condition, is_backend_enabled
 
 
+TARGET_OPSET = min(DEFAULT_OPSET_NUMBER, onnx_opset_version())
+
+
 def dump_data_and_model(data, model, onnx=None, basename="model", folder=None,
                         inputs=None, backend="onnxruntime", context=None,
                         allow_failure=None, verbose=False):
@@ -248,7 +253,8 @@ def dump_one_class_classification(model, suffix="", folder=None, allow_failure=N
     X = numpy.array(X, dtype=numpy.float32)
     y = [1, 1, 1]
     model.fit(X, y)
-    model_onnx, prefix = convert_model(model, 'one_class', [('input', FloatTensorType([None, 2]))])
+    model_onnx, prefix = convert_model(model, 'one_class', [('input', FloatTensorType([None, 2]))],
+                                       target_opset=TARGET_OPSET)
     return dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
                                basename=prefix + "One" + model.__class__.__name__ + suffix)
 
@@ -274,7 +280,8 @@ def dump_binary_classification(model, suffix="", folder=None, allow_failure=None
     X = numpy.array(X, dtype=numpy.float32)
     y = [0, 1, 0]
     model.fit(X, y)
-    model_onnx, prefix = convert_model(model, 'tree-based binary classifier', [('input', FloatTensorType([None, 2]))])
+    model_onnx, prefix = convert_model(model, 'tree-based binary classifier', [('input', FloatTensorType([None, 2]))],
+                                       target_opset=TARGET_OPSET)
     dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
                         basename=prefix + "Bin" + model.__class__.__name__ + suffix,
                         verbose=verbose)
@@ -299,7 +306,8 @@ def dump_multiple_classification(model, suffix="", folder=None, allow_failure=No
     X = numpy.array(X, dtype=numpy.float32)
     y = [0, 1, 2, 1, 1, 2]
     model.fit(X, y)
-    model_onnx, prefix = convert_model(model, 'tree-based multi-output regressor', [('input', FloatTensorType([None, 2]))])
+    model_onnx, prefix = convert_model(model, 'tree-based multi-output regressor', [('input', FloatTensorType([None, 2]))],
+                                       target_opset=TARGET_OPSET)
     dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
                         basename=prefix + "Mcl" + model.__class__.__name__ + suffix)
 
@@ -324,7 +332,8 @@ def dump_multiple_regression(model, suffix="", folder=None, allow_failure=None):
     X = numpy.array(X, dtype=numpy.float32)
     y = numpy.array([[100, 50], [100, 49], [100, 99]], dtype=numpy.float32)
     model.fit(X, y)
-    model_onnx, prefix = convert_model(model, 'tree-based multi-output regressor', [('input', FloatTensorType([None, 2]))])
+    model_onnx, prefix = convert_model(model, 'tree-based multi-output regressor', [('input', FloatTensorType([None, 2]))],
+                                       target_opset=TARGET_OPSET)
     dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
                         basename=prefix + "MRg" + model.__class__.__name__ + suffix)
 
@@ -350,7 +359,8 @@ def dump_single_regression(model, suffix="", folder=None, allow_failure=None):
     X = numpy.array(X, dtype=numpy.float32)
     y = numpy.array([100, -10, 50], dtype=numpy.float32)
     model.fit(X, y)
-    model_onnx, prefix = convert_model(model, 'tree-based regressor', [('input', FloatTensorType([None, 2]))])
+    model_onnx, prefix = convert_model(model, 'tree-based regressor', [('input', FloatTensorType([None, 2]))],
+                                       target_opset=TARGET_OPSET)
     dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
                         basename=prefix + "Reg" + model.__class__.__name__ + suffix)
 

tests/baseline/test_convert_baseline.py

@@ -6,10 +6,15 @@
 import os
 import re
 import unittest
+from onnx.defs import onnx_opset_version
 from onnxmltools.convert import convert_coreml
+from onnxconverter_common.onnx_ex import DEFAULT_OPSET_NUMBER
 import coremltools
 
 
+TARGET_OPSET = min(DEFAULT_OPSET_NUMBER, onnx_opset_version())
+
+
 class TestBaseLine(unittest.TestCase):
 
     def check_baseline(self, input_file, ref_file):
@@ -20,7 +25,7 @@ def get_diff(self, input_file, ref_file):
         this = os.path.dirname(__file__)
         coreml_file = os.path.join(this, "models", input_file)
         cml = coremltools.utils.load_spec(coreml_file)
-        onnx_model = convert_coreml(cml)
+        onnx_model = convert_coreml(cml, target_opset=TARGET_OPSET)
         output_dir = os.path.join(this, "outmodels")
         output_file = os.path.join(this, "outmodels", ref_file)
         if not os.path.exists(output_dir):