Fix examples after update to onnx 1.11 (#831)

* Fix examples after update to onnx 1.11

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

@@ -24,7 +24,7 @@ jobs:
         sklearn.version: '>=1.0'
         lgbm.version: ''
         onnxcc.version: '>=1.8.1'  # git
-        run.example: '0'
+        run.example: '1'
       Py39-Onnx1102-Rt110-Skl10:
         do.bench: '0'
         python.version: '3.9'

docs/examples/plot_pipeline_lightgbm.py

@@ -86,7 +86,7 @@
 model_onnx = convert_sklearn(
     pipe, 'pipeline_lightgbm',
     [('input', FloatTensorType([None, 2]))],
-    target_opset=12)
+    target_opset={'': 12, 'ai.onnx.ml': 2})
 
 # And save.
 with open("pipeline_lightgbm.onnx", "wb") as f:

docs/examples/plot_pipeline_xgboost.py

@@ -60,7 +60,7 @@
 try:
     convert_sklearn(pipe, 'pipeline_xgboost',
                     [('input', FloatTensorType([None, 2]))],
-                    target_opset=12)
+                    target_opset={'': 12, 'ai.onnx.ml': 2})
 except Exception as e:
     print(e)
 
@@ -102,7 +102,7 @@
 model_onnx = convert_sklearn(
     pipe, 'pipeline_xgboost',
     [('input', FloatTensorType([None, 2]))],
-    target_opset=12)
+    target_opset={'': 12, 'ai.onnx.ml': 2})
 
 # And save.
 with open("pipeline_xgboost.onnx", "wb") as f:

docs/tutorial/plot_cbegin_opset.py

@@ -59,7 +59,8 @@
 # ++++
 
 
-onx = to_onnx(model, X[:1].astype(numpy.float32))
+onx = to_onnx(model, X[:1].astype(numpy.float32),
+              target_opset={'': 15, 'ai.onnx.ml': 2})
 print(onx)
 
 ##########################
@@ -89,9 +90,10 @@ def get_domain_opset(onx):
     return {d['domain']: d['version'] for d in res}
 
 
-for opset in range(1, onnx_opset_version() + 1):
+for opset in range(6, onnx_opset_version() + 1):
     try:
-        onx = to_onnx(model, X[:1].astype(numpy.float32), target_opset=opset)
+        onx = to_onnx(model, X[:1].astype(numpy.float32),
+                      target_opset={'': opset, 'ai.onnx.ml': 2})
     except RuntimeError as e:
         print('target: %r error: %r' % (opset, e))
         continue
@@ -112,7 +114,7 @@ def get_domain_opset(onx):
 # ``''`` but the other opset domain can be changed as well.
 
 for opset in range(9, onnx_opset_version() + 1):
-    for opset_ml in range(1, 3):
+    for opset_ml in range(1, 4):
         tops = {'': opset, 'ai.onnx.ml': opset_ml}
         try:
             onx = to_onnx(

docs/tutorial/plot_gexternal_lightgbm.py

@@ -73,7 +73,7 @@
 model_onnx = convert_sklearn(
     pipe, 'pipeline_lightgbm',
     [('input', FloatTensorType([None, 2]))],
-    target_opset=12)
+    target_opset={'': 12, 'ai.onnx.ml': 2})
 
 # And save.
 with open("pipeline_lightgbm.onnx", "wb") as f:

docs/tutorial/plot_gexternal_lightgbm_reg.py

@@ -102,8 +102,10 @@ def skl2onnx_convert_lightgbm(scope, operator, container):
 # TreeEnsembleRegressor node, or more. *split* parameter is the number of
 # trees per node TreeEnsembleRegressor.
 
-model_onnx = to_onnx(reg, X[:1].astype(numpy.float32), target_opset=14)
-model_onnx_split = to_onnx(reg, X[:1].astype(numpy.float32), target_opset=14,
+model_onnx = to_onnx(reg, X[:1].astype(numpy.float32),
+                     target_opset={'': 14, 'ai.onnx.ml': 2})
+model_onnx_split = to_onnx(reg, X[:1].astype(numpy.float32),
+                           target_opset={'': 14, 'ai.onnx.ml': 2},
                            options={'split': 100})
 
 ##########################
@@ -158,7 +160,7 @@ def skl2onnx_convert_lightgbm(scope, operator, container):
 res = []
 for i in tqdm(list(range(20, 170, 20)) + [200, 300, 400, 500]):
     model_onnx_split = to_onnx(reg, X[:1].astype(numpy.float32),
-                               target_opset=14,
+                               target_opset={'': 14, 'ai.onnx.ml': 2},
                                options={'split': i})
     sess_split = InferenceSession(model_onnx_split.SerializeToString())
     got_split = sess_split.run(None, {'X': X32})[0].ravel()

docs/tutorial/plot_gexternal_xgboost.py

@@ -60,7 +60,7 @@
 try:
     convert_sklearn(pipe, 'pipeline_xgboost',
                     [('input', FloatTensorType([None, 2]))],
-                    target_opset=12)
+                    target_opset={'': 12, 'ai.onnx.ml': 2})
 except Exception as e:
     print(e)
 
@@ -97,7 +97,7 @@
 model_onnx = convert_sklearn(
     pipe, 'pipeline_xgboost',
     [('input', FloatTensorType([None, 2]))],
-    target_opset=12)
+    target_opset={'': 12, 'ai.onnx.ml': 2})
 
 # And save.
 with open("pipeline_xgboost.onnx", "wb") as f:
@@ -154,7 +154,8 @@
 #############################
 # ONNX
 
-onx = to_onnx(pipe, X_train.astype(numpy.float32))
+onx = to_onnx(pipe, X_train.astype(numpy.float32),
+              target_opset={'': 12, 'ai.onnx.ml': 2})
 
 sess = rt.InferenceSession(onx.SerializeToString())
 pred_onx = sess.run(None, {"X": X_test[:5].astype(numpy.float32)})

docs/tutorial/plot_usparse_xgboost.py

@@ -161,7 +161,8 @@ def make_pipelines(df_train, y_train, models=None,
                 pipe,
                 initial_types=[('input', FloatTensorType([None, 2])),
                                ('text', StringTensorType([None, 1]))],
-                target_opset=12, options=options)
+                target_opset={'': 12, 'ai.onnx.ml': 2},
+                options=options)
 
         with open('model.onnx', 'wb') as f:
             f.write(model_onnx.SerializeToString())

docs/tutorial/plot_wext_pyod_forest.py

@@ -172,7 +172,7 @@ def pyod_iforest_converter(scope, operator, container):
 
 if IForest is not None:
     onx = to_onnx(model1, initial_types=initial_type,
-                  target_opset=14)
+                  target_opset={'': 14, 'ai.onnx.ml': 2})
 
 ###############################################
 # Checking discrepencies

skl2onnx/common/_topology.py

@@ -42,7 +42,7 @@ def _default_OPSET_TO_IR_VERSION():
     return {
         1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3,
         7: 3, 8: 4, 9: 4, 10: 5, 11: 6, 12: 7,
-        13: 7, 14: 7, 15: 8
+        13: 7, 14: 7, 15: 8, 16: 8
     }
 
 
@@ -52,11 +52,29 @@ def _default_OPSET_TO_IR_VERSION():
 except (ImportError, KeyError):
     OPSET_TO_IR_VERSION = _default_OPSET_TO_IR_VERSION()
 
-OPSET_ML_TO_OPSET = {1: 11, 2: 15}
+OPSET_ML_TO_OPSET = {1: 11, 2: 15, 3: 16}
 
 logger = getLogger('skl2onnx')
 
 
+def get_default_opset_for_domain(domain):
+    """
+    Returns the associated for a domain given the main opset.
+    """
+    from .. import __max_supported_opset__ as main_opset
+    if domain == '':
+        return main_opset
+    if domain == 'ai.onnx.ml':
+        if main_opset >= 16:
+            return 3
+        if main_opset < 6:
+            return 1
+        return 2
+    if domain == 'ai.onnx.training':
+        return 1
+    return None
+
+
 class Variable:
     """
     Defines a variable which holds any data defined
@@ -1516,7 +1534,16 @@ def _update_domain_version(container, onnx_model, verbose=0):
             print('[_update_domain_version] +opset %d: name=%r, version=%s' % (
                 i, op_domain, op_version))
         op_set.domain = op_domain
+        if op_set != '':
+            max_supported = get_default_opset_for_domain(op_domain)
+            if max_supported is not None and max_supported < op_version:
+                raise RuntimeError(
+                    "The model is using version %d of domain %r not supported "
+                    "yet by this library. You need to specify "
+                    "target_opset={%r: %r}." % (
+                        op_version, op_domain, op_domain, max_supported))
         op_set.version = op_version
+
         i += 1
         if container.target_opset_any_domain(op_domain) < op_version:
             raise RuntimeError(