rename into TorchOnnxEvaluator

CHANGELOGS.rst

@@ -4,7 +4,7 @@ Change Logs
 0.6.1
 +++++
 
-* :pr:`115`, :pr:`116`, :pr:`117`: first steps for TorchEvaluator
+* :pr:`115`, :pr:`116`, :pr:`117`, :pr:`118`: first steps for TorchOnnxEvaluator
 * :pr:`114`: extends the list of known rewritings
 * :pr:`113`: fixes a couple of issues with ModelBuilder
 

_doc/api/reference/index.rst

@@ -29,10 +29,10 @@ OnnxruntimeEvaluator
 .. autoclass:: onnx_diagnostic.reference.OnnxruntimeEvaluator
     :members:
 
-TorchEvaluator
-++++++++++++++
+TorchOnnxEvaluator
+++++++++++++++++++
 
-.. autoclass:: onnx_diagnostic.reference.TorchEvaluator
+.. autoclass:: onnx_diagnostic.reference.TorchOnnxEvaluator
     :members:
 
 Other functions

_doc/api/reference/torch_evaluator.rst

@@ -5,4 +5,4 @@ onnx_diagnostic.reference.torch_evaluator
 .. automodule:: onnx_diagnostic.reference.torch_evaluator
     :members:
     :no-undoc-members:
-    :exclude-members: TorchEvaluator
+    :exclude-members: TorchOnnxEvaluator

_unittests/ut_reference/test_torch_evaluator.py

@@ -5,15 +5,15 @@
 import onnx.numpy_helper as onh
 import torch
 from onnx_diagnostic.ext_test_case import ExtTestCase
-from onnx_diagnostic.reference import ExtendedReferenceEvaluator, TorchEvaluator
+from onnx_diagnostic.reference import ExtendedReferenceEvaluator, TorchOnnxEvaluator
 from onnx_diagnostic.reference.torch_evaluator import get_kernels
 
 
 TFLOAT = onnx.TensorProto.FLOAT
 TINT64 = onnx.TensorProto.INT64
 
 
-class TestTorchEvaluator(ExtTestCase):
+class TestTorchOnnxEvaluator(ExtTestCase):
     def test_kernels(self):
         ker = get_kernels()
         self.assertIsInstance(ker, dict)
@@ -36,7 +36,7 @@ def _finalize_test(self, model, *args, atol: float = 0, use_ort: bool = False):
             expected = sess.run(None, feeds_numpy)
         else:
             expected = ExtendedReferenceEvaluator(model).run(None, feeds_numpy)
-        rt = TorchEvaluator(model)
+        rt = TorchOnnxEvaluator(model)
         got = rt.run(None, feeds)
         self.assertEqualAny(expected, [g.detach().numpy() for g in got], atol=atol)
 
@@ -68,7 +68,7 @@ def test_op_binary(self):
         )
         onnx.checker.check_model(model)
 
-        rt = TorchEvaluator(model)
+        rt = TorchOnnxEvaluator(model)
         self.assertEqual(5, len(rt.kernels))
         self.assertEqual(2, len(rt.constants))
 
@@ -144,7 +144,7 @@ def test_op_slice_squeeze(self):
         expected = ExtendedReferenceEvaluator(model).run(
             None, {k: v.numpy() for k, v in feeds.items()}
         )
-        rt = TorchEvaluator(model)
+        rt = TorchOnnxEvaluator(model)
         got = rt.run(None, feeds)
         self.assertEqualAny(expected, [g.detach().numpy() for g in got])
 
@@ -171,7 +171,7 @@ def test_op_shape(self):
         expected = ExtendedReferenceEvaluator(model).run(
             None, {k: v.numpy() for k, v in feeds.items()}
         )
-        rt = TorchEvaluator(model)
+        rt = TorchOnnxEvaluator(model)
         got = rt.run(None, feeds)
         self.assertEqualAny(expected, [g.detach().numpy() for g in got])
 

onnx_diagnostic/reference/__init__.py

@@ -1,3 +1,3 @@
 from .evaluator import ExtendedReferenceEvaluator
 from .ort_evaluator import OnnxruntimeEvaluator
-from .torch_evaluator import TorchEvaluator
+from .torch_evaluator import TorchOnnxEvaluator

onnx_diagnostic/reference/torch_evaluator.py

@@ -19,7 +19,7 @@ def get_kernels() -> Dict[Tuple[str, str, int], type[torch_ops.OpRun]]:
     return res
 
 
-class TorchEvaluator:
+class TorchOnnxEvaluator:
     """
     Torch evaluator for onnx models.
     The model does not stores the original proto it evaluates to avoid