add

Author: xadupre

_unittests/ut_reference/test_torch_onnx_evaluator.py

@@ -1193,6 +1193,42 @@ def test_conv_autopad_valid(self):
         B = torch.tensor([[[[0]]]], dtype=torch.float32)
         self._finalize_test(model, X, W, B, use_ort=True)
 
+    def test_conv_autopad_upper(self):
+        model = oh.make_model(
+            oh.make_graph(
+                [
+                    oh.make_node(
+                        "Conv",
+                        ["X", "W", "B"],
+                        ["Y"],
+                        dilations=[1, 1],
+                        strides=[2, 2],
+                        auto_pad="SAME_UPPER",
+                    )
+                ],
+                "g",
+                [
+                    oh.make_tensor_value_info("X", TFLOAT, [None, None, None, None]),
+                    oh.make_tensor_value_info("W", TFLOAT, [None, None, None, None]),
+                    oh.make_tensor_value_info("B", TFLOAT, [None, None, None, None]),
+                ],
+                [oh.make_tensor_value_info("Y", TFLOAT, [None, None, None, None])],
+            ),
+            opset_imports=[oh.make_opsetid("", 18)],
+            ir_version=10,
+        )
+        sH, sW = 5, 5
+        i = sH // 2
+        j = sW // 2
+        X = torch.zeros((1, 1, sH, sW), dtype=torch.float32)
+        X[0, 0, i, j] = 1.0
+        W = torch.zeros((1, 1, 3, 3), dtype=torch.float32)
+        W[0, 0, :, :] = torch.minimum(
+            2 ** torch.arange(9).reshape((3, -1)), torch.tensor([256])
+        )
+        B = torch.tensor([[[[0]]]], dtype=torch.float32)
+        self._finalize_test(model, X, W, B, use_ort=True)
+
     def test_nonzero(self):
         model = oh.make_model(
             oh.make_graph(

onnx_diagnostic/reference/torch_ops/access_ops.py

@@ -1,6 +1,5 @@
 from typing import Optional
 import onnx
-import numpy as np
 import torch
 from . import OpRun, OpRunTensor
 
@@ -29,21 +28,6 @@ def __init__(self, node: onnx.NodeProto, version: Optional[int] = None):
         super().__init__(node, version)
         self.reduction = self.get_attribute_string(node, "reduction", "none")
 
-    def _scatter_nd_impl(data, indices, updates, reduction=None):  # type: ignore
-        output = np.copy(data)
-        for i in np.ndindex(indices.shape[:-1]):
-            if reduction == "add":
-                output[tuple(indices[i])] += updates[i]
-            elif reduction == "mul":
-                output[tuple(indices[i])] *= updates[i]
-            elif reduction == "max":
-                output[tuple(indices[i])] = np.maximum(output[indices[i]], updates[i])
-            elif reduction == "min":
-                output[tuple(indices[i])] = np.minimum(output[indices[i]], updates[i])
-            else:
-                output[tuple(indices[i])] = updates[i]
-        return output
-
     def run(
         self, data: OpRunTensor, indices: OpRunTensor, updates: OpRunTensor
     ) -> OpRunTensor: