ReduceMean + resnet

validation/.gitignore

@@ -8,3 +8,7 @@ networks/**/outputs
 networks/**/raptor
 networks/**/runner
 networks/**/simulation
+networks/**/real_image_val
+networks/**/*.png
+networks/**/*.jpg
+networks/**/*.csv

validation/operations/gen_tests.py

@@ -1053,6 +1053,92 @@ def reducemean_large_dimension_1024():
     save_model(model, "reduce_mean/large_dimension_1024", "reduce_mean_large_dimension_1024.onnx")
 
 
+def make_legacy_reducemean_model(name, shape, output_shape, directory, filename, *, axes, keepdims=1,
+                                 noop_with_empty_axes=0):
+    """Create an opset-18 ReduceMean model that lowers to ONNXReduceMeanOp."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, shape)
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, output_shape)
+
+    initializers = []
+    node_inputs = ["X", ""]
+    if axes is not None:
+        initializers.append(make_int64_initializer("axes", axes))
+        node_inputs = ["X", "axes"]
+
+    node = helper.make_node("ReduceMean", node_inputs, ["Y"],
+                            keepdims=keepdims, noop_with_empty_axes=noop_with_empty_axes)
+    graph = helper.make_graph([node], name, [X], [Y], initializer=initializers)
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 18)])
+    save_model(model, directory, filename)
+
+
+def reducemean_legacy_axis1_keepdims_1():
+    """Opset-18 ReduceMean over one positive axis, preserving rank."""
+    make_legacy_reducemean_model("reducemean_legacy_axis1_keepdims_1",
+                                 [2, 3, 4], [2, 1, 4],
+                                 "reduce_mean/legacy_axis1_keepdims_1",
+                                 "reduce_mean_legacy_axis1_keepdims_1.onnx",
+                                 axes=[1], keepdims=1)
+
+
+def reducemean_legacy_axis1_keepdims_0():
+    """Opset-18 ReduceMean over one positive axis, dropping the reduced axis."""
+    make_legacy_reducemean_model("reducemean_legacy_axis1_keepdims_0",
+                                 [2, 3, 4], [2, 4],
+                                 "reduce_mean/legacy_axis1_keepdims_0",
+                                 "reduce_mean_legacy_axis1_keepdims_0.onnx",
+                                 axes=[1], keepdims=0)
+
+
+def reducemean_legacy_axes_1_2_keepdims_1():
+    """Opset-18 ReduceMean over multiple positive axes."""
+    make_legacy_reducemean_model("reducemean_legacy_axes_1_2_keepdims_1",
+                                 [2, 3, 4], [2, 1, 1],
+                                 "reduce_mean/legacy_axes_1_2_keepdims_1",
+                                 "reduce_mean_legacy_axes_1_2_keepdims_1.onnx",
+                                 axes=[1, 2], keepdims=1)
+
+
+def reducemean_legacy_negative_axis():
+    """Opset-18 ReduceMean using a negative axis."""
+    make_legacy_reducemean_model("reducemean_legacy_negative_axis",
+                                 [2, 3, 4], [2, 3, 1],
+                                 "reduce_mean/legacy_negative_axis",
+                                 "reduce_mean_legacy_negative_axis.onnx",
+                                 axes=[-1], keepdims=1)
+
+
+def reducemean_legacy_reduce_all_keepdims_1():
+    """Opset-18 ReduceMean over all axes with the optional axes input omitted."""
+    make_legacy_reducemean_model("reducemean_legacy_reduce_all_keepdims_1",
+                                 [2, 3, 4], [1, 1, 1],
+                                 "reduce_mean/legacy_reduce_all_keepdims_1",
+                                 "reduce_mean_legacy_reduce_all_keepdims_1.onnx",
+                                 axes=None, keepdims=1)
+
+
+def reducemean_legacy_empty_axes_noop():
+    """Opset-18 ReduceMean with empty axes and noop_with_empty_axes enabled."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 4])
+    axes = make_int64_initializer("axes", [])
+    reduce = helper.make_node("ReduceMean", ["X", "axes"], ["R"],
+                              keepdims=1, noop_with_empty_axes=1)
+    relu = helper.make_node("Relu", ["R"], ["Y"])
+    graph = helper.make_graph([reduce, relu], "reducemean_legacy_empty_axes_noop", [X], [Y], initializer=[axes])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 18)])
+    save_model(model, "reduce_mean/legacy_empty_axes_noop", "reduce_mean_legacy_empty_axes_noop.onnx")
+
+
+def reducemean_legacy_nchw_spatial():
+    """Opset-18 ReduceMean over H and W on an NCHW tensor."""
+    make_legacy_reducemean_model("reducemean_legacy_nchw_spatial",
+                                 [1, 3, 5, 5], [1, 3, 1, 1],
+                                 "reduce_mean/legacy_nchw_spatial",
+                                 "reduce_mean_legacy_nchw_spatial.onnx",
+                                 axes=[2, 3], keepdims=1)
+
+
 # ---------------------------------------------------------------------------
 # Relu tests
 # ---------------------------------------------------------------------------
@@ -1974,6 +2060,13 @@ if __name__ == "__main__":
     reducemean_4d_spatial_keepdims_0()
     reducemean_channel_axis_nchw()
     reducemean_large_dimension_1024()
+    reducemean_legacy_axis1_keepdims_1()
+    reducemean_legacy_axis1_keepdims_0()
+    reducemean_legacy_axes_1_2_keepdims_1()
+    reducemean_legacy_negative_axis()
+    reducemean_legacy_reduce_all_keepdims_1()
+    reducemean_legacy_empty_axes_noop()
+    reducemean_legacy_nchw_spatial()
 
     print("\nGenerating Relu tests:")
     relu_basic()