Merge branch 4.x

testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis0_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis0_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis0_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis1_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis1_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis1_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis_negative_1_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis_negative_1_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis_negative_1_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis_negative_2_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis_negative_2_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_2d_axis_negative_2_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis0_epsilon_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis0_epsilon_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis0_epsilon_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis1_epsilon_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis1_epsilon_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis1_epsilon_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis2_epsilon_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis2_epsilon_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis2_epsilon_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis_negative_1_epsilon_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis_negative_1_epsilon_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis_negative_1_epsilon_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis_negative_2_epsilon_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis_negative_2_epsilon_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis_negative_3_epsilon_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis_negative_3_epsilon_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_3d_axis_negative_3_epsilon_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis0_0.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis0_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis1_0.pb

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XJx?h>z?j@$?.z8s?bhdӽ9>(>%?^B?0=B>]ת>=?RiJ>Z/d#S'?K]?=C@(Hm;= ?2??>>Ec! >*z??Oƾmǚ6&õgڿ?xFKྙ[	G?4οYL=e> kQN>.:=ݚ>b"6^&,Z[*Pܿ35>;;Wп>DhT=:?>ב?>O/^~/Ѓ
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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis2_0.pb

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XJx?h>z?j@$?.z8s?bhdӽ9>(>%?^B?0=B>]ת>=?RiJ>Z/d#S'?K]?=C@(Hm;= ?2??>>Ec! >*z??Oƾmǚ6&õgڿ?xFKྙ[	G?4οYL=e> kQN>.:=ݚ>b"6^&,Z[*Pܿ35>;;Wп>DhT=:?>ב?>O/^~/Ѓ
f=#f?Ok>Ŀ??@?7>8lF?5mξy?	FU>z?u>4?G,<h?>
>>?^Ab*x?(?Ӿ3Y?"??,?g?Iy\}?7mM?r?N4?l?
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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis2_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis2_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis3_0.pb

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XJx?h>z?j@$?.z8s?bhdӽ9>(>%?^B?0=B>]ת>=?RiJ>Z/d#S'?K]?=C@(Hm;= ?2??>>Ec! >*z??Oƾmǚ6&õgڿ?xFKྙ[	G?4οYL=e> kQN>.:=ݚ>b"6^&,Z[*Pܿ35>;;Wп>DhT=:?>ב?>O/^~/Ѓ
f=#f?Ok>Ŀ??@?7>8lF?5mξy?	FU>z?u>4?G,<h?>
>>?^Ab*x?(?Ӿ3Y?"??,?g?Iy\}?7mM?r?N4?l?
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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis3_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis3_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis_negative_1_0.pb

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XJx?h>z?j@$?.z8s?bhdӽ9>(>%?^B?0=B>]ת>=?RiJ>Z/d#S'?K]?=C@(Hm;= ?2??>>Ec! >*z??Oƾmǚ6&õgڿ?xFKྙ[	G?4οYL=e> kQN>.:=ݚ>b"6^&,Z[*Pܿ35>;;Wп>DhT=:?>ב?>O/^~/Ѓ
f=#f?Ok>Ŀ??@?7>8lF?5mξy?	FU>z?u>4?G,<h?>
>>?^Ab*x?(?Ӿ3Y?"??,?g?Iy\}?7mM?r?N4?l?
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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis_negative_1_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis_negative_1_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis_negative_2_0.pb

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XJx?h>z?j@$?.z8s?bhdӽ9>(>%?^B?0=B>]ת>=?RiJ>Z/d#S'?K]?=C@(Hm;= ?2??>>Ec! >*z??Oƾmǚ6&õgڿ?xFKྙ[	G?4οYL=e> kQN>.:=ݚ>b"6^&,Z[*Pܿ35>;;Wп>DhT=:?>ב?>O/^~/Ѓ
f=#f?Ok>Ŀ??@?7>8lF?5mξy?	FU>z?u>4?G,<h?>
>>?^Ab*x?(?Ӿ3Y?"??,?g?Iy\}?7mM?r?N4?l?
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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis_negative_2_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis_negative_2_2.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis_negative_3_0.pb

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XJx?h>z?j@$?.z8s?bhdӽ9>(>%?^B?0=B>]ת>=?RiJ>Z/d#S'?K]?=C@(Hm;= ?2??>>Ec! >*z??Oƾmǚ6&õgڿ?xFKྙ[	G?4οYL=e> kQN>.:=ݚ>b"6^&,Z[*Pܿ35>;;Wп>DhT=:?>ב?>O/^~/Ѓ
f=#f?Ok>Ŀ??@?7>8lF?5mξy?	FU>z?u>4?G,<h?>
>>?^Ab*x?(?Ӿ3Y?"??,?g?Iy\}?7mM?r?N4?l?
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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis_negative_3_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_4d_axis_negative_4_0.pb

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XJx?h>z?j@$?.z8s?bhdӽ9>(>%?^B?0=B>]ת>=?RiJ>Z/d#S'?K]?=C@(Hm;= ?2??>>Ec! >*z??Oƾmǚ6&õgڿ?xFKྙ[	G?4οYL=e> kQN>.:=ݚ>b"6^&,Z[*Pܿ35>;;Wп>DhT=:?>ב?>O/^~/Ѓ
f=#f?Ok>Ŀ??@?7>8lF?5mξy?	FU>z?u>4?G,<h?>
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testdata/dnn/onnx/data/input_test_layer_normalization_default_axis_0.pb

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>>?^Ab*x?(?Ӿ3Y?"??,?g?Iy\}?7mM?r?N4?l?
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testdata/dnn/onnx/data/input_test_layer_normalization_default_axis_1.pb

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testdata/dnn/onnx/data/input_test_layer_normalization_default_axis_2.pb

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testdata/dnn/onnx/data/output_test_layer_normalization_2d_axis0.pb

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testdata/dnn/onnx/data/output_test_layer_normalization_2d_axis1.pb

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testdata/dnn/onnx/data/output_test_layer_normalization_2d_axis_negative_2.pb

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testdata/dnn/onnx/data/output_test_layer_normalization_3d_axis1_epsilon.pb

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testdata/dnn/onnx/data/output_test_layer_normalization_3d_axis2_epsilon.pb

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testdata/dnn/onnx/data/output_test_layer_normalization_3d_axis_negative_2_epsilon.pb

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testdata/dnn/onnx/data/output_test_layer_normalization_3d_axis_negative_3_epsilon.pb

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testdata/dnn/onnx/generate_onnx_models.py

@@ -709,6 +709,21 @@ model = DepthWiseAdd()
 model.eval()
 save_data_and_model("depthwiseconv_add", input, model)
 
+class DepthWiseStride2(nn.Module):
+
+    def __init__(self):
+        super(DepthWiseStride2, self).__init__()
+        self.dconv1 = nn.Conv2d(8, 8, kernel_size=3, stride=2, padding=1, groups=8)
+
+    def forward(self, x):
+        a = self.dconv1(x)
+        return a
+
+input = Variable(torch.randn(1, 8, 6, 6))
+model = DepthWiseStride2()
+model.eval()
+save_data_and_model("depthwise_stride2", input, model)
+
 class Clip(nn.Module):
 
     def __init__(self):
@@ -2461,3 +2476,100 @@ tile=dict(
 )
 
 generate_onnx_single_operator(tile, "tile")
+
+def gen_layer_norm_expanded(input_shape=[1, 4, 5], axis=-1, constant_as_initializers=False):
+    X = onnx.helper.make_tensor_value_info("X", onnx.TensorProto.FLOAT, input_shape)
+    Y = onnx.helper.make_tensor_value_info("Y", onnx.TensorProto.FLOAT, input_shape)
+    nodes = []
+    initializers = []
+
+    class NodeNameManager:
+        def __init__(self):
+            self.name_dict = dict()
+
+        def get_name(self, op_type):
+            if op_type in self.name_dict:
+                self.name_dict[op_type] += 1
+            else:
+                self.name_dict[op_type] = 0
+            
+            return "{}.{}".format(op_type, self.name_dict[op_type])
+
+    node_name_manager = NodeNameManager()
+
+    def make_node(op_type, inputs=None, outputs=None, *args, **kwargs):
+        nonlocal node_name_manager, nodes
+        node_name = node_name_manager.get_name(op_type)
+
+        if inputs is None:
+            inputs = [nodes[-1].output[0]]
+        if outputs is None:
+            outputs = ["{}.out".format(node_name)]
+        return [onnx.helper.make_node(op_type, inputs, outputs, *args, **kwargs)]
+
+    def make_node_with_constant(op_type, constant_value, inputs=None, outputs=None, is_constant_scalar=False):
+        nonlocal node_name_manager, nodes, initializers, constant_as_initializers
+        node_name = node_name_manager.get_name(op_type)
+
+        constant_shape = [] if is_constant_scalar else constant_value.shape
+        tensor = onnx.helper.make_tensor(
+            "Const.{}.tensor".format(node_name),
+            onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[constant_value.dtype],
+            constant_shape,
+            vals=constant_value
+        )
+        if inputs is None:
+            inputs = [nodes[-1].output[0]]
+        if outputs is None:
+            outputs = ["{}.out".format(node_name)]
+
+        if constant_as_initializers:
+            inputs = inputs + [tensor.name]
+            initializers += [tensor]
+            return [onnx.helper.make_node(op_type, inputs, outputs, node_name)]
+        else:
+            node_const = onnx.helper.make_node("Constant", [], ["Const.{}.out".format(node_name)], value=tensor)
+            inputs = inputs + [node_const.output[0]]
+            return [node_const, onnx.helper.make_node(op_type, inputs, outputs, node_name)]
+
+    #   -> ReduceMean ->     -> Pow(2) -> ReduceMean -> Add(epsilon) -> Sqrt ->
+    # x                  Sub                                                    Div -> Mul(weight) -> Add(bias)
+    #   --------------->     ------------------------------------------------->
+
+    nodes += make_node("ReduceMean", inputs=["X"], axes=np.array([axis], dtype=np.int64))
+    nodes += make_node("Sub", inputs=["X", nodes[-1].output[0]])
+    node_sub_0_outname = nodes[-1].output[0]
+    nodes += make_node_with_constant("Pow", np.array([2], dtype=np.float32), is_constant_scalar=True)
+    nodes += make_node("ReduceMean", axes=np.array([axis], dtype=np.int64))
+    nodes += make_node_with_constant("Add", np.array([1e-5], dtype=np.float32), is_constant_scalar=True)
+    nodes += make_node("Sqrt")
+    nodes += make_node("Div", inputs=[node_sub_0_outname, nodes[-1].output[0]])
+    nodes += make_node_with_constant("Mul", np.random.rand(*input_shape[axis:]).astype(np.float32))
+    nodes += make_node_with_constant("Add", np.random.rand(*input_shape[axis:]).astype(np.float32), outputs=["Y"])
+
+    graph_name = "layer_norm_expanded"
+    if constant_as_initializers:
+        graph_name += "with_initializers"
+    graph_def = onnx.helper.make_graph(
+        nodes,
+        graph_name,
+        [X],
+        [Y],
+        initializers
+    )
+    model_def = onnx.helper.make_model(graph_def, producer_name="github.com/opencv/opencv_extra")
+    onnx.checker.check_model(model_def)
+    shape_inferred_model_def = onnx.shape_inference.infer_shapes(model_def)
+    onnx.save(shape_inferred_model_def, "models/{}.onnx".format(graph_name))
+
+    # infer & save data
+    input_blob = np.random.rand(*input_shape).astype(np.float32)
+
+    import onnxruntime as ort
+    sess = ort.InferenceSession("models/{}.onnx".format(graph_name))
+    output_blobs = sess.run(["Y"], {"X": input_blob})
+    np.save("data/input_{}.npy".format(graph_name), input_blob)
+    np.save("data/output_{}.npy".format(graph_name), output_blobs[0])
+
+gen_layer_norm_expanded()
+gen_layer_norm_expanded(constant_as_initializers=True)

testdata/dnn/onnx/models/test_layer_normalization_2d_axis1.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+-
+
X
+
W
+
B
Y"LayerNormalization*
+axis

!test_layer_normalization_2d_axis1Z
+
X
+

+
+Z
+
W
+
+

+Z
+
B
+
+

+b
+
Y
+

+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_2d_axis_negative_1.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+6
+
X
+
W
+
B
Y"LayerNormalization*
+axis

+test_layer_normalization_2d_axis_negative_1Z
+
X
+

+
+Z
+
W
+
+

+Z
+
B
+
+

+b
+
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+

+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_2d_axis_negative_2.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+6
+
X
+
W
+
B
Y"LayerNormalization*
+axis

+test_layer_normalization_2d_axis_negative_2Z
+
X
+

+
+Z
+
W
+

+
+Z
+
B
+

+
+b
+
Y
+

+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_3d_axis1_epsilon.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+@
+
X
+
W
+
B
Y"LayerNormalization*
+axis

*
+epsilon=

)test_layer_normalization_3d_axis1_epsilonZ
+
X
+

+
+
+Z
+
W
+

+
+Z
+
B
+

+
+b
+
Y
+

+
+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_3d_axis2_epsilon.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+@
+
X
+
W
+
B
Y"LayerNormalization*
+axis
*
+epsilon=

)test_layer_normalization_3d_axis2_epsilonZ
+
X
+

+
+
+Z
+
W
+
+

+Z
+
B
+
+

+b
+
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+

+
+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_3d_axis_negative_1_epsilon.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+I
+
X
+
W
+
B
Y"LayerNormalization*
+axis

*
+epsilon=

3test_layer_normalization_3d_axis_negative_1_epsilonZ
+
X
+

+
+
+Z
+
W
+
+

+Z
+
B
+
+

+b
+
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+

+
+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_3d_axis_negative_2_epsilon.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+I
+
X
+
W
+
B
Y"LayerNormalization*
+axis

*
+epsilon=

3test_layer_normalization_3d_axis_negative_2_epsilonZ
+
X
+

+
+
+Z
+
W
+

+
+Z
+
B
+

+
+b
+
Y
+

+
+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_3d_axis_negative_3_epsilon.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+I
+
X
+
W
+
B
Y"LayerNormalization*
+axis

*
+epsilon=

3test_layer_normalization_3d_axis_negative_3_epsilonZ
+
X
+

+
+
+Z
+
W
+

+
+
+Z
+
B
+

+
+
+b
+
Y
+

+
+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_4d_axis1.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+-
+
X
+
W
+
B
Y"LayerNormalization*
+axis

!test_layer_normalization_4d_axis1Z
+
X
+

+
+
+
+Z
+
W
+

+
+
+Z
+
B
+

+
+
+b
+
Y
+

+
+
+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_4d_axis2.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+-
+
X
+
W
+
B
Y"LayerNormalization*
+axis
!test_layer_normalization_4d_axis2Z
+
X
+

+
+
+
+Z
+
W
+

+
+Z
+
B
+

+
+b
+
Y
+

+
+
+
+B
\ No newline at end of file

testdata/dnn/onnx/models/test_layer_normalization_4d_axis3.onnx

+7backend-test modified by github.com/opencv/opencv_extra:

+-
+
X
+
W
+
B
Y"LayerNormalization*
+axis
!test_layer_normalization_4d_axis3Z
+
X
+

+
+
+
+Z
+
W
+
+

+Z
+
B
+
+

+b
+
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+

+
+
+
+B
\ No newline at end of file