diff --git a/tests/test_backend.py b/tests/test_backend.py
old mode 100644
new mode 100755
index 8bf466ea7..9e00f9e56
--- a/tests/test_backend.py
+++ b/tests/test_backend.py
@@ -2830,7 +2830,7 @@ def func(x):
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-04)
 
     @check_opset_min_version(7, "batchnorm")
-    @check_tf_min_version("2.0", "tf-1.x does not support NDHWC")
+    @check_tf_min_version("2.4", "tf version above 2.4 supports NDHWC")
     def test_fused_batchnorm_3d(self):
         x_shape = [1, 28, 28, 2, 2]
         x_dtype = np.float32
diff --git a/tf2onnx/rewriter/conv2d_with_add_rewriter.py b/tf2onnx/rewriter/conv2d_with_add_rewriter.py
index aa941d75b..5fd516935 100644
--- a/tf2onnx/rewriter/conv2d_with_add_rewriter.py
+++ b/tf2onnx/rewriter/conv2d_with_add_rewriter.py
@@ -13,31 +13,39 @@
 # pylint: disable=missing-docstring
 
 def rewrite_biasadd_with_conv2d(g, ops):
-    pattern = \
+    pattern1 = \
         OpTypePattern('BiasAdd', name='biasadd', inputs=[
             OpTypePattern('Conv2D|Conv2DBackpropInput', name='conv', inputs=['*', '*']), '*'])
-    matcher = GraphMatcher(pattern)
-    match_results = list(matcher.match_ops(ops))
-    for match in match_results:
-        biasadd = match.get_op('biasadd')
-        conv = match.get_op('conv')
-
-        #backup the conv and biasadd values
-        conv_type = conv.type
-        conv_input = conv.input
-        conv_attr = conv.attr
-        dtype = g.get_dtype(conv.output[0])
-        shape = g.get_shape(conv.output[0])
-        conv_name = biasadd.name
-        conv_output = biasadd.output
-        conv_inputs = [conv_input[0], conv_input[1], biasadd.input[1]]
-
-        if len(g.find_output_consumers(conv.output[0])) > 1:
-            continue
-        # Remove the Conv and BiasAdd node
-        g.remove_node(conv.name)
-        g.remove_node(biasadd.name)
-
-        g.make_node(conv_type, conv_inputs, attr=conv_attr, name=conv_name, outputs=conv_output,
-                    shapes=[shape], dtypes=[dtype], skip_conversion=False)
+    pattern2 = \
+        OpTypePattern('BiasAdd', name='biasadd', inputs=[
+            OpTypePattern('Conv2D|Conv2DBackpropInput', name='conv', inputs=['*', '*', '*']), '*'], allow_reorder = True)
+
+    for pattern in [pattern1, pattern2]:
+        matcher = GraphMatcher(pattern)
+        match_results = list(matcher.match_ops(ops))
+        for match in match_results:
+            biasadd = match.get_op('biasadd')
+            conv = match.get_op('conv')
+
+            #backup the conv and biasadd values
+            conv_type = conv.type
+            conv_input = conv.input
+            conv_attr = conv.attr
+            dtype = g.get_dtype(conv.output[0])
+            shape = g.get_shape(conv.output[0])
+            conv_name = biasadd.name
+            conv_output = biasadd.output
+            if pattern == pattern2:
+                conv_inputs = [conv_input[0], conv_input[1], conv_input[2], biasadd.input[1]]
+            else:
+                conv_inputs = [conv_input[0], conv_input[1],  biasadd.input[1]]
+
+            if len(g.find_output_consumers(conv.output[0])) > 1:
+                continue
+            # Remove the Conv and BiasAdd node
+            g.remove_node(conv.name)
+            g.remove_node(biasadd.name)
+
+            g.make_node(conv_type, conv_inputs, attr=conv_attr, name=conv_name, outputs=conv_output,
+                        shapes=[shape], dtypes=[dtype], skip_conversion=False)
     return ops