diff --git a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
index 18055caaaeba..0e7a05669cdc 100644
--- a/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
@@ -139,13 +139,6 @@ static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
   return it->second;
 }
 
-template<typename DType>
-static MKLDNNBNForward &GetBNForward(const BatchNormParam& param,
-                                     const OpContext &ctx, const NDArray &in_data,
-                                     mkldnn::normalization_flags flags) {
-  return GetBNForward<DType>(param, ctx, in_data.GetMKLDNNData(), flags);
-}
-
 template <typename DType>
 void MKLDNNBatchNormForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
                             const std::vector<NDArray> &inputs, const std::vector<OpReqType> &req,
@@ -176,8 +169,12 @@ void MKLDNNBatchNormForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
                                                 aux_states,
                                                 param,
                                                 ctx.is_train && !param.use_global_stats);
-  const NDArray &data = in_data[batchnorm::kData];
-  auto &fwd = GetBNForward<DType>(param, ctx, data, flags);
+
+  NDArray &data = in_data[batchnorm::kData];
+  if (data.IsMKLDNNData() && data.IsView())
+    data = data.Reorder2Default();
+  auto data_mem = data.GetMKLDNNData();
+  auto &fwd = GetBNForward<DType>(param, ctx, data_mem, flags);
 
   // for output memory
   auto out_mem = const_cast<NDArray &>(out).CreateMKLDNNData(fwd.GetPd().dst_desc());
@@ -215,7 +212,7 @@ void MKLDNNBatchNormForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
     }
 
     mkldnn_args_map_t net_args;
-    net_args[MKLDNN_ARG_SRC] = *data.GetMKLDNNData();
+    net_args[MKLDNN_ARG_SRC] = *data_mem;
     net_args[MKLDNN_ARG_SCALE_SHIFT] = weight_mem;
     net_args[MKLDNN_ARG_DST] = *out_mem;
 
diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index 44e7d3cf2be9..3be71f4e9da6 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -294,7 +294,7 @@ def test_mkldnn_sum_inplace_with_cpu_layout():
 @with_seed()
 def test_batchnorm():
     def check_batchnorm_training(stype):
-        for shape in [(2, 3), (2, 3, 2, 2)]:
+        for shape in [(2, 3), (2, 4), (2, 3, 2, 2), (2, 4, 2, 2)]:
             data_tmp = np.random.normal(-0.1, 0.1, size=shape)
             s = shape[1],
             gamma = np.ones(s)
diff --git a/tests/python/unittest/test_gluon.py b/tests/python/unittest/test_gluon.py
index a02682557954..ae5af33a811c 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -20,6 +20,7 @@
 
 import mxnet as mx
 from mxnet import gluon
+from mxnet import init
 from mxnet.gluon import nn
 from mxnet.base import py_str, MXNetError
 from mxnet.test_utils import assert_almost_equal
@@ -2160,6 +2161,40 @@ def hybrid_forward(self, F, x):
             check_layer_forward_withinput(net, x)
 
 
+@with_seed()
+def test_batchnorm_chnls():
+    chn_list = [1024, 512, 256, 128, 64, 45, 32, 16, 3]
+    class Net(gluon.HybridBlock):
+        def __init__(self,
+                     chn_num,
+                     norm_kwargs=None,
+                     in_channels=3,
+                     **kwargs):
+            super(Net, self).__init__(**kwargs)
+            self.in_channels = in_channels
+            self.conv1 = gluon.nn.Conv3D(
+                    in_channels=self.in_channels,
+                    channels=chn_num,
+                    kernel_size=(1, 7, 7),
+                    strides=(1, 2, 2),
+                    padding=(0, 3, 3),
+                    use_bias=False,
+                    )
+            self.bn1 = gluon.nn.BatchNorm(in_channels=chn_num, **({} if norm_kwargs is None else norm_kwargs))
+
+        def hybrid_forward(self, F, x):
+            """Hybrid forward of R2+1D net"""
+            conv = self.conv1(x)
+            out = self.bn1(conv)
+            return out
+
+    for i in range(len(chn_list)):
+        net = Net(chn_list[i])
+        net.initialize(init=init.Constant(1))
+        x = mx.nd.zeros((1, 3, 8, 160, 160))
+        net(x).asnumpy()
+
+
 @with_seed()
 def test_concat():
     chn_list = [16, 64]