diff --git a/paddle/fluid/operators/batch_norm_op.cc b/paddle/fluid/operators/batch_norm_op.cc
index b2cffc3f9063c1fd3b33baa9c740c2402fa00080..be17bf9a03fc1908fccb2bb6a5f32ce49db3353d 100644
--- a/paddle/fluid/operators/batch_norm_op.cc
+++ b/paddle/fluid/operators/batch_norm_op.cc
@@ -295,8 +295,7 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
     bool global_stats = test_mode || use_global_stats;
 
     const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
-    const DataLayout data_layout =
-        framework::StringToDataLayout(data_layout_str);
+    DataLayout data_layout = framework::StringToDataLayout(data_layout_str);
 
     const auto *x = ctx.Input<Tensor>("X");
     const auto &x_dims = x->dims();
@@ -332,6 +331,12 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
     saved_mean->mutable_data<T>(ctx.GetPlace());
     saved_variance->mutable_data<T>(ctx.GetPlace());
 
+    // input dimension is 2 and the format is NCHW. The input can be regarded
+    // as NHWC format
+    if (x_dims.size() == 2 && data_layout == DataLayout::kNCHW) {
+      data_layout = DataLayout::kNHWC;
+    }
+
     if (!global_stats) {
       // saved_xx is use just in this batch of data
       EigenVectorArrayMap<T> saved_mean_e(
@@ -578,8 +583,7 @@ class BatchNormGradKernel<platform::CPUDeviceContext, T>
     bool use_global_stats = ctx.Attr<bool>("use_global_stats");
     const bool is_test = ctx.Attr<bool>("is_test");
     const float epsilon = ctx.Attr<float>("epsilon");
-    const DataLayout data_layout =
-        framework::StringToDataLayout(data_layout_str);
+    DataLayout data_layout = framework::StringToDataLayout(data_layout_str);
 
     auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
@@ -633,6 +637,12 @@ class BatchNormGradKernel<platform::CPUDeviceContext, T>
                                           : x_dims[x_dims.size() - 1]);
     const int sample_size = x->numel() / N / C;
 
+    // input dimension is 2 and the format is NCHW. The input can be regarded as
+    // NHWC format
+    if (x_dims.size() == 2 && data_layout == DataLayout::kNCHW) {
+      data_layout = DataLayout::kNHWC;
+    }
+
     // init output
     if (d_x) {
       d_x->mutable_data<T>(ctx.GetPlace());