diff --git a/mindspore/lite/src/runtime/kernel/opencl/cl/batchnorm.cl b/mindspore/lite/src/runtime/kernel/opencl/cl/batchnorm.cl
index c3cc89ad1f1..7301135455c 100644
--- a/mindspore/lite/src/runtime/kernel/opencl/cl/batchnorm.cl
+++ b/mindspore/lite/src/runtime/kernel/opencl/cl/batchnorm.cl
@@ -2,10 +2,10 @@
 #define INT4 int4
 #define INT2 int2
 __constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
-__kernel void batch_normalization(__read_only image2d_t input, __read_only image2d_t scale,
-                                  __read_only image2d_t offset, __read_only image2d_t mean,
-                                  __read_only image2d_t variance, __write_only image2d_t output, const INT4 input_shape,
-                                  float epsilon) {
+__kernel void Batch_normalization_NHWC4(__read_only image2d_t input, __read_only image2d_t scale,
+                                        __read_only image2d_t offset, __read_only image2d_t mean,
+                                        __read_only image2d_t variance, __write_only image2d_t output,
+                                        const INT4 input_shape, float epsilon) {
   int X = get_global_id(0);  // H
   int Y = get_global_id(1);  // W
   int Z = get_global_id(2);  // C/4
@@ -25,3 +25,27 @@ __kernel void batch_normalization(__read_only image2d_t input, __read_only image
   result.w = result_scale.w * ((result.w - result_mean.w) / sqrt(result_var.w + epsilon)) + result_offset.w;
   WRITE_IMAGE(output, (int2)((Y)*input_shape.w + Z, (X)), result);
 }
+
+__kernel void Batch_normalization_NC4HW4(__read_only image2d_t input, __read_only image2d_t scale,
+                                         __read_only image2d_t offset, __read_only image2d_t mean,
+                                         __read_only image2d_t variance, __write_only image2d_t output,
+                                         const INT4 input_shape, float epsilon) {
+  int X = get_global_id(0);  // H
+  int Y = get_global_id(1);  // W
+  int Z = get_global_id(2);  // C/4
+  if (X >= input_shape.y || Y >= input_shape.z || Z >= input_shape.w) {
+    return;
+  }
+  FLT4 result = READ_IMAGE(input, smp_none, (int2)((Y), (Z * input_shape.y + X)));
+
+  FLT4 result_mean = READ_IMAGE(mean, smp_none, (int2)((0), (Z)));
+  FLT4 result_var = READ_IMAGE(variance, smp_none, (int2)((0), (Z)));
+  FLT4 result_scale = READ_IMAGE(scale, smp_none, (int2)((0), (Z)));
+  FLT4 result_offset = READ_IMAGE(offset, smp_none, (int2)((0), (Z)));
+
+  result.x = result_scale.x * ((result.x - result_mean.x) / sqrt(result_var.x + epsilon)) + result_offset.x;
+  result.y = result_scale.y * ((result.y - result_mean.y) / sqrt(result_var.y + epsilon)) + result_offset.y;
+  result.z = result_scale.z * ((result.z - result_mean.z) / sqrt(result_var.z + epsilon)) + result_offset.z;
+  result.w = result_scale.w * ((result.w - result_mean.w) / sqrt(result_var.w + epsilon)) + result_offset.w;
+  WRITE_IMAGE(output, (int2)((Y), (Z * input_shape.y + X)), result);
+}
diff --git a/mindspore/lite/src/runtime/kernel/opencl/kernel/batchnorm.cc b/mindspore/lite/src/runtime/kernel/opencl/kernel/batchnorm.cc
index 5c98f9dfe06..30c6c347ae3 100644
--- a/mindspore/lite/src/runtime/kernel/opencl/kernel/batchnorm.cc
+++ b/mindspore/lite/src/runtime/kernel/opencl/kernel/batchnorm.cc
@@ -14,7 +14,6 @@
  * limitations under the License.
  */
 #include <cstring>
-#include <string>
 #include <algorithm>
 #include <set>
 #include "src/kernel_registry.h"
@@ -35,7 +34,7 @@ int BatchNormOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_siz
     im_dst_x = out_tensors_[0]->Width() * CO4;
     im_dst_y = out_tensors_[0]->Height();
   } else {
-    im_dst_y = out_tensors_[0]->Height() * CO4;
+    im_dst_y = out_tensors_[0]->Batch() * out_tensors_[0]->Height() * CO4;
     im_dst_x = out_tensors_[0]->Width();
   }
   size_t img_dtype = CL_FLOAT;
@@ -50,17 +49,29 @@ int BatchNormOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_siz
   return RET_OK;
 }
 int BatchNormOpenCLKernel::Init() {
+  auto in_format = op_format_;
+  if (in_format != schema::Format_NHWC4 && in_format != schema::Format_NC4HW4) {
+    MS_LOG(ERROR) << "input format(" << in_format << ") "
+                  << "format not support!";
+      return RET_ERROR;
+  }
+  in_ori_format_ = in_tensors_[0]->GetFormat();
+  in_tensors_[0]->SetFormat(op_format_);
+  out_ori_format_ = out_tensors_[0]->GetFormat();
+  out_tensors_[0]->SetFormat(op_format_);
+  std::string kernel_name = "Batch_normalization";
+  if (in_format == schema::Format_NC4HW4) {
+    kernel_name += "_NC4HW4";
+  } else if (in_format == schema::Format_NHWC4) {
+    kernel_name += "_NHWC4";
+  }
+
   std::set<std::string> build_options;
   std::string source = batchnorm_source;
-  std::string program_name = "batch_normalization";
-  std::string kernel_name = "batch_normalization";
+  std::string program_name = "Batch_normalization";
   auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
   ocl_runtime->LoadSource(program_name, source);
   ocl_runtime->BuildKernel(kernel_, program_name, kernel_name, build_options);
-  in_ori_format_ = in_tensors_[0]->GetFormat();
-  in_tensors_[0]->SetFormat(schema::Format_NHWC4);
-  out_ori_format_ = out_tensors_[0]->GetFormat();
-  out_tensors_[0]->SetFormat(schema::Format_NHWC4);
 
   return RET_OK;
 }
diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc
index 7ab29a333a8..e9c90cb7765 100644
--- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc
+++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc
@@ -197,7 +197,7 @@ TEST_F(TestBatchnormOpenCLfp32, Batchnormfp32input_dim4) {
     return;
   }
   auto *output_tensor =
-    new (std::nothrow) lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensor_type);
+    new (std::nothrow) lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC, tensor_type);
   if (output_tensor == nullptr) {
     MS_LOG(INFO) << " init tensor failed ";
     delete tensor_data;
diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc
index c0b744bfc9a..5c8830e26fb 100644
--- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc
+++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc
@@ -180,8 +180,8 @@ TEST_F(TestConcatOpenCLfp32, ConcatFp32_2input_dim4_axis3) {
   MS_LOG(INFO) << " init tensors ";
   constexpr int INPUT_NUM = 3;
   std::array<std::vector<int>, INPUT_NUM> input_shapes = {
-    std::vector<int>{1, 2, 4, 8}, std::vector<int>{1, 2, 4, 8}, std::vector<int>{1, 2, 4, 8}};
-  std::vector<int> output_shape = {3, 2, 4, 8};
+    std::vector<int>{1, 16, 256, 80}, std::vector<int>{1, 16, 256, 80}, std::vector<int>{1, 16, 256, 80}};
+  std::vector<int> output_shape = {1, 48, 256, 80};
   auto data_type = kNumberTypeFloat32;
   auto tensor_type = schema::NodeType_ValueNode;
   std::vector<lite::tensor::Tensor *> inputs;
@@ -217,7 +217,7 @@ TEST_F(TestConcatOpenCLfp32, ConcatFp32_2input_dim4_axis3) {
     }
     return;
   }
-  param->axis_ = 0;
+  param->axis_ = 1;
   auto *concat_kernel =
     new (std::nothrow) kernel::ConcatOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
   if (concat_kernel == nullptr) {