diff --git a/mindspore/lite/src/runtime/kernel/opencl/cl/activation.cl b/mindspore/lite/src/runtime/kernel/opencl/cl/activation.cl
index 26b65e626fb22970d5c9bdcdb1bd96c3c88b29e0..7698a0228acc8b33180782b7fb0899a15a44893b 100644
--- a/mindspore/lite/src/runtime/kernel/opencl/cl/activation.cl
+++ b/mindspore/lite/src/runtime/kernel/opencl/cl/activation.cl
@@ -5,63 +5,71 @@
 #define MIN(X, Y) (X < Y ? X : Y)
 __constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
 
-__kernel void LeakyRelu(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape,
-                        __global FLT *alpha) {
-  int C = input_shape.w;     // channel size
-  int Y = get_global_id(0);  // height id
-  int X = get_global_id(1);  // weight id
-  for (int num = 0; num < UP_DIV(C, SLICES); ++num) {
-    FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X * UP_DIV(C, SLICES) + num, Y));  // NHWC4: H WC
-    FLT4 tmp;
-    tmp.x = in_c4.x > 0.0f ? in_c4.x : in_c4.x * alpha[0];
-    tmp.y = in_c4.y > 0.0f ? in_c4.y : in_c4.y * alpha[0];
-    tmp.z = in_c4.z > 0.0f ? in_c4.z : in_c4.z * alpha[0];
-    tmp.w = in_c4.w > 0.0f ? in_c4.w : in_c4.w * alpha[0];
-    WRITE_IMAGE(output, (int2)(X * UP_DIV(C, SLICES) + num, Y), tmp);  // NHWC4: H WC
-  }
+__kernel void LeakyRelu_NHWC4(__read_only image2d_t input, __write_only image2d_t output, const int4 img_shape,
+                              __global FLT4 *alpha, const int4 input_shape) {
+  int Y = get_global_id(0);  // H
+  int X = get_global_id(1);  // W C4
+  if (X >= img_shape.z || Y >= img_shape.y) return;
+  int C = X % UP_DIV(input_shape.w, SLICES);
+  FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
+  FLT4 tmp;
+  tmp.x = in_c4.x > 0.0f ? in_c4.x : in_c4.x * alpha[C].x;
+  tmp.y = in_c4.y > 0.0f ? in_c4.y : in_c4.y * alpha[C].y;
+  tmp.z = in_c4.z > 0.0f ? in_c4.z : in_c4.z * alpha[C].z;
+  tmp.w = in_c4.w > 0.0f ? in_c4.w : in_c4.w * alpha[C].w;
+  WRITE_IMAGE(output, (int2)(X, Y), tmp);
+}
+
+__kernel void LeakyRelu_NC4HW4(__read_only image2d_t input, __write_only image2d_t output, const int4 img_shape,
+                               __global FLT4 *alpha, const int4 input_shape) {
+  int Y = get_global_id(0);  // C4 H
+  int X = get_global_id(1);  // W
+  if (X >= img_shape.z || Y >= img_shape.y) return;
+  int C = Y / input_shape.y;
+  FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
+  FLT4 tmp;
+  tmp.x = in_c4.x > 0.0f ? in_c4.x : in_c4.x * alpha[C].x;
+  tmp.y = in_c4.y > 0.0f ? in_c4.y : in_c4.y * alpha[C].y;
+  tmp.z = in_c4.z > 0.0f ? in_c4.z : in_c4.z * alpha[C].z;
+  tmp.w = in_c4.w > 0.0f ? in_c4.w : in_c4.w * alpha[C].w;
+  WRITE_IMAGE(output, (int2)(X, Y), tmp);
 }
 
 __kernel void Relu(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape) {
-  int C = input_shape.w;     // channel size
-  int Y = get_global_id(0);  // height id
-  int X = get_global_id(1);  // weight id
-  for (int num = 0; num < UP_DIV(C, SLICES); ++num) {
-    FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X * UP_DIV(C, SLICES) + num, Y));  // NHWC4: H WC
-    FLT4 tmp;
-    tmp.x = in_c4.x > 0.0f ? in_c4.x : 0.0f;
-    tmp.y = in_c4.y > 0.0f ? in_c4.y : 0.0f;
-    tmp.z = in_c4.z > 0.0f ? in_c4.z : 0.0f;
-    tmp.w = in_c4.w > 0.0f ? in_c4.w : 0.0f;
-    WRITE_IMAGE(output, (int2)(X * UP_DIV(C, SLICES) + num, Y), tmp);  // NHWC4: H WC
-  }
+  int Y = get_global_id(0);
+  int X = get_global_id(1);
+  if (X >= input_shape.z || Y >= input_shape.y) return;
+  FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
+  FLT4 tmp;
+  tmp.x = in_c4.x > 0.0f ? in_c4.x : 0.0f;
+  tmp.y = in_c4.y > 0.0f ? in_c4.y : 0.0f;
+  tmp.z = in_c4.z > 0.0f ? in_c4.z : 0.0f;
+  tmp.w = in_c4.w > 0.0f ? in_c4.w : 0.0f;
+  WRITE_IMAGE(output, (int2)(X, Y), tmp);
 }
 
 __kernel void Relu6(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape) {
-  int C = input_shape.w;     // channel size
-  int Y = get_global_id(0);  // height id
-  int X = get_global_id(1);  // weight id
-  for (int num = 0; num < UP_DIV(C, SLICES); ++num) {
-    FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X * UP_DIV(C, SLICES) + num, Y));  // NHWC4: H WC
-    FLT4 tmp;
-    tmp.x = in_c4.x > 0.0f ? MIN(in_c4.x, 6.0f) : 0.0f;
-    tmp.y = in_c4.y > 0.0f ? MIN(in_c4.y, 6.0f) : 0.0f;
-    tmp.z = in_c4.z > 0.0f ? MIN(in_c4.z, 6.0f) : 0.0f;
-    tmp.w = in_c4.w > 0.0f ? MIN(in_c4.w, 6.0f) : 0.0f;
-    WRITE_IMAGE(output, (int2)(X * UP_DIV(C, SLICES) + num, Y), tmp);  // NHWC4: H WC
-  }
+  int Y = get_global_id(0);
+  int X = get_global_id(1);
+  if (X >= input_shape.z || Y >= input_shape.y) return;
+  FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
+  FLT4 tmp;
+  tmp.x = in_c4.x > 0.0f ? MIN(in_c4.x, 6.0f) : 0.0f;
+  tmp.y = in_c4.y > 0.0f ? MIN(in_c4.y, 6.0f) : 0.0f;
+  tmp.z = in_c4.z > 0.0f ? MIN(in_c4.z, 6.0f) : 0.0f;
+  tmp.w = in_c4.w > 0.0f ? MIN(in_c4.w, 6.0f) : 0.0f;
+  WRITE_IMAGE(output, (int2)(X, Y), tmp);
 }
 
 __kernel void Sigmoid(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape) {
-  int C = input_shape.w;     // channel size
-  int Y = get_global_id(0);  // height id
-  int X = get_global_id(1);  // weight id
-  for (int num = 0; num < UP_DIV(C, SLICES); ++num) {
-    FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X * UP_DIV(C, SLICES) + num, Y));  // NHWC4: H WC
-    FLT4 tmp;
-    tmp.x = 1.0f / (1.0f + exp(-in_c4.x));
-    tmp.y = 1.0f / (1.0f + exp(-in_c4.y));
-    tmp.z = 1.0f / (1.0f + exp(-in_c4.z));
-    tmp.w = 1.0f / (1.0f + exp(-in_c4.w));
-    WRITE_IMAGE(output, (int2)(X * UP_DIV(C, SLICES) + num, Y), tmp);  // NHWC4: H WC
-  }
+  int Y = get_global_id(0);
+  int X = get_global_id(1);
+  if (X >= input_shape.z || Y >= input_shape.y) return;
+  FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X, Y));
+  FLT4 tmp;
+  tmp.x = 1.0f / (1.0f + exp(-in_c4.x));
+  tmp.y = 1.0f / (1.0f + exp(-in_c4.y));
+  tmp.z = 1.0f / (1.0f + exp(-in_c4.z));
+  tmp.w = 1.0f / (1.0f + exp(-in_c4.w));
+  WRITE_IMAGE(output, (int2)(X, Y), tmp);
 }
diff --git a/mindspore/lite/src/runtime/kernel/opencl/kernel/activation.cc b/mindspore/lite/src/runtime/kernel/opencl/kernel/activation.cc
index 19685cec4f1d72f9c5fb39738be755f7c07f7b83..4030148870f37d572776838e96064df24fb09bdd 100644
--- a/mindspore/lite/src/runtime/kernel/opencl/kernel/activation.cc
+++ b/mindspore/lite/src/runtime/kernel/opencl/kernel/activation.cc
@@ -24,6 +24,7 @@
 #include "src/kernel_registry.h"
 #include "src/runtime/runtime_api.h"
 #include "include/errorcode.h"
+#include "nnacl/fp32/common_func.h"
 #include "src/runtime/kernel/opencl/cl/activation.cl.inc"
 
 using mindspore::kernel::KERNEL_ARCH::kGPU;
@@ -40,14 +41,41 @@ namespace mindspore::kernel {
 
 void ActivationOpenClKernel::InitBuffer() {
   auto allocator = lite::opencl::OpenCLRuntime::GetInstance()->GetAllocator();
-  alpha_buff_ = allocator->Malloc(fp_size);
+  int elem_num = UP_ROUND(nhwc_shape_[3], C4NUM);
+  alpha_buff_ = allocator->Malloc(elem_num * fp_size);
   alpha_buff_ = allocator->MapBuffer(alpha_buff_, CL_MAP_WRITE, nullptr, true);
-  memset(alpha_buff_, 0x00, fp_size);
-  if (enable_fp16_) {
-    auto fp16 = (int16_t)alpha_;
-    memcpy(alpha_buff_, &fp16, fp_size);
+  memset(alpha_buff_, 0x00, elem_num * fp_size);
+  if (in_tensors_.size() == 1) {
+    if (enable_fp16_) {
+      uint16_t alpha_fp16 = Float32ToShort(alpha_);
+      auto alpha_buff_fp16 = reinterpret_cast<uint16_t *>(alpha_buff_);
+      for (int i = 0; i < nhwc_shape_[3]; i++) {
+        alpha_buff_fp16[i] = alpha_fp16;
+      }
+    } else {
+      auto alpha_buff_fp16 = reinterpret_cast<float *>(alpha_buff_);
+      for (int i = 0; i < nhwc_shape_[3]; i++) {
+        alpha_buff_fp16[i] = alpha_;
+      }
+    }
   } else {
-    memcpy(alpha_buff_, &alpha_, fp_size);
+    if (enable_fp16_) {
+      if (in_tensors_[1]->data_type() == kNumberTypeFloat32) {
+        auto alpha_buff_fp16 = reinterpret_cast<uint16_t *>(alpha_buff_);
+        for (int i = 0; i < nhwc_shape_[3]; i++) {
+          alpha_buff_fp16[i] = Float32ToShort(reinterpret_cast<float *>(in_tensors_[0]->Data())[i]);
+        }
+      } else {
+        memcpy(alpha_buff_, in_tensors_[0]->Data(), nhwc_shape_[3] * fp_size);
+      }
+    } else {
+      if (in_tensors_[1]->data_type() == kNumberTypeFloat16) {
+        MS_LOG(WARNING) << "fp16 model run in fp32 mode not support.";
+        memcpy(alpha_buff_, in_tensors_[0]->Data(), nhwc_shape_[3] * fp_size);
+      } else {
+        memcpy(alpha_buff_, in_tensors_[0]->Data(), nhwc_shape_[3] * fp_size);
+      }
+    }
   }
   allocator->UnmapBuffer(alpha_buff_);
 }
@@ -55,6 +83,18 @@ void ActivationOpenClKernel::InitBuffer() {
 int ActivationOpenClKernel::Init() {
   in_size_ = in_tensors_[0]->shape().size();
   out_size_ = out_tensors_[0]->shape().size();
+  size_t n, h, w, c;
+  if (in_size_ == 2) {
+    n = in_tensors_[0]->shape()[0];
+    c = in_tensors_[0]->shape()[1];
+    h = w = 1;
+  } else {
+    n = in_tensors_[0]->shape()[0];
+    h = in_tensors_[0]->shape()[1];
+    w = in_tensors_[0]->shape()[2];
+    c = in_tensors_[0]->shape()[3];
+  }
+  nhwc_shape_ = {n, h, w, c};
   auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
   enable_fp16_ = ocl_runtime->GetFp16Enable();
   fp_size = enable_fp16_ ? sizeof(uint16_t) : sizeof(float);
@@ -62,7 +102,9 @@ int ActivationOpenClKernel::Init() {
     MS_LOG(ERROR) << "Activate fun only support dim=4 or 2, but your dim=" << in_size_;
     return RET_ERROR;
   }
-  InitBuffer();
+  if (type_ == ActivationType_LEAKY_RELU) {
+    InitBuffer();
+  }
   std::map<int, std::vector<std::string>> Program_Kernel{
     {ActivationType_LEAKY_RELU, std::vector<std::string>{"LEAKY_RELU", "LeakyRelu"}},
     {ActivationType_RELU, std::vector<std::string>{"RELU", "Relu"}},
@@ -76,7 +118,11 @@ int ActivationOpenClKernel::Init() {
   std::string source = activation_source;
   std::set<std::string> build_options;
   ocl_runtime->LoadSource(Program_Kernel[type_][0], source);
-  ocl_runtime->BuildKernel(kernel_, Program_Kernel[type_][0], Program_Kernel[type_][1], build_options);
+  std::string kernel_name = Program_Kernel[type_][1];
+  if (type_ == ActivationType_LEAKY_RELU) {
+    kernel_name += "_" + std::string(EnumNameFormat(op_format_));
+  }
+  ocl_runtime->BuildKernel(kernel_, Program_Kernel[type_][0], kernel_name, build_options);
   in_ori_format_ = in_tensors_[0]->GetFormat();
   out_ori_format_ = out_tensors_[0]->GetFormat();
   in_tensors_[0]->SetFormat(op_format_);
@@ -95,8 +141,11 @@ int ActivationOpenClKernel::Run() {
   ocl_runtime->SetKernelArg(kernel_, arg_idx++, img2d_shape);
   if (type_ == ActivationType_LEAKY_RELU) {
     ocl_runtime->SetKernelArg(kernel_, arg_idx++, alpha_buff_, lite::opencl::MemType::BUF);
+    cl_int4 input_shape = {static_cast<int>(nhwc_shape_[0]), static_cast<int>(nhwc_shape_[1]),
+                           static_cast<int>(nhwc_shape_[2]), static_cast<int>(nhwc_shape_[3])};
+    ocl_runtime->SetKernelArg(kernel_, arg_idx++, input_shape);
   }
-  std::vector<size_t> local = {1, 1};
+  std::vector<size_t> local = {};
   std::vector<size_t> global = {static_cast<size_t>(img2d_shape.s[1]), static_cast<size_t>(img2d_shape.s[2])};
   auto ret = ocl_runtime->RunKernel(kernel_, global, local, nullptr);
   if (ret != RET_OK) {
@@ -107,17 +156,15 @@ int ActivationOpenClKernel::Run() {
 }
 
 cl_int4 ActivationOpenClKernel::GetImg2dShape() {
-  cl_int4 img2d_shape = {0, 0, 0, 0};
-  for (int i = 0; i < in_size_; ++i) {
-    img2d_shape.s[i + 4 - in_size_] = in_tensors_[0]->shape()[i];
-  }
-  if (op_format_ == schema::Format_NC4) {
-    img2d_shape.s[1] = img2d_shape.s[2];
-    img2d_shape.s[2] = UP_DIV(img2d_shape.s[3], C4NUM);
+  cl_int4 img2d_shape = {1, 1, 1, 1};
+  if (op_format_ == schema::Format_NHWC4) {
+    img2d_shape.s[1] = nhwc_shape_[1];
+    img2d_shape.s[2] = nhwc_shape_[2] * UP_DIV(nhwc_shape_[3], C4NUM);
     img2d_shape.s[3] = C4NUM;
   }
   if (op_format_ == schema::Format_NC4HW4) {
-    img2d_shape.s[1] = UP_DIV(img2d_shape.s[3], C4NUM) * img2d_shape.s[1];  // UP(c / 4) * H
+    img2d_shape.s[1] = UP_DIV(nhwc_shape_[3], C4NUM) * nhwc_shape_[1];
+    img2d_shape.s[2] = nhwc_shape_[2];
     img2d_shape.s[3] = C4NUM;
   }
   return img2d_shape;
@@ -130,7 +177,7 @@ int ActivationOpenClKernel::GetImageSize(size_t idx, std::vector<size_t> *img_si
     img_dtype = CL_HALF_FLOAT;
   }
   img_size->clear();
-  img_size->push_back(img_shape.s[2] * UP_DIV(img_shape.s[3], C4NUM));
+  img_size->push_back(img_shape.s[2]);
   img_size->push_back(img_shape.s[1]);
   img_size->push_back(img_dtype);
   return RET_OK;
diff --git a/mindspore/lite/src/runtime/kernel/opencl/kernel/activation.h b/mindspore/lite/src/runtime/kernel/opencl/kernel/activation.h
index 039a4a419cbb434b948c1c202514bc8977282bd7..72ed7a0f8e6b57102e978f5ce89a99a45fee1f16 100644
--- a/mindspore/lite/src/runtime/kernel/opencl/kernel/activation.h
+++ b/mindspore/lite/src/runtime/kernel/opencl/kernel/activation.h
@@ -50,6 +50,7 @@ class ActivationOpenClKernel : public OpenCLKernel {
   int out_size_;
   size_t fp_size;
   bool enable_fp16_{false};
+  std::vector<size_t> nhwc_shape_;
 };
 
 }  // namespace mindspore::kernel
diff --git a/mindspore/lite/test/models_tflite_gpu.cfg b/mindspore/lite/test/models_tflite_gpu.cfg
index ac008b9b1829e4e986f8f35d0d83e4ebf8f467cf..39becae923b3fe0b6cf0b06695430ea07fdbf76e 100644
--- a/mindspore/lite/test/models_tflite_gpu.cfg
+++ b/mindspore/lite/test/models_tflite_gpu.cfg
@@ -3,3 +3,4 @@ mobilenet_v2_1.0_224.tflite
 resnet.tflite
 hiai_cn_recognize_modify_padv2.tflite
 hiai_cv_focusShootOCRModel_08.tflite
+hiai_model_normalize_object_scene_ps_20200519.tflite