add new ops named slice

mindspore/lite/src/runtime/kernel/opencl/cl/slice.cl

@@ -0,0 +1,81 @@
+#define INT2 int2
+#define INT4 int4
+#define FLT4 float4
+__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
+__kernel void slice(__read_only image2d_t input, __write_only image2d_t output, INT4 input_shape, INT4 out_shape,
+                    INT4 begin, INT2 sharedNoUpdiv) {
+  int X = get_global_id(1);  // H
+  int Y = get_global_id(2);  // W
+  if (X >= out_shape.y || Y >= out_shape.z) {
+    return;
+  }
+  FLT4 result;
+  if (sharedNoUpdiv.x % 4 == 0) {
+    for (int i = 0; i < out_shape.w; i++) {
+      result = read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (i + begin.w), (X + begin.y)));
+      write_imagef(output, (INT2)((Y)*out_shape.w + i, (X)), result);
+    }
+  } else {
+    int begin_postion = sharedNoUpdiv.y % 4;
+    FLT4 first = read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + begin.w, (X + begin.y)));
+    if (begin_postion == 1) {
+      for (int i = 1; i <= out_shape.w; i++) {
+        FLT4 second =
+          read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (begin.w + i), (X + begin.y)));
+        result.x = first.y;
+        result.y = first.z;
+        result.z = first.w;
+        result.w = second.x;
+        write_imagef(output, (INT2)((Y)*out_shape.w + i - 1, (X)), result);
+        first.y = second.y;
+        first.z = second.z;
+        first.w = second.w;
+      }
+    } else if (begin_postion == 2) {
+      for (int i = 1; i <= out_shape.w; i++) {
+        FLT4 second =
+          read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (begin.w + i), (X + begin.y)));
+        result.x = first.z;
+        result.y = first.w;
+        result.z = second.x;
+        result.w = second.y;
+        write_imagef(output, (INT2)((Y)*out_shape.w + i - 1, (X)), result);
+        first.z = second.z;
+        first.w = second.w;
+      }
+    } else {
+      for (int i = 1; i <= out_shape.w; i++) {
+        FLT4 second =
+          read_imagef(input, smp_none, (INT2)((Y + begin.z) * input_shape.w + (begin.w + i), (X + begin.y)));
+        result.x = first.w;
+        result.y = second.x;
+        result.z = second.y;
+        result.w = second.z;
+        write_imagef(output, (INT2)((Y)*out_shape.w + i - 1, (X)), result);
+        first.w = second.w;
+      }
+    }
+  }
+  // judge the line of size
+  int size = sharedNoUpdiv.y % 4;
+  FLT4 result_fill0;
+  if (size == 1) {
+    result_fill0.x = result.x;
+    result_fill0.y = 0;
+    result_fill0.z = 0;
+    result_fill0.w = 0;
+    write_imagef(output, (INT2)((Y)*out_shape.w + out_shape.w - 1, (X)), result_fill0);
+  } else if (size == 2) {
+    result_fill0.x = result.x;
+    result_fill0.y = result.y;
+    result_fill0.z = 0;
+    result_fill0.w = 0;
+    write_imagef(output, (INT2)((Y)*out_shape.w + out_shape.w - 1, (X)), result_fill0);
+  } else if (size == 3) {
+    result_fill0.x = result.x;
+    result_fill0.y = result.y;
+    result_fill0.z = result.z;
+    result_fill0.w = 0;
+    write_imagef(output, (INT2)((Y)*out_shape.w + out_shape.w - 1, (X)), result_fill0);
+  }
+}

mindspore/lite/src/runtime/kernel/opencl/kernel/batchnorm.cc

@@ -137,7 +137,7 @@ kernel::LiteKernel *OpenCLBatchnormKernelCreator(const std::vector<lite::tensor:
     return nullptr;
   }
   auto ret = kernel->Init();
-  if (0 != ret) {
+  if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init kernel failed, name: Convolution";
     delete kernel;
     return nullptr;

mindspore/lite/src/runtime/kernel/opencl/kernel/concat.cc

@@ -214,7 +214,7 @@ kernel::LiteKernel *OpenCLConcatKernelCreator(const std::vector<lite::tensor::Te
     return nullptr;
   }
   auto ret = kernel->Init();
-  if (0 != ret) {
+  if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init kernel failed, name: Convolution";
     delete kernel;
     return nullptr;

mindspore/lite/src/runtime/kernel/opencl/kernel/convolution.cc

@@ -273,7 +273,7 @@ int ConvolutionOpenCLKernel::Run() {
   }
 
   if (use_winograd_) {
-    ocl_runtime->RunKernel(kernel_4x4to36, {size_t(TILES_XY), 6, size_t(CI_SLICES)}, {16, 6, 4}, nullptr);
+    ocl_runtime->RunKernel(kernel_4x4to36, {size_t(TILES_XY), 6, size_t(CI_SLICES)}, {8, 6, 4}, nullptr);
     ocl_runtime->RunKernel(kernel_conv, {size_t(TILES_XY / 2), 36, size_t(CO_SLICES / 2)}, {8, 6, 2}, nullptr);
     ocl_runtime->RunKernel(kernel_36to4x4, {size_t(TILES_XY), 4, size_t(CO_SLICES)}, {32, 4, 2}, nullptr);
   } else {
@@ -674,7 +674,7 @@ kernel::LiteKernel *OpenCLConvolutionKernelCreator(const std::vector<lite::tenso
     return nullptr;
   }
   auto ret = kernel->Init();
-  if (0 != ret) {
+  if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init kernel failed, name: Convolution";
     delete kernel;
     return nullptr;

mindspore/lite/src/runtime/kernel/opencl/kernel/slice.cc

@@ -0,0 +1,144 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cstring>
+#include <string>
+#include <algorithm>
+#include <set>
+#include "src/kernel_registry.h"
+#include "src/runtime/opencl/opencl_runtime.h"
+#include "src/runtime/kernel/opencl/kernel/slice.h"
+#include "src/runtime/kernel/opencl/cl/slice.cl.inc"
+
+using mindspore::kernel::KERNEL_ARCH::kGPU;
+using mindspore::lite::KernelRegistrar;
+using mindspore::schema::PrimitiveType_Slice;
+
+namespace mindspore::kernel {
+
+int SliceOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size) {
+  size_t CO4 = UP_DIV(out_tensors_[0]->Channel(), C4NUM);
+  size_t im_dst_x, im_dst_y;
+  if (in_tensors_[0]->GetFormat() == schema::Format_NHWC4) {
+    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_x = out_tensors_[0]->Width();
+  }
+#ifdef ENABLE_FP16
+  size_t img_dtype = CL_HALF_FLOAT;
+#else
+  size_t img_dtype = CL_FLOAT;
+#endif
+  img_size->clear();
+  std::vector<size_t> vec{im_dst_x, im_dst_y, img_dtype};
+  *img_size = vec;
+  return RET_OK;
+}
+int SliceOpenCLKernel::Init() {
+  std::set<std::string> build_options;
+  std::string source = slice_source;
+  std::string program_name = "slice";
+  std::string kernel_name = "slice";
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  ocl_runtime->LoadSource(program_name, source);
+  ocl_runtime->BuildKernel(kernel_, program_name, kernel_name, build_options);
+  ori_format_ = out_tensors_[0]->GetFormat();
+  out_tensors_[0]->SetFormat(schema::Format_NHWC4);
+
+  return RET_OK;
+}
+
+int SliceOpenCLKernel::ReSize() { return RET_OK; }
+
+int SliceGetBiggestDividerWithPriority(int number, int max_divider) {
+  if (number % 8 == 0 && 8 <= max_divider) {
+    return number / 8;
+  } else if (number % 4 == 0 && 4 <= max_divider) {
+    return number / 4;
+  } else if (number % 2 == 0 && 2 <= max_divider) {
+    return number / 2;
+  }
+  for (int i = max_divider; i != 0; i--) {
+    if (number % i == 0) {
+      return i;
+    }
+  }
+  return 1;
+}
+
+void SlcieGetWorkGroup(const std::vector<size_t> &global, std::vector<size_t> *local, int max_size) {
+  const int max_divider = 8;
+  const int max_x = 4, max_y = 8;
+  int x = std::min(SliceGetBiggestDividerWithPriority(global[0], max_divider), max_x);
+  int yz = max_size / x;
+  int y = std::min(std::min(SliceGetBiggestDividerWithPriority(global[1], max_divider), yz), max_y);
+  int z = std::min(yz / y, static_cast<int>(UP_DIV(global[2], 2)));
+
+  local->clear();
+  local->push_back(x);
+  local->push_back(y);
+  local->push_back(z);
+}
+int SliceOpenCLKernel::Run() {
+  MS_LOG(DEBUG) << this->name() << " Running!";
+  auto param = reinterpret_cast<SliceParameter *>(this->op_parameter_);
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  auto input_shape = in_tensors_[0]->shape();
+  cl_int4 input_shape_ = {input_shape[0], input_shape[1], input_shape[2], UP_DIV(input_shape[3], C4NUM)};
+  cl_int4 size_ = {param->size_[0], param->size_[1], param->size_[2], UP_DIV(param->size_[3], C4NUM)};
+  cl_int4 begin_ = {param->begin_[0], param->begin_[1], param->begin_[2], param->begin_[3] / 4};
+  cl_int2 sharedNoUpdiv = {param->begin_[3], param->size_[3]};
+  uint32_t OH = param->size_[1];
+  uint32_t OW = param->size_[2];
+
+  const std::vector<size_t> &max_global = ocl_runtime->GetWorkItemSize();
+  std::vector<size_t> local = {1, 1, 1};  // init local
+  std::vector<size_t> global = {1, OH, OW};
+  SlcieGetWorkGroup(global, &local, max_global[0]);
+  int arg_cn = 0;
+  ocl_runtime->SetKernelArg(kernel_, arg_cn++, in_tensors_[0]->Data());   // input tensor
+  ocl_runtime->SetKernelArg(kernel_, arg_cn++, out_tensors_[0]->Data());  // out tensor
+  ocl_runtime->SetKernelArg(kernel_, arg_cn++, input_shape_);
+  ocl_runtime->SetKernelArg(kernel_, arg_cn++, size_);
+  ocl_runtime->SetKernelArg(kernel_, arg_cn++, begin_);
+  ocl_runtime->SetKernelArg(kernel_, arg_cn++, sharedNoUpdiv);
+  ocl_runtime->RunKernel(kernel_, global, local, nullptr);
+
+  return RET_OK;
+}  // namespace mindspore::kernel
+
+kernel::LiteKernel *OpenCLSliceKernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
+                                             const std::vector<lite::tensor::Tensor *> &outputs,
+                                             OpParameter *opParameter, const lite::Context *ctx,
+                                             const kernel::KernelKey &desc,
+                                             const mindspore::lite::PrimitiveC *primitive) {
+  auto *kernel = new (std::nothrow) SliceOpenCLKernel(opParameter, inputs, outputs);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << "new SliceOpenCLKernel failed";
+    return nullptr;
+  }
+  auto ret = kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Init kernel failed, name: Convolution";
+    delete kernel;
+    return nullptr;
+  }
+  return kernel;
+}
+
+REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Slice, OpenCLSliceKernelCreator);
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/opencl/kernel/slice.h

@@ -0,0 +1,49 @@
+/**
+ * Copyright 2019 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_LITE_SRC_BACKEND_OPENCL_SLICE_H_
+#define MINDSPORE_LITE_SRC_BACKEND_OPENCL_SLICE_H_
+
+#include <vector>
+#include "ir/anf.h"
+#include "src/runtime/kernel/opencl/opencl_kernel.h"
+#include "src/runtime/opencl/opencl_runtime.h"
+#include "src/runtime/kernel/arm/nnacl/fp32/slice.h"
+
+namespace mindspore::kernel {
+
+class SliceOpenCLKernel : public OpenCLKernel {
+ public:
+  explicit SliceOpenCLKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
+                             const std::vector<lite::tensor::Tensor *> &outputs)
+      : OpenCLKernel(parameter, inputs, outputs) {}
+
+  ~SliceOpenCLKernel() override{};
+
+  int Init() override;
+
+  int ReSize() override;
+
+  int Run() override;
+
+  int GetImageSize(size_t idx, std::vector<size_t> *img_size) override;
+
+ private:
+  cl::Kernel kernel_;
+};
+
+}  // namespace mindspore::kernel
+#endif

mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.cc

@@ -46,8 +46,10 @@ int SubGraphOpenCLKernel::GenToFormatOp(const std::vector<lite::tensor::Tensor *
       }
       if (mem_type == OpenCLMemType::IMG) {
         jv->set_in_tensors({});
+        jv->SetInKernel({});
       } else {
         jv->set_out_tensors({});
+        jv->SetOutKernel({});
       }
     }
   }
@@ -120,13 +122,21 @@ int SubGraphOpenCLKernel::GenToFormatOp(const std::vector<lite::tensor::Tensor *
     if (mem_type == OpenCLMemType::IMG) {
       for (auto &iv : in_kernels[i]) {
         in_opencl_op->AddOutKernel(iv);
-        reinterpret_cast<OpenCLKernel *>(iv)->SetInKernel({in_convert_op});
-        reinterpret_cast<OpenCLKernel *>(iv)->set_in_tensors({new_tensor});
+        auto kernels = iv->in_kernels();
+        kernels.emplace_back(in_convert_op);
+        iv->SetInKernel(kernels);
+        auto tensors = iv->in_tensors();
+        tensors.emplace_back(new_tensor);
+        iv->set_in_tensors(tensors);
       }
     } else {
       for (auto &iv : in_kernels[i]) {
-        reinterpret_cast<OpenCLKernel *>(iv)->SetOutKernel({in_convert_op});
-        reinterpret_cast<OpenCLKernel *>(iv)->set_out_tensors({new_tensor});
+        auto kernels = iv->out_kernels();
+        kernels.emplace_back(in_convert_op);
+        iv->SetOutKernel(kernels);
+        auto tensors = iv->out_tensors();
+        tensors.emplace_back(new_tensor);
+        iv->set_out_tensors(tensors);
         in_convert_op->AddInKernel(iv);
       }
     }

mindspore/lite/test/CMakeLists.txt

@@ -145,6 +145,7 @@ if (SUPPORT_GPU)
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/softmax.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/concat.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/batchnorm.cc
+            ${LITE_DIR}/src/runtime/kernel/opencl/kernel/slice.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/activation.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/conv2d_transpose.cc
             ${LITE_DIR}/src/runtime/kernel/opencl/kernel/transpose.cc
@@ -318,6 +319,7 @@ if (SUPPORT_GPU)
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/depthwise_conv2d_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/concat_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/batchnorm_tests.cc
+            ${TEST_DIR}/ut/src/runtime/kernel/opencl/slice_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/softmax_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/arithmetic_tests.cc
             ${TEST_DIR}/ut/src/runtime/kernel/opencl/avg_pooling_tests.cc

mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc

@@ -43,8 +43,8 @@ TEST_F(TestBatchnormOpenCL, Batchnorminput_dim4) {
   auto allocator = ocl_runtime->GetAllocator();
 
   MS_LOG(INFO) << "Read tensors from .bin";
-  std::vector<int> input_shape = {1, 256, 256, 48};
-  std::vector<int> output_shape = {1, 256, 256, 48};
+  std::vector<int> input_shape = {1, 256, 256, 16};
+  std::vector<int> output_shape = {1, 256, 256, 16};
   auto data_type = kNumberTypeFloat32;
   auto tensor_type = schema::NodeType_ValueNode;
 

mindspore/lite/test/ut/src/runtime/kernel/opencl/slice_tests.cc

@@ -0,0 +1,149 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <iostream>
+#include <memory>
+#include "utils/log_adapter.h"
+#include "common/common_test.h"
+#include "mindspore/lite/src/runtime/opencl/opencl_runtime.h"
+#include "mindspore/lite/src/common/file_utils.h"
+#include "mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
+#include "mindspore/lite/src/runtime/kernel/opencl/kernel/slice.h"
+
+namespace mindspore {
+class TestSliceOpenCL : public mindspore::CommonTest {
+ public:
+  TestSliceOpenCL() {}
+};
+
+template <typename T>
+void CompareOutputData1(T *output_data, T *correct_data, int size, float err_bound) {
+  for (size_t i = 0; i < size; i++) {
+    T abs = fabs(output_data[i] - correct_data[i]);
+    ASSERT_LE(abs, err_bound);
+  }
+}
+
+TEST_F(TestSliceOpenCL, Sliceinput_dim4) {
+  MS_LOG(INFO) << "begin test";
+  auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+  ocl_runtime->Init();
+  auto allocator = ocl_runtime->GetAllocator();
+
+  MS_LOG(INFO) << "Read tensors from .bin";
+  std::vector<int> input_shape = {1, 256, 256, 48};
+  std::vector<int> output_shape = {1, 255, 255, 15};
+  std::vector<int> begin = {0, 1, 1, 7};
+  std::vector<int> size = {1, 255, 255, 15};
+  auto data_type = kNumberTypeFloat32;
+  auto tensor_type = schema::NodeType_ValueNode;
+
+  // get the input from .bin
+  size_t input_size, output_size;
+  std::string input_path = "./test_data/in_data.bin";
+  std::string output_path = "./test_data/out_data.bin";
+  auto input_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(input_path.c_str(), &input_size));
+  auto correct_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(output_path.c_str(), &output_size));
+
+  MS_LOG(INFO) << "construct tensors";
+  lite::tensor::Tensor *tensor_data =
+    new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC, tensor_type);
+  if (tensor_data == nullptr) {
+    MS_LOG(INFO) << "init tensor failed";
+    return;
+  }
+  auto *output_tensor =
+    new (std::nothrow) lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensor_type);
+  if (output_tensor == nullptr) {
+    delete tensor_data;
+    MS_LOG(INFO) << "init tensor failed";
+    return;
+  }
+  std::vector<lite::tensor::Tensor *> inputs = {tensor_data};
+  std::vector<lite::tensor::Tensor *> outputs = {output_tensor};
+
+  MS_LOG(INFO) << "setting  SliceParameter";
+  auto param = new (std::nothrow) SliceParameter();
+  if (param == nullptr) {
+    for (auto tensor : inputs) {
+      delete tensor;
+    }
+    for (auto tensor : outputs) {
+      delete tensor;
+    }
+    MS_LOG(INFO) << "new SliceParameter failed";
+    return;
+  }
+  for (int i = 0; i < 4; i++) {
+    param->begin_[i] = begin[i];
+    param->size_[i] = size[i];
+  }
+
+  auto *slice_kernel =
+    new (std::nothrow) kernel::SliceOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  if (slice_kernel == nullptr) {
+    for (auto tensor : inputs) {
+      delete tensor;
+    }
+    for (auto tensor : outputs) {
+      delete tensor;
+    }
+    delete param;
+    MS_LOG(INFO) << "new kernel::slice_kernel failed";
+    return;
+  }
+  slice_kernel->Init();
+
+  // to do allocate memory for inputs and outputs
+  for (auto &input_tensor : inputs) {
+    input_tensor->MallocData(allocator);
+  }
+
+  MS_LOG(INFO) << "initialize sub_graph";
+  std::vector<kernel::LiteKernel *> kernels{slice_kernel};
+  auto *sub_graph = new (std::nothrow) kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  if (sub_graph == nullptr) {
+    for (auto tensor : inputs) {
+      delete tensor;
+    }
+    for (auto tensor : outputs) {
+      delete tensor;
+    }
+    delete param;
+    delete slice_kernel;
+    MS_LOG(INFO) << "new kernel::SubGraphOpenCLKernel failed";
+    return;
+  }
+  sub_graph->Init();
+
+  MS_LOG(INFO) << "init tensors";
+  memcpy(inputs[0]->Data(), input_data, input_size);
+
+  std::cout << "==================output data================" << std::endl;
+  sub_graph->Run();
+
+  auto *output_data_gpu = reinterpret_cast<float *>(output_tensor->Data());
+  CompareOutputData1(output_data_gpu, correct_data, output_tensor->ElementsNum(), 0.0001);
+  for (auto tensor : inputs) {
+    delete tensor;
+  }
+  for (auto tensor : outputs) {
+    delete tensor;
+  }
+  delete slice_kernel;
+  delete sub_graph;
+  lite::opencl::OpenCLRuntime::DeleteInstance();
+}
+}  // namespace mindspore