!7688 clear warning of compiler

Merge pull request !7688 from zhanghaibo/master

mindspore/lite/nnacl/fp32/deconv_winograd.c

@@ -227,17 +227,17 @@ void DeConvWgMerge(const float *src, float *dst, size_t src_stride, size_t dst_s
     size_t src_step = src_stride * sizeof(float);
     size_t dst_step = dst_stride * sizeof(float);
     asm volatile(
-      "mov r7, %[src_ptr]\n"
+      "mov r11, %[src_ptr]\n"
       "mov r8, %[dst_ptr]\n"
       "mov r10, r8\n"
 
-      "vld1.32 {q0}, [r7], %[src_step]\n"
+      "vld1.32 {q0}, [r11], %[src_step]\n"
       "vld1.32 {q1}, [r8], %[dst_step]\n"
-      "vld1.32 {q2}, [r7], %[src_step]\n"
+      "vld1.32 {q2}, [r11], %[src_step]\n"
       "vld1.32 {q3}, [r8], %[dst_step]\n"
 
       "vadd.f32 q0, q0, q1\n"
-      "vld1.32 {q8}, [r7], %[src_step]\n"
+      "vld1.32 {q8}, [r11], %[src_step]\n"
       "vadd.f32 q2, q2, q3\n"
 
       "vst1.32 {q0}, [r10], %[dst_step]\n"
@@ -245,19 +245,19 @@ void DeConvWgMerge(const float *src, float *dst, size_t src_stride, size_t dst_s
 
       "vld1.32 {q9}, [r8], %[dst_step]\n"
 
-      "vld1.32 {q10}, [r7], %[src_step]\n"
+      "vld1.32 {q10}, [r11], %[src_step]\n"
 
       "vadd.f32 q8, q8, q9\n"
       "vld1.32 {q11}, [r8], %[dst_step]\n"
       "vadd.f32 q10, q10, q11\n"
 
-      "vld1.32 {q0}, [r7], %[src_step]\n"
+      "vld1.32 {q0}, [r11], %[src_step]\n"
       "vst1.32 {q8}, [r10], %[dst_step]\n"
       "vst1.32 {q10}, [r10], %[dst_step]\n"
 
       "vld1.32 {q1}, [r8], %[dst_step]\n"
 
-      "vld1.32 {q2}, [r7], %[src_step]\n"
+      "vld1.32 {q2}, [r11], %[src_step]\n"
       "vld1.32 {q3}, [r8], %[dst_step]\n"
 
       "vadd.f32 q0, q0, q1\n"
@@ -266,10 +266,10 @@ void DeConvWgMerge(const float *src, float *dst, size_t src_stride, size_t dst_s
       "vst1.32 {q0}, [r10], %[dst_step]\n"
       "vst1.32 {q2}, [r10], %[dst_step]\n"
 
-      "vld1.32 {q8}, [r7], %[src_step]\n"
+      "vld1.32 {q8}, [r11], %[src_step]\n"
       "vld1.32 {q9}, [r8], %[dst_step]\n"
 
-      "vld1.32 {q10}, [r7], %[src_step]\n"
+      "vld1.32 {q10}, [r11], %[src_step]\n"
       "vld1.32 {q11}, [r8], %[dst_step]\n"
 
       "vadd.f32 q8, q8, q9\n"
@@ -280,7 +280,7 @@ void DeConvWgMerge(const float *src, float *dst, size_t src_stride, size_t dst_s
 
       :
       : [ src_ptr ] "r"(src_ptr), [ dst_ptr ] "r"(dst_ptr), [ src_step ] "r"(src_step), [ dst_step ] "r"(dst_step)
-      : "r7", "r8", "r10", "q0", "q1", "q2", "q3", "q8", "q9", "q10", "q11");
+      : "r8", "r10", "r11", "q0", "q1", "q2", "q3", "q8", "q9", "q10", "q11");
 #else
     for (int j = 0; j < 8; j++) {
       const float *s = src_ptr + j * src_stride;

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

@@ -28,7 +28,9 @@ class SoftmaxOpenCLKernel : public OpenCLKernel {
  public:
   SoftmaxOpenCLKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
                       const std::vector<lite::Tensor *> &outputs)
-      : OpenCLKernel(parameter, inputs, outputs), parameter_(reinterpret_cast<SoftmaxParameter *>(parameter)) {}
+      : OpenCLKernel(parameter, inputs, outputs) {
+    parameter_ = reinterpret_cast<SoftmaxParameter *>(parameter);
+  }
 
   ~SoftmaxOpenCLKernel() override = default;
   int Init() override;

mindspore/lite/test/CMakeLists.txt

@@ -6,8 +6,6 @@ include_directories(${TOP_DIR})
 include_directories(${TEST_DIR})
 include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/dependency_gtest.cmake)
 
-string(REPLACE " -Werror " " " CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
-string(REPLACE " -Werror " " " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 STRING(REPLACE " -fvisibility=hidden " " -fvisibility=default " CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
 STRING(REPLACE " -fvisibility=hidden " " -fvisibility=default " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 

mindspore/lite/test/common/common_test.h

@@ -50,7 +50,7 @@ class CommonTest : public testing::Test {
 
   template <typename T>
   static void CompareOutputData(T *output_data, T *correct_data, int size, float err_bound) {
-    for (size_t i = 0; i < size; i++) {
+    for (int i = 0; i < size; i++) {
       T abs = fabs(output_data[i] - correct_data[i]);
       ASSERT_LE(abs, err_bound);
     }

mindspore/lite/test/ut/internal/CMakeLists.txt

@@ -6,8 +6,6 @@ include_directories(${TOP_DIR})
 include_directories(${TEST_DIR})
 add_compile_definitions(ENABLE_NNACL_INFER_SHAPE)
 
-string(REPLACE " -Werror " " " CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
-string(REPLACE " -Werror " " " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 STRING(REPLACE " -fvisibility=hidden " " -fvisibility=default " CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
 STRING(REPLACE " -fvisibility=hidden " " -fvisibility=default " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
 

mindspore/lite/test/ut/src/infer_test.cc

@@ -149,7 +149,7 @@ TEST_F(InferTest, TestConvNode) {
   ASSERT_NE(nullptr, output_data);
   //===================================================
   ASSERT_EQ(output_size, outTensor->Size());
-  for (size_t i = 0; i < outTensor->ElementsNum(); i++) {
+  for (int i = 0; i < outTensor->ElementsNum(); i++) {
     ASSERT_LE((output_data[i] - outData[i]), 0.001);
   }
   MS_LOG(INFO) << "Passed";

mindspore/lite/test/ut/src/runtime/kernel/arm/common/pack_tests.cc

@@ -184,7 +184,7 @@ TEST_F(TestPack, PackWeightUint8) {
   std::string weight_path = "./test_data/conv/convuint8_weight_32_3_3_3.bin";
   auto weight_data = reinterpret_cast<uint8_t *>(mindspore::lite::ReadFile(weight_path.c_str(), &weight_size));
   auto int8_weight = reinterpret_cast<int8_t *>(malloc(weight_size));
-  for (int i = 0; i < weight_size; i++) {
+  for (unsigned int i = 0; i < weight_size; i++) {
     int8_weight[i] = (int8_t)(weight_data[i] - 128);
   }
   int32_t filter_zp = 20;

mindspore/lite/test/ut/src/runtime/kernel/arm/common/strided_slice_tests.cc

@@ -117,7 +117,7 @@ TEST_F(TestStridedSlice, StridedSliceInt8) {
   EXPECT_EQ(0, ret);
 
   int8_t expect[4] = {-6, -5, 7, 8};
-  for (int i = 0; i < sizeof(expect); ++i) {
+  for (unsigned int i = 0; i < sizeof(expect); ++i) {
     EXPECT_EQ(output_data[i], expect[i]);
   }
 

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/constant_of_shape_fp32_test.cc

@@ -33,7 +33,7 @@ int ConstantOfShapeTestInit(std::vector<lite::Tensor *> *inputs_, std::vector<li
   inputs_->push_back(in_t);
 
   std::vector<int> c_shape(in_t->ElementsNum());
-  for (int i = 0; i < c_shape.size(); ++i) {
+  for (unsigned int i = 0; i < c_shape.size(); ++i) {
     c_shape[i] = a_ptr[i];
   }
   auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR);

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/convolution_depthwise_fp32_tests.cc

@@ -138,10 +138,10 @@ TEST_F(TestConvolutionDwFp32, ConvDwFp32Accuracy) {
   CompareOutputData(output_ptr, correct_data, outputs[0]->ElementsNum(), 0.0001);
 
   delete conv_param;
-  for (int i = 0; i < inputs.size(); i++) {
+  for (unsigned int i = 0; i < inputs.size(); i++) {
     delete inputs[i];
   }
-  for (int i = 0; i < outputs.size(); i++) {
+  for (unsigned int i = 0; i < outputs.size(); i++) {
     delete outputs[i];
   }
   delete kernel;
@@ -189,10 +189,10 @@ TEST_F(TestConvolutionDwFp32, ConvDwFp32Performance) {
   printf("Convolution_depthwise fp32 average time : %f ms\n", time_avg / 1000.0f);
 
   delete conv_param;
-  for (int i = 0; i < inputs.size(); i++) {
+  for (unsigned int i = 0; i < inputs.size(); i++) {
     delete inputs[i];
   }
-  for (int i = 0; i < outputs.size(); i++) {
+  for (unsigned int i = 0; i < outputs.size(); i++) {
     delete outputs[i];
   }
   delete kernel;

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/elu_fp32_test.cc

@@ -60,7 +60,7 @@ TEST_F(TestEluFp32, EluTest) {
   elu->Run();
 
   std::cout << "output shape:" << std::endl;
-  for (int i = 0; i < outputs_.front()->shape().size(); ++i) {
+  for (unsigned int i = 0; i < outputs_.front()->shape().size(); ++i) {
     std::cout << outputs_.front()->shape()[i] << ' ';
   }
   std::cout << std::endl;

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/embedding_lookup_fp32_test.cc

@@ -75,7 +75,7 @@ TEST_F(TestEmbeddingLookupFp32, ElTest) {
   el->Run();
 
   std::cout << "output shape:" << std::endl;
-  for (int i = 0; i < outputs_.front()->shape().size(); ++i) {
+  for (unsigned int i = 0; i < outputs_.front()->shape().size(); ++i) {
     std::cout << outputs_.front()->shape()[i] << ' ';
   }
   std::cout << std::endl;

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/fullconnection_fp32_tests.cc

@@ -146,8 +146,8 @@ TEST_F(TestFcFp32, FcTest2) {
   CompareOutputData(reinterpret_cast<float *>(outputs_[0]->MutableData()), correct, total_size, 0.0001);
 }
 
-int FcTestInit3(std::vector<lite::Tensor *> *inputs_, std::vector<lite::Tensor *> *outputs_,
-                MatMulParameter *matmal_param, float **correct) {
+void FcTestInit3(std::vector<lite::Tensor *> *inputs_, std::vector<lite::Tensor *> *outputs_,
+                 MatMulParameter *matmal_param, float **correct) {
   Tensor *in_t = new Tensor(kNumberTypeFloat, {1, 1, 1, 20}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR);
   in_t->MallocData();
   float in[] = {1, 0, 3, 0, 4, 5, 2, 5, 2, 5, 1, 5, 0, 1, 2, 0, 2, 1, 0, 5};
@@ -177,7 +177,6 @@ int FcTestInit3(std::vector<lite::Tensor *> *inputs_, std::vector<lite::Tensor *
   matmal_param->a_transpose_ = false;
   matmal_param->has_bias_ = false;
   matmal_param->act_type_ = ActType_No;
-  return out_t->ElementsNum();
 }
 
 TEST_F(TestFcFp32, FcTest3) {
@@ -185,7 +184,7 @@ TEST_F(TestFcFp32, FcTest3) {
   std::vector<lite::Tensor *> outputs_;
   auto matmul_param = new MatMulParameter();
   float *correct;
-  int total_size = FcTestInit3(&inputs_, &outputs_, matmul_param, &correct);
+  FcTestInit3(&inputs_, &outputs_, matmul_param, &correct);
   lite::InnerContext *ctx = new lite::InnerContext;
   ctx->thread_num_ = 1;
   ASSERT_EQ(lite::RET_OK, ctx->Init());

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/lstm_fp32_tests.cc

@@ -171,10 +171,10 @@ TEST_F(LstmFp32, LstmForwardFp32Accuracy) {
   CompareOutput(outputs[2], output2_data);
 
   delete lstm_param;
-  for (int i = 0; i < inputs.size() - 1; i++) {
+  for (unsigned int i = 0; i < inputs.size() - 1; i++) {
     delete inputs[i];
   }
-  for (int i = 0; i < outputs.size(); i++) {
+  for (unsigned int i = 0; i < outputs.size(); i++) {
     delete outputs[i];
   }
   delete kernel;
@@ -321,10 +321,10 @@ TEST_F(LstmFp32, LstmBackwardFp32Accuracy) {
   CompareOutput(outputs[2], output2_data);
 
   delete lstm_param;
-  for (int i = 0; i < inputs.size() - 1; i++) {
+  for (unsigned int i = 0; i < inputs.size() - 1; i++) {
     delete inputs[i];
   }
-  for (int i = 0; i < outputs.size(); i++) {
+  for (unsigned int i = 0; i < outputs.size(); i++) {
     delete outputs[i];
   }
   delete kernel;

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/power_fp32_tests.cc

@@ -79,7 +79,6 @@ TEST_F(TestPowerFp32, Simple) {
   op->Init();
   op->Run();
   float correct[] = {1, 64, 2187, 65536};
-  float *output = reinterpret_cast<float *>(outputs_[0]->MutableData());
   CompareOutputData(reinterpret_cast<float *>(outputs_[0]->MutableData()), correct, total_size, 0.0001);
   delete op;
   for (auto t : inputs_) delete t;

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/skip_gram_fp32.cc

@@ -67,7 +67,7 @@ TEST_F(TestSkipGramFp32, ElTest) {
   el->Run();
 
   std::vector<StringPack> output = mindspore::lite::ParseTensorBuffer(outputs_[0]);
-  for (int i = 0; i < output.size(); i++) {
+  for (unsigned int i = 0; i < output.size(); i++) {
     for (int j = 0; j < output[i].len; j++) {
       printf("%c", output[i].data[j]);
     }

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/space_to_batch_fp32_tests.cc

@@ -39,7 +39,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest4) {
   param.block_sizes_[0] = 2;
   param.block_sizes_[1] = 1;
   DoSpaceToBatchNHWC(input.data(), out, param.block_sizes_, in_shape.data(), out_shape.data());
-  for (int i = 0; i < kOutSize; ++i) {
+  for (unsigned int i = 0; i < kOutSize; ++i) {
     std::cout << out[i] << " ";
   }
   std::cout << "\n";
@@ -57,7 +57,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest5) {
   param.block_sizes_[0] = 1;
   param.block_sizes_[1] = 2;
   DoSpaceToBatchNHWC(input.data(), out, param.block_sizes_, in_shape.data(), out_shape.data());
-  for (int i = 0; i < kOutSize; ++i) {
+  for (unsigned int i = 0; i < kOutSize; ++i) {
     std::cout << out[i] << " ";
   }
   std::cout << "\n";
@@ -75,7 +75,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest6) {
   param.block_sizes_[0] = 2;
   param.block_sizes_[1] = 2;
   DoSpaceToBatchNHWC(input.data(), out, param.block_sizes_, in_shape.data(), out_shape.data());
-  for (int i = 0; i < kOutSize; ++i) {
+  for (unsigned int i = 0; i < kOutSize; ++i) {
     std::cout << out[i] << " ";
   }
   std::cout << "\n";
@@ -97,7 +97,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest7) {
   param.block_sizes_[0] = 2;
   param.block_sizes_[1] = 2;
   DoSpaceToBatchNHWC(input.data(), out, param.block_sizes_, in_shape.data(), out_shape.data());
-  for (int i = 0; i < kOutSize; ++i) {
+  for (unsigned int i = 0; i < kOutSize; ++i) {
     std::cout << out[i] << " ";
   }
   std::cout << "\n";
@@ -116,7 +116,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest8) {
   std::vector<int> out_shape = {1, 5, 5, 2};
   std::vector<int> padding = {0, 1, 0, 1};
   DoSpaceToBatchPaddingNHWC(input.data(), out, in_shape.data(), padding.data(), out_shape.data());
-  for (int i = 0; i < kOutSize; ++i) {
+  for (unsigned int i = 0; i < kOutSize; ++i) {
     std::cout << out[i] << " ";
   }
   std::cout << "\n";
@@ -136,7 +136,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest9) {
   std::vector<int> out_shape = {1, 6, 6, 2};
   std::vector<int> padding = {1, 1, 1, 1};
   DoSpaceToBatchPaddingNHWC(input.data(), out, in_shape.data(), padding.data(), out_shape.data());
-  for (int i = 0; i < kOutSize; ++i) {
+  for (unsigned int i = 0; i < kOutSize; ++i) {
     std::cout << out[i] << " ";
   }
   std::cout << "\n";
@@ -162,7 +162,7 @@ TEST_F(SpaceToBatchTestFp32, SpaceToBatchTest10) {
   param.block_sizes_[0] = 2;
   param.block_sizes_[1] = 2;
   DoSpaceToBatchNHWC(pedding_out, out, param.block_sizes_, pedding_out_shape.data(), out_shape.data());
-  for (int i = 0; i < kOutSize; ++i) {
+  for (unsigned int i = 0; i < kOutSize; ++i) {
     std::cout << out[i] << " ";
   }
   std::cout << "\n";

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/pooling_grad_fp32_tests.cc

@@ -276,10 +276,7 @@ TEST_F(TestPoolingGradFp32, AvgPoolGradStride2Fp32) {
 
   kernel->Init();
 
-  auto time_start = mindspore::lite::GetTimeUs();
   kernel->Run();
-  auto time_end = mindspore::lite::GetTimeUs();
-  printf("single thread running time : %lu ms\n", time_end - time_start);
 
   std::string output_path = "./test_data/pooling/avgpoolgradfp32_s2_dx_3_28_28_3.bin";
   auto res = lite::CompareRelativeOutput(out_data, output_path);
@@ -340,10 +337,7 @@ TEST_F(TestPoolingGradFp32, AvgPoolGradStride3Fp32) {
 
   kernel->Init();
 
-  auto time_start = mindspore::lite::GetTimeUs();
   kernel->Run();
-  auto time_end = mindspore::lite::GetTimeUs();
-  printf("single thread running time : %lu ms\n", time_end - time_start);
 
   std::string output_path = "./test_data/pooling/avgpoolgradfp32_s3_dx_3_28_28_3.bin";
   auto res = lite::CompareRelativeOutput(out_data, output_path);
@@ -461,10 +455,7 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradBatchFp32) {
 
   kernel->Init();
 
-  auto time_start = mindspore::lite::GetTimeUs();
   kernel->Run();
-  auto time_end = mindspore::lite::GetTimeUs();
-  printf("single thread running time : %lu ms\n", time_end - time_start);
 
   std::string output_path = "./test_data/pooling/maxpoolgradfp32_1_xgrad_3_28_28_3.bin";
   auto res = lite::CompareRelativeOutput(out_data, output_path);
@@ -535,10 +526,7 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradStride2Fp32) {
 
   kernel->Init();
 
-  auto time_start = mindspore::lite::GetTimeUs();
   kernel->Run();
-  auto time_end = mindspore::lite::GetTimeUs();
-  printf("single thread running time : %lu ms\n", time_end - time_start);
 
   std::string output_path = "./test_data/pooling/maxpoolgradfp32_s2_xgrad_3_28_28_3.bin";
   auto res = lite::CompareRelativeOutput(out_data, output_path);
@@ -609,10 +597,7 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradStride3Fp32) {
 
   kernel->Init();
 
-  auto time_start = mindspore::lite::GetTimeUs();
   kernel->Run();
-  auto time_end = mindspore::lite::GetTimeUs();
-  printf("single thread running time : %lu ms\n", time_end - time_start);
 
   std::string output_path = "./test_data/pooling/maxpoolgradfp32_s3_xgrad_3_28_28_3.bin";
   auto res = lite::CompareRelativeOutput(out_data, output_path);

mindspore/lite/test/ut/src/runtime/kernel/arm/int8/relux_int8_tests.cc

@@ -108,7 +108,7 @@ TEST_F(TestReluXInt8, Relu6) {
 
   // 0.0f, 0.0f, 1.25f, 3.0f, 4.5f, 6.0f, 6.0f, 6.0f
   int8_t expect[8] = {-128, -128, -96, -52, -14, 25, 25, 25};
-  for (int i = 0; i < sizeof(expect); ++i) {
+  for (unsigned int i = 0; i < sizeof(expect); ++i) {
     EXPECT_EQ(output_data[i], expect[i]);
   }
 

mindspore/lite/test/ut/src/runtime/kernel/arm/string/normalize.cc

@@ -68,12 +68,12 @@ TEST_F(TestNormalize, TestSentence) {
   kernel_ = creator_(inputs_, outputs_, &parameter_, &ctx_, desc_, nullptr);
   ASSERT_NE(kernel_, nullptr);
   auto ret = kernel_->Init();
-  MS_ASSERT(ret == 0);
+  ASSERT_EQ(ret, 0);
   ret = kernel_->Run();
-  MS_ASSERT(ret == 0);
+  ASSERT_EQ(ret, 0);
 
   std::vector<StringPack> output = mindspore::lite::ParseTensorBuffer(outputs_[0]);
-  for (int i = 0; i < output.size(); i++) {
+  for (unsigned int i = 0; i < output.size(); i++) {
     for (int j = 0; j < output[i].len; j++) {
       printf("%c", output[i].data[j]);
     }

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

@@ -91,7 +91,6 @@ TEST_F(TestActivationOpenCL, ReluFp_dim4) {
   MS_LOG(INFO) << "Init tensors.";
   std::vector<int> input_shape = {1, 9};
   schema::Format format = schema::Format_NC;
-  schema::Format op_format = schema::Format_NC4;
   auto tensor_type = lite::Tensor::CONST_TENSOR;
   auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
   if (input_tensor == nullptr) {
@@ -198,7 +197,6 @@ TEST_F(TestActivationOpenCL, Relu6Fp_dim4) {
   MS_LOG(INFO) << "Init tensors.";
   std::vector<int> input_shape = {1, 9};
   schema::Format format = schema::Format_NC;
-  schema::Format op_format = schema::Format_NC4;
   auto tensor_type = lite::Tensor::CONST_TENSOR;
   auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
   if (input_tensor == nullptr) {
@@ -308,7 +306,6 @@ TEST_F(TestActivationOpenCL, SigmoidFp_dim4) {
   MS_LOG(INFO) << "Init tensors.";
   std::vector<int> input_shape = {1, 9};
   schema::Format format = schema::Format_NC;
-  schema::Format op_format = schema::Format_NC4;
   auto tensor_type = lite::Tensor::CONST_TENSOR;
   auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
   if (input_tensor == nullptr) {
@@ -411,15 +408,14 @@ TEST_F(TestActivationOpenCL, LeakyReluFp_dim4) {
   MS_LOG(INFO) << "Leaky relu Begin test!";
   auto ocl_runtime = lite::opencl::OpenCLRuntimeWrapper().GetInstance();
   ocl_runtime->Init();
-  auto data_type = kNumberTypeFloat16;  // need modify
+  auto data_type = kNumberTypeFloat16;
   ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16);
   bool enable_fp16 = ocl_runtime->GetFp16Enable();
 
   MS_LOG(INFO) << "Init tensors.";
-  std::vector<int> input_shape = {1, 9};  // need modify
+  std::vector<int> input_shape = {1, 9};
   auto tensor_type = lite::Tensor::CONST_TENSOR;
-  schema::Format format = schema::Format_NC;        // need modify
-  schema::Format op_format = schema::Format_NHWC4;  // need modify
+  schema::Format format = schema::Format_NC;
   auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
   if (input_tensor == nullptr) {
     MS_LOG(ERROR) << "new input tensor error!";
@@ -527,7 +523,6 @@ TEST_F(TestActivationOpenCLTanh, TanhFp_dim4) {
   MS_LOG(INFO) << "Init tensors.";
   std::vector<int> input_shape = {1, 2, 3, 9};
   schema::Format format = schema::Format_NHWC;
-  schema::Format op_format = schema::Format_NC4HW4;
   auto tensor_type = lite::Tensor::CONST_TENSOR;
   auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
   if (input_tensor == nullptr) {

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

@@ -77,13 +77,12 @@ TEST_F(TestBiasAddOpenCL, BiasAddFp32_dim4) {
   MS_LOG(INFO) << "BiasAdd Begin test:";
   auto ocl_runtime = lite::opencl::OpenCLRuntimeWrapper().GetInstance();
   ocl_runtime->Init();
-  auto data_type = kNumberTypeFloat16;  // need modify
+  auto data_type = kNumberTypeFloat16;
   ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16);
-  std::vector<int> input_shape = {1, 9};   // need modify
-  std::vector<int> output_shape = {1, 9};  // need modify
+  std::vector<int> input_shape = {1, 9};
+  std::vector<int> output_shape = {1, 9};
   auto tensor_type = lite::Tensor::CONST_TENSOR;
-  schema::Format type = schema::Format_NC;        // need modify
-  schema::Format op_format = schema::Format_NC4;  // need modify
+  schema::Format type = schema::Format_NC;
   int weight_shape = 0;
   if (input_shape.size() == 4) {
     weight_shape = input_shape[3];

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

@@ -86,7 +86,6 @@ TEST_F(TestPReluOpenCL, PReluFp32_dim4) {
   auto data_type = kNumberTypeFloat16;
   ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16);
   schema::Format format = schema::Format_NHWC;
-  schema::Format op_format = schema::Format_NC4HW4;
   auto tensor_type = lite::Tensor::CONST_TENSOR;
   auto input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type);
   if (input_tensor == nullptr) {