!4752 code clean for opencl

Merge pull request !4752 from chenzhongming/lite

mindspore/lite/src/runtime/kernel/opencl/kernel/pooling2d.cc

@@ -112,7 +112,6 @@ int PoolingOpenCLKernel::Run() {
   MS_LOG(DEBUG) << this->name() << " Running!";
   auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
 
-  // attribute
   int slices = UP_DIV(out_tensors_[0]->Channel(), C4NUM);
   cl_int4 input_shape = {in_tensors_[0]->Height(), in_tensors_[0]->Width(), in_tensors_[0]->Channel(), slices};
   cl_int4 output_shape = {out_tensors_[0]->Height(), out_tensors_[0]->Width(), out_tensors_[0]->Channel(), slices};
@@ -120,7 +119,6 @@ int PoolingOpenCLKernel::Run() {
   cl_int2 kernel_size = {parameter_->window_h_, parameter_->window_w_};
   cl_int2 padding = {parameter_->pad_u_, parameter_->pad_l_};
 
-  // binding parameters
   int arg_idx = 0;
   ocl_runtime->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->Data());
   ocl_runtime->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
@@ -130,14 +128,12 @@ int PoolingOpenCLKernel::Run() {
   ocl_runtime->SetKernelArg(kernel_, arg_idx++, kernel_size);
   ocl_runtime->SetKernelArg(kernel_, arg_idx++, padding);
 
-  // set work group size
   std::vector<size_t> local_size;
   std::vector<size_t> global_size = InitGlobalSize();
   int max_work_group_size = ocl_runtime->GetKernelMaxWorkGroupSize(kernel_(), (*ocl_runtime->Device())());
   local_size = GetCommonLocalSize(global_size, max_work_group_size);
   global_size = GetCommonGlobalSize(local_size, global_size);
 
-  // run opengl kernel
   ocl_runtime->RunKernel(kernel_, global_size, local_size, nullptr);
   return RET_OK;
 }

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

@@ -171,6 +171,7 @@ kernel::LiteKernel *OpenCLSoftMaxKernelCreator(const std::vector<lite::tensor::T
   auto *kernel = new (std::nothrow) SoftmaxOpenCLKernel(reinterpret_cast<OpParameter *>(opParameter), inputs, outputs);
   if (kernel == nullptr) {
     MS_LOG(ERROR) << "kernel " << opParameter->name_ << "is nullptr.";
+    delete kernel;
     return nullptr;
   }
   if (inputs[0]->shape()[0] > 1) {

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

@@ -58,7 +58,7 @@ TEST_F(TestAvgPoolingOpenCL, AvgPoolFp32) {
   ocl_runtime->Init();
 
   MS_LOG(INFO) << "create PoolingParameter";
-  auto param = new PoolingParameter();
+  auto param = new (std::nothrow) PoolingParameter();
   InitAvgPoolingParam(param);
 
   MS_LOG(INFO) << "create Tensors";
@@ -76,18 +76,37 @@ TEST_F(TestAvgPoolingOpenCL, AvgPoolFp32) {
   };
   auto data_type = kNumberTypeFloat32;
   auto tensorType = schema::NodeType_ValueNode;
-  lite::tensor::Tensor *tensor_in = new lite::tensor::Tensor(data_type, shape_in, schema::Format_NHWC, tensorType);
-  lite::tensor::Tensor *tensor_out = new lite::tensor::Tensor(data_type, shape_out, schema::Format_NHWC, tensorType);
+  lite::tensor::Tensor *tensor_in =
+    new (std::nothrow) lite::tensor::Tensor(data_type, shape_in, schema::Format_NHWC, tensorType);
+  lite::tensor::Tensor *tensor_out =
+    new (std::nothrow) lite::tensor::Tensor(data_type, shape_out, schema::Format_NHWC, tensorType);
+  if (tensor_in == nullptr) {
+    MS_LOG(ERROR) << "tensor_in null";
+    return;
+  }
+  if (tensor_out == nullptr) {
+    MS_LOG(ERROR) << "tensor_out null";
+    return;
+  }
   std::vector<lite::tensor::Tensor *> inputs{tensor_in};
   std::vector<lite::tensor::Tensor *> outputs{tensor_out};
 
   MS_LOG(INFO) << "create OpenCL Kernel";
-  auto *pooling_kernel = new kernel::PoolingOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  auto *pooling_kernel =
+    new (std::nothrow) kernel::PoolingOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  if (pooling_kernel == nullptr) {
+    MS_LOG(ERROR) << "pooling_kernel null";
+    return;
+  }
   pooling_kernel->Init();
   std::vector<kernel::LiteKernel *> kernels{pooling_kernel};
 
   MS_LOG(INFO) << "create SubGraphOpenCLKernel";
-  auto *pGraph = new kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  auto *pGraph = new (std::nothrow) kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  if (pGraph == nullptr) {
+    MS_LOG(ERROR) << "pGraph null";
+    return;
+  }
   pGraph->Init();
 
   MS_LOG(INFO) << "initialize data";

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

@@ -46,7 +46,7 @@ TEST_F(TestMaxPoolingOpenCL, MaxPool_1_32_512_96) {
   auto allocator = ocl_runtime->GetAllocator();
 
   MS_LOG(INFO) << "PoolingParameter";
-  auto param = new PoolingParameter;
+  auto param = new (std::nothrow) PoolingParameter;
   InitParameter(param);
 
   // define tensor
@@ -56,21 +56,39 @@ TEST_F(TestMaxPoolingOpenCL, MaxPool_1_32_512_96) {
   auto data_type = kNumberTypeFloat32;
   auto tensorType = schema::NodeType_ValueNode;
   MS_LOG(INFO) << "define tensor2";
-  auto input_tensor = new lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC4, tensorType);
-  auto output_tensor = new lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensorType);
+  auto input_tensor = new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC4, tensorType);
+  auto output_tensor =
+    new (std::nothrow) lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensorType);
+  if (input_tensor == nullptr) {
+    MS_LOG(ERROR) << "input_tensor null";
+    return;
+  }
+  if (output_tensor == nullptr) {
+    MS_LOG(ERROR) << "output_tensor null";
+    return;
+  }
   MS_LOG(INFO) << "define input";
   std::vector<lite::tensor::Tensor *> inputs{input_tensor};
   std::vector<lite::tensor::Tensor *> outputs{output_tensor};
 
   // run
   MS_LOG(INFO) << "pooling_kernel";
-  auto *pooling_kernel = new kernel::PoolingOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  auto *pooling_kernel =
+    new (std::nothrow) kernel::PoolingOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  if (pooling_kernel == nullptr) {
+    MS_LOG(ERROR) << "pooling_kernel null";
+    return;
+  }
   MS_LOG(INFO) << "pooling_kernel init";
   pooling_kernel->Init();
 
   std::vector<kernel::LiteKernel *> kernels{pooling_kernel};
   inputs[0]->MallocData(allocator);
-  auto *pGraph = new kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  auto *pGraph = new (std::nothrow) kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  if (pGraph == nullptr) {
+    MS_LOG(ERROR) << "pGraph null";
+    return;
+  }
   MS_LOG(INFO) << "pGraph init";
   pGraph->Init();
 

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

@@ -28,41 +28,49 @@ class TestSoftmaxOpenCL : public mindspore::CommonTest {};
 
 void RunTestCase(std::vector<int> input_shape, std::vector<int> output_shape, std::string input_file,
                  std::string expect_file, SoftmaxParameter *param, schema::Format format) {
-  std::cout << "runtime" << std::endl;
   auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
   ocl_runtime->Init();
   auto allocator = ocl_runtime->GetAllocator();
 
   // define tensor
   MS_LOG(INFO) << "defineTensor";
-  std::cout << "defineTensor" << std::endl;
-
   auto data_type = kNumberTypeFloat32;
   auto tensorType = schema::NodeType_ValueNode;
-  auto input_tensor = new lite::tensor::Tensor(data_type, input_shape, format, tensorType);
-  auto output_tensor = new lite::tensor::Tensor(data_type, output_shape, format, tensorType);
+  auto input_tensor = new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, format, tensorType);
+  auto output_tensor = new (std::nothrow) lite::tensor::Tensor(data_type, output_shape, format, tensorType);
+  if (input_tensor == nullptr) {
+    MS_LOG(ERROR) << "input tensor null";
+    return;
+  }
+  if (output_tensor == nullptr) {
+    MS_LOG(ERROR) << "output tensor null";
+    return;
+  }
   std::vector<lite::tensor::Tensor *> inputs{input_tensor};
   std::vector<lite::tensor::Tensor *> outputs{output_tensor};
 
   // run
   MS_LOG(INFO) << "NewOpenCLKernel";
-  std::cout << "NewOpenCLKernel" << std::endl;
   auto *kernel = new kernel::SoftmaxOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << "kernel null";
+    return;
+  }
   MS_LOG(INFO) << "KernelInit";
-  std::cout << "KernelInit" << std::endl;
   kernel->Init();
 
-  std::cout << "LiteKernel" << std::endl;
   std::vector<kernel::LiteKernel *> kernels{kernel};
   inputs[0]->MallocData(allocator);
-  std::cout << "SubGraphOpenCLKernel" << std::endl;
-  auto *pGraph = new kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  auto *pGraph = new (std::nothrow) kernel::SubGraphOpenCLKernel(inputs, outputs, kernels, kernels, kernels);
+  if (pGraph == nullptr) {
+    MS_LOG(ERROR) << "pGraph null";
+    return;
+  }
   MS_LOG(INFO) << "pGraphinit";
   pGraph->Init();
 
   // load data
   MS_LOG(INFO) << "load data1";
-
   LoadTestData(input_tensor->Data(), input_tensor->Size(), input_file);
   auto *input_data = reinterpret_cast<float *>(input_tensor->Data());
   printf("\ninput[0:10]:");
@@ -75,7 +83,6 @@ void RunTestCase(std::vector<int> input_shape, std::vector<int> output_shape, st
   pGraph->Run();
 
   MS_LOG(INFO) << "compare result";
-  std::cout << "compare result" << std::endl;
   CompareOutput(output_tensor, expect_file);
   for (auto tensor : inputs) {
     delete tensor;
@@ -93,23 +100,11 @@ TEST_F(TestSoftmaxOpenCL, Softmax_1) {
   std::vector<int> output_shape = {1, 2, 2, 8};
   std::string input_file = "softmax_in.bin";
   std::string expect_file = "softmax_out.bin";
-  auto param = new SoftmaxParameter;
+  auto param = new (std::nothrow) SoftmaxParameter;
   param->axis_ = 3;
   schema::Format format = schema::Format_NHWC4;
 
   RunTestCase(input_shape, output_shape, input_file, expect_file, param, format);
 }
 
-// TEST_F(TestSoftmaxOpenCL, Softmax_1x1) {
-//  std::vector<int> input_shape = {1, 100};
-//  std::vector<int> output_shape = {1, 100};
-//  std::string input_file = "softmax1x1_in.bin";
-//  std::string expect_file = "softmax1x1_out.bin";
-//  auto param = new SoftmaxParameter;
-//  param->axis_ = 1;
-//  schema::Format format = schema::Format_NHWC4;
-//
-//  RunTestCase(input_shape, output_shape, input_file, expect_file, param, format);
-//}
-
 }  // namespace mindspore