fix codex checking

mindspore/lite/include/train_session.h

@@ -18,7 +18,6 @@
 #include <vector>
 #include <string>
 #include <unordered_map>
-// #include "include/lite_session.h"
 #include "src/lite_session.h"
 
 namespace mindspore {

mindspore/lite/nnacl/fp32_grad/pooling_grad.h

@@ -23,7 +23,6 @@
 extern "C" {
 #endif
 void AvgPoolingGrad(const float *input_ptr, float *output_ptr, PoolingParameter *pooling_param);
-// void MaxPoolingGrad(const float *dy, const int *indices_ptr, float *output_ptr, PoolingParameter *pooling_param);
 void MaxPoolingGrad(const float *input_ptr, const float *dx_ptr, const float *dy_ptr, float *output_ptr,
                     PoolingParameter *pooling_param);
 #ifdef __cplusplus

mindspore/lite/src/ops/apply_momentum.cc

@@ -65,10 +65,6 @@ int ApplyMomentum::InferShape(std::vector<lite::Tensor *> inputs, std::vector<li
     MS_LOG(ERROR) << "ApplyMomentum should have at 5 input tensors";
     return RET_ERROR;
   }
-  // if (outputs.empty()) {
-  //  MS_LOG(ERROR) << "ApplyMomentumCPUKernel error input output size!";
-  //  return RET_ERROR;
-  // }
 
   if (inputs[0]->ElementsNum() != inputs[1]->ElementsNum() || inputs[0]->ElementsNum() != inputs[3]->ElementsNum() ||
       inputs[2]->ElementsNum() != 1 || inputs[4]->ElementsNum() != 1) {

mindspore/lite/src/runtime/kernel/arm/fp32_grad/bn_grad.cc

@@ -58,7 +58,6 @@ int BNGradCPUKernel::Run() {
   auto *output_dx = out_tensors_.at(0);
   auto *output_scale = out_tensors_.at(1);
   auto *output_bias = out_tensors_.at(2);
-  // Tensor *bias = input[5];
   int batch = input_x->Batch();
   int channels = input_x->Channel();
   int spatial = input_x->Height() * input_x->Width();

mindspore/lite/src/runtime/kernel/arm/fp32_grad/convolution_grad_input.h

@@ -40,9 +40,6 @@ class ConvolutionGradInputCPUKernel : public LiteKernel {
  private:
   float *workspace;
 };
-
-// OpParameter *PopulateConvolutionGradInputParameter(const lite::Primitive *primitive);
-
 }  // namespace mindspore::kernel
 
 #endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_GRAD_CONVOLUTION_GRAD_INPUT_H

mindspore/lite/src/runtime/kernel/arm/fp32_grad/depend.cc

@@ -33,17 +33,15 @@ int DependCPUKernel::Init() { return RET_OK; }
 int DependCPUKernel::ReSize() { return 0; }
 
 int DependCPUKernel::Run() {
-#if 0
-  auto ret = Prepare();
-  if (ret != RET_OK) {
-    MS_LOG(ERROR) << "Prepare failed.";
-    return RET_ERROR;
-  }
-  auto in = reinterpret_cast<float *>(in_tensors_.at(0)->MutableData());
-  auto out = reinterpret_cast<float *>(out_tensors_.at(0)->MutableData());
-
-  memcpy(out, in, in_tensors_.at(0)->Size());
-#endif
+//  auto ret = Prepare();
+//  if (ret != RET_OK) {
+//    MS_LOG(ERROR) << "Prepare failed.";
+//    return RET_ERROR;
+//  }
+//  auto in = reinterpret_cast<float *>(in_tensors_.at(0)->MutableData());
+//  auto out = reinterpret_cast<float *>(out_tensors_.at(0)->MutableData());
+//
+//  memcpy(out, in, in_tensors_.at(0)->Size());
   return RET_OK;
 }
 

mindspore/lite/src/runtime/kernel/arm/fp32_grad/softmax_grad.cc

@@ -22,11 +22,9 @@
 #include "src/kernel_registry.h"
 #include "include/errorcode.h"
 
-// using mindspore::kernel::KERNEL_ARCH::kCPU;
 using mindspore::lite::KernelRegistrar;
 using mindspore::lite::RET_ERROR;
 using mindspore::lite::RET_OK;
-// using mindspore::schema::PrimitiveType_SoftMaxGrad;
 
 namespace mindspore::kernel {
 int SoftmaxGradCPUKernel::Init() {
@@ -71,7 +69,6 @@ int SoftmaxGradCPUKernel::Init() {
 int SoftmaxGradCPUKernel::ReSize() { return RET_OK; }
 
 int SoftmaxGradCPUKernel::Run() {
-  // auto input_ptr = reinterpret_cast<float *>(in_tensors_.at(kInputIndex)->MutableData());
   auto input_ptr = reinterpret_cast<float *>(in_tensors_.at(kInputIndex)->MutableData());
   auto yt_ptr = reinterpret_cast<float *>(in_tensors_.at(1)->MutableData());
   auto output_ptr = reinterpret_cast<float *>(out_tensors_.at(kOutputIndex)->MutableData());
@@ -85,7 +82,6 @@ kernel::LiteKernel *CpuSoftmaxGradFp32KernelCreator(const std::vector<lite::Tens
                                                     const kernel::KernelKey &desc,
                                                     const mindspore::lite::PrimitiveC *primitive) {
   MS_ASSERT(opParameter != nullptr);
-  // MS_ASSERT(desc.type == schema::PrimitiveType_SoftMaxGrad);
   auto *kernel = new (std::nothrow) SoftmaxGradCPUKernel(opParameter, inputs, outputs, ctx, primitive);
   if (kernel == nullptr) {
     MS_LOG(ERROR) << "new SoftmaxGradCPUKernel fail!";
@@ -101,5 +97,4 @@ kernel::LiteKernel *CpuSoftmaxGradFp32KernelCreator(const std::vector<lite::Tens
   return kernel;
 }
 
-// REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_SoftMaxGrad, CpuSoftmaxGradFp32KernelCreator)
 }  // namespace mindspore::kernel

mindspore/lite/src/train/train_session.cc

@@ -59,7 +59,6 @@ TrainSession::~TrainSession() {
 }
 
 void *TrainSession::ExportToBuf(lite::Model *model, void *buf, size_t *len) const {
-  // return model->ExportBuf(buf, len);
   return nullptr;
 }
 
@@ -79,9 +78,6 @@ int TrainSession::RunGraph(const session::KernelCallBack &before, const session:
   }
 
   MS_EXCEPTION_IF_NULL(this->context_);
-  // TODO(Emir)
-  // SetMaxWokerNum(context_->thread_num_);
-  // context_->running_ = true;
   lite::Executor executor;
   if (before == nullptr && after == nullptr) {
     return executor.Run(this->inputs_, this->outputs_, infference_kernels, this->context_->allocator.get());