fix some mistake of codex

mindspore/lite/nnacl/fp16/conv_fp16.c

@@ -279,7 +279,7 @@ void ConvSWFp16(const float16_t *input_data, const float16_t *packed_weight, con
   bool relu6 = conv_param->act_type_ == ActType_Relu6;
   int oc4_res = conv_param->output_channel_ % C4NUM;
   const float16_t *src = input_data;
-  float16_t *dst;
+  float16_t *dst = NULL;
   if (oc4_res == 0) {
     dst = output_data;
   } else {

mindspore/lite/nnacl/fp32/common_func.c

@@ -17,8 +17,14 @@
 #include "nnacl/fp32/common_func.h"
 void PostConvFuncComm(const float *src_ptr_, float *out_ptr, const float *bias_ptr, size_t output_channel,
                       size_t plane_size, size_t stride, bool is_relu, bool is_relu6, int size) {
+  int oc_div = 0, oc_mod = 0;
   for (int oc = 0; oc < output_channel; oc++) {
-    int oc_div = oc / size, oc_mod = oc % size;
+    if (size != 0) {
+      oc_div = oc / size;
+      oc_mod = oc % size;
+    } else {
+      return;
+    }
     for (int hw = 0; hw < plane_size; hw++) {
       int src_index = oc_div * size * plane_size + hw * size + oc_mod;
       int dst_index = hw * stride + oc;

mindspore/lite/nnacl/fp32/roi_pooling.c

@@ -35,7 +35,7 @@ int ROIPooling(float *in_ptr, float *out_ptr, float *roi, int tid, ROIPoolingPar
   int scale = param->scale_;
   int pooled_height = param->pooledH_;
   int pooled_width = param->pooledW_;
-  int roi_stride = 5;
+  const int roi_stride = 5;
   int roi_ind_st = roi_st * roi_stride;
   float *max_c = malloc(channels_ * sizeof(float));
   for (int i = roi_st; i < roi_end; ++i) {

mindspore/lite/nnacl/int8/leaky_relu_int8.c

@@ -17,6 +17,9 @@
 #include "nnacl/int8/leaky_relu_int8.h"
 
 void DoLeakReluInt8(int8_t *inputs, int8_t *output_ptr, LeakyReluQuantArg *quant_prelu_parm, int task_id) {
+  if (quant_prelu_parm == NULL) {
+    return;
+  }
   float output_scale = quant_prelu_parm->quant_arg.out_args_.scale_;
   int output_zp = quant_prelu_parm->quant_arg.out_args_.zp_;
   const float output_inverse_scale = 1.f / output_scale;

mindspore/lite/nnacl/int8/matmul_int8.c

@@ -328,7 +328,7 @@ void CalcWeightBiasSums(int8_t *weight, int row, int col, int input_zp, int weig
       }
     }
     dst[c] = row * input_zp * weight_zp - input_zp * sum;
-    if (bias) {
+    if (bias != NULL) {
       dst[c] += bias[c];
     }
   }

mindspore/lite/src/ops/conv2d.cc

@@ -218,8 +218,8 @@ void Conv2D::PopulaterConv2DSingleGroup(const Primitive &prim, schema::Primitive
 }
 
 void Conv2D::CalQuantParam(const double &mean, const double &stdDev, float *mMin, float *mMax) {
-  constexpr float qmin = 0;
-  constexpr float qmax = 255;
+  const float qmin = 0;
+  const float qmax = 255;
   *mMin = static_cast<float>((qmin - mean) / stdDev);
   *mMax = static_cast<float>((qmax - mean) / stdDev);
 }

mindspore/lite/src/ops/depthwise_conv2d.cc

@@ -70,8 +70,8 @@ void DepthwiseConv2D::SetActivationType(int activation_type) {
 }
 
 void DepthwiseConv2D::CalQuantParam(const double &mean, const double &stdDev, float *mMin, float *mMax) {
-  constexpr float qmin = 0;
-  constexpr float qmax = 255;
+  const float qmin = 0;
+  const float qmax = 255;
   *mMin = static_cast<float>((qmin - mean) / stdDev);
   *mMax = static_cast<float>((qmax - mean) / stdDev);
 }

mindspore/lite/src/ops/matmul.cc

@@ -31,8 +31,8 @@ void MatMul::SetTransposeA(bool transpose_a) { this->primitive_->value.AsMatMul(
 void MatMul::SetTransposeB(bool transpose_b) { this->primitive_->value.AsMatMul()->transposeB = transpose_b; }
 
 void MatMul::CalQuantParam(const double &mean, const double &stdDev, float *mMin, float *mMax) {
-  constexpr float qmin = 0;
-  constexpr float qmax = 255;
+  const float qmin = 0;
+  const float qmax = 255;
   *mMin = static_cast<float>((qmin - mean) / stdDev);
   *mMax = static_cast<float>((qmax - mean) / stdDev);
 }

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

@@ -119,8 +119,8 @@ int ToFormatOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size
     im_dst_x = w * UP_DIV(c, C4NUM);
     im_dst_y = h;
   } else if (out_tensors_[0]->GetFormat() == schema::Format_NC4) {
-    int h = 1;
-    int w = 1;
+    const int h = 1;
+    const int w = 1;
     int c = shapex[1];
     im_dst_x = w * UP_DIV(c, C4NUM);
     im_dst_y = h;

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

@@ -109,7 +109,7 @@ int SubGraphOpenCLKernel::GenToFormatOp(const std::vector<lite::tensor::Tensor *
     parameter->dst_format = dst_format;
     parameter->out_mem_type = mem_type;
     out_parameters->emplace_back(parameter);
-    LiteKernel *in_convert_op;
+    LiteKernel *in_convert_op = nullptr;
     if (mem_type == OpenCLMemType::IMG) {
       in_convert_op =
         lite::GetOpenCLKernel({in_tensors[i]}, {new_tensor}, reinterpret_cast<OpParameter *>(parameter), nullptr, desc);

mindspore/lite/src/runtime/opencl/opencl_runtime.cc

@@ -198,7 +198,7 @@ int OpenCLRuntime::Init() {
   MS_LOG(INFO) << "Compute Unit: " << compute_units_;
   MS_LOG(INFO) << "Clock Frequency: " << max_freq_ << " MHz";
 
-  cl_command_queue_properties properties = 0;
+  const cl_command_queue_properties properties = 0;
 #if MS_OPENCL_PROFILE
   properties |= CL_QUEUE_PROFILING_ENABLE;
 #endif

mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.cc

@@ -402,6 +402,7 @@ STATUS OnnxModelParser::CopyOnnxTensorData(const onnx::TensorProto &onnx_const_v
       data_size = data_count * sizeof(int32_t);
       buffer = std::make_unique<int32_t[]>(data_count);
       const int64_t *in_data;
+      in_data = nullptr;
       if (onnx_const_value.int64_data_size() == 0) {
         in_data = reinterpret_cast<const int64_t *>(onnx_const_value.raw_data().data());
       } else {