diff --git a/mindspore/lite/nnacl/fp32_grad/batch_norm.c b/mindspore/lite/nnacl/fp32_grad/batch_norm.c
index a6d2b20ed8d..a353dbc85f0 100644
--- a/mindspore/lite/nnacl/fp32_grad/batch_norm.c
+++ b/mindspore/lite/nnacl/fp32_grad/batch_norm.c
@@ -48,7 +48,7 @@ void backwardX(const float *in, const float *dout, const float *scale, const int
   meanVar(in, size, channels, eps, mean, invar);
   for (int i = 0; i < size; i++) {
     for (int f = 0; f < channels; f++) {
-      int ix = i*channels + f;
+      int ix = i * channels + f;
       float x_hat = (in[ix] - mean[f]) * invar[f];
       float dxhat = dout[ix] * scale[f];
       dxhat_sum[f] += dxhat;
@@ -57,7 +57,7 @@ void backwardX(const float *in, const float *dout, const float *scale, const int
   }
   for (int i = 0; i < size; i++) {
     for (int f = 0; f < channels; f++) {
-      int ix = i*channels + f;
+      int ix = i * channels + f;
       float x_hat = (in[ix] - mean[f]) * invar[f];
       float dxhat = dout[ix] * scale[f];
       out[ix] = 1.f / size * invar[f] * (size * dxhat - dxhat_sum[f] - x_hat * dxhathat_sum[f]);
diff --git a/mindspore/lite/nnacl/minimal_filtering_generator.h b/mindspore/lite/nnacl/minimal_filtering_generator.h
index c6ce595a58d..95098d5b4dc 100644
--- a/mindspore/lite/nnacl/minimal_filtering_generator.h
+++ b/mindspore/lite/nnacl/minimal_filtering_generator.h
@@ -43,7 +43,7 @@ void MatrixTranspose(float *matrix, float *trans_matrix, int row, int col);
 void MatrixMultiply(const float *matrix_a, const float *matrix_b, float *matrix_c, int m, int k, int n);
 
 int CookToomFilter(float *matrix_a, float *matrix_at, float *matrix_b, float *matrix_bt, float *matrix_g,
-                    float *matrix_gt, float coefficient, int out_unit, int filter_size);
+                   float *matrix_gt, float coefficient, int out_unit, int filter_size);
 
 #ifdef ENABLE_ARM
 void MatrixMultiplyVec(const float32x4_t *matrix_a, const float32x4_t *matrix_b, float32x4_t *matrix_c,
diff --git a/mindspore/lite/nnacl/optimized_kernel.h b/mindspore/lite/nnacl/optimized_kernel.h
index 162aa0433c1..7b3d7332e01 100644
--- a/mindspore/lite/nnacl/optimized_kernel.h
+++ b/mindspore/lite/nnacl/optimized_kernel.h
@@ -37,7 +37,6 @@ class OptimizeModule {
 #ifdef ENABLE_ARM64
     int hwcap_type = 16;
     uint32_t hwcap = getHwCap(hwcap_type);
-
     if (hwcap & HWCAP_ASIMDDP) {
       MS_LOG(INFO) << "Hw cap support SMID Dot Product, hwcap: 0x" << hwcap;
       support_optimize_ops = true;
@@ -72,7 +71,6 @@ class Float16Module {
 #ifdef ENABLE_ARM64
     int hwcap_type = 16;
     uint32_t hwcap = getHwCap(hwcap_type);
-
     if (hwcap & HWCAP_FPHP) {
       MS_LOG(INFO) << "Hw cap support FP16, hwcap: 0x" << hwcap;
       support_fp16 = true;
diff --git a/mindspore/lite/tools/time_profiler/time_profiler.cc b/mindspore/lite/tools/time_profiler/time_profiler.cc
index 1fcf0800c50..555d0af4c21 100644
--- a/mindspore/lite/tools/time_profiler/time_profiler.cc
+++ b/mindspore/lite/tools/time_profiler/time_profiler.cc
@@ -80,6 +80,7 @@ int TimeProfiler::ReadInputFile() {
   }
   auto input_data = inTensor->MutableData();
   memcpy(input_data, bin_buf, tensor_data_size);
+  delete bin_buf;
   return RET_OK;
 }
 
@@ -104,15 +105,10 @@ int TimeProfiler::LoadInput() {
 }
 
 int TimeProfiler::InitSession() {
-  size_t size = 0;
-  char *graph_buf = ReadFile(_flags->model_path_.c_str(), &size);
-  if (graph_buf == nullptr) {
-    MS_LOG(ERROR) << "Load graph failed, path " << _flags->model_path_;
-    std::cerr << "Load graph failed, path " << _flags->model_path_ << std::endl;
+  ctx = new (std::nothrow) lite::Context;
+  if (ctx == nullptr) {
     return RET_ERROR;
   }
-
-  auto ctx = new lite::Context;
   ctx->cpu_bind_mode_ = static_cast<CpuBindMode>(_flags->cpu_bind_mode_);
   ctx->device_type_ = lite::DT_CPU;
   ctx->thread_num_ = _flags->num_threads_;
@@ -239,7 +235,7 @@ int TimeProfiler::Init() {
 }
 
 int TimeProfiler::PrintResult(const std::vector<std::string> &title,
-                             const std::map<std::string, std::pair<int, float>> &result) {
+                              const std::map<std::string, std::pair<int, float>> &result) {
   std::vector<size_t> columnLenMax(5);
   std::vector<std::vector<std::string>> rows;
 
diff --git a/mindspore/lite/tools/time_profiler/time_profiler.h b/mindspore/lite/tools/time_profiler/time_profiler.h
index c43429476cf..8b1121b03b7 100644
--- a/mindspore/lite/tools/time_profiler/time_profiler.h
+++ b/mindspore/lite/tools/time_profiler/time_profiler.h
@@ -57,7 +57,11 @@ class MS_API TimeProfilerFlags : public virtual FlagParser {
 class MS_API TimeProfiler {
  public:
   explicit TimeProfiler(TimeProfilerFlags *flags) : _flags(flags) {}
-  ~TimeProfiler() = default;
+  ~TimeProfiler() {
+    if (ctx != nullptr) {
+      delete ctx;
+    }
+  }
 
   int Init();
   int RunTimeProfiler();
@@ -72,6 +76,7 @@ class MS_API TimeProfiler {
   int PrintResult(const std::vector<std::string> &title, const std::map<std::string, std::pair<int, float>> &result);
 
  private:
+  Context *ctx = nullptr;
   TimeProfilerFlags *_flags;
   std::vector<mindspore::tensor::MSTensor *> ms_inputs_;
   session::LiteSession *session_;