!8108 modify files for static check

Merge pull request !8108 from lyvette/check_master

mindspore/lite/src/common/file_utils.cc

@@ -16,7 +16,7 @@
 
 #include "src/common/file_utils.h"
 #include <fcntl.h>
-#include <stdlib.h>
+#include <cstdlib>
 #include <climits>
 #include <cmath>
 #include "securec/include/securec.h"
@@ -107,7 +107,7 @@ int CompareOutputData(const float *output_data, size_t output_size, const float
   return 0;
 }
 
-int CompareOutput(const float *output_data, size_t output_num, std::string file_path) {
+int CompareOutput(const float *output_data, size_t output_num, const std::string &file_path) {
   size_t ground_truth_size = 0;
   auto ground_truth = reinterpret_cast<float *>(mindspore::lite::ReadFile(file_path.c_str(), &ground_truth_size));
   size_t ground_truth_num = ground_truth_size / sizeof(float);

mindspore/lite/src/common/file_utils.h

@@ -59,7 +59,7 @@ inline int WriteToBin(const std::string &file_path, void *data, size_t size) {
 }
 
 int CompareOutputData(const float *output_data, size_t output_num, const float *correct_data, size_t data_size);
-int CompareOutput(const float *output_data, size_t output_num, std::string file_path);
+int CompareOutput(const float *output_data, size_t output_num, const std::string &file_path);
 
 std::string GetAndroidPackageName();
 std::string GetAndroidPackagePath();

mindspore/lite/src/common/string_util.cc

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#include "src/common/string_util.h"
 #include <algorithm>
+#include "src/common/string_util.h"
 #include "include/ms_tensor.h"
 
 namespace mindspore {
@@ -35,7 +35,7 @@ std::vector<StringPack> ParseStringBuffer(const void *data) {
     MS_LOG(ERROR) << "data is nullptr";
     return buffer;
   }
-  const int32_t *offset = reinterpret_cast<const int32_t *>(data);
+  const auto *offset = reinterpret_cast<const int32_t *>(data);
   int32_t num = *offset;
   for (int i = 0; i < num; i++) {
     offset += 1;
@@ -59,7 +59,7 @@ int WriteStringsToTensor(Tensor *tensor, const std::vector<StringPack> &string_b
     return RET_ERROR;
   }
 
-  int32_t *string_info = reinterpret_cast<int32_t *>(data);
+  auto *string_info = reinterpret_cast<int32_t *>(data);
   char *string_data = reinterpret_cast<char *>(data);
 
   string_info[0] = num;
@@ -140,13 +140,13 @@ static uint64_t k1 = 0xb492b66fbe98f273ULL;
 static uint64_t k2 = 0x9ae16a3b2f90404fULL;
 
 uint64_t Fetch64Bit(const char *p) {
-  uint64_t result;
+  uint64_t result = 0;
   memcpy(&result, p, sizeof(uint64_t));
   return result;
 }
 
 uint32_t Fetch32Bit(const char *p) {
-  uint32_t result;
+  uint32_t result = 0;
   memcpy(&result, p, sizeof(uint32_t));
   return result;
 }
@@ -226,7 +226,7 @@ std::pair<uint64_t, uint64_t> HashLen32WithSeeds(const char *s, uint64_t a, uint
 }
 
 uint64_t StringHash64(const char *s, size_t len) {
-  uint64_t seed_value = 81;
+  const uint64_t seed_value = 81;
   if (len <= 16) {
     return HashStringLen0to16(s, len);
   } else if (len <= 32) {

mindspore/lite/src/model_common.cc

@@ -13,7 +13,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 #include "src/model_common.h"
 #include "include/version.h"
 #ifndef PRIMITIVE_WRITEABLE
@@ -23,7 +22,7 @@
 namespace mindspore::lite {
 bool ConvertNodes(const schema::MetaGraph *meta_graph, Model *model) {
   for (size_t i = 0; i < meta_graph->nodes()->size(); ++i) {
-    Model::Node *node = new (std::nothrow) Model::Node();
+    auto *node = new (std::nothrow) Model::Node();
     if (node == nullptr) {
       MS_LOG(ERROR) << "new node fail!";
       return false;

mindspore/lite/src/runtime/thread_pool.c

@@ -384,6 +384,7 @@ int SetArch(CpuInfo *freq_set, int core_num) {
     freq_set[i].arch = archs[i];
   }
   free(archs);
+  fclose(fp);
   return RET_TP_OK;
 }
 

mindspore/lite/src/train/train_model.cc

@@ -49,8 +49,8 @@ TrainModel *TrainModel::Import(const char *model_buf, size_t size) {
   model->buf_size_ = size;
   auto meta_graph = schema::GetMetaGraph(model->buf);
   if (meta_graph == nullptr) {
-    delete model;
     free(model->buf);
+    delete model;
     MS_LOG(ERROR) << "meta_graph is nullptr!";
     return nullptr;
   }

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

@@ -16,6 +16,7 @@
 
 #include "tools/converter/parser/onnx/onnx_expand_parser.h"
 #include <memory>
+#include <vector>
 
 namespace mindspore {
 namespace lite {

mindspore/lite/tools/converter/quantizer/post_training_quantizer.cc

@@ -19,10 +19,10 @@
 #include <sys/stat.h>
 #include <future>
 #include <map>
-#include <unordered_map>
+#include <memory>
 #include <algorithm>
+#include <unordered_map>
 #include <functional>
-#include <memory>
 #include <numeric>
 #include <utility>
 #include <string>
@@ -55,7 +55,7 @@ STATUS DivergInfo::RecordMaxValue(const std::vector<float> &datas) {
 }
 
 STATUS DivergInfo::RecordMaxValueArray(const std::vector<float> &datas) {
-  if (datas.size() == 0) {
+  if (datas.empty()) {
     return RET_ERROR;
   }
   float max_num = datas.at(0);
@@ -106,7 +106,7 @@ STATUS DivergInfo::ComputeThreshold() {
     return RET_OK;
   }
 
-  constexpr int quant_bint_nums = 128;
+  const constexpr int quant_bint_nums = 128;
   int threshold = quant_bint_nums;
   float min_kl = FLT_MAX;
   float after_threshold_sum = std::accumulate(this->histogram.begin() + quant_bint_nums, this->histogram.end(), 0.0f);
@@ -247,18 +247,19 @@ std::pair<CNodePtr, int32_t> DivergInfo::GetZeropoint() {
 std::unordered_map<CNodePtr, float> Calibrator::GetScale(
   std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) {
   std::unordered_map<CNodePtr, float> result;
-  for (auto iter = diverg_info->begin(); iter != diverg_info->end(); iter++) {
-    DivergInfo *info = iter->second.get();
+  for (auto &iter : *diverg_info) {
+    DivergInfo *info = iter.second.get();
     auto item = info->GetScale();
     result.insert(item);
   }
   return result;
 }
+
 std::unordered_map<CNodePtr, int32_t> Calibrator::GetZeropoint(
   std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) {
   std::unordered_map<CNodePtr, int32_t> result;
-  for (auto iter = diverg_info->begin(); iter != diverg_info->end(); iter++) {
-    DivergInfo *info = iter->second.get();
+  for (auto &iter : *diverg_info) {
+    DivergInfo *info = iter.second.get();
     auto zeropoint = info->GetZeropoint();
     result.insert(zeropoint);
   }
@@ -268,8 +269,8 @@ std::unordered_map<CNodePtr, int32_t> Calibrator::GetZeropoint(
 std::map<CNodePtr, MaxMin> Calibrator::GetMinMax(
   std::unordered_map<std::string, std::unique_ptr<DivergInfo>> *diverg_info) {
   std::map<CNodePtr, MaxMin> result;
-  for (auto iter = diverg_info->begin(); iter != diverg_info->end(); iter++) {
-    DivergInfo *info = iter->second.get();
+  for (auto &iter : *diverg_info) {
+    DivergInfo *info = iter.second.get();
     mindspore::lite::quant::MaxMin input_maxmin{};
     input_maxmin.min = info->min;
     input_maxmin.max = info->max;
@@ -358,16 +359,16 @@ STATUS Calibrator::UpdateDataFrequency(const vector<float> &data, const std::uni
   return RET_OK;
 }
 
-STATUS Calibrator::AddQuantizedOp(CNodePtr node) {
+STATUS Calibrator::AddQuantizedOp(const CNodePtr &node) {
   if (node == nullptr) {
     MS_LOG(ERROR) << "To be quantized node is null";
     return RET_ERROR;
   }
   string node_name = node->fullname_with_scope();
-  std::unique_ptr<DivergInfo> input_diverg = std::unique_ptr<DivergInfo>(
-    new DivergInfo(node, kDefaultBinNumber, bit_num_, quant_max_, quant_min_, config_param_.method_x));
-  std::unique_ptr<DivergInfo> output_diverg = std::unique_ptr<DivergInfo>(
-    new DivergInfo(node, kDefaultBinNumber, bit_num_, quant_max_, quant_min_, config_param_.method_x));
+  std::unique_ptr<DivergInfo> input_diverg =
+    std::make_unique<DivergInfo>(node, kDefaultBinNumber, bit_num_, quant_max_, quant_min_, config_param_.method_x);
+  std::unique_ptr<DivergInfo> output_diverg =
+    std::make_unique<DivergInfo>(node, kDefaultBinNumber, bit_num_, quant_max_, quant_min_, config_param_.method_x);
 
   inputs_diverg_info_[node_name].push_back(std::move(input_diverg));
   outputs_diverg_info_[node_name].push_back(std::move(output_diverg));
@@ -379,7 +380,7 @@ void Calibrator::AddImage(const string &file, size_t index) {
     MS_LOG(ERROR) << "images_ size: " << images_.size() << " but index: " << index;
     return;
   }
-  auto exist = [](const string file) {
+  auto exist = [](const string &file) {
     struct stat buf {};
     return stat(file.c_str(), &buf) == 0;
   };
@@ -408,6 +409,7 @@ STATUS Calibrator::GenerateInputData(int input_index, int image_index, mindspore
   auto ret = memcpy_s(data, tensor->Size(), bin_buf, size);
   if (ret != EOK) {
     MS_LOG(ERROR) << "memcpy_s error: " << ret;
+    delete[] bin_buf;
     return RET_ERROR;
   }
   delete[] bin_buf;
@@ -536,11 +538,11 @@ STATUS Calibrator::ReadConfig() {
 }
 
 Calibrator::Calibrator(string path, size_t bit_num, int quant_max, int quant_min)
-    : config_path_(path), bit_num_(bit_num), quant_max_(quant_max), quant_min_(quant_min) {}
+    : config_path_(std::move(path)), bit_num_(bit_num), quant_max_(quant_max), quant_min_(quant_min) {}
 
 PostTrainingQuantizer::PostTrainingQuantizer(FuncGraphPtr graph, string path, int bit_num, TypeId target_type,
                                              bool per_channel)
-    : Quantizer(graph) {
+    : Quantizer(std::move(graph)) {
   this->per_channel_ = per_channel;
   this->bit_num = bit_num;
   this->target_type_ = target_type;
@@ -553,7 +555,7 @@ PostTrainingQuantizer::PostTrainingQuantizer(FuncGraphPtr graph, string path, in
   } else {
     MS_LOG(ERROR) << "unsupported quant value type: " << target_type;
   }
-  calibrator_ = std::unique_ptr<Calibrator>(new Calibrator(path, this->bit_num, quant_max, quant_min));
+  calibrator_ = std::make_unique<Calibrator>(std::move(path), this->bit_num, quant_max, quant_min);
   if (calibrator_ == nullptr) {
     MS_LOG(ERROR) << "creat calibrator failed!";
     return;
@@ -561,7 +563,7 @@ PostTrainingQuantizer::PostTrainingQuantizer(FuncGraphPtr graph, string path, in
 }
 
 STATUS PostTrainingQuantizer::DoQuantInput(double scale, int32_t zeropoint, struct MaxMin *max_min,
-                                           std::shared_ptr<PrimitiveC> lite_primitive) {
+                                           const std::shared_ptr<PrimitiveC> &lite_primitive) const {
   schema::QuantParamT quant_param;
   quant_param.scale = scale;
   quant_param.zeroPoint = zeropoint;
@@ -576,7 +578,7 @@ STATUS PostTrainingQuantizer::DoQuantInput(double scale, int32_t zeropoint, stru
 }
 
 STATUS PostTrainingQuantizer::DoQuantOutput(double scale, int zeropoint, struct MaxMin *max_min,
-                                            std::shared_ptr<PrimitiveC> lite_primitive) {
+                                            const std::shared_ptr<PrimitiveC> &lite_primitive) const {
   schema::QuantParamT quant_param;
   quant_param.scale = scale;
   quant_param.zeroPoint = zeropoint;
@@ -590,8 +592,8 @@ STATUS PostTrainingQuantizer::DoQuantOutput(double scale, int zeropoint, struct
   return RET_OK;
 }
 
-STATUS PostTrainingQuantizer::DoWeightQuant(AnfNodePtr weight, std::shared_ptr<PrimitiveC> primitive_c,
-                                            bool perchanel) {
+STATUS PostTrainingQuantizer::DoWeightQuant(const AnfNodePtr &weight, std::shared_ptr<PrimitiveC> primitive_c,
+                                            bool perchanel) const {
   // perlayer
   if (!weight->isa<Parameter>()) {
     MS_LOG(ERROR) << "not a parameter";
@@ -607,8 +609,8 @@ STATUS PostTrainingQuantizer::DoWeightQuant(AnfNodePtr weight, std::shared_ptr<P
     MS_LOG(ERROR) << weight->fullname_with_scope() << " can not get value";
     return RET_ERROR;
   }
-  auto status =
-    QuantFilter<int8_t>(paramValue, primitive_c, QuantType_PostTraining, quant_max, quant_min, bit_num, perchanel);
+  auto status = QuantFilter<int8_t>(paramValue, std::move(primitive_c), QuantType_PostTraining, quant_max, quant_min,
+                                    bit_num, perchanel);
   if (status != RET_OK) {
     MS_LOG(ERROR) << "QuantFilter failed: " << status;
     return status;
@@ -628,7 +630,7 @@ STATUS PostTrainingQuantizer::DoWeightQuant(AnfNodePtr weight, std::shared_ptr<P
   return RET_OK;
 }
 
-STATUS PostTrainingQuantizer::DoBiasQuant(AnfNodePtr bias, std::shared_ptr<PrimitiveC> primitive_c) {
+STATUS PostTrainingQuantizer::DoBiasQuant(const AnfNodePtr &bias, const std::shared_ptr<PrimitiveC> &primitive_c) {
   if (primitive_c == nullptr || bias == nullptr) {
     MS_LOG(ERROR) << "null pointer!";
     return RET_NULL_PTR;
@@ -675,22 +677,22 @@ STATUS PostTrainingQuantizer::DoBiasQuant(AnfNodePtr bias, std::shared_ptr<Primi
 
   // set bias quant param
   vector<schema::QuantParamT> quant_params;
-  for (size_t i = 0; i < bias_scales.size(); i++) {
+  for (double bias_scale : bias_scales) {
     schema::QuantParamT quant_param;
-    quant_param.scale = bias_scales[i];
+    quant_param.scale = bias_scale;
     quant_param.zeroPoint = 0;
     quant_param.inited = true;
     quant_params.emplace_back(quant_param);
   }
   // quant bias data
-  int32_t *quant_datas = new (std::nothrow) int32_t[shape_size];
+  auto *quant_datas = new (std::nothrow) int32_t[shape_size];
   if (quant_datas == nullptr) {
     MS_LOG(ERROR) << "null pointer dereferencing.";
     return RET_NULL_PTR;
   }
-  float *raw_datas = static_cast<float *>(bias_param->tensor_addr());
+  auto *raw_datas = static_cast<float *>(bias_param->tensor_addr());
   double bias_scale_tmp;
-  constexpr int32_t quanted_bias_abs_limit = 0.5 * INT32_MAX;
+  const constexpr int32_t quanted_bias_abs_limit = 0.5 * INT32_MAX;
   for (size_t i = 0; i < shape_size; i++) {
     if (bias_scales.size() == 1) {
       bias_scale_tmp = bias_scales[0];
@@ -944,7 +946,7 @@ STATUS PostTrainingQuantizer::PreProcess() {
 
 STATUS PostTrainingQuantizer::CheckFp32TensorVec(const std::string &node_name,
                                                  const std::vector<mindspore::tensor::MSTensor *> &tensor_vec) const {
-  if (tensor_vec.size() < 1) {
+  if (tensor_vec.empty()) {
     MS_LOG(ERROR) << "node: " << node_name << " input tensors is 0";
     return RET_ERROR;
   }
@@ -1000,7 +1002,7 @@ STATUS PostTrainingQuantizer::DoInference() {
       }
       for (size_t i = 0; i < (*diverg_info_map)[callParam.node_name].size(); i++) {
         auto tensor = beforeInputs[i];
-        const float *tensor_data = static_cast<const float *>(tensor->MutableData());
+        const auto *tensor_data = static_cast<const float *>(tensor->MutableData());
         size_t elem_count = tensor->ElementsNum();
         vector<float> data(tensor_data, tensor_data + elem_count);
         this->calibrator_->RecordMaxValue(data, (*diverg_info_map)[callParam.node_name][i]);
@@ -1027,7 +1029,7 @@ STATUS PostTrainingQuantizer::DoInference() {
       }
       size_t output_i = 0;
       for (const auto &tensor : afterOutputs) {
-        const float *tensor_data = static_cast<const float *>(tensor->MutableData());
+        const auto *tensor_data = static_cast<const float *>(tensor->MutableData());
         size_t elem_count = tensor->ElementsNum();
         vector<float> data(tensor_data, tensor_data + elem_count);
         this->calibrator_->RecordMaxValue(data, (*diverg_info_map)[callParam.node_name][output_i]);
@@ -1184,7 +1186,7 @@ STATUS PostTrainingQuantizer::Int8Inference() {
   return RET_OK;
 }
 
-STATUS PostTrainingQuantizer::BiasCorrection(FuncGraphPtr func_graph) {
+STATUS PostTrainingQuantizer::BiasCorrection(const FuncGraphPtr &func_graph) {
   auto ret = RET_OK;
   std::future<STATUS> int8_inference = std::async(std::launch::async, &PostTrainingQuantizer::Int8Inference, this);
   // get input tensor
@@ -1233,7 +1235,7 @@ STATUS PostTrainingQuantizer::BiasCorrection(FuncGraphPtr func_graph) {
           return false;
         }
         auto tensor = afterOutputs[0];
-        const float *tensor_data = static_cast<const float *>(tensor->MutableData());
+        const auto *tensor_data = static_cast<const float *>(tensor->MutableData());
         size_t elem_count = tensor->ElementsNum();
         auto shapes = tensor->shape();
         if (shapes.size() != 4) {
@@ -1318,7 +1320,7 @@ STATUS PostTrainingQuantizer::BiasCorrection(FuncGraphPtr func_graph) {
         for (int i = 0; i < bias_param->tensor_shape_size(); i++) {
           auto scale = bias_quant_params[i].scale;
           double after_correct = std::round(bias_diff[i] / scale) + bias_datas[i];
-          constexpr int32_t corrected_bias_abs_limit = 0.6 * INT32_MAX;
+          const constexpr int32_t corrected_bias_abs_limit = 0.6 * INT32_MAX;
           if (after_correct > corrected_bias_abs_limit) {
             MS_LOG(WARNING) << op_name << " ch: " << i << " bias after_corrected too large: " << after_correct
                             << " origin value: " << bias_datas[i] << " bias_diff: " << bias_diff[i]
@@ -1359,6 +1361,8 @@ STATUS PostTrainingQuantizer::BiasCorrection(FuncGraphPtr func_graph) {
         ret = ::memcpy_s(tensor_data, size * sizeof(char), bias_diff.data(), size * sizeof(char));
         if (ret != EOK) {
           MS_LOG(ERROR) << "memcpy_s error: " << ret;
+          free(tensor_data);
+          tensor_data = nullptr;
           return false;
         }
         param_value->set_tensor_addr(tensor_data);
@@ -1372,6 +1376,8 @@ STATUS PostTrainingQuantizer::BiasCorrection(FuncGraphPtr func_graph) {
           auto conv2d = primitive_c->GetPrimitiveT()->value.AsConv2D();
           if (conv2d == nullptr) {
             MS_LOG(ERROR) << "conv2d is null";
+            free(tensor_data);
+            tensor_data = nullptr;
             return RET_ERROR;
           }
           conv2d->hasBias = true;
@@ -1379,10 +1385,14 @@ STATUS PostTrainingQuantizer::BiasCorrection(FuncGraphPtr func_graph) {
           auto depthwise_conv2d = primitive_c->GetPrimitiveT()->value.AsDepthwiseConv2D();
           if (depthwise_conv2d == nullptr) {
             MS_LOG(ERROR) << "conv2d is null";
+            free(tensor_data);
+            tensor_data = nullptr;
             return RET_ERROR;
           }
           depthwise_conv2d->hasBias = true;
         }
+        free(tensor_data);
+        tensor_data = nullptr;
       } else {
         MS_LOG(ERROR) << "unexpected input_quant_params size: " << input_quant_params.size();
         continue;
@@ -1423,7 +1433,7 @@ STATUS PostTrainingQuantizer::CollectDataFrequency() {
       }
       for (size_t i = 0; i < (*diverg_info_map)[callParam.node_name].size(); i++) {
         auto tensor = beforeInputs[i];
-        const float *tensor_data = static_cast<const float *>(tensor->MutableData());
+        const auto *tensor_data = static_cast<const float *>(tensor->MutableData());
         size_t elem_count = tensor->ElementsNum();
         vector<float> data(tensor_data, tensor_data + elem_count);
         this->calibrator_->UpdateDataFrequency(data, (*diverg_info_map)[callParam.node_name][i]);
@@ -1443,7 +1453,7 @@ STATUS PostTrainingQuantizer::CollectDataFrequency() {
       }
       int output_i = 0;
       for (const auto &tensor : after_outputs) {
-        const float *tensor_data = static_cast<const float *>(tensor->MutableData());
+        const auto *tensor_data = static_cast<const float *>(tensor->MutableData());
         size_t elem_count = tensor->ElementsNum();
         vector<float> data(tensor_data, tensor_data + elem_count);
         this->calibrator_->UpdateDataFrequency(data, (*diverg_info_map)[call_param.node_name][output_i]);

mindspore/lite/tools/converter/quantizer/post_training_quantizer.h

@@ -110,14 +110,15 @@ class PostTrainingQuantizer : public Quantizer {
   STATUS QuantNode();
 
   STATUS DoQuantInput(double scale, int32_t zeropoint, struct MaxMin *max_min,
-                      std::shared_ptr<PrimitiveC> lite_primitive);
-  STATUS DoQuantOutput(double scale, int32_t zeropoint, struct MaxMin *max_min, std::shared_ptr<PrimitiveC>);
+                      const std::shared_ptr<PrimitiveC> &lite_primitive) const;
+  STATUS DoQuantOutput(double scale, int32_t zeropoint, struct MaxMin *max_min,
+                       const std::shared_ptr<PrimitiveC> &) const;
 
-  STATUS DoWeightQuant(AnfNodePtr weight, std::shared_ptr<PrimitiveC> primitive_c, bool perchannel);
+  STATUS DoWeightQuant(const AnfNodePtr &weight, std::shared_ptr<PrimitiveC> primitive_c, bool perchannel) const;
 
-  STATUS DoBiasQuant(AnfNodePtr bias, std::shared_ptr<PrimitiveC> primitive_c);
+  STATUS DoBiasQuant(const AnfNodePtr &bias, const std::shared_ptr<PrimitiveC> &primitive_c);
   STATUS Int8Inference();
-  STATUS BiasCorrection(FuncGraphPtr func_graph);
+  STATUS BiasCorrection(const FuncGraphPtr &func_graph);
 };
 
 struct DivergInfo {
@@ -189,7 +190,7 @@ class Calibrator {
 
   size_t GetInputNum() const { return config_param_.image_paths.size(); }
 
-  STATUS AddQuantizedOp(CNodePtr node);
+  STATUS AddQuantizedOp(const CNodePtr &node);
 
   STATUS RecordMaxValue(const std::vector<float> &data, const std::unique_ptr<DivergInfo> &diverg_info);