code clean 2

mindspore/lite/tools/common/tensor_util.cc

@@ -238,11 +238,15 @@ size_t GetElementSize(const TypeId &dataType) {
   }
 }
 
-int GetShapeSize(const TensorT &tensor) {
+size_t GetShapeSize(const TensorT &tensor) {
   auto shape = tensor.dims;
-  int shapeSize = 1;
+  size_t shapeSize = 1;
   for (auto dim : shape) {
-    shapeSize *= dim;
+    if (dim <= 0) {
+      MS_LOG(WARNING) << "Dim value less than or equal to 0 found in tensor's shape.";
+      return 0;
+    }
+    shapeSize *= static_cast<size_t>(dim);
   }
   return shapeSize;
 }
@@ -277,10 +281,15 @@ size_t GetRefCount(MetaGraphT *graphT, uint32_t tensorIdx) {
   }
   return refCount;
 }
-int GetShapeSize(const std::vector<int32_t> &shape) {
-  int shapeSize = 1;
+
+size_t GetShapeSize(const std::vector<int32_t> &shape) {
+  size_t shapeSize = 1;
   for (auto dim : shape) {
-    shapeSize *= dim;
+    if (dim <= 0) {
+      MS_LOG(WARNING) << "Dim value less than or equal to 0 found in tensor's shape.";
+      return 0;
+    }
+    shapeSize *= static_cast<size_t>(dim);
   }
   return shapeSize;
 }

mindspore/lite/tools/common/tensor_util.h

@@ -68,9 +68,9 @@ size_t GetElementSize(const TensorT &tensor);
 
 size_t GetElementSize(const TypeId &dataType);
 
-int GetShapeSize(const TensorT &tensor);
+size_t GetShapeSize(const TensorT &tensor);
 
-int GetShapeSize(const std::vector<int32_t> &shape);
+size_t GetShapeSize(const std::vector<int32_t> &shape);
 
 std::unique_ptr<TensorT> CopyTensorDefT(const std::unique_ptr<TensorT> &);
 

mindspore/lite/tools/converter/legacy_optimizer/graph/convert_fp32_to_fp16_pass.cc

@@ -43,10 +43,6 @@ STATUS ConvertFP32ToFP16Pass::Run(schema::MetaGraphT *graph) {
       continue;
     }
     auto ele_num = lite::GetShapeSize(tensor->dims);
-    if (ele_num < 0) {
-      MS_LOG(ERROR) << "Tensor data length error.";
-      return RET_ERROR;
-    }
     auto origin_data = tensor->data;
     if (origin_data.size() != ele_num * sizeof(float) || origin_data.size() % kFp16ToFp32Multiply != 0) {
       MS_LOG(ERROR) << "Tensor data length error.";
@@ -58,7 +54,7 @@ STATUS ConvertFP32ToFP16Pass::Run(schema::MetaGraphT *graph) {
     auto fp16_data = reinterpret_cast<float16 *>(new_data.data());
     CHECK_NULL_RETURN(fp32_data);
     CHECK_NULL_RETURN(fp16_data);
-    for (int i = 0; i < ele_num; i++) {
+    for (size_t i = 0; i < ele_num; i++) {
       fp16_data[i] = float16(fp32_data[i]);
     }
     tensor->data.swap(new_data);

mindspore/lite/tools/converter/legacy_optimizer/graph/tensor_quant_pass.cc

@@ -48,11 +48,7 @@ constexpr int kHalfUInt = 128;
 
 STATUS ComputeDataToInt8(const std::unique_ptr<TensorT> &tensor) {
   MS_ASSERT(tensor != nullptr);
-  int wShapeSize = tensor->data.empty() ? 0 : GetShapeSize(*(tensor.get()));
-  if (wShapeSize < 0) {
-    MS_LOG(ERROR) << "Invalid shape.";
-    return RET_ERROR;
-  }
+  size_t wShapeSize = tensor->data.empty() ? 0 : GetShapeSize(*(tensor.get()));
   void *oriWeightData = tensor->data.data();
   if (oriWeightData == nullptr) {
     return RET_OK;
@@ -62,12 +58,12 @@ STATUS ComputeDataToInt8(const std::unique_ptr<TensorT> &tensor) {
   if (tensor->dataType == TypeId::kNumberTypeFloat ||
       tensor->dataType == TypeId::kNumberTypeFloat32) {  // normal awareing quant
     auto *weightData = static_cast<float *>(oriWeightData);
-    for (int j = 0; j < wShapeSize; j++) {
+    for (size_t j = 0; j < wShapeSize; j++) {
       qDatas[j] = QuantizeData<int8_t>(weightData[j], weightQauntParam.get());
     }
   } else {  // convert uint8 to int8
     auto *weightData = static_cast<uint8_t *>(oriWeightData);
-    for (int j = 0; j < wShapeSize; j++) {
+    for (size_t j = 0; j < wShapeSize; j++) {
       qDatas[j] = static_cast<int8_t>(static_cast<int32_t>(weightData[j]) - kHalfUInt);
     }
     weightQauntParam->zeroPoint -= kHalfUInt;
@@ -90,11 +86,7 @@ STATUS ComputeDataToInt8(const std::unique_ptr<TensorT> &tensor) {
 
 STATUS ComputeDataToInt32(const std::unique_ptr<TensorT> &tensor) {
   MS_ASSERT(tensor != nullptr);
-  int bShapeSize = GetShapeSize(*(tensor));
-  if (bShapeSize < 0) {
-    MS_LOG(ERROR) << "Invalid shape.";
-    return RET_ERROR;
-  }
+  size_t bShapeSize = GetShapeSize(*(tensor));
   auto qDatas = std::make_unique<int32_t[]>(bShapeSize);
   if (qDatas == nullptr) {
     MS_LOG(ERROR) << "new qDatas failed";
@@ -106,7 +98,7 @@ STATUS ComputeDataToInt32(const std::unique_ptr<TensorT> &tensor) {
     MS_LOG(ERROR) << "divisor 'scale' cannot be 0";
     return RET_ERROR;
   }
-  for (int i = 0; i < bShapeSize; ++i) {
+  for (size_t i = 0; i < bShapeSize; ++i) {
     qDatas[i] = (int32_t)std::round(rawDatas[i] / tensor->quantParams.front()->scale);
   }
   tensor->dataType = TypeId::kNumberTypeInt32;