self check onnx caffe parser

mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp32/reduce_fp32.h

@@ -16,6 +16,7 @@
 
 #ifndef MINDSPORE_NNACL_FP32_REDUCE_H_
 #define MINDSPORE_NNACL_FP32_REDUCE_H_
+#include <stddef.h>
 #include "nnacl/op_base.h"
 #include "nnacl/reduce_parameter.h"
 

mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp32/unique_fp32.c

@@ -17,6 +17,9 @@
 #include "nnacl/fp32/unique_fp32.h"
 
 int Find(const float *array, int len, float target) {
+  if (array == NULL) {
+    return -1;
+  }
   for (int i = 0; i < len; ++i) {
     if (array[i] == target) {
       return i;

mindspore/lite/src/runtime/kernel/arm/fp32/ragged_range_fp32.cc

@@ -37,16 +37,19 @@ int RaggedRangeCPUKernel::Prepare() {
 int RaggedRangeCPUKernel::ReSize() { return RET_OK; }
 
 int RaggedRangeCPUKernel::Run() {
-  if (in_tensors_[0]->data_type() == kNumberTypeFloat32) {
-    RaggedRangeFp32(static_cast<float *>(in_tensors_.at(0)->data()), static_cast<float *>(in_tensors_.at(1)->data()),
-                    static_cast<float *>(in_tensors_.at(2)->data()), static_cast<int *>(out_tensors_.at(0)->data()),
-                    static_cast<float *>(out_tensors_.at(1)->data()),
+  if (in_tensors_[FIRST_INPUT]->data_type() == kNumberTypeFloat32) {
+    RaggedRangeFp32(static_cast<float *>(in_tensors_.at(FIRST_INPUT)->data()),
+                    static_cast<float *>(in_tensors_.at(SECOND_INPUT)->data()),
+                    static_cast<float *>(in_tensors_.at(THIRD_INPUT)->data()),
+                    static_cast<int *>(out_tensors_.at(FIRST_INPUT)->data()),
+                    static_cast<float *>(out_tensors_.at(SECOND_INPUT)->data()),
                     reinterpret_cast<RaggedRangeParameter *>(op_parameter_));
   } else {
-    RaggedRangeInt(static_cast<int *>(in_tensors_.at(0)->data()), static_cast<int *>(in_tensors_.at(1)->data()),
-                   static_cast<int *>(in_tensors_.at(2)->data()), static_cast<int *>(out_tensors_.at(0)->data()),
-                   static_cast<int *>(out_tensors_.at(1)->data()),
-                   reinterpret_cast<RaggedRangeParameter *>(op_parameter_));
+    RaggedRangeInt(
+      static_cast<int *>(in_tensors_.at(FIRST_INPUT)->data()), static_cast<int *>(in_tensors_.at(SECOND_INPUT)->data()),
+      static_cast<int *>(in_tensors_.at(THIRD_INPUT)->data()), static_cast<int *>(out_tensors_.at(FIRST_INPUT)->data()),
+      static_cast<int *>(out_tensors_.at(SECOND_INPUT)->data()),
+      reinterpret_cast<RaggedRangeParameter *>(op_parameter_));
   }
   return RET_OK;
 }

mindspore/lite/src/runtime/kernel/arm/fp32/relative_position_attention_fp32.cc

@@ -37,7 +37,7 @@ constexpr int k3DimsLeftMatrixDeepIndex = 2;
 constexpr int kRightMatrixDeepIndex = 0;
 constexpr int kRelativePositionHasBiasInputSize = 15;
 
-bool AttentionActivationTensorCheck(lite::Tensor *tensor) {
+bool AttentionActivationTensorCheck(const lite::Tensor *tensor) {
   if (tensor == nullptr || tensor->data_type() != kNumberTypeFloat32 ||
       tensor->shape().size() != kActivationTensorShapeSize ||
       tensor->shape().at(kTensorShapeBatchIndex) != kActivationTensorBatch) {
@@ -99,7 +99,7 @@ int RelativePositionAttentionCPUKernel::CheckInputs() {
 
 namespace {
 constexpr int kWeightTensorShapeSize = 2;
-bool AttentionWeightTensorCheck(lite::Tensor *tensor) {
+bool AttentionWeightTensorCheck(const lite::Tensor *tensor) {
   if (tensor == nullptr || !tensor->IsConst() || tensor->data_type() != kNumberTypeFloat32 ||
       tensor->shape().size() != kWeightTensorShapeSize) {
     return false;
@@ -152,7 +152,7 @@ int RelativePositionAttentionCPUKernel::CheckWeights() {
 }
 
 namespace {
-bool AttentionBiasTensorCheck(lite::Tensor *tensor) {
+bool AttentionBiasTensorCheck(const lite::Tensor *tensor) {
   if (tensor == nullptr || !tensor->IsConst() || tensor->data_type() != kNumberTypeFloat32 ||
       tensor->shape().size() != 1) {
     return false;

mindspore/lite/src/runtime/kernel/arm/fp32/relative_position_attention_fp32.h

@@ -39,7 +39,6 @@ class RelativePositionAttentionCPUKernel : public InnerKernel {
   int ReSize() override;
   int Run() override;
 
- private:
   // check inputs
   int CheckInputs();
   int CheckWeights();

mindspore/lite/src/runtime/kernel/arm/fp32/roi_pooling_fp32.cc

@@ -48,12 +48,12 @@ int ROIPoolingCPUKernel::ReSize() {
   auto in_shape = in_tensors_.front()->shape();
   auto out_shape = out_tensors_.front()->shape();
   int ndims = static_cast<int>(in_shape.size());
-  if (ndims < C4NUM) {
-    MS_LOG(ERROR) << "ROIPooling in_shape.size() error ,shape dim greater than or equal to 4!";
+  if (ndims != C4NUM) {
+    MS_LOG(ERROR) << "ROIPooling in_shape.size() error ,shape must be equal to 4!";
     return RET_ERROR;
   }
-  if (out_shape.size() < C4NUM) {
-    MS_LOG(ERROR) << "ROIPooling out_shape.size() error ,shape dim greater than or equal to 4!";
+  if (out_shape.size() != C4NUM) {
+    MS_LOG(ERROR) << "ROIPooling out_shape.size() error ,shape must be equal to 4!";
     return RET_ERROR;
   }
   param_->ndim_ = ndims;

mindspore/lite/src/runtime/kernel/arm/fp32/scale_fp32.cc

@@ -53,7 +53,7 @@ int ScaleCPUKernel::InitScaleOffset() {
       MS_LOG(ERROR) << "Malloc buffer failed.";
       return RET_ERROR;
     }
-    memcpy(scale_, scale_tensor->data(), scale_tensor->ElementsNum() * sizeof(float));
+    (void)memcpy(scale_, scale_tensor->data(), scale_tensor->ElementsNum() * sizeof(float));
   } else {
     scale_param_->const_scale_ = false;
     scale_ = nullptr;
@@ -67,7 +67,7 @@ int ScaleCPUKernel::InitScaleOffset() {
       return RET_ERROR;
     }
     memset(offset_, 0, scale_tensor->ElementsNum() * sizeof(float));
-  } else if (in_tensors_.size() == 3 && reinterpret_cast<float *>(in_tensors_.at(2)->data()) != nullptr) {
+  } else if (in_tensors_.size() == C3NUM && reinterpret_cast<float *>(in_tensors_.at(THIRD_INPUT)->data()) != nullptr) {
     scale_param_->const_offset_ = true;
     auto offset_tensor = in_tensors_.at(2);
     MS_CHECK_TRUE_RET(scale_tensor->ElementsNum() == offset_tensor->ElementsNum(), RET_ERROR);
@@ -76,7 +76,7 @@ int ScaleCPUKernel::InitScaleOffset() {
       MS_LOG(ERROR) << "Malloc data failed";
       return RET_ERROR;
     }
-    memcpy(offset_, offset_tensor->data(), offset_tensor->ElementsNum() * sizeof(float));
+    (void)memcpy(offset_, offset_tensor->data(), offset_tensor->ElementsNum() * sizeof(float));
   } else {
     scale_param_->const_offset_ = false;
     offset_ = nullptr;
@@ -179,6 +179,7 @@ int ScaleRun(void *cdata, int task_id, float lhs_scale, float rhs_scale) {
 int ScaleCPUKernel::Run() {
   auto in_tensor = in_tensors_.front();
   input_ptr_ = reinterpret_cast<float *>(in_tensor->data());
+  CHECK_NULL_RETURN(input_ptr_);
   if (!scale_param_->const_scale_) {
     auto scale_tensor = in_tensors_.at(1);
     scale_ = reinterpret_cast<float *>(scale_tensor->data());

mindspore/lite/src/runtime/kernel/arm/fp32/space_to_batch_fp32.h

@@ -36,7 +36,6 @@ class SpaceToBatchCPUKernel : public InnerKernel {
   int Run() override;
   void ProcessInput();
 
- public:
   int DoRun(int task_id);
 
  protected:

mindspore/lite/src/runtime/kernel/arm/fp32/transpose_fp32.cc

@@ -62,6 +62,10 @@ int TransposeCPUKernel::ReSize() {
   } else {
     MS_ASSERT(in_tensors_.size() == C2NUM);
     auto perm_tensor = in_tensors_.at(1);
+    if (perm_tensor->data_type() != kNumberTypeInt32) {
+      MS_LOG(ERROR) << "Unsupported type id: " << perm_tensor->data_type() << " of perm tensor.";
+      return RET_ERROR;
+    }
     perm_data = reinterpret_cast<int *>(perm_tensor->data());
     MSLITE_CHECK_PTR(perm_data);
   }

mindspore/lite/src/runtime/kernel/arm/fp32/uniform_real_fp32.cc

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 #include "src/runtime/kernel/arm/fp32/uniform_real_fp32.h"
-#include <stdlib.h>
-#include <string.h>
+#include <cstdlib>
+#include <cstring>
 #include <vector>
 #include "src/kernel_registry.h"
 #include "include/errorcode.h"

mindspore/lite/src/runtime/kernel/arm/fp32/where_fp32.cc

@@ -98,7 +98,6 @@ int WhereCPUKernel::RunWithSingleInput() {
   int data_num_int = where_param_->condition_num_ * where_param_->rank_;
   MS_CHECK_TRUE_RET(data_num_int >= 0, RET_ERROR);
   size_t data_num = static_cast<size_t>(data_num_int);
-  MS_CHECK_FALSE_MSG(SIZE_MUL_OVERFLOW(sizeof(int32_t), data_num), RET_ERROR, "mul overflow");
   size_t data_size = data_num * sizeof(int32_t);
   auto data = ms_context_->allocator->Malloc(data_size);
   if (data == nullptr) {
@@ -113,13 +112,13 @@ int WhereCPUKernel::RunWithSingleInput() {
     bool condition = false;
     switch (input_data_type) {
       case kNumberTypeInt32:
-        condition = int32_condition_[index];
+        condition = static_cast<bool>(int32_condition_[index]);
         break;
       case kNumberTypeFloat32:
-        condition = fp32_condition_[index];
+        condition = static_cast<bool>(fp32_condition_[index]);
         break;
       case kNumberTypeBool:
-        condition = condition_[index];
+        condition = static_cast<bool>(condition_[index]);
         break;
       default:
         MS_LOG(ERROR) << "Unsupported data type: " << input_data_type << " of where cpu kernel.";