!12981 fix tensorlist too many malloc， transform two level pointer to one level

From: @zhaodezan
Reviewed-by: @hangangqiang
Signed-off-by:

mindspore/lite/nnacl/infer/common_infer.c

@@ -17,17 +17,6 @@
 #include <stdlib.h>
 #include <string.h>
 
-int FreeTensorListData(TensorListC *tensor_list) {
-  // del each tensor in tensors_ and clear tensors_
-  if (tensor_list->element_num_ == 0) {
-    return NNACL_OK;
-  }
-  for (int i = 0; i < tensor_list->element_num_; ++i) {
-    tensor_list->tensors_[i] = NULL;
-  }
-  return NNACL_OK;
-}
-
 int MallocTensorListData(TensorListC *tensor_list, TypeIdC dtype, vvector *tensor_shape) {
   // This function will create a new tensors_
   // Your must to set shape(param2: tensor_shape) and data_type_(tensors_data_type_ = param1: dtype) of each tensor in
@@ -40,21 +29,16 @@ int MallocTensorListData(TensorListC *tensor_list, TypeIdC dtype, vvector *tenso
     return NNACL_ERR;
   }
   tensor_list->tensors_data_type_ = dtype;
-  tensor_list->tensors_ = (TensorC **)malloc(tensor_list->element_num_ * sizeof(TensorC *));  // free in infer_manager
+  tensor_list->tensors_ = (TensorC *)malloc(tensor_list->element_num_ * sizeof(TensorC));  // free in infer_manager
   if (tensor_list->tensors_ == NULL) {
     return NNACL_NULL_PTR;
   }
-  memset(tensor_list->tensors_, 0, tensor_list->element_num_ * sizeof(TensorC *));
+  memset(tensor_list->tensors_, 0, tensor_list->element_num_ * sizeof(TensorC));
   for (int i = 0; i < tensor_list->element_num_; ++i) {
-    TensorC *tensor_ptr = (TensorC *)malloc(sizeof(TensorC));
-    if (tensor_ptr == NULL) {
-      return NNACL_ERR;
-    }
-    memset(tensor_ptr, 0, sizeof(TensorC));
-    tensor_ptr->format_ = Format_NHWC;
-    tensor_ptr->data_type_ = dtype;
-    ShapeSet(tensor_ptr->shape_, &(tensor_ptr->shape_size_), tensor_shape->shape_[i], tensor_shape->shape_size_[i]);
-    tensor_list->tensors_[i] = tensor_ptr;
+    tensor_list->tensors_[i].format_ = Format_NHWC;
+    tensor_list->tensors_[i].data_type_ = dtype;
+    ShapeSet(tensor_list->tensors_[i].shape_, &(tensor_list->tensors_[i].shape_size_), tensor_shape->shape_[i],
+             tensor_shape->shape_size_[i]);
   }
   return NNACL_OK;
 }

mindspore/lite/nnacl/infer/common_infer.h

@@ -137,12 +137,12 @@ typedef struct TensorListC {
   int data_type_;
   int format_;
 
-  TensorC **tensors_;
-  size_t element_num_;
   int tensors_data_type_;  // element_data_type_, keep same as c++
-  int element_shape_[MAX_SHAPE_SIZE];
-  size_t element_shape_size_;
   int max_elements_num_;
+  int element_shape_[8];
+  size_t element_num_;
+  size_t element_shape_size_;
+  TensorC *tensors_;
 } TensorListC;
 
 typedef struct VectorC {

mindspore/lite/nnacl/infer/merge_infer.c

@@ -42,7 +42,7 @@ int MergeInfer(const TensorC *const *inputs, size_t inputs_size, TensorC **outpu
 
       output_tensorlist->element_num_ = input_tensorlist->element_num_;
       for (size_t j = 0; j < output_tensorlist->element_num_; j++) {
-        memcpy(output_tensorlist->tensors_[j], input_tensorlist->tensors_[j], sizeof(TensorC));
+        memcpy(&output_tensorlist->tensors_[j], &input_tensorlist->tensors_[j], sizeof(TensorC));
       }
     } else {
       SetShapeTensor(outputs[i], inputs[i]);

mindspore/lite/nnacl/infer/select_infer.c

@@ -43,7 +43,7 @@ int SelectInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
       output_tensorlist->element_num_ = input_tensorlist->element_num_;
 
       for (size_t j = 0; j < output_tensorlist->element_num_; j++) {
-        memcpy(output_tensorlist->tensors_[j], input_tensorlist->tensors_[j], sizeof(TensorC));
+        memcpy(&output_tensorlist->tensors_[j], &input_tensorlist->tensors_[j], sizeof(TensorC));
       }
     } else {
       SetShapeTensor(output, input);

mindspore/lite/nnacl/infer/switch_infer.c

@@ -51,8 +51,8 @@ int SwitchInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
 
       // note: need delete below?
       for (size_t j = 0; j < output_false_tensorlist->element_num_; j++) {
-        memcpy(output_true_tensorlist->tensors_[j], input_tensorlist->tensors_[j], sizeof(TensorC));
-        memcpy(output_false_tensorlist->tensors_[j], input_tensorlist->tensors_[j], sizeof(TensorC));
+        memcpy(&output_true_tensorlist->tensors_[j], &input_tensorlist->tensors_[j], sizeof(TensorC));
+        memcpy(&output_false_tensorlist->tensors_[j], &input_tensorlist->tensors_[j], sizeof(TensorC));
       }
 
     } else {
@@ -89,8 +89,8 @@ int SwitchInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
       output_true_tensorlist->element_num_ = input_tensorlist->element_num_;
 
       for (size_t j = 0; j < output_false_tensorlist->element_num_; j++) {
-        memcpy(output_true_tensorlist->tensors_[j], input_tensorlist->tensors_[j], sizeof(TensorC));
-        memcpy(output_false_tensorlist->tensors_[j], input_tensorlist->tensors_[j], sizeof(TensorC));
+        memcpy(&output_true_tensorlist->tensors_[j], &input_tensorlist->tensors_[j], sizeof(TensorC));
+        memcpy(&output_false_tensorlist->tensors_[j], &input_tensorlist->tensors_[j], sizeof(TensorC));
       }
 
     } else {

mindspore/lite/nnacl/infer/tensorlist_fromtensor_infer.c

@@ -41,35 +41,29 @@ int TensorListFromTensorInferShape(const TensorC *const *inputs, size_t inputs_s
   int *ele_shape_ptr = (int *)(input1->data_);
   TensorListC *output = (TensorListC *)(outputs[0]);
 
-  vvector *tensor_shape = (vvector *)malloc(sizeof(vvector));
-  if (tensor_shape == NULL) {
+  vvector tensor_shape;
+  tensor_shape.size_ = dim0;
+  tensor_shape.shape_ = (int **)malloc(tensor_shape.size_ * sizeof(int *));
+  if (tensor_shape.shape_ == NULL) {
     return NNACL_NULL_PTR;
   }
-  tensor_shape->size_ = dim0;
-  tensor_shape->shape_ = (int **)malloc(tensor_shape->size_ * sizeof(int *));
-  if (tensor_shape->shape_ == NULL) {
-    free(tensor_shape);
-    return NNACL_NULL_PTR;
-  }
-  tensor_shape->shape_size_ = (int *)malloc(tensor_shape->size_ * sizeof(int));
-  if (tensor_shape->shape_size_ == NULL) {
-    free(tensor_shape->shape_);
-    free(tensor_shape);
+  tensor_shape.shape_size_ = (int *)malloc(tensor_shape.size_ * sizeof(int));
+  if (tensor_shape.shape_size_ == NULL) {
+    free(tensor_shape.shape_);
     return NNACL_NULL_PTR;
   }
 
   for (size_t i = 0; i < dim0; i++) {
-    tensor_shape->shape_[i] = (int *)(input0->shape_ + 1);
-    tensor_shape->shape_size_[i] = input0->shape_size_ - 1;
+    tensor_shape.shape_[i] = (int *)(input0->shape_ + 1);
+    tensor_shape.shape_size_[i] = input0->shape_size_ - 1;
   }
 
   ShapeSet(output->element_shape_, &(output->element_shape_size_), ele_shape_ptr, GetElementNum(input1));
   output->element_num_ = dim0;
   output->data_type_ = kObjectTypeTensorType;
   output->format_ = Format_NHWC;
-  MallocTensorListData(output, input0->data_type_, tensor_shape);
-  free(tensor_shape->shape_);
-  free(tensor_shape->shape_size_);
-  free(tensor_shape);
+  MallocTensorListData(output, input0->data_type_, &tensor_shape);
+  free(tensor_shape.shape_);
+  free(tensor_shape.shape_size_);
   return NNACL_OK;
 }

mindspore/lite/nnacl/infer/tensorlist_getitem_infer.c

@@ -37,7 +37,7 @@ int TensorListGetItemInferShape(const TensorC *const *inputs, size_t inputs_size
   if (index < 0 || index > (input0->element_num_ - 1)) {
     return NNACL_ERR;
   }
-  TensorC *tensor_index = input0->tensors_[index];
+  TensorC *tensor_index = &input0->tensors_[index];
   TensorC *output = outputs[0];
   if (tensor_index->data_type_ != kTypeUnknown) {
     output->data_type_ = tensor_index->data_type_;
@@ -60,7 +60,7 @@ int TensorListGetItemInferShape(const TensorC *const *inputs, size_t inputs_size
     }
     if (!TensorListIsFullyDefined(element_shape, element_shape_size)) {
       for (int i = 0; i < input0->element_num_; ++i) {
-        TensorC *input = input0->tensors_[i];
+        TensorC *input = &input0->tensors_[i];
         if (input->data_type_ != kTypeUnknown) {
           status = TensorListMergeShape(element_shape, &element_shape_size, input->shape_, input->shape_size_);
           if (status != NNACL_OK) {
@@ -75,6 +75,6 @@ int TensorListGetItemInferShape(const TensorC *const *inputs, size_t inputs_size
     output->data_type_ = input0->tensors_data_type_;
     SetShapeArray(output, element_shape, element_shape_size);
   }
-  output->format_ = input0->tensors_[index]->format_;
+  output->format_ = input0->tensors_[index].format_;
   return NNACL_OK;
 }

mindspore/lite/nnacl/infer/tensorlist_reserve_infer.c

@@ -50,30 +50,24 @@ int TensorListReserveInferShape(const TensorC *const *inputs, size_t inputs_size
   ShapeSet(output->element_shape_, &(output->element_shape_size_), ele_shape_ptr, GetElementNum(input0));
   output->element_num_ = num_elements;
 
-  vvector *tmp_shape = (vvector *)malloc(sizeof(vvector));
-  if (tmp_shape == NULL) {
+  vvector tmp_shape;
+  tmp_shape.size_ = num_elements;
+  tmp_shape.shape_ = (int **)malloc(tmp_shape.size_ * sizeof(int *));
+  if (tmp_shape.shape_ == NULL) {
     return NNACL_NULL_PTR;
   }
-  tmp_shape->size_ = num_elements;
-  tmp_shape->shape_ = (int **)malloc(tmp_shape->size_ * sizeof(int *));
-  if (tmp_shape->shape_ == NULL) {
-    free(tmp_shape);
-    return NNACL_NULL_PTR;
-  }
-  tmp_shape->shape_size_ = (int *)malloc(tmp_shape->size_ * sizeof(int));
-  if (tmp_shape->shape_size_ == NULL) {
-    free(tmp_shape->shape_);
-    free(tmp_shape);
+  tmp_shape.shape_size_ = (int *)malloc(tmp_shape.size_ * sizeof(int));
+  if (tmp_shape.shape_size_ == NULL) {
+    free(tmp_shape.shape_);
     return NNACL_NULL_PTR;
   }
 
   for (size_t i = 0; i < num_elements; i++) {
-    tmp_shape->shape_size_[i] = 0;
-    tmp_shape->shape_[i] = NULL;
+    tmp_shape.shape_size_[i] = 0;
+    tmp_shape.shape_[i] = NULL;
   }
-  MallocTensorListData(output, kTypeUnknown, tmp_shape);
-  free(tmp_shape->shape_size_);
-  free(tmp_shape->shape_);
-  free(tmp_shape);
+  MallocTensorListData(output, kTypeUnknown, &tmp_shape);
+  free(tmp_shape.shape_size_);
+  free(tmp_shape.shape_);
   return NNACL_OK;
 }

mindspore/lite/nnacl/infer/tensorlist_setitem_infer.c

@@ -70,46 +70,35 @@ int TensorListSetItemInferShape(const TensorC *const *inputs, size_t inputs_size
              input0->element_shape_size_);
   }
 
-  vvector *out_shape = (vvector *)malloc(sizeof(vvector));
-  if (out_shape == NULL) {
+  vvector out_shape;
+  out_shape.size_ = 0;
+  out_shape.shape_ = (int **)malloc((input0->element_num_ + 1) * sizeof(int *));
+  if (out_shape.shape_ == NULL) {
     return NNACL_NULL_PTR;
   }
-  out_shape->size_ = 0;
-  out_shape->shape_ = (int **)malloc((input0->element_num_ + 1) * sizeof(int *));
-  if (out_shape->shape_ == NULL) {
-    free(out_shape);
-    return NNACL_NULL_PTR;
-  }
-  out_shape->shape_size_ = (int *)malloc((input0->element_num_ + 1) * sizeof(int));
-  if (out_shape->shape_size_ == NULL) {
-    free(out_shape->shape_);
-    free(out_shape);
+  out_shape.shape_size_ = (int *)malloc((input0->element_num_ + 1) * sizeof(int));
+  if (out_shape.shape_size_ == NULL) {
+    free(out_shape.shape_);
     return NNACL_NULL_PTR;
   }
 
   if (index == 0 && input0->element_num_ == 0) {  // uninitialized tensorlist
-    out_shape->shape_[out_shape->size_] = (int *)(value_tensor->shape_);
-    out_shape->shape_size_[out_shape->size_] = value_tensor->shape_size_;
-    out_shape->size_++;
+    out_shape.shape_[out_shape.size_] = (int *)(value_tensor->shape_);
+    out_shape.shape_size_[out_shape.size_] = value_tensor->shape_size_;
+    out_shape.size_++;
     output0->element_num_ = 1;
   } else {
     output0->element_num_ = input0->element_num_;
     for (int i = 0; i < input0->element_num_; ++i) {
-      TensorC *src_ptr = input0->tensors_[i];
-      if (src_ptr == NULL) {
-        free(out_shape->shape_);
-        free(out_shape->shape_size_);
-        free(out_shape);
-        return NNACL_ERR;
-      }
+      TensorC *src_ptr = &input0->tensors_[i];
       if (src_ptr->data_type_ != kTypeUnknown) {
-        out_shape->shape_[out_shape->size_] = src_ptr->shape_;
-        out_shape->shape_size_[out_shape->size_] = src_ptr->shape_size_;
-        out_shape->size_++;
+        out_shape.shape_[out_shape.size_] = src_ptr->shape_;
+        out_shape.shape_size_[out_shape.size_] = src_ptr->shape_size_;
+        out_shape.size_++;
       } else {
-        out_shape->shape_[out_shape->size_] = NULL;
-        out_shape->shape_size_[out_shape->size_] = 0;
-        out_shape->size_++;
+        out_shape.shape_[out_shape.size_] = NULL;
+        out_shape.shape_size_[out_shape.size_] = 0;
+        out_shape.size_++;
       }
     }
   }
@@ -118,11 +107,10 @@ int TensorListSetItemInferShape(const TensorC *const *inputs, size_t inputs_size
     input0->tensors_data_type_ = value_tensor->data_type_;
   }
 
-  out_shape->shape_[index] = (int *)(value_tensor->shape_);
-  out_shape->shape_size_[index] = value_tensor->shape_size_;
-  MallocTensorListData(output0, input0->tensors_data_type_, out_shape);
-  free(out_shape->shape_);
-  free(out_shape->shape_size_);
-  free(out_shape);
+  out_shape.shape_[index] = (int *)(value_tensor->shape_);
+  out_shape.shape_size_[index] = value_tensor->shape_size_;
+  MallocTensorListData(output0, input0->tensors_data_type_, &out_shape);
+  free(out_shape.shape_);
+  free(out_shape.shape_size_);
   return NNACL_OK;
 }

mindspore/lite/nnacl/infer/tensorlist_stack_infer.c

@@ -50,7 +50,7 @@ int TensorListStackInferShape(const TensorC *const *inputs, size_t inputs_size,
   }
   if (!TensorListIsFullyDefined(input0->element_shape_, input0->element_shape_size_)) {
     for (int i = 0; i < input0->element_num_; ++i) {
-      TensorC *tensor_ele = input0->tensors_[i];
+      TensorC *tensor_ele = &input0->tensors_[i];
       if (tensor_ele->data_type_ != kTypeUnknown) {
         status = TensorListMergeShape(output_shape, &output_shape_size, tensor_ele->shape_, tensor_ele->shape_size_);
         if (status == NNACL_ERR) {

mindspore/lite/src/common/tensor_util.cc

@@ -87,10 +87,6 @@ void FreeAllTensorC(std::vector<TensorC *> *tensors_in) {
 }
 
 void FreeTensorListC(TensorListC *tensorlist_c) {
-  for (size_t i = 0; i < tensorlist_c->element_num_; i++) {
-    free(tensorlist_c->tensors_[i]);
-    tensorlist_c->tensors_[i] = nullptr;
-  }
   if (tensorlist_c->tensors_ != nullptr) {
     free(tensorlist_c->tensors_);
     tensorlist_c->tensors_ = nullptr;
@@ -132,18 +128,13 @@ int TensorList2TensorListC(TensorList *src, TensorListC *dst) {
   dst->format_ = src->format();
   dst->element_num_ = src->shape().empty() ? 0 : src->tensors().size();
 
-  dst->tensors_ = reinterpret_cast<TensorC **>(malloc(dst->element_num_ * sizeof(TensorC *)));
+  dst->tensors_ = reinterpret_cast<TensorC *>(malloc(dst->element_num_ * sizeof(TensorC)));
   if (dst->tensors_ == nullptr) {
     return RET_ERROR;
   }
-  memset(dst->tensors_, 0, dst->element_num_ * sizeof(TensorC *));
+  memset(dst->tensors_, 0, dst->element_num_ * sizeof(TensorC));
   for (size_t i = 0; i < dst->element_num_; i++) {
-    dst->tensors_[i] = reinterpret_cast<TensorC *>(malloc(sizeof(TensorC)));
-    if (dst->tensors_[i] == nullptr) {
-      return NNACL_ERR;
-    }
-    memset(dst->tensors_[i], 0, sizeof(TensorC));
-    Tensor2TensorC(src->tensors().at(i), dst->tensors_[i]);
+    Tensor2TensorC(src->tensors().at(i), &dst->tensors_[i]);
   }
 
   dst->tensors_data_type_ = src->tensors_data_type();
@@ -163,7 +154,7 @@ void TensorListC2TensorList(TensorListC *src, TensorList *dst) {
 
   // Set Tensors
   for (size_t i = 0; i < src->element_num_; i++) {
-    TensorC2Tensor(src->tensors_[i], dst->GetTensor(i));
+    TensorC2Tensor(&src->tensors_[i], dst->GetTensor(i));
   }
 
   dst->set_element_shape(std::vector<int>(src->element_shape_, src->element_shape_ + src->element_shape_size_));
@@ -183,28 +174,13 @@ int GenerateMergeOutTensorC(const std::vector<lite::Tensor *> &inputs, std::vect
       output_tensorlist->element_num_ = inputs[i]->shape().empty() ? 0 : inputs[i]->shape().at(0);
       if (output_tensorlist->element_num_ != 0) {
         output_tensorlist->tensors_ =
-          reinterpret_cast<TensorC **>(malloc(output_tensorlist->element_num_ * sizeof(TensorC *)));
+          reinterpret_cast<TensorC *>(malloc(output_tensorlist->element_num_ * sizeof(TensorC)));
         if (output_tensorlist->tensors_ == nullptr) {
           free(output_tensorlist);
           output_tensorlist = nullptr;
           return RET_ERROR;
         }
-        memset(output_tensorlist->tensors_, 0, output_tensorlist->element_num_ * sizeof(TensorC *));
-        for (size_t j = 0; j < output_tensorlist->element_num_; j++) {
-          output_tensorlist->tensors_[j] = reinterpret_cast<TensorC *>(malloc(sizeof(TensorC)));
-          if (output_tensorlist->tensors_[j] == nullptr) {
-            for (size_t k = 0; k < j; k++) {
-              free(output_tensorlist->tensors_[k]);
-              output_tensorlist->tensors_[k] = nullptr;
-            }
-            free(output_tensorlist->tensors_);
-            output_tensorlist->tensors_ = nullptr;
-            free(output_tensorlist);
-            output_tensorlist = nullptr;
-            return RET_ERROR;
-          }
-          memset(output_tensorlist->tensors_[j], 0, sizeof(TensorC));
-        }
+        memset(output_tensorlist->tensors_, 0, output_tensorlist->element_num_ * sizeof(TensorC));
       }
 
       out_tensor_c->push_back(reinterpret_cast<TensorC *const>(output_tensorlist));
@@ -239,26 +215,13 @@ int GenerateSwitchOutTensorC(const std::vector<lite::Tensor *> &inputs, std::vec
       output_tensorlist1->element_num_ = inputs[i + 1]->shape().empty() ? 0 : inputs[i + 1]->shape().at(0);
       if (output_tensorlist1->element_num_ != 0) {
         output_tensorlist1->tensors_ =
-          reinterpret_cast<TensorC **>(malloc(output_tensorlist1->element_num_ * sizeof(TensorC *)));
+          reinterpret_cast<TensorC *>(malloc(output_tensorlist1->element_num_ * sizeof(TensorC)));
         if (output_tensorlist1->tensors_ == nullptr) {
           free(output_tensorlist1);
           output_tensorlist1 = nullptr;
           return RET_ERROR;
         }
-        memset(output_tensorlist1->tensors_, 0, output_tensorlist1->element_num_ * sizeof(TensorC *));
-        for (size_t j = 0; j < output_tensorlist1->element_num_; j++) {
-          output_tensorlist1->tensors_[j] = reinterpret_cast<TensorC *>(malloc(sizeof(TensorC)));
-          if (output_tensorlist1->tensors_[j] == nullptr) {
-            for (size_t k = 0; k < j; k++) {
-              free(output_tensorlist1->tensors_[k]);
-              output_tensorlist1->tensors_[k] = nullptr;
-            }
-            free(output_tensorlist1->tensors_);
-            output_tensorlist1->tensors_ = nullptr;
-            return RET_ERROR;
-          }
-          memset(output_tensorlist1->tensors_[j], 0, sizeof(TensorC));
-        }
+        memset(output_tensorlist1->tensors_, 0, output_tensorlist1->element_num_ * sizeof(TensorC));
       }
 
       out_tensor_c->at(i) = reinterpret_cast<TensorC *const>(output_tensorlist1);
@@ -271,28 +234,13 @@ int GenerateSwitchOutTensorC(const std::vector<lite::Tensor *> &inputs, std::vec
       output_tensorlist2->element_num_ = inputs[i + 1]->shape().empty() ? 0 : inputs[i + 1]->shape().at(0);
       if (output_tensorlist2->element_num_ != 0) {
         output_tensorlist2->tensors_ =
-          reinterpret_cast<TensorC **>(malloc(output_tensorlist2->element_num_ * sizeof(TensorC *)));
+          reinterpret_cast<TensorC *>(malloc(output_tensorlist2->element_num_ * sizeof(TensorC)));
         if (output_tensorlist2->tensors_ == nullptr) {
           free(output_tensorlist2);
           output_tensorlist2 = nullptr;
           return RET_ERROR;
         }
-        memset(output_tensorlist2->tensors_, 0, output_tensorlist2->element_num_ * sizeof(TensorC *));
-        for (size_t j = 0; j < output_tensorlist2->element_num_; j++) {
-          output_tensorlist2->tensors_[j] = reinterpret_cast<TensorC *>(malloc(sizeof(TensorC)));
-          if (output_tensorlist2->tensors_[j] == nullptr) {
-            for (size_t k = 0; k < j; k++) {
-              free(output_tensorlist2->tensors_[k]);
-              output_tensorlist2->tensors_[k] = nullptr;
-            }
-            free(output_tensorlist2->tensors_);
-            output_tensorlist2->tensors_ = nullptr;
-            free(output_tensorlist2);
-            output_tensorlist2 = nullptr;
-            return RET_ERROR;
-          }
-          memset(output_tensorlist2->tensors_[j], 0, sizeof(TensorC));
-        }
+        memset(output_tensorlist2->tensors_, 0, output_tensorlist2->element_num_ * sizeof(TensorC));
       }
 
       out_tensor_c->at(i + outputs->size() / 2) = reinterpret_cast<TensorC *const>(output_tensorlist2);

mindspore/lite/test/ut/nnacl/infer/tensorlist_fromtensor_infer_test.cc

@@ -58,9 +58,9 @@ TEST_F(TensorlistFromtensorInferTest, TensorlistFromtensorInferTest0) {
   ASSERT_EQ(out->tensors_data_type_, kNumberTypeInt32);
   // ASSERT_EQ(outputs[0]->format_, Format_NHWC);
   for (size_t i = 0; i < out->element_num_; i++) {
-    ASSERT_EQ(out->tensors_[i]->shape_size_, 2);
-    ASSERT_EQ(out->tensors_[i]->shape_[0], 6);
-    ASSERT_EQ(out->tensors_[i]->shape_[1], 5);
+    ASSERT_EQ(out->tensors_[i].shape_size_, 2);
+    ASSERT_EQ(out->tensors_[i].shape_[0], 6);
+    ASSERT_EQ(out->tensors_[i].shape_[1], 5);
   }
   delete parameter;
   for (size_t i = 0; i < inputs_size; i++) {

mindspore/lite/test/ut/nnacl/infer/tensorlist_getitem_infer_test.cc

@@ -30,27 +30,22 @@ TEST_F(TensorlistGetItemInferTest, TensorlistGetItemInferTest0) {
   std::vector<TensorC *> inputs(inputs_size, NULL);
   TensorListC *input0 = reinterpret_cast<TensorListC *>(malloc(sizeof(TensorListC)));
   input0->element_num_ = 3;
-  input0->tensors_ = reinterpret_cast<TensorC **>(malloc(input0->element_num_ * sizeof(TensorC *)));
-  input0->tensors_[0] = reinterpret_cast<TensorC *>(malloc(sizeof(TensorC)));
-  input0->tensors_[0]->shape_size_ = 2;
-  input0->tensors_[0]->shape_[0] = 1;
-  input0->tensors_[0]->shape_[1] = 2;
-  input0->tensors_[0]->data_type_ = kNumberTypeInt32;
-  // input0->tensors_[0]->format_ = Format_NHWC;
-  input0->tensors_[1] = reinterpret_cast<TensorC *>(malloc(sizeof(TensorC)));
-  input0->tensors_[1]->shape_size_ = 3;
-  input0->tensors_[1]->shape_[0] = 3;
-  input0->tensors_[1]->shape_[1] = 4;
-  input0->tensors_[1]->shape_[2] = 5;
-  input0->tensors_[1]->data_type_ = kNumberTypeInt32;
-  // input0->tensors_[1]->format_ = Format_NHWC;
-  input0->tensors_[2] = reinterpret_cast<TensorC *>(malloc(sizeof(TensorC)));
-  input0->tensors_[2]->shape_size_ = 4;
-  input0->tensors_[2]->shape_[0] = 6;
-  input0->tensors_[2]->shape_[1] = 7;
-  input0->tensors_[2]->shape_[2] = 8;
-  input0->tensors_[2]->shape_[3] = 9;
-  input0->tensors_[2]->data_type_ = kNumberTypeInt32;
+  input0->tensors_ = reinterpret_cast<TensorC *>(malloc(input0->element_num_ * sizeof(TensorC)));
+  input0->tensors_[0].shape_size_ = 2;
+  input0->tensors_[0].shape_[0] = 1;
+  input0->tensors_[0].shape_[1] = 2;
+  input0->tensors_[0].data_type_ = kNumberTypeInt32;
+  input0->tensors_[1].shape_size_ = 3;
+  input0->tensors_[1].shape_[0] = 3;
+  input0->tensors_[1].shape_[1] = 4;
+  input0->tensors_[1].shape_[2] = 5;
+  input0->tensors_[1].data_type_ = kNumberTypeInt32;
+  input0->tensors_[2].shape_size_ = 4;
+  input0->tensors_[2].shape_[0] = 6;
+  input0->tensors_[2].shape_[1] = 7;
+  input0->tensors_[2].shape_[2] = 8;
+  input0->tensors_[2].shape_[3] = 9;
+  input0->tensors_[2].data_type_ = kNumberTypeInt32;
   // input0->tensors_[2]->format_ = Format_NHWC;
   inputs[0] = reinterpret_cast<TensorC *>(input0);
   inputs[0]->data_type_ = kObjectTypeTensorType;

mindspore/lite/test/ut/nnacl/infer/tensorlist_reserve_infer_test.cc

@@ -57,7 +57,7 @@ TEST_F(TensorlistReserveInferTest, TensorlistReserveInferTest0) {
   ASSERT_EQ(out->tensors_data_type_, kTypeUnknown);
   // ASSERT_EQ(outputs[0]->format_, Format_NHWC);
   for (size_t i = 0; i < out->element_num_; i++) {
-    ASSERT_EQ(out->tensors_[i]->shape_size_, 0);
+    ASSERT_EQ(out->tensors_[i].shape_size_, 0);
   }
   delete parameter;
   for (size_t i = 0; i < inputs_size; i++) {

mindspore/lite/test/ut/nnacl/infer/tensorlist_setitem_infer_test.cc

@@ -29,29 +29,27 @@ TEST_F(TensorlistSetItemInferTest, TensorlistSetItemInferTest0) {
   std::vector<TensorC *> inputs(inputs_size, NULL);
   TensorListC *input0 = new TensorListC;
   input0->element_num_ = 3;
-  input0->tensors_ = reinterpret_cast<TensorC **>(malloc(input0->element_num_ * sizeof(TensorC *)));
+  input0->tensors_ = reinterpret_cast<TensorC *>(malloc(input0->element_num_ * sizeof(TensorC)));
   input0->element_shape_size_ = 2;
   input0->element_shape_[0] = 2;
   input0->element_shape_[1] = 4;
   input0->tensors_data_type_ = kNumberTypeInt32;
   input0->data_type_ = kObjectTypeTensorType;
-  input0->tensors_[0] = new TensorC;
-  input0->tensors_[0]->shape_size_ = 2;
-  input0->tensors_[0]->shape_[0] = 2;
-  input0->tensors_[0]->shape_[1] = 4;
-  input0->tensors_[0]->data_type_ = kNumberTypeInt32;
-  // input0->tensors_[0]->format_ = Format_NHWC;
-  input0->tensors_[1] = new TensorC;
-  input0->tensors_[1]->shape_size_ = 2;
-  input0->tensors_[1]->shape_[0] = 2;
-  input0->tensors_[1]->shape_[1] = 4;
-  input0->tensors_[1]->data_type_ = kNumberTypeInt32;
-  // input0->tensors_[1]->format_ = Format_NHWC;
-  input0->tensors_[2] = new TensorC;
-  input0->tensors_[2]->shape_size_ = 2;
-  input0->tensors_[2]->shape_[0] = 2;
-  input0->tensors_[2]->shape_[1] = 4;
-  input0->tensors_[2]->data_type_ = kNumberTypeInt32;
+
+  input0->tensors_[0].shape_size_ = 2;
+  input0->tensors_[0].shape_[0] = 2;
+  input0->tensors_[0].shape_[1] = 4;
+  input0->tensors_[0].data_type_ = kNumberTypeInt32;
+
+  input0->tensors_[1].shape_size_ = 2;
+  input0->tensors_[1].shape_[0] = 2;
+  input0->tensors_[1].shape_[1] = 4;
+  input0->tensors_[1].data_type_ = kNumberTypeInt32;
+
+  input0->tensors_[2].shape_size_ = 2;
+  input0->tensors_[2].shape_[0] = 2;
+  input0->tensors_[2].shape_[1] = 4;
+  input0->tensors_[2].data_type_ = kNumberTypeInt32;
   // input0->tensors_[2]->format_ = Format_NHWC;
   inputs[0] = reinterpret_cast<TensorC *>(input0);
 
@@ -67,6 +65,8 @@ TEST_F(TensorlistSetItemInferTest, TensorlistSetItemInferTest0) {
   inputs[2]->shape_[0] = 5;
   inputs[2]->shape_[1] = 6;
   inputs[2]->data_type_ = kNumberTypeInt32;
+  std::vector<int> inputs2_data = {3};
+  inputs[2]->data_ = inputs2_data.data();
 
   std::vector<TensorC *> outputs(1, NULL);
   outputs[0] = reinterpret_cast<TensorC *>(new TensorListC);
@@ -82,15 +82,15 @@ TEST_F(TensorlistSetItemInferTest, TensorlistSetItemInferTest0) {
   ASSERT_EQ(res->element_shape_[1], 4);
   ASSERT_EQ(res->tensors_data_type_, kNumberTypeInt32);
   ASSERT_EQ(res->data_type_, kObjectTypeTensorType);
-  ASSERT_EQ(res->tensors_[0]->shape_size_, 2);
-  ASSERT_EQ(res->tensors_[0]->shape_[0], 2);
-  ASSERT_EQ(res->tensors_[0]->shape_[1], 4);
-  ASSERT_EQ(res->tensors_[1]->shape_size_, 2);
-  ASSERT_EQ(res->tensors_[1]->shape_[0], 2);
-  ASSERT_EQ(res->tensors_[1]->shape_[1], 4);
-  ASSERT_EQ(res->tensors_[2]->shape_size_, 2);
-  ASSERT_EQ(res->tensors_[2]->shape_[0], 5);
-  ASSERT_EQ(res->tensors_[2]->shape_[1], 6);
+  ASSERT_EQ(res->tensors_[0].shape_size_, 2);
+  ASSERT_EQ(res->tensors_[0].shape_[0], 2);
+  ASSERT_EQ(res->tensors_[0].shape_[1], 4);
+  ASSERT_EQ(res->tensors_[1].shape_size_, 2);
+  ASSERT_EQ(res->tensors_[1].shape_[0], 2);
+  ASSERT_EQ(res->tensors_[1].shape_[1], 4);
+  ASSERT_EQ(res->tensors_[2].shape_size_, 2);
+  ASSERT_EQ(res->tensors_[2].shape_[0], 5);
+  ASSERT_EQ(res->tensors_[2].shape_[1], 6);
 
   // ASSERT_EQ(outputs[0]->format_, Format_NHWC);
 

mindspore/lite/test/ut/nnacl/infer/tensorlist_stack_infer_test.cc

@@ -29,28 +29,26 @@ TEST_F(TensorlistStackInferTest, TensorlistStackInferTest0) {
   std::vector<TensorC *> inputs(inputs_size, NULL);
   TensorListC *input0 = new TensorListC;
   input0->element_num_ = 3;
-  input0->tensors_ = reinterpret_cast<TensorC **>(malloc(input0->element_num_ * sizeof(TensorC *)));
+  input0->tensors_ = reinterpret_cast<TensorC *>(malloc(input0->element_num_ * sizeof(TensorC)));
   input0->element_shape_size_ = 2;
   input0->element_shape_[0] = 2;
   input0->element_shape_[1] = 4;
   input0->tensors_data_type_ = kNumberTypeInt32;
-  input0->tensors_[0] = new TensorC;
-  input0->tensors_[0]->shape_size_ = 2;
-  input0->tensors_[0]->shape_[0] = 2;
-  input0->tensors_[0]->shape_[1] = 4;
-  input0->tensors_[0]->data_type_ = kNumberTypeInt32;
-  // input0->tensors_[0]->format_ = Format_NHWC;
-  input0->tensors_[1] = new TensorC;
-  input0->tensors_[1]->shape_size_ = 2;
-  input0->tensors_[1]->shape_[0] = 2;
-  input0->tensors_[1]->shape_[1] = 4;
-  input0->tensors_[1]->data_type_ = kNumberTypeInt32;
-  // input0->tensors_[1]->format_ = Format_NHWC;
-  input0->tensors_[2] = new TensorC;
-  input0->tensors_[2]->shape_size_ = 2;
-  input0->tensors_[2]->shape_[0] = 2;
-  input0->tensors_[2]->shape_[1] = 4;
-  input0->tensors_[2]->data_type_ = kNumberTypeInt32;
+
+  input0->tensors_[0].shape_size_ = 2;
+  input0->tensors_[0].shape_[0] = 2;
+  input0->tensors_[0].shape_[1] = 4;
+  input0->tensors_[0].data_type_ = kNumberTypeInt32;
+
+  input0->tensors_[1].shape_size_ = 2;
+  input0->tensors_[1].shape_[0] = 2;
+  input0->tensors_[1].shape_[1] = 4;
+  input0->tensors_[1].data_type_ = kNumberTypeInt32;
+
+  input0->tensors_[2].shape_size_ = 2;
+  input0->tensors_[2].shape_[0] = 2;
+  input0->tensors_[2].shape_[1] = 4;
+  input0->tensors_[2].data_type_ = kNumberTypeInt32;
   // input0->tensors_[2]->format_ = Format_NHWC;
   inputs[0] = reinterpret_cast<TensorC *>(input0);
   inputs[0]->data_type_ = kObjectTypeTensorType;