fix magic number

mindspore/lite/src/runtime/kernel/arm/base/slice_base.cc

@@ -83,7 +83,8 @@ int SliceCPUKernel::Run() {
     return RET_NULL_PTR;
   }
   // param_ shape info has already been extended to 8d
-  if (param_->size_[5] < op_parameter_->thread_num_) {
+  constexpr size_t kDimHUnder8D = 5;
+  if (param_->size_[kDimHUnder8D] < op_parameter_->thread_num_) {
     DoSliceNoParallel(input_data, output_data, param_, lite::DataTypeSize(in_tensors_.at(0)->data_type()));
     return RET_OK;
   }

mindspore/lite/src/runtime/kernel/arm/fp32/crop_and_resize_fp32.cc

@@ -28,6 +28,10 @@ using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_CropAndResize;
 
 namespace mindspore::kernel {
+namespace {
+constexpr size_t kBoxIndex = 1;
+constexpr size_t kBoxIdIndex = 2;
+}  // namespace
 int CropAndResizeCPUKernel::Init() {
   if (!InferShapeDone()) {
     return RET_OK;
@@ -112,11 +116,11 @@ int CropAndResizeCPUKernel::RunImpl(int task_id) {
   if (input_data == nullptr) {
     return RET_NULL_PTR;
   }
-  auto boxes = reinterpret_cast<float *>(in_tensors_.at(1)->data_c());
+  auto boxes = reinterpret_cast<float *>(in_tensors_.at(kBoxIndex)->data_c());
   if (boxes == nullptr) {
     return RET_NULL_PTR;
   }
-  auto box_idx = reinterpret_cast<int32_t *>(in_tensors_.at(2)->data_c());
+  auto box_idx = reinterpret_cast<int32_t *>(in_tensors_.at(kBoxIdIndex)->data_c());
   if (box_idx == nullptr) {
     return RET_NULL_PTR;
   }

mindspore/lite/src/runtime/kernel/arm/fp32/cumsum_fp32.cc

@@ -54,7 +54,7 @@ int CumSumCPUKernel::Init() {
 }
 
 int CumSumCPUKernel::ReSize() {
-  MS_ASSERT(in_tensors_.size() == 2);
+  MS_ASSERT(in_tensors_.size() == kInputSize1);
   auto input_tensor = in_tensors_.at(0);
   auto axis_tensor = in_tensors_.at(1);
   int *axis_data = reinterpret_cast<int *>(axis_tensor->data_c());

mindspore/lite/tools/optimizer/fusion/conv_transform_fusion.cc

@@ -30,6 +30,7 @@ constexpr size_t kConvNoBiasLen = 3;
 constexpr size_t kConvWithBiasLen = 4;
 constexpr size_t kNumDim1 = 1;
 constexpr size_t kNumDim2 = 2;
+constexpr size_t kDim4D = 4;
 int GetOutChannels(const CNodePtr &conv_node) {
   MS_ASSERT(conv_node != nullptr);
   auto value_node = conv_node->input(0);
@@ -230,7 +231,7 @@ void ConvTransformFusion::CalNewWeightTensor(const CNodePtr &conv_node, const te
                                              int kernel_num, const float *trans_scale) const {
   MS_ASSERT(weight_data != nullptr);
   MS_ASSERT(trans_scale != nullptr);
-  if (weight_tensor->shape().size() > 4) {
+  if (weight_tensor->shape().size() > kDim4D) {
     MS_LOG(ERROR) << "weight tensor shape error";
     return;
   }

mindspore/lite/tools/optimizer/graph/reduce_same_act_pass.cc

@@ -23,6 +23,9 @@
 
 namespace mindspore {
 namespace opt {
+namespace {
+constexpr size_t kMinUsersSize = 2;
+}
 bool ReduceSameActPass::Run(const FuncGraphPtr &func_graph) {
   auto node_list = TopoSort(func_graph->get_return());
   auto manager = Manage(func_graph, true);
@@ -37,7 +40,7 @@ bool ReduceSameActPass::Run(const FuncGraphPtr &func_graph) {
       continue;
     }
     auto cur_node_users = func_graph->manager()->node_users()[node];
-    if (cur_node_users.size() < 2) {
+    if (cur_node_users.size() < kMinUsersSize) {
       continue;
     }
 

mindspore/lite/tools/optimizer/graph/split_one_pass.cc

@@ -23,6 +23,9 @@
 
 namespace mindspore {
 namespace opt {
+namespace {
+constexpr size_t kMinCnodeSize = 2;
+}  // namespace
 bool SplitOnePass::Run(const FuncGraphPtr &func_graph) {
   auto node_list = TopoSort(func_graph->get_return());
   auto manager = Manage(func_graph, true);
@@ -50,7 +53,7 @@ bool SplitOnePass::Run(const FuncGraphPtr &func_graph) {
     if (primitive_c->get_output_num() != 1) {
       continue;
     }
-    if (cnode->size() < 2) {
+    if (cnode->size() < kMinCnodeSize) {
       return false;
     }
     func_graph->manager()->Replace(node, cnode->input(1));