Rename space_to_batch helper function name for readability.

mace/ops/batch_to_space.h

@@ -24,18 +24,18 @@ class BatchToSpaceNDOp : public Operator<D, T> {
 
   bool Run(StatsFuture *future) override {
     const Tensor *batch_tensor = this->Input(INPUT);
-    Tensor *space_tensor= this->Output(OUTPUT);
+    Tensor *space_tensor = this->Output(OUTPUT);
 
     std::vector<index_t> output_shape(4, 0);
-    BatchToSpaceHelper(batch_tensor, space_tensor, output_shape);
+    CalculateOutputShape(batch_tensor, space_tensor, output_shape.data());
     functor_(space_tensor, output_shape, const_cast<Tensor *>(batch_tensor), future);
     return true;
   }
 
  private:
-  inline void BatchToSpaceHelper(const Tensor *input_tensor,
-                                 Tensor *output,
-                                 std::vector<index_t> &output_shape) {
+  inline void CalculateOutputShape(const Tensor *input_tensor,
+                                   Tensor *output,
+                                   index_t *output_shape) {
     auto crops = OperatorBase::GetRepeatedArgument<int>("crops", {0, 0, 0, 0});
     auto block_shape = OperatorBase::GetRepeatedArgument<int>("block_shape", {1, 1});
     MACE_CHECK(input_tensor->dim_size() == 4, "Input's shape should be 4D");

mace/ops/space_to_batch.h

@@ -12,7 +12,6 @@
 
 namespace mace {
 
-
 template<DeviceType D, typename T>
 class SpaceToBatchNDOp : public Operator<D, T> {
  public:
@@ -24,20 +23,20 @@ class SpaceToBatchNDOp : public Operator<D, T> {
             false) {}
 
   bool Run(StatsFuture *future) override {
-    const Tensor *space_tensor= this->Input(INPUT);
-    Tensor *batch_tensor= this->Output(OUTPUT);
+    const Tensor *space_tensor = this->Input(INPUT);
+    Tensor *batch_tensor = this->Output(OUTPUT);
 
     std::vector<index_t> output_shape(4, 0);
-    SpaceToBatchHelper(space_tensor, batch_tensor, output_shape);
+    CalculateOutputShape(space_tensor, batch_tensor, output_shape.data());
     functor_(const_cast<Tensor *>(space_tensor), output_shape, batch_tensor, future);
     return true;
   }
 
  private:
 
-  inline void SpaceToBatchHelper(const Tensor *input_tensor,
-                                 Tensor *output,
-                                 std::vector<index_t> &output_shape) {
+  inline void CalculateOutputShape(const Tensor *input_tensor,
+                                   Tensor *output,
+                                   index_t *output_shape) {
     auto paddings = OperatorBase::GetRepeatedArgument<int>("paddings", {0, 0, 0, 0});
     auto block_shape = OperatorBase::GetRepeatedArgument<int>("block_shape", {1, 1});
     MACE_CHECK(input_tensor->dim_size() == 4, "Input's shape should be 4D");
@@ -47,15 +46,15 @@ class SpaceToBatchNDOp : public Operator<D, T> {
     const index_t block_dims = block_shape.size();
     index_t block_shape_product = 1;
     for (uint32_t block_dim = 0; block_dim < block_dims; ++block_dim) {
-        MACE_CHECK(block_shape[block_dim] > 1, "block_shape's value should be great to 1");
-        const index_t block_shape_value = block_shape[block_dim];
-        const index_t padded_input_size = input_tensor->dim(block_dim + 1)
-            + paddings[block_dim * 2]
-            + paddings[block_dim * 2 + 1];
-        MACE_CHECK(padded_input_size % block_shape_value == 0,
-                   "padded input ", padded_input_size, " is not divisible by block_shape");
-        block_shape_product *= block_shape_value;
-        output_shape[block_dim + 1] = padded_input_size / block_shape_value;
+      MACE_CHECK(block_shape[block_dim] > 1, "block_shape's value should be great to 1");
+      const index_t block_shape_value = block_shape[block_dim];
+      const index_t padded_input_size = input_tensor->dim(block_dim + 1)
+          + paddings[block_dim * 2]
+          + paddings[block_dim * 2 + 1];
+      MACE_CHECK(padded_input_size % block_shape_value == 0,
+                 "padded input ", padded_input_size, " is not divisible by block_shape");
+      block_shape_product *= block_shape_value;
+      output_shape[block_dim + 1] = padded_input_size / block_shape_value;
     }
     output_shape[0] = input_tensor->dim(0) * block_shape_product;
     output_shape[3] = input_tensor->dim(3);