Add Tensor related utilities to the new API

A couple of utility functions to get the information
about tensors are added. Those functions are placed
at an additional header file for better grouping.
Related test cases are also added.

Resolves: COMPMID-4376

Change-Id: I6bd09cbf60fddcf4fe651906982397afb0451392
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5405
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 49 insertions, 4 deletions

diff --git a/src/common/utils/LegacySupport.cpp b/src/common/utils/LegacySupport.cpp
index 5981c657bd..569b2abd89 100644
--- a/src/common/utils/LegacySupport.cpp
+++ b/src/common/utils/LegacySupport.cpp
@@ -29,7 +29,7 @@ namespace detail
 {
 namespace
 {
-DataType data_type_mapper(AclDataType data_type)
+DataType convert_to_legacy_data_type(AclDataType data_type)
 {
     switch(data_type)
     {
@@ -41,11 +41,25 @@ DataType data_type_mapper(AclDataType data_type)
             return DataType::BFLOAT16;
         default:
             return DataType::UNKNOWN;
-            ;
     }
 }
 
-TensorShape tensor_shape_mapper(int32_t ndims, int32_t *shape)
+AclDataType convert_to_c_data_type(DataType data_type)
+{
+    switch(data_type)
+    {
+        case DataType::F32:
+            return AclDataType::AclFloat32;
+        case DataType::F16:
+            return AclDataType::AclFloat16;
+        case DataType::BFLOAT16:
+            return AclDataType::AclBFloat16;
+        default:
+            return AclDataType::AclDataTypeUnknown;
+    }
+}
+
+TensorShape create_legacy_tensor_shape(int32_t ndims, int32_t *shape)
 {
     TensorShape legacy_shape{};
     for(int32_t d = 0; d < ndims; ++d)
@@ -54,13 +68,44 @@ TensorShape tensor_shape_mapper(int32_t ndims, int32_t *shape)
     }
     return legacy_shape;
 }
+int32_t *create_tensor_shape_array(const TensorInfo &info)
+{
+    const auto num_dims = info.num_dimensions();
+    if(num_dims <= 0)
+    {
+        return nullptr;
+    }
+
+    int32_t *shape_array = new int32_t[num_dims];
+
+    for(size_t d = 0; d < num_dims; ++d)
+    {
+        shape_array[d] = info.tensor_shape()[d];
+    }
+
+    return shape_array;
+}
 } // namespace
 
 TensorInfo convert_to_legacy_tensor_info(const AclTensorDescriptor &desc)
 {
     TensorInfo legacy_desc;
-    legacy_desc.init(tensor_shape_mapper(desc.ndims, desc.shape), 1, data_type_mapper(desc.data_type));
+    legacy_desc.init(create_legacy_tensor_shape(desc.ndims, desc.shape), 1, convert_to_legacy_data_type(desc.data_type));
     return legacy_desc;
 }
+
+AclTensorDescriptor convert_to_descriptor(const TensorInfo &info)
+{
+    const auto          num_dims = info.num_dimensions();
+    AclTensorDescriptor desc
+    {
+        static_cast<int32_t>(num_dims),
+        create_tensor_shape_array(info),
+        convert_to_c_data_type(info.data_type()),
+        nullptr,
+        0
+    };
+    return desc;
+}
 } // namespace detail
 } // namespace arm_compute