COMPMID-1367: Enable NHWC in graph examples

Change-Id: Iabc54a3a1bdcd46a9a921cda39c7c85fef672b72
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141449
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 38 insertions, 22 deletions

diff --git a/arm_compute/core/Helpers.h b/arm_compute/core/Helpers.h
index 374e36442b..ef59323073 100644
--- a/arm_compute/core/Helpers.h
+++ b/arm_compute/core/Helpers.h
@@ -111,28 +111,6 @@ struct is_contained<T, std::tuple<U, Ts...>> : is_contained<T, std::tuple<Ts...>
 };
 }
 
-/** Calculate the number of output tiles required by Winograd Convolution layer. This utility function can be used by the Winograd input transform
- *  to know the number of tiles on the x and y direction
- *
- * @param[in] in_dims          Spatial dimensions of the input tensor of convolution layer
- * @param[in] kernel_size      Kernel size
- * @param[in] output_tile_size Size of a single output tile
- * @param[in] conv_info        Convolution info (i.e. pad, stride,...)
- *
- * @return the number of output tiles along the x and y directions of size "output_tile_size"
- */
-inline Size2D compute_winograd_convolution_tiles(const Size2D &in_dims, const Size2D &kernel_size, const Size2D &output_tile_size, const PadStrideInfo &conv_info)
-{
-    int num_tiles_x = std::ceil((in_dims.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width));
-    int num_tiles_y = std::ceil((in_dims.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height));
-
-    // Clamp in case we provide paddings but we have 1D convolution
-    num_tiles_x = std::min(num_tiles_x, static_cast<int>(in_dims.width));
-    num_tiles_y = std::min(num_tiles_y, static_cast<int>(in_dims.height));
-
-    return Size2D(num_tiles_x, num_tiles_y);
-}
-
 /** Computes bilinear interpolation using the pointer to the top-left pixel and the pixel's distance between
  * the real coordinates and the smallest following integer coordinates. Input must be in single channel format.
  *
@@ -694,6 +672,44 @@ inline int coords2index(const TensorShape &shape, const Coordinates &coord);
  * @return The int conversion of the requested data layout index.
  */
 inline size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension);
+
+/** Calculate the normalization dimension index for a given normalization type
+ *
+ * @param[in] layout Data layout of the input and output tensor
+ * @param[in] info   Normalization info
+ *
+ * @return Normalization dimension index
+ */
+inline unsigned int get_normalization_dimension_index(DataLayout layout, const NormalizationLayerInfo &info)
+{
+    const unsigned int width_idx   = get_data_layout_dimension_index(layout, DataLayoutDimension::WIDTH);
+    const unsigned int channel_idx = get_data_layout_dimension_index(layout, DataLayoutDimension::CHANNEL);
+
+    return info.is_in_map() ? width_idx : channel_idx;
+}
+
+/** Calculate the number of output tiles required by Winograd Convolution layer. This utility function can be used by the Winograd input transform
+ *  to know the number of tiles on the x and y direction
+ *
+ * @param[in] in_dims          Spatial dimensions of the input tensor of convolution layer
+ * @param[in] kernel_size      Kernel size
+ * @param[in] output_tile_size Size of a single output tile
+ * @param[in] conv_info        Convolution info (i.e. pad, stride,...)
+ *
+ * @return the number of output tiles along the x and y directions of size "output_tile_size"
+ */
+inline Size2D compute_winograd_convolution_tiles(const Size2D &in_dims, const Size2D &kernel_size, const Size2D &output_tile_size, const PadStrideInfo &conv_info)
+{
+    int num_tiles_x = std::ceil((in_dims.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width));
+    int num_tiles_y = std::ceil((in_dims.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height));
+
+    // Clamp in case we provide paddings but we have 1D convolution
+    num_tiles_x = std::min(num_tiles_x, static_cast<int>(in_dims.width));
+    num_tiles_y = std::min(num_tiles_y, static_cast<int>(in_dims.height));
+
+    return Size2D(num_tiles_x, num_tiles_y);
+}
+
 } // namespace arm_compute
 
 #include "arm_compute/core/Helpers.inl"