COMPMID-556 - Fix examples

- Fixed data type issue in cl_sgemm
- Added support for NEON and OpenCL targets in graph examples. Before we
  could run only OpenCL target
- Add auto_init() in NEDepthwiseVectorToTensorKernel

Change-Id: I4410ce6f4992b2375b980634fe55f1083cf3c471
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/112850
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>

2 files changed, 27 insertions, 6 deletions

diff --git a/utils/GraphUtils.h b/utils/GraphUtils.h
index d7d5cd6778..429394d1cc 100644
--- a/utils/GraphUtils.h
+++ b/utils/GraphUtils.h
@@ -25,6 +25,7 @@
 #define __ARM_COMPUTE_GRAPH_UTILS_H__
 
 #include "arm_compute/core/PixelValue.h"
+#include "arm_compute/graph/Graph.h"
 #include "arm_compute/graph/ITensorAccessor.h"
 #include "arm_compute/graph/Types.h"
 
@@ -220,6 +221,26 @@ inline std::unique_ptr<graph::ITensorAccessor> get_input_accessor(const std::str
     }
 }
 
+/** Utility function to return the TargetHint
+ *
+ * @param[in] target Integer value which expresses the selected target. Must be 0 for NEON or 1 for OpenCL
+ *
+ * @return the TargetHint
+ */
+inline graph::TargetHint set_target_hint(int target)
+{
+    ARM_COMPUTE_ERROR_ON_MSG(target > 1, "Invalid target. Target must be 0 (NEON) or 1 (OpenCL)");
+    if(target == 1 && graph::Graph::opencl_is_available())
+    {
+        // If type of target is OpenCL, check if OpenCL is available and initialize the scheduler
+        return graph::TargetHint::OPENCL;
+    }
+    else
+    {
+        return graph::TargetHint::NEON;
+    }
+}
+
 /** Generates appropriate output accessor according to the specified labels_path
  *
  * @note If labels_path is empty will generate a DummyAccessor else will generate a TopNPredictionsAccessor
diff --git a/utils/Utils.h b/utils/Utils.h
index 9133fd0ac0..eb4e846e80 100644
--- a/utils/Utils.h
+++ b/utils/Utils.h
@@ -708,7 +708,7 @@ void save_to_ppm(T &tensor, const std::string &ppm_filename)
 
 /** Template helper function to save a tensor image to a NPY file.
  *
- * @note Only F32 format supported.
+ * @note Only F32 data type supported.
  * @note Only works with 2D tensors.
  * @note If the input tensor is a CLTensor, the function maps and unmaps the image
  *
@@ -719,7 +719,7 @@ void save_to_ppm(T &tensor, const std::string &ppm_filename)
 template <typename T>
 void save_to_npy(T &tensor, const std::string &npy_filename, bool fortran_order)
 {
-    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(&tensor, arm_compute::Format::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(&tensor, arm_compute::DataType::F32);
     ARM_COMPUTE_ERROR_ON(tensor.info()->num_dimensions() > 2);
 
     std::ofstream fs;
@@ -745,9 +745,9 @@ void save_to_npy(T &tensor, const std::string &npy_filename, bool fortran_order)
         // Map buffer if creating a CLTensor
         map(tensor, true);
 
-        switch(tensor.info()->format())
+        switch(tensor.info()->data_type())
         {
-            case arm_compute::Format::F32:
+            case arm_compute::DataType::F32:
             {
                 std::vector<float> tmp; /* Used only to get the typestring */
                 npy::Typestring    typestring_o{ tmp };
@@ -851,9 +851,9 @@ void fill_random_tensor(T &tensor, float lower_bound, float upper_bound)
 
     Iterator it(&tensor, window);
 
-    switch(tensor.info()->format())
+    switch(tensor.info()->data_type())
     {
-        case arm_compute::Format::F32:
+        case arm_compute::DataType::F32:
         {
             std::uniform_real_distribution<float> dist(lower_bound, upper_bound);