COMPMID-1330: Add support for NormalizePlanarYUV operator in CL

Change-Id: Id0754b9e2bc3ef7ff2c4c21c3b89709588c41bd3
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/146637
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>

1 files changed, 22 insertions, 17 deletions

diff --git a/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h b/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h
index 09905cfef7..cc73e530ef 100644
--- a/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h
+++ b/tests/validation/fixtures/NormalizePlanarYUVLayerFixture.h
@@ -45,16 +45,16 @@ class NormalizePlanarYUVLayerValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
-    void setup(TensorShape shape0, TensorShape shape1, DataType dt)
+    void setup(TensorShape shape0, TensorShape shape1, DataType dt, DataLayout data_layout)
     {
         _data_type = dt;
-        _target    = compute_target(shape0, shape1, dt);
+        _target    = compute_target(shape0, shape1, dt, data_layout);
         _reference = compute_reference(shape0, shape1, dt);
     }
 
 protected:
     template <typename U>
-    void fill(U &&src_tensor, U &&mean_tensor, U &&sd_tensor)
+    void fill(U &&src_tensor, U &&mean_tensor, U &&std_tensor)
     {
         if(is_data_type_float(_data_type))
         {
@@ -62,43 +62,48 @@ protected:
             float max_bound = 0.f;
             std::tie(min_bound, max_bound) = get_normalize_planar_yuv_layer_test_bounds<T>();
             std::uniform_real_distribution<> distribution(min_bound, max_bound);
-            std::uniform_real_distribution<> distribution_sd(0.1, max_bound);
+            std::uniform_real_distribution<> distribution_std(0.1, max_bound);
             library->fill(src_tensor, distribution, 0);
             library->fill(mean_tensor, distribution, 1);
-            library->fill(sd_tensor, distribution_sd, 2);
+            library->fill(std_tensor, distribution_std, 2);
         }
     }
 
-    TensorType compute_target(const TensorShape &shape0, const TensorShape &shape1, DataType dt)
+    TensorType compute_target(TensorShape shape0, const TensorShape &shape1, DataType dt, DataLayout data_layout)
     {
+        if(data_layout == DataLayout::NHWC)
+        {
+            permute(shape0, PermutationVector(2U, 0U, 1U));
+        }
+
         // Create tensors
-        TensorType src  = create_tensor<TensorType>(shape0, dt, 1);
-        TensorType dst  = create_tensor<TensorType>(shape0, dt, 1);
+        TensorType src  = create_tensor<TensorType>(shape0, dt, 1, QuantizationInfo(), data_layout);
+        TensorType dst  = create_tensor<TensorType>(shape0, dt, 1, QuantizationInfo(), data_layout);
         TensorType mean = create_tensor<TensorType>(shape1, dt, 1);
-        TensorType sd   = create_tensor<TensorType>(shape1, dt, 1);
+        TensorType std  = create_tensor<TensorType>(shape1, dt, 1);
 
         // Create and configure function
         FunctionType norm;
-        norm.configure(&src, &dst, &mean, &sd);
+        norm.configure(&src, &dst, &mean, &std);
 
         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(mean.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(sd.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(std.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Allocate tensors
         src.allocator()->allocate();
         dst.allocator()->allocate();
         mean.allocator()->allocate();
-        sd.allocator()->allocate();
+        std.allocator()->allocate();
 
         ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!mean.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(!sd.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!std.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Fill tensors
-        fill(AccessorType(src), AccessorType(mean), AccessorType(sd));
+        fill(AccessorType(src), AccessorType(mean), AccessorType(std));
 
         // Compute function
         norm.run();
@@ -111,12 +116,12 @@ protected:
         // Create reference
         SimpleTensor<T> ref_src{ shape0, dt, 1 };
         SimpleTensor<T> ref_mean{ shape1, dt, 1 };
-        SimpleTensor<T> ref_sd{ shape1, dt, 1 };
+        SimpleTensor<T> ref_std{ shape1, dt, 1 };
 
         // Fill reference
-        fill(ref_src, ref_mean, ref_sd);
+        fill(ref_src, ref_mean, ref_std);
 
-        return reference::normalize_planar_yuv_layer(ref_src, ref_mean, ref_sd);
+        return reference::normalize_planar_yuv_layer(ref_src, ref_mean, ref_std);
     }
 
     TensorType      _target{};