Implicit padding testing along the X axis on high priority operators

Add artificial implicit padding testing for the following fixtures:
- Scale
- FullyConnected
- Pooling
- DepthwiseConvolution
- DirectConvolution
- Winograd
- FFT
- GEMM/GEMMLowp

Create utility function that loops through a list of tensor and adds random padding based on the global seed (only for NHWC layer layout).

Remove GEMMLowpAssemblyFixture since it wasn't used
Remove some AssetsLibrary headers since they weren't used

Resolve COMPMID-4161

Change-Id: Ib6f4f7f113ae69b993d7b2a9e04abbf3de8c99fe
Signed-off-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5327
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 19 insertions, 15 deletions

diff --git a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
index f956963e14..a1433e9115 100644
--- a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
@@ -62,12 +62,11 @@ public:
     {
         ARM_COMPUTE_UNUSED(dilation);
         _mixed_layout = mixed_layout;
-        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info, data_layout);
-        _reference = compute_reference(input_shape, weights_shape, bias_shape, info, data_type, act_info);
+        _target       = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info, data_layout);
+        _reference    = compute_reference(input_shape, weights_shape, bias_shape, info, data_type, act_info);
     }
 
 protected:
-
     void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst)
     {
         const DataLayout data_layout = src.info()->data_layout();
@@ -134,6 +133,8 @@ protected:
         ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
+        add_padding_x({ &src, &weights, &bias, &dst }, data_layout);
+
         // Allocate tensors
         src.allocator()->allocate();
         weights.allocator()->allocate();
@@ -235,7 +236,7 @@ protected:
 
     TensorType      _target{};
     SimpleTensor<T> _reference{};
-    bool            _mixed_layout{false};
+    bool            _mixed_layout{ false };
 };
 
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
@@ -246,13 +247,12 @@ public:
     void setup(TensorShape input_shape, WinogradInfo winograd_info, DataLayout data_layout, DataType data_type)
     {
         TensorShape output_shape = compute_winograd_input_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
-        _mixed_layout = mixed_layout;
-        _target    = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type);
-        _reference = compute_reference(input_shape, output_shape, winograd_info, data_type);
+        _mixed_layout            = mixed_layout;
+        _target                  = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type);
+        _reference               = compute_reference(input_shape, output_shape, winograd_info, data_type);
     }
 
 protected:
-
     void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst)
     {
         const DataLayout data_layout_src = src.info()->data_layout();
@@ -311,6 +311,8 @@ protected:
         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
+        add_padding_x({ &src, &dst }, data_layout);
+
         // Allocate tensors
         src.allocator()->allocate();
         dst.allocator()->allocate();
@@ -344,7 +346,7 @@ protected:
         return reference::winograd_input_transform<T>(src, output_shape, winograd_info);
     }
 
-    bool _mixed_layout {false};
+    bool            _mixed_layout{ false };
     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };
@@ -360,12 +362,11 @@ public:
         TensorShape  output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
 
         _mixed_layout = mixed_layout;
-        _target    = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type);
-        _reference = compute_reference(input_shape, output_shape, winograd_info, data_type);
+        _target       = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type);
+        _reference    = compute_reference(input_shape, output_shape, winograd_info, data_type);
     }
 
 protected:
-
     void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst)
     {
         const DataLayout data_layout_src = src.info()->data_layout();
@@ -425,6 +426,8 @@ protected:
         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
+        add_padding_x({ &src, &dst }, data_layout);
+
         // Allocate tensors
         src.allocator()->allocate();
         dst.allocator()->allocate();
@@ -458,7 +461,7 @@ protected:
         return reference::winograd_filter_transform<T>(src, output_shape, winograd_info);
     }
 
-    bool    _mixed_layout {false};
+    bool            _mixed_layout{ false };
     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };
@@ -475,7 +478,6 @@ public:
     }
 
 protected:
-
     void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst)
     {
         const DataLayout data_layout_src = src.info()->data_layout();
@@ -534,6 +536,8 @@ protected:
         ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
+        add_padding_x({ &src, &bias, &dst }, winograd_info.output_data_layout);
+
         // Allocate tensors
         src.allocator()->allocate();
         bias.allocator()->allocate();
@@ -577,7 +581,7 @@ protected:
         return (act_info.enabled()) ? reference::activation_layer<T>(winograd_output, act_info) : winograd_output;
     }
 
-    bool    _mixed_layout {false};
+    bool            _mixed_layout{ false };
     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };