COMPMID-617 Add validation methods to Kernels

- NEActivationLayer
- NESoftmax
- NEDirectConvolutionLayer
- NENormalizationLayer
- NEPoolingLayer

Change-Id: Ib279f1c1b7f9247679b0d6593aed7393da8fe87b
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111335
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

5 files changed, 192 insertions, 0 deletions

diff --git a/tests/validation/NEON/ActivationLayer.cpp b/tests/validation/NEON/ActivationLayer.cpp
index b13cb59b9a..6ed482e4e7 100644
--- a/tests/validation/NEON/ActivationLayer.cpp
+++ b/tests/validation/NEON/ActivationLayer.cpp
@@ -137,6 +137,36 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(conca
     }
 }
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+    framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),     // Mismatching data types
+                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),     // Mismatching shapes
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 2),  // Mismatching fixed point
+                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2),
+                                          }),
+    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
+                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
+                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 3),
+                                            TensorInfo(),
+                                          })),
+    framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                               })),
+    framework::dataset::make("Expected", { false, true, false, false, true })),
+    input_info, output_info, act_info, expected)
+{
+    bool is_valid = bool(NEActivationLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), act_info));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 template <typename T>
 using NEActivationLayerFixture = ActivationValidationFixture<Tensor, Accessor, NEActivationLayer, T>;
 
diff --git a/tests/validation/NEON/DirectConvolutionLayer.cpp b/tests/validation/NEON/DirectConvolutionLayer.cpp
index 52e2b2c034..cd23ce4bc3 100644
--- a/tests/validation/NEON/DirectConvolutionLayer.cpp
+++ b/tests/validation/NEON/DirectConvolutionLayer.cpp
@@ -91,6 +91,68 @@ const auto data_qs16 = combine(datasets::SmallDirectConvolutionShapes(),
 TEST_SUITE(NEON)
 TEST_SUITE(DirectConvolutionLayer)
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+        framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type input/weights
+                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching input feature maps
+                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Unsupported kernel width
+                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Non-rectangular weights dimensions
+                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid weights dimensions
+                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid stride
+                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases size
+                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases dimensions
+                                                TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid output size
+                                              }),
+        framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16, 0),
+                                                 TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32, 0),
+                                                 TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32, 0),
+                                                 TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32, 0),
+                                                 TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32, 0),
+                                                 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+                                                 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+                                                 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+                                                 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0),
+                                              })),
+        framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(3U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(4U, 2U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(4U), 1, DataType::F32, 0),
+                                              })),
+        framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0),
+                                                TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32, 0),
+                                              })),
+        framework::dataset::make("ConvInfo",  { PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(3, 3, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                               })),
+        framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false })),
+        input_info, weights_info, biases_info, output_info, conv_info, expected)
+{
+        bool is_valid = bool(NEDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info));
+        ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 //TODO(COMPMID-415): Configuration tests?
 
 template <typename T>
diff --git a/tests/validation/NEON/NormalizationLayer.cpp b/tests/validation/NEON/NormalizationLayer.cpp
index 3afa52cb4c..4d504a8972 100644
--- a/tests/validation/NEON/NormalizationLayer.cpp
+++ b/tests/validation/NEON/NormalizationLayer.cpp
@@ -66,6 +66,42 @@ TEST_SUITE(NormalizationLayer)
 
 //TODO(COMPMID-415): Missing configuration?
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+    framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type input/output
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching shapes
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Even normalization
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Non implemented IN_MAP_2D
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 4), // Mismatching fixed point position
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Window shrink
+                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32, 0),
+                                          }),
+    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16, 0),
+                                            TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 3),
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32, 0),
+                                          })),
+    framework::dataset::make("NormInfo",  { NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
+                                            NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
+                                            NormalizationLayerInfo(NormType::IN_MAP_1D, 4),
+                                            NormalizationLayerInfo(NormType::IN_MAP_2D, 5),
+                                            NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
+                                            NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
+                                            NormalizationLayerInfo(NormType::CROSS_MAP, 1),
+                                           })),
+    framework::dataset::make("Expected", { false, false, false, false, false, false, true })),
+    input_info, output_info, norm_info, expected)
+{
+    bool is_valid = bool(NENormalizationLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), norm_info));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 template <typename T>
 using NENormalizationLayerFixture = NormalizationValidationFixture<Tensor, Accessor, NENormalizationLayer, T>;
 
diff --git a/tests/validation/NEON/PoolingLayer.cpp b/tests/validation/NEON/PoolingLayer.cpp
index 78c326975c..79a732a59a 100644
--- a/tests/validation/NEON/PoolingLayer.cpp
+++ b/tests/validation/NEON/PoolingLayer.cpp
@@ -66,6 +66,48 @@ TEST_SUITE(PoolingLayer)
 
 //TODO(COMPMID-415): Configuration tests?
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
+    framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),     // Mismatching data type
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),     // Window shrink
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 4),     // Mismatching fixed point position
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS16, 11),   // Window shrink
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),     // Invalid pad/size combination
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0),     // Invalid pad/size combination
+                                            TensorInfo(TensorShape(15U, 13U, 5U), 1, DataType::F32, 0),     // Non-rectangular Global Pooling
+                                            TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32, 0),     // Invalid output Global Pooling
+                                            TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32, 0),
+                                          }),
+    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16, 0),
+                                            TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::QS8, 5),
+                                            TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::QS16, 11),
+                                            TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(25U, 16U, 2U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(1U, 1U, 5U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(2U, 2U, 5U), 1, DataType::F32, 0),
+                                            TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+                                          })),
+    framework::dataset::make("PoolInfo",  { PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)),
+                                            PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)),
+                                            PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)),
+                                            PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 0, 0)),
+                                            PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(1, 1, 2, 0)),
+                                            PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(1, 1, 0, 2)),
+                                            PoolingLayerInfo(PoolingType::AVG),
+                                            PoolingLayerInfo(PoolingType::MAX),
+                                            PoolingLayerInfo(PoolingType::AVG),
+                                           })),
+    framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, true })),
+    input_info, output_info, pool_info, expected)
+{
+    bool is_valid = bool(NEPoolingLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pool_info));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 template <typename T>
 using NEPoolingLayerFixture = PoolingLayerValidationFixture<Tensor, Accessor, NEPoolingLayer, T>;
 
diff --git a/tests/validation/NEON/SoftmaxLayer.cpp b/tests/validation/NEON/SoftmaxLayer.cpp
index 1a303e14a3..0b688dfd1b 100644
--- a/tests/validation/NEON/SoftmaxLayer.cpp
+++ b/tests/validation/NEON/SoftmaxLayer.cpp
@@ -93,6 +93,28 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(concat(datase
     validate(dst.info()->padding(), padding);
 }
 
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(
+    framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),    // Mismatching data types
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),    // Mismatching shapes
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 2), // Mismatching fixed point
+                                            TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
+                                           }),
+    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
+                                            TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
+                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 3),
+                                            TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
+                                          })),
+    framework::dataset::make("Expected", { false, false, false, true })),
+    input_info, output_info, expected)
+{
+    bool is_valid = bool(NESoftmaxLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false)));
+    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
 template <typename T>
 using NESoftmaxLayerFixture = SoftmaxValidationFixture<Tensor, Accessor, NESoftmaxLayer, T>;