COMPMID-415: Move NormalizationLayer to new validation

Change-Id: Icf5781c920836fe87d2db27ca3f9cc4eb2bea554
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/80999
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>

4 files changed, 579 insertions, 0 deletions

diff --git a/tests/validation_new/CPP/NormalizationLayer.cpp b/tests/validation_new/CPP/NormalizationLayer.cpp
new file mode 100644
index 0000000000..72f49007cc
--- /dev/null
+++ b/tests/validation_new/CPP/NormalizationLayer.cpp
@@ -0,0 +1,274 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "NormalizationLayer.h"
+
+#include "tests/validation_new/FixedPoint.h"
+#include "tests/validation_new/half.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
+SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info)
+{
+    // Create reference
+    SimpleTensor<T> dst{ src.shape(), src.data_type(), 1, src.fixed_point_position() };
+
+    // Compute reference
+    const uint32_t norm_size = info.norm_size();
+    NormType       type      = info.type();
+    float          beta      = info.beta();
+    uint32_t       kappa     = info.kappa();
+
+    const int cols       = src.shape()[0];
+    const int rows       = src.shape()[1];
+    const int depth      = src.shape()[2];
+    int       upper_dims = src.shape().total_size() / (cols * rows);
+
+    float coeff       = info.scale_coeff();
+    int   radius_cols = norm_size / 2;
+
+    // IN_MAP_1D and CROSS_MAP normalize over a single axis only
+    int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0;
+
+    if(type == NormType::CROSS_MAP)
+    {
+        // Remove also depth from upper dimensions since it is the dimension we
+        // want to use for normalization
+        upper_dims /= depth;
+
+        for(int r = 0; r < upper_dims; ++r)
+        {
+            for(int i = 0; i < rows; ++i)
+            {
+                for(int k = 0; k < cols; ++k)
+                {
+                    for(int l = 0; l < depth; ++l)
+                    {
+                        float accumulated_scale = 0.f;
+
+                        for(int j = -radius_cols; j <= radius_cols; ++j)
+                        {
+                            const int z = l + j;
+
+                            if(z >= 0 && z < depth)
+                            {
+                                const T value = src[k + i * cols + z * rows * cols + r * cols * rows * depth];
+                                accumulated_scale += value * value;
+                            }
+                        }
+
+                        dst[k + i * cols + l * rows * cols + r * cols * rows * depth] = kappa + accumulated_scale * coeff;
+                    }
+                }
+            }
+        }
+    }
+    else
+    {
+        for(int r = 0; r < upper_dims; ++r)
+        {
+            for(int i = 0; i < rows; ++i)
+            {
+                for(int k = 0; k < cols; ++k)
+                {
+                    float accumulated_scale = 0.f;
+
+                    for(int j = -radius_rows; j <= radius_rows; ++j)
+                    {
+                        const int y = i + j;
+                        for(int l = -radius_cols; l <= radius_cols; ++l)
+                        {
+                            const int x = k + l;
+
+                            if((x >= 0 && y >= 0) && (x < cols && y < rows))
+                            {
+                                const T value = src[x + y * cols + r * cols * rows];
+                                accumulated_scale += value * value;
+                            }
+                        }
+                    }
+
+                    dst[k + i * cols + r * cols * rows] = kappa + accumulated_scale * coeff;
+                }
+            }
+        }
+    }
+
+    if(beta == 1.f)
+    {
+        for(int i = 0; i < dst.num_elements(); ++i)
+        {
+            dst[i] = src[i] / dst[i];
+        }
+    }
+    else if(beta == 0.5f)
+    {
+        for(int i = 0; i < dst.num_elements(); ++i)
+        {
+            dst[i] = src[i] / std::sqrt(dst[i]);
+        }
+    }
+    else
+    {
+        for(int i = 0; i < dst.num_elements(); ++i)
+        {
+            dst[i] = src[i] * std::exp(std::log(dst[i]) * -beta);
+        }
+    }
+
+    return dst;
+}
+
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type>
+SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info)
+{
+    using namespace fixed_point_arithmetic;
+
+    // Create reference
+    SimpleTensor<T> dst{ src.shape(), src.data_type(), 1, src.fixed_point_position() };
+
+    // Compute reference
+    const int fixed_point_position = src.fixed_point_position();
+
+    const uint32_t norm_size = info.norm_size();
+    NormType       type      = info.type();
+    fixed_point<T> beta(info.beta(), fixed_point_position);
+    fixed_point<T> kappa(info.kappa(), fixed_point_position);
+
+    const int cols       = src.shape()[0];
+    const int rows       = src.shape()[1];
+    const int depth      = src.shape()[2];
+    int       upper_dims = src.shape().total_size() / (cols * rows);
+
+    fixed_point<T> coeff(info.scale_coeff(), fixed_point_position);
+    int            radius_cols = norm_size / 2;
+
+    // IN_MAP_1D and CROSS_MAP normalize over a single axis only
+    int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0;
+
+    if(type == NormType::CROSS_MAP)
+    {
+        // Remove also depth from upper dimensions since it is the dimension we
+        // want to use for normalization
+        upper_dims /= depth;
+
+        for(int r = 0; r < upper_dims; ++r)
+        {
+            for(int i = 0; i < rows; ++i)
+            {
+                for(int k = 0; k < cols; ++k)
+                {
+                    for(int l = 0; l < depth; ++l)
+                    {
+                        fixed_point<T> accumulated_scale(0.f, fixed_point_position);
+
+                        for(int j = -radius_cols; j <= radius_cols; ++j)
+                        {
+                            const int z = l + j;
+
+                            if(z >= 0 && z < depth)
+                            {
+                                const T              value = src[k + i * cols + z * rows * cols + r * cols * rows * depth];
+                                const fixed_point<T> fp_value(value, fixed_point_position, true);
+                                accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value));
+                            }
+                        }
+
+                        accumulated_scale                                             = add(kappa, mul(accumulated_scale, coeff));
+                        dst[k + i * cols + l * rows * cols + r * cols * rows * depth] = accumulated_scale.raw();
+                    }
+                }
+            }
+        }
+    }
+    else
+    {
+        for(int r = 0; r < upper_dims; ++r)
+        {
+            for(int i = 0; i < rows; ++i)
+            {
+                for(int k = 0; k < cols; ++k)
+                {
+                    fixed_point<T> accumulated_scale(0.f, fixed_point_position);
+
+                    for(int j = -radius_rows; j <= radius_rows; ++j)
+                    {
+                        const int y = i + j;
+
+                        for(int l = -radius_cols; l <= radius_cols; ++l)
+                        {
+                            const int x = k + l;
+
+                            if((x >= 0 && y >= 0) && (x < cols && y < rows))
+                            {
+                                const T              value = src[x + y * cols + r * cols * rows];
+                                const fixed_point<T> fp_value(value, fixed_point_position, true);
+                                accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value));
+                            }
+                        }
+                    }
+
+                    accumulated_scale                   = add(kappa, mul(accumulated_scale, coeff));
+                    dst[k + i * cols + r * cols * rows] = accumulated_scale.raw();
+                }
+            }
+        }
+    }
+
+    if(info.beta() == 1.f)
+    {
+        for(int i = 0; i < dst.num_elements(); ++i)
+        {
+            fixed_point<T> res = div(fixed_point<T>(src[i], fixed_point_position, true), fixed_point<T>(dst[i], fixed_point_position, true));
+            dst[i]             = res.raw();
+        }
+    }
+    else
+    {
+        const fixed_point<T> beta(info.beta(), fixed_point_position);
+
+        for(int i = 0; i < dst.num_elements(); ++i)
+        {
+            fixed_point<T> res = pow(fixed_point<T>(dst[i], fixed_point_position, true), beta);
+            res                = div(fixed_point<T>(src[i], fixed_point_position, true), res);
+            dst[i]             = res.raw();
+        }
+    }
+
+    return dst;
+}
+
+template SimpleTensor<float> normalization_layer(const SimpleTensor<float> &src, NormalizationLayerInfo info);
+template SimpleTensor<half_float::half> normalization_layer(const SimpleTensor<half_float::half> &src, NormalizationLayerInfo info);
+template SimpleTensor<qint8_t> normalization_layer(const SimpleTensor<qint8_t> &src, NormalizationLayerInfo info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation_new/CPP/NormalizationLayer.h b/tests/validation_new/CPP/NormalizationLayer.h
new file mode 100644
index 0000000000..54284b1d50
--- /dev/null
+++ b/tests/validation_new/CPP/NormalizationLayer.h
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef __ARM_COMPUTE_TEST_NORMALIZATION_LAYER_H__
+#define __ARM_COMPUTE_TEST_NORMALIZATION_LAYER_H__
+
+#include "tests/validation_new/Helpers.h"
+#include "tests/validation_new/SimpleTensor.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
+SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info);
+
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
+SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_TEST_NORMALIZATION_LAYER_H__ */
diff --git a/tests/validation_new/NEON/NormalizationLayer.cpp b/tests/validation_new/NEON/NormalizationLayer.cpp
new file mode 100644
index 0000000000..f364975332
--- /dev/null
+++ b/tests/validation_new/NEON/NormalizationLayer.cpp
@@ -0,0 +1,125 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/NEON/functions/NENormalizationLayer.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "arm_compute/runtime/TensorAllocator.h"
+#include "framework/Asserts.h"
+#include "framework/Macros.h"
+#include "framework/datasets/Datasets.h"
+#include "tests/NEON/Accessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets_new/NormalizationTypesDataset.h"
+#include "tests/datasets_new/ShapeDatasets.h"
+#include "tests/validation_new/Validation.h"
+#include "tests/validation_new/fixtures/NormalizationLayerFixture.h"
+#include "tests/validation_new/half.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+/** Tolerance for float operations */
+#ifdef ARM_COMPUTE_ENABLE_FP16
+constexpr float tolerance_f16 = 0.001f;
+#endif /* ARM_COMPUTE_ENABLE_FP16 */
+constexpr float tolerance_f32 = 0.00001f;
+/** Tolerance for fixed point operations */
+constexpr int8_t tolerance_qs8 = 2;
+
+/** Input data set. */
+const auto NormalizationDataset = combine(combine(combine(datasets::SmallShapes(), datasets::NormalizationTypes()), framework::dataset::make("NormalizationSize", 3, 9, 2)),
+                                          framework::dataset::make("Beta", { 0.5f, 1.f, 2.f }));
+} // namespace
+
+TEST_SUITE(NEON)
+TEST_SUITE(NormalizationLayer)
+
+//TODO(COMPMID-415): Missing configuration?
+
+template <typename T>
+using NENormalizationLayerFixture = NormalizationValidationFixture<Tensor, Accessor, NENormalizationLayer, T>;
+
+TEST_SUITE(Float)
+#ifdef ARM_COMPUTE_ENABLE_FP16
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, NENormalizationLayerFixture<half_float::half>, framework::DatasetMode::PRECOMMIT, combine(NormalizationDataset, framework::dataset::make("DataType", DataType::F16)))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, tolerance_f16);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixture<half_float::half>, framework::DatasetMode::NIGHTLY, combine(NormalizationDataset, framework::dataset::make("DataType", DataType::F16)))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END()
+#endif /* ARM_COMPUTE_ENABLE_FP16 */
+
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, NENormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(NormalizationDataset, framework::dataset::make("DataType", DataType::F32)))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(NormalizationDataset, framework::dataset::make("DataType", DataType::F32)))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+template <typename T>
+using NENormalizationLayerFixedPointFixture = NormalizationValidationFixedPointFixture<Tensor, Accessor, NENormalizationLayer, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QS8)
+// Testing for fixed point position [1,6) as reciprocal limits the maximum fixed point position to 5
+FIXTURE_DATA_TEST_CASE(RunSmall, NENormalizationLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(NormalizationDataset, framework::dataset::make("DataType",
+                       DataType::QS8)),
+                       framework::dataset::make("FractionalBits", 1, 6)))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, tolerance_qs8);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(NormalizationDataset, framework::dataset::make("DataType",
+                       DataType::QS8)),
+                       framework::dataset::make("FractionalBits", 1, 6)))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, tolerance_qs8);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation_new/fixtures/NormalizationLayerFixture.h b/tests/validation_new/fixtures/NormalizationLayerFixture.h
new file mode 100644
index 0000000000..044405473b
--- /dev/null
+++ b/tests/validation_new/fixtures/NormalizationLayerFixture.h
@@ -0,0 +1,133 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_TEST_NORMALIZATION_LAYER_FIXTURE
+#define ARM_COMPUTE_TEST_NORMALIZATION_LAYER_FIXTURE
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "framework/Asserts.h"
+#include "framework/Fixture.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/validation_new/CPP/NormalizationLayer.h"
+
+#include <random>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class NormalizationValidationFixedPointFixture : public framework::Fixture
+{
+public:
+    template <typename...>
+    void setup(TensorShape shape, NormType norm_type, int norm_size, float beta, DataType data_type, int fractional_bits)
+    {
+        _fractional_bits = fractional_bits;
+        NormalizationLayerInfo info(norm_type, norm_size, 5, beta);
+
+        _target    = compute_target(shape, info, data_type, fractional_bits);
+        _reference = compute_reference(shape, info, data_type, fractional_bits);
+    }
+
+protected:
+    template <typename U>
+    void fill(U &&tensor)
+    {
+        if(_fractional_bits == 0)
+        {
+            library->fill_tensor_uniform(tensor, 0);
+        }
+        else
+        {
+            const int                       one_fixed = 1 << _fractional_bits;
+            std::uniform_int_distribution<> distribution(-one_fixed, one_fixed);
+            library->fill(tensor, distribution, 0);
+        }
+    }
+
+    TensorType compute_target(const TensorShape &shape, NormalizationLayerInfo info, DataType data_type, int fixed_point_position = 0)
+    {
+        // Create tensors
+        TensorType src = create_tensor<TensorType>(shape, data_type, 1, fixed_point_position);
+        TensorType dst = create_tensor<TensorType>(shape, data_type, 1, fixed_point_position);
+
+        // Create and configure function
+        FunctionType norm_layer;
+        norm_layer.configure(&src, &dst, info);
+
+        ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Allocate tensors
+        src.allocator()->allocate();
+        dst.allocator()->allocate();
+
+        ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Fill tensors
+        fill(AccessorType(src));
+
+        // Compute function
+        norm_layer.run();
+
+        return dst;
+    }
+
+    SimpleTensor<T> compute_reference(const TensorShape &shape, NormalizationLayerInfo info, DataType data_type, int fixed_point_position = 0)
+    {
+        // Create reference
+        SimpleTensor<T> src{ shape, data_type, 1, fixed_point_position };
+
+        // Fill reference
+        fill(src);
+
+        return reference::normalization_layer<T>(src, info);
+    }
+
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+    int             _fractional_bits{};
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class NormalizationValidationFixture : public NormalizationValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(TensorShape shape, NormType norm_type, int norm_size, float beta, DataType data_type)
+    {
+        NormalizationValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, norm_type, norm_size, beta, data_type, 0);
+    }
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_NORMALIZATION_LAYER_FIXTURE */