tests/validation/NEON/ActivationLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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 "AssetsLibrary.h"
 #include "Globals.h"
 #include "NEON/Accessor.h"
 #include "PaddingCalculator.h"
 #include "TypePrinter.h"
 #include "Utils.h"
 #include "validation/Datasets.h"
 #include "validation/Helpers.h"
 #include "validation/Reference.h"
 #include "validation/Validation.h"

 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
 #include "arm_compute/runtime/Tensor.h"
 #include "arm_compute/runtime/TensorAllocator.h"

 #include "boost_wrapper.h"

 #include <random>
 #include <string>
 #include <tuple>

 using namespace arm_compute;
 using namespace arm_compute::test;
 using namespace arm_compute::test::validation;

 namespace
 {
 /** Define tolerance of the activation layer
  *
  * @param[in] dt                   The data type used.
  * @param[in] activation           The activation function used.
  * @param[in] fixed_point_position Number of bits for the fractional part..
  *
  * @return Tolerance depending on the activation function.
  */
 float activation_layer_tolerance(DataType dt, ActivationLayerInfo::ActivationFunction activation, int fixed_point_position = 0)
 {
     switch(activation)
     {
         case ActivationLayerInfo::ActivationFunction::LOGISTIC:
         case ActivationLayerInfo::ActivationFunction::SOFT_RELU:
         case ActivationLayerInfo::ActivationFunction::SQRT:
         case ActivationLayerInfo::ActivationFunction::TANH:
             switch(dt)
             {
                 case DataType::QS8:
                     return 5.f;
                 case DataType::QS16:
                     return 11.f;
                 case DataType::F16:
                     return 0.01f;
                 default:
                     return 0.00001f;
             }
             break;
         default:
             return 0.f;
     }
 }

 /** Compute Neon activation layer function.
  *
  * @param[in] in_place             Compute the activation layer in-place.
  * @param[in] shape                Shape of the input and output tensors.
  * @param[in] dt                   Shape Data type of tensors.
  * @param[in] act_info             Activation layer information.
  * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of fixed point numbers.
  *
  * @return Computed output tensor.
  */
 Tensor compute_activation_layer(bool in_place, const TensorShape &shape, DataType dt, ActivationLayerInfo act_info, int fixed_point_position = 0)
 {
     // Create tensors
     Tensor src = create_tensor<Tensor>(shape, dt, 1, fixed_point_position);
     Tensor dst = create_tensor<Tensor>(shape, dt, 1, fixed_point_position);

     // Create and configure function
     NEActivationLayer act_layer;

     if(in_place)
     {
         act_layer.configure(&src, nullptr, act_info);
     }
     else
     {
         act_layer.configure(&src, &dst, act_info);
     }

     // Allocate tensors
     src.allocator()->allocate();
     BOOST_TEST(!src.info()->is_resizable());

     if(!in_place)
     {
         dst.allocator()->allocate();
         BOOST_TEST(!dst.info()->is_resizable());
     }
     // Fill tensors
     switch(dt)
     {
         case DataType::QS8:
         {
             const std::pair<int8_t, int8_t> bounds = get_activation_layer_test_bounds<int8_t>(act_info.activation(), fixed_point_position);
             std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
             library->fill(Accessor(src), distribution, 0);
             break;
         }
         case DataType::QS16:
         {
             const std::pair<int16_t, int16_t> bounds = get_activation_layer_test_bounds<int16_t>(act_info.activation(), fixed_point_position);
             std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
             library->fill(Accessor(src), distribution, 0);
             break;
         }
 #ifdef ARM_COMPUTE_ENABLE_FP16
         case DataType::F16:
         {
             const std::pair<float16_t, float16_t> bounds = get_activation_layer_test_bounds<float16_t>(act_info.activation());
             std::uniform_real_distribution<> distribution(bounds.first, bounds.second);
             library->fill(Accessor(src), distribution, 0);
             break;
         }
 #endif /* ARM_COMPUTE_ENABLE_FP16 */
         case DataType::F32:
         {
             const std::pair<float, float> bounds = get_activation_layer_test_bounds<float>(act_info.activation());
             std::uniform_real_distribution<> distribution(bounds.first, bounds.second);
             library->fill(Accessor(src), distribution, 0);
             break;
         }
         default:
         {
             ARM_COMPUTE_ERROR("Not supported");
             break;
         }
     }

     // Compute function
     act_layer.run();

     if(in_place)
     {
         return src;
     }
     else
     {
         return dst;
     }
 }
 } // namespace

 #ifndef DOXYGEN_SKIP_THIS
 BOOST_AUTO_TEST_SUITE(NEON)
 BOOST_AUTO_TEST_SUITE(ActivationLayer)

 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly"))
 BOOST_DATA_TEST_CASE(Configuration, boost::unit_test::data::make({ false, true }) * (SmallShapes() + LargeShapes()) * CNNDataTypes(), in_place, shape, dt)
 {
     // Set fixed point position data type allowed
     const int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0;

     // Create tensors
     Tensor src = create_tensor<Tensor>(shape, dt, 1, fixed_point_position);
     Tensor dst = create_tensor<Tensor>(shape, dt, 1, fixed_point_position);

     BOOST_TEST(src.info()->is_resizable());
     BOOST_TEST(dst.info()->is_resizable());

     // Create and configure function
     NEActivationLayer act_layer;

     if(in_place)
     {
         act_layer.configure(&src, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS));
     }
     else
     {
         act_layer.configure(&src, &dst, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS));
     }

     // Validate valid region
     const ValidRegion valid_region = shape_to_valid_region(shape);
     validate(src.info()->valid_region(), valid_region);

     if(!in_place)
     {
         validate(dst.info()->valid_region(), valid_region);
     }

     // Validate padding
     const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
     validate(src.info()->padding(), padding);

     if(!in_place)
     {
         validate(dst.info()->padding(), padding);
     }
 }

 #ifdef ARM_COMPUTE_ENABLE_FP16
 BOOST_AUTO_TEST_SUITE(Float16)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(RunSmall, boost::unit_test::data::make({ false, true }) * SmallShapes() * boost::unit_test::data::make(DataType::F16) * ActivationFunctions() * boost::unit_test::data::make({ 0.5f, 1.f }),
                      in_place, shape, dt, act_function, alpha_beta)
 {
     // Create activation layer info
     const ActivationLayerInfo act_info(act_function, alpha_beta);

     // Compute function
     Tensor dst = compute_activation_layer(in_place, shape, dt, act_info);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, dt, act_info);

     // Validate output
     validate(Accessor(dst), ref_dst, activation_layer_tolerance(dt, act_function));
 }
 BOOST_AUTO_TEST_SUITE_END()
 #endif /* ARM_COMPUTE_ENABLE_FP16 */

 BOOST_AUTO_TEST_SUITE(Float)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(RunSmall, boost::unit_test::data::make({ false, true }) * SmallShapes() * CNNFloatDataTypes() * ActivationFunctions() * boost::unit_test::data::make({ 0.5f, 1.f }),
                      in_place, shape, dt, act_function, alpha_beta)
 {
     // Create activation layer info
     ActivationLayerInfo act_info(act_function, alpha_beta, alpha_beta);

     // Compute function
     Tensor dst = compute_activation_layer(in_place, shape, dt, act_info);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, dt, act_info);

     // Validate output
     validate(Accessor(dst), ref_dst, activation_layer_tolerance(dt, act_function));
 }

 BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
 BOOST_DATA_TEST_CASE(RunLarge, boost::unit_test::data::make({ false, true }) * LargeShapes() * CNNFloatDataTypes() * ActivationFunctions() * boost::unit_test::data::make({ 0.5f, 1.f }),
                      in_place, shape, dt, act_function, alpha_beta)
 {
     // Create activation layer info
     ActivationLayerInfo act_info(act_function, alpha_beta, alpha_beta);

     // Compute function
     Tensor dst = compute_activation_layer(in_place, shape, dt, act_info);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, dt, act_info);

     // Validate output
     validate(Accessor(dst), ref_dst, activation_layer_tolerance(dt, act_function));
 }
 BOOST_AUTO_TEST_SUITE_END()

 /** @note We test for fixed point precision [3,5] because [1,2] and [6,7] ranges
  *        cause overflowing issues in most of the transcendentals functions.
  */
 BOOST_AUTO_TEST_SUITE(Quantized)
 BOOST_AUTO_TEST_SUITE(QS8)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(RunSmall, boost::unit_test::data::make({ false, true }) * SmallShapes() * ActivationFunctions() * boost::unit_test::data::xrange(3, 6, 1) * boost::unit_test::data::make({ 0.5f, 1.f }),
                      in_place, shape, act_function, fixed_point_position, alpha_beta)
 {
     // Create activation layer info
     ActivationLayerInfo act_info(act_function, alpha_beta, alpha_beta);

     // Compute function
     Tensor dst = compute_activation_layer(in_place, shape, DataType::QS8, act_info, fixed_point_position);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, DataType::QS8, act_info, fixed_point_position);

     // Validate output
     validate(Accessor(dst), ref_dst, activation_layer_tolerance(DataType::QS8, act_function, fixed_point_position));
 }
 BOOST_AUTO_TEST_SUITE_END()

 BOOST_AUTO_TEST_SUITE(QS16)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(RunSmall, boost::unit_test::data::make({ false, true }) * SmallShapes() * ActivationFunctions() * boost::unit_test::data::xrange(3, 14, 1) * boost::unit_test::data::make({ 0.5f, 1.f }),
                      in_place, shape, act_function, fixed_point_position, alpha_beta)
 {
     // Create activation layer info
     ActivationLayerInfo act_info(act_function, alpha_beta, alpha_beta);

     // Compute function
     Tensor dst = compute_activation_layer(in_place, shape, DataType::QS16, act_info, fixed_point_position);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, DataType::QS16, act_info, fixed_point_position);

     // Validate output
     validate(Accessor(dst), ref_dst, activation_layer_tolerance(DataType::QS16, act_function, fixed_point_position));
 }
 BOOST_AUTO_TEST_SUITE_END()

 BOOST_AUTO_TEST_SUITE_END()

 BOOST_AUTO_TEST_SUITE_END()
 BOOST_AUTO_TEST_SUITE_END()
 #endif /* DOXYGEN_SKIP_THIS */
	/*
	* 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 "AssetsLibrary.h"
	#include "Globals.h"
	#include "NEON/Accessor.h"
	#include "PaddingCalculator.h"
	#include "TypePrinter.h"
	#include "Utils.h"
	#include "validation/Datasets.h"
	#include "validation/Helpers.h"
	#include "validation/Reference.h"
	#include "validation/Validation.h"

	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
	#include "arm_compute/runtime/Tensor.h"
	#include "arm_compute/runtime/TensorAllocator.h"

	#include "boost_wrapper.h"

	#include <random>
	#include <string>
	#include <tuple>

	using namespace arm_compute;
	using namespace arm_compute::test;
	using namespace arm_compute::test::validation;

	namespace
	{
	/** Define tolerance of the activation layer
	*
	* @param[in] dt The data type used.
	* @param[in] activation The activation function used.
	* @param[in] fixed_point_position Number of bits for the fractional part..
	*
	* @return Tolerance depending on the activation function.
	*/
	float activation_layer_tolerance(DataType dt, ActivationLayerInfo::ActivationFunction activation, int fixed_point_position = 0)
	{
	switch(activation)
	{
	case ActivationLayerInfo::ActivationFunction::LOGISTIC:
	case ActivationLayerInfo::ActivationFunction::SOFT_RELU:
	case ActivationLayerInfo::ActivationFunction::SQRT:
	case ActivationLayerInfo::ActivationFunction::TANH:
	switch(dt)
	{
	case DataType::QS8:
	return 5.f;
	case DataType::QS16:
	return 11.f;
	case DataType::F16:
	return 0.01f;
	default:
	return 0.00001f;
	}
	break;
	default:
	return 0.f;
	}
	}

	/** Compute Neon activation layer function.
	*
	* @param[in] in_place Compute the activation layer in-place.
	* @param[in] shape Shape of the input and output tensors.
	* @param[in] dt Shape Data type of tensors.
	* @param[in] act_info Activation layer information.
	* @param[in] fixed_point_position (Optional) Number of bits for the fractional part of fixed point numbers.
	*
	* @return Computed output tensor.
	*/
	Tensor compute_activation_layer(bool in_place, const TensorShape &shape, DataType dt, ActivationLayerInfo act_info, int fixed_point_position = 0)
	{
	// Create tensors
	Tensor src = create_tensor<Tensor>(shape, dt, 1, fixed_point_position);
	Tensor dst = create_tensor<Tensor>(shape, dt, 1, fixed_point_position);

	// Create and configure function
	NEActivationLayer act_layer;

	if(in_place)
	{
	act_layer.configure(&src, nullptr, act_info);
	}
	else
	{
	act_layer.configure(&src, &dst, act_info);
	}

	// Allocate tensors
	src.allocator()->allocate();
	BOOST_TEST(!src.info()->is_resizable());

	if(!in_place)
	{
	dst.allocator()->allocate();
	BOOST_TEST(!dst.info()->is_resizable());
	}
	// Fill tensors
	switch(dt)
	{
	case DataType::QS8:
	{
	const std::pair<int8_t, int8_t> bounds = get_activation_layer_test_bounds<int8_t>(act_info.activation(), fixed_point_position);
	std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
	library->fill(Accessor(src), distribution, 0);
	break;
	}
	case DataType::QS16:
	{
	const std::pair<int16_t, int16_t> bounds = get_activation_layer_test_bounds<int16_t>(act_info.activation(), fixed_point_position);
	std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
	library->fill(Accessor(src), distribution, 0);
	break;
	}
	#ifdef ARM_COMPUTE_ENABLE_FP16
	case DataType::F16:
	{
	const std::pair<float16_t, float16_t> bounds = get_activation_layer_test_bounds<float16_t>(act_info.activation());
	std::uniform_real_distribution<> distribution(bounds.first, bounds.second);
	library->fill(Accessor(src), distribution, 0);
	break;
	}
	#endif /* ARM_COMPUTE_ENABLE_FP16 */
	case DataType::F32:
	{
	const std::pair<float, float> bounds = get_activation_layer_test_bounds<float>(act_info.activation());
	std::uniform_real_distribution<> distribution(bounds.first, bounds.second);
	library->fill(Accessor(src), distribution, 0);
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Not supported");
	break;
	}
	}

	// Compute function
	act_layer.run();

	if(in_place)
	{
	return src;
	}
	else
	{
	return dst;
	}
	}
	} // namespace

	#ifndef DOXYGEN_SKIP_THIS
	BOOST_AUTO_TEST_SUITE(NEON)
	BOOST_AUTO_TEST_SUITE(ActivationLayer)

	BOOST_TEST_DECORATOR(boost::unit_test::label("precommit") boost::unit_test::label("nightly"))
	BOOST_DATA_TEST_CASE(Configuration, boost::unit_test::data::make({ false, true }) * (SmallShapes() + LargeShapes()) * CNNDataTypes(), in_place, shape, dt)
	{
	// Set fixed point position data type allowed
	const int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0;

	// Create tensors
	Tensor src = create_tensor<Tensor>(shape, dt, 1, fixed_point_position);
	Tensor dst = create_tensor<Tensor>(shape, dt, 1, fixed_point_position);

	BOOST_TEST(src.info()->is_resizable());
	BOOST_TEST(dst.info()->is_resizable());

	// Create and configure function
	NEActivationLayer act_layer;

	if(in_place)
	{
	act_layer.configure(&src, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS));
	}
	else
	{
	act_layer.configure(&src, &dst, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS));
	}

	// Validate valid region
	const ValidRegion valid_region = shape_to_valid_region(shape);
	validate(src.info()->valid_region(), valid_region);

	if(!in_place)
	{
	validate(dst.info()->valid_region(), valid_region);
	}

	// Validate padding
	const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
	validate(src.info()->padding(), padding);

	if(!in_place)
	{
	validate(dst.info()->padding(), padding);
	}
	}

	#ifdef ARM_COMPUTE_ENABLE_FP16
	BOOST_AUTO_TEST_SUITE(Float16)
	BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
	BOOST_DATA_TEST_CASE(RunSmall, boost::unit_test::data::make({ false, true }) * SmallShapes() * boost::unit_test::data::make(DataType::F16) * ActivationFunctions() * boost::unit_test::data::make({ 0.5f, 1.f }),
	in_place, shape, dt, act_function, alpha_beta)
	{
	// Create activation layer info
	const ActivationLayerInfo act_info(act_function, alpha_beta);

	// Compute function
	Tensor dst = compute_activation_layer(in_place, shape, dt, act_info);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, dt, act_info);

	// Validate output
	validate(Accessor(dst), ref_dst, activation_layer_tolerance(dt, act_function));
	}
	BOOST_AUTO_TEST_SUITE_END()
	#endif /* ARM_COMPUTE_ENABLE_FP16 */

	BOOST_AUTO_TEST_SUITE(Float)
	BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
	BOOST_DATA_TEST_CASE(RunSmall, boost::unit_test::data::make({ false, true }) * SmallShapes() * CNNFloatDataTypes() * ActivationFunctions() * boost::unit_test::data::make({ 0.5f, 1.f }),
	in_place, shape, dt, act_function, alpha_beta)
	{
	// Create activation layer info
	ActivationLayerInfo act_info(act_function, alpha_beta, alpha_beta);

	// Compute function
	Tensor dst = compute_activation_layer(in_place, shape, dt, act_info);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, dt, act_info);

	// Validate output
	validate(Accessor(dst), ref_dst, activation_layer_tolerance(dt, act_function));
	}

	BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
	BOOST_DATA_TEST_CASE(RunLarge, boost::unit_test::data::make({ false, true }) * LargeShapes() * CNNFloatDataTypes() * ActivationFunctions() * boost::unit_test::data::make({ 0.5f, 1.f }),
	in_place, shape, dt, act_function, alpha_beta)
	{
	// Create activation layer info
	ActivationLayerInfo act_info(act_function, alpha_beta, alpha_beta);

	// Compute function
	Tensor dst = compute_activation_layer(in_place, shape, dt, act_info);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, dt, act_info);

	// Validate output
	validate(Accessor(dst), ref_dst, activation_layer_tolerance(dt, act_function));
	}
	BOOST_AUTO_TEST_SUITE_END()

	/** @note We test for fixed point precision [3,5] because [1,2] and [6,7] ranges
	* cause overflowing issues in most of the transcendentals functions.
	*/
	BOOST_AUTO_TEST_SUITE(Quantized)
	BOOST_AUTO_TEST_SUITE(QS8)
	BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
	BOOST_DATA_TEST_CASE(RunSmall, boost::unit_test::data::make({ false, true }) * SmallShapes() * ActivationFunctions() * boost::unit_test::data::xrange(3, 6, 1) * boost::unit_test::data::make({ 0.5f, 1.f }),
	in_place, shape, act_function, fixed_point_position, alpha_beta)
	{
	// Create activation layer info
	ActivationLayerInfo act_info(act_function, alpha_beta, alpha_beta);

	// Compute function
	Tensor dst = compute_activation_layer(in_place, shape, DataType::QS8, act_info, fixed_point_position);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, DataType::QS8, act_info, fixed_point_position);

	// Validate output
	validate(Accessor(dst), ref_dst, activation_layer_tolerance(DataType::QS8, act_function, fixed_point_position));
	}
	BOOST_AUTO_TEST_SUITE_END()

	BOOST_AUTO_TEST_SUITE(QS16)
	BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
	BOOST_DATA_TEST_CASE(RunSmall, boost::unit_test::data::make({ false, true }) * SmallShapes() * ActivationFunctions() * boost::unit_test::data::xrange(3, 14, 1) * boost::unit_test::data::make({ 0.5f, 1.f }),
	in_place, shape, act_function, fixed_point_position, alpha_beta)
	{
	// Create activation layer info
	ActivationLayerInfo act_info(act_function, alpha_beta, alpha_beta);

	// Compute function
	Tensor dst = compute_activation_layer(in_place, shape, DataType::QS16, act_info, fixed_point_position);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, DataType::QS16, act_info, fixed_point_position);

	// Validate output
	validate(Accessor(dst), ref_dst, activation_layer_tolerance(DataType::QS16, act_function, fixed_point_position));
	}
	BOOST_AUTO_TEST_SUITE_END()

	BOOST_AUTO_TEST_SUITE_END()

	BOOST_AUTO_TEST_SUITE_END()
	BOOST_AUTO_TEST_SUITE_END()
	#endif /* DOXYGEN_SKIP_THIS */