tests/validation/NEON/BatchNormalizationLayer.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 "NEON/NEAccessor.h"
 #include "TypePrinter.h"
 #include "dataset/BatchNormalizationLayerDataset.h"
 #include "tests/Globals.h"
 #include "tests/Utils.h"
 #include "tests/validation/Helpers.h"
 #include "validation/Datasets.h"
 #include "validation/Reference.h"
 #include "validation/Validation.h"

 #include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h"

 #include <random>

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

 namespace
 {
 const float tolerance_f = 1e-05; /**< Tolerance value for comparing reference's output against floating point implementation's output */
 const float tolerance_q = 3;     /**< Tolerance value for comparing reference's output against quantized implementation's output */

 /** Compute Neon batch normalization function.
  *
  * @param[in] shape     Shape of the input and output tensors.
  * @param[in] dt        Data type of input and output tensors.
  * @param[in] norm_info Normalization Layer information.
  *
  * @return Computed output tensor.
  */
 Tensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0)
 {
     // Create tensors
     Tensor src   = create_tensor<Tensor>(shape0, dt, 1, fixed_point_position);
     Tensor dst   = create_tensor<Tensor>(shape0, dt, 1, fixed_point_position);
     Tensor mean  = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
     Tensor var   = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
     Tensor beta  = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
     Tensor gamma = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);

     // Create and configure function
     NEBatchNormalizationLayer norm;
     norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon);

     // Allocate tensors
     src.allocator()->allocate();
     dst.allocator()->allocate();
     mean.allocator()->allocate();
     var.allocator()->allocate();
     beta.allocator()->allocate();
     gamma.allocator()->allocate();

     BOOST_TEST(!src.info()->is_resizable());
     BOOST_TEST(!dst.info()->is_resizable());
     BOOST_TEST(!mean.info()->is_resizable());
     BOOST_TEST(!var.info()->is_resizable());
     BOOST_TEST(!beta.info()->is_resizable());
     BOOST_TEST(!gamma.info()->is_resizable());

     // Fill tensors
     if(dt == DataType::F32)
     {
         float min_bound = 0.f;
         float max_bound = 0.f;
         std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<float>();
         std::uniform_real_distribution<> distribution(min_bound, max_bound);
         std::uniform_real_distribution<> distribution_var(0, max_bound);
         library->fill(NEAccessor(src), distribution, 0);
         library->fill(NEAccessor(mean), distribution, 1);
         library->fill(NEAccessor(var), distribution_var, 0);
         library->fill(NEAccessor(beta), distribution, 3);
         library->fill(NEAccessor(gamma), distribution, 4);
     }
     else
     {
         int min_bound = 0;
         int max_bound = 0;
         std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<int8_t>(fixed_point_position);
         std::uniform_int_distribution<> distribution(min_bound, max_bound);
         std::uniform_int_distribution<> distribution_var(0, max_bound);
         library->fill(NEAccessor(src), distribution, 0);
         library->fill(NEAccessor(mean), distribution, 1);
         library->fill(NEAccessor(var), distribution_var, 0);
         library->fill(NEAccessor(beta), distribution, 3);
         library->fill(NEAccessor(gamma), distribution, 4);
     }

     // Compute function
     norm.run();

     return dst;
 }
 } // namespace

 #ifndef DOXYGEN_SKIP_THIS
 BOOST_AUTO_TEST_SUITE(NEON)
 BOOST_AUTO_TEST_SUITE(BatchNormalizationLayer)

 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly"))
 BOOST_DATA_TEST_CASE(Configuration, RandomBatchNormalizationLayerDataset() * (boost::unit_test::data::make(DataType::F32) + boost::unit_test::data::make(DataType::QS8)), obj, dt)
 {
     // Set fixed point position data type allowed
     int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0;

     // Create tensors
     Tensor src   = create_tensor<Tensor>(obj.shape0, dt, 1, fixed_point_position);
     Tensor dst   = create_tensor<Tensor>(obj.shape0, dt, 1, fixed_point_position);
     Tensor mean  = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);
     Tensor var   = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);
     Tensor beta  = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);
     Tensor gamma = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);

     BOOST_TEST(src.info()->is_resizable());
     BOOST_TEST(dst.info()->is_resizable());
     BOOST_TEST(mean.info()->is_resizable());
     BOOST_TEST(var.info()->is_resizable());
     BOOST_TEST(beta.info()->is_resizable());
     BOOST_TEST(gamma.info()->is_resizable());

     // Create and configure function
     NEBatchNormalizationLayer norm;
     norm.configure(&src, &dst, &mean, &var, &beta, &gamma, obj.epsilon);

     // Validate valid region
     const ValidRegion valid_region     = shape_to_valid_region(obj.shape0);
     const ValidRegion valid_region_vec = shape_to_valid_region(obj.shape1);
     validate(src.info()->valid_region(), valid_region);
     validate(dst.info()->valid_region(), valid_region);
     validate(mean.info()->valid_region(), valid_region_vec);
     validate(var.info()->valid_region(), valid_region_vec);
     validate(beta.info()->valid_region(), valid_region_vec);
     validate(gamma.info()->valid_region(), valid_region_vec);
 }

 BOOST_AUTO_TEST_SUITE(Float)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(Random,
                      RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::F32),
                      obj, dt)
 {
     // Compute function
     Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon);

     // Validate output
     validate(NEAccessor(dst), ref_dst, tolerance_f, 0);
 }
 BOOST_AUTO_TEST_SUITE_END()

 BOOST_AUTO_TEST_SUITE(Quantized)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(Random,
                      RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(1, 6),
                      obj, dt, fixed_point_position)
 {
     // Compute function
     Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position);

     // Validate output
     validate(NEAccessor(dst), ref_dst, tolerance_q, 0);
 }
 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 "NEON/NEAccessor.h"
	#include "TypePrinter.h"
	#include "dataset/BatchNormalizationLayerDataset.h"
	#include "tests/Globals.h"
	#include "tests/Utils.h"
	#include "tests/validation/Helpers.h"
	#include "validation/Datasets.h"
	#include "validation/Reference.h"
	#include "validation/Validation.h"

	#include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h"

	#include <random>

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

	namespace
	{
	const float tolerance_f = 1e-05; /*< Tolerance value for comparing reference's output against floating point implementation's output /
	const float tolerance_q = 3; /*< Tolerance value for comparing reference's output against quantized implementation's output /

	/** Compute Neon batch normalization function.
	*
	* @param[in] shape Shape of the input and output tensors.
	* @param[in] dt Data type of input and output tensors.
	* @param[in] norm_info Normalization Layer information.
	*
	* @return Computed output tensor.
	*/
	Tensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0)
	{
	// Create tensors
	Tensor src = create_tensor<Tensor>(shape0, dt, 1, fixed_point_position);
	Tensor dst = create_tensor<Tensor>(shape0, dt, 1, fixed_point_position);
	Tensor mean = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
	Tensor var = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
	Tensor beta = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
	Tensor gamma = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);

	// Create and configure function
	NEBatchNormalizationLayer norm;
	norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon);

	// Allocate tensors
	src.allocator()->allocate();
	dst.allocator()->allocate();
	mean.allocator()->allocate();
	var.allocator()->allocate();
	beta.allocator()->allocate();
	gamma.allocator()->allocate();

	BOOST_TEST(!src.info()->is_resizable());
	BOOST_TEST(!dst.info()->is_resizable());
	BOOST_TEST(!mean.info()->is_resizable());
	BOOST_TEST(!var.info()->is_resizable());
	BOOST_TEST(!beta.info()->is_resizable());
	BOOST_TEST(!gamma.info()->is_resizable());

	// Fill tensors
	if(dt == DataType::F32)
	{
	float min_bound = 0.f;
	float max_bound = 0.f;
	std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<float>();
	std::uniform_real_distribution<> distribution(min_bound, max_bound);
	std::uniform_real_distribution<> distribution_var(0, max_bound);
	library->fill(NEAccessor(src), distribution, 0);
	library->fill(NEAccessor(mean), distribution, 1);
	library->fill(NEAccessor(var), distribution_var, 0);
	library->fill(NEAccessor(beta), distribution, 3);
	library->fill(NEAccessor(gamma), distribution, 4);
	}
	else
	{
	int min_bound = 0;
	int max_bound = 0;
	std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<int8_t>(fixed_point_position);
	std::uniform_int_distribution<> distribution(min_bound, max_bound);
	std::uniform_int_distribution<> distribution_var(0, max_bound);
	library->fill(NEAccessor(src), distribution, 0);
	library->fill(NEAccessor(mean), distribution, 1);
	library->fill(NEAccessor(var), distribution_var, 0);
	library->fill(NEAccessor(beta), distribution, 3);
	library->fill(NEAccessor(gamma), distribution, 4);
	}

	// Compute function
	norm.run();

	return dst;
	}
	} // namespace

	#ifndef DOXYGEN_SKIP_THIS
	BOOST_AUTO_TEST_SUITE(NEON)
	BOOST_AUTO_TEST_SUITE(BatchNormalizationLayer)

	BOOST_TEST_DECORATOR(boost::unit_test::label("precommit") boost::unit_test::label("nightly"))
	BOOST_DATA_TEST_CASE(Configuration, RandomBatchNormalizationLayerDataset() * (boost::unit_test::data::make(DataType::F32) + boost::unit_test::data::make(DataType::QS8)), obj, dt)
	{
	// Set fixed point position data type allowed
	int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0;

	// Create tensors
	Tensor src = create_tensor<Tensor>(obj.shape0, dt, 1, fixed_point_position);
	Tensor dst = create_tensor<Tensor>(obj.shape0, dt, 1, fixed_point_position);
	Tensor mean = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);
	Tensor var = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);
	Tensor beta = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);
	Tensor gamma = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);

	BOOST_TEST(src.info()->is_resizable());
	BOOST_TEST(dst.info()->is_resizable());
	BOOST_TEST(mean.info()->is_resizable());
	BOOST_TEST(var.info()->is_resizable());
	BOOST_TEST(beta.info()->is_resizable());
	BOOST_TEST(gamma.info()->is_resizable());

	// Create and configure function
	NEBatchNormalizationLayer norm;
	norm.configure(&src, &dst, &mean, &var, &beta, &gamma, obj.epsilon);

	// Validate valid region
	const ValidRegion valid_region = shape_to_valid_region(obj.shape0);
	const ValidRegion valid_region_vec = shape_to_valid_region(obj.shape1);
	validate(src.info()->valid_region(), valid_region);
	validate(dst.info()->valid_region(), valid_region);
	validate(mean.info()->valid_region(), valid_region_vec);
	validate(var.info()->valid_region(), valid_region_vec);
	validate(beta.info()->valid_region(), valid_region_vec);
	validate(gamma.info()->valid_region(), valid_region_vec);
	}

	BOOST_AUTO_TEST_SUITE(Float)
	BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
	BOOST_DATA_TEST_CASE(Random,
	RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::F32),
	obj, dt)
	{
	// Compute function
	Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon);

	// Validate output
	validate(NEAccessor(dst), ref_dst, tolerance_f, 0);
	}
	BOOST_AUTO_TEST_SUITE_END()

	BOOST_AUTO_TEST_SUITE(Quantized)
	BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
	BOOST_DATA_TEST_CASE(Random,
	RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(1, 6),
	obj, dt, fixed_point_position)
	{
	// Compute function
	Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position);

	// Validate output
	validate(NEAccessor(dst), ref_dst, tolerance_q, 0);
	}
	BOOST_AUTO_TEST_SUITE_END()

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