tests/validation/NEON/ConvolutionLayer.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/Helper.h"
 #include "NEON/NEAccessor.h"
 #include "TypePrinter.h"
 #include "dataset/ConvolutionLayerDataset.h"
 #include "validation/Datasets.h"
 #include "validation/Reference.h"
 #include "validation/Validation.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.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_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
 const float tolerance_qs8 = 3.0f;   /**< Tolerance value for comparing reference's output against implementation's output for DataType::QS8 */

 Tensor compute_convolution_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt,
                                  const PadStrideInfo &conv_info, int fixed_point_position)
 {
     // Create tensors
     Tensor src     = create_tensor(input_shape, dt, 1, fixed_point_position);
     Tensor weights = create_tensor(weights_shape, dt, 1, fixed_point_position);
     Tensor bias    = create_tensor(bias_shape, dt, 1, fixed_point_position);
     Tensor dst     = create_tensor(output_shape, dt, 1, fixed_point_position);

     // Create and configure function
     NEConvolutionLayer conv;
     conv.configure(&src, &weights, &bias, &dst, conv_info);

     // Allocate tensors
     src.allocator()->allocate();
     weights.allocator()->allocate();
     bias.allocator()->allocate();
     dst.allocator()->allocate();

     BOOST_TEST(!src.info()->is_resizable());
     BOOST_TEST(!weights.info()->is_resizable());
     BOOST_TEST(!bias.info()->is_resizable());
     BOOST_TEST(!dst.info()->is_resizable());

     // Fill tensors
     if(dt == DataType::F32)
     {
         std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
         library->fill(NEAccessor(src), distribution, 0);
         library->fill(NEAccessor(weights), distribution, 1);
         library->fill(NEAccessor(bias), distribution, 2);
     }
     else
     {
         library->fill_tensor_uniform(NEAccessor(src), 0);
         library->fill_tensor_uniform(NEAccessor(weights), 1);
         library->fill_tensor_uniform(NEAccessor(bias), 2);
     }

     // Compute NEConvolutionLayer function
     conv.run();

     return dst;
 }
 } // namespace

 #ifndef DOXYGEN_SKIP_THIS
 BOOST_AUTO_TEST_SUITE(NEON)
 BOOST_AUTO_TEST_SUITE(ConvolutionLayer)
 BOOST_AUTO_TEST_SUITE(GEMM)

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

     // Create tensors
     Tensor src     = create_tensor(conv_set.src_shape, dt, 1, fixed_point_position);
     Tensor weights = create_tensor(conv_set.weights_shape, dt, 1, fixed_point_position);
     Tensor bias    = create_tensor(conv_set.bias_shape, dt, 1, fixed_point_position);
     Tensor dst     = create_tensor(conv_set.dst_shape, dt, 1, fixed_point_position);

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

     // Create and configure function
     NEConvolutionLayer conv;
     conv.configure(&src, &weights, &bias, &dst, conv_set.info);

     // Validate valid region
     const ValidRegion src_valid_region     = shape_to_valid_region(conv_set.src_shape);
     const ValidRegion weights_valid_region = shape_to_valid_region(conv_set.weights_shape);
     const ValidRegion bias_valid_region    = shape_to_valid_region(conv_set.bias_shape);
     const ValidRegion dst_valid_region     = shape_to_valid_region(conv_set.dst_shape);

     validate(src.info()->valid_region(), src_valid_region);
     validate(weights.info()->valid_region(), weights_valid_region);
     validate(bias.info()->valid_region(), bias_valid_region);
     validate(dst.info()->valid_region(), dst_valid_region);
 }

 BOOST_AUTO_TEST_SUITE(Float)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(SmallConvolutionLayer,
                      SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F32),
                      conv_set, dt)
 {
     // Compute function
     Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0);

     // Validate output
     validate(NEAccessor(dst), ref_dst, tolerance_f32);
 }

 BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
 BOOST_DATA_TEST_CASE(LargeConvolutionLayer,
                      AlexNetConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F32),
                      conv_set, dt)
 {
     // Compute function
     Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0);

     // Validate output
     validate(NEAccessor(dst), ref_dst, tolerance_f32);
 }
 BOOST_AUTO_TEST_SUITE_END()

 BOOST_AUTO_TEST_SUITE(Quantized)
 BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
 BOOST_DATA_TEST_CASE(SmallConvolutionLayer,
                      SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(4, 7),
                      conv_set, dt, fixed_point_position)
 {
     // Compute function
     Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);

     // Validate output
     validate(NEAccessor(dst), ref_dst, tolerance_qs8);
 }

 BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
 BOOST_DATA_TEST_CASE(LargeConvolutionLayer,
                      AlexNetConvolutionLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(4, 7),
                      conv_set, dt, fixed_point_position)
 {
     // Compute function
     Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);

     // Compute reference
     RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);

     // Validate output
     validate(NEAccessor(dst), ref_dst, tolerance_qs8);
 }
 BOOST_AUTO_TEST_SUITE_END()

 BOOST_AUTO_TEST_SUITE_END()
 BOOST_AUTO_TEST_SUITE_END()
 BOOST_AUTO_TEST_SUITE_END()
 #endif
	/*
	* 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/Helper.h"
	#include "NEON/NEAccessor.h"
	#include "TypePrinter.h"
	#include "dataset/ConvolutionLayerDataset.h"
	#include "validation/Datasets.h"
	#include "validation/Reference.h"
	#include "validation/Validation.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.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_f32 = 1e-03f; /*< Tolerance value for comparing reference's output against implementation's output for DataType::F32 /
	const float tolerance_qs8 = 3.0f; /*< Tolerance value for comparing reference's output against implementation's output for DataType::QS8 /

	Tensor compute_convolution_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt,
	const PadStrideInfo &conv_info, int fixed_point_position)
	{
	// Create tensors
	Tensor src = create_tensor(input_shape, dt, 1, fixed_point_position);
	Tensor weights = create_tensor(weights_shape, dt, 1, fixed_point_position);
	Tensor bias = create_tensor(bias_shape, dt, 1, fixed_point_position);
	Tensor dst = create_tensor(output_shape, dt, 1, fixed_point_position);

	// Create and configure function
	NEConvolutionLayer conv;
	conv.configure(&src, &weights, &bias, &dst, conv_info);

	// Allocate tensors
	src.allocator()->allocate();
	weights.allocator()->allocate();
	bias.allocator()->allocate();
	dst.allocator()->allocate();

	BOOST_TEST(!src.info()->is_resizable());
	BOOST_TEST(!weights.info()->is_resizable());
	BOOST_TEST(!bias.info()->is_resizable());
	BOOST_TEST(!dst.info()->is_resizable());

	// Fill tensors
	if(dt == DataType::F32)
	{
	std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
	library->fill(NEAccessor(src), distribution, 0);
	library->fill(NEAccessor(weights), distribution, 1);
	library->fill(NEAccessor(bias), distribution, 2);
	}
	else
	{
	library->fill_tensor_uniform(NEAccessor(src), 0);
	library->fill_tensor_uniform(NEAccessor(weights), 1);
	library->fill_tensor_uniform(NEAccessor(bias), 2);
	}

	// Compute NEConvolutionLayer function
	conv.run();

	return dst;
	}
	} // namespace

	#ifndef DOXYGEN_SKIP_THIS
	BOOST_AUTO_TEST_SUITE(NEON)
	BOOST_AUTO_TEST_SUITE(ConvolutionLayer)
	BOOST_AUTO_TEST_SUITE(GEMM)

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

	// Create tensors
	Tensor src = create_tensor(conv_set.src_shape, dt, 1, fixed_point_position);
	Tensor weights = create_tensor(conv_set.weights_shape, dt, 1, fixed_point_position);
	Tensor bias = create_tensor(conv_set.bias_shape, dt, 1, fixed_point_position);
	Tensor dst = create_tensor(conv_set.dst_shape, dt, 1, fixed_point_position);

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

	// Create and configure function
	NEConvolutionLayer conv;
	conv.configure(&src, &weights, &bias, &dst, conv_set.info);

	// Validate valid region
	const ValidRegion src_valid_region = shape_to_valid_region(conv_set.src_shape);
	const ValidRegion weights_valid_region = shape_to_valid_region(conv_set.weights_shape);
	const ValidRegion bias_valid_region = shape_to_valid_region(conv_set.bias_shape);
	const ValidRegion dst_valid_region = shape_to_valid_region(conv_set.dst_shape);

	validate(src.info()->valid_region(), src_valid_region);
	validate(weights.info()->valid_region(), weights_valid_region);
	validate(bias.info()->valid_region(), bias_valid_region);
	validate(dst.info()->valid_region(), dst_valid_region);
	}

	BOOST_AUTO_TEST_SUITE(Float)
	BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
	BOOST_DATA_TEST_CASE(SmallConvolutionLayer,
	SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F32),
	conv_set, dt)
	{
	// Compute function
	Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0);

	// Validate output
	validate(NEAccessor(dst), ref_dst, tolerance_f32);
	}

	BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
	BOOST_DATA_TEST_CASE(LargeConvolutionLayer,
	AlexNetConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F32),
	conv_set, dt)
	{
	// Compute function
	Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0);

	// Validate output
	validate(NEAccessor(dst), ref_dst, tolerance_f32);
	}
	BOOST_AUTO_TEST_SUITE_END()

	BOOST_AUTO_TEST_SUITE(Quantized)
	BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
	BOOST_DATA_TEST_CASE(SmallConvolutionLayer,
	SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(4, 7),
	conv_set, dt, fixed_point_position)
	{
	// Compute function
	Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);

	// Validate output
	validate(NEAccessor(dst), ref_dst, tolerance_qs8);
	}

	BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
	BOOST_DATA_TEST_CASE(LargeConvolutionLayer,
	AlexNetConvolutionLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(4, 7),
	conv_set, dt, fixed_point_position)
	{
	// Compute function
	Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);

	// Compute reference
	RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position);

	// Validate output
	validate(NEAccessor(dst), ref_dst, tolerance_qs8);
	}
	BOOST_AUTO_TEST_SUITE_END()

	BOOST_AUTO_TEST_SUITE_END()
	BOOST_AUTO_TEST_SUITE_END()
	BOOST_AUTO_TEST_SUITE_END()
	#endif