From 27b386cb7596542a3296c32e41f7a5168b4d53be Mon Sep 17 00:00:00 2001 From: steniu01 Date: Tue, 18 Jul 2017 17:37:43 +0100 Subject: COMPMID-355 Implement 3x3 CL direct convolution Change-Id: I1b44dc375045964e65557f0ead57a7c12d6bf097 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/81418 Tested-by: Kaizen Reviewed-by: Anthony Barbier --- tests/validation/NEON/CMakeLists.txt | 2 +- tests/validation/NEON/ConvolutionLayerDirect.cpp | 264 ----------------------- tests/validation/NEON/DirectConvolutionLayer.cpp | 264 +++++++++++++++++++++++ 3 files changed, 265 insertions(+), 265 deletions(-) delete mode 100644 tests/validation/NEON/ConvolutionLayerDirect.cpp create mode 100644 tests/validation/NEON/DirectConvolutionLayer.cpp (limited to 'tests/validation/NEON') diff --git a/tests/validation/NEON/CMakeLists.txt b/tests/validation/NEON/CMakeLists.txt index 988e1633f5..9dda17d149 100644 --- a/tests/validation/NEON/CMakeLists.txt +++ b/tests/validation/NEON/CMakeLists.txt @@ -37,7 +37,7 @@ set(arm_compute_test_validation_NEON_SOURCE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/BitwiseXor.cpp ${CMAKE_CURRENT_SOURCE_DIR}/Box3x3.cpp ${CMAKE_CURRENT_SOURCE_DIR}/ConvolutionLayer.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/ConvolutionLayerDirect.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/DirectConvolutionLayer.cpp ${CMAKE_CURRENT_SOURCE_DIR}/DepthConvert.cpp ${CMAKE_CURRENT_SOURCE_DIR}/FillBorder.cpp ${CMAKE_CURRENT_SOURCE_DIR}/Fixedpoint/Exp_QS8.cpp diff --git a/tests/validation/NEON/ConvolutionLayerDirect.cpp b/tests/validation/NEON/ConvolutionLayerDirect.cpp deleted file mode 100644 index effb898428..0000000000 --- a/tests/validation/NEON/ConvolutionLayerDirect.cpp +++ /dev/null @@ -1,264 +0,0 @@ -/* - * 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 "TypePrinter.h" -#include "Utils.h" -#include "validation/Datasets.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/NEDirectConvolutionLayer.h" -#include "arm_compute/runtime/Tensor.h" -#include "arm_compute/runtime/TensorAllocator.h" - -#include "boost_wrapper.h" - -#include -#include -#include - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -const float tolerance_fp32 = 1e-3f; /**< Tolerance for floating point tests */ -#ifdef ARM_COMPUTE_ENABLE_FP16 -const float tolerance_fp16 = 0.01f; /**< Tolerance for half precision floating point tests */ -#endif /* ARM_COMPUTE_ENABLE_FP16 */ -const float tolerance_qs8 = 1; /**< Tolerance for fixed point tests */ - -/** Compute NEON direct convolution layer function. - * - * @param[in] src_shape Shape of the input tensor. - * @param[in] weights_shape Shape of the weights. - * @param[in] bias_shape Shape of the bias tensor. - * @param[in] dst_shape Shape of the output tensor. - * @param[in] dt Data type of input, convolution matrix and output tensors. - * @param[in] conv_info Padding and stride information. - * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers - * - * @return Computed output tensor. -*/ -Tensor compute_convolution_layer(const TensorShape &src_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &dst_shape, - DataType dt, PadStrideInfo conv_info, int fixed_point_position = 0) -{ - // Create tensors - Tensor src = create_tensor(src_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(dst_shape, dt, 1, fixed_point_position); - - // Create and configure function - NEDirectConvolutionLayer conv_layer; - conv_layer.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::F16 || dt == DataType::F32) - { - std::uniform_real_distribution<> distribution(-1.f, 1.f); - library->fill(Accessor(src), distribution, 0); - library->fill(Accessor(weights), distribution, 1); - library->fill(Accessor(bias), distribution, 2); - } - else - { - library->fill_tensor_uniform(Accessor(src), 0); - library->fill_tensor_uniform(Accessor(weights), 1); - library->fill_tensor_uniform(Accessor(bias), 2); - } - - // Compute function - conv_layer.run(); - - return dst; -} - -TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &conv_info) -{ - TensorShape out_shape(in_shape); - const std::pair scaled_dims = arm_compute::scaled_dimensions(in_shape.x(), - in_shape.y(), - kernel_shape.x(), - kernel_shape.y(), - conv_info); - out_shape.set(0, scaled_dims.first); - out_shape.set(1, scaled_dims.second); - out_shape.set(2, kernel_shape[3]); - return out_shape; -} - -} // namespace - -#ifndef DOXYGEN_SKIP_THIS -BOOST_AUTO_TEST_SUITE(NEON) -BOOST_AUTO_TEST_SUITE(ConvolutionLayer) -BOOST_AUTO_TEST_SUITE(Direct) - -#ifdef ARM_COMPUTE_ENABLE_FP16 -BOOST_AUTO_TEST_SUITE(Float16) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W1x1, - DirectConvolutionShapes() * boost::unit_test::data::make(DataType::F16) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), - input_shape, dt, sx, sy, num_kernels) -{ - const unsigned int kernel_size = 1; - const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); - const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); - const TensorShape b_shape(static_cast(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); - - // Validate output - validate(Accessor(dst), ref); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::make(DataType::F16) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, - 1) - * boost::unit_test::data::xrange(0, 2, - 1) - * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), - input_shape, dt, sx, sy, px, py, num_kernels) -{ - const unsigned int kernel_size = 3; - const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR); - const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); - const TensorShape b_shape(static_cast(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); - - // Validate output - validate(Accessor(dst), ref, tolerance_fp16); -} -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(W1x1, - DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), - input_shape, dt, sx, sy, num_kernels) -{ - const unsigned int kernel_size = 1; - const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); - const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); - const TensorShape b_shape(static_cast(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); - - // Validate output - validate(Accessor(dst), ref); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2, - 1) - * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), - input_shape, dt, sx, sy, px, py, num_kernels) -{ - const unsigned int kernel_size = 3; - const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR); - const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); - const TensorShape b_shape(static_cast(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); - - // Validate output - validate(Accessor(dst), ref, tolerance_fp32); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE(Quantized) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W1x1, - DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }), - input_shape, sx, sy, num_kernels, fixed_point_position) -{ - const unsigned int kernel_size = 1; - const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); - const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); - const TensorShape b_shape(static_cast(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); - - // Validate output - validate(Accessor(dst), ref); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2, 1) - * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }), - input_shape, sx, sy, px, py, num_kernels, fixed_point_position) -{ - const unsigned int kernel_size = 3; - const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR); - const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); - const TensorShape b_shape(static_cast(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); - - // Validate output - validate(Accessor(dst), ref, tolerance_qs8); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() -#endif /* DOXYGEN_SKIP_THIS */ diff --git a/tests/validation/NEON/DirectConvolutionLayer.cpp b/tests/validation/NEON/DirectConvolutionLayer.cpp new file mode 100644 index 0000000000..034a8b2045 --- /dev/null +++ b/tests/validation/NEON/DirectConvolutionLayer.cpp @@ -0,0 +1,264 @@ +/* + * 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 "TypePrinter.h" +#include "Utils.h" +#include "validation/Datasets.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/NEDirectConvolutionLayer.h" +#include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/TensorAllocator.h" + +#include "boost_wrapper.h" + +#include +#include +#include + +using namespace arm_compute; +using namespace arm_compute::test; +using namespace arm_compute::test::validation; + +namespace +{ +const float tolerance_fp32 = 1e-3f; /**< Tolerance for floating point tests */ +#ifdef ARM_COMPUTE_ENABLE_FP16 +const float tolerance_fp16 = 0.01f; /**< Tolerance for half precision floating point tests */ +#endif /* ARM_COMPUTE_ENABLE_FP16 */ +const float tolerance_qs8 = 1; /**< Tolerance for fixed point tests */ + +/** Compute NEON direct convolution layer function. + * + * @param[in] src_shape Shape of the input tensor. + * @param[in] weights_shape Shape of the weights. + * @param[in] bias_shape Shape of the bias tensor. + * @param[in] dst_shape Shape of the output tensor. + * @param[in] dt Data type of input, convolution matrix and output tensors. + * @param[in] conv_info Padding and stride information. + * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers + * + * @return Computed output tensor. +*/ +Tensor compute_convolution_layer(const TensorShape &src_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &dst_shape, + DataType dt, PadStrideInfo conv_info, int fixed_point_position = 0) +{ + // Create tensors + Tensor src = create_tensor(src_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(dst_shape, dt, 1, fixed_point_position); + + // Create and configure function + NEDirectConvolutionLayer conv_layer; + conv_layer.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::F16 || dt == DataType::F32) + { + std::uniform_real_distribution<> distribution(-1.f, 1.f); + library->fill(Accessor(src), distribution, 0); + library->fill(Accessor(weights), distribution, 1); + library->fill(Accessor(bias), distribution, 2); + } + else + { + library->fill_tensor_uniform(Accessor(src), 0); + library->fill_tensor_uniform(Accessor(weights), 1); + library->fill_tensor_uniform(Accessor(bias), 2); + } + + // Compute function + conv_layer.run(); + + return dst; +} + +TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &conv_info) +{ + TensorShape out_shape(in_shape); + const std::pair scaled_dims = arm_compute::scaled_dimensions(in_shape.x(), + in_shape.y(), + kernel_shape.x(), + kernel_shape.y(), + conv_info); + out_shape.set(0, scaled_dims.first); + out_shape.set(1, scaled_dims.second); + out_shape.set(2, kernel_shape[3]); + return out_shape; +} + +} // namespace + +#ifndef DOXYGEN_SKIP_THIS +BOOST_AUTO_TEST_SUITE(NEON) +BOOST_AUTO_TEST_SUITE(ConvolutionLayer) +BOOST_AUTO_TEST_SUITE(Direct) + +#ifdef ARM_COMPUTE_ENABLE_FP16 +BOOST_AUTO_TEST_SUITE(Float16) +BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) +BOOST_DATA_TEST_CASE(W1x1, + DirectConvolutionShapes() * boost::unit_test::data::make(DataType::F16) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), + input_shape, dt, sx, sy, num_kernels) +{ + const unsigned int kernel_size = 1; + const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); + const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); + const TensorShape b_shape(static_cast(num_kernels)); + const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); + + Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); + + RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); + + // Validate output + validate(NEAccessor(dst), ref); +} + +BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) +BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::make(DataType::F16) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, + 1) + * boost::unit_test::data::xrange(0, 2, + 1) + * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), + input_shape, dt, sx, sy, px, py, num_kernels) +{ + const unsigned int kernel_size = 3; + const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR); + const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); + const TensorShape b_shape(static_cast(num_kernels)); + const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); + + Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); + + RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); + + // Validate output + validate(NEAccessor(dst), ref, tolerance_fp16); +} +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(W1x1, + DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), + input_shape, dt, sx, sy, num_kernels) +{ + const unsigned int kernel_size = 1; + const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); + const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); + const TensorShape b_shape(static_cast(num_kernels)); + const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); + + Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); + + RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); + + // Validate output + validate(Accessor(dst), ref); +} + +BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) +BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2, + 1) + * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), + input_shape, dt, sx, sy, px, py, num_kernels) +{ + const unsigned int kernel_size = 3; + const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR); + const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); + const TensorShape b_shape(static_cast(num_kernels)); + const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); + + Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); + + RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); + + // Validate output + validate(Accessor(dst), ref, tolerance_fp32); +} +BOOST_AUTO_TEST_SUITE_END() + +BOOST_AUTO_TEST_SUITE(Quantized) +BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) +BOOST_DATA_TEST_CASE(W1x1, + DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }), + input_shape, sx, sy, num_kernels, fixed_point_position) +{ + const unsigned int kernel_size = 1; + const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); + const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); + const TensorShape b_shape(static_cast(num_kernels)); + const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); + + Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); + + RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); + + // Validate output + validate(Accessor(dst), ref); +} + +BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) +BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2, 1) + * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }), + input_shape, sx, sy, px, py, num_kernels, fixed_point_position) +{ + const unsigned int kernel_size = 3; + const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR); + const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast(num_kernels)); + const TensorShape b_shape(static_cast(num_kernels)); + const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); + + Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); + + RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); + + // Validate output + validate(Accessor(dst), ref, tolerance_qs8); +} +BOOST_AUTO_TEST_SUITE_END() + +BOOST_AUTO_TEST_SUITE_END() +BOOST_AUTO_TEST_SUITE_END() +BOOST_AUTO_TEST_SUITE_END() +#endif /* DOXYGEN_SKIP_THIS */ -- cgit v1.2.1