diff options
Diffstat (limited to 'tests/validation/CL')
-rw-r--r-- | tests/validation/CL/ConvolutionLayer.cpp | 222 | ||||
-rw-r--r-- | tests/validation/CL/DirectConvolutionLayer.cpp | 197 |
2 files changed, 0 insertions, 419 deletions
diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp deleted file mode 100644 index 570077120e..0000000000 --- a/tests/validation/CL/ConvolutionLayer.cpp +++ /dev/null @@ -1,222 +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 "CL/CLAccessor.h" - -#include "TypePrinter.h" -#include "dataset/ConvolutionLayerDataset.h" -#include "tests/Globals.h" -#include "tests/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/CL/functions/CLConvolutionLayer.h" -#include "arm_compute/runtime/Tensor.h" -#include "arm_compute/runtime/TensorAllocator.h" - -#include <random> - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -const float tolerance_f16 = 0.1f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ -const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ -const float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ - -CLTensor 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 - CLTensor src = create_tensor<CLTensor>(input_shape, dt, 1, fixed_point_position); - CLTensor weights = create_tensor<CLTensor>(weights_shape, dt, 1, fixed_point_position); - CLTensor bias = create_tensor<CLTensor>(bias_shape, dt, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(output_shape, dt, 1, fixed_point_position); - - // Create and configure function - CLConvolutionLayer 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 || dt == DataType::F16) - { - std::uniform_real_distribution<> distribution(-1.0f, 1.0f); - library->fill(CLAccessor(src), distribution, 0); - library->fill(CLAccessor(weights), distribution, 1); - library->fill(CLAccessor(bias), distribution, 2); - } - else - { - library->fill_tensor_uniform(CLAccessor(src), 0); - library->fill_tensor_uniform(CLAccessor(weights), 1); - library->fill_tensor_uniform(CLAccessor(bias), 2); - } - - // Compute CLConvolutionLayer function - conv.run(); - - return dst; -} -} // namespace - -#ifndef DOXYGEN_SKIP_THIS -BOOST_AUTO_TEST_SUITE(CL) -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, DataType::QS16 }), - conv_set, dt) -{ - // Set fixed point position data type allowed - int fixed_point_position = (dt == DataType::F32) ? 0 : 3; - - // Create tensors - CLTensor src = create_tensor<CLTensor>(conv_set.src_shape, dt, 1, fixed_point_position); - CLTensor weights = create_tensor<CLTensor>(conv_set.weights_shape, dt, 1, fixed_point_position); - CLTensor bias = create_tensor<CLTensor>(conv_set.bias_shape, dt, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(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 - CLConvolutionLayer 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(Float16) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(SmallConvolutionLayer, - SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F16), - conv_set, dt) -{ - // Compute function - CLTensor 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(CLAccessor(dst), ref_dst, tolerance_f16); -} -BOOST_AUTO_TEST_SUITE_END() - -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 - CLTensor 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(CLAccessor(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 - CLTensor 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(CLAccessor(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, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), - conv_set, dt, fixed_point_position) -{ - // Compute function - CLTensor 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(CLAccessor(dst), ref_dst, tolerance_q); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(LargeConvolutionLayer, - AlexNetConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), - conv_set, dt, fixed_point_position) -{ - // Compute function - CLTensor 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(CLAccessor(dst), ref_dst, tolerance_q); -} -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/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp deleted file mode 100644 index d9dd34b9ec..0000000000 --- a/tests/validation/CL/DirectConvolutionLayer.cpp +++ /dev/null @@ -1,197 +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 "CL/CLAccessor.h" -#include "Globals.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/CL/functions/CLDirectConvolutionLayer.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 direct convolution layer - * - * @param[in] dt DataType of the tensor. - * - * @return Tolerance depending on the data type. - */ -float direct_convolution_layer_tolerance(DataType dt) -{ - switch(dt) - { - case DataType::F16: - return 0.1f; - case DataType::F32: - return 1e-3f; - default: - return 0.f; - } -} - -/** Compute CL 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. -*/ -CLTensor 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 - CLTensor src = create_tensor<CLTensor>(src_shape, dt, 1, fixed_point_position); - CLTensor weights = create_tensor<CLTensor>(weights_shape, dt, 1, fixed_point_position); - - CLTensor bias = create_tensor<CLTensor>(bias_shape, dt, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(dst_shape, dt, 1, fixed_point_position); - - // Create and configure function - CLDirectConvolutionLayer conv_layer; - conv_layer.configure(&src, &weights, &bias, &dst, conv_info); - - // Allocate tensors - src.allocator()->allocate(); - weights.allocator()->allocate(); - dst.allocator()->allocate(); - bias.allocator()->allocate(); - - BOOST_TEST(!src.info()->is_resizable()); - BOOST_TEST(!weights.info()->is_resizable()); - BOOST_TEST(!dst.info()->is_resizable()); - BOOST_TEST(!bias.info()->is_resizable()); - - // Fill tensors - switch(dt) - { - case DataType::F16: - case DataType::F32: - { - std::uniform_real_distribution<> distribution(-1.f, 1.f); - library->fill(CLAccessor(src), distribution, 0); - library->fill(CLAccessor(weights), distribution, 1); - library->fill(CLAccessor(bias), distribution, 2); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - } - } - - // 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<unsigned int, unsigned int> 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(CL) -BOOST_AUTO_TEST_SUITE(DirectConvolutionLayer) - -BOOST_AUTO_TEST_SUITE(Float) - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W1x1, DirectConvolutionShapes() * boost::unit_test::data::make({ DataType::F16, DataType::F32 }) * boost::unit_test::data::xrange(1, 4, 1) * boost::unit_test::data::xrange(1, 4, - 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<unsigned int>(num_kernels)); - const TensorShape b_shape(static_cast<unsigned int>(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - CLTensor 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(CLAccessor(dst), ref, direct_convolution_layer_tolerance(dt)); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::make({ DataType::F16, DataType::F32 }) * 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<unsigned int>(num_kernels)); - const TensorShape b_shape(static_cast<unsigned int>(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - CLTensor 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(CLAccessor(dst), ref, direct_convolution_layer_tolerance(dt)); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() -#endif /* DOXYGEN_SKIP_THIS */ |