diff options
21 files changed, 1219 insertions, 1169 deletions
diff --git a/tests/datasets_new/LargeConvolutionLayerDataset.h b/tests/datasets_new/LargeConvolutionLayerDataset.h new file mode 100644 index 0000000000..6fef77009e --- /dev/null +++ b/tests/datasets_new/LargeConvolutionLayerDataset.h @@ -0,0 +1,57 @@ +/* + * 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. + */ +#ifndef ARM_COMPUTE_TEST_LARGE_CONVOLUTION_LAYER_DATASET +#define ARM_COMPUTE_TEST_LARGE_CONVOLUTION_LAYER_DATASET + +#include "tests/datasets_new/ConvolutionLayerDataset.h" + +#include "tests/TypePrinter.h" + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" + +namespace arm_compute +{ +namespace test +{ +namespace datasets +{ +class LargeConvolutionLayerDataset final : public ConvolutionLayerDataset +{ +public: + LargeConvolutionLayerDataset() + { + add_config(TensorShape(227U, 227U, 3U), TensorShape(11U, 11U, 3U, 96U), TensorShape(96U), TensorShape(55U, 55U, 96U), PadStrideInfo(4, 4, 0, 0)); + add_config(TensorShape(27U, 27U, 96U), TensorShape(5U, 5U, 96U, 256U), TensorShape(256U), TensorShape(27U, 27U, 256U), PadStrideInfo(1, 1, 2, 2)); + add_config(TensorShape(13U, 13U, 256U), TensorShape(3U, 3U, 256U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U), PadStrideInfo(1, 1, 1, 1)); + add_config(TensorShape(13U, 13U, 384U), TensorShape(3U, 3U, 384U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U), PadStrideInfo(1, 1, 1, 1)); + add_config(TensorShape(13U, 13U, 384U), TensorShape(3U, 3U, 384U, 256U), TensorShape(256U), TensorShape(13U, 13U, 256U), PadStrideInfo(1, 1, 1, 1)); + add_config(TensorShape(224U, 224U, 3U), TensorShape(7U, 7U, 3U, 64U), TensorShape(64U), TensorShape(112U, 112U, 64U), PadStrideInfo(2, 2, 3, 3)); + add_config(TensorShape(28U, 28U, 256U), TensorShape(1U, 1U, 256U, 64U), TensorShape(64U), TensorShape(28U, 28U, 64U), PadStrideInfo(1, 1, 0, 0)); + } +}; +} // namespace datasets +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_LARGE_CONVOLUTION_LAYER_DATASET */ diff --git a/tests/datasets_new/ShapeDatasets.h b/tests/datasets_new/ShapeDatasets.h index ba142cae0c..14f7851621 100644 --- a/tests/datasets_new/ShapeDatasets.h +++ b/tests/datasets_new/ShapeDatasets.h @@ -35,7 +35,7 @@ namespace test { namespace datasets { -/** Data set containing one 1D tensor shape. */ +/** Data set containing 1D tensor shapes. */ class Small1DShape final : public framework::dataset::SingletonDataset<TensorShape> { public: @@ -48,7 +48,7 @@ public: /** Parent type for all for shape datasets. */ using ShapeDataset = framework::dataset::ContainerDataset<std::vector<TensorShape>>; -/** Data set containing two small 2D tensor shapes. */ +/** Data set containing small 2D tensor shapes. */ class Small2DShapes final : public ShapeDataset { public: @@ -93,7 +93,7 @@ public: } }; -/** Data set containing two 2D large tensor shapes. */ +/** Data set containing large 2D tensor shapes. */ class Large2DShapes final : public ShapeDataset { public: @@ -107,6 +107,21 @@ public: { } }; + +/** Data set containing small tensor shapes for direct convolution. */ +class SmallDirectConvolutionShapes final : public ShapeDataset +{ +public: + SmallDirectConvolutionShapes() + : ShapeDataset("InputShape", + { + TensorShape{ 3U, 3U, 3U, 2U, 4U, 5U }, + TensorShape{ 32U, 37U, 3U }, + TensorShape{ 13U, 15U, 8U, 3U } + }) + { + } +}; } // namespace datasets } // namespace test } // namespace arm_compute diff --git a/tests/datasets_new/SmallConvolutionLayerDataset.h b/tests/datasets_new/SmallConvolutionLayerDataset.h new file mode 100644 index 0000000000..2cd8da0b27 --- /dev/null +++ b/tests/datasets_new/SmallConvolutionLayerDataset.h @@ -0,0 +1,56 @@ +/* + * 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. + */ +#ifndef ARM_COMPUTE_TEST_SMALL_CONVOLUTION_LAYER_DATASET +#define ARM_COMPUTE_TEST_SMALL_CONVOLUTION_LAYER_DATASET + +#include "tests/datasets_new/ConvolutionLayerDataset.h" + +#include "tests/TypePrinter.h" + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" + +namespace arm_compute +{ +namespace test +{ +namespace datasets +{ +class SmallConvolutionLayerDataset final : public ConvolutionLayerDataset +{ +public: + SmallConvolutionLayerDataset() + { + add_config(TensorShape(23U, 27U, 5U), TensorShape(3U, 3U, 5U, 21U), TensorShape(21U), TensorShape(11U, 25U, 21U), PadStrideInfo(2, 1, 0, 0)); + add_config(TensorShape(33U, 27U, 7U), TensorShape(5U, 5U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U), PadStrideInfo(3, 2, 1, 0)); + add_config(TensorShape(17U, 31U, 2U, 7U), TensorShape(5U, 5U, 2U, 19U), TensorShape(19U), TensorShape(15U, 15U, 19U, 7U), PadStrideInfo(1, 2, 1, 1)); + add_config(TensorShape(23U, 27U, 5U), TensorShape(3U, 1U, 5U, 21U), TensorShape(21U), TensorShape(11U, 27U, 21U), PadStrideInfo(2, 1, 0, 0)); + add_config(TensorShape(33U, 27U, 7U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(11U, 11U, 16U), PadStrideInfo(3, 2, 1, 0)); + add_config(TensorShape(17U, 31U, 2U, 7U), TensorShape(5U, 3U, 2U, 19U), TensorShape(19U), TensorShape(15U, 16U, 19U, 7U), PadStrideInfo(1, 2, 1, 1)); + } +}; +} // namespace datasets +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_SMALL_CONVOLUTION_LAYER_DATASET */ 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 */ diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp deleted file mode 100644 index ce96a6b321..0000000000 --- a/tests/validation/NEON/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 "NEON/Accessor.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/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::validation; - -namespace -{ -const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ -#ifdef ARM_COMPUTE_ENABLE_FP16 -const float tolerance_f16 = 0.01f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ -#endif /* ARM_COMPUTE_ENABLE_FP16 */ -const float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ - -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<Tensor>(input_shape, dt, 1, fixed_point_position); - Tensor weights = create_tensor<Tensor>(weights_shape, dt, 1, fixed_point_position); - Tensor bias = create_tensor<Tensor>(bias_shape, dt, 1, fixed_point_position); - Tensor dst = create_tensor<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::F16 || dt == DataType::F32) - { - std::uniform_real_distribution<> distribution(-1.0f, 1.0f); - 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 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, DataType::QS16 }), - 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<Tensor>(conv_set.src_shape, dt, 1, fixed_point_position); - Tensor weights = create_tensor<Tensor>(conv_set.weights_shape, dt, 1, fixed_point_position); - Tensor bias = create_tensor<Tensor>(conv_set.bias_shape, dt, 1, fixed_point_position); - Tensor dst = create_tensor<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); -} - -#ifdef ARM_COMPUTE_ENABLE_FP16 -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 - 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(Accessor(dst), ref_dst, tolerance_f16); -} -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(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(Accessor(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(Accessor(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 - 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(Accessor(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 - 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(Accessor(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/NEON/DirectConvolutionLayer.cpp b/tests/validation/NEON/DirectConvolutionLayer.cpp deleted file mode 100644 index 7022d656e9..0000000000 --- a/tests/validation/NEON/DirectConvolutionLayer.cpp +++ /dev/null @@ -1,280 +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 <random> -#include <string> -#include <tuple> - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -const float tolerance_qs = 1.f; /**< Tolerance for 8 bit fixed 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_fp32 = 1e-3f; /**< Tolerance for floating 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<Tensor>(src_shape, dt, 1, fixed_point_position); - Tensor weights = create_tensor<Tensor>(weights_shape, dt, 1, fixed_point_position); - Tensor bias = create_tensor<Tensor>(bias_shape, dt, 1, fixed_point_position); - Tensor dst = create_tensor<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 - switch(dt) - { - case DataType::F16: - case 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); - break; - } - case DataType::QS8: - case DataType::QS16: - { - library->fill_tensor_uniform(Accessor(src), 0); - library->fill_tensor_uniform(Accessor(weights), 1); - library->fill_tensor_uniform(Accessor(bias), 2); - break; - } - default: - { - ARM_COMPUTE_ERROR("Data type not supported."); - break; - } - } - - // 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(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<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)); - - 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<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)); - - 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<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)); - - 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<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)); - - 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::make({ DataType::QS8, DataType::QS16 }) * 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, dt, 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<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)); - - Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, fixed_point_position); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, fixed_point_position); - - // Validate output - validate(Accessor(dst), ref, tolerance_qs); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::make(DataType::QS8) * 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, dt, 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<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)); - - Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, fixed_point_position); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, fixed_point_position); - - // Validate output - validate(Accessor(dst), ref, tolerance_qs); -} -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/Reference.cpp b/tests/validation/Reference.cpp index 145f308c50..99e3095007 100644 --- a/tests/validation/Reference.cpp +++ b/tests/validation/Reference.cpp @@ -515,48 +515,6 @@ RawTensor Reference::compute_reference_batch_normalization_layer(const TensorSha return ref_dst; } -RawTensor Reference::compute_reference_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 reference - RawTensor ref_src(input_shape, dt, 1, fixed_point_position); - RawTensor ref_weights(weights_shape, dt, 1, fixed_point_position); - RawTensor ref_bias(bias_shape, dt, 1, fixed_point_position); - RawTensor ref_dst(output_shape, dt, 1, fixed_point_position); - - // Fill reference - switch(dt) - { - case DataType::F32: - case DataType::F16: - { - std::uniform_real_distribution<> distribution(-1.0f, 1.0f); - library->fill(ref_src, distribution, 0); - library->fill(ref_weights, distribution, 1); - library->fill(ref_bias, distribution, 2); - break; - } - case DataType::QS16: - case DataType::QS8: - { - library->fill_tensor_uniform(ref_src, 0); - library->fill_tensor_uniform(ref_weights, 1); - library->fill_tensor_uniform(ref_bias, 2); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - break; - } - } - - // Compute reference - ReferenceCPP::convolution_layer(ref_src, ref_weights, ref_bias, ref_dst, conv_info); - - return ref_dst; -} - RawTensor Reference::compute_reference_fully_connected_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt, bool transpose_weights, int fixed_point_position) { diff --git a/tests/validation/Reference.h b/tests/validation/Reference.h index 8c22545cb1..f3216fbaf9 100644 --- a/tests/validation/Reference.h +++ b/tests/validation/Reference.h @@ -306,20 +306,6 @@ public: * @return Computed raw tensor. */ static RawTensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0); - /** Compute reference convolution layer - * - * @param[in] input_shape Shape for the input tensor - * @param[in] weights_shape Shape for the weights tensor - * @param[in] bias_shape Shape for the bias tensor - * @param[in] output_shape Shape for the output tensor - * @param[in] dt Data type to use - * @param[in] conv_info Pads and strides information for the convolution layer - * @param[in] fixed_point_position Number of bits for the fractional part of the fixed point numbers - * - * @return Computed raw tensor. - */ - static RawTensor compute_reference_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); /** Compute reference for fully connected layer function * * @param[in] input_shape Shape for the input tensor diff --git a/tests/validation/ReferenceCPP.cpp b/tests/validation/ReferenceCPP.cpp index 5f3fa1fcbc..6b902ae3f2 100644 --- a/tests/validation/ReferenceCPP.cpp +++ b/tests/validation/ReferenceCPP.cpp @@ -296,16 +296,6 @@ void ReferenceCPP::batch_normalization_layer(const RawTensor &src, RawTensor &ds boost::apply_visitor(tensor_visitors::batch_normalization_layer_visitor(s, m, v, b, g, epsilon, fixed_point_position), d); } -// Convolution Layer -void ReferenceCPP::convolution_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst, const PadStrideInfo &conv_info) -{ - const TensorVariant s = TensorFactory::get_tensor(src); - const TensorVariant w = TensorFactory::get_tensor(weights); - const TensorVariant b = TensorFactory::get_tensor(bias); - TensorVariant d = TensorFactory::get_tensor(dst); - boost::apply_visitor(tensor_visitors::convolution_layer_visitor(s, w, b, conv_info), d); -} - // Fully connected layer void ReferenceCPP::fully_connected_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst) { diff --git a/tests/validation/ReferenceCPP.h b/tests/validation/ReferenceCPP.h index ab77d783b6..d289e8e57e 100644 --- a/tests/validation/ReferenceCPP.h +++ b/tests/validation/ReferenceCPP.h @@ -266,15 +266,6 @@ public: */ static void batch_normalization_layer(const RawTensor &src, RawTensor &dst, const RawTensor &mean, const RawTensor &var, const RawTensor &beta, const RawTensor &gamma, float epsilon, int fixed_point_position = 0); - /** Convolution layer function - * - * @param[in] src Input tensor. - * @param[in] weights Weights tensor. - * @param[in] bias Bias tensor. - * @param[out] dst Result tensor. - * @param[in] conv_info Pads and strides information for the convolution layer. - */ - static void convolution_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst, const PadStrideInfo &conv_info); /** Fully connected layer function * * @param[in] src Input tensor diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h index 84aa965a9f..f4d2110387 100644 --- a/tests/validation/TensorOperations.h +++ b/tests/validation/TensorOperations.h @@ -59,100 +59,6 @@ struct is_floating_point { }; -bool is_valid_pixel(int i, int min, int max) -{ - return (i >= min && i < max); -} - -// 3D convolution for floating point type -template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> -void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int8_t fixed_point_position) -{ - const int half_width_weights = width_weights / 2; - const int half_height_weights = height_weights / 2; - - // Reset accumulator - T acc = static_cast<T>(0); - - // Compute a 2D convolution for each IFM and accumulate the result - for(int ifm = 0; ifm < depth_in; ++ifm) - { - // Compute the offset for the input slice - const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; - - // Compute 2D convolution - for(int yk = -half_height_weights; yk <= half_height_weights; ++yk) - { - for(int xk = -half_width_weights; xk <= half_width_weights; ++xk) - { - // Check if the pixel is out-of-bound - if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in)) - { - const int idx = xk + half_width_weights; - const int idy = yk + half_height_weights; - - const T i_value = in[offset_slice_in + xk + yk * width_in]; - const T w_value = weights[idx + idy * width_weights + ifm * width_weights * height_weights]; - - acc += i_value * w_value; - } - } - } - } - - // Accumulate the bias and store the result - *out = acc + (*bias); -} - -// 3D convolution for fixed point type -template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> -void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, - int8_t fixed_point_position) -{ - const int half_width_weights = width_weights / 2; - const int half_height_weights = height_weights / 2; - - using namespace fixed_point_arithmetic; - using promoted_type = typename fixed_point_arithmetic::traits::promote<T>::type; - - // Reset accumulator - fixed_point<promoted_type> acc(0, fixed_point_position); - - // Compute a 2D convolution for each IFM and accumulate the result - for(int ifm = 0; ifm < depth_in; ++ifm) - { - // Compute the offset for the input slice - const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; - - // Compute 2D convolution - for(int yk = -half_height_weights; yk <= half_height_weights; ++yk) - { - for(int xk = -half_width_weights; xk <= half_width_weights; ++xk) - { - // Check if the pixel is out-of-bound - if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in)) - { - const int idx = xk + half_width_weights; - const int idy = yk + half_height_weights; - - const fixed_point<promoted_type> i_value(in[offset_slice_in + xk + yk * width_in], fixed_point_position, true); - const fixed_point<promoted_type> w_value(weights[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true); - const fixed_point<promoted_type> iw = i_value * w_value; - acc = iw + acc; - } - } - } - } - - // Get the bias - const fixed_point<promoted_type> b(*bias, fixed_point_position, true); - - // Accumulate the bias and covert back - acc = acc + b; - fixed_point<T> res(acc); - *out = res.raw(); -} - template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out, int cols_weights, int rows_weights, uint8_t fixed_point_position) { @@ -999,58 +905,6 @@ void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor } } -// Convolution layer -template <typename T> -void convolution_layer(const Tensor<T> &in, const Tensor<T> &weights, const Tensor<T> &bias, Tensor<T> &out, const PadStrideInfo &conv_info) -{ - const int width_in = in.shape().x(); - const int height_in = in.shape().y(); - const int depth_in = in.shape().z(); - const int width_out = out.shape().x(); - const int height_out = out.shape().y(); - const int depth_out = out.shape().z(); - const int width_weights = weights.shape().x(); - const int height_weights = weights.shape().y(); - const int depth_weights = weights.shape().z(); - const int pad_xi = std::min(static_cast<int>(conv_info.pad().first), width_weights / 2); - const int pad_yi = std::min(static_cast<int>(conv_info.pad().second), height_weights / 2); - const int start_xi = width_weights / 2 - pad_xi; - const int start_yi = height_weights / 2 - pad_yi; - const int end_xi = width_in - start_xi; - const int end_yi = height_in - start_yi; - const int stride_xi = conv_info.stride().first; - const int stride_yi = conv_info.stride().second; - const int num_batches = in.shape().total_size() / (width_in * height_in * depth_in); - - for(int r = 0; r < num_batches; ++r) - { - for(int yi = start_yi; yi < end_yi; yi += stride_yi) - { - for(int xi = start_xi; xi < end_xi; xi += stride_xi) - { - for(int ofm = 0; ofm < depth_out; ++ofm) - { - // Compute input and output offsets - const int offset_in = r * width_in * height_in * depth_in; - const int xo = (xi - start_xi) / stride_xi; - const int yo = (yi - start_yi) / stride_yi; - const int offset_out = xo + yo * width_out + ofm * width_out * height_out + r * width_out * height_out * depth_out; - - // Compute 3D convolution - convolution3d(in.data() + offset_in, - weights.data() + ofm * width_weights * height_weights * depth_weights, - bias.data() + ofm, - out.data() + offset_out, - xi, yi, - width_in, height_in, depth_in, - width_weights, height_weights, - static_cast<int8_t>(in.fixed_point_position())); - } - } - } - } -} - // Fully connected layer template <typename T> void fully_connected_layer(const Tensor<T> &in, const Tensor<T> &weights, const Tensor<T> &bias, Tensor<T> &out) diff --git a/tests/validation/TensorVisitors.h b/tests/validation/TensorVisitors.h index d72b437344..67f1d8a001 100644 --- a/tests/validation/TensorVisitors.h +++ b/tests/validation/TensorVisitors.h @@ -254,30 +254,6 @@ private: float _epsilon; int _fixed_point_position; }; -// Convolution Layer visitor -struct convolution_layer_visitor : public boost::static_visitor<> -{ -public: - explicit convolution_layer_visitor(const TensorVariant &in, const TensorVariant &weights, const TensorVariant &bias, PadStrideInfo conv_info) - : _in(in), _weights(weights), _bias(bias), _conv_info(conv_info) - { - } - - template <typename T> - void operator()(Tensor<T> &out) const - { - const Tensor<T> &in = boost::get<Tensor<T>>(_in); - const Tensor<T> &weights = boost::get<Tensor<T>>(_weights); - const Tensor<T> &bias = boost::get<Tensor<T>>(_bias); - tensor_operations::convolution_layer(in, weights, bias, out, _conv_info); - } - -private: - const TensorVariant &_in; - const TensorVariant &_weights; - const TensorVariant &_bias; - PadStrideInfo _conv_info; -}; // Fully Connected Layer visitor struct fully_connected_layer_visitor : public boost::static_visitor<> { diff --git a/tests/validation_new/CL/ConvolutionLayer.cpp b/tests/validation_new/CL/ConvolutionLayer.cpp new file mode 100644 index 0000000000..398feb7966 --- /dev/null +++ b/tests/validation_new/CL/ConvolutionLayer.cpp @@ -0,0 +1,186 @@ +/* + * 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 CONCLCTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/CLTensorAllocator.h" +#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" +#include "framework/Asserts.h" +#include "framework/Macros.h" +#include "framework/datasets/Datasets.h" +#include "tests/CL/CLAccessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets_new/LargeConvolutionLayerDataset.h" +#include "tests/datasets_new/SmallConvolutionLayerDataset.h" +#include "tests/validation_new/Validation.h" +#include "tests/validation_new/fixtures/ConvolutionLayerFixture.h" +#include "tests/validation_new/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +constexpr float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ +constexpr float tolerance_f16 = 0.1f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ +constexpr float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ + +/** CNN data types */ +const auto CNNDataTypes = framework::dataset::make("DataType", +{ + DataType::F16, + DataType::F32, + DataType::QS8, + DataType::QS16, +}); +} // namespace + +TEST_SUITE(CL) +TEST_SUITE(ConvolutionLayer) + +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()), CNNDataTypes), + input_shape, weights_shape, bias_shape, output_shape, info, data_type) +{ + // Set fixed point position data type allowed + int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0; + + // Create tensors + CLTensor src = create_tensor<CLTensor>(input_shape, data_type, 1, fixed_point_position); + CLTensor weights = create_tensor<CLTensor>(weights_shape, data_type, 1, fixed_point_position); + CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type, 1, fixed_point_position); + CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, fixed_point_position); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function + CLConvolutionLayer conv; + conv.configure(&src, &weights, &bias, &dst, info); + + // Validate valid region + const ValidRegion src_valid_region = shape_to_valid_region(input_shape); + const ValidRegion weights_valid_region = shape_to_valid_region(weights_shape); + const ValidRegion bias_valid_region = shape_to_valid_region(bias_shape); + const ValidRegion dst_valid_region = shape_to_valid_region(output_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); + + // Validate padding + //TODO(COMPMID-415) Need to validate padding? +} + +template <typename T> +using CLConvolutionLayerFixture = ConvolutionValidationFixture<CLTensor, CLAccessor, CLConvolutionLayer, T>; + +TEST_SUITE(Float) +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixture<half_float::half>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixture<half_float::half>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallConvolutionLayerDataset(), framework::dataset::make("DataType", + DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeConvolutionLayerDataset(), framework::dataset::make("DataType", + DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() +TEST_SUITE_END() + +template <typename T> +using CLConvolutionLayerFixedPointFixture = ConvolutionValidationFixedPointFixture<CLTensor, CLAccessor, CLConvolutionLayer, T>; + +TEST_SUITE(Quantized) +TEST_SUITE(QS8) +// We test for fixed point precision [4,6] +FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 4, 7))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_q); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 4, 7))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_q); +} +TEST_SUITE_END() + +TEST_SUITE(QS16) +// Testing for fixed point position [1,14) +FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_q); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_q); +} +TEST_SUITE_END() +TEST_SUITE_END() + +TEST_SUITE_END() +TEST_SUITE_END() +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/CL/DirectConvolutionLayer.cpp b/tests/validation_new/CL/DirectConvolutionLayer.cpp new file mode 100644 index 0000000000..9cffabae42 --- /dev/null +++ b/tests/validation_new/CL/DirectConvolutionLayer.cpp @@ -0,0 +1,92 @@ +/* + * 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 "arm_compute/core/Types.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/CLTensorAllocator.h" +#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" +#include "framework/Asserts.h" +#include "framework/Macros.h" +#include "framework/datasets/Datasets.h" +#include "tests/CL/CLAccessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets_new/ShapeDatasets.h" +#include "tests/validation_new/Validation.h" +#include "tests/validation_new/fixtures/DirectConvolutionLayerFixture.h" +#include "tests/validation_new/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +constexpr float tolerance_fp16 = 0.1f; /**< Tolerance for floating point tests */ +constexpr float tolerance_fp32 = 0.001f; /**< Tolerance for floating point tests */ + +/** Direct convolution data set. */ +const auto data = combine(datasets::SmallDirectConvolutionShapes(), + combine(framework::dataset::make("StrideX", 1, 3), + combine(framework::dataset::make("StrideY", 1, 3), + combine(concat(combine(framework::dataset::make("PadX", 0), + combine(framework::dataset::make("PadY", 0), + framework::dataset::make("KernelSize", 1))), + combine(framework::dataset::make("PadX", 0, 2), + combine(framework::dataset::make("PadY", 0, 2), + framework::dataset::make("KernelSize", 3)))), + framework::dataset::make("NumKernels", { 1, 4, 8, 16 }))))); +} // namespace + +TEST_SUITE(CL) +TEST_SUITE(DirectConvolutionLayer) + +//TODO(COMPMID-415): Configuration tests? + +template <typename T> +using CLDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>; + +TEST_SUITE(Float) +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixture<half_float::half>, framework::DatasetMode::ALL, combine(data, framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_fp16); +} +TEST_SUITE_END() + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(data, framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_fp32); +} +TEST_SUITE_END() +TEST_SUITE_END() + +TEST_SUITE_END() +TEST_SUITE_END() +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/CPP/ConvolutionLayer.cpp b/tests/validation_new/CPP/ConvolutionLayer.cpp new file mode 100644 index 0000000000..a24621a3f2 --- /dev/null +++ b/tests/validation_new/CPP/ConvolutionLayer.cpp @@ -0,0 +1,205 @@ +/* + * 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 "ConvolutionLayer.h" + +#include "tests/validation_new/FixedPoint.h" +#include "tests/validation_new/Helpers.h" +#include "tests/validation_new/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +namespace +{ +inline bool is_valid_pixel(int i, int min, int max) +{ + return (i >= min && i < max); +} + +// 3D convolution for floating point type +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0> +void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int fixed_point_position) +{ + ARM_COMPUTE_UNUSED(fixed_point_position); + + const int half_width_weights = width_weights / 2; + const int half_height_weights = height_weights / 2; + + // Reset accumulator + T acc(0); + + // Compute a 2D convolution for each IFM and accumulate the result + for(int ifm = 0; ifm < depth_in; ++ifm) + { + // Compute the offset for the input slice + const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; + + // Compute 2D convolution + for(int yk = -half_height_weights; yk <= half_height_weights; ++yk) + { + for(int xk = -half_width_weights; xk <= half_width_weights; ++xk) + { + // Check if the pixel is out-of-bound + if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in)) + { + const int idx = xk + half_width_weights; + const int idy = yk + half_height_weights; + + const T i_value = in[offset_slice_in + xk + yk * width_in]; + const T w_value = weights[idx + idy * width_weights + ifm * width_weights * height_weights]; + + acc += i_value * w_value; + } + } + } + } + + // Accumulate the bias and store the result + *out = acc + (*bias); +} + +// 3D convolution for fixed point type +template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0> +void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, + int fixed_point_position) +{ + const int half_width_weights = width_weights / 2; + const int half_height_weights = height_weights / 2; + + using namespace fixed_point_arithmetic; + using promoted_type = fixed_point_arithmetic::traits::promote_t<T>; + + // Reset accumulator + fixed_point<promoted_type> acc(0, fixed_point_position); + + // Compute a 2D convolution for each IFM and accumulate the result + for(int ifm = 0; ifm < depth_in; ++ifm) + { + // Compute the offset for the input slice + const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; + + // Compute 2D convolution + for(int yk = -half_height_weights; yk <= half_height_weights; ++yk) + { + for(int xk = -half_width_weights; xk <= half_width_weights; ++xk) + { + // Check if the pixel is out-of-bound + if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in)) + { + const int idx = xk + half_width_weights; + const int idy = yk + half_height_weights; + + const fixed_point<promoted_type> i_value(in[offset_slice_in + xk + yk * width_in], fixed_point_position, true); + const fixed_point<promoted_type> w_value(weights[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true); + const fixed_point<promoted_type> iw = i_value * w_value; + acc = iw + acc; + } + } + } + } + + // Get the bias + const fixed_point<promoted_type> b(*bias, fixed_point_position, true); + + // Accumulate the bias and covert back + acc = acc + b; + fixed_point<T> res(acc); + *out = res.raw(); +} +} // namespace + +template <typename T> +SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &output_shape, const PadStrideInfo &info) +{ + // Create reference + SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position() }; + + // Compute reference + const int width_in = src.shape().x(); + const int height_in = src.shape().y(); + const int depth_in = src.shape().z(); + const int width_out = dst.shape().x(); + const int height_out = dst.shape().y(); + const int depth_out = dst.shape().z(); + const int width_weights = weights.shape().x(); + const int height_weights = weights.shape().y(); + const int depth_weights = weights.shape().z(); + const int pad_xi = std::min(static_cast<int>(info.pad().first), width_weights / 2); + const int pad_yi = std::min(static_cast<int>(info.pad().second), height_weights / 2); + const int start_xi = width_weights / 2 - pad_xi; + const int start_yi = height_weights / 2 - pad_yi; + const int end_xi = width_in - start_xi; + const int end_yi = height_in - start_yi; + const int stride_xi = info.stride().first; + const int stride_yi = info.stride().second; + const int num_batches = src.shape().total_size() / (width_in * height_in * depth_in); + + for(int r = 0; r < num_batches; ++r) + { + for(int yi = start_yi; yi < end_yi; yi += stride_yi) + { + for(int xi = start_xi; xi < end_xi; xi += stride_xi) + { + for(int ofm = 0; ofm < depth_out; ++ofm) + { + // Compute input and output offsets + const int offset_in = r * width_in * height_in * depth_in; + const int xo = (xi - start_xi) / stride_xi; + const int yo = (yi - start_yi) / stride_yi; + const int offset_out = xo + yo * width_out + ofm * width_out * height_out + r * width_out * height_out * depth_out; + + // Compute 3D convolution + convolution3d(src.data() + offset_in, + weights.data() + ofm * width_weights * height_weights * depth_weights, + bias.data() + ofm, + dst.data() + offset_out, + xi, yi, + width_in, height_in, depth_in, + width_weights, height_weights, + src.fixed_point_position()); + } + } + } + } + + return dst; +} + +template SimpleTensor<float> convolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape, + const PadStrideInfo &info); +template SimpleTensor<half_float::half> convolution_layer(const SimpleTensor<half_float::half> &src, const SimpleTensor<half_float::half> &weights, const SimpleTensor<half_float::half> &bias, + const TensorShape &output_shape, const PadStrideInfo &info); +template SimpleTensor<qint8_t> convolution_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &output_shape, + const PadStrideInfo &info); +template SimpleTensor<qint16_t> convolution_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &output_shape, + const PadStrideInfo &info); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/CPP/ConvolutionLayer.h b/tests/validation_new/CPP/ConvolutionLayer.h new file mode 100644 index 0000000000..fd46567910 --- /dev/null +++ b/tests/validation_new/CPP/ConvolutionLayer.h @@ -0,0 +1,44 @@ +/* + * 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. + */ +#ifndef __ARM_COMPUTE_TEST_CONVOLUTION_LAYER_H__ +#define __ARM_COMPUTE_TEST_CONVOLUTION_LAYER_H__ + +#include "tests/validation_new/Helpers.h" +#include "tests/validation_new/SimpleTensor.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +template <typename T> +SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &output_shape, const PadStrideInfo &info); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* __ARM_COMPUTE_TEST_CONVOLUTION_LAYER_H__ */ diff --git a/tests/validation_new/NEON/ConvolutionLayer.cpp b/tests/validation_new/NEON/ConvolutionLayer.cpp new file mode 100644 index 0000000000..af33cc0707 --- /dev/null +++ b/tests/validation_new/NEON/ConvolutionLayer.cpp @@ -0,0 +1,192 @@ +/* + * 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 "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" +#include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/TensorAllocator.h" +#include "framework/Asserts.h" +#include "framework/Macros.h" +#include "framework/datasets/Datasets.h" +#include "tests/NEON/Accessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets_new/LargeConvolutionLayerDataset.h" +#include "tests/datasets_new/SmallConvolutionLayerDataset.h" +#include "tests/validation_new/Validation.h" +#include "tests/validation_new/fixtures/ConvolutionLayerFixture.h" +#include "tests/validation_new/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ +#ifdef ARM_COMPUTE_ENABLE_FP16 +const float tolerance_f16 = 0.01f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ +#endif /* ARM_COMPUTE_ENABLE_FP16 */ +const float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ + +/** CNN data types */ +const auto CNNDataTypes = framework::dataset::make("DataType", +{ +#ifdef ARM_COMPUTE_ENABLE_FP16 + DataType::F16, +#endif /* ARM_COMPUTE_ENABLE_FP16 */ + DataType::F32, + DataType::QS8, + DataType::QS16, +}); +} // namespace + +TEST_SUITE(NEON) +TEST_SUITE(ConvolutionLayer) + +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()), CNNDataTypes), + input_shape, weights_shape, bias_shape, output_shape, info, data_type) +{ + // Set fixed point position data type allowed + int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0; + + // Create tensors + Tensor src = create_tensor<Tensor>(input_shape, data_type, 1, fixed_point_position); + Tensor weights = create_tensor<Tensor>(weights_shape, data_type, 1, fixed_point_position); + Tensor bias = create_tensor<Tensor>(bias_shape, data_type, 1, fixed_point_position); + Tensor dst = create_tensor<Tensor>(output_shape, data_type, 1, fixed_point_position); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function + NEConvolutionLayer conv; + conv.configure(&src, &weights, &bias, &dst, info); + + // Validate valid region + const ValidRegion src_valid_region = shape_to_valid_region(input_shape); + const ValidRegion weights_valid_region = shape_to_valid_region(weights_shape); + const ValidRegion bias_valid_region = shape_to_valid_region(bias_shape); + const ValidRegion dst_valid_region = shape_to_valid_region(output_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); + + // Validate padding + //TODO(COMPMID-415) Need to validate padding? +} + +template <typename T> +using NEConvolutionLayerFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolutionLayer, T>; + +TEST_SUITE(Float) +#ifdef ARM_COMPUTE_ENABLE_FP16 +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionLayerFixture<half_float::half>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::F16))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionLayerFixture<half_float::half>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::F16))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() +#endif /* ARM_COMPUTE_ENABLE_FP16 */ + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallConvolutionLayerDataset(), framework::dataset::make("DataType", + DataType::F32))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeConvolutionLayerDataset(), framework::dataset::make("DataType", + DataType::F32))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() +TEST_SUITE_END() + +template <typename T> +using NEConvolutionLayerFixedPointFixture = ConvolutionValidationFixedPointFixture<Tensor, Accessor, NEConvolutionLayer, T>; + +TEST_SUITE(Quantized) +TEST_SUITE(QS8) +// We test for fixed point precision [4,6] +FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 4, 7))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_q); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 4, 7))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_q); +} +TEST_SUITE_END() + +TEST_SUITE(QS16) +// Testing for fixed point position [1,14) +FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_q); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeConvolutionLayerDataset(), + framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_q); +} +TEST_SUITE_END() +TEST_SUITE_END() + +TEST_SUITE_END() +TEST_SUITE_END() +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/NEON/DirectConvolutionLayer.cpp b/tests/validation_new/NEON/DirectConvolutionLayer.cpp new file mode 100644 index 0000000000..a46f5a5dcc --- /dev/null +++ b/tests/validation_new/NEON/DirectConvolutionLayer.cpp @@ -0,0 +1,131 @@ +/* + * 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 "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 "framework/Asserts.h" +#include "framework/Macros.h" +#include "framework/datasets/Datasets.h" +#include "tests/NEON/Accessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets_new/ShapeDatasets.h" +#include "tests/validation_new/Validation.h" +#include "tests/validation_new/fixtures/DirectConvolutionLayerFixture.h" +#include "tests/validation_new/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +constexpr float tolerance_qs = 1.f; /**< Tolerance for fixed point tests */ +#ifdef ARM_COMPUTE_ENABLE_FP16 +constexpr float tolerance_fp16 = 0.01f; /**< Tolerance for half precision floating point tests */ +#endif /* ARM_COMPUTE_ENABLE_FP16 */ +constexpr float tolerance_fp32 = 0.001f; /**< Tolerance for floating point tests */ + +/** Direct convolution data set. */ +const auto data = combine(datasets::SmallDirectConvolutionShapes(), + combine(framework::dataset::make("StrideX", 1, 3), + combine(framework::dataset::make("StrideY", 1, 3), + combine(concat(combine(framework::dataset::make("PadX", 0), + combine(framework::dataset::make("PadY", 0), + framework::dataset::make("KernelSize", 1))), + combine(framework::dataset::make("PadX", 0, 2), + combine(framework::dataset::make("PadY", 0, 2), + framework::dataset::make("KernelSize", 3)))), + framework::dataset::make("NumKernels", { 1, 4, 8, 16 }))))); + +/** Direct convolution QS16 data set. */ +const auto data_qs16 = combine(datasets::SmallDirectConvolutionShapes(), + combine(framework::dataset::make("StrideX", 1, 3), + combine(framework::dataset::make("StrideY", 1, 3), + combine(framework::dataset::make("PadX", 0), + combine(framework::dataset::make("PadY", 0), + combine(framework::dataset::make("KernelSize", 1), + framework::dataset::make("NumKernels", { 1, 4, 8, 16 }))))))); +} // namespace + +TEST_SUITE(NEON) +TEST_SUITE(DirectConvolutionLayer) + +//TODO(COMPMID-415): Configuration tests? + +template <typename T> +using NEDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<Tensor, Accessor, NEDirectConvolutionLayer, T>; + +TEST_SUITE(Float) +#ifdef ARM_COMPUTE_ENABLE_FP16 +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixture<half_float::half>, framework::DatasetMode::ALL, combine(data, framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp16); +} +TEST_SUITE_END() +#endif /* ARM_COMPUTE_ENABLE_FP16 */ + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(data, framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32); +} +TEST_SUITE_END() +TEST_SUITE_END() + +template <typename T> +using NEDirectConvolutionLayerFixedPointFixture = DirectConvolutionValidationFixedPointFixture<Tensor, Accessor, NEDirectConvolutionLayer, T>; + +TEST_SUITE(Quantized) +TEST_SUITE(QS8) +// We test for fixed point precision [4,6] +FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(data, framework::dataset::make("DataType", DataType::QS8)), + framework::dataset::make("FractionalBits", 4, 7))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qs); +} +TEST_SUITE_END() + +TEST_SUITE(QS16) +// We test for fixed point precision [4,13] +FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::ALL, combine(combine(data_qs16, framework::dataset::make("DataType", DataType::QS16)), + framework::dataset::make("FractionalBits", 4, 14))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qs); +} +TEST_SUITE_END() +TEST_SUITE_END() + +TEST_SUITE_END() +TEST_SUITE_END() +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/fixtures/ConvolutionLayerFixture.h b/tests/validation_new/fixtures/ConvolutionLayerFixture.h new file mode 100644 index 0000000000..25a53d0c1d --- /dev/null +++ b/tests/validation_new/fixtures/ConvolutionLayerFixture.h @@ -0,0 +1,152 @@ +/* + * 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. + */ +#ifndef ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE +#define ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "framework/Asserts.h" +#include "framework/Fixture.h" +#include "tests/AssetsLibrary.h" +#include "tests/Globals.h" +#include "tests/IAccessor.h" +#include "tests/validation_new/CPP/ConvolutionLayer.h" +#include "tests/validation_new/Helpers.h" + +#include <random> + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class ConvolutionValidationFixedPointFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, DataType data_type, int fractional_bits) + { + _fractional_bits = fractional_bits; + _data_type = data_type; + + _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); + _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); + } + +protected: + template <typename U> + void fill(U &&tensor, int i) + { + switch(tensor.data_type()) + { + case DataType::F16: + case DataType::F32: + { + std::uniform_real_distribution<> distribution(-1.0f, 1.0f); + library->fill(tensor, distribution, i); + break; + } + default: + library->fill_tensor_uniform(tensor, i); + } + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, + DataType data_type, int fixed_point_position) + { + // Create tensors + TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position); + TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, fixed_point_position); + TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position); + TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position); + + // Create and configure function + FunctionType conv; + conv.configure(&src, &weights, &bias, &dst, info); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + weights.allocator()->allocate(); + bias.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src), 0); + fill(AccessorType(weights), 1); + fill(AccessorType(bias), 2); + + // Compute NEConvolutionLayer function + conv.run(); + + return dst; + } + + SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, + DataType data_type, int fixed_point_position) + { + // Create reference + SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position }; + SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position }; + SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position }; + + // Fill reference + fill(src, 0); + fill(weights, 1); + fill(bias, 2); + + return reference::convolution_layer<T>(src, weights, bias, output_shape, info); + } + + TensorType _target{}; + SimpleTensor<T> _reference{}; + int _fractional_bits{}; + DataType _data_type{}; +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class ConvolutionValidationFixture : public ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, DataType data_type) + { + ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, 0); + } +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE */ diff --git a/tests/validation_new/fixtures/DirectConvolutionLayerFixture.h b/tests/validation_new/fixtures/DirectConvolutionLayerFixture.h new file mode 100644 index 0000000000..0d138b47d9 --- /dev/null +++ b/tests/validation_new/fixtures/DirectConvolutionLayerFixture.h @@ -0,0 +1,86 @@ +/* + * 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 "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "framework/Asserts.h" +#include "framework/Fixture.h" +#include "tests/AssetsLibrary.h" +#include "tests/Globals.h" +#include "tests/IAccessor.h" +#include "tests/validation_new/CPP/ConvolutionLayer.h" +#include "tests/validation_new/Helpers.h" +#include "tests/validation_new/fixtures/ConvolutionLayerFixture.h" + +#include <random> + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class DirectConvolutionValidationFixedPointFixture : public ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, int fractional_bits) + { + const TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels); + const TensorShape bias_shape(num_kernels); + const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR); + const TensorShape output_shape = get_output_shape(input_shape, weights_shape, info); + + ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); + } + +private: + TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &info) + { + TensorShape out_shape(in_shape); + const std::pair<unsigned int, unsigned int> scaled_dims = scaled_dimensions(in_shape.x(), + in_shape.y(), + kernel_shape.x(), + kernel_shape.y(), + 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; + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class DirectConvolutionValidationFixture : public DirectConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type) + { + DirectConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, 0); + } +}; +} // namespace validation +} // namespace test +} // namespace arm_compute |