diff options
Diffstat (limited to 'tests/validation')
-rw-r--r-- | tests/validation/CL/ConvolutionLayer.cpp | 222 | ||||
-rw-r--r-- | tests/validation/CL/DirectConvolutionLayer.cpp | 197 | ||||
-rw-r--r-- | tests/validation/NEON/ConvolutionLayer.cpp | 222 | ||||
-rw-r--r-- | tests/validation/NEON/DirectConvolutionLayer.cpp | 280 | ||||
-rw-r--r-- | tests/validation/Reference.cpp | 42 | ||||
-rw-r--r-- | tests/validation/Reference.h | 14 | ||||
-rw-r--r-- | tests/validation/ReferenceCPP.cpp | 10 | ||||
-rw-r--r-- | tests/validation/ReferenceCPP.h | 9 | ||||
-rw-r--r-- | tests/validation/TensorOperations.h | 146 | ||||
-rw-r--r-- | tests/validation/TensorVisitors.h | 24 |
10 files changed, 0 insertions, 1166 deletions
diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp deleted file mode 100644 index 570077120e..0000000000 --- a/tests/validation/CL/ConvolutionLayer.cpp +++ /dev/null @@ -1,222 +0,0 @@ -/* - * Copyright (c) 2017 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "CL/CLAccessor.h" - -#include "TypePrinter.h" -#include "dataset/ConvolutionLayerDataset.h" -#include "tests/Globals.h" -#include "tests/Utils.h" -#include "validation/Datasets.h" -#include "validation/Reference.h" -#include "validation/Validation.h" - -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" -#include "arm_compute/runtime/Tensor.h" -#include "arm_compute/runtime/TensorAllocator.h" - -#include <random> - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -const float tolerance_f16 = 0.1f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ -const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ -const float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ - -CLTensor compute_convolution_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt, - const PadStrideInfo &conv_info, int fixed_point_position) -{ - // Create tensors - CLTensor src = create_tensor<CLTensor>(input_shape, dt, 1, fixed_point_position); - CLTensor weights = create_tensor<CLTensor>(weights_shape, dt, 1, fixed_point_position); - CLTensor bias = create_tensor<CLTensor>(bias_shape, dt, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(output_shape, dt, 1, fixed_point_position); - - // Create and configure function - CLConvolutionLayer conv; - conv.configure(&src, &weights, &bias, &dst, conv_info); - - // Allocate tensors - src.allocator()->allocate(); - weights.allocator()->allocate(); - bias.allocator()->allocate(); - dst.allocator()->allocate(); - - BOOST_TEST(!src.info()->is_resizable()); - BOOST_TEST(!weights.info()->is_resizable()); - BOOST_TEST(!bias.info()->is_resizable()); - BOOST_TEST(!dst.info()->is_resizable()); - - // Fill tensors - if(dt == DataType::F32 || dt == DataType::F16) - { - std::uniform_real_distribution<> distribution(-1.0f, 1.0f); - library->fill(CLAccessor(src), distribution, 0); - library->fill(CLAccessor(weights), distribution, 1); - library->fill(CLAccessor(bias), distribution, 2); - } - else - { - library->fill_tensor_uniform(CLAccessor(src), 0); - library->fill_tensor_uniform(CLAccessor(weights), 1); - library->fill_tensor_uniform(CLAccessor(bias), 2); - } - - // Compute CLConvolutionLayer function - conv.run(); - - return dst; -} -} // namespace - -#ifndef DOXYGEN_SKIP_THIS -BOOST_AUTO_TEST_SUITE(CL) -BOOST_AUTO_TEST_SUITE(ConvolutionLayer) -BOOST_AUTO_TEST_SUITE(GEMM) - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(Configuration, - AlexNetConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8, DataType::QS16 }), - conv_set, dt) -{ - // Set fixed point position data type allowed - int fixed_point_position = (dt == DataType::F32) ? 0 : 3; - - // Create tensors - CLTensor src = create_tensor<CLTensor>(conv_set.src_shape, dt, 1, fixed_point_position); - CLTensor weights = create_tensor<CLTensor>(conv_set.weights_shape, dt, 1, fixed_point_position); - CLTensor bias = create_tensor<CLTensor>(conv_set.bias_shape, dt, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(conv_set.dst_shape, dt, 1, fixed_point_position); - - BOOST_TEST(src.info()->is_resizable()); - BOOST_TEST(weights.info()->is_resizable()); - BOOST_TEST(bias.info()->is_resizable()); - BOOST_TEST(dst.info()->is_resizable()); - - // Create and configure function - CLConvolutionLayer conv; - conv.configure(&src, &weights, &bias, &dst, conv_set.info); - - // Validate valid region - const ValidRegion src_valid_region = shape_to_valid_region(conv_set.src_shape); - const ValidRegion weights_valid_region = shape_to_valid_region(conv_set.weights_shape); - const ValidRegion bias_valid_region = shape_to_valid_region(conv_set.bias_shape); - const ValidRegion dst_valid_region = shape_to_valid_region(conv_set.dst_shape); - - validate(src.info()->valid_region(), src_valid_region); - validate(weights.info()->valid_region(), weights_valid_region); - validate(bias.info()->valid_region(), bias_valid_region); - validate(dst.info()->valid_region(), dst_valid_region); -} - -BOOST_AUTO_TEST_SUITE(Float16) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(SmallConvolutionLayer, - SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F16), - conv_set, dt) -{ - // Compute function - CLTensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_f16); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE(Float) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(SmallConvolutionLayer, - SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F32), - conv_set, dt) -{ - // Compute function - CLTensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_f32); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(LargeConvolutionLayer, - AlexNetConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F32), - conv_set, dt) -{ - // Compute function - CLTensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_f32); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE(Quantized) -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(SmallConvolutionLayer, - SmallConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), - conv_set, dt, fixed_point_position) -{ - // Compute function - CLTensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_q); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) -BOOST_DATA_TEST_CASE(LargeConvolutionLayer, - AlexNetConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), - conv_set, dt, fixed_point_position) -{ - // Compute function - CLTensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position); - - // Compute reference - RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position); - - // Validate output - validate(CLAccessor(dst), ref_dst, tolerance_q); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() -#endif /* DOXYGEN_SKIP_THIS */ diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp deleted file mode 100644 index d9dd34b9ec..0000000000 --- a/tests/validation/CL/DirectConvolutionLayer.cpp +++ /dev/null @@ -1,197 +0,0 @@ -/* - * Copyright (c) 2017 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "AssetsLibrary.h" -#include "CL/CLAccessor.h" -#include "Globals.h" -#include "TypePrinter.h" -#include "Utils.h" -#include "validation/Datasets.h" -#include "validation/Reference.h" -#include "validation/Validation.h" - -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" -#include "arm_compute/runtime/Tensor.h" -#include "arm_compute/runtime/TensorAllocator.h" - -#include "boost_wrapper.h" - -#include <random> -#include <string> -#include <tuple> - -using namespace arm_compute; -using namespace arm_compute::test; -using namespace arm_compute::test::validation; - -namespace -{ -/** Define tolerance of the direct convolution layer - * - * @param[in] dt DataType of the tensor. - * - * @return Tolerance depending on the data type. - */ -float direct_convolution_layer_tolerance(DataType dt) -{ - switch(dt) - { - case DataType::F16: - return 0.1f; - case DataType::F32: - return 1e-3f; - default: - return 0.f; - } -} - -/** Compute CL direct convolution layer function. - * - * @param[in] src_shape Shape of the input tensor. - * @param[in] weights_shape Shape of the weights. - * @param[in] bias_shape Shape of the bias tensor. - * @param[in] dst_shape Shape of the output tensor. - * @param[in] dt Data type of input, convolution matrix and output tensors. - * @param[in] conv_info Padding and stride information. - * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers - * - * @return Computed output tensor. -*/ -CLTensor compute_convolution_layer(const TensorShape &src_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &dst_shape, - DataType dt, PadStrideInfo conv_info, int fixed_point_position = 0) -{ - // Create tensors - CLTensor src = create_tensor<CLTensor>(src_shape, dt, 1, fixed_point_position); - CLTensor weights = create_tensor<CLTensor>(weights_shape, dt, 1, fixed_point_position); - - CLTensor bias = create_tensor<CLTensor>(bias_shape, dt, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(dst_shape, dt, 1, fixed_point_position); - - // Create and configure function - CLDirectConvolutionLayer conv_layer; - conv_layer.configure(&src, &weights, &bias, &dst, conv_info); - - // Allocate tensors - src.allocator()->allocate(); - weights.allocator()->allocate(); - dst.allocator()->allocate(); - bias.allocator()->allocate(); - - BOOST_TEST(!src.info()->is_resizable()); - BOOST_TEST(!weights.info()->is_resizable()); - BOOST_TEST(!dst.info()->is_resizable()); - BOOST_TEST(!bias.info()->is_resizable()); - - // Fill tensors - switch(dt) - { - case DataType::F16: - case DataType::F32: - { - std::uniform_real_distribution<> distribution(-1.f, 1.f); - library->fill(CLAccessor(src), distribution, 0); - library->fill(CLAccessor(weights), distribution, 1); - library->fill(CLAccessor(bias), distribution, 2); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - } - } - - // Compute function - conv_layer.run(); - - return dst; -} - -TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &conv_info) -{ - TensorShape out_shape(in_shape); - const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(), - in_shape.y(), - kernel_shape.x(), - kernel_shape.y(), - conv_info); - out_shape.set(0, scaled_dims.first); - out_shape.set(1, scaled_dims.second); - out_shape.set(2, kernel_shape[3]); - return out_shape; -} - -} // namespace - -#ifndef DOXYGEN_SKIP_THIS -BOOST_AUTO_TEST_SUITE(CL) -BOOST_AUTO_TEST_SUITE(DirectConvolutionLayer) - -BOOST_AUTO_TEST_SUITE(Float) - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W1x1, DirectConvolutionShapes() * boost::unit_test::data::make({ DataType::F16, DataType::F32 }) * boost::unit_test::data::xrange(1, 4, 1) * boost::unit_test::data::xrange(1, 4, - 1) - * boost::unit_test::data::make({ 1, 4, 8, 16 }), - input_shape, dt, sx, sy, num_kernels) -{ - const unsigned int kernel_size = 1; - const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); - const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels)); - const TensorShape b_shape(static_cast<unsigned int>(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - CLTensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); - - // Validate output - validate(CLAccessor(dst), ref, direct_convolution_layer_tolerance(dt)); -} - -BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) -BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::make({ DataType::F16, DataType::F32 }) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, - 1) - * boost::unit_test::data::xrange(0, 2, 1) - * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), - input_shape, dt, sx, sy, px, py, num_kernels) -{ - const unsigned int kernel_size = 3; - const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR); - const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels)); - const TensorShape b_shape(static_cast<unsigned int>(num_kernels)); - const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); - - CLTensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); - - RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); - - // Validate output - validate(CLAccessor(dst), ref, direct_convolution_layer_tolerance(dt)); -} -BOOST_AUTO_TEST_SUITE_END() - -BOOST_AUTO_TEST_SUITE_END() -BOOST_AUTO_TEST_SUITE_END() -#endif /* DOXYGEN_SKIP_THIS */ 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<> { |