diff options
Diffstat (limited to 'tests')
-rw-r--r-- | tests/datasets/ShapeDatasets.h | 32 | ||||
-rw-r--r-- | tests/datasets/WinogradFilterTransformDataset.h | 128 | ||||
-rw-r--r-- | tests/validation/CL/Winograd.cpp | 85 | ||||
-rw-r--r--[-rwxr-xr-x] | tests/validation/Helpers.h | 0 | ||||
-rw-r--r-- | tests/validation/fixtures/WinogradLayerFixture.h | 84 | ||||
-rw-r--r-- | tests/validation/reference/Winograd.cpp | 105 | ||||
-rw-r--r-- | tests/validation/reference/Winograd.h | 5 |
7 files changed, 432 insertions, 7 deletions
diff --git a/tests/datasets/ShapeDatasets.h b/tests/datasets/ShapeDatasets.h index 4b563708e1..e939a6f5a7 100644 --- a/tests/datasets/ShapeDatasets.h +++ b/tests/datasets/ShapeDatasets.h @@ -238,6 +238,38 @@ public: } }; +/** Data set containing medium 3D tensor shapes. */ +class Medium3DShapes final : public ShapeDataset +{ +public: + Medium3DShapes() + : ShapeDataset("Shape", + { + TensorShape{ 42U, 37U, 8U }, + TensorShape{ 57U, 60U, 13U }, + TensorShape{ 128U, 64U, 21U }, + TensorShape{ 83U, 72U, 14U } + }) + { + } +}; + +/** Data set containing medium 4D tensor shapes. */ +class Medium4DShapes final : public ShapeDataset +{ +public: + Medium4DShapes() + : ShapeDataset("Shape", + { + TensorShape{ 42U, 37U, 8U, 15U }, + TensorShape{ 57U, 60U, 13U, 8U }, + TensorShape{ 128U, 64U, 21U, 13U }, + TensorShape{ 83U, 72U, 14U, 5U } + }) + { + } +}; + /** Data set containing large tensor shapes. */ class LargeShapes final : public ShapeDataset { diff --git a/tests/datasets/WinogradFilterTransformDataset.h b/tests/datasets/WinogradFilterTransformDataset.h new file mode 100644 index 0000000000..07d0283b55 --- /dev/null +++ b/tests/datasets/WinogradFilterTransformDataset.h @@ -0,0 +1,128 @@ +/* + * Copyright (c) 2018 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_WINOGRAD_FILTER_TRANSFORM_DATASET +#define ARM_COMPUTE_TEST_WINOGRAD_FILTER_TRANSFORM_DATASET + +#include "utils/TypePrinter.h" + +#include "arm_compute/core/TensorShape.h" + +namespace arm_compute +{ +namespace test +{ +namespace datasets +{ +class WinogradFilterTransformDataset +{ +public: + using type = std::tuple<TensorShape, bool>; + + struct iterator + { + iterator(std::vector<TensorShape>::const_iterator a_it, + std::vector<bool>::const_iterator is_nchw_it) + : _a_it{ std::move(a_it) }, + _is_nchw_it{ std::move(is_nchw_it) } + { + } + + std::string description() const + { + std::stringstream description; + description << "Input=" << *_a_it << ":"; + description << "IsNCHW=" << *_is_nchw_it << ":"; + return description.str(); + } + + WinogradFilterTransformDataset::type operator*() const + { + return std::make_tuple(*_a_it, *_is_nchw_it); + } + + iterator &operator++() + { + ++_a_it; + ++_is_nchw_it; + + return *this; + } + + private: + std::vector<TensorShape>::const_iterator _a_it; + std::vector<bool>::const_iterator _is_nchw_it; + }; + + iterator begin() const + { + return iterator(_a_shapes.begin(), _is_nchw.begin()); + } + + int size() const + { + return std::min(_a_shapes.size(), _is_nchw.size()); + } + + void add_config(TensorShape a, bool is_nchw) + { + _a_shapes.emplace_back(std::move(a)); + _is_nchw.emplace_back(std::move(is_nchw)); + } + +protected: + WinogradFilterTransformDataset() = default; + WinogradFilterTransformDataset(WinogradFilterTransformDataset &&) = default; + +private: + std::vector<TensorShape> _a_shapes{}; + std::vector<bool> _is_nchw{}; +}; + +class SmallWinogradFilterTransformDataset final : public WinogradFilterTransformDataset +{ +public: + SmallWinogradFilterTransformDataset() + { + add_config(TensorShape(3U, 3U, 7U, 4U), true); + add_config(TensorShape(3U, 3U, 4U, 13U), true); + add_config(TensorShape(3U, 3U, 9U, 2U), true); + add_config(TensorShape(3U, 3U, 3U, 5U), true); + } +}; + +class LargeWinogradFilterTransformDataset final : public WinogradFilterTransformDataset +{ +public: + LargeWinogradFilterTransformDataset() + { + add_config(TensorShape(3U, 3U, 32U, 64U), true); + add_config(TensorShape(3U, 3U, 51U, 13U), true); + add_config(TensorShape(3U, 3U, 53U, 47U), true); + add_config(TensorShape(3U, 3U, 128U, 384U), true); + } +}; +} // namespace datasets +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_WINOGRAD_FILTER_TRANSFORM_DATASET */ diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp index 664b3f4ef8..0b21ed2577 100644 --- a/tests/validation/CL/Winograd.cpp +++ b/tests/validation/CL/Winograd.cpp @@ -18,15 +18,20 @@ * 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 + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ +#include "arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/CLTensorAllocator.h" #include "arm_compute/runtime/CL/functions/CLWinogradInputTransform.h" #include "tests/CL/CLAccessor.h" +#include "tests/CL/Helper.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets/ShapeDatasets.h" +#include "tests/datasets/WinogradFilterTransformDataset.h" #include "tests/datasets/WinogradInputTransformDataset.h" #include "tests/framework/Asserts.h" #include "tests/framework/Macros.h" @@ -40,6 +45,13 @@ namespace test { namespace validation { +namespace +{ +constexpr AbsoluteTolerance<float> tolerance_f32(0.0001f); +} // namespace + +using namespace arm_compute::misc::shape_calculator; + TEST_SUITE(CL) TEST_SUITE(Winograd) @@ -125,11 +137,76 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::Dat { validate(CLAccessor(_target), _reference); } +TEST_SUITE_END() // InputTransform + +TEST_SUITE(FilterTransform) +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip( + framework::dataset::make("InputInfo",{ + TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), // F16 not supported + TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::QASYMM8), // QASYMM8 not supported + TensorInfo(TensorShape(5U, 5U, 5U, 3U), 1, DataType::F32), // Kernel size not supported + TensorInfo(TensorShape(3U, 3U), 1, DataType::F32), // valid + TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F32), // valid + TensorInfo(TensorShape(3U, 3U, 37U, 2U), 1, DataType::F32), // valid + TensorInfo(TensorShape(3U, 3U, 37U, 22U), 1, DataType::F32) // valid + }), + framework::dataset::make("OutputInfo", { + TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F16), + TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::QASYMM8), + TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(1U, 1U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(2U, 37U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(22U, 37U, 16U), 1, DataType::F32) + })), + framework::dataset::make("Expected", { false, false, false, true, true, true, true })), + input_info, output_info, expected) +{ + ARM_COMPUTE_EXPECT(bool(CLWinogradFilterTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* -TEST_SUITE_END() +using CLWinogradFilterTransform = CLSynthetizeFunctionWithZeroConstantBorder<CLWinogradFilterTransformKernel, 0>; +using CLWinogradFilterTransformFixture = WinogradFilterTransformValidationFixture<CLTensor, CLAccessor, CLWinogradFilterTransform, float>; + +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallWinogradFilterTransformDataset(), datasets::LargeWinogradFilterTransformDataset()), + framework::dataset::make("DataType", { DataType::F32 })), + shape_a, is_nchw_format, data_type) +{ + ARM_COMPUTE_UNUSED(is_nchw_format); + + TensorShape shape_b = compute_winograd_filter_transform_shape(TensorInfo(shape_a, 1, data_type)); + + // Create tensors + CLTensor a = create_tensor<CLTensor>(shape_a, data_type); + CLTensor b = create_tensor<CLTensor>(shape_b, data_type); + + ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function + CLWinogradFilterTransform winograd_filter_transform; + winograd_filter_transform.configure(&a, &b); +} + +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::ALL, combine(datasets::SmallWinogradFilterTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradFilterTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() // FilterTransform -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE_END() // Winograd +TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute diff --git a/tests/validation/Helpers.h b/tests/validation/Helpers.h index b192f317b4..b192f317b4 100755..100644 --- a/tests/validation/Helpers.h +++ b/tests/validation/Helpers.h diff --git a/tests/validation/fixtures/WinogradLayerFixture.h b/tests/validation/fixtures/WinogradLayerFixture.h index 95e331560d..bfe1efce3b 100644 --- a/tests/validation/fixtures/WinogradLayerFixture.h +++ b/tests/validation/fixtures/WinogradLayerFixture.h @@ -27,7 +27,6 @@ #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" #include "tests/AssetsLibrary.h" #include "tests/Globals.h" #include "tests/IAccessor.h" @@ -42,8 +41,6 @@ namespace arm_compute { -class NEWinogradLayer; - namespace test { namespace validation @@ -224,6 +221,87 @@ protected: TensorType _target{}; SimpleTensor<T> _reference{}; }; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class WinogradFilterTransformValidationFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(TensorShape input_shape, bool is_nchw_format, DataType data_type) + { + TensorShape output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type)); + + _target = compute_target(input_shape, output_shape, is_nchw_format, data_type); + _reference = compute_reference(input_shape, output_shape, is_nchw_format, data_type); + } + +protected: + template <typename U> + void fill(U &&tensor, int i, float min, float max) + { + switch(tensor.data_type()) + { + case DataType::F32: + { + std::uniform_real_distribution<> distribution(min, max); + library->fill(tensor, distribution, i); + break; + } + default: + { + ARM_COMPUTE_ERROR("Not supported"); + library->fill_tensor_uniform(tensor, i); + break; + } + } + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, DataType data_type) + { + ARM_COMPUTE_UNUSED(is_nchw_format); + + // Create tensors + TensorType src = create_tensor<TensorType>(input_shape, data_type); + TensorType dst = create_tensor<TensorType>(output_shape, data_type); + + // Create and configure function + FunctionType filter_transform; + filter_transform.configure(&src, &dst); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src), 0, -1.f, 1.f); + + filter_transform.run(); + + return dst; + } + + SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, DataType data_type) + { + ARM_COMPUTE_ERROR_ON(!is_nchw_format); + + // Create reference + SimpleTensor<T> src{ input_shape, data_type, 1 }; + + // Fill reference + fill(src, 0, -1.f, 1.f); + + return reference::winograd_filter_transform<T>(src, output_shape); + } + + TensorType _target{}; + SimpleTensor<T> _reference{}; +}; } // namespace validation } // namespace test } // namespace arm_compute diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp index 371bb6348e..3ed55fb9fc 100644 --- a/tests/validation/reference/Winograd.cpp +++ b/tests/validation/reference/Winograd.cpp @@ -26,6 +26,8 @@ #include "tests/validation/Helpers.h" #include "tests/validation/reference/Utils.h" +#include "arm_compute/core/Types.h" + namespace arm_compute { namespace test @@ -108,6 +110,87 @@ void winograd_input_transform3x3(const SimpleTensor<T> &src, SimpleTensor<T> &ds } } } + +template <typename T> +void winograd_filter_transform3x3(const SimpleTensor<T> &in, SimpleTensor<T> &out) +{ + // Simple tensor for the 3x3 input tile + SimpleTensor<T> input_tile{ TensorShape(3u, 3u), in.data_type(), 1 }; + + // Simple tensor for the transformation matrix + SimpleTensor<T> trans_matrix{ TensorShape(3u, 4u), in.data_type(), 1 }; + + // Simple tensor for the transformation matrix transpose + SimpleTensor<T> trans_matrix_transposed{ TensorShape(4u, 3u), in.data_type(), 1 }; + + // Simple tensor for the 4x3 temporary tile + SimpleTensor<T> tmp_tile{ TensorShape(3u, 4u), in.data_type(), 1 }; + + // Simple tensor for the 4x4 output tile + SimpleTensor<T> output_tile{ TensorShape(4u, 4u), in.data_type(), 1 }; + + // Initialize transformation matrix + // 1 | 0 | 0 + // 0.5 | 0.5 | 0.5 + // 0.5 |-0.5 | 0.5 + // 0 | 0 | 1 + trans_matrix[0 + 0 * 3] = 1.0f; + trans_matrix[1 + 0 * 3] = 0.0f; + trans_matrix[2 + 0 * 3] = 0.0f; + trans_matrix[0 + 1 * 3] = 0.5f; + trans_matrix[1 + 1 * 3] = 0.5f; + trans_matrix[2 + 1 * 3] = 0.5f; + trans_matrix[0 + 2 * 3] = 0.5f; + trans_matrix[1 + 2 * 3] = -0.5f; + trans_matrix[2 + 2 * 3] = 0.5f; + trans_matrix[0 + 3 * 3] = 0.0f; + trans_matrix[1 + 3 * 3] = 0.0f; + trans_matrix[2 + 3 * 3] = 1.0f; + + // Transpose the transformation matrix + transpose_matrix(trans_matrix, trans_matrix_transposed); + + const int num_channels = in.shape()[2]; + const int num_filters = in.shape()[3]; + const int num_batches = in.shape().total_size() / (9 * num_channels * num_filters); + + for(int n = 0; n < num_batches; ++n) + { + for(int w = 0; w < num_filters; ++w) + { + for(int z = 0; z < num_channels; ++z) + { + // Load the 3x3 tile from the input tensor + get_tile(in, input_tile, Coordinates(0, 0, z, w, n)); + + // First transformation + matrix_multiply(trans_matrix, input_tile, tmp_tile); + + // Second transformation + matrix_multiply(tmp_tile, trans_matrix_transposed, output_tile); + + // Store the 4x4 output tile across the 16 channels + const int output_offset = w + z * num_filters; + out[output_offset + 0 * num_filters * num_channels] = output_tile[0 + 0 * 4]; + out[output_offset + 1 * num_filters * num_channels] = output_tile[1 + 0 * 4]; + out[output_offset + 2 * num_filters * num_channels] = output_tile[2 + 0 * 4]; + out[output_offset + 3 * num_filters * num_channels] = output_tile[3 + 0 * 4]; + out[output_offset + 4 * num_filters * num_channels] = output_tile[0 + 1 * 4]; + out[output_offset + 5 * num_filters * num_channels] = output_tile[1 + 1 * 4]; + out[output_offset + 6 * num_filters * num_channels] = output_tile[2 + 1 * 4]; + out[output_offset + 7 * num_filters * num_channels] = output_tile[3 + 1 * 4]; + out[output_offset + 8 * num_filters * num_channels] = output_tile[0 + 2 * 4]; + out[output_offset + 9 * num_filters * num_channels] = output_tile[1 + 2 * 4]; + out[output_offset + 10 * num_filters * num_channels] = output_tile[2 + 2 * 4]; + out[output_offset + 11 * num_filters * num_channels] = output_tile[3 + 2 * 4]; + out[output_offset + 12 * num_filters * num_channels] = output_tile[0 + 3 * 4]; + out[output_offset + 13 * num_filters * num_channels] = output_tile[1 + 3 * 4]; + out[output_offset + 14 * num_filters * num_channels] = output_tile[2 + 3 * 4]; + out[output_offset + 15 * num_filters * num_channels] = output_tile[3 + 3 * 4]; + } + } + } +} } // namespace template <typename T> @@ -130,7 +213,29 @@ SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &src, const Tenso return dst; } +template <typename T> +SimpleTensor<T> winograd_filter_transform(const SimpleTensor<T> &in, const TensorShape &output_shape) +{ + ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format"); + + // Create reference + SimpleTensor<T> out{ output_shape, in.data_type(), 1 }; + + switch(in.shape()[0]) + { + case 3: + winograd_filter_transform3x3(in, out); + break; + default: + ARM_COMPUTE_ERROR("Only supported 3x3 kernel"); + break; + } + + return out; +} + template SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims); +template SimpleTensor<float> winograd_filter_transform(const SimpleTensor<float> &in, const TensorShape &output_shape); } // namespace reference } // namespace validation } // namespace test diff --git a/tests/validation/reference/Winograd.h b/tests/validation/reference/Winograd.h index ed95239db3..ba8e5c1cb6 100644 --- a/tests/validation/reference/Winograd.h +++ b/tests/validation/reference/Winograd.h @@ -24,6 +24,8 @@ #ifndef __ARM_COMPUTE_TEST_WINOGRAD_H__ #define __ARM_COMPUTE_TEST_WINOGRAD_H__ +#include "arm_compute/core/TensorShape.h" + #include "tests/SimpleTensor.h" namespace arm_compute @@ -36,6 +38,9 @@ namespace reference { template <typename T> SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims); + +template <typename T> +SimpleTensor<T> winograd_filter_transform(const SimpleTensor<T> &in, const TensorShape &output_shape); } // namespace reference } // namespace validation } // namespace test |