/* * Copyright (c) 2017-2021 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_DEPTHWISE_CONVOLUTION_DATASET #define ARM_COMPUTE_TEST_DEPTHWISE_CONVOLUTION_DATASET #include "utils/TypePrinter.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" namespace arm_compute { namespace test { namespace datasets { class DepthwiseConvolutionLayerDataset { public: using type = std::tuple; struct iterator { iterator(std::vector::const_iterator src_it, std::vector::const_iterator weights_it, std::vector::const_iterator infos_it, std::vector::const_iterator dilation_it) : _src_it{ std::move(src_it) }, _weights_it{ std::move(weights_it) }, _infos_it{ std::move(infos_it) }, _dilation_it{ std::move(dilation_it) } { } std::string description() const { std::stringstream description; description << "In=" << *_src_it << ":"; description << "Weights=" << *_weights_it << ":"; description << "Info=" << *_infos_it << ":"; description << "Dilation=" << *_dilation_it; return description.str(); } DepthwiseConvolutionLayerDataset::type operator*() const { return std::make_tuple(*_src_it, *_weights_it, *_infos_it, *_dilation_it); } iterator &operator++() { ++_src_it; ++_weights_it; ++_infos_it; ++_dilation_it; return *this; } private: std::vector::const_iterator _src_it; std::vector::const_iterator _weights_it; std::vector::const_iterator _infos_it; std::vector::const_iterator _dilation_it; }; iterator begin() const { return iterator(_src_shapes.begin(), _weight_shapes.begin(), _infos.begin(), _dilations.begin()); } int size() const { return std::min(_src_shapes.size(), std::min(_weight_shapes.size(), std::min(_infos.size(), _dilations.size()))); } void add_config(TensorShape src, Size2D weights, PadStrideInfo info, Size2D dilation = Size2D(1U, 1U)) { _src_shapes.emplace_back(std::move(src)); _weight_shapes.emplace_back(std::move(weights)); _infos.emplace_back(std::move(info)); _dilations.emplace_back(std::move(dilation)); } protected: DepthwiseConvolutionLayerDataset() = default; DepthwiseConvolutionLayerDataset(DepthwiseConvolutionLayerDataset &&) = default; private: std::vector _src_shapes{}; std::vector _weight_shapes{}; std::vector _infos{}; std::vector _dilations{}; }; /** Dataset containing small, generic depthwise convolution shapes. */ class SmallDepthwiseConvolutionLayerDataset final : public DepthwiseConvolutionLayerDataset { public: SmallDepthwiseConvolutionLayerDataset() { add_config(TensorShape(7U, 7U, 1U), Size2D(1U, 1U), PadStrideInfo(1, 1, 0, 0)); add_config(TensorShape(3U, 3U, 2U), Size2D(2U, 2U), PadStrideInfo(1, 1, 0, 0)); add_config(TensorShape(33U, 27U, 7U), Size2D(7U, 3U), PadStrideInfo(3, 2, 1, 0)); // Asymmetric padding add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR)); add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 1, 1, 0, 2, DimensionRoundingType::FLOOR)); // Ceil rounding add_config(TensorShape(7U, 8U, 5U, 9U), Size2D(8U, 6U), PadStrideInfo(2, 3, 1, 1, 1, 3, DimensionRoundingType::CEIL), Size2D(1U, 2U)); } }; /** Dataset containing large, generic depthwise convolution shapes. */ class LargeDepthwiseConvolutionLayerDataset final : public DepthwiseConvolutionLayerDataset { public: LargeDepthwiseConvolutionLayerDataset() { add_config(TensorShape(33U, 27U, 11U), Size2D(3U, 4U), PadStrideInfo(1, 2, 0, 1)); add_config(TensorShape(17U, 31U, 2U), Size2D(5U, 9U), PadStrideInfo(1, 2, 1, 1)); add_config(TensorShape(23U, 27U, 5U), Size2D(11U, 3U), PadStrideInfo(1, 2, 0, 0)); add_config(TensorShape(17U, 31U, 2U, 3U), Size2D(5U, 9U), PadStrideInfo(1, 2, 1, 1)); add_config(TensorShape(233U, 277U, 55U), Size2D(1U, 1U), PadStrideInfo(2, 1, 0, 0)); add_config(TensorShape(333U, 277U, 77U), Size2D(1U, 1U), PadStrideInfo(3, 2, 1, 0)); add_config(TensorShape(177U, 311U, 22U), Size2D(3U, 4U), PadStrideInfo(1, 2, 1, 1)); add_config(TensorShape(233U, 277U, 55U), Size2D(3U, 4U), PadStrideInfo(1, 2, 0, 0)); add_config(TensorShape(333U, 277U, 77U), Size2D(3U, 4U), PadStrideInfo(2, 3, 0, 1)); add_config(TensorShape(177U, 311U, 22U), Size2D(3U, 4U), PadStrideInfo(2, 1, 1, 1)); // Asymmetric padding add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 2, 1, 2, 0, DimensionRoundingType::FLOOR)); add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 1, 3, 0, 2, DimensionRoundingType::FLOOR)); add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 1, 0, 1, 0, DimensionRoundingType::FLOOR)); add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)); } }; /** Dataset containing large kernel size for generic depthwise convolution. */ class LargeKernelSizeDepthwiseConvolutionLayerNHWCDataset final : public DepthwiseConvolutionLayerDataset { public: LargeKernelSizeDepthwiseConvolutionLayerNHWCDataset() { add_config(TensorShape(6U, 210U, 8U), Size2D(4U, 194U), PadStrideInfo(1, 1, 0, 0)); } }; /** Dataset containing small, 3x3 depthwise convolution shapes. */ class SmallDepthwiseConvolutionLayerDataset3x3 final : public DepthwiseConvolutionLayerDataset { public: SmallDepthwiseConvolutionLayerDataset3x3() { add_config(TensorShape(1U, 1U, 2U), Size2D(3U, 3U), PadStrideInfo(1, 1, 2, 2)); add_config(TensorShape(7U, 8U, 3U, 2U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0)); add_config(TensorShape(32U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0)); // Asymmetric padding add_config(TensorShape(33U, 27U, 11U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)); } }; class SmallDepthwiseConvolutionLayerDataset3x3NCHW final : public DepthwiseConvolutionLayerDataset { public: SmallDepthwiseConvolutionLayerDataset3x3NCHW() { add_config(TensorShape(33U, 27U, 11U), Size2D(3U, 3U), PadStrideInfo(3, 2, 1, 1)); // Asymmetric padding add_config(TensorShape(33U, 27U, 11U), Size2D(3U, 3U), PadStrideInfo(2, 2, 3, 1, 2, 1, DimensionRoundingType::FLOOR)); } }; /** Dataset containing large, 3x3 depthwise convolution shapes. */ class LargeDepthwiseConvolutionLayerDataset3x3 final : public DepthwiseConvolutionLayerDataset { public: LargeDepthwiseConvolutionLayerDataset3x3() { add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 1)); add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(1, 2, 1, 0)); add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 2, 0, 1)); add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 2, 1, 1)); add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(2, 1, 1, 0)); add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 1, 0, 1)); add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 1, 1, 1)); add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(2, 2, 1, 0)); add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 1)); add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 2, 1, 1)); add_config(TensorShape(177U, 311U, 22U), Size2D(3U, 3U), PadStrideInfo(1, 2, 1, 1)); add_config(TensorShape(233U, 277U, 55U), Size2D(3U, 3U), PadStrideInfo(1, 2, 0, 0)); add_config(TensorShape(333U, 277U, 77U), Size2D(3U, 3U), PadStrideInfo(2, 3, 0, 0)); add_config(TensorShape(177U, 311U, 22U), Size2D(3U, 3U), PadStrideInfo(2, 1, 1, 1)); // Width and height are a multipile of the processing tile size add_config(TensorShape(32U, 21U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 1)); } }; /** Dataset containing optimized, 3x3 depthwise convolution shapes. */ class SmallOptimizedDepthwiseConvolutionLayerDataset3x3 final : public DepthwiseConvolutionLayerDataset { public: SmallOptimizedDepthwiseConvolutionLayerDataset3x3() { // Stride 1 add_config(TensorShape(7U, 7U, 16U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)); add_config(TensorShape(7U, 7U, 16U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL), Size2D(2U, 2U)); add_config(TensorShape(7U, 7U, 16U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)); add_config(TensorShape(7U, 7U, 16U), Size2D(3U, 3U), PadStrideInfo(1, 1, 2, 2, DimensionRoundingType::CEIL), Size2D(2U, 2U)); // Stride 2 add_config(TensorShape(9U, 9U, 32U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)); add_config(TensorShape(9U, 9U, 32U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), Size2D(2U, 2U)); add_config(TensorShape(9U, 9U, 32U), Size2D(3U, 3U), PadStrideInfo(2, 2, 1, 1, DimensionRoundingType::CEIL)); } }; /** Dataset containing optimized, 3x3 depthwise convolution shapes. */ class LargeOptimizedDepthwiseConvolutionLayerDataset3x3 final : public DepthwiseConvolutionLayerDataset { public: LargeOptimizedDepthwiseConvolutionLayerDataset3x3() { // Stride 1 add_config(TensorShape(233U, 277U, 16U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)); add_config(TensorShape(233U, 7U, 16U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)); add_config(TensorShape(7U, 7U, 21U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)); add_config(TensorShape(28U, 28U, 16U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)); add_config(TensorShape(28U, 28U, 16U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)); // Stride 2 add_config(TensorShape(233U, 277U, 32U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)); add_config(TensorShape(233U, 277U, 32U), Size2D(3U, 3U), PadStrideInfo(2, 2, 1, 1, 1, 1, DimensionRoundingType::CEIL)); add_config(TensorShape(8U, 8U, 32U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::FLOOR)); add_config(TensorShape(8U, 8U, 32U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL)); add_config(TensorShape(8U, 8U, 33U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL)); add_config(TensorShape(64U, 64U, 128U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL)); } }; /** Dataset containing optimized, 5x5 depthwise convolution shapes. */ class SmallOptimizedDepthwiseConvolutionLayerDataset5x5 final : public DepthwiseConvolutionLayerDataset { public: SmallOptimizedDepthwiseConvolutionLayerDataset5x5() { // Stride 1 add_config(TensorShape(7U, 7U, 16U), Size2D(5U, 5U), PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)); add_config(TensorShape(11U, 11U, 16U), Size2D(5U, 5U), PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL), Size2D(2U, 2U)); add_config(TensorShape(7U, 7U, 16U), Size2D(5U, 5U), PadStrideInfo(1, 1, 2, 2, DimensionRoundingType::CEIL)); add_config(TensorShape(7U, 7U, 16U), Size2D(5U, 5U), PadStrideInfo(1, 1, 4, 4, DimensionRoundingType::CEIL), Size2D(2U, 2U)); // Stride 2 add_config(TensorShape(9U, 9U, 32U), Size2D(5U, 5U), PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)); add_config(TensorShape(9U, 9U, 32U), Size2D(5U, 5U), PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), Size2D(2U, 2U)); add_config(TensorShape(9U, 9U, 32U), Size2D(5U, 5U), PadStrideInfo(2, 2, 2, 2, 2, 2, DimensionRoundingType::CEIL)); add_config(TensorShape(9U, 9U, 32U), Size2D(5U, 5U), PadStrideInfo(2, 2, 4, 4, 4, 4, DimensionRoundingType::CEIL), Size2D(2U, 2U)); } }; /** Dataset containing in-place 1x1 depthwise convolution shapes. * * For a depthwise convolution op to be in-place: * * Output has the same shape as the input; * * 1x1 filter * * stride == 1 * * dilations == 1 * * No paddings */ class SmallInPlaceDepthwiseConvolutionLayerDataset final : public DepthwiseConvolutionLayerDataset { public: SmallInPlaceDepthwiseConvolutionLayerDataset() { add_config(TensorShape(7U, 7U, 1U), Size2D(1U, 1U), PadStrideInfo(1, 1, 0, 0)); add_config(TensorShape(11U, 13U, 16U), Size2D(1U, 1U), PadStrideInfo(1, 1, 0, 0)); } }; } // namespace datasets } // namespace test } // namespace arm_compute #endif /* ARM_COMPUTE_TEST_DEPTHWISE_CONVOLUTION_DATASET */