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Diffstat (limited to 'tests/dataset/ConvolutionLayerDataset.h')
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diff --git a/tests/dataset/ConvolutionLayerDataset.h b/tests/dataset/ConvolutionLayerDataset.h new file mode 100644 index 0000000000..e66117e0d8 --- /dev/null +++ b/tests/dataset/ConvolutionLayerDataset.h @@ -0,0 +1,269 @@ +/* + * 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_DATASET_CONVOLUTION_LAYER_DATASET_H__ +#define __ARM_COMPUTE_TEST_DATASET_CONVOLUTION_LAYER_DATASET_H__ + +#include "TypePrinter.h" + +#include "arm_compute/core/TensorShape.h" +#include "dataset/GenericDataset.h" +#include "dataset/ShapeDatasets.h" + +#include <sstream> +#include <type_traits> + +#ifdef BOOST +#include "boost_wrapper.h" +#endif + +namespace arm_compute +{ +namespace test +{ +/** Convolution Layer data object */ +class ConvolutionLayerDataObject +{ +public: + operator std::string() const + { + std::stringstream ss; + ss << "ConvolutionLayer"; + ss << "_I" << src_shape; + ss << "_K" << weights_shape; + ss << "_PS" << info; + return ss.str(); + } + + friend std::ostream &operator<<(std::ostream &os, const ConvolutionLayerDataObject &obj) + { + os << static_cast<std::string>(obj); + return os; + } + +public: + TensorShape src_shape; + TensorShape weights_shape; + TensorShape bias_shape; + TensorShape dst_shape; + PadStrideInfo info; +}; + +template <unsigned int Size> +using ConvolutionLayerDataset = GenericDataset<ConvolutionLayerDataObject, Size>; + +/** Data set containing small convolution layer shapes */ +class SmallConvolutionLayerDataset final : public ConvolutionLayerDataset<3> +{ +public: + SmallConvolutionLayerDataset() + : GenericDataset + { + ConvolutionLayerDataObject{ TensorShape(23U, 27U, 5U), TensorShape(3U, 3U, 5U, 21U), TensorShape(21U), TensorShape(11U, 25U, 21U), PadStrideInfo(2, 1, 0, 0) }, + ConvolutionLayerDataObject{ TensorShape(33U, 27U, 7U), TensorShape(5U, 5U, 7U, 11U), TensorShape(11U), TensorShape(11U, 12U, 11U), PadStrideInfo(3, 2, 1, 0) }, + ConvolutionLayerDataObject{ TensorShape(17U, 31U, 2U, 7U), TensorShape(5U, 5U, 2U, 5U), TensorShape(5U), TensorShape(15U, 15U, 5U, 7U), PadStrideInfo(1, 2, 1, 1) } + } + { + } + + ~SmallConvolutionLayerDataset() = default; +}; + +/** Data set containing direct convolution tensor shapes. */ +class DirectConvolutionShapes final : public ShapeDataset<3> +{ +public: + DirectConvolutionShapes() + : ShapeDataset(TensorShape(3U, 3U, 3U, 2U, 4U, 5U), + TensorShape(32U, 37U, 3U), + TensorShape(13U, 15U, 8U, 3U)) + { + } +}; + +/** AlexNet's convolution layers tensor shapes. */ +class AlexNetConvolutionLayerDataset final : public ConvolutionLayerDataset<5> +{ +public: + AlexNetConvolutionLayerDataset() + : GenericDataset + { + ConvolutionLayerDataObject{ TensorShape(227U, 227U, 3U), TensorShape(11U, 11U, 3U, 96U), TensorShape(96U), TensorShape(55U, 55U, 96U), PadStrideInfo(4, 4, 0, 0) }, + ConvolutionLayerDataObject{ TensorShape(27U, 27U, 96U), TensorShape(5U, 5U, 96U, 256U), TensorShape(256U), TensorShape(27U, 27U, 256U), PadStrideInfo(1, 1, 2, 2) }, + ConvolutionLayerDataObject{ TensorShape(13U, 13U, 256U), TensorShape(3U, 3U, 256U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U), PadStrideInfo(1, 1, 1, 1) }, + ConvolutionLayerDataObject{ TensorShape(13U, 13U, 384U), TensorShape(3U, 3U, 384U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U), PadStrideInfo(1, 1, 1, 1) }, + ConvolutionLayerDataObject{ TensorShape(13U, 13U, 384U), TensorShape(3U, 3U, 384U, 256U), TensorShape(256U), TensorShape(13U, 13U, 256U), PadStrideInfo(1, 1, 1, 1) } + } + { + } + + ~AlexNetConvolutionLayerDataset() = default; +}; + +/** LeNet5's convolution layers tensor shapes. */ +class LeNet5ConvolutionLayerDataset final : public ConvolutionLayerDataset<2> +{ +public: + LeNet5ConvolutionLayerDataset() + : GenericDataset + { + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 1U), TensorShape(5U, 5U, 1U, 20U), TensorShape(20U), TensorShape(24U, 24U, 20U), PadStrideInfo(1, 1, 0, 0) }, + ConvolutionLayerDataObject{ TensorShape(12U, 12U, 20U), TensorShape(5U, 5U, 20U, 50U), TensorShape(50U), TensorShape(8U, 8U, 50U), PadStrideInfo(1, 1, 0, 0) }, + } + { + } + + ~LeNet5ConvolutionLayerDataset() = default; +}; + +/** GoogleLeNet v1 convolution layers tensor shapes (Part 1). + * + * @note Dataset is split into two to avoid a register allocation failure produced by clang in Android debug builds. + */ +class GoogLeNetConvolutionLayerDataset1 final : public ConvolutionLayerDataset<32> +{ +public: + GoogLeNetConvolutionLayerDataset1() + : GenericDataset + { + // conv1/7x7_s2 + ConvolutionLayerDataObject{ TensorShape(224U, 224U, 3U), TensorShape(7U, 7U, 3U, 64U), TensorShape(64U), TensorShape(112U, 112U, 64U), PadStrideInfo(2, 2, 3, 3) }, + // conv2/3x3_reduce + ConvolutionLayerDataObject{ TensorShape(56U, 56U, 64U), TensorShape(1U, 1U, 64U, 64U), TensorShape(64U), TensorShape(56U, 56U, 64U), PadStrideInfo(1, 1, 0, 0) }, + // conv2/3x3 + ConvolutionLayerDataObject{ TensorShape(56U, 56U, 64U), TensorShape(3U, 3U, 64U, 192U), TensorShape(192U), TensorShape(56U, 56U, 192U), PadStrideInfo(1, 1, 1, 1) }, + // inception_3a/1x1 + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(1U, 1U, 192U, 64U), TensorShape(64U), TensorShape(28U, 28U, 64U), PadStrideInfo(1, 1, 0, 0) }, + // inception_3a/3x3_reduce + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(1U, 1U, 192U, 96U), TensorShape(96U), TensorShape(28U, 28U, 96U), PadStrideInfo(1, 1, 0, 0) }, + // inception_3a/3x3 + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 96U), TensorShape(3U, 3U, 96U, 128U), TensorShape(128U), TensorShape(28U, 28U, 128U), PadStrideInfo(1, 1, 1, 1) }, + // inception_3a/5x5_reduce + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(1U, 1U, 192U, 16U), TensorShape(16U), TensorShape(28U, 28U, 16U), PadStrideInfo(1, 1, 0, 0) }, + // inception_3a/5x5 + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 16U), TensorShape(5U, 5U, 16U, 32U), TensorShape(32U), TensorShape(28U, 28U, 32U), PadStrideInfo(1, 1, 2, 2) }, + // inception_3a/pool_proj + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(1U, 1U, 192U, 32U), TensorShape(32U), TensorShape(28U, 28U, 32U), PadStrideInfo(1, 1, 0, 0) }, + // inception_3b/1x1, inception_3b/3x3_reduce + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 256U), TensorShape(1U, 1U, 256U, 128U), TensorShape(128U), TensorShape(28U, 28U, 128U), PadStrideInfo(1, 1, 0, 0) }, + // inception_3b/3x3 + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 128U), TensorShape(3U, 3U, 128U, 192U), TensorShape(192U), TensorShape(28U, 28U, 192U), PadStrideInfo(1, 1, 1, 1) }, + // inception_3b/5x5_reduce + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 256U), TensorShape(1U, 1U, 256U, 32U), TensorShape(32U), TensorShape(28U, 28U, 32U), PadStrideInfo(1, 1, 0, 0) }, + // inception_3b/5x5 + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 32U), TensorShape(5U, 5U, 32U, 96U), TensorShape(96U), TensorShape(28U, 28U, 96U), PadStrideInfo(1, 1, 2, 2) }, + // inception_3b/pool_proj + ConvolutionLayerDataObject{ TensorShape(28U, 28U, 256U), TensorShape(1U, 1U, 256U, 64U), TensorShape(64U), TensorShape(28U, 28U, 64U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4a/1x1 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(1U, 1U, 480U, 192U), TensorShape(192U), TensorShape(14U, 14U, 192U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4a/3x3_reduce + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(1U, 1U, 480U, 96U), TensorShape(96U), TensorShape(14U, 14U, 96U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4a/3x3 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 96U), TensorShape(3U, 3U, 96U, 208U), TensorShape(208U), TensorShape(14U, 14U, 208U), PadStrideInfo(1, 1, 1, 1) }, + // inception_4a/5x5_reduce + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(1U, 1U, 480U, 16U), TensorShape(16U), TensorShape(14U, 14U, 16U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4a/5x5 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 16U), TensorShape(5U, 5U, 16U, 48U), TensorShape(48U), TensorShape(14U, 14U, 48U), PadStrideInfo(1, 1, 2, 2) }, + // inception_4a/pool_proj + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(1U, 1U, 480U, 64U), TensorShape(64U), TensorShape(14U, 14U, 64U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4b/1x1 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 160U), TensorShape(160U), TensorShape(14U, 14U, 160U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4b/3x3_reduce, inception_4d/1x1 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 112U), TensorShape(112U), TensorShape(14U, 14U, 112U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4b/3x3 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 112U), TensorShape(3U, 3U, 112U, 224U), TensorShape(224U), TensorShape(14U, 14U, 224U), PadStrideInfo(1, 1, 1, 1) }, + // inception_4b/5x5_reduce, inception_4c/5x5_reduce + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 24U), TensorShape(24U), TensorShape(14U, 14U, 24U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4b/5x5, inception_4c/5x5 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 24U), TensorShape(5U, 5U, 24U, 64U), TensorShape(64U), TensorShape(14U, 14U, 64U), PadStrideInfo(1, 1, 2, 2) }, + // inception_4b/pool_proj, inception_4c/pool_proj, inception_4d/pool_proj + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 64U), TensorShape(64U), TensorShape(14U, 14U, 64U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4c/1x1, inception_4c/3x3_reduce + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 128U), TensorShape(128U), TensorShape(14U, 14U, 128U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4c/3x3 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 128U), TensorShape(3U, 3U, 128U, 256U), TensorShape(256U), TensorShape(14U, 14U, 256U), PadStrideInfo(1, 1, 1, 1) }, + // inception_4d/3x3_reduce + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 144U), TensorShape(144U), TensorShape(14U, 14U, 144U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4d/3x3 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 144U), TensorShape(3U, 3U, 144U, 288U), TensorShape(288U), TensorShape(14U, 14U, 288U), PadStrideInfo(1, 1, 1, 1) }, + // inception_4d/5x5_reduce + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 32U), TensorShape(32U), TensorShape(14U, 14U, 32U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4d/5x5 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 32U), TensorShape(5U, 5U, 32U, 64U), TensorShape(64U), TensorShape(14U, 14U, 64U), PadStrideInfo(1, 1, 2, 2) }, + } + { + } + + ~GoogLeNetConvolutionLayerDataset1() = default; +}; + +/** GoogleLeNet v1 convolution layers tensor shapes (Part 2). */ +class GoogLeNetConvolutionLayerDataset2 final : public ConvolutionLayerDataset<17> +{ +public: + GoogLeNetConvolutionLayerDataset2() + : GenericDataset + { + // inception_4e/1x1 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(1U, 1U, 528U, 256U), TensorShape(256U), TensorShape(14U, 14U, 256U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4e/3x3_reduce + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(1U, 1U, 528U, 160U), TensorShape(160U), TensorShape(14U, 14U, 160U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4e/3x3 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 160U), TensorShape(3U, 3U, 160U, 320U), TensorShape(320U), TensorShape(14U, 14U, 320U), PadStrideInfo(1, 1, 1, 1) }, + // inception_4e/5x5_reduce + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(1U, 1U, 528U, 32U), TensorShape(32U), TensorShape(14U, 14U, 32U), PadStrideInfo(1, 1, 0, 0) }, + // inception_4e/5x5 + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 32U), TensorShape(5U, 5U, 32U, 128U), TensorShape(128U), TensorShape(14U, 14U, 128U), PadStrideInfo(1, 1, 2, 2) }, + // inception_4e/pool_proj + ConvolutionLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(1U, 1U, 528U, 128U), TensorShape(128U), TensorShape(14U, 14U, 128U), PadStrideInfo(1, 1, 0, 0) }, + // inception_5a/1x1 + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 256U), TensorShape(256U), TensorShape(7U, 7U, 256U), PadStrideInfo(1, 1, 0, 0) }, + // inception_5a/3x3_reduce + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 160U), TensorShape(160U), TensorShape(7U, 7U, 160U), PadStrideInfo(1, 1, 0, 0) }, + // inception_5a/3x3 + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 160U), TensorShape(3U, 3U, 160U, 320U), TensorShape(320U), TensorShape(7U, 7U, 320U), PadStrideInfo(1, 1, 1, 1) }, + // inception_5a/5x5_reduce + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 32U), TensorShape(32U), TensorShape(7U, 7U, 32U), PadStrideInfo(1, 1, 0, 0) }, + // inception_5a/5x5 + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 32U), TensorShape(5U, 5U, 32U, 128U), TensorShape(128U), TensorShape(7U, 7U, 128U), PadStrideInfo(1, 1, 2, 2) }, + // inception_5a/pool_proj, inception_5b/pool_proj + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 128U), TensorShape(128U), TensorShape(7U, 7U, 128U), PadStrideInfo(1, 1, 0, 0) }, + // inception_5b/1x1 + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 384U), TensorShape(384U), TensorShape(7U, 7U, 384U), PadStrideInfo(1, 1, 0, 0) }, + // inception_5b/3x3_reduce + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 192U), TensorShape(192U), TensorShape(7U, 7U, 192U), PadStrideInfo(1, 1, 0, 0) }, + // inception_5b/3x3 + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 192U), TensorShape(3U, 3U, 192U, 384U), TensorShape(384U), TensorShape(7U, 7U, 384U), PadStrideInfo(1, 1, 1, 1) }, + // inception_5b/5x5_reduce + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 48U), TensorShape(48U), TensorShape(7U, 7U, 48U), PadStrideInfo(1, 1, 0, 0) }, + // inception_5b/5x5 + ConvolutionLayerDataObject{ TensorShape(7U, 7U, 48U), TensorShape(5U, 5U, 48U, 128U), TensorShape(128U), TensorShape(7U, 7U, 128U), PadStrideInfo(1, 1, 2, 2) } + } + { + } + + ~GoogLeNetConvolutionLayerDataset2() = default; +}; +} // namespace test +} // namespace arm_compute +#endif //__ARM_COMPUTE_TEST_DATASET_CONVOLUTION_LAYER_DATASET_H__ |