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Diffstat (limited to 'tests/dataset/PoolingLayerDataset.h')
| -rw-r--r-- | tests/dataset/PoolingLayerDataset.h | 159 |
1 files changed, 159 insertions, 0 deletions
diff --git a/tests/dataset/PoolingLayerDataset.h b/tests/dataset/PoolingLayerDataset.h new file mode 100644 index 0000000000..0a50a763ce --- /dev/null +++ b/tests/dataset/PoolingLayerDataset.h @@ -0,0 +1,159 @@ +/* + * 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_POOLING_LAYER_DATASET_H__ +#define __ARM_COMPUTE_TEST_DATASET_POOLING_LAYER_DATASET_H__ + +#include "TypePrinter.h" + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "dataset/GenericDataset.h" + +#include <type_traits> + +#ifdef BOOST +#include "boost_wrapper.h" +#endif + +namespace arm_compute +{ +namespace test +{ +class PoolingLayerDataObject +{ +public: + operator std::string() const + { + std::stringstream ss; + ss << "PoolingLayer"; + ss << "_I" << src_shape; + ss << "_S_" << info.pool_size(); + ss << "_F_" << info.pool_type(); + ss << "_PS" << info.pad_stride_info(); + return ss.str(); + } + + friend std::ostream &operator<<(std::ostream &s, const PoolingLayerDataObject &obj) + { + s << static_cast<std::string>(obj); + return s; + } + +public: + TensorShape src_shape; + TensorShape dst_shape; + PoolingLayerInfo info; +}; + +template <unsigned int Size> +using PoolingLayerDataset = GenericDataset<PoolingLayerDataObject, Size>; + +class AlexNetPoolingLayerDataset final : public PoolingLayerDataset<3> +{ +public: + AlexNetPoolingLayerDataset() + : GenericDataset + { + PoolingLayerDataObject{ TensorShape(55U, 55U, 96U), TensorShape(27U, 27U, 96U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(27U, 27U, 256U), TensorShape(13U, 13U, 256U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(13U, 13U, 256U), TensorShape(6U, 6U, 256U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) }, + } + { + } + + ~AlexNetPoolingLayerDataset() = default; +}; + +class LeNet5PoolingLayerDataset final : public PoolingLayerDataset<2> +{ +public: + LeNet5PoolingLayerDataset() + : GenericDataset + { + PoolingLayerDataObject{ TensorShape(24U, 24U, 20U), TensorShape(12U, 12U, 20U), PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(8U, 8U, 50U), TensorShape(4U, 4U, 50U), PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)) }, + } + { + } + + ~LeNet5PoolingLayerDataset() = default; +}; + +class GoogLeNetPoolingLayerDataset final : public PoolingLayerDataset<10> +{ +public: + GoogLeNetPoolingLayerDataset() + : GenericDataset + { + // FIXME: Add support for 7x7 pooling layer pool5/7x7_s1 + // pool1/3x3_s2 + PoolingLayerDataObject{ TensorShape(112U, 112U, 64U), TensorShape(56U, 56U, 64U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)) }, + // pool2/3x3_s2 + PoolingLayerDataObject{ TensorShape(56U, 56U, 192U), TensorShape(28U, 28U, 192U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)) }, + // inception_3a/pool + PoolingLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(28U, 28U, 192U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) }, + // inception_3b/pool + PoolingLayerDataObject{ TensorShape(28U, 28U, 256U), TensorShape(28U, 28U, 256U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) }, + // pool3/3x3_s2 + PoolingLayerDataObject{ TensorShape(28U, 28U, 480U), TensorShape(14U, 14U, 480U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)) }, + // inception_4a/pool + PoolingLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(14U, 14U, 480U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) }, + // inception_4b/pool, inception_4c/pool, inception_4d/pool + PoolingLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(14U, 14U, 512U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) }, + // inception_4e/pool + PoolingLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(14U, 14U, 528U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) }, + // pool4/3x3_s2 + PoolingLayerDataObject{ TensorShape(14U, 14U, 832U), TensorShape(7U, 7U, 832U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)) }, + // inception_5a/pool, inception_5b/pool + PoolingLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(7U, 7U, 832U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) }, + } + { + } + + ~GoogLeNetPoolingLayerDataset() = default; +}; + +class RandomPoolingLayerDataset final : public PoolingLayerDataset<8> +{ +public: + RandomPoolingLayerDataset() + : GenericDataset + { + PoolingLayerDataObject{ TensorShape(27U, 27U, 16U), TensorShape(13U, 13U, 16U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(13U, 13U, 32U), TensorShape(6U, 6U, 32U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(24U, 24U, 10U), TensorShape(12U, 12U, 10U), PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(8U, 8U, 30U), TensorShape(4U, 4U, 30U), PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(27U, 27U, 16U), TensorShape(13U, 13U, 16U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(13U, 13U, 32U), TensorShape(6U, 6U, 32U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(24U, 24U, 10U), TensorShape(12U, 12U, 10U), PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2, 2, 0, 0)) }, + PoolingLayerDataObject{ TensorShape(8U, 8U, 30U), TensorShape(4U, 4U, 30U), PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2, 2, 0, 0)) }, + } + { + } + + ~RandomPoolingLayerDataset() = default; +}; +} // namespace test +} // namespace arm_compute +#endif //__ARM_COMPUTE_TEST_DATASET_POOLING_LAYER_DATASET_H__ |