237 lines
17 KiB
C++
237 lines
17 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_LARGE_CONVOLUTION_LAYER_DATASET
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#define ARM_COMPUTE_TEST_LARGE_CONVOLUTION_LAYER_DATASET
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#include "tests/datasets/ConvolutionLayerDataset.h"
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#include "utils/TypePrinter.h"
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace datasets
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{
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class LargeWinogradConvolutionLayer3x3Dataset final : public ConvolutionLayerDataset
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{
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public:
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LargeWinogradConvolutionLayer3x3Dataset()
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{
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// Kernel size 3
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// Batch size 1
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add_config(TensorShape(224U, 222U, 64U), TensorShape(3U, 3U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U), PadStrideInfo(1, 1, 1, 1));
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add_config(TensorShape(112U, 113U, 64U), TensorShape(3U, 3U, 64U, 128U), TensorShape(128U), TensorShape(112U, 113U, 128U), PadStrideInfo(1, 1, 1, 1));
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add_config(TensorShape(112U, 112U, 128U), TensorShape(3U, 3U, 128U, 129U), TensorShape(129U), TensorShape(112U, 112U, 129U), PadStrideInfo(1, 1, 1, 1));
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add_config(TensorShape(53U, 56U, 125U), TensorShape(3U, 3U, 125U, 256U), TensorShape(256U), TensorShape(51U, 54U, 256U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(56U, 56U, 256U), TensorShape(3U, 3U, 256U, 256U), TensorShape(256U), TensorShape(54U, 54U, 256U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(28U, 28U, 257U), TensorShape(3U, 3U, 257U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 1, 1));
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add_config(TensorShape(28U, 28U, 512U), TensorShape(3U, 3U, 512U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 1, 1));
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add_config(TensorShape(14U, 14U, 512U), TensorShape(3U, 3U, 512U, 512U), TensorShape(512U), TensorShape(12U, 12U, 512U), PadStrideInfo(1, 1, 0, 0));
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// Batch size 3, 2 and 4
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add_config(TensorShape(224U, 222U, 64U, 3U), TensorShape(3U, 3U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U, 3U), PadStrideInfo(1, 1, 1, 1));
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add_config(TensorShape(112U, 113U, 64U, 2U), TensorShape(3U, 3U, 64U, 128U), TensorShape(128U), TensorShape(110U, 111U, 128U, 2U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(111U, 112U, 127U, 4U), TensorShape(3U, 3U, 127U, 128U), TensorShape(128U), TensorShape(111U, 112U, 128U, 4U), PadStrideInfo(1, 1, 1, 1));
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}
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};
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class LargeWinogradConvolutionLayer3x1Dataset final : public ConvolutionLayerDataset
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{
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public:
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LargeWinogradConvolutionLayer3x1Dataset()
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{
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// Kernel size 3
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// Batch size 1
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add_config(TensorShape(224U, 222U, 64U), TensorShape(3U, 1U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U), PadStrideInfo(1, 1, 1, 0));
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add_config(TensorShape(112U, 113U, 64U), TensorShape(3U, 1U, 64U, 128U), TensorShape(128U), TensorShape(112U, 113U, 128U), PadStrideInfo(1, 1, 1, 0));
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add_config(TensorShape(112U, 112U, 128U), TensorShape(3U, 1U, 128U, 129U), TensorShape(129U), TensorShape(112U, 112U, 129U), PadStrideInfo(1, 1, 1, 0));
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add_config(TensorShape(53U, 56U, 125U), TensorShape(3U, 1U, 125U, 256U), TensorShape(256U), TensorShape(51U, 56U, 256U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(56U, 56U, 256U), TensorShape(3U, 1U, 256U, 256U), TensorShape(256U), TensorShape(56U, 56U, 256U), PadStrideInfo(1, 1, 1, 0));
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add_config(TensorShape(28U, 28U, 257U), TensorShape(3U, 1U, 257U, 512U), TensorShape(512U), TensorShape(26U, 28U, 512U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(28U, 28U, 512U), TensorShape(3U, 1U, 512U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 1, 0));
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add_config(TensorShape(14U, 14U, 512U), TensorShape(3U, 1U, 512U, 512U), TensorShape(512U), TensorShape(12U, 14U, 512U), PadStrideInfo(1, 1, 0, 0));
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// Batch size 3, 2 and 4
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add_config(TensorShape(224U, 222U, 64U, 3U), TensorShape(3U, 1U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U, 3U), PadStrideInfo(1, 1, 1, 0));
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add_config(TensorShape(112U, 113U, 64U, 2U), TensorShape(3U, 1U, 64U, 128U), TensorShape(128U), TensorShape(110U, 113U, 128U, 2U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(111U, 112U, 127U, 4U), TensorShape(3U, 1U, 127U, 128U), TensorShape(128U), TensorShape(111U, 112U, 128U, 4U), PadStrideInfo(1, 1, 1, 0));
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}
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};
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class LargeWinogradConvolutionLayer1x3Dataset final : public ConvolutionLayerDataset
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{
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public:
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LargeWinogradConvolutionLayer1x3Dataset()
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{
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// Kernel size 3
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// Batch size 1
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add_config(TensorShape(224U, 222U, 64U), TensorShape(1U, 3U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U), PadStrideInfo(1, 1, 0, 1));
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add_config(TensorShape(112U, 113U, 64U), TensorShape(1U, 3U, 64U, 128U), TensorShape(128U), TensorShape(112U, 113U, 128U), PadStrideInfo(1, 1, 0, 1));
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add_config(TensorShape(112U, 112U, 128U), TensorShape(1U, 3U, 128U, 129U), TensorShape(129U), TensorShape(112U, 110U, 129U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(53U, 56U, 125U), TensorShape(1U, 3U, 125U, 256U), TensorShape(256U), TensorShape(53U, 56U, 256U), PadStrideInfo(1, 1, 0, 1));
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add_config(TensorShape(56U, 56U, 256U), TensorShape(1U, 3U, 256U, 256U), TensorShape(256U), TensorShape(56U, 54U, 256U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(28U, 28U, 257U), TensorShape(1U, 3U, 257U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 0, 1));
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add_config(TensorShape(28U, 28U, 512U), TensorShape(1U, 3U, 512U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 0, 1));
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add_config(TensorShape(14U, 14U, 512U), TensorShape(1U, 3U, 512U, 512U), TensorShape(512U), TensorShape(14U, 12U, 512U), PadStrideInfo(1, 1, 0, 0));
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// Batch size 3, 2 and 4
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add_config(TensorShape(224U, 222U, 64U, 3U), TensorShape(1U, 3U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U, 3U), PadStrideInfo(1, 1, 0, 1));
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add_config(TensorShape(112U, 113U, 64U, 2U), TensorShape(1U, 3U, 64U, 128U), TensorShape(128U), TensorShape(112U, 113U, 128U, 2U), PadStrideInfo(1, 1, 0, 1));
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add_config(TensorShape(111U, 112U, 127U, 4U), TensorShape(1U, 3U, 127U, 128U), TensorShape(128U), TensorShape(111U, 112U, 128U, 4U), PadStrideInfo(1, 1, 0, 1));
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}
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};
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class LargeWinogradConvolutionLayer5x5Dataset final : public ConvolutionLayerDataset
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{
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public:
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LargeWinogradConvolutionLayer5x5Dataset()
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{
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// Kernel size 5
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// Batch size 1
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add_config(TensorShape(224U, 224U, 3U), TensorShape(5U, 5U, 3U, 64U), TensorShape(64U), TensorShape(220U, 220U, 64U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(123U, 134U, 16U), TensorShape(5U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U), PadStrideInfo(1, 1, 2, 2));
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add_config(TensorShape(181U, 152U, 42U), TensorShape(5U, 5U, 42U, 100U), TensorShape(100U), TensorShape(177U, 148U, 100U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(200U, 201U, 24U), TensorShape(5U, 5U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 2, 2));
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// Batch size 2, 3 and 4
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add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(5U, 5U, 3U, 64U), TensorShape(64U), TensorShape(220U, 220U, 64U, 2U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(5U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U, 3U), PadStrideInfo(1, 1, 2, 2));
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add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(5U, 5U, 42U, 100U), TensorShape(100U), TensorShape(177U, 148U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
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}
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};
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class LargeWinogradConvolutionLayer5x1Dataset final : public ConvolutionLayerDataset
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{
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public:
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LargeWinogradConvolutionLayer5x1Dataset()
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{
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// Batch size 1
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add_config(TensorShape(224U, 224U, 3U), TensorShape(5U, 1U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U), PadStrideInfo(1, 1, 2, 0));
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add_config(TensorShape(123U, 134U, 16U), TensorShape(5U, 1U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U), PadStrideInfo(1, 1, 2, 0));
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add_config(TensorShape(181U, 152U, 42U), TensorShape(5U, 1U, 42U, 100U), TensorShape(100U), TensorShape(177U, 152U, 100U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(200U, 201U, 24U), TensorShape(5U, 1U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 2, 0));
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// Batch size 2, 3 and 4
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add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(5U, 1U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U, 2U), PadStrideInfo(1, 1, 2, 0));
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add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(5U, 1U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U, 3U), PadStrideInfo(1, 1, 2, 0));
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add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(5U, 1U, 42U, 100U), TensorShape(100U), TensorShape(177U, 152U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
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}
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};
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class LargeWinogradConvolutionLayer7x1Dataset final : public ConvolutionLayerDataset
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{
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public:
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LargeWinogradConvolutionLayer7x1Dataset()
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{
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// Batch size 1
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add_config(TensorShape(224U, 224U, 3U), TensorShape(7U, 1U, 3U, 64U), TensorShape(64U), TensorShape(218U, 224U, 64U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(123U, 134U, 16U), TensorShape(7U, 1U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U), PadStrideInfo(1, 1, 3, 0));
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add_config(TensorShape(181U, 152U, 42U), TensorShape(7U, 1U, 42U, 100U), TensorShape(100U), TensorShape(175U, 152U, 100U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(200U, 201U, 24U), TensorShape(7U, 1U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 3, 0));
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// Batch size 2, 3 and 4
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add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(7U, 1U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U, 2U), PadStrideInfo(1, 1, 3, 0));
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add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(7U, 1U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U, 3U), PadStrideInfo(1, 1, 3, 0));
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add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(7U, 1U, 42U, 100U), TensorShape(100U), TensorShape(175U, 152U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
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}
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};
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class LargeWinogradConvolutionLayer1x7Dataset final : public ConvolutionLayerDataset
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{
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public:
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LargeWinogradConvolutionLayer1x7Dataset()
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{
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// Batch size 1
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add_config(TensorShape(224U, 224U, 3U), TensorShape(1U, 7U, 3U, 64U), TensorShape(64U), TensorShape(224U, 218U, 64U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(123U, 134U, 16U), TensorShape(1U, 7U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U), PadStrideInfo(1, 1, 0, 3));
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add_config(TensorShape(181U, 152U, 42U), TensorShape(1U, 7U, 42U, 100U), TensorShape(100U), TensorShape(181U, 146U, 100U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(200U, 201U, 24U), TensorShape(1U, 7U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 0, 3));
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// Batch size 2, 3 and 4
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add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(1U, 7U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U, 2U), PadStrideInfo(1, 1, 0, 3));
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add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(1U, 7U, 16U, 7U), TensorShape(7U), TensorShape(123U, 134U, 7U, 3U), PadStrideInfo(1, 1, 0, 3));
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add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(1U, 7U, 42U, 100U), TensorShape(100U), TensorShape(181U, 146U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
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}
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};
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class LargeWinogradConvolutionLayer1x5Dataset final : public ConvolutionLayerDataset
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{
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public:
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LargeWinogradConvolutionLayer1x5Dataset()
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{
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// Batch size 1
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add_config(TensorShape(224U, 224U, 3U), TensorShape(1U, 5U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U), PadStrideInfo(1, 1, 0, 2));
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add_config(TensorShape(123U, 134U, 16U), TensorShape(1U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 130U, 7U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(181U, 152U, 42U), TensorShape(1U, 5U, 42U, 100U), TensorShape(100U), TensorShape(181U, 148U, 100U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(200U, 201U, 24U), TensorShape(1U, 5U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 0, 2));
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// Batch size 2, 3 and 4
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add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(1U, 5U, 3U, 64U), TensorShape(64U), TensorShape(224U, 224U, 64U, 2U), PadStrideInfo(1, 1, 0, 2));
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add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(1U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 130U, 7U, 3U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(1U, 5U, 42U, 100U), TensorShape(100U), TensorShape(181U, 148U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
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}
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};
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class LargeConvolutionLayerDataset final : public ConvolutionLayerDataset
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{
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public:
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/** Shapes taken from use-cases such as AlexNet, MobileNet, SqueezeNet, etc. */
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LargeConvolutionLayerDataset()
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{
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// Batch size 1
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add_config(TensorShape(227U, 227U, 3U), TensorShape(11U, 11U, 3U, 96U), TensorShape(96U), TensorShape(55U, 55U, 96U), PadStrideInfo(4, 4, 0, 0));
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add_config(TensorShape(27U, 27U, 96U), TensorShape(5U, 5U, 96U, 256U), TensorShape(256U), TensorShape(27U, 27U, 256U), PadStrideInfo(1, 1, 2, 2));
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add_config(TensorShape(13U, 13U, 256U), TensorShape(1U, 1U, 256U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(224U, 224U, 3U), TensorShape(7U, 7U, 3U, 64U), TensorShape(64U), TensorShape(112U, 112U, 64U), PadStrideInfo(2, 2, 3, 3));
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// Batch size 4
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add_config(TensorShape(227U, 227U, 3U, 4U), TensorShape(11U, 11U, 3U, 96U), TensorShape(96U), TensorShape(55U, 55U, 96U, 4U), PadStrideInfo(4, 4, 0, 0));
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add_config(TensorShape(27U, 27U, 96U, 4U), TensorShape(5U, 5U, 96U, 256U), TensorShape(256U), TensorShape(27U, 27U, 256U, 4U), PadStrideInfo(1, 1, 2, 2));
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add_config(TensorShape(13U, 13U, 256U, 4U), TensorShape(1U, 1U, 256U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U, 4U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(224U, 224U, 3U, 4U), TensorShape(7U, 7U, 3U, 64U), TensorShape(64U), TensorShape(112U, 112U, 64U, 4U), PadStrideInfo(2, 2, 3, 3));
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}
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};
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class LargeGroupedConvolutionLayerDataset final : public ConvolutionLayerDataset
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{
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public:
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LargeGroupedConvolutionLayerDataset()
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{
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// Batch size 1
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add_config(TensorShape(227U, 227U, 4U), TensorShape(11U, 11U, 2U, 96U), TensorShape(96U), TensorShape(55U, 55U, 96U), PadStrideInfo(4, 4, 0, 0));
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add_config(TensorShape(27U, 27U, 96U), TensorShape(5U, 5U, 24U, 256U), TensorShape(256U), TensorShape(27U, 27U, 256U), PadStrideInfo(1, 1, 2, 2));
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add_config(TensorShape(13U, 13U, 256U), TensorShape(1U, 1U, 128U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U), PadStrideInfo(1, 1, 0, 0));
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add_config(TensorShape(13U, 13U, 384U), TensorShape(3U, 3U, 128U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U), PadStrideInfo(1, 1, 1, 1));
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// Batch size 4
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add_config(TensorShape(227U, 227U, 4U, 4U), TensorShape(11U, 11U, 2U, 96U), TensorShape(96U), TensorShape(55U, 55U, 96U, 4U), PadStrideInfo(4, 4, 0, 0));
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add_config(TensorShape(27U, 27U, 96U, 4U), TensorShape(5U, 5U, 24U, 256U), TensorShape(256U), TensorShape(27U, 27U, 256U, 4U), PadStrideInfo(1, 1, 2, 2));
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add_config(TensorShape(13U, 13U, 256U, 4U), TensorShape(3U, 3U, 128U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U, 4U), PadStrideInfo(1, 1, 1, 1));
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add_config(TensorShape(13U, 13U, 384U, 4U), TensorShape(3U, 3U, 128U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U, 4U), PadStrideInfo(1, 1, 1, 1));
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}
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};
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} // namespace datasets
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_LARGE_CONVOLUTION_LAYER_DATASET */
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