/******************************************************************************* * * MIT License * * Copyright (c) 2017 Advanced Micro Devices, Inc. * * 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 GUARD_MIOPEN_TEST_NETWORK_DATA_HPP #define GUARD_MIOPEN_TEST_NETWORK_DATA_HPP #include #include #include #ifndef MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR #define MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR 0 #endif inline int pick_batch_size(int x, int y) { if(y == 0 || y > x) return 1; else return x / y; } inline std::set> get_inputs(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size(32, n), 1, 14, 14 }, { pick_batch_size(100, n), 1, 8, 8 }, { pick_batch_size(256, n), 1, 27, 27 }, { pick_batch_size(100, n), 19, 1024,2048}, { pick_batch_size(100, n), 3, 32, 32 }, { pick_batch_size(100, n), 32, 16, 16 }, { pick_batch_size(100, n), 32, 8, 8 }, { pick_batch_size(128, n), 1024, 12, 12 }, { pick_batch_size(128, n), 256, 12, 12 }, { pick_batch_size(128, n), 3, 231, 231 }, { pick_batch_size(128, n), 512, 12, 12 }, { pick_batch_size(128, n), 96, 28, 28 }, { pick_batch_size(256, n), 256, 13, 13 }, { pick_batch_size(256, n), 3, 227, 227 }, { pick_batch_size(256, n), 384, 13, 13 }, { pick_batch_size(256, n), 96, 27, 27 }, { pick_batch_size(128, n), 64, 27, 27 }, { pick_batch_size(32, n), 112, 14, 14 }, { pick_batch_size(32, n), 128, 14, 14 }, { pick_batch_size(32, n), 128, 28, 28 }, { pick_batch_size(32, n), 144, 14, 14 }, { pick_batch_size(32, n), 16, 14, 14 }, { pick_batch_size(32, n), 16, 28, 28 }, { pick_batch_size(32, n), 160, 14, 14 }, { pick_batch_size(32, n), 160, 7, 7 }, #if MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR > 0 { pick_batch_size(32, n), 192, 128, 256 }, { pick_batch_size(32, n), 192, 256, 512 }, #endif { pick_batch_size(32, n), 192, 28, 28 }, { pick_batch_size(32, n), 192, 7, 7 }, { pick_batch_size(32, n), 24, 14, 14 }, { pick_batch_size(32, n), 256, 28, 28 }, { pick_batch_size(32, n), 3, 224, 224 }, { pick_batch_size(32, n), 32, 14, 14 }, { pick_batch_size(32, n), 32, 28, 28 }, { pick_batch_size(32, n), 32, 7, 7 }, { pick_batch_size(32, n), 48, 7, 7 }, { pick_batch_size(32, n), 480, 128, 256 }, { pick_batch_size(32, n), 480, 14, 14 }, { pick_batch_size(32, n), 480, 64, 128 }, { pick_batch_size(32, n), 512, 14, 14 }, { pick_batch_size(32, n), 512, 4, 4 }, { pick_batch_size(32, n), 512, 64, 128 }, { pick_batch_size(32, n), 528, 14, 14 }, { pick_batch_size(32, n), 528, 4, 4 }, { pick_batch_size(32, n), 528, 64, 128 }, // { pick_batch_size(1, n), 64 , 512, 1024}, { pick_batch_size(16, n), 64, 56, 56 }, { pick_batch_size(32, n), 64, 56, 56 }, { pick_batch_size(32, n), 832, 64, 128 }, { pick_batch_size(32, n), 832, 64, 128 }, { pick_batch_size(32, n), 832, 7, 7 }, { pick_batch_size(32, n), 96, 14, 14 }, { pick_batch_size(32, n), 96, 28, 28 }, { pick_batch_size(64, n), 128, 56, 56 }, { pick_batch_size(16, n), 256, 28, 28 }, { pick_batch_size(64, n), 256, 28, 28 }, { pick_batch_size(64, n), 256, 56, 56 }, { pick_batch_size(64, n), 3, 224, 224 }, { pick_batch_size(64, n), 512, 14, 14 }, { pick_batch_size(64, n), 256, 14, 14 }, { pick_batch_size(16, n), 256, 14, 14 }, { pick_batch_size(64, n), 512, 28, 28 }, { pick_batch_size(64, n), 64, 112, 112 }, { pick_batch_size(32, n), 64, 28, 28 }, { pick_batch_size(32, n), 64, 14, 14 }, { pick_batch_size(32, n), 192, 14, 14 }, { pick_batch_size(32, n), 224, 7, 7 }, { pick_batch_size(16, n), 512, 7, 7 }, { pick_batch_size(32, n), 320, 28, 28 }, { pick_batch_size(32, n), 576, 14, 14 }, { pick_batch_size(32, n), 576, 4, 4 }, { pick_batch_size(32, n), 608, 14, 14 }, { pick_batch_size(32, n), 608, 4, 4 }, { pick_batch_size(32, n), 1056, 7, 7 }, { pick_batch_size(32, n), 1024, 7, 7 }, { pick_batch_size(32, n), 2048, 11, 11 }, { pick_batch_size(32, n), 16, 2048, 2048 }, { pick_batch_size(32, n), 16, 3072, 3072 }, { pick_batch_size(32, n), 16, 4096, 4096 }, { 1, 1, 1, 1 } }; // clang-format on } inline std::set> get_weights(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size(1024, n),1024, 3, 3 }, { pick_batch_size(1024, n),512, 3, 3 }, { pick_batch_size(112, n), 512, 1, 1 }, { pick_batch_size(128, n), 256, 1, 1 }, { pick_batch_size(128, n), 32, 5, 5 }, { pick_batch_size(128, n), 48, 5, 5 }, { pick_batch_size(128, n), 512, 1, 1 }, { pick_batch_size(128, n), 528, 1, 1 }, { pick_batch_size(128, n), 64, 3, 3 }, { pick_batch_size(128, n), 832, 1, 1 }, { pick_batch_size(128, n), 96, 3, 3 }, { pick_batch_size(144, n), 512, 1, 1 }, { pick_batch_size(16, n), 192, 1, 1 }, { pick_batch_size(16, n), 480, 1, 1 }, { pick_batch_size(160, n), 512, 1, 1 }, { pick_batch_size(160, n), 528, 1, 1 }, { pick_batch_size(160, n), 832, 1, 1 }, { pick_batch_size(192, n), 128, 3, 3 }, { pick_batch_size(192, n), 480, 1, 1 }, { pick_batch_size(192, n), 64, 3, 3 }, { pick_batch_size(192, n), 832, 1, 1 }, { pick_batch_size(208, n), 96, 3, 3 }, { pick_batch_size(224, n), 112, 3, 3 }, { pick_batch_size(24, n), 512, 1, 1 }, { pick_batch_size(256, n), 128, 3, 3 }, { pick_batch_size(256, n), 256, 3, 3 }, { pick_batch_size(256, n), 384, 3, 3 }, { pick_batch_size(256, n), 528, 1, 1 }, { pick_batch_size(256, n), 832, 1, 1 }, { pick_batch_size(256, n), 96, 5, 5 }, { pick_batch_size(288, n), 144, 3, 3 }, { pick_batch_size(32, n), 16, 5, 5 }, { pick_batch_size(32, n), 192, 1, 1 }, { pick_batch_size(32, n), 256, 1, 1 }, { pick_batch_size(32, n), 3, 5, 5 }, { pick_batch_size(32, n), 32, 5, 5 }, { pick_batch_size(32, n), 512, 1, 1 }, { pick_batch_size(32, n), 528, 1, 1 }, { pick_batch_size(32, n), 832, 1, 1 }, { pick_batch_size(320, n), 160, 3, 3 }, { pick_batch_size(384, n), 192, 3, 3 }, { pick_batch_size(384, n), 256, 3, 3 }, { pick_batch_size(384, n), 384, 3, 3 }, { pick_batch_size(384, n), 832, 1, 1 }, { pick_batch_size(48, n), 16, 5, 5 }, { pick_batch_size(48, n), 832, 1, 1 }, { pick_batch_size(512, n), 256, 3, 3 }, { pick_batch_size(512, n), 512, 3, 3 }, { pick_batch_size(64, n), 192, 1, 1 }, { pick_batch_size(64, n), 24, 5, 5 }, { pick_batch_size(64, n), 256, 1, 1 }, { pick_batch_size(1024, n),256, 1, 1 }, { pick_batch_size(64, n), 1, 2, 2 }, { pick_batch_size(64, n), 1, 3, 3 }, { pick_batch_size(64, n), 3, 3, 3 }, { pick_batch_size(64, n), 3, 7, 7 }, { pick_batch_size(32, n), 3, 7, 7 }, { pick_batch_size(16, n), 3, 7, 7 }, { pick_batch_size(64, n), 32, 5, 5 }, { pick_batch_size(64, n), 480, 1, 1 }, { pick_batch_size(64, n), 512, 1, 1 }, { pick_batch_size(2048, n),512, 1, 1 }, { pick_batch_size(64, n), 64, 1, 1 }, { pick_batch_size(256, n), 64, 1, 1 }, { pick_batch_size(96, n), 192, 1, 1 }, { pick_batch_size(96, n), 3, 11, 11 }, { pick_batch_size(96, n), 32, 5, 5 }, { pick_batch_size(192, n), 64, 5, 5 }, { pick_batch_size(96, n), 480, 1, 1 }, { pick_batch_size(64, n), 64, 3, 3 }, { pick_batch_size(96, n), 64, 3, 3 }, { pick_batch_size(96, n), 96, 3, 3 }, { pick_batch_size(128, n), 96, 3, 3 }, { pick_batch_size(128, n), 128, 3, 3 }, { pick_batch_size(256, n), 256, 3, 3 }, { pick_batch_size(512, n), 512, 3, 3 }, { pick_batch_size(96, n), 128, 3, 3 }, { pick_batch_size(160, n), 128, 3, 3 }, { pick_batch_size(160, n), 160, 3, 3 }, { pick_batch_size(192, n), 160, 3, 3 }, { pick_batch_size(192, n), 192, 3, 3 }, { pick_batch_size(256, n), 192, 3, 3 }, { pick_batch_size(320, n), 192, 3, 3 }, { pick_batch_size(224, n), 160, 3, 3 }, { pick_batch_size(224, n), 224, 3, 3 }, { pick_batch_size(224, n), 192, 3, 3 }, { pick_batch_size(512, n), 128, 1, 1 }, { pick_batch_size(32, n), 192, 1, 1 }, { pick_batch_size(128, n), 320, 1, 1 }, { pick_batch_size(64, n), 320, 1, 1 }, { pick_batch_size(224, n), 576, 1, 1 }, { pick_batch_size(64, n), 576, 1, 1 }, { pick_batch_size(96, n), 576, 1, 1 }, { pick_batch_size(128, n), 576, 1, 1 }, { pick_batch_size(192, n), 576, 1, 1 }, { pick_batch_size(160, n), 576, 1, 1 }, { pick_batch_size(96, n), 608, 1, 1 }, { pick_batch_size(128, n), 608, 1, 1 }, { pick_batch_size(160, n), 608, 1, 1 }, { pick_batch_size(128, n), 608, 1, 1 }, { pick_batch_size(192, n), 608, 1, 1 }, { pick_batch_size(352, n), 1056, 1, 1 }, { pick_batch_size(192, n), 1056, 1, 1 }, { pick_batch_size(160, n), 1056, 1, 1 }, { pick_batch_size(128, n), 1056, 1, 1 }, { pick_batch_size(352, n), 1024, 1, 1 }, { pick_batch_size(256, n), 1024, 1, 1 }, { pick_batch_size(192, n), 1024, 1, 1 }, { pick_batch_size(128, n), 1024, 1, 1 }, { pick_batch_size(512, n), 2048, 1, 1 } }; // clang-format on } inline std::set> get_immed_inputs(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size(32, n), 1, 14, 14 }, { pick_batch_size(256, n), 1, 27, 27 }, { pick_batch_size(128, n), 512, 12, 12 }, { pick_batch_size(256, n), 256, 13, 13 }, { pick_batch_size(256, n), 3, 227, 227 }, { pick_batch_size(32, n), 64, 56, 56 }, // { pick_batch_size(32, n), 832, 64, 128 }, // { pick_batch_size(32, n), 832, 7, 7 }, { pick_batch_size(32, n), 96, 14, 14 }, { pick_batch_size(32, n), 96, 28, 28 }, { pick_batch_size(64, n), 128, 56, 56 }, { pick_batch_size(64, n), 3, 224, 224 }, { pick_batch_size(64, n), 256, 14, 14 }, { 1, 1, 1, 1 } }; // clang-format on } inline std::set> get_immed_weights(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size(208, n), 96, 3, 3 }, { pick_batch_size(24, n), 512, 1, 1 }, { pick_batch_size(256, n), 128, 3, 3 }, { pick_batch_size(256, n), 256, 3, 3 }, // { pick_batch_size(256, n), 832, 1, 1 }, { pick_batch_size(256, n), 64, 5, 5 }, { pick_batch_size(288, n), 144, 3, 3 }, { pick_batch_size(96, n), 3, 11, 11 }, { pick_batch_size(32, n), 128, 5, 5 }, { pick_batch_size(32, n), 128, 1, 1 }, { pick_batch_size(256, n), 256, 3, 3 }, { pick_batch_size(512, n), 512, 3, 3 }, { pick_batch_size(160, n), 128, 3, 3 }, { pick_batch_size(32, n), 3, 7, 7 } }; // clang-format on } inline std::set> get_3d_conv_input_shapes(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size(128, n), 1, 1, 2, 2}, { pick_batch_size(128, n), 64, 1, 1, 1}, { pick_batch_size(128, n), 64, 3, 4, 4}, { pick_batch_size(352, n), 32, 4, 9, 9}, { pick_batch_size(192, n), 512, 3, 14, 14}, { pick_batch_size(352, n), 512, 4, 28, 28}, { pick_batch_size(256, n), 512, 4, 56, 56}, { pick_batch_size(192, n), 3, 4, 227, 227}, { pick_batch_size(128, n), 4, 4, 161, 700} }; // clang-format on } inline std::set> get_3d_conv_weight_shapes(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size( 128, n), 1, 1, 1, 1}, { pick_batch_size( 352, n), 128, 1, 1, 1}, { pick_batch_size( 256, n), 128, 1, 1, 1}, { pick_batch_size( 352, n), 32, 3, 3, 3}, { pick_batch_size( 352, n), 4, 3, 3, 3}, { pick_batch_size( 160, n), 4, 3, 5, 5}, { pick_batch_size( 128, n), 64, 5, 7, 7}, { pick_batch_size( 192, n), 4, 3, 11, 11}, { pick_batch_size( 128, n), 1, 3, 1, 7}, { pick_batch_size( 128, n), 1, 3, 7, 1}, { pick_batch_size( 128, n), 1, 3, 5, 20} }; // clang-format on } inline std::set> get_bn_peract_inputs(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size(32, n), 4, 1024,2048}, //Making this much smaller { pick_batch_size(100, n), 3, 32, 32 }, { pick_batch_size(100, n), 32, 8, 8 }, { pick_batch_size(128, n), 256, 12, 12 }, { pick_batch_size(256, n), 3, 227, 227 }, { pick_batch_size(64, n), 64, 112, 112 },//Batch-norm ResNet 152 after this line { pick_batch_size(256, n), 1024, 14, 14 },// n is from the paper @ 256 { pick_batch_size(256, n), 128, 28, 28 }, { pick_batch_size(256, n), 2048, 7, 7 }, { pick_batch_size(256, n), 256, 56, 56 }, { pick_batch_size(256, n), 256, 14, 14 }, { pick_batch_size(256, n), 512, 28, 28 }, { pick_batch_size(256, n), 512, 7, 7 }, { pick_batch_size(256, n), 64, 112, 112 }, { pick_batch_size(256, n), 64, 56, 56 },//Batch-norm Inception_v3 after this { pick_batch_size(32, n), 1024, 1, 1 },// n is from the paper @ 32 { pick_batch_size(32, n), 128, 14, 14 }, { pick_batch_size(32, n), 128, 28, 28 }, { pick_batch_size(32, n), 128, 4, 4 }, { pick_batch_size(32, n), 128, 7, 7 }, { pick_batch_size(32, n), 160, 14, 14 }, { pick_batch_size(32, n), 160, 7, 7 }, { pick_batch_size(32, n), 192, 14, 14 }, { pick_batch_size(32, n), 192, 56, 56 }, { pick_batch_size(32, n), 192, 7, 7 }, { pick_batch_size(32, n), 224, 14, 14 }, { pick_batch_size(32, n), 256, 7, 7 }, { pick_batch_size(32, n), 256, 14, 14 }, { pick_batch_size(32, n), 32, 28, 28 }, { pick_batch_size(32, n), 352, 7, 7 }, { pick_batch_size(32, n), 64, 112, 112 }, { pick_batch_size(32, n), 64, 14, 14 }, { pick_batch_size(32, n), 64, 28, 28 }, { pick_batch_size(32, n), 64, 56, 56 }, { pick_batch_size(32, n), 96, 28, 28 }, { pick_batch_size(32, n), 32, 256, 512 }, //Killing this config. Takes way too long on the CPU { pick_batch_size(32, n), 256, 28, 28 }, { pick_batch_size(32, n), 3, 224, 224 }, { pick_batch_size(32, n), 480, 128, 256 }, { pick_batch_size(32, n), 528, 64, 128 } }; // clang-format on } inline std::set> get_bn_spatial_inputs(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size(32, n), 4, 1024,2048}, //Making this much smaller { pick_batch_size(32, n), 192, 256, 512 }, { pick_batch_size(32, n), 480, 128, 256 }, { pick_batch_size(256, n), 3, 227, 227 }, { pick_batch_size(256, n), 64, 112, 112 }, { pick_batch_size(512, n), 16, 32, 32 }, { pick_batch_size(32, n), 64, 112, 112 }, //{ pick_batch_size(100, n), 3, 32, 32 },// causing issues with Jenkins { pick_batch_size(100, n), 32, 8, 8 }, { pick_batch_size(128, n), 256, 12, 12 }, { pick_batch_size(256, n), 1024, 14, 14 },// n is from the paper @ 256 { pick_batch_size(256, n), 128, 28, 28 }, { pick_batch_size(256, n), 2048, 7, 7 }, { pick_batch_size(256, n), 256, 56, 56 }, { pick_batch_size(256, n), 256, 14, 14 }, { pick_batch_size(256, n), 512, 28, 28 }, { pick_batch_size(256, n), 512, 7, 7 }, { pick_batch_size(256, n), 64, 56, 56 },//Batch-norm Inception_v3 after this { pick_batch_size(32, n), 1024, 1, 1 },// n is from the paper @ 32 { pick_batch_size(32, n), 128, 14, 14 }, { pick_batch_size(32, n), 128, 28, 28 }, { pick_batch_size(32, n), 128, 4, 4 }, { pick_batch_size(32, n), 128, 7, 7 }, { pick_batch_size(32, n), 160, 14, 14 }, { pick_batch_size(32, n), 160, 7, 7 }, { pick_batch_size(32, n), 192, 14, 14 }, { pick_batch_size(32, n), 192, 56, 56 }, { pick_batch_size(32, n), 192, 7, 7 }, { pick_batch_size(32, n), 224, 14, 14 }, { pick_batch_size(32, n), 256, 7, 7 }, { pick_batch_size(32, n), 256, 14, 14 }, { pick_batch_size(32, n), 32, 28, 28 }, { pick_batch_size(32, n), 352, 7, 7 }, { pick_batch_size(32, n), 64, 14, 14 }, { pick_batch_size(32, n), 64, 28, 28 }, { pick_batch_size(32, n), 64, 56, 56 }, { pick_batch_size(32, n), 96, 28, 28 }, { pick_batch_size(32, n), 192, 256, 512 }, { pick_batch_size(32, n), 256, 28, 28 }, { pick_batch_size(32, n), 3, 224, 224 }, { pick_batch_size(32, n), 480, 128, 256 }, { pick_batch_size(32, n), 528, 64, 128 } }; // clang-format on } inline std::set> get_3d_bn_peract_inputs(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size(32, n), 1, 32, 32, 32 }, // 32x32x32 based on VoxNet arch { pick_batch_size(32, n), 1, 14, 14, 14 }, { pick_batch_size(32, n), 32, 14, 14, 14 }, { pick_batch_size(32, n), 32, 12, 12, 12 }, { pick_batch_size(32, n), 32, 6, 6, 6 }, { pick_batch_size(256, n), 1, 32, 32, 32 }, // 32x32x32 based on VoxNet arch { pick_batch_size(256, n), 32, 14, 14, 14 }, { pick_batch_size(256, n), 32, 12, 12, 12 }, { pick_batch_size(256, n), 32, 6, 6, 6 }, { pick_batch_size(512, n), 1, 32, 32, 32 }, // 32x32x32 based on VoxNet arch { pick_batch_size(512, n), 32, 14, 14, 14 }, { pick_batch_size(512, n), 32, 12, 12, 12 }, { pick_batch_size(512, n), 32, 6, 6, 6 }, { pick_batch_size(32, n), 2, 32, 57, 125 }, // Hand-gesture recognition CVPR 2015 paper High Res Net Path { pick_batch_size(32, n), 32, 14, 25, 59 }, { pick_batch_size(32, n), 32, 6, 10, 27 }, { pick_batch_size(32, n), 32, 4, 6, 11 }, { pick_batch_size(32, n), 32, 2, 2, 3 }, { pick_batch_size(32, n), 32, 32, 28, 62 }, // Hand-gesture recognition CVPR 2015 paper Low Res Net Path { pick_batch_size(32, n), 32, 14, 12, 29 }, { pick_batch_size(32, n), 32, 6, 4, 12 }, { pick_batch_size(32, n), 32, 4, 2, 2 }, { pick_batch_size(16, n), 32, 6, 50, 50 }, // Multi-view 3D convnet { pick_batch_size(1, n), 3, 8, 240, 320 }, // 3D convet on video { pick_batch_size(1, n), 3, 16, 240, 320 }, // 3D convet on video { pick_batch_size(1, n), 3, 8, 128, 171 }, // 3D convet on video { pick_batch_size(1, n), 3, 16, 128, 171 }, // 3D convet on video { pick_batch_size(1, n), 3, 8, 112, 112 }, // 3D convet on video { pick_batch_size(1, n), 3, 16, 112, 112 } // 3D convet on video }; // clang-format on } inline std::set> get_3d_bn_spatial_inputs(int n = MIOPEN_TEST_DEFAULT_BATCH_SIZE_FACTOR) { // clang-format off return { { pick_batch_size(32, n), 1, 32, 32, 32 }, // 32x32x32 based on VoxNet arch { pick_batch_size(32, n), 1, 14, 14, 14 }, { pick_batch_size(32, n), 32, 14, 14, 14 }, { pick_batch_size(32, n), 32, 12, 12, 12 }, { pick_batch_size(32, n), 32, 6, 6, 6 }, { pick_batch_size(256, n), 1, 32, 32, 32 }, // 32x32x32 based on VoxNet arch { pick_batch_size(256, n), 32, 14, 14, 14 }, { pick_batch_size(256, n), 32, 12, 12, 12 }, { pick_batch_size(256, n), 32, 6, 6, 6 }, { pick_batch_size(512, n), 1, 32, 32, 32 }, // 32x32x32 based on VoxNet arch { pick_batch_size(512, n), 32, 14, 14, 14 }, { pick_batch_size(512, n), 32, 12, 12, 12 }, { pick_batch_size(512, n), 32, 6, 6, 6 }, { pick_batch_size(32, n), 2, 32, 57, 125 }, // Hand-gesture recognition CVPR 2015 paper High Res Net Path { pick_batch_size(32, n), 32, 14, 25, 59 }, { pick_batch_size(32, n), 32, 6, 10, 27 }, { pick_batch_size(32, n), 32, 4, 6, 11 }, { pick_batch_size(32, n), 32, 2, 2, 3 }, { pick_batch_size(32, n), 32, 32, 28, 62 }, // Hand-gesture recognition CVPR 2015 paper Low Res Net Path { pick_batch_size(32, n), 32, 14, 12, 29 }, { pick_batch_size(32, n), 32, 6, 4, 12 }, { pick_batch_size(32, n), 32, 4, 2, 2 }, { pick_batch_size(16, n), 32, 6, 50, 50 }, // Multi-view 3D convnet { pick_batch_size(1, n), 3, 8, 240, 320 }, // 3D convet on video { pick_batch_size(1, n), 3, 16, 240, 320 }, // 3D convet on video { pick_batch_size(1, n), 3, 8, 128, 171 }, // 3D convet on video { pick_batch_size(1, n), 3, 16, 128, 171 }, // 3D convet on video { pick_batch_size(1, n), 3, 8, 112, 112 }, // 3D convet on video { pick_batch_size(1, n), 3, 16, 112, 112 } // 3D convet on video }; // clang-format on } inline std::vector> get_sub_tensor() { return {{16, 4, 8, 1, 4}, {2, 4, 8, 8, 4}, {16, 4, 8, 4}, {13, 8, 4, 8}, {3, 8, 7}, {16, 4, 10}, {3, 8}, {16, 4}, {4}}; } inline std::vector> get_tensor_offsets() { return {{0, 0}, {0, 2}, {4, 0}, {5, 7}}; } inline std::vector get_tensor_offset() { return {0, 1, 2, 3, 4, 5}; } #endif