/* * Copyright (c) 2019-2020 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. */ #include "arm_compute/core/Types.h" #include "arm_compute/runtime/CPP/functions/CPPTopKV.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/runtime/TensorAllocator.h" #include "tests/NEON/Accessor.h" #include "tests/PaddingCalculator.h" #include "tests/datasets/ShapeDatasets.h" #include "tests/framework/Asserts.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Validation.h" #include "tests/validation/fixtures/PermuteFixture.h" namespace arm_compute { namespace test { namespace validation { namespace { template inline void fill_tensor(U &&tensor, const std::vector &v) { std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size()); } } // namespace TEST_SUITE(CPP) TEST_SUITE(TopKV) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( framework::dataset::make("PredictionsInfo", { TensorInfo(TensorShape(20, 10), 1, DataType::F32), TensorInfo(TensorShape(10, 20), 1, DataType::F16), // Mismatching batch_size TensorInfo(TensorShape(20, 10), 1, DataType::S8), // Unsupported data type TensorInfo(TensorShape(10, 10, 10), 1, DataType::F32), // Wrong predictions dimensions TensorInfo(TensorShape(20, 10), 1, DataType::F32)}), // Wrong output dimension framework::dataset::make("TargetsInfo",{ TensorInfo(TensorShape(10), 1, DataType::U32), TensorInfo(TensorShape(10), 1, DataType::U32), TensorInfo(TensorShape(10), 1, DataType::U32), TensorInfo(TensorShape(10), 1, DataType::U32), TensorInfo(TensorShape(10), 1, DataType::U32)})), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(10), 1, DataType::U8), TensorInfo(TensorShape(10), 1, DataType::U8), TensorInfo(TensorShape(10), 1, DataType::U8), TensorInfo(TensorShape(10), 1, DataType::U8), TensorInfo(TensorShape(1), 1, DataType::U8)})), framework::dataset::make("k",{ 0, 1, 2, 3, 4 })), framework::dataset::make("Expected", {true, false, false, false, false })), prediction_info, targets_info, output_info, k, expected) { const Status status = CPPTopKV::validate(&prediction_info.clone()->set_is_resizable(false),&targets_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), k); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* TEST_CASE(Float, framework::DatasetMode::ALL) { const unsigned int k = 5; Tensor predictions = create_tensor(TensorShape(10, 20), DataType::F32); Tensor targets = create_tensor(TensorShape(20), DataType::U32); predictions.allocator()->allocate(); targets.allocator()->allocate(); // Fill the tensors with random pre-generated values fill_tensor(Accessor(predictions), std::vector { 0.8147, 0.6557, 0.4387, 0.7513, 0.3517, 0.1622, 0.1067, 0.8530, 0.7803, 0.5470, 0.9058, 0.0357, 0.3816, 0.2551, 0.8308, 0.7943, 0.9619, 0.6221, 0.3897, 0.2963, 0.1270, 0.8491, 0.7655, 0.5060, 0.5853, 0.3112, 0.0046, 0.3510, 0.2417, 0.7447, 0.9134, 0.9340, 0.7952, 0.6991, 0.5497, 0.5285, 0.7749, 0.5132, 0.4039, 0.1890, 0.6324, 0.6787, 0.1869, 0.8909, 0.9172, 0.1656, 0.8173, 0.4018, 0.0965, 0.6868, 0.0975, 0.7577, 0.4898, 0.9593, 0.2858, 0.6020, 0.8687, 0.0760, 0.1320, 0.1835, 0.2785, 0.7431, 0.4456, 0.5472, 0.7572, 0.2630, 0.0844, 0.2399, 0.9421, 0.3685, 0.5469, 0.3922, 0.6463, 0.1386, 0.7537, 0.6541, 0.3998, 0.1233, 0.9561, 0.6256, 0.9575, 0.6555, 0.7094, 0.1493, 0.3804, 0.6892, 0.2599, 0.1839, 0.5752, 0.7802, 0.9649, 0.1712, 0.7547, 0.2575, 0.5678, 0.7482, 0.8001, 0.2400, 0.0598, 0.0811, 0.1576, 0.7060, 0.2760, 0.8407, 0.0759, 0.4505, 0.4314, 0.4173, 0.2348, 0.9294, 0.9706, 0.0318, 0.6797, 0.2543, 0.0540, 0.0838, 0.9106, 0.0497, 0.3532, 0.7757, 0.9572, 0.2769, 0.6551, 0.8143, 0.5308, 0.2290, 0.1818, 0.9027, 0.8212, 0.4868, 0.4854, 0.0462, 0.1626, 0.2435, 0.7792, 0.9133, 0.2638, 0.9448, 0.0154, 0.4359, 0.8003, 0.0971, 0.1190, 0.9293, 0.9340, 0.1524, 0.1455, 0.4909, 0.0430, 0.4468, 0.1419, 0.8235, 0.4984, 0.3500, 0.1299, 0.8258, 0.1361, 0.4893, 0.1690, 0.3063, 0.4218, 0.6948, 0.9597, 0.1966, 0.5688, 0.5383, 0.8693, 0.3377, 0.6491, 0.5085, 0.9157, 0.3171, 0.3404, 0.2511, 0.4694, 0.9961, 0.5797, 0.9001, 0.7317, 0.5108, 0.7922, 0.9502, 0.5853, 0.6160, 0.0119, 0.0782, 0.5499, 0.3692, 0.6477, 0.8176, 0.9595, 0.0344, 0.2238, 0.4733, 0.3371, 0.4427, 0.1450, 0.1112, 0.4509, 0.7948 }); fill_tensor(Accessor(targets), std::vector { 1, 5, 7, 2, 8, 1, 2, 1, 2, 4, 3, 9, 4, 1, 9, 9, 4, 1, 2, 4 }); // Determine the output through the CPP kernel Tensor output; CPPTopKV topkv; topkv.configure(&predictions, &targets, &output, k); output.allocator()->allocate(); // Run the kernel topkv.run(); // Validate against the expected values SimpleTensor expected_output(TensorShape(20), DataType::U8); fill_tensor(expected_output, std::vector { 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0 }); validate(Accessor(output), expected_output); } TEST_CASE(QASYMM8, framework::DatasetMode::ALL) { const unsigned int k = 5; Tensor predictions = create_tensor(TensorShape(10, 20), DataType::QASYMM8, 1, QuantizationInfo()); Tensor targets = create_tensor(TensorShape(20), DataType::U32); predictions.allocator()->allocate(); targets.allocator()->allocate(); // Fill the tensors with random pre-generated values fill_tensor(Accessor(predictions), std::vector { 133, 235, 69, 118, 140, 179, 189, 203, 137, 157, 242, 1, 196, 170, 166, 25, 102, 244, 24, 254, 164, 119, 49, 198, 140, 135, 175, 84, 29, 136, 246, 109, 74, 90, 185, 136, 181, 172, 35, 123, 62, 118, 24, 170, 134, 221, 114, 113, 174, 206, 174, 198, 148, 107, 255, 125, 6, 214, 127, 59, 75, 83, 175, 216, 56, 101, 85, 197, 49, 128, 172, 201, 140, 214, 28, 172, 109, 43, 127, 231, 178, 121, 109, 66, 29, 190, 70, 221, 38, 148, 18, 10, 165, 158, 17, 134, 51, 254, 15, 217, 66, 46, 166, 150, 104, 90, 211, 132, 218, 190, 58, 185, 174, 139, 115, 39, 111, 227, 144, 151, 171, 122, 163, 223, 94, 151, 228, 151, 238, 64, 217, 40, 242, 68, 196, 68, 101, 40, 179, 171, 89, 88, 54, 82, 161, 12, 197, 52, 150, 22, 200, 156, 182, 31, 198, 194, 102, 105, 209, 161, 173, 50, 61, 241, 239, 63, 207, 192, 226, 170, 2, 190, 31, 166, 250, 114, 194, 212, 254, 187, 155, 63, 156, 123, 50, 177, 97, 203, 1, 229, 100, 235, 116, 164, 36, 92, 56, 82, 222, 252 }); fill_tensor(Accessor(targets), std::vector { 1, 5, 7, 2, 8, 1, 2, 1, 2, 4, 3, 9, 4, 1, 9, 9, 4, 1, 2, 4 }); // Determine the output through the CPP kernel Tensor output; CPPTopKV topkv; topkv.configure(&predictions, &targets, &output, k); output.allocator()->allocate(); // Run the kernel topkv.run(); // Validate against the expected values SimpleTensor expected_output(TensorShape(20), DataType::U8); fill_tensor(expected_output, std::vector { 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0 }); validate(Accessor(output), expected_output); } TEST_CASE(QASYMM8_SIGNED, framework::DatasetMode::ALL) { const unsigned int k = 5; Tensor predictions = create_tensor(TensorShape(10, 20), DataType::QASYMM8_SIGNED, 1, QuantizationInfo()); Tensor targets = create_tensor(TensorShape(20), DataType::U32); predictions.allocator()->allocate(); targets.allocator()->allocate(); // Fill the tensors with random pre-generated values fill_tensor(Accessor(predictions), std::vector { 123, -34, 69, 118, 20, -45, 99, -98, 127, 117, //-34 -99, 1, -128, 90, 60, 25, 102, 76, 24, -110, //25 99, 119, 49, 43, -40, 60, 43, 84, 29, 67, //84 33, 109, 74, 90, 90, 44, 98, 90, 35, 123, //74 62, 118, 24, -32, 34, 21, 114, 113, 124, 20, //124 74, 98, 48, 107, 127, 125, 6, -98, 127, 59, //98 75, 83, 75, -118, 56, 101, 85, 97, 49, 127, //75 72, -20, 40, 14, 28, -30, 109, 43, 127, -31, //-20 78, 121, 109, 66, 29, 90, 70, 21, 38, 48, //109 18, 10, 115, 124, 17, 123, 51, 54, 15, 17, //17 66, 46, -66, 125, 104, 90, 123, 113, -54, -126, //125 58, -85, 74, 39, 115, 39, 111, -27, 44, 51, //51 71, 122, -34, -123, 94, 113, 125, 111, 38, 64, //94 -17, 40, 42, 68, 96, 68, 101, 40, 79, 71, //40 89, 88, 54, 82, 127, 12, 112, 52, 125, 22, //22 -128, 56, 82, 31, 98, 94, 102, 105, 127, 123, //123 112, 50, 61, 41, 39, 63, -77, 92, 26, 70, //39 2, 90, 31, 99, -34, 114, 112, 126, 127, 87, //90 125, 63, 56, 123, 50, -77, 97, -93, 1, 29, //56 100, -35, 116, 64, 36, 92, 56, 82, -22, -118 //36 }); fill_tensor(Accessor(targets), std::vector { 1, 5, 7, 2, 8, 1, 2, 1, 2, 4, 3, 9, 4, 1, 9, 9, 4, 1, 2, 4 }); // Determine the output through the CPP kernel Tensor output; CPPTopKV topkv; topkv.configure(&predictions, &targets, &output, k); output.allocator()->allocate(); // Run the kernel topkv.run(); // Validate against the expected values SimpleTensor expected_output(TensorShape(20), DataType::U8); fill_tensor(expected_output, std::vector { 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0 }); validate(Accessor(output), expected_output); } TEST_SUITE_END() // TopKV TEST_SUITE_END() // CPP } // namespace validation } // namespace test } // namespace arm_compute