1 /* 2 * Copyright (c) 2017-2020 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24 #ifndef ARM_COMPUTE_TEST_CONVOLUTION_FIXTURE 25 #define ARM_COMPUTE_TEST_CONVOLUTION_FIXTURE 26 27 #include "arm_compute/core/TensorShape.h" 28 #include "arm_compute/core/Types.h" 29 #include "tests/AssetsLibrary.h" 30 #include "tests/Globals.h" 31 #include "tests/IAccessor.h" 32 #include "tests/framework/Asserts.h" 33 #include "tests/framework/Fixture.h" 34 #include "tests/validation/reference/Convolution.h" 35 36 #include <random> 37 38 namespace arm_compute 39 { 40 namespace test 41 { 42 namespace validation 43 { 44 template <typename TensorType, typename AccessorType, typename FunctionType, typename T> 45 class ConvolutionValidationFixture : public framework::Fixture 46 { 47 protected: 48 template <typename...> 49 void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width, const unsigned int height, const bool is_separable = false) 50 { 51 std::mt19937 gen(library->seed()); 52 std::uniform_int_distribution<uint8_t> distribution(0, 255); 53 std::uniform_int_distribution<uint8_t> scale_distribution(1, 255); 54 const uint8_t constant_border_value = distribution(gen); 55 56 // Generate random scale value between 1 and 255. 57 const uint32_t scale = scale_distribution(gen); 58 59 ARM_COMPUTE_ERROR_ON(3 != width && 5 != width && 7 != width && 9 != width); 60 ARM_COMPUTE_ERROR_ON(3 != height && 5 != height && 7 != height && 9 != height); 61 62 std::vector<int16_t> conv(width * height); 63 64 _width = width; 65 _height = height; 66 67 if(is_separable) 68 { 69 init_separable_conv(conv.data(), width, height, library->seed()); 70 } 71 else 72 { 73 init_conv(conv.data(), width, height, library->seed()); 74 } 75 76 _target = compute_target(shape, output_data_type, conv.data(), scale, border_mode, constant_border_value); 77 _reference = compute_reference(shape, output_data_type, conv.data(), scale, border_mode, constant_border_value); 78 } 79 80 template <typename U> fill(U && tensor,int i)81 void fill(U &&tensor, int i) 82 { 83 library->fill_tensor_uniform(tensor, i); 84 } 85 compute_reference(const TensorShape & shape,DataType output_data_type,const int16_t * conv,uint32_t scale,BorderMode border_mode,uint8_t constant_border_value)86 SimpleTensor<T> compute_reference(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value) 87 { 88 // Create reference 89 SimpleTensor<uint8_t> src{ shape, DataType::U8 }; 90 91 // Fill reference 92 fill(src, 0); 93 94 // Compute reference 95 return reference::convolution<T>(src, output_data_type, conv, scale, border_mode, constant_border_value, _width, _height); 96 } 97 98 virtual TensorType compute_target(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value) = 0; 99 100 BorderMode _border_mode{}; 101 TensorType _target{}; 102 SimpleTensor<T> _reference{}; 103 unsigned int _width{}; 104 unsigned int _height{}; 105 }; 106 107 template <typename TensorType, typename AccessorType, typename FunctionType, typename T> 108 class ConvolutionSquareValidationFixture : public ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T> 109 { 110 public: 111 template <typename...> setup(TensorShape shape,DataType output_data_type,BorderMode border_mode,const unsigned int width)112 void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width) 113 { 114 ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, output_data_type, border_mode, width, width); 115 } 116 117 protected: compute_target(const TensorShape & shape,DataType output_data_type,const int16_t * conv,uint32_t scale,BorderMode border_mode,uint8_t constant_border_value)118 TensorType compute_target(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value) 119 { 120 // Create tensors 121 TensorType src = create_tensor<TensorType>(shape, DataType::U8); 122 TensorType dst = create_tensor<TensorType>(shape, output_data_type); 123 124 // Create and configure function 125 FunctionType convolution; 126 convolution.configure(&src, &dst, conv, scale, border_mode, constant_border_value); 127 128 ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); 129 ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); 130 131 // Allocate tensors 132 src.allocator()->allocate(); 133 dst.allocator()->allocate(); 134 135 ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); 136 ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); 137 138 // Fill tensors 139 this->fill(AccessorType(src), 0); 140 this->fill(AccessorType(dst), 1); 141 142 // Compute function 143 convolution.run(); 144 145 return dst; 146 } 147 }; 148 149 template <typename TensorType, typename AccessorType, typename FunctionType, typename T> 150 class ConvolutionSeparableValidationFixture : public ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T> 151 { 152 public: 153 template <typename...> setup(TensorShape shape,DataType output_data_type,BorderMode border_mode,const unsigned int width)154 void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width) 155 { 156 ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, output_data_type, border_mode, width, width, true); 157 } 158 159 protected: compute_target(const TensorShape & shape,DataType output_data_type,const int16_t * conv,uint32_t scale,BorderMode border_mode,uint8_t constant_border_value)160 TensorType compute_target(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value) 161 { 162 // Create tensors 163 TensorType src = create_tensor<TensorType>(shape, DataType::U8); 164 TensorType dst = create_tensor<TensorType>(shape, output_data_type); 165 166 // Create and configure function 167 FunctionType convolution; 168 convolution.configure(&src, &dst, conv, scale, border_mode, constant_border_value); 169 170 ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); 171 ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); 172 173 // Allocate tensors 174 src.allocator()->allocate(); 175 dst.allocator()->allocate(); 176 177 ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); 178 ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); 179 180 // Fill tensors 181 this->fill(AccessorType(src), 0); 182 this->fill(AccessorType(dst), 1); 183 184 // Compute function 185 convolution.run(); 186 187 return dst; 188 } 189 }; 190 191 template <typename TensorType, typename AccessorType, typename FunctionType, typename T> 192 class ConvolutionRectangleValidationFixture : public ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T> 193 { 194 public: 195 template <typename...> setup(TensorShape shape,DataType output_data_type,BorderMode border_mode,const unsigned int width,const unsigned int height)196 void setup(TensorShape shape, DataType output_data_type, BorderMode border_mode, const unsigned int width, const unsigned int height) 197 { 198 ConvolutionValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, output_data_type, border_mode, width, height); 199 } 200 201 protected: compute_target(const TensorShape & shape,DataType output_data_type,const int16_t * conv,uint32_t scale,BorderMode border_mode,uint8_t constant_border_value)202 TensorType compute_target(const TensorShape &shape, DataType output_data_type, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value) 203 { 204 // Create tensors 205 TensorType src = create_tensor<TensorType>(shape, DataType::U8); 206 TensorType dst = create_tensor<TensorType>(shape, output_data_type); 207 208 // Create and configure function 209 FunctionType convolution; 210 convolution.configure(&src, &dst, conv, this->_width, this->_height, scale, border_mode, constant_border_value); 211 212 ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); 213 ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); 214 215 // Allocate tensors 216 src.allocator()->allocate(); 217 dst.allocator()->allocate(); 218 219 ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); 220 ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); 221 222 // Fill tensors 223 this->fill(AccessorType(src), 0); 224 this->fill(AccessorType(dst), 1); 225 226 // Compute function 227 convolution.run(); 228 229 return dst; 230 } 231 }; 232 } // namespace validation 233 } // namespace test 234 } // namespace arm_compute 235 #endif /* ARM_COMPUTE_TEST_CONVOLUTION_FIXTURE */ 236