1 /* 2 * Copyright (c) 2017-2022 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_VALIDATION_HELPERS_H 25 #define ARM_COMPUTE_TEST_VALIDATION_HELPERS_H 26 27 #include "arm_compute/core/Types.h" 28 #include "arm_compute/core/Utils.h" 29 #include "support/Half.h" 30 #include "tests/Globals.h" 31 #include "tests/SimpleTensor.h" 32 33 #include <math.h> 34 #include <random> 35 #include <type_traits> 36 #include <utility> 37 38 namespace arm_compute 39 { 40 namespace test 41 { 42 namespace validation 43 { 44 template <typename T> 45 struct is_floating_point : public std::is_floating_point<T> 46 { 47 }; 48 49 template <> 50 struct is_floating_point<half> : public std::true_type 51 { 52 }; 53 54 /** Helper function to get the testing range for each activation layer. 55 * 56 * @param[in] activation Activation function to test. 57 * @param[in] data_type Data type. 58 * 59 * @return A pair containing the lower upper testing bounds for a given function. 60 */ 61 template <typename T> 62 std::pair<T, T> get_activation_layer_test_bounds(ActivationLayerInfo::ActivationFunction activation, DataType data_type) 63 { 64 std::pair<T, T> bounds; 65 66 switch(data_type) 67 { 68 case DataType::F16: 69 { 70 using namespace half_float::literal; 71 72 switch(activation) 73 { 74 case ActivationLayerInfo::ActivationFunction::TANH: 75 case ActivationLayerInfo::ActivationFunction::SQUARE: 76 case ActivationLayerInfo::ActivationFunction::LOGISTIC: 77 case ActivationLayerInfo::ActivationFunction::SOFT_RELU: 78 // Reduce range as exponent overflows 79 bounds = std::make_pair(-2._h, 2._h); 80 break; 81 case ActivationLayerInfo::ActivationFunction::SQRT: 82 // Reduce range as sqrt should take a non-negative number 83 bounds = std::make_pair(0._h, 128._h); 84 break; 85 default: 86 bounds = std::make_pair(-255._h, 255._h); 87 break; 88 } 89 break; 90 } 91 case DataType::F32: 92 switch(activation) 93 { 94 case ActivationLayerInfo::ActivationFunction::SOFT_RELU: 95 // Reduce range as exponent overflows 96 bounds = std::make_pair(-40.f, 40.f); 97 break; 98 case ActivationLayerInfo::ActivationFunction::SQRT: 99 // Reduce range as sqrt should take a non-negative number 100 bounds = std::make_pair(0.f, 255.f); 101 break; 102 default: 103 bounds = std::make_pair(-255.f, 255.f); 104 break; 105 } 106 break; 107 default: 108 ARM_COMPUTE_ERROR("Unsupported data type"); 109 } 110 111 return bounds; 112 } 113 114 /** Calculate output tensor shape give a vector of input tensor to concatenate 115 * 116 * @param[in] input_shapes Shapes of the tensors to concatenate across depth. 117 * 118 * @return The shape of output concatenated tensor. 119 */ 120 TensorShape calculate_depth_concatenate_shape(const std::vector<TensorShape> &input_shapes); 121 122 /** Calculate output tensor shape for the concatenate operation along a given axis 123 * 124 * @param[in] input_shapes Shapes of the tensors to concatenate across width. 125 * @param[in] axis Axis to use for the concatenate operation 126 * 127 * @return The shape of output concatenated tensor. 128 */ 129 TensorShape calculate_concatenate_shape(const std::vector<TensorShape> &input_shapes, size_t axis); 130 131 /** Convert an asymmetric quantized simple tensor into float using tensor quantization information. 132 * 133 * @param[in] src Quantized tensor. 134 * 135 * @return Float tensor. 136 */ 137 template <typename T> 138 SimpleTensor<float> convert_from_asymmetric(const SimpleTensor<T> &src); 139 140 /** Convert float simple tensor into quantized using specified quantization information. 141 * 142 * @param[in] src Float tensor. 143 * @param[in] quantization_info Quantification information. 144 * 145 * @return Quantized tensor. 146 */ 147 template <typename T> 148 SimpleTensor<T> convert_to_asymmetric(const SimpleTensor<float> &src, const QuantizationInfo &quantization_info); 149 150 /** Convert quantized simple tensor into float using tensor quantization information. 151 * 152 * @param[in] src Quantized tensor. 153 * 154 * @return Float tensor. 155 */ 156 template <typename T> 157 SimpleTensor<float> convert_from_symmetric(const SimpleTensor<T> &src); 158 159 /** Convert float simple tensor into quantized using specified quantization information. 160 * 161 * @param[in] src Float tensor. 162 * @param[in] quantization_info Quantification information. 163 * 164 * @return Quantized tensor. 165 */ 166 template <typename T> 167 SimpleTensor<T> convert_to_symmetric(const SimpleTensor<float> &src, const QuantizationInfo &quantization_info); 168 169 /** Matrix multiply between 2 float simple tensors 170 * 171 * @param[in] a Input tensor A 172 * @param[in] b Input tensor B 173 * @param[out] out Output tensor 174 * 175 */ 176 template <typename T> 177 void matrix_multiply(const SimpleTensor<T> &a, const SimpleTensor<T> &b, SimpleTensor<T> &out); 178 179 /** Transpose matrix 180 * 181 * @param[in] in Input tensor 182 * @param[out] out Output tensor 183 * 184 */ 185 template <typename T> 186 void transpose_matrix(const SimpleTensor<T> &in, SimpleTensor<T> &out); 187 188 /** Get a 2D tile from a tensor 189 * 190 * @note In case of out-of-bound reads, the tile will be filled with zeros 191 * 192 * @param[in] in Input tensor 193 * @param[out] tile Tile 194 * @param[in] coord Coordinates 195 */ 196 template <typename T> 197 void get_tile(const SimpleTensor<T> &in, SimpleTensor<T> &tile, const Coordinates &coord); 198 199 /** Fill with zeros the input tensor in the area defined by anchor and shape 200 * 201 * @param[in] in Input tensor to fill with zeros 202 * @param[out] anchor Starting point of the zeros area 203 * @param[in] shape Ending point of the zeros area 204 */ 205 template <typename T> 206 void zeros(SimpleTensor<T> &in, const Coordinates &anchor, const TensorShape &shape); 207 208 /** Helper function to compute quantized min and max bounds 209 * 210 * @param[in] quant_info Quantization info to be used for conversion 211 * @param[in] min Floating point minimum value to be quantized 212 * @param[in] max Floating point maximum value to be quantized 213 */ 214 std::pair<int, int> get_quantized_bounds(const QuantizationInfo &quant_info, float min, float max); 215 216 /** Helper function to compute asymmetric quantized signed min and max bounds 217 * 218 * @param[in] quant_info Quantization info to be used for conversion 219 * @param[in] min Floating point minimum value to be quantized 220 * @param[in] max Floating point maximum value to be quantized 221 */ 222 std::pair<int, int> get_quantized_qasymm8_signed_bounds(const QuantizationInfo &quant_info, float min, float max); 223 224 /** Helper function to compute symmetric quantized min and max bounds 225 * 226 * @param[in] quant_info Quantization info to be used for conversion 227 * @param[in] min Floating point minimum value to be quantized 228 * @param[in] max Floating point maximum value to be quantized 229 * @param[in] channel_id Channel id for per channel quantization info. 230 */ 231 std::pair<int, int> get_symm_quantized_per_channel_bounds(const QuantizationInfo &quant_info, float min, float max, size_t channel_id = 0); 232 233 /** Add random padding along the X axis (between 1 and 16 columns per side) to all the input tensors. 234 * This is used in our validation suite in order to simulate implicit padding addition after configuring, but before allocating. 235 * 236 * @param[in] tensors List of tensors to add padding to 237 * @param[in] data_layout (Optional) Data layout of the operator 238 * @param[in] only_right_pad (Optional) Only right padding testing, in case of cl image padding 239 * 240 * @note This function adds padding to the input tensors only if data_layout == DataLayout::NHWC 241 */ 242 void add_padding_x(std::initializer_list<ITensor *> tensors, const DataLayout &data_layout = DataLayout::NHWC, bool only_right_pad = false); 243 244 /** Add random padding along the Y axis (between 1 and 4 rows per side) to all the input tensors. 245 * This is used in our validation suite in order to simulate implicit padding addition after configuring, but before allocating. 246 * 247 * @param[in] tensors List of tensors to add padding to 248 * @param[in] data_layout (Optional) Data layout of the operator 249 * 250 * @note This function adds padding to the input tensors only if data_layout == DataLayout::NHWC 251 */ 252 void add_padding_y(std::initializer_list<ITensor *> tensors, const DataLayout &data_layout = DataLayout::NHWC); 253 } // namespace validation 254 } // namespace test 255 } // namespace arm_compute 256 #endif /* ARM_COMPUTE_TEST_VALIDATION_HELPERS_H */ 257