/* * Copyright (c) 2017-2021 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. */ #ifndef ARM_COMPUTE_TEST_VALIDATION_HELPERS_H #define ARM_COMPUTE_TEST_VALIDATION_HELPERS_H #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" #include "support/Half.h" #include "tests/Globals.h" #include "tests/SimpleTensor.h" #include #include #include #include namespace arm_compute { namespace test { namespace validation { template struct is_floating_point : public std::is_floating_point { }; template <> struct is_floating_point : public std::true_type { }; /** Helper function to get the testing range for each activation layer. * * @param[in] activation Activation function to test. * @param[in] data_type Data type. * * @return A pair containing the lower upper testing bounds for a given function. */ template std::pair get_activation_layer_test_bounds(ActivationLayerInfo::ActivationFunction activation, DataType data_type) { std::pair bounds; switch(data_type) { case DataType::F16: { using namespace half_float::literal; switch(activation) { case ActivationLayerInfo::ActivationFunction::TANH: case ActivationLayerInfo::ActivationFunction::SQUARE: case ActivationLayerInfo::ActivationFunction::LOGISTIC: case ActivationLayerInfo::ActivationFunction::SOFT_RELU: // Reduce range as exponent overflows bounds = std::make_pair(-2._h, 2._h); break; case ActivationLayerInfo::ActivationFunction::SQRT: // Reduce range as sqrt should take a non-negative number bounds = std::make_pair(0._h, 128._h); break; default: bounds = std::make_pair(-255._h, 255._h); break; } break; } case DataType::F32: switch(activation) { case ActivationLayerInfo::ActivationFunction::SOFT_RELU: // Reduce range as exponent overflows bounds = std::make_pair(-40.f, 40.f); break; case ActivationLayerInfo::ActivationFunction::SQRT: // Reduce range as sqrt should take a non-negative number bounds = std::make_pair(0.f, 255.f); break; default: bounds = std::make_pair(-255.f, 255.f); break; } break; default: ARM_COMPUTE_ERROR("Unsupported data type"); } return bounds; } /** Calculate output tensor shape give a vector of input tensor to concatenate * * @param[in] input_shapes Shapes of the tensors to concatenate across depth. * * @return The shape of output concatenated tensor. */ TensorShape calculate_depth_concatenate_shape(const std::vector &input_shapes); /** Calculate output tensor shape for the concatenate operation along a given axis * * @param[in] input_shapes Shapes of the tensors to concatenate across width. * @param[in] axis Axis to use for the concatenate operation * * @return The shape of output concatenated tensor. */ TensorShape calculate_concatenate_shape(const std::vector &input_shapes, size_t axis); /** Convert an asymmetric quantized simple tensor into float using tensor quantization information. * * @param[in] src Quantized tensor. * * @return Float tensor. */ template SimpleTensor convert_from_asymmetric(const SimpleTensor &src); /** Convert float simple tensor into quantized using specified quantization information. * * @param[in] src Float tensor. * @param[in] quantization_info Quantification information. * * @return Quantized tensor. */ template SimpleTensor convert_to_asymmetric(const SimpleTensor &src, const QuantizationInfo &quantization_info); /** Convert quantized simple tensor into float using tensor quantization information. * * @param[in] src Quantized tensor. * * @return Float tensor. */ template SimpleTensor convert_from_symmetric(const SimpleTensor &src); /** Convert float simple tensor into quantized using specified quantization information. * * @param[in] src Float tensor. * @param[in] quantization_info Quantification information. * * @return Quantized tensor. */ template SimpleTensor convert_to_symmetric(const SimpleTensor &src, const QuantizationInfo &quantization_info); /** Matrix multiply between 2 float simple tensors * * @param[in] a Input tensor A * @param[in] b Input tensor B * @param[out] out Output tensor * */ template void matrix_multiply(const SimpleTensor &a, const SimpleTensor &b, SimpleTensor &out); /** Transpose matrix * * @param[in] in Input tensor * @param[out] out Output tensor * */ template void transpose_matrix(const SimpleTensor &in, SimpleTensor &out); /** Get a 2D tile from a tensor * * @note In case of out-of-bound reads, the tile will be filled with zeros * * @param[in] in Input tensor * @param[out] tile Tile * @param[in] coord Coordinates */ template void get_tile(const SimpleTensor &in, SimpleTensor &tile, const Coordinates &coord); /** Fill with zeros the input tensor in the area defined by anchor and shape * * @param[in] in Input tensor to fill with zeros * @param[out] anchor Starting point of the zeros area * @param[in] shape Ending point of the zeros area */ template void zeros(SimpleTensor &in, const Coordinates &anchor, const TensorShape &shape); /** Helper function to compute quantized min and max bounds * * @param[in] quant_info Quantization info to be used for conversion * @param[in] min Floating point minimum value to be quantized * @param[in] max Floating point maximum value to be quantized */ std::pair get_quantized_bounds(const QuantizationInfo &quant_info, float min, float max); /** Helper function to compute asymmetric quantized signed min and max bounds * * @param[in] quant_info Quantization info to be used for conversion * @param[in] min Floating point minimum value to be quantized * @param[in] max Floating point maximum value to be quantized */ std::pair get_quantized_qasymm8_signed_bounds(const QuantizationInfo &quant_info, float min, float max); /** Helper function to compute symmetric quantized min and max bounds * * @param[in] quant_info Quantization info to be used for conversion * @param[in] min Floating point minimum value to be quantized * @param[in] max Floating point maximum value to be quantized * @param[in] channel_id Channel id for per channel quantization info. */ std::pair get_symm_quantized_per_channel_bounds(const QuantizationInfo &quant_info, float min, float max, size_t channel_id = 0); /** Add random padding along the X axis (between 1 and 16 columns per side) to all the input tensors. * This is used in our validation suite in order to simulate implicit padding addition after configuring, but before allocating. * * @param[in] tensors List of tensors to add padding to * @param[in] data_layout (Optional) Data layout of the operator * * @note This function adds padding to the input tensors only if data_layout == DataLayout::NHWC */ void add_padding_x(std::initializer_list tensors, const DataLayout &data_layout = DataLayout::NHWC); } // namespace validation } // namespace test } // namespace arm_compute #endif /* ARM_COMPUTE_TEST_VALIDATION_HELPERS_H */