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Diffstat (limited to 'tests/validation_old/TensorOperations.h')
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diff --git a/tests/validation_old/TensorOperations.h b/tests/validation_old/TensorOperations.h deleted file mode 100644 index e03336505b..0000000000 --- a/tests/validation_old/TensorOperations.h +++ /dev/null @@ -1,687 +0,0 @@ -/* - * Copyright (c) 2017 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_TENSOR_OPERATIONS_H__ -#define __ARM_COMPUTE_TEST_TENSOR_OPERATIONS_H__ - -#include "arm_compute/core/FixedPoint.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/Types.h" -#include "support/ToolchainSupport.h" -#include "tests/Types.h" -#include "tests/Utils.h" -#include "tests/validation_old/FixedPoint.h" -#include "tests/validation_old/Tensor.h" -#include "tests/validation_old/ValidationUserConfiguration.h" -#include "tests/validation_old/half.h" - -#include <algorithm> -#include <array> -#include <cmath> -#include <random> -#include <string> -#include <vector> - -namespace arm_compute -{ -namespace test -{ -namespace validation -{ -namespace tensor_operations -{ -namespace -{ -template <class T> -struct is_floating_point - : std::integral_constant < bool, - std::is_same<float, typename std::remove_cv<T>::type>::value || std::is_same<half_float::half, typename std::remove_cv<T>::type>::value - || std::is_same<double, typename std::remove_cv<T>::type>::value || std::is_same<long double, typename std::remove_cv<T>::type>::value > -{ -}; - -// Return a tensor element at a specified coordinate with different border modes -template <typename T> -T tensor_elem_at(const Tensor<T> &in, Coordinates coord, BorderMode border_mode, T constant_border_value) -{ - const int x = coord.x(); - const int y = coord.y(); - const int width = static_cast<int>(in.shape().x()); - const int height = static_cast<int>(in.shape().y()); - - // If coordinates beyond range of tensor's width or height - if(x < 0 || y < 0 || x >= width || y >= height) - { - if(border_mode == BorderMode::REPLICATE) - { - coord.set(0, std::max(0, std::min(x, width - 1))); - coord.set(1, std::max(0, std::min(y, height - 1))); - } - else - { - return constant_border_value; - } - } - - return in[coord2index(in.shape(), coord)]; -} - -/** Apply 2D spatial filter on a single element of @p in at coordinates @p coord - * - * - filter sizes have to be odd number - * - Row major order of filter assumed - * - TO_ZERO rounding policy assumed - * - SATURATE convert policy assumed - * - */ -template <typename T1, typename T2, typename T3> -void apply_2d_spatial_filter(Coordinates coord, const Tensor<T1> &in, Tensor<T3> &out, const TensorShape &filter_shape, const T2 *filter_itr, float scale, BorderMode border_mode, - T1 constant_border_value = 0) -{ - double val = 0; - const int x = coord.x(); - const int y = coord.y(); - for(int j = y - static_cast<int>(filter_shape[1] / 2); j <= y + static_cast<int>(filter_shape[1] / 2); ++j) - { - for(int i = x - static_cast<int>(filter_shape[0] / 2); i <= x + static_cast<int>(filter_shape[0] / 2); ++i) - { - coord.set(0, i); - coord.set(1, j); - val += static_cast<double>(*filter_itr) * tensor_elem_at(in, coord, border_mode, constant_border_value); - ++filter_itr; - } - } - coord.set(0, x); - coord.set(1, y); - const double rounded_val = support::cpp11::trunc(val * static_cast<double>(scale)); - out[coord2index(in.shape(), coord)] = saturate_cast<T3>(rounded_val); -} -} // namespace - -// Sobel 3x3 -template <typename T1, typename T2> -void sobel_3x3(Tensor<T1> &in, Tensor<T2> &out_x, Tensor<T2> &out_y, BorderMode border_mode, uint8_t constant_border_value) -{ - const std::array<int8_t, 9> sobel_x{ { -1, 0, 1, -2, 0, 2, -1, 0, 1 } }; - const std::array<int8_t, 9> sobel_y{ { -1, -2, -1, 0, 0, 0, 1, 2, 1 } }; - - for(int element_idx = 0; element_idx < in.num_elements(); ++element_idx) - { - const Coordinates id = index2coord(in.shape(), element_idx); - - apply_2d_spatial_filter(id, in, out_x, TensorShape(3U, 3U), sobel_x.data(), 1.f, border_mode, constant_border_value); - apply_2d_spatial_filter(id, in, out_y, TensorShape(3U, 3U), sobel_y.data(), 1.f, border_mode, constant_border_value); - } -} - -// Sobel 5x5 -template <typename T1, typename T2> -void sobel_5x5(Tensor<T1> &in, Tensor<T2> &out_x, Tensor<T2> &out_y, BorderMode border_mode, uint8_t constant_border_value) -{ - const std::array<int8_t, 25> sobel_x{ { - -1, -2, 0, 2, 1, - -4, -8, 0, 8, 4, - -6, -12, 0, 12, 6, - -4, -8, 0, 8, 4, - -1, -2, 0, 2, 1 - } }; - - const std::array<int8_t, 25> sobel_y{ { - -1, -4, -6, -4, -1, - -2, -8, -12, -8, -2, - 0, 0, 0, 0, 0, - 2, 8, 12, 8, 2, - 1, 4, 6, 4, 1 - } }; - - for(int element_idx = 0; element_idx < in.num_elements(); ++element_idx) - { - const Coordinates id = index2coord(in.shape(), element_idx); - - apply_2d_spatial_filter(id, in, out_x, TensorShape(5U, 5U), sobel_x.data(), 1.f, border_mode, constant_border_value); - apply_2d_spatial_filter(id, in, out_y, TensorShape(5U, 5U), sobel_y.data(), 1.f, border_mode, constant_border_value); - } -} - -// Sobel 7x7 -template <typename T1, typename T2> -void sobel_7x7(Tensor<T1> &in, Tensor<T2> &out_x, Tensor<T2> &out_y, BorderMode border_mode, uint8_t constant_border_value) -{ - const std::array<int8_t, 49> sobel_x{ { - -1, -4, -5, 0, 5, 4, 1, - -6, -24, -30, 0, 30, 24, 6, - -15, -60, -75, 0, 75, 60, 15, - -20, -80, -100, 0, 100, 80, 20, - -15, -60, -75, 0, 75, 60, 15, - -6, -24, -30, 0, 30, 24, 6, - -1, -4, -5, 0, 5, 4, 1 - } }; - - const std::array<int8_t, 49> sobel_y{ { - -1, -6, -15, -20, -15, -6, -1, - -4, -24, -60, -80, -60, -24, -4, - -5, -30, -75, -100, -75, -30, -5, - 0, 0, 0, 0, 0, 0, 0, - 5, 30, 75, 100, 75, 30, 5, - 4, 24, 60, 80, 60, 24, 4, - 1, 6, 15, 20, 15, 6, 1 - } }; - - for(int element_idx = 0; element_idx < in.num_elements(); ++element_idx) - { - const Coordinates id = index2coord(in.shape(), element_idx); - - apply_2d_spatial_filter(id, in, out_x, TensorShape(7U, 7U), sobel_x.data(), 1.f, border_mode, constant_border_value); - apply_2d_spatial_filter(id, in, out_y, TensorShape(7U, 7U), sobel_y.data(), 1.f, border_mode, constant_border_value); - } -} - -template <typename T> -void non_maxima_suppression_3x3(Tensor<T> &in, Tensor<T> &out, BorderMode border_mode) -{ - for(int i = 0; i < in.num_elements(); ++i) - { - Coordinates coord = index2coord(in.shape(), i); - int x = coord.x(); - int y = coord.y(); - - if(in[i] >= tensor_elem_at(in, Coordinates(x - 1, y - 1), border_mode, 0.f) && in[i] >= tensor_elem_at(in, Coordinates(x, y - 1), border_mode, 0.f) - && in[i] >= tensor_elem_at(in, Coordinates(x + 1, y - 1), border_mode, 0.f) && in[i] >= tensor_elem_at(in, Coordinates(x - 1, y), border_mode, 0.f) - && in[i] > tensor_elem_at(in, Coordinates(x + 1, y), border_mode, 0.f) && in[i] > tensor_elem_at(in, Coordinates(x - 1, y + 1), border_mode, 0.f) - && in[i] > tensor_elem_at(in, Coordinates(x, y + 1), border_mode, 0.f) && in[i] > tensor_elem_at(in, Coordinates(x + 1, y + 1), border_mode, 0.f)) - { - out[i] = in[i]; - } - else - { - out[i] = 0; - } - } -} - -// Harris corners -template <typename T1, typename T2, typename T3> -void harris_corners(Tensor<T1> &in, Tensor<T2> &Gx, Tensor<T2> &Gy, Tensor<T3> &candidates, Tensor<T3> &non_maxima, float threshold, float min_dist, float sensitivity, - int32_t gradient_size, int32_t block_size, KeyPointArray &corners, BorderMode border_mode, uint8_t constant_border_value) -{ - ARM_COMPUTE_ERROR_ON(block_size != 3 && block_size != 5 && block_size != 7); - - ValidRegion valid_region = shape_to_valid_region(candidates.shape()); - float norm_factor = 0.f; - - // Sobel - switch(gradient_size) - { - case 3: - sobel_3x3(in, Gx, Gy, border_mode, constant_border_value); - norm_factor = 1.f / (4 * 255 * block_size); - break; - case 5: - sobel_5x5(in, Gx, Gy, border_mode, constant_border_value); - norm_factor = 1.f / (16 * 255 * block_size); - break; - case 7: - sobel_7x7(in, Gx, Gy, border_mode, constant_border_value); - norm_factor = 1.f / (64 * 255 * block_size); - break; - default: - ARM_COMPUTE_ERROR("Gradient size not supported."); - } - - //Calculate scores - for(int i = 0; i < in.num_elements(); ++i) - { - Coordinates in_coord = index2coord(in.shape(), i); - - float Gx2 = 0; - float Gy2 = 0; - float Gxy = 0; - - // Calculate Gx^2, Gy^2 and Gxy within the given window - for(int y = in_coord.y() - block_size / 2; y <= in_coord.y() + block_size / 2; ++y) - { - for(int x = in_coord.x() - block_size / 2; x <= in_coord.x() + block_size / 2; ++x) - { - Coordinates block_coord(x, y); - - float norm_gx = tensor_elem_at(Gx, block_coord, border_mode, static_cast<T2>(constant_border_value)) * norm_factor; - float norm_gy = tensor_elem_at(Gy, block_coord, border_mode, static_cast<T2>(constant_border_value)) * norm_factor; - - Gx2 += std::pow(norm_gx, 2); - Gy2 += std::pow(norm_gy, 2); - Gxy += norm_gx * norm_gy; - } - } - - float trace2 = std::pow(Gx2 + Gy2, 2); - float det = Gx2 * Gy2 - std::pow(Gxy, 2); - float response = det - sensitivity * trace2; - - if(response > threshold) - { - candidates[i] = response; - } - else - { - candidates[i] = 0.f; - } - } - - // Update valid region and remove candidates on borders for border_mode == UNDEFINED - if(border_mode == BorderMode::UNDEFINED) - { - valid_region = shape_to_valid_region(candidates.shape(), true, BorderSize((gradient_size / 2) + (block_size / 2))); - - for(int i = 0; i < candidates.num_elements(); ++i) - { - if(!is_in_valid_region(valid_region, index2coord(candidates.shape(), i))) - { - candidates[i] = 0.f; - } - } - } - - // Suppress non-maxima candidates - non_maxima_suppression_3x3(candidates, non_maxima, border_mode != BorderMode::UNDEFINED ? BorderMode::CONSTANT : BorderMode::UNDEFINED); - if(border_mode == BorderMode::UNDEFINED) - { - valid_region = shape_to_valid_region(non_maxima.shape(), true, BorderSize((gradient_size / 2) + (block_size / 2) + 1)); - } - - // Create vector of candidate corners - KeyPointArray candidates_vector(corners.max_num_values()); - for(int i = 0; i < non_maxima.num_elements(); ++i) - { - Coordinates coord = index2coord(non_maxima.shape(), i); - - if(non_maxima[i] != 0.f && is_in_valid_region(valid_region, coord)) - { - KeyPoint corner; - corner.x = coord.x(); - corner.y = coord.y(); - corner.tracking_status = 1; - corner.strength = non_maxima[i]; - - corner.scale = 0.f; - corner.orientation = 0.f; - corner.error = 0.f; - - candidates_vector.push_back(corner); - } - } - - // If there are any candidates, sort them by strength and add them to the output corners vector if there are no stronger corners within the given euclidean radius - if(candidates_vector.num_values() > 0) - { - std::sort(candidates_vector.buffer(), candidates_vector.buffer() + candidates_vector.num_values(), [](KeyPoint a, KeyPoint b) - { - return a.strength > b.strength; - }); - corners.push_back(candidates_vector.at(0)); - - for(size_t j = 0; j < candidates_vector.num_values(); ++j) - { - bool found = false; - int32_t x = candidates_vector.at(j).x; - int32_t y = candidates_vector.at(j).y; - - for(size_t i = 0; i < corners.num_values(); ++i) - { - int32_t corners_x = corners.at(i).x; - int32_t corners_y = corners.at(i).y; - - // Euclidean distance - if(std::sqrt((std::pow(x - corners_x, 2) + std::pow(y - corners_y, 2))) < min_dist) - { - found = true; - } - } - - // If no stronger corners within the given euclidean radius - if(!found) - { - corners.push_back(candidates_vector.at(j)); - } - } - } -} - -// Integral Image -void integral_image(const Tensor<uint8_t> &in, Tensor<uint32_t> &out) -{ - // Length of dimensions - const size_t width = in.shape().x(); - const size_t height = in.shape().y(); - const size_t depth = in.shape().z() * in.shape()[3] * in.shape()[4] * in.shape()[5]; - - const size_t image_size = width * height; - - for(size_t z = 0; z < depth; ++z) - { - size_t current_image = z * image_size; - - //First element of each image - out[current_image] = in[current_image]; - - // First row of each image (add only pixel on the left) - for(size_t x = 1; x < width; ++x) - { - out[current_image + x] = static_cast<uint32_t>(in[current_image + x]) + out[current_image + x - 1]; - } - - // Subsequent rows - for(size_t y = 1; y < height; ++y) - { - size_t current_row = current_image + (width * y); - - // First element of each row (add only pixel up) - out[current_row] = static_cast<uint32_t>(in[current_row]) + out[current_row - width]; - - // Following row elements - for(size_t x = 1; x < width; ++x) - { - size_t current_pixel = current_row + x; - - // out = in + up(out) + left(out) - up_left(out) - out[current_pixel] = static_cast<uint32_t>(in[current_pixel]) + out[current_pixel - 1] - + out[current_pixel - width] - out[current_pixel - width - 1]; - } - } - } -} - -// Absolute difference -template <typename T1, typename T2, typename T3> -void absolute_difference(const Tensor<T1> &in1, const Tensor<T2> &in2, Tensor<T3> &out) -{ - using intermediate_type = typename common_promoted_signed_type<T1, T2, T3>::intermediate_type; - - for(int i = 0; i < in1.num_elements(); ++i) - { - intermediate_type val(std::abs(static_cast<intermediate_type>(in1[i]) - static_cast<intermediate_type>(in2[i]))); - out[i] = saturate_cast<T3>(val); - } -} - -// Accumulate -template <typename T1, typename T2> -void accumulate(const Tensor<T1> &in, Tensor<T2> &out) -{ - using intermediate_type = typename common_promoted_signed_type<T1, T2>::intermediate_type; - - for(int i = 0; i < in.num_elements(); ++i) - { - intermediate_type val = static_cast<intermediate_type>(out[i]) + static_cast<intermediate_type>(in[i]); - out[i] = saturate_cast<T2>(val); - } -} - -// Accumulate squared -template <typename T1, typename T2> -void accumulate_squared(const Tensor<T1> &in, Tensor<T2> &out, uint32_t shift) -{ - if(shift > 15) - { - ARM_COMPUTE_ERROR("Shift in accumulate_squared must be within the range [0, 15]"); - } - using intermediate_type = typename common_promoted_signed_type<T1, T2>::intermediate_type; - intermediate_type denom = 1 << shift; - - for(int i = 0; i < in.num_elements(); ++i) - { - intermediate_type val = static_cast<intermediate_type>(out[i]) + (static_cast<intermediate_type>(in[i]) * static_cast<intermediate_type>(in[i]) / denom); - out[i] = saturate_cast<T2>(val); - } -} - -// Accumulate weighted total_size = init_auto_padding(tensor_shape, num_channels, type); -template <typename T> -void accumulate_weighted(const Tensor<T> &in, Tensor<T> &out, float alpha) -{ - if(alpha < 0.f || alpha > 1.f) - { - ARM_COMPUTE_ERROR("Weight (alpha) specified in accumulate_weighted must be within the range [0, 1]"); - } - using intermediate_type = typename common_promoted_signed_type<T>::intermediate_type; - - for(int i = 0; i < in.num_elements(); ++i) - { - double val = (1. - static_cast<double>(alpha)) * static_cast<intermediate_type>(out[i]) + static_cast<double>(alpha) * static_cast<intermediate_type>(in[i]); - out[i] = static_cast<T>(val); - } -} - -// Non linear filter -template <typename T> -void non_linear_filter(const Tensor<T> &in, Tensor<T> &out, NonLinearFilterFunction function, unsigned int mask_size, - MatrixPattern pattern, const uint8_t *mask, BorderMode border_mode, uint8_t constant_border_value) -{ - ARM_COMPUTE_ERROR_ON(pattern == MatrixPattern::OTHER && mask == nullptr); - - using intermediate_type = typename common_promoted_signed_type<T>::intermediate_type; - - const int sq_mask_size = mask_size * mask_size; - const int half_mask_size = mask_size / 2; - std::vector<intermediate_type> vals(sq_mask_size); - intermediate_type current_value = 0; - - const ValidRegion valid_region = shape_to_valid_region(in.shape(), border_mode == BorderMode::UNDEFINED, BorderSize(half_mask_size)); - - for(int element_idx = 0, count = 0, index = 0; element_idx < in.num_elements(); ++element_idx, count = 0, index = 0) - { - Coordinates id = index2coord(in.shape(), element_idx); - if(is_in_valid_region(valid_region, id)) - { - int idx = id.x(); - int idy = id.y(); - for(int y = idy - half_mask_size; y <= idy + half_mask_size; ++y) - { - for(int x = idx - half_mask_size; x <= idx + half_mask_size; ++x, ++index) - { - id.set(0, x); - id.set(1, y); - current_value = tensor_elem_at(in, id, border_mode, constant_border_value); - - if(mask[index] == 255) - { - vals[count] = static_cast<intermediate_type>(current_value); - ++count; - } - } - } - std::sort(vals.begin(), vals.begin() + count); - switch(function) - { - case NonLinearFilterFunction::MIN: - out[element_idx] = saturate_cast<T>(vals[0]); - break; - case NonLinearFilterFunction::MAX: - out[element_idx] = saturate_cast<T>(vals[count - 1]); - break; - case NonLinearFilterFunction::MEDIAN: - out[element_idx] = saturate_cast<T>(vals[count / 2]); - break; - default: - ARM_COMPUTE_ERROR("Unsupported NonLinearFilter function."); - } - } - } -} - -// Threshold -template <typename T> -void threshold(const Tensor<T> &in, Tensor<T> &out, uint8_t threshold, uint8_t false_value, uint8_t true_value, ThresholdType type, uint8_t upper) -{ - switch(type) - { - case ThresholdType::BINARY: - for(int i = 0; i < in.num_elements(); ++i) - { - out[i] = ((in[i] > threshold) ? true_value : false_value); - } - break; - case ThresholdType::RANGE: - for(int i = 0; i < in.num_elements(); ++i) - { - if(in[i] > upper) - { - out[i] = false_value; - } - else if(in[i] < threshold) - { - out[i] = false_value; - } - else - { - out[i] = true_value; - } - } - break; - default: - ARM_COMPUTE_ERROR("Thresholding type not recognised"); - break; - } -} - -// ROI Pooling layer -template <typename T> -void roi_pooling_layer(const Tensor<T> &in, Tensor<T> &out, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info) -{ - const int num_rois = rois.size(); - const int width_in = in.shape().x(); - const int height_in = in.shape().y(); - const int fms = in.shape().z(); - const int volume_in = width_in * height_in * fms; - const int pool_w = pool_info.pooled_width(); - const int pool_h = pool_info.pooled_height(); - const int volume_out = pool_w * pool_h * fms; - const float roi_scale = pool_info.spatial_scale(); - - // Iterate through all rois - for(int roi_idx = 0; roi_idx < num_rois; ++roi_idx) - { - // Get dimensions of current ROI - const ROI &roi = rois[roi_idx]; - - int batch_id = roi.batch_idx; - int roi_start_x = support::cpp11::round(roi.rect.x * roi_scale); - int roi_start_y = support::cpp11::round(roi.rect.y * roi_scale); - int roi_width = std::max(support::cpp11::round(roi.rect.width * roi_scale), 1.f); - int roi_height = std::max(support::cpp11::round(roi.rect.height * roi_scale), 1.f); - - // Iterate through all channel - for(int fm = 0; fm < fms; ++fm) - { - // Calculate each output pixel - for(int py = 0; py < pool_h; ++py) - { - for(int px = 0; px < pool_w; ++px) - { - int region_start_x = static_cast<int>(std::floor((static_cast<float>(px) / pool_w) * roi_width)); - int region_end_x = static_cast<int>(std::floor((static_cast<float>(px + 1) / pool_w) * roi_width)); - int region_start_y = static_cast<int>(std::floor((static_cast<float>(py) / pool_h) * roi_height)); - int region_end_y = static_cast<int>(std::floor((static_cast<float>(py + 1) / pool_h) * roi_height)); - - region_start_x = std::min(std::max(region_start_x + roi_start_x, 0), width_in); - region_end_x = std::min(std::max(region_end_x + roi_start_x, 0), width_in); - region_start_y = std::min(std::max(region_start_y + roi_start_y, 0), height_in); - region_end_y = std::min(std::max(region_end_y + roi_start_y, 0), height_in); - - // Iterate through each pixel in the pooling region - if((region_end_x <= region_start_x) || (region_end_y <= region_start_y)) - { - out[roi_idx * volume_out + fm * pool_w * pool_h + py * pool_w + px] = 0; - } - else - { - T curr_max = std::numeric_limits<T>::lowest(); - for(int j = region_start_y; j < region_end_y; ++j) - { - for(int i = region_start_x; i < region_end_x; ++i) - { - const auto val = in[batch_id * volume_in + fm * width_in * height_in + j * width_in + i]; - curr_max = std::max(val, curr_max); - } - } - out[roi_idx * volume_out + fm * pool_w * pool_h + py * pool_w + px] = curr_max; - } - } - } - } - } -} - -// Fixed point operations -template <typename T> -void fixed_point_operation(const Tensor<T> &in, Tensor<T> &out, FixedPointOp op) -{ - int p = in.fixed_point_position(); - switch(op) - { - case FixedPointOp::EXP: - for(int i = 0; i < in.num_elements(); ++i) - { - out[i] = fixed_point_arithmetic::exp(fixed_point_arithmetic::fixed_point<T>(in[i], p, true)).raw(); - } - break; - case FixedPointOp::LOG: - for(int i = 0; i < in.num_elements(); ++i) - { - out[i] = fixed_point_arithmetic::log(fixed_point_arithmetic::fixed_point<T>(in[i], p, true)).raw(); - } - break; - case FixedPointOp::INV_SQRT: - for(int i = 0; i < in.num_elements(); ++i) - { - out[i] = fixed_point_arithmetic::inv_sqrt(fixed_point_arithmetic::fixed_point<T>(in[i], p, true)).raw(); - } - break; - case FixedPointOp::RECIPROCAL: - for(int i = 0; i < in.num_elements(); ++i) - { - out[i] = fixed_point_arithmetic::div(fixed_point_arithmetic::fixed_point<T>(1, p), fixed_point_arithmetic::fixed_point<T>(in[i], p, true)).raw(); - } - break; - default: - ARM_COMPUTE_ERROR("Fixed point operation not supported"); - break; - } -} - -// Tensor print -template <typename T> -void print(const Tensor<T> &in, std::ostream &out) -{ - out << "\n"; - for(int i = 0; i < in.num_elements(); ++i) - { - out << in[i] << " "; - } - out << "\n"; -} -} // namespace tensor_operations -} // namespace validation -} // namespace test -} // namespace arm_compute - -#endif /* __ARM_COMPUTE_TEST_TENSOR_OPERATIONS_H__ */ |