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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 new file mode 100644 index 0000000000..48661bbab9 --- /dev/null +++ b/tests/validation_old/TensorOperations.h @@ -0,0 +1,1178 @@ +/* + * 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 + +template <typename T> +T bilinear_policy(const Tensor<T> &in, Coordinates id, float xn, float yn, BorderMode border_mode, uint8_t constant_border_value) +{ + int idx = std::floor(xn); + int idy = std::floor(yn); + + const float dx = xn - idx; + const float dy = yn - idy; + const float dx_1 = 1.0f - dx; + const float dy_1 = 1.0f - dy; + + id.set(0, idx); + id.set(1, idy); + const T tl = tensor_elem_at(in, id, border_mode, constant_border_value); + id.set(0, idx + 1); + id.set(1, idy); + const T tr = tensor_elem_at(in, id, border_mode, constant_border_value); + id.set(0, idx); + id.set(1, idy + 1); + const T bl = tensor_elem_at(in, id, border_mode, constant_border_value); + id.set(0, idx + 1); + id.set(1, idy + 1); + const T br = tensor_elem_at(in, id, border_mode, constant_border_value); + + return tl * (dx_1 * dy_1) + tr * (dx * dy_1) + bl * (dx_1 * dy) + br * (dx * dy); +} + +bool valid_bilinear_policy(float xn, float yn, int width, int height, BorderMode border_mode) +{ + if(border_mode != BorderMode::UNDEFINED) + { + return true; + } + if((0 <= yn + 1) && (yn + 1 < height) && (0 <= xn + 1) && (xn + 1 < width)) + { + return true; + } + return false; +} + +// 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)); + } + } + } +} + +template <typename T> +void compute_min_max(const Tensor<T> &in, void *min, void *max) +{ + using type = typename std::conditional<std::is_same<T, float>::value, float, int32_t>::type; + + // Set min and max to first pixel + type tmp_min = static_cast<type>(in[0]); + type tmp_max = static_cast<type>(in[0]); + + // Look for min and max values + for(int i = 1; i < in.num_elements(); ++i) + { + if(static_cast<type>(in[i]) < tmp_min) + { + tmp_min = static_cast<type>(in[i]); + } + if(static_cast<type>(in[i]) > tmp_max) + { + tmp_max = static_cast<type>(in[i]); + } + } + + *static_cast<type *>(min) = tmp_min; + *static_cast<type *>(max) = tmp_max; +} + +// Min max location +template <typename T1> +void min_max_location(const Tensor<T1> &in, void *min, void *max, IArray<Coordinates2D> &min_loc, IArray<Coordinates2D> &max_loc, uint32_t &min_count, uint32_t &max_count) +{ + const size_t width = in.shape().x(); + + compute_min_max(in, min, max); + + using type = typename std::conditional<std::is_same<T1, float>::value, float, int32_t>::type; + + type min_value = *static_cast<type *>(min); + type max_value = *static_cast<type *>(max); + + min_count = 0; + max_count = 0; + for(int i = 0; i < in.num_elements(); ++i) + { + if(static_cast<type>(in[i]) == min_value) + { + Coordinates2D min_coord; + min_coord.x = static_cast<int32_t>(i % width); + min_coord.y = static_cast<int32_t>(i / width); + + min_loc.push_back(min_coord); + + min_count++; + } + if(static_cast<type>(in[i]) == max_value) + { + Coordinates2D max_coord; + max_coord.x = static_cast<int32_t>(i % width); + max_coord.y = static_cast<int32_t>(i / width); + + max_loc.push_back(max_coord); + + max_count++; + } + } +} + +// 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); + } +} + +// Arithmetic addition +template <typename T1, typename T2, typename T3> +void arithmetic_addition(const Tensor<T1> &in1, const Tensor<T2> &in2, Tensor<T3> &out, ConvertPolicy convert_policy) +{ + 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 = static_cast<intermediate_type>(in1[i]) + static_cast<intermediate_type>(in2[i]); + out[i] = (convert_policy == ConvertPolicy::SATURATE) ? saturate_cast<T3>(val) : static_cast<T3>(val); + } +} + +// Arithmetic Subtraction +template <typename T1, typename T2, typename T3> +void arithmetic_subtraction(const Tensor<T1> &in1, const Tensor<T2> &in2, Tensor<T3> &out, ConvertPolicy convert_policy) +{ + 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 = static_cast<intermediate_type>(in1[i]) - static_cast<intermediate_type>(in2[i]); + out[i] = (convert_policy == ConvertPolicy::SATURATE) ? saturate_cast<T3>(val) : static_cast<T3>(val); + } +} + +// Box3x3 filter +template <typename T, typename = typename std::enable_if<std::is_integral<T>::value>::type> +void box3x3(const Tensor<T> &in, Tensor<T> &out, BorderMode border_mode, T constant_border_value) +{ + const std::array<T, 9> filter{ { 1, 1, 1, 1, 1, 1, 1, 1, 1 } }; + float scale = 1.f / static_cast<float>(filter.size()); + 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, TensorShape(3U, 3U), filter.data(), scale, border_mode, constant_border_value); + } +} + +// Depth conversion +template < typename T1, typename T2, typename std::enable_if < std::is_integral<T1>::value &&is_floating_point<T2>::value, int >::type = 0 > +void depth_convert(const Tensor<T1> &in, Tensor<T2> &out, ConvertPolicy policy, uint32_t shift) +{ + using namespace fixed_point_arithmetic; + + const int fixed_point_position = in.fixed_point_position(); + for(int i = 0; i < in.num_elements(); ++i) + { + out[i] = static_cast<float>(fixed_point<T1>(in[i], fixed_point_position, true)); + } +} + +template < typename T1, typename T2, typename std::enable_if < is_floating_point<T1>::value &&std::is_integral<T2>::value, int >::type = 0 > +void depth_convert(const Tensor<T1> &in, Tensor<T2> &out, ConvertPolicy policy, uint32_t shift) +{ + using namespace fixed_point_arithmetic; + + const int fixed_point_position = out.fixed_point_position(); + for(int i = 0; i < in.num_elements(); ++i) + { + out[i] = fixed_point<T2>(in[i], fixed_point_position).raw(); + } +} + +template < typename T1, typename T2, typename std::enable_if < std::is_integral<T1>::value &&std::is_integral<T2>::value &&!std::is_same<T1, T2>::value, int >::type = 0 > +void depth_convert(const Tensor<T1> &in, Tensor<T2> &out, ConvertPolicy policy, uint32_t shift) +{ + // Up-casting + if(std::numeric_limits<T1>::digits <= std::numeric_limits<T2>::digits) + { + for(int i = 0; i < in.num_elements(); ++i) + { + out[i] = static_cast<T2>(in[i]) << shift; + } + } + // Down-casting + else + { + for(int i = 0; i < in.num_elements(); ++i) + { + T1 val = in[i] >> shift; + out[i] = ((policy == ConvertPolicy::SATURATE) ? saturate_cast<T2>(val) : static_cast<T2>(val)); + } + } +} + +template < typename T1, typename T2, typename std::enable_if < std::is_integral<T1>::value &&std::is_integral<T2>::value &&std::is_same<T1, T2>::value, int >::type = 0 > +void depth_convert(const Tensor<T1> &in, Tensor<T2> &out, ConvertPolicy policy, uint32_t shift) +{ + using namespace fixed_point_arithmetic; + bool is_in_place = (&in == &out); + + const int fixed_point_position_in = in.fixed_point_position(); + const int fixed_point_position_out = (is_in_place) ? static_cast<int>(shift) : out.fixed_point_position(); + + if(!is_in_place || (fixed_point_position_in != fixed_point_position_out)) + { + for(int i = 0; i < in.num_elements(); ++i) + { + auto x = fixed_point<T2>(in[i], fixed_point_position_in, true); + x.rescale(fixed_point_position_out); + out[i] = x.raw(); + } + } +} + +template < typename T1, typename T2, typename std::enable_if < is_floating_point<T1>::value &&is_floating_point<T2>::value, int >::type = 0 > +void depth_convert(const Tensor<T1> &in, Tensor<T2> &out, ConvertPolicy policy, uint32_t shift) +{ + for(int i = 0; i < in.num_elements(); ++i) + { + out[i] = static_cast<T2>(in[i]); + } +} + +// Gaussian3x3 filter +template <typename T, typename = typename std::enable_if<std::is_integral<T>::value>::type> +void gaussian3x3(const Tensor<T> &in, Tensor<T> &out, BorderMode border_mode, T constant_border_value) +{ + const std::array<T, 9> filter{ { 1, 2, 1, 2, 4, 2, 1, 2, 1 } }; + const float scale = 1.f / 16.f; + 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, TensorShape(3U, 3U), filter.data(), scale, border_mode, constant_border_value); + } +} + +// Gaussian5x5 filter +template <typename T, typename = typename std::enable_if<std::is_integral<T>::value>::type> +void gaussian5x5(const Tensor<T> &in, Tensor<T> &out, BorderMode border_mode, T constant_border_value) +{ + const std::array<T, 25> filter{ { + 1, 4, 6, 4, 1, + 4, 16, 24, 16, 4, + 6, 24, 36, 24, 6, + 4, 16, 24, 16, 4, + 1, 4, 6, 4, 1 + } }; + const float scale = 1.f / 256.f; + 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, TensorShape(5U, 5U), filter.data(), scale, border_mode, constant_border_value); + } +} + +// 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."); + } + } + } +} + +// Pixel-wise multiplication +template <typename T1, typename T2, typename T3> +void pixel_wise_multiplication(const Tensor<T1> &in1, const Tensor<T2> &in2, Tensor<T3> &out, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy) +{ + if(scale < 0) + { + ARM_COMPUTE_ERROR("Scale of pixel-wise multiplication must be non-negative"); + } + using intermediate_type = typename common_promoted_signed_type<T1, T2, T3>::intermediate_type; + for(int i = 0; i < in1.num_elements(); ++i) + { + double val = static_cast<intermediate_type>(in1[i]) * static_cast<intermediate_type>(in2[i]) * static_cast<double>(scale); + if(is_floating_point<T3>::value) + { + out[i] = val; + } + else + { + double rounded_val = 0; + switch(rounding_policy) + { + case(RoundingPolicy::TO_ZERO): + rounded_val = support::cpp11::trunc(val); + break; + case(RoundingPolicy::TO_NEAREST_UP): + rounded_val = round_half_up(val); + break; + case(RoundingPolicy::TO_NEAREST_EVEN): + rounded_val = round_half_even(val); + break; + default: + ARM_COMPUTE_ERROR("Unsupported rounding policy"); + } + out[i] = (convert_policy == ConvertPolicy::SATURATE) ? saturate_cast<T3>(rounded_val) : static_cast<T3>(rounded_val); + } + } +} + +// Fixed-point Pixel-wise Multiplication +template <typename T, typename = typename std::enable_if<std::is_integral<T>::value>::type> +void fixed_point_pixel_wise_multiplication(const Tensor<T> &in1, const Tensor<T> &in2, Tensor<T> &out, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy) +{ + using namespace fixed_point_arithmetic; + + const int fixed_point_position = in1.fixed_point_position(); + + ARM_COMPUTE_ERROR_ON_MSG(in1.data_type() != in2.data_type() || in1.data_type() != out.data_type(), + "Tensors must all have the same DataType"); + ARM_COMPUTE_ERROR_ON_MSG(fixed_point_position != in2.fixed_point_position() || fixed_point_position != out.fixed_point_position(), + "Fixed-point position must be the same for both inputs and outputs"); + + // Validate fixed_point_position + ARM_COMPUTE_ERROR_ON((in1.data_type() == DataType::QS8) && (fixed_point_position == 0 || fixed_point_position > 7)); + ARM_COMPUTE_ERROR_ON((in1.data_type() == DataType::QS16) && (fixed_point_position == 0 || fixed_point_position > 15)); + + const fixed_point<T> fp_scale(scale, fixed_point_position); + const bool is_sat = convert_policy == ConvertPolicy::SATURATE; + + for(int i = 0; i < in1.num_elements(); ++i) + { + const fixed_point<T> val1(in1[i], fixed_point_position, true); + fixed_point<T> res(in2[i], fixed_point_position, true); + if(is_sat) + { + res = mul(mul(res, val1), fp_scale); + } + else + { + res = mul<OverflowPolicy::WRAP>(mul<OverflowPolicy::WRAP>(res, val1), fp_scale); + } + out[i] = res.raw(); + } +} + +//Table Lookup +template <typename T, typename T1> +void table_lookup(const Tensor<T> &in, Tensor<T> &out, std::map<T1, T1> &lut) +{ + for(int i = 0; i < in.num_elements(); ++i) + { + out[i] = static_cast<T>(lut[in[i]]); + } +} + +// 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; + } +} + +// Warp Perspective +template <typename T> +void warp_perspective(const Tensor<T> &in, Tensor<T> &out, Tensor<T> &valid_mask, const float *matrix, InterpolationPolicy policy, BorderMode border_mode, uint8_t constant_border_value) +{ + // x0 = M00 * x + M01 * y + M02 + // y0 = M10 * x + M11 * y + M12 + // z0 = M20 * x + M21 * y + M22 + // xn = x0 / z0 + // yn = y0 / z0 + const float M00 = matrix[0]; + const float M10 = matrix[1]; + const float M20 = matrix[2]; + const float M01 = matrix[0 + 1 * 3]; + const float M11 = matrix[1 + 1 * 3]; + const float M21 = matrix[2 + 1 * 3]; + const float M02 = matrix[0 + 2 * 3]; + const float M12 = matrix[1 + 2 * 3]; + const float M22 = matrix[2 + 2 * 3]; + + const int width = in.shape().x(); + const int height = in.shape().y(); + + for(int element_idx = 0; element_idx < in.num_elements(); ++element_idx) + { + valid_mask[element_idx] = 1; + Coordinates id = index2coord(in.shape(), element_idx); + int idx = id.x(); + int idy = id.y(); + const float z0 = M20 * idx + M21 * idy + M22; + + float x0 = (M00 * idx + M01 * idy + M02); + float y0 = (M10 * idx + M11 * idy + M12); + + float xn = x0 / z0; + float yn = y0 / z0; + id.set(0, static_cast<int>(std::floor(xn))); + id.set(1, static_cast<int>(std::floor(yn))); + if((0 <= yn) && (yn < height) && (0 <= xn) && (xn < width)) + { + switch(policy) + { + case InterpolationPolicy::NEAREST_NEIGHBOR: + out[element_idx] = tensor_elem_at(in, id, border_mode, constant_border_value); + break; + case InterpolationPolicy::BILINEAR: + (valid_bilinear_policy(xn, yn, width, height, border_mode)) ? out[element_idx] = bilinear_policy(in, id, xn, yn, border_mode, constant_border_value) : valid_mask[element_idx] = 0; + break; + case InterpolationPolicy::AREA: + default: + ARM_COMPUTE_ERROR("Interpolation not supported"); + } + } + else + { + if(border_mode == BorderMode::UNDEFINED) + { + valid_mask[element_idx] = 0; + } + else + { + switch(policy) + { + case InterpolationPolicy::NEAREST_NEIGHBOR: + if(border_mode == BorderMode::CONSTANT) + { + out[element_idx] = constant_border_value; + } + else if(border_mode == BorderMode::REPLICATE) + { + id.set(0, std::max(0, std::min(static_cast<int>(xn), width - 1))); + id.set(1, std::max(0, std::min(static_cast<int>(yn), height - 1))); + out[element_idx] = in[coord2index(in.shape(), id)]; + } + break; + case InterpolationPolicy::BILINEAR: + out[element_idx] = bilinear_policy(in, id, xn, yn, border_mode, constant_border_value); + break; + case InterpolationPolicy::AREA: + default: + ARM_COMPUTE_ERROR("Interpolation not supported"); + } + } + } + } +} + +// Batch Normalization Layer for fixed point type +template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> +void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor<T> &mean, const Tensor<T> &var, const Tensor<T> &beta, const Tensor<T> &gamma, float epsilon, int fixed_point_position) +{ + const int cols = static_cast<int>(in.shape()[0]); + const int rows = static_cast<int>(in.shape()[1]); + const int depth = static_cast<int>(in.shape()[2]); + int upper_dims = in.shape().total_size() / (cols * rows * depth); + + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < depth; ++i) + { + for(int k = 0; k < rows; ++k) + { + for(int l = 0; l < cols; ++l) + { + const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth; + fixed_point_arithmetic::fixed_point<T> in_qs(in[pos], fixed_point_position, true); + fixed_point_arithmetic::fixed_point<T> var_qs(var[i], fixed_point_position, true); + fixed_point_arithmetic::fixed_point<T> mean_qs(mean[i], fixed_point_position, true); + fixed_point_arithmetic::fixed_point<T> beta_qs(beta[i], fixed_point_position, true); + fixed_point_arithmetic::fixed_point<T> gamma_qs(gamma[i], fixed_point_position, true); + fixed_point_arithmetic::fixed_point<T> epsilon_qs(epsilon, fixed_point_position); + + auto denominator = fixed_point_arithmetic::inv_sqrt(var_qs + epsilon_qs); + auto numerator = in_qs - mean_qs; + auto x_bar = numerator * denominator; + x_bar = beta_qs + x_bar * gamma_qs; + out[pos] = x_bar.raw(); + } + } + } + } +} + +// Batch Normalization Layer for floating point type +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> +void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor<T> &mean, const Tensor<T> &var, const Tensor<T> &beta, const Tensor<T> &gamma, float epsilon, int fixed_point_position) +{ + const int cols = static_cast<int>(in.shape()[0]); + const int rows = static_cast<int>(in.shape()[1]); + const int depth = static_cast<int>(in.shape()[2]); + int upper_dims = in.shape().total_size() / (cols * rows * depth); + + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < depth; ++i) + { + for(int k = 0; k < rows; ++k) + { + for(int l = 0; l < cols; ++l) + { + const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth; + const float denominator = sqrt(var[i] + epsilon); + const float numerator = in[pos] - mean[i]; + const float x_bar = numerator / denominator; + out[pos] = beta[i] + x_bar * gamma[i]; + } + } + } + } +} + +// 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); + + // Determine pooling regions + float pool_region_size_x = static_cast<float>(roi_width) / pool_w; + float pool_region_size_y = static_cast<float>(roi_height) / pool_h; + + // 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(px * pool_region_size_x)); + int region_end_x = static_cast<int>(std::ceil((px + 1) * pool_region_size_x)); + int region_start_y = static_cast<int>(std::floor(py * pool_region_size_y)); + int region_end_y = static_cast<int>(std::ceil((py + 1) * pool_region_size_y)); + + 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__ */ |