| /* |
| * 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)); |
| } |
| } |
| } |
| } |
| |
| // 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."); |
| } |
| } |
| } |
| } |
| |
| // 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(); |
| } |
| } |
| |
| // 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"); |
| } |
| } |
| } |
| } |
| } |
| |
| // 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__ */ |