| /* |
| * 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 "FixedPoint.h" |
| #include "Tensor.h" |
| #include "Types.h" |
| #include "Utils.h" |
| #include "support/ToolchainSupport.h" |
| |
| #include "FixedPoint.h" |
| #include "Types.h" |
| #include "arm_compute/core/FixedPoint.h" |
| #include "arm_compute/core/Types.h" |
| #include "tests/validation/FixedPoint.h" |
| #include "tests/validation/ValidationUserConfiguration.h" |
| |
| #include <algorithm> |
| #include <array> |
| #include <cmath> |
| #include <random> |
| |
| 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 || |
| #if ARM_COMPUTE_ENABLE_FP16 |
| std::is_same<float16_t, typename std::remove_cv<T>::type>::value || |
| #endif |
| std::is_same<double, typename std::remove_cv<T>::type>::value || std::is_same<long double, typename std::remove_cv<T>::type>::value > |
| { |
| }; |
| |
| bool is_valid_pixel(int i, int min, int max) |
| { |
| return (i >= min && i < max); |
| } |
| |
| // 3D convolution for floating point type |
| template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> |
| void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int8_t fixed_point_position) |
| { |
| const int half_width_weights = width_weights / 2; |
| const int half_height_weights = height_weights / 2; |
| |
| // Reset accumulator |
| T acc = static_cast<T>(0); |
| |
| // Compute a 2D convolution for each IFM and accumulate the result |
| for(int ifm = 0; ifm < depth_in; ++ifm) |
| { |
| // Compute the offset for the input slice |
| const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; |
| |
| // Compute 2D convolution |
| for(int yk = -half_height_weights; yk <= half_height_weights; ++yk) |
| { |
| for(int xk = -half_width_weights; xk <= half_width_weights; ++xk) |
| { |
| // Check if the pixel is out-of-bound |
| if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in)) |
| { |
| const int idx = xk + half_width_weights; |
| const int idy = yk + half_height_weights; |
| |
| const T i_value = in[offset_slice_in + xk + yk * width_in]; |
| const T w_value = weights[idx + idy * width_weights + ifm * width_weights * height_weights]; |
| |
| acc += i_value * w_value; |
| } |
| } |
| } |
| } |
| |
| // Accumulate the bias and store the result |
| *out = acc + (*bias); |
| } |
| |
| // 3D convolution for fixed point type |
| template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> |
| void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, |
| int8_t fixed_point_position) |
| { |
| const int half_width_weights = width_weights / 2; |
| const int half_height_weights = height_weights / 2; |
| |
| using namespace fixed_point_arithmetic; |
| using promoted_type = typename fixed_point_arithmetic::traits::promote<T>::type; |
| |
| // Reset accumulator |
| fixed_point<promoted_type> acc(0, fixed_point_position); |
| |
| // Compute a 2D convolution for each IFM and accumulate the result |
| for(int ifm = 0; ifm < depth_in; ++ifm) |
| { |
| // Compute the offset for the input slice |
| const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; |
| |
| // Compute 2D convolution |
| for(int yk = -half_height_weights; yk <= half_height_weights; ++yk) |
| { |
| for(int xk = -half_width_weights; xk <= half_width_weights; ++xk) |
| { |
| // Check if the pixel is out-of-bound |
| if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in)) |
| { |
| const int idx = xk + half_width_weights; |
| const int idy = yk + half_height_weights; |
| |
| const fixed_point<promoted_type> i_value(in[offset_slice_in + xk + yk * width_in], fixed_point_position, true); |
| const fixed_point<promoted_type> w_value(weights[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true); |
| const fixed_point<promoted_type> iw = i_value * w_value; |
| acc = iw + acc; |
| } |
| } |
| } |
| } |
| |
| // Get the bias |
| const fixed_point<promoted_type> b(*bias, fixed_point_position, true); |
| |
| // Accumulate the bias and covert back |
| acc = acc + b; |
| fixed_point<T> res(acc); |
| *out = res.raw(); |
| } |
| |
| template <typename T> |
| void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out, int cols_weights, int rows_weights, uint8_t fixed_point_position) |
| { |
| for(int x = 0; x < cols_weights; ++x) |
| { |
| T acc = 0.0f; |
| for(int y = 0; y < rows_weights; ++y) |
| { |
| acc += in[y] * weights[x + y * cols_weights]; |
| } |
| out[x] = acc + bias[x]; |
| } |
| } |
| |
| template <> |
| void vector_matrix_multiply(const int8_t *in, const int8_t *weights, const int8_t *bias, int8_t *out, int cols_weights, int rows_weights, uint8_t fixed_point_position) |
| { |
| using namespace fixed_point_arithmetic; |
| using promoted_type = typename fixed_point_arithmetic::traits::promote<int8_t>::type; |
| |
| for(int x = 0; x < cols_weights; ++x) |
| { |
| // Reset accumulator |
| fixed_point<promoted_type> acc(0, fixed_point_position); |
| |
| for(int y = 0; y < rows_weights; ++y) |
| { |
| const fixed_point<promoted_type> i_value(in[y], fixed_point_position, true); |
| const fixed_point<promoted_type> w_value(weights[x + y * cols_weights], fixed_point_position, true); |
| const fixed_point<promoted_type> iw = i_value * w_value; |
| acc = iw + acc; |
| } |
| |
| // Get the bias |
| const fixed_point<int8_t> b(bias[x], fixed_point_position, true); |
| |
| // Convert back and accumulate the bias |
| fixed_point<int8_t> res(acc); |
| res = res + b; |
| |
| // Store the result |
| out[x] = res.raw(); |
| } |
| } |
| |
| // Return a tensor element at a specified coordinate with different border modes |
| template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0> |
| 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))); |
| return in[coord2index(in.shape(), coord)]; |
| } |
| else |
| { |
| return constant_border_value; |
| } |
| } |
| else |
| { |
| 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); |
| } |
| } |
| |
| // Mean Standard Deviation |
| template <typename T1> |
| void mean_and_standard_deviation(const Tensor<T1> &in, float &mean, float &std_dev) |
| { |
| int num_elements = in.num_elements(); |
| |
| // Calculate mean |
| mean = 0.f; |
| for(int i = 0; i < num_elements; ++i) |
| { |
| mean += in[i]; |
| } |
| mean /= num_elements; |
| |
| // Calculate standard deviation |
| std_dev = 0.f; |
| for(int i = 0; i < num_elements; ++i) |
| { |
| std_dev += (mean - in[i]) * (mean - in[i]); |
| } |
| std_dev = sqrt(std_dev / num_elements); |
| } |
| |
| // 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 |
| 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); |
| } |
| } |
| |
| // Bitwise and |
| template <typename T, typename = typename std::enable_if<std::is_integral<T>::value>::type> |
| void bitwise_and(const Tensor<T> &in1, const Tensor<T> &in2, Tensor<T> &out) |
| { |
| for(int i = 0; i < in1.num_elements(); ++i) |
| { |
| out[i] = in1[i] & in2[i]; |
| } |
| } |
| |
| // Bitwise or |
| template <typename T, typename = typename std::enable_if<std::is_integral<T>::value>::type> |
| void bitwise_or(const Tensor<T> &in1, const Tensor<T> &in2, Tensor<T> &out) |
| { |
| for(int i = 0; i < in1.num_elements(); ++i) |
| { |
| out[i] = in1[i] | in2[i]; |
| } |
| } |
| |
| // Bitwise xor |
| template <typename T, typename = typename std::enable_if<std::is_integral<T>::value>::type> |
| void bitwise_xor(const Tensor<T> &in1, const Tensor<T> &in2, Tensor<T> &out) |
| { |
| for(int i = 0; i < in1.num_elements(); ++i) |
| { |
| out[i] = in1[i] ^ in2[i]; |
| } |
| } |
| |
| // Bitwise not |
| template <typename T, typename = typename std::enable_if<std::is_integral<T>::value>::type> |
| void bitwise_not(const Tensor<T> &in, Tensor<T> &out) |
| { |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| out[i] = ~in[i]; |
| } |
| } |
| |
| // 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> |
| void depth_convert(const Tensor<T1> &in, Tensor<T2> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| ARM_COMPUTE_ERROR("The conversion is not supported"); |
| } |
| |
| template <> |
| void depth_convert<int8_t, float>(const Tensor<int8_t> &in, Tensor<float> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| const int8_t fixed_point_position = static_cast<int8_t>(in.fixed_point_position()); |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| out[i] = static_cast<float>(in[i]) * (1.0f / (1 << fixed_point_position)); |
| } |
| } |
| |
| template <> |
| void depth_convert<float, int8_t>(const Tensor<float> &in, Tensor<int8_t> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| const int8_t fixed_point_position = static_cast<int8_t>(in.fixed_point_position()); |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| float val = in[i] * (1 << fixed_point_position) + 0.5f; |
| out[i] = ((policy == ConvertPolicy::SATURATE) ? saturate_cast<int8_t>(val) : static_cast<int8_t>(val)); |
| } |
| } |
| |
| template <> |
| void depth_convert<uint8_t, uint16_t>(const Tensor<uint8_t> &in, Tensor<uint16_t> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| out[i] = static_cast<uint16_t>(in[i]) << shift; |
| } |
| } |
| |
| template <> |
| void depth_convert<uint8_t, int16_t>(const Tensor<uint8_t> &in, Tensor<int16_t> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| out[i] = static_cast<int16_t>(in[i]) << shift; |
| } |
| } |
| |
| template <> |
| void depth_convert<uint8_t, int32_t>(const Tensor<uint8_t> &in, Tensor<int32_t> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| out[i] = static_cast<int32_t>(in[i]) << shift; |
| } |
| } |
| |
| template <> |
| void depth_convert<uint16_t, uint8_t>(const Tensor<uint16_t> &in, Tensor<uint8_t> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| uint16_t val = in[i] >> shift; |
| out[i] = ((policy == ConvertPolicy::SATURATE) ? saturate_cast<uint8_t>(val) : static_cast<uint8_t>(val)); |
| } |
| } |
| |
| template <> |
| void depth_convert<uint16_t, uint32_t>(const Tensor<uint16_t> &in, Tensor<uint32_t> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| out[i] = static_cast<uint32_t>(in[i]) << shift; |
| } |
| } |
| |
| template <> |
| void depth_convert<int16_t, uint8_t>(const Tensor<int16_t> &in, Tensor<uint8_t> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| int16_t val = in[i] >> shift; |
| out[i] = ((policy == ConvertPolicy::SATURATE) ? saturate_cast<uint8_t>(val) : static_cast<uint8_t>(val)); |
| } |
| } |
| template <> |
| void depth_convert<int16_t, int32_t>(const Tensor<int16_t> &in, Tensor<int32_t> &out, ConvertPolicy policy, uint32_t shift) |
| { |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| out[i] = static_cast<int32_t>(in[i]) << shift; |
| } |
| } |
| |
| // 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); |
| } |
| } |
| |
| // Matrix multiplication for floating point type |
| template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> |
| void gemm(const Tensor<T> &in1, const Tensor<T> &in2, const Tensor<T> &in3, Tensor<T> &out, float alpha, float beta) |
| { |
| const int M = out.shape().y(); |
| const int N = out.shape().x(); |
| const int K = in1.shape().x(); |
| |
| for(int r = 0; r < M; ++r) |
| { |
| for(int c = 0; c < N; ++c) |
| { |
| T acc = 0.0f; |
| |
| for(int k = 0; k < K; ++k) |
| { |
| const T a0 = in1[r * K + k]; |
| const T b0 = in2[k * N + c]; |
| |
| acc += a0 * b0; |
| } |
| |
| // Finalize the result: A * B * alpha + C * beta |
| const T c0 = in3[c + r * N]; |
| out[c + r * N] = alpha * acc + beta * c0; |
| } |
| } |
| } |
| |
| // Matrix multiplication for fixed point type |
| template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> |
| void gemm(const Tensor<T> &in1, const Tensor<T> &in2, const Tensor<T> &in3, Tensor<T> &out, float alpha, float beta) |
| { |
| using namespace fixed_point_arithmetic; |
| |
| using promoted_type = typename fixed_point_arithmetic::traits::promote<T>::type; |
| |
| const int M = out.shape().y(); |
| const int N = out.shape().x(); |
| const int K = in1.shape().x(); |
| const int8_t fixed_point_position = static_cast<int8_t>(in1.fixed_point_position()); |
| |
| const fixed_point<T> alpha_q(alpha, fixed_point_position); |
| const fixed_point<T> beta_q(beta, fixed_point_position); |
| |
| for(int r = 0; r < M; ++r) |
| { |
| for(int c = 0; c < N; ++c) |
| { |
| fixed_point<promoted_type> acc_q(0, fixed_point_position); |
| |
| for(int k = 0; k < K; ++k) |
| { |
| const fixed_point<promoted_type> a0_q(in1[r * K + k], fixed_point_position, true); |
| const fixed_point<promoted_type> b0_q(in2[k * N + c], fixed_point_position, true); |
| const fixed_point<promoted_type> axb_q = a0_q * b0_q; |
| |
| acc_q = axb_q + acc_q; |
| } |
| |
| // Finalize the result: A * B * alpha + C * beta |
| const fixed_point<T> c0_q(in3[c + r * N], fixed_point_position, true); |
| |
| fixed_point<T> res_q(acc_q); |
| res_q = alpha_q * res_q; |
| res_q = (c0_q * beta_q) + res_q; |
| |
| // Store the result |
| out[c + r * N] = res_q.raw(); |
| } |
| } |
| } |
| |
| // 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, int 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)); |
| |
| fixed_point<T> fp_scale(scale, fixed_point_position); |
| const bool is_sat = convert_policy == ConvertPolicy::SATURATE; |
| const bool do_scaling = scale != 1; |
| |
| for(int i = 0; i < in1.num_elements(); ++i) |
| { |
| fixed_point<T> val1(in1[i], fixed_point_position, true); |
| fixed_point<T> val2(in2[i], fixed_point_position, true); |
| fixed_point<T> res = (is_sat) ? val1 * val2 : mul<OverflowPolicy::WRAP>(val1, val2); |
| if(do_scaling) |
| { |
| res = (is_sat) ? res * fp_scale : mul<OverflowPolicy::WRAP>(res, 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; |
| } |
| } |
| |
| // Activation Layer for floating point type |
| template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> |
| void activation_layer(const Tensor<T> &in, Tensor<T> &out, ActivationLayerInfo act_info) |
| { |
| const T a = static_cast<T>(act_info.a()); |
| const T b = static_cast<T>(act_info.b()); |
| |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| T x = in[i]; |
| switch(act_info.activation()) |
| { |
| case ActivationLayerInfo::ActivationFunction::ABS: |
| out[i] = std::abs(x); |
| break; |
| case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU: |
| out[i] = std::min<T>(a, std::max<T>(0, x)); |
| break; |
| case ActivationLayerInfo::ActivationFunction::LINEAR: |
| out[i] = a * x + b; |
| break; |
| case ActivationLayerInfo::ActivationFunction::LOGISTIC: |
| out[i] = static_cast<T>(1) / (static_cast<T>(1) + std::exp(-x)); |
| break; |
| case ActivationLayerInfo::ActivationFunction::RELU: |
| out[i] = std::max<T>(0, x); |
| break; |
| case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| out[i] = std::log(static_cast<T>(1) + std::exp(x)); |
| break; |
| case ActivationLayerInfo::ActivationFunction::SQRT: |
| out[i] = std::sqrt(x); |
| break; |
| case ActivationLayerInfo::ActivationFunction::SQUARE: |
| out[i] = x * x; |
| break; |
| case ActivationLayerInfo::ActivationFunction::TANH: |
| out[i] = a * std::tanh(b * x); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Activation function not recognised"); |
| break; |
| } |
| } |
| } |
| |
| // Activation Layer for fixed point type |
| template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> |
| void activation_layer(const Tensor<T> &in, Tensor<T> &out, ActivationLayerInfo act_info) |
| { |
| using namespace fixed_point_arithmetic; |
| int fixed_point_position = in.fixed_point_position(); |
| ActivationLayerInfo::ActivationFunction act_func = act_info.activation(); |
| const fixed_point<T> a(act_info.a(), fixed_point_position); |
| const fixed_point<T> b(act_info.b(), fixed_point_position); |
| const fixed_point<T> const_0(0, fixed_point_position); |
| const fixed_point<T> const_1(1, fixed_point_position); |
| |
| for(int i = 0; i < in.num_elements(); ++i) |
| { |
| fixed_point<T> x(in[i], fixed_point_position, true); |
| switch(act_func) |
| { |
| case ActivationLayerInfo::ActivationFunction::ABS: |
| out[i] = abs(x).raw(); |
| break; |
| case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU: |
| out[i] = min(a, max(const_0, x)).raw(); |
| break; |
| case ActivationLayerInfo::ActivationFunction::LINEAR: |
| out[i] = add(b, mul(a, x)).raw(); |
| break; |
| case ActivationLayerInfo::ActivationFunction::LOGISTIC: |
| out[i] = (const_1 / (const_1 + exp(-x))).raw(); |
| break; |
| case ActivationLayerInfo::ActivationFunction::RELU: |
| out[i] = max(const_0, x).raw(); |
| break; |
| case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| out[i] = log(const_1 + exp(x)).raw(); |
| break; |
| case ActivationLayerInfo::ActivationFunction::SQRT: |
| out[i] = (const_1 / inv_sqrt(x)).raw(); |
| break; |
| case ActivationLayerInfo::ActivationFunction::SQUARE: |
| out[i] = mul(x, x).raw(); |
| break; |
| case ActivationLayerInfo::ActivationFunction::TANH: |
| out[i] = tanh(x).raw(); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Activation function not recognised"); |
| break; |
| } |
| } |
| } |
| |
| // 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_qs8(in[pos], fixed_point_position, true); |
| fixed_point_arithmetic::fixed_point<T> var_qs8(var[i], fixed_point_position, true); |
| fixed_point_arithmetic::fixed_point<T> mean_qs8(mean[i], fixed_point_position, true); |
| fixed_point_arithmetic::fixed_point<T> beta_qs8(beta[i], fixed_point_position, true); |
| fixed_point_arithmetic::fixed_point<T> gamma_qs8(gamma[i], fixed_point_position, true); |
| fixed_point_arithmetic::fixed_point<T> epsilon_qs8(epsilon, fixed_point_position); |
| |
| auto denominator = fixed_point_arithmetic::inv_sqrt(var_qs8 + epsilon_qs8); |
| auto numerator = in_qs8 - mean_qs8; |
| auto x_bar = numerator * denominator; |
| x_bar = beta_qs8 + x_bar * gamma_qs8; |
| 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]; |
| } |
| } |
| } |
| } |
| } |
| |
| // Convolution layer |
| template <typename T> |
| void convolution_layer(const Tensor<T> &in, const Tensor<T> &weights, const Tensor<T> &bias, Tensor<T> &out, const PadStrideInfo &conv_info) |
| { |
| const int width_in = in.shape().x(); |
| const int height_in = in.shape().y(); |
| const int depth_in = in.shape().z(); |
| const int width_out = out.shape().x(); |
| const int height_out = out.shape().y(); |
| const int depth_out = out.shape().z(); |
| const int width_weights = weights.shape().x(); |
| const int height_weights = weights.shape().y(); |
| const int depth_weights = weights.shape().z(); |
| const int pad_xi = std::min(static_cast<int>(conv_info.pad().first), width_weights / 2); |
| const int pad_yi = std::min(static_cast<int>(conv_info.pad().second), height_weights / 2); |
| const int start_xi = width_weights / 2 - pad_xi; |
| const int start_yi = height_weights / 2 - pad_yi; |
| const int end_xi = width_in - start_xi; |
| const int end_yi = height_in - start_yi; |
| const int stride_xi = conv_info.stride().first; |
| const int stride_yi = conv_info.stride().second; |
| const int num_batches = in.shape().total_size() / (width_in * height_in * depth_in); |
| |
| for(int r = 0; r < num_batches; ++r) |
| { |
| for(int yi = start_yi; yi < end_yi; yi += stride_yi) |
| { |
| for(int xi = start_xi; xi < end_xi; xi += stride_xi) |
| { |
| for(int ofm = 0; ofm < depth_out; ++ofm) |
| { |
| // Compute input and output offsets |
| const int offset_in = r * width_in * height_in * depth_in; |
| const int xo = (xi - start_xi) / stride_xi; |
| const int yo = (yi - start_yi) / stride_yi; |
| const int offset_out = xo + yo * width_out + ofm * width_out * height_out + r * width_out * height_out * depth_out; |
| |
| // Compute 3D convolution |
| convolution3d(in.data() + offset_in, |
| weights.data() + ofm * width_weights * height_weights * depth_weights, |
| bias.data() + ofm, |
| out.data() + offset_out, |
| xi, yi, |
| width_in, height_in, depth_in, |
| width_weights, height_weights, |
| static_cast<int8_t>(in.fixed_point_position())); |
| } |
| } |
| } |
| } |
| } |
| |
| // Fully connected layer |
| template <typename T> |
| void fully_connected_layer(const Tensor<T> &in, const Tensor<T> &weights, const Tensor<T> &bias, Tensor<T> &out) |
| { |
| ARM_COMPUTE_ERROR_ON(weights.shape().x() != out.shape().x()); |
| ARM_COMPUTE_ERROR_ON(weights.shape().y() != in.shape().x() * in.shape().y() * in.shape().z()); |
| const int cols_weights = weights.shape().x(); |
| const int rows_weights = weights.shape().y(); |
| const int num_batches = in.shape().total_size() / rows_weights; |
| |
| for(int k = 0; k < num_batches; ++k) |
| { |
| vector_matrix_multiply<T>(in.data() + k * rows_weights, |
| weights.data(), |
| bias.data(), |
| out.data() + k * cols_weights, |
| cols_weights, |
| rows_weights, |
| in.fixed_point_position()); |
| } |
| } |
| |
| // Normalization Layer for floating point type |
| template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> |
| void normalization_layer(const Tensor<T> &in, Tensor<T> &out, NormalizationLayerInfo norm_info) |
| { |
| const uint32_t norm_size = norm_info.norm_size(); |
| NormType type = norm_info.type(); |
| float beta = norm_info.beta(); |
| uint32_t kappa = norm_info.kappa(); |
| |
| 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); |
| |
| float coeff = norm_info.scale_coeff(); |
| int radius_cols = norm_size / 2; |
| // IN_MAP_1D and CROSS_MAP normalize over a single axis only |
| int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0; |
| |
| if(type == NormType::CROSS_MAP) |
| { |
| // Remove also depth from upper dimensions since it is the axes we want |
| // to use for normalization |
| upper_dims /= depth; |
| for(int r = 0; r < upper_dims; ++r) |
| { |
| for(int i = 0; i < rows; ++i) |
| { |
| for(int k = 0; k < cols; ++k) |
| { |
| for(int l = 0; l < depth; ++l) |
| { |
| float accumulated_scale = 0.f; |
| for(int j = -radius_cols; j <= radius_cols; ++j) |
| { |
| const int z = l + j; |
| if(z >= 0 && z < depth) |
| { |
| const T value = in[k + i * cols + z * rows * cols + r * cols * rows * depth]; |
| accumulated_scale += value * value; |
| } |
| } |
| out[k + i * cols + l * rows * cols + r * cols * rows * depth] = kappa + accumulated_scale * coeff; |
| } |
| } |
| } |
| } |
| } |
| else |
| { |
| for(int r = 0; r < upper_dims; ++r) |
| { |
| for(int i = 0; i < rows; ++i) |
| { |
| for(int k = 0; k < cols; ++k) |
| { |
| float accumulated_scale = 0.f; |
| for(int j = -radius_rows; j <= radius_rows; ++j) |
| { |
| const int y = i + j; |
| for(int l = -radius_cols; l <= radius_cols; ++l) |
| { |
| const int x = k + l; |
| if((x >= 0 && y >= 0) && (x < cols && y < rows)) |
| { |
| const T value = in[x + y * cols + r * cols * rows]; |
| accumulated_scale += value * value; |
| } |
| } |
| } |
| out[k + i * cols + r * cols * rows] = kappa + accumulated_scale * coeff; |
| } |
| } |
| } |
| } |
| |
| if(beta == 1.f) |
| { |
| for(int i = 0; i < out.num_elements(); ++i) |
| { |
| out[i] = in[i] / out[i]; |
| } |
| } |
| else if(beta == 0.5f) |
| { |
| for(int i = 0; i < out.num_elements(); ++i) |
| { |
| out[i] = in[i] / std::sqrt(out[i]); |
| } |
| } |
| else |
| { |
| for(int i = 0; i < out.num_elements(); ++i) |
| { |
| out[i] = in[i] * std::exp(std::log(out[i]) * -beta); |
| } |
| } |
| } |
| // Normalization Layer for fixed-point types |
| template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> |
| void normalization_layer(const Tensor<T> &in, Tensor<T> &out, NormalizationLayerInfo norm_info) |
| { |
| using namespace fixed_point_arithmetic; |
| |
| const int fixed_point_position = in.fixed_point_position(); |
| |
| const uint32_t norm_size = norm_info.norm_size(); |
| NormType type = norm_info.type(); |
| fixed_point<T> beta(norm_info.beta(), fixed_point_position); |
| fixed_point<T> kappa(norm_info.kappa(), 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); |
| |
| fixed_point<T> coeff(norm_info.scale_coeff(), fixed_point_position); |
| int radius_cols = norm_size / 2; |
| // IN_MAP_1D and CROSS_MAP normalize over a single axis only |
| int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0; |
| |
| if(type == NormType::CROSS_MAP) |
| { |
| // Remove also depth from upper dimensions since it is the axes we want |
| // to use for normalization |
| upper_dims /= depth; |
| for(int r = 0; r < upper_dims; ++r) |
| { |
| for(int i = 0; i < rows; ++i) |
| { |
| for(int k = 0; k < cols; ++k) |
| { |
| for(int l = 0; l < depth; ++l) |
| { |
| fixed_point<T> accumulated_scale(0.f, fixed_point_position); |
| for(int j = -radius_cols; j <= radius_cols; ++j) |
| { |
| const int z = l + j; |
| if(z >= 0 && z < depth) |
| { |
| const T value = in[k + i * cols + z * rows * cols + r * cols * rows * depth]; |
| const fixed_point<T> fp_value(value, fixed_point_position, true); |
| accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value)); |
| } |
| } |
| accumulated_scale = add(kappa, mul(accumulated_scale, coeff)); |
| out[k + i * cols + l * rows * cols + r * cols * rows * depth] = accumulated_scale.raw(); |
| } |
| } |
| } |
| } |
| } |
| else |
| { |
| for(int r = 0; r < upper_dims; ++r) |
| { |
| for(int i = 0; i < rows; ++i) |
| { |
| for(int k = 0; k < cols; ++k) |
| { |
| fixed_point<T> accumulated_scale(0.f, fixed_point_position); |
| for(int j = -radius_rows; j <= radius_rows; ++j) |
| { |
| const int y = i + j; |
| for(int l = -radius_cols; l <= radius_cols; ++l) |
| { |
| const int x = k + l; |
| if((x >= 0 && y >= 0) && (x < cols && y < rows)) |
| { |
| const T value = in[x + y * cols + r * cols * rows]; |
| const fixed_point<T> fp_value(value, fixed_point_position, true); |
| accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value)); |
| } |
| } |
| } |
| accumulated_scale = add(kappa, mul(accumulated_scale, coeff)); |
| out[k + i * cols + r * cols * rows] = accumulated_scale.raw(); |
| } |
| } |
| } |
| } |
| |
| if(norm_info.beta() == 1.f) |
| { |
| for(int i = 0; i < out.num_elements(); ++i) |
| { |
| fixed_point<T> res = div(fixed_point<T>(in[i], fixed_point_position, true), fixed_point<T>(out[i], fixed_point_position, true)); |
| out[i] = res.raw(); |
| } |
| } |
| else |
| { |
| const fixed_point<T> beta(norm_info.beta(), fixed_point_position); |
| for(int i = 0; i < out.num_elements(); ++i) |
| { |
| fixed_point<T> res = pow(fixed_point<T>(out[i], fixed_point_position, true), beta); |
| res = div(fixed_point<T>(in[i], fixed_point_position, true), res); |
| out[i] = res.raw(); |
| } |
| } |
| } |
| |
| // Pooling layer |
| template <typename T> |
| void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_info, int fixed_point_position) |
| { |
| const int pool_size = pool_info.pool_size(); |
| PoolingType type = pool_info.pool_type(); |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| int pad_x = 0; |
| int pad_y = 0; |
| std::tie(pool_stride_x, pool_stride_y) = pool_info.pad_stride_info().stride(); |
| std::tie(pad_x, pad_y) = pool_info.pad_stride_info().pad(); |
| |
| const int w_in = static_cast<int>(in.shape()[0]); |
| const int h_in = static_cast<int>(in.shape()[1]); |
| |
| const int w_out = static_cast<int>(out.shape()[0]); |
| const int h_out = static_cast<int>(out.shape()[1]); |
| |
| int upper_dims = in.shape().total_size() / (w_in * h_in); |
| |
| int pooled_w = 0; |
| int pooled_h = 0; |
| if(pool_info.pad_stride_info().round() == DimensionRoundingType::CEIL) |
| { |
| pooled_w = static_cast<int>(ceil(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1; |
| pooled_h = static_cast<int>(ceil(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1; |
| } |
| else |
| { |
| pooled_w = static_cast<int>(floor(static_cast<float>(w_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1; |
| pooled_h = static_cast<int>(floor(static_cast<float>(h_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1; |
| } |
| |
| if((pooled_w - 1) * pool_stride_x >= w_in + pad_x) |
| { |
| --pooled_w; |
| } |
| if((pooled_h - 1) * pool_stride_y >= h_in + pad_y) |
| { |
| --pooled_h; |
| } |
| |
| if(type == PoolingType::MAX) |
| { |
| for(int r = 0; r < upper_dims; ++r) |
| { |
| for(int h = 0; h < pooled_h; ++h) |
| { |
| for(int w = 0; w < pooled_w; ++w) |
| { |
| int wstart = w * pool_stride_x - pad_x; |
| int hstart = h * pool_stride_y - pad_y; |
| int wend = std::min(wstart + pool_size, w_in); |
| int hend = std::min(hstart + pool_size, h_in); |
| wstart = std::max(wstart, 0); |
| hstart = std::max(hstart, 0); |
| |
| T max_val = std::numeric_limits<T>::lowest(); |
| for(int y = hstart; y < hend; ++y) |
| { |
| for(int x = wstart; x < wend; ++x) |
| { |
| T val = in[r * h_in * w_in + y * w_in + x]; |
| if(val > max_val) |
| { |
| max_val = val; |
| } |
| } |
| } |
| |
| out[r * h_out * w_out + h * pooled_w + w] = max_val; |
| } |
| } |
| } |
| } |
| else // Average pooling |
| { |
| for(int r = 0; r < upper_dims; ++r) |
| { |
| for(int h = 0; h < pooled_h; ++h) |
| { |
| for(int w = 0; w < pooled_w; ++w) |
| { |
| T avg_val = 0; |
| int wstart = w * pool_stride_x - pad_x; |
| int hstart = h * pool_stride_y - pad_y; |
| int wend = std::min(wstart + pool_size, w_in + pad_x); |
| int hend = std::min(hstart + pool_size, h_in + pad_y); |
| int pool = (hend - hstart) * (wend - wstart); |
| wstart = std::max(wstart, 0); |
| hstart = std::max(hstart, 0); |
| wend = std::min(wend, w_in); |
| hend = std::min(hend, h_in); |
| if(is_floating_point<T>::value) |
| { |
| for(int y = hstart; y < hend; ++y) |
| { |
| for(int x = wstart; x < wend; ++x) |
| { |
| avg_val += in[r * h_in * w_in + y * w_in + x]; |
| } |
| } |
| out[r * h_out * w_out + h * pooled_w + w] = avg_val / pool; |
| } |
| else |
| { |
| static std::array<qint8_t, 10> scale_values_q8 = |
| { { 0x0, 0x0, 0x40, 0x2A, 0x20, 0x19, 0x15, 0x12, 0x10, 0xE } }; |
| |
| for(int y = hstart; y < hend; ++y) |
| { |
| for(int x = wstart; x < wend; ++x) |
| { |
| avg_val = sqadd_qs8(avg_val, in[r * h_in * w_in + y * w_in + x]); |
| } |
| } |
| out[r * h_out * w_out + h * pooled_w + w] = sqmul_qs8(avg_val, (scale_values_q8[pool] >> (7 - fixed_point_position)), fixed_point_position); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| // Softmax Layer |
| template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> |
| void softmax_layer(const Tensor<T> &in, Tensor<T> &out) |
| { |
| const int cols = static_cast<int>(in.shape()[0]); |
| const int upper_dims = in.shape().total_size() / cols; |
| for(int r = 0; r < upper_dims; ++r) |
| { |
| // Find max |
| T max = std::numeric_limits<T>::lowest(); |
| for(int c = 0; c < cols; ++c) |
| { |
| const T x = in[r * cols + c]; |
| if(x > max) |
| { |
| max = x; |
| } |
| } |
| |
| // Regularize |
| T sum = 0; |
| for(int c = 0; c < cols; ++c) |
| { |
| const T res = exp(in[r * cols + c] - max); |
| out[r * cols + c] = res; |
| sum += res; |
| } |
| |
| // Normalize |
| const T norm_val = 1 / sum; |
| for(int c = 0; c < cols; ++c) |
| { |
| out[r * cols + c] *= norm_val; |
| } |
| } |
| } |
| template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr> |
| void softmax_layer(const Tensor<T> &in, Tensor<T> &out) |
| { |
| using namespace fixed_point_arithmetic; |
| using promoted_T = typename test::traits::promote<T>::type; |
| |
| const int fixed_point_position = in.fixed_point_position(); |
| const int cols = static_cast<int>(in.shape()[0]); |
| const int upper_dims = in.shape().total_size() / cols; |
| |
| for(int r = 0; r < upper_dims; ++r) |
| { |
| // Find max |
| fixed_point<T> max(std::numeric_limits<T>::lowest(), fixed_point_position, true); |
| for(int c = 0; c < cols; ++c) |
| { |
| const fixed_point<T> x(in[r * cols + c], fixed_point_position, true); |
| if(x > max) |
| { |
| max = x; |
| } |
| } |
| |
| // Regularize |
| fixed_point<promoted_T> sum(0, fixed_point_position); |
| for(int c = 0; c < cols; ++c) |
| { |
| const fixed_point<T> x(in[r * cols + c], fixed_point_position, true); |
| fixed_point<T> res = exp(x - max); |
| out[r * cols + c] = res.raw(); |
| sum = add(sum, static_cast<fixed_point<promoted_T>>(res)); |
| } |
| |
| // Normalize |
| fixed_point<T> sat_sum(sum); |
| for(int c = 0; c < cols; ++c) |
| { |
| const fixed_point<T> x(out[r * cols + c], fixed_point_position, true); |
| out[r * cols + c] = div(x, sat_sum).raw(); |
| } |
| } |
| } |
| |
| // 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__ */ |