COMPMID-344 Updated doxygen
Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae
diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h
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+++ b/tests/validation/TensorOperations.h
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+/*
+ * 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 "FixedPoint.h"
+#include "Types.h"
+#include "arm_compute/core/FixedPoint.h"
+#include "arm_compute/core/Types.h"
+#include "tests/validation/FixedPoint.h"
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace tensor_operations
+{
+namespace
+{
+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<std::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();
+ }
+}
+
+/** Apply 2D spatial filter on a single element of @p in at coordinates @p coord
+ *
+ * - filter sizes have to be odd number
+ * - Valid region assumed
+ * - 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)
+{
+ using intermediate_type = typename common_promoted_signed_type<T1, T2, T3>::intermediate_type;
+ intermediate_type val = 0;
+ int x = coord.x();
+ int y = coord.y();
+ for(size_t j = y - filter_shape[1] / 2; j <= y + filter_shape[1] / 2; ++j)
+ {
+ for(size_t i = x - filter_shape[0] / 2; i <= x + filter_shape[0] / 2; ++i)
+ {
+ coord.set(0, i);
+ coord.set(1, j);
+ val += static_cast<intermediate_type>(*filter_itr) * static_cast<intermediate_type>(in[coord2index(in.shape(), coord)]);
+ ++filter_itr;
+ }
+ }
+ coord.set(0, x);
+ coord.set(1, y);
+ double rounded_val = cpp11::trunc(val * static_cast<double>(scale));
+ out[coord2index(in.shape(), coord)] = saturate_cast<T3>(rounded_val);
+}
+} // namespace
+
+// 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];
+ }
+}
+
+// 3-by-3 box filter
+template <typename T, typename = typename std::enable_if<std::is_integral<T>::value>::type>
+void box3x3(const Tensor<T> &in, Tensor<T> &out)
+{
+ const std::array<T, 9> filter{ { 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
+ float scale = 1.f / static_cast<float>(filter.size());
+ const ValidRegion valid_region = shape_to_valid_region_undefined_border(in.shape(), BorderSize(1));
+ for(int element_idx = 0; element_idx < in.num_elements(); ++element_idx)
+ {
+ const Coordinates id = index2coord(in.shape(), element_idx);
+ if(is_in_valid_region(valid_region, id))
+ {
+ apply_2d_spatial_filter(id, in, out, TensorShape(3U, 3U), filter.data(), scale);
+ }
+ }
+}
+
+// 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;
+ }
+}
+
+// Matrix multiplication for floating point type
+template <typename T, typename std::enable_if<std::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();
+ }
+ }
+}
+
+// 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(std::is_floating_point<T3>::value)
+ {
+ out[i] = val;
+ }
+ else
+ {
+ double rounded_val = 0;
+ switch(rounding_policy)
+ {
+ case(RoundingPolicy::TO_ZERO):
+ rounded_val = cpp11::trunc(val);
+ break;
+ case(RoundingPolicy::TO_NEAREST_UP):
+ rounded_val = cpp11::round_half_up(val);
+ break;
+ case(RoundingPolicy::TO_NEAREST_EVEN):
+ rounded_val = cpp11::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<std::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<std::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<std::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 cols_in = static_cast<int>(in.shape()[0]);
+ const int rows_in = static_cast<int>(in.shape()[1]);
+
+ const int cols_out = static_cast<int>(out.shape()[0]);
+ const int rows_out = static_cast<int>(out.shape()[1]);
+
+ int upper_dims = in.shape().total_size() / (cols_in * rows_in);
+
+ int pooled_height = static_cast<int>(ceil(static_cast<float>(rows_in + 2 * pad_x - pool_size) / pool_stride_x)) + 1;
+ int pooled_width = static_cast<int>(ceil(static_cast<float>(cols_in + 2 * pad_y - pool_size) / pool_stride_y)) + 1;
+
+ if((pooled_height - 1) * pool_stride_x >= rows_in + pad_x)
+ {
+ --pooled_height;
+ }
+ if((pooled_width - 1) * pool_stride_y >= cols_in + pad_y)
+ {
+ --pooled_width;
+ }
+
+ if(type == PoolingType::MAX)
+ {
+ for(int r = 0; r < upper_dims; ++r)
+ {
+ for(int i = 0; i < pooled_height; ++i)
+ {
+ for(int k = 0; k < pooled_width; ++k)
+ {
+ int hstart = i * pool_stride_x - pad_x;
+ int wstart = k * pool_stride_y - pad_y;
+ int hend = std::min(hstart + pool_size, rows_in);
+ int wend = std::min(wstart + pool_size, cols_in);
+ hstart = std::max(hstart, 0);
+ wstart = std::max(wstart, 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 * cols_in * rows_in + y * cols_in + x];
+ if(val > max_val)
+ {
+ max_val = val;
+ }
+ }
+ }
+
+ out[r * rows_out * cols_out + i * pooled_width + k] = max_val;
+ }
+ }
+ }
+ }
+ else // Average pooling
+ {
+ for(int r = 0; r < upper_dims; ++r)
+ {
+ for(int i = 0; i < pooled_height; ++i)
+ {
+ for(int k = 0; k < pooled_width; ++k)
+ {
+ T avg_val = 0;
+
+ int hstart = i * pool_stride_x - pad_x;
+ int wstart = k * pool_stride_y - pad_y;
+ int hend = std::min(hstart + pool_size, cols_in + pad_x);
+ int wend = std::min(wstart + pool_size, rows_in + pad_y);
+ int pool = (hend - hstart) * (wend - wstart);
+ hstart = std::max(hstart, 0);
+ wstart = std::max(wstart, 0);
+ hend = std::min(hend, rows_in);
+ wend = std::min(wend, cols_in);
+
+ if(std::is_floating_point<T>::value)
+ {
+ for(int y = hstart; y < hend; ++y)
+ {
+ for(int x = wstart; x < wend; ++x)
+ {
+ avg_val += in[r * cols_in * rows_in + y * cols_in + x];
+ }
+ }
+ out[r * rows_out * cols_out + i * pooled_width + k] = 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 * cols_in * rows_in + y * cols_in + x]);
+ }
+ }
+ out[r * rows_out * cols_out + i * pooled_width + k] = sqmul_qs8(avg_val, (scale_values_q8[pool] >> (7 - fixed_point_position)), fixed_point_position);
+ }
+ }
+ }
+ }
+ }
+}
+
+// Softmax Layer
+template <typename T, typename std::enable_if<std::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__ */