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Diffstat (limited to 'tests/validation_new/CPP/ConvolutionLayer.cpp')
| -rw-r--r-- | tests/validation_new/CPP/ConvolutionLayer.cpp | 205 |
1 files changed, 0 insertions, 205 deletions
diff --git a/tests/validation_new/CPP/ConvolutionLayer.cpp b/tests/validation_new/CPP/ConvolutionLayer.cpp deleted file mode 100644 index a24621a3f2..0000000000 --- a/tests/validation_new/CPP/ConvolutionLayer.cpp +++ /dev/null @@ -1,205 +0,0 @@ -/* - * Copyright (c) 2017 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "ConvolutionLayer.h" - -#include "tests/validation_new/FixedPoint.h" -#include "tests/validation_new/Helpers.h" -#include "tests/validation_new/half.h" - -namespace arm_compute -{ -namespace test -{ -namespace validation -{ -namespace reference -{ -namespace -{ -inline 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 = 0> -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, int fixed_point_position) -{ - ARM_COMPUTE_UNUSED(fixed_point_position); - - const int half_width_weights = width_weights / 2; - const int half_height_weights = height_weights / 2; - - // Reset accumulator - T acc(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 = 0> -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, - int 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 = fixed_point_arithmetic::traits::promote_t<T>; - - // 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(); -} -} // namespace - -template <typename T> -SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &output_shape, const PadStrideInfo &info) -{ - // Create reference - SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position() }; - - // Compute reference - const int width_in = src.shape().x(); - const int height_in = src.shape().y(); - const int depth_in = src.shape().z(); - const int width_out = dst.shape().x(); - const int height_out = dst.shape().y(); - const int depth_out = dst.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>(info.pad().first), width_weights / 2); - const int pad_yi = std::min(static_cast<int>(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 = info.stride().first; - const int stride_yi = info.stride().second; - const int num_batches = src.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(src.data() + offset_in, - weights.data() + ofm * width_weights * height_weights * depth_weights, - bias.data() + ofm, - dst.data() + offset_out, - xi, yi, - width_in, height_in, depth_in, - width_weights, height_weights, - src.fixed_point_position()); - } - } - } - } - - return dst; -} - -template SimpleTensor<float> convolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape, - const PadStrideInfo &info); -template SimpleTensor<half_float::half> convolution_layer(const SimpleTensor<half_float::half> &src, const SimpleTensor<half_float::half> &weights, const SimpleTensor<half_float::half> &bias, - const TensorShape &output_shape, const PadStrideInfo &info); -template SimpleTensor<qint8_t> convolution_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &output_shape, - const PadStrideInfo &info); -template SimpleTensor<qint16_t> convolution_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &output_shape, - const PadStrideInfo &info); -} // namespace reference -} // namespace validation -} // namespace test -} // namespace arm_compute |