| /* |
| * 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/FixedPoint.h" |
| #include "tests/validation/Helpers.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| template <typename T> |
| SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a) |
| { |
| // Create reference |
| TensorShape scaled_shape = src.shape(); |
| scaled_shape.set(0, output_shape.x()); |
| scaled_shape.set(1, output_shape.y()); |
| SimpleTensor<T> scaled{ scaled_shape, src.data_type(), 1, src.fixed_point_position() }; |
| |
| const int width_in = src.shape().x(); |
| const int height_in = src.shape().y(); |
| const int width_scaled = scaled.shape().x(); |
| const int height_scaled = scaled.shape().y(); |
| const int num_2d_slices = src.shape().total_size() / (width_in * height_in); |
| const float width_ratio = static_cast<float>(width_in) / static_cast<float>(width_scaled); |
| const float height_ratio = static_cast<float>(height_in) / static_cast<float>(height_scaled); |
| const int ax = a.first; // The number of zeros added to right edge of the input. |
| const int ay = a.second; // The number of zeros added to bottom edge of the input. |
| const unsigned int kernel_size = weights.shape().x(); |
| ARM_COMPUTE_ERROR_ON(info.pad().first > (kernel_size - 1)); |
| const int transposed_convolution_padx = kernel_size - info.pad().first - 1; |
| const int transposed_convolution_pady = kernel_size - info.pad().second - 1; |
| const int stridex = info.stride().first; |
| const int stridey = info.stride().second; |
| for(int j = 0; j < scaled.num_elements(); ++j) |
| { |
| scaled[j] = T(0); |
| } |
| |
| for(int slice = 0; slice < num_2d_slices; ++slice) |
| { |
| const int offset_slice_in = slice * width_in * height_in; |
| const int offset_slice_out = slice * width_scaled * height_scaled; |
| for(int yi = ay; yi < height_scaled; yi += stridey) |
| { |
| for(int xi = transposed_convolution_padx; xi < width_scaled; xi += stridex) |
| { |
| const float x_src = (xi + 0.5f) * width_ratio - 0.5f; |
| const float y_src = (yi + 0.5f) * height_ratio - 0.5f; |
| T *out = scaled.data() + offset_slice_out + xi + yi * width_scaled; |
| const bool in_bounds = x_src > -1 && y_src > -1 && x_src < width_in && y_src < height_in; |
| const bool in_axy = xi < transposed_convolution_padx || xi >= (width_scaled - ax) // this is checking if the x coordinate is in the padded left/right area |
| || yi < ay || yi >= (height_scaled - transposed_convolution_pady); // like above but top and bottom padding in the upscaled XY plane |
| if(!in_axy) |
| { |
| if(in_bounds) |
| { |
| const int in_scaled_x = (x_src < 0.f) ? static_cast<int>(x_src - 0.5f) : static_cast<int>(x_src + 0.5f); |
| const int in_scaled_y = (y_src < 0.f) ? static_cast<int>(y_src - 0.5f) : static_cast<int>(y_src + 0.5f); |
| const T *in = src.data() + offset_slice_in + in_scaled_x + in_scaled_y * width_in; |
| *out = *in; |
| } |
| else |
| { |
| *out = T(0); |
| } |
| } |
| } |
| } |
| } |
| const PadStrideInfo conv_info(1, 1, 1, 1, DimensionRoundingType::CEIL); |
| return convolution_layer(scaled, weights, bias, output_shape, conv_info); |
| } |
| |
| template SimpleTensor<float> deconvolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |