| /* |
| * Copyright (c) 2017-2019 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/Helpers.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| template <typename T, typename TB> |
| SimpleTensor<T> deconvolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info, QuantizationInfo out_qinfo) |
| { |
| // Create reference |
| const unsigned int pad_left = info.pad_left(); |
| const unsigned int pad_right = info.pad_right(); |
| const unsigned int pad_top = info.pad_top(); |
| const unsigned int pad_bottom = info.pad_bottom(); |
| const int stride_x = info.stride().first; |
| const int stride_y = info.stride().second; |
| const int weights_width = weights.shape().x(); |
| const int weights_height = weights.shape().y(); |
| const int weights_upper_dims = weights.shape().total_size() / (weights_width * weights_height); |
| |
| ARM_COMPUTE_ERROR_ON(pad_left > (weights.shape().x() - 1)); |
| ARM_COMPUTE_ERROR_ON(pad_right > (weights.shape().x() - 1)); |
| ARM_COMPUTE_ERROR_ON(pad_top > (weights.shape().y() - 1)); |
| ARM_COMPUTE_ERROR_ON(pad_bottom > (weights.shape().y() - 1)); |
| |
| // Find the upsampled dimensions |
| unsigned int out_x = (src.shape().x() - 1) * stride_x + 1; |
| unsigned int out_y = (src.shape().y() - 1) * stride_y + 1; |
| |
| // Find the padding needed for the convolution with stride 1 in order to match output shape |
| unsigned int deconv_pad_x = output_shape.x() - (out_x - weights_width + 1); |
| unsigned int deconv_pad_y = output_shape.y() - (out_y - weights_height + 1); |
| out_x += deconv_pad_x; |
| out_y += deconv_pad_y; |
| |
| unsigned int deconv_pad_left = pad_right > pad_left ? pad_right - pad_left : 0; |
| unsigned int deconv_pad_right = pad_left > pad_right ? pad_left - pad_right : 0; |
| deconv_pad_x -= deconv_pad_left + deconv_pad_right; |
| ARM_COMPUTE_ERROR_ON((deconv_pad_x % 2) != 0); |
| deconv_pad_left += deconv_pad_x / 2; |
| deconv_pad_right += deconv_pad_x / 2; |
| |
| unsigned int deconv_pad_top = pad_bottom > pad_top ? pad_bottom - pad_top : 0; |
| unsigned int deconv_pad_bottom = pad_top > pad_bottom ? pad_top - pad_bottom : 0; |
| deconv_pad_y -= deconv_pad_top + deconv_pad_bottom; |
| ARM_COMPUTE_ERROR_ON((deconv_pad_y % 2) != 0); |
| deconv_pad_top += deconv_pad_y / 2; |
| deconv_pad_bottom += deconv_pad_y / 2; |
| |
| TensorShape scaled_shape = src.shape(); |
| scaled_shape.set(0, out_x); |
| scaled_shape.set(1, out_y); |
| SimpleTensor<T> scaled{ scaled_shape, src.data_type(), 1, src.quantization_info() }; |
| |
| 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); |
| |
| if(src.data_type() == DataType::QASYMM8) |
| { |
| const uint8_t quantized_zero = src.quantization_info().uniform().offset; |
| std::fill_n(scaled.data(), scaled.num_elements(), quantized_zero); |
| } |
| else |
| { |
| std::fill_n(scaled.data(), scaled.num_elements(), T(0)); |
| } |
| |
| // Flip weights by 180 degrees |
| SimpleTensor<T> weights_flipped{ weights.shape(), weights.data_type(), 1, weights.quantization_info() }; |
| for(int ud = 0; ud < weights_upper_dims; ++ud) |
| { |
| const int offset = ud * weights_width * weights_height; |
| for(int y = 0; y < weights_height; ++y) |
| { |
| for(int x = 0; x < weights_width; ++x) |
| { |
| weights_flipped[offset + (weights_height - 1 - y) * weights_width + (weights_width - 1 - x)] = weights[offset + y * weights_width + x]; |
| } |
| } |
| } |
| |
| 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; |
| const int start_x = deconv_pad_left; |
| const int start_y = deconv_pad_top; |
| const int end_x = width_scaled - deconv_pad_right; |
| const int end_y = height_scaled - deconv_pad_bottom; |
| |
| for(int yi = start_y, in_y = 0; yi < end_y; yi += stride_y, in_y++) |
| { |
| for(int xi = start_x, in_x = 0; xi < end_x; xi += stride_x, in_x++) |
| { |
| const T *in = src.data() + offset_slice_in + in_y * width_in + in_x; |
| T *out = scaled.data() + offset_slice_out + xi + yi * width_scaled; |
| *out = *in; |
| } |
| } |
| } |
| |
| const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); |
| return convolution_layer(scaled, weights_flipped, bias, output_shape, conv_info, Size2D(1U, 1U), 1, out_qinfo); |
| } |
| |
| template SimpleTensor<uint8_t> deconvolution_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info, QuantizationInfo out_quant_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, QuantizationInfo out_quant_info); |
| template SimpleTensor<half> deconvolution_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info, QuantizationInfo out_quant_info); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |