/* * Copyright (c) 2017-2020 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 SimpleTensor deconvolution_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &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 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 || src.data_type() == DataType::QASYMM8_SIGNED) { const auto quantized_zero = static_cast(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 weights_flipped{ weights.shape(), weights.data_type(), 1, weights.quantization_info() }; #if defined(_OPENMP) #pragma omp parallel for #endif /* _OPENMP */ 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]; } } } #if defined(_OPENMP) #pragma omp parallel for #endif /* _OPENMP */ 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 deconvolution_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &output_shape, const PadStrideInfo &info, QuantizationInfo out_quant_info); template SimpleTensor deconvolution_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &output_shape, const PadStrideInfo &info, QuantizationInfo out_quant_info); template SimpleTensor deconvolution_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &output_shape, const PadStrideInfo &info, QuantizationInfo out_quant_info); template SimpleTensor deconvolution_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &output_shape, const PadStrideInfo &info, QuantizationInfo out_quant_info); } // namespace reference } // namespace validation } // namespace test } // namespace arm_compute