139 lines
7.6 KiB
C++
139 lines
7.6 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "ConvolutionLayer.h"
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#include "tests/validation/Helpers.h"
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#include "tests/validation/reference/Convolution3d.h"
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#include "tests/validation/reference/Permute.h"
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#include "tests/validation/reference/Utils.h"
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#include "tests/validation/reference/UtilsQuantizedAsymm.h"
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#include "tests/framework/Asserts.h"
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#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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template <typename T, typename TW, typename TB>
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SimpleTensor<T> convolution_layer_nchw(const SimpleTensor<T> &src, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, const PadStrideInfo &info,
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const Size2D &dilation, unsigned int num_groups)
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{
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ARM_COMPUTE_ERROR_ON((src.shape()[2] / num_groups) != weights.shape()[2]);
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// Compute reference
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const int width_in = src.shape().x();
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const int height_in = src.shape().y();
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const int depth_in = src.shape().z();
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const int width_out = dst.shape().x();
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const int height_out = dst.shape().y();
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const int depth_out = dst.shape().z();
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const int width_weights = weights.shape().x();
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const int height_weights = weights.shape().y();
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const int depth_weights = weights.shape().z();
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const int pad_left = info.pad_left();
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const int pad_top = info.pad_top();
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const int stride_xi = info.stride().first;
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const int stride_yi = info.stride().second;
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auto output_wh = scaled_dimensions(width_in, height_in, width_weights, height_weights, info, dilation);
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const int start_xi = (dilation.x() * (width_weights - 1) + 1) / 2 - pad_left;
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const int start_yi = (dilation.y() * (height_weights - 1) + 1) / 2 - pad_top;
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const int end_xi = output_wh.first * stride_xi;
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const int end_yi = output_wh.second * stride_yi;
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const int num_batches = src.shape().total_size() / (width_in * height_in * depth_in);
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#if defined(_OPENMP) && !( defined(__arm__) && defined(__ANDROID__))
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#pragma omp parallel for collapse(5)
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#endif /* _OPENMP */
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for(int r = 0; r < num_batches; ++r)
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{
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for(int yi = start_yi; yi < start_yi + end_yi; yi += stride_yi)
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{
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for(int xi = start_xi; xi < start_xi + end_xi; xi += stride_xi)
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{
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for(int group = 0; group < static_cast<int>(num_groups); ++group)
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{
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for(int ofm = 0; ofm < static_cast<int>(depth_out / num_groups); ++ofm)
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{
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// Compute input and output offsets
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const int offset_in = r * width_in * height_in * depth_in + (group * (depth_in / num_groups) * width_in * height_in);
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const int xo = (xi - start_xi) / stride_xi;
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const int yo = (yi - start_yi) / stride_yi;
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const int offset_out = xo + yo * width_out + ((ofm + group * (depth_out / num_groups)) * width_out * height_out) + (r * width_out * height_out * depth_out);
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const int offset_w = (ofm + group * (depth_out / num_groups)) * width_weights * height_weights * depth_weights;
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const int offset_b = (ofm + group * (depth_out / num_groups));
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ARM_COMPUTE_ASSERT(xo < width_out);
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ARM_COMPUTE_ASSERT(yo < height_out);
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// Compute 3D convolution
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convolution_3d::detail::convolution3d(src, weights, bias, dst,
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offset_in, offset_w, offset_b, offset_out,
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xi, yi,
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width_in, height_in, (depth_in / num_groups),
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width_weights, height_weights, dilation.x(), dilation.y(), ofm);
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}
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}
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}
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}
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}
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return dst;
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}
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template <typename T, typename TW, typename TB>
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SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info,
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const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info)
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{
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// if no explicit quantization has been set you the same as src
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if(out_quant_info == QuantizationInfo())
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{
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out_quant_info = src.quantization_info();
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}
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// Create reference
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SimpleTensor<T> dst{ output_shape, src.data_type(), 1, out_quant_info };
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return convolution_layer_nchw(src, weights, bias, dst, info, dilation, num_groups);
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}
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template SimpleTensor<float> convolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape,
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const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info);
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template SimpleTensor<half> convolution_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, const TensorShape &output_shape,
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const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info);
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template SimpleTensor<uint8_t> convolution_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &output_shape,
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const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info);
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template SimpleTensor<uint8_t> convolution_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<int8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &output_shape,
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const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info);
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template SimpleTensor<int8_t> convolution_layer(const SimpleTensor<int8_t> &src, const SimpleTensor<int8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &output_shape,
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const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info);
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} // namespace reference
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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