| /* |
| * 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" |
| #include "tests/validation/reference/Convolution3d.h" |
| #include "tests/validation/reference/Permute.h" |
| #include "tests/validation/reference/Utils.h" |
| #include "tests/validation/reference/UtilsQuantizedAsymm.h" |
| |
| #include "tests/framework/Asserts.h" |
| |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| template <typename T, typename TW, typename TB> |
| SimpleTensor<T> convolution_layer_nchw(const SimpleTensor<T> &src, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, const PadStrideInfo &info, |
| const Size2D &dilation, unsigned int num_groups) |
| { |
| ARM_COMPUTE_ERROR_ON((src.shape()[2] / num_groups) != weights.shape()[2]); |
| |
| // 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_left = info.pad_left(); |
| const int pad_top = info.pad_top(); |
| const int stride_xi = info.stride().first; |
| const int stride_yi = info.stride().second; |
| |
| auto output_wh = scaled_dimensions(width_in, height_in, width_weights, height_weights, info, dilation); |
| |
| const int start_xi = (dilation.x() * (width_weights - 1) + 1) / 2 - pad_left; |
| const int start_yi = (dilation.y() * (height_weights - 1) + 1) / 2 - pad_top; |
| const int end_xi = output_wh.first * stride_xi; |
| const int end_yi = output_wh.second * stride_yi; |
| 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 < start_yi + end_yi; yi += stride_yi) |
| { |
| for(int xi = start_xi; xi < start_xi + end_xi; xi += stride_xi) |
| { |
| for(int group = 0; group < static_cast<int>(num_groups); ++group) |
| { |
| for(int ofm = 0; ofm < static_cast<int>(depth_out / num_groups); ++ofm) |
| { |
| // Compute input and output offsets |
| const int offset_in = r * width_in * height_in * depth_in + (group * (depth_in / num_groups) * width_in * height_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 + group * (depth_out / num_groups)) * width_out * height_out) + (r * width_out * height_out * depth_out); |
| const int offset_w = (ofm + group * (depth_out / num_groups)) * width_weights * height_weights * depth_weights; |
| const int offset_b = (ofm + group * (depth_out / num_groups)); |
| |
| ARM_COMPUTE_ASSERT(xo < width_out); |
| ARM_COMPUTE_ASSERT(yo < height_out); |
| |
| // Compute 3D convolution |
| convolution_3d::detail::convolution3d(src, weights, bias, dst, |
| offset_in, offset_w, offset_b, offset_out, |
| xi, yi, |
| width_in, height_in, (depth_in / num_groups), |
| width_weights, height_weights, dilation.x(), dilation.y(), ofm); |
| } |
| } |
| } |
| } |
| } |
| return dst; |
| } |
| template <typename T, typename TW, typename TB> |
| SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<TW> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info, |
| const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info) |
| { |
| // if no explicit quantization has been set you the same as src |
| if(out_quant_info == QuantizationInfo()) |
| { |
| out_quant_info = src.quantization_info(); |
| } |
| // Create reference |
| SimpleTensor<T> dst{ output_shape, src.data_type(), 1, out_quant_info }; |
| |
| if(src.data_layout() == DataLayout::NHWC) |
| { |
| SimpleTensor<T> src_nchw = reference::permute<T>(src, PermutationVector(1U, 2U, 0U)); |
| SimpleTensor<TW> weights_nchw = reference::permute<TW>(weights, PermutationVector(1U, 2U, 0U)); |
| SimpleTensor<T> dst_nchw = reference::permute<T>(dst, PermutationVector(1U, 2U, 0U)); |
| |
| return reference::permute<T>(convolution_layer_nchw(src_nchw, weights_nchw, bias, dst_nchw, info, dilation, num_groups), PermutationVector(2U, 0U, 1U)); |
| } |
| else |
| { |
| return convolution_layer_nchw(src, weights, bias, dst, info, dilation, num_groups); |
| } |
| } |
| |
| template SimpleTensor<float> convolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info); |
| template SimpleTensor<half> convolution_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info); |
| 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, |
| const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info); |
| 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, |
| const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info); |
| 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, |
| const PadStrideInfo &info, const Size2D &dilation, unsigned int num_groups, QuantizationInfo out_quant_info); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |