/* * Copyright (c) 2016, 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 "helpers.h" #if STRIDE_X == 2 #define CONVOLVE1x3(left_pixel_position, left_coeff, middle_coeff, right_coeff) convolution1x3_stride2(left_pixel_position, left_coeff, middle_coeff, right_coeff) #elif STRIDE_X == 1 /* STRIDE_X == 1 */ #define CONVOLVE1x3(left_pixel_position, left_coeff, middle_coeff, right_coeff) convolution1x3_stride1(left_pixel_position, left_coeff, middle_coeff, right_coeff) #else /* STRIDE_X not equals 1 or 2 */ #error "STRIDE_X larger than 2 is not supported" #endif /* STRIDE_X == 2 */ /** Compute a 1D horizontal convolution of size 3 with stride as 1. * * @param[in] left_pixel Pointer to the left pixel. * @param[in] left_coeff Weight of the left pixel * @param[in] middle_coeff Weight of the middle pixel * @param[in] right_coeff Weight of the right pixel * * @return a convoluted values. */ inline VEC_DATA_TYPE(DATA_TYPE, 8) convolution1x3_stride1(__global const DATA_TYPE *left_pixel, const DATA_TYPE left_coeff, const DATA_TYPE middle_coeff, const DATA_TYPE right_coeff) { VEC_DATA_TYPE(DATA_TYPE, 16) temp = vload16(0, left_pixel); VEC_DATA_TYPE(DATA_TYPE, 8) left = temp.s01234567; VEC_DATA_TYPE(DATA_TYPE, 8) middle = temp.s12345678; VEC_DATA_TYPE(DATA_TYPE, 8) right = temp.s23456789; return left * (VEC_DATA_TYPE(DATA_TYPE, 8))left_coeff + middle * (VEC_DATA_TYPE(DATA_TYPE, 8))middle_coeff + right * (VEC_DATA_TYPE(DATA_TYPE, 8))right_coeff; } /** Compute a 1D horizontal convolution of size 3 with stride as 2. * * @param[in] left_pixel Pointer to the left pixel. * @param[in] left_coeff Weight of the left pixel * @param[in] middle_coeff Weight of the middle pixel * @param[in] right_coeff Weight of the right pixel * * @return a convoluted values. */ inline VEC_DATA_TYPE(DATA_TYPE, 8) convolution1x3_stride2(__global const DATA_TYPE *left_pixel, const DATA_TYPE left_coeff, const DATA_TYPE middle_coeff, const DATA_TYPE right_coeff) { const int stride_size = 2; VEC_DATA_TYPE(DATA_TYPE, 16) temp1 = vload16(0, left_pixel); VEC_DATA_TYPE(DATA_TYPE, 16) temp2 = vload16(0, left_pixel + 8); VEC_DATA_TYPE(DATA_TYPE, 8) left = (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s0246, temp2.s0246); VEC_DATA_TYPE(DATA_TYPE, 8) middle = (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s1357, temp2.s1357); VEC_DATA_TYPE(DATA_TYPE, 8) right = (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s2468, temp2.s2468); return left * (VEC_DATA_TYPE(DATA_TYPE, 8))left_coeff + middle * (VEC_DATA_TYPE(DATA_TYPE, 8))middle_coeff + right * (VEC_DATA_TYPE(DATA_TYPE, 8))right_coeff; } /** Apply a 3x3 2D convolution matrix on the input and return the result. * * Convolution matrix layout: * * [ mat0, mat1, mat2 ]\n * [ mat3, mat4, mat5 ]\n * [ mat6, mat7, mat8 ]\n * * @param[in] src A pointer to source Image structure * @param[in] mat0 Coefficient from the convolution matrix * @param[in] mat1 Coefficient from the convolution matrix * @param[in] mat2 Coefficient from the convolution matrix * @param[in] mat3 Coefficient from the convolution matrix * @param[in] mat4 Coefficient from the convolution matrix * @param[in] mat5 Coefficient from the convolution matrix * @param[in] mat6 Coefficient from the convolution matrix * @param[in] mat0 Coefficient from the convolution matrix * @param[in] mat7 Coefficient from the convolution matrix * @param[in] mat8 Coefficient from the convolution matrix * * @return convoluted values. */ inline VEC_DATA_TYPE(DATA_TYPE, 8) convolution3x3( Image *src, const DATA_TYPE mat0, const DATA_TYPE mat1, const DATA_TYPE mat2, const DATA_TYPE mat3, const DATA_TYPE mat4, const DATA_TYPE mat5, const DATA_TYPE mat6, const DATA_TYPE mat7, const DATA_TYPE mat8) { // Output pixels VEC_DATA_TYPE(DATA_TYPE, 8) pixels; // Row 0 pixels = CONVOLVE1x3((__global DATA_TYPE *)offset(src, 0, 0), mat0, mat1, mat2); // Row pixels += CONVOLVE1x3((__global DATA_TYPE *)offset(src, 0, 1), mat3, mat4, mat5); // Row 2 pixels += CONVOLVE1x3((__global DATA_TYPE *)offset(src, 0, 2), mat6, mat7, mat8); return pixels; } /** This kernel performs a direct convolution to convolve the low three dimensions. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @note The convolution stride x and stride y must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1, _DSTRIDE_Y=1 * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[out] weights_ptr Pointer to the weights tensor. Supported data types: same as @p weights_ptr * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor * @param[in] weights_stride_w Stride of the weights tensor in W dimension * @param[in] filter_depth The depth size of the filter */ __kernel void direct_convolution3x3( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), TENSOR3D_DECLARATION(weights), #ifdef HAS_BIAS VECTOR_DECLARATION(biases), #endif /* defined(HAS_BIAS) */ unsigned int weights_stride_w, unsigned int filter_depth) { Image src = CONVERT_TO_IMAGE_STRUCT(src); Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); #endif /* defined(HAS_BIAS) */ VEC_DATA_TYPE(DATA_TYPE, 8) pixels = 0; const uint z_index = get_global_id(2); weights.ptr += z_index * weights_stride_w; for(int d = 0; d < filter_depth; ++d) { VEC_DATA_TYPE(DATA_TYPE, 4) weights_row1 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 0, 0)); VEC_DATA_TYPE(DATA_TYPE, 4) weights_row2 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 1, 0)); VEC_DATA_TYPE(DATA_TYPE, 4) weights_row3 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 2, 0)); pixels += convolution3x3(&src, weights_row1.s0, weights_row1.s1, weights_row1.s2, weights_row2.s0, weights_row2.s1, weights_row2.s2, weights_row3.s0, weights_row3.s1, weights_row3.s2); src.ptr += src_stride_z; weights.ptr += weights_stride_z; } #ifdef HAS_BIAS pixels += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, z_index))); #endif /* defined(HAS_BIAS) */ vstore8(pixels, 0, (__global DATA_TYPE *)dst.ptr); }