1/* 2 * Copyright (c) 2018-2021 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24#include "helpers.h" 25 26#if defined(DATA_TYPE) && defined(VEC_SIZE) 27 28#define TYPE VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) 29 30/** Apply normalize_planar_yuv layer on tensors with NCHW data layout. 31 * 32 * @note Data type should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float 33 * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE e.g. -DVEC_SIZE=8 34 * @note The depth of the input tensor should be given as a preprocessor argument using -DNUM_CHANNELS e.g. -DNUM_CHANNELS=8 35 * 36 * @param[in] src_ptr Pointer to the first source tensor. Supported data types: F16/F32 37 * @param[in] src_stride_x Stride of the first source tensor in X dimension (in bytes) 38 * @param[in] src_step_x input_stride_x * number of elements along X processed per workitem(in bytes) 39 * @param[in] src_stride_y Stride of the first source tensor in Y dimension (in bytes) 40 * @param[in] src_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) 41 * @param[in] src_stride_z Stride of the first source tensor in Z dimension (in bytes) 42 * @param[in] src_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) 43 * @param[in] src_offset_first_element_in_bytes The offset of the first element in the first source tensor 44 * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr 45 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) 46 * @param[in] dst_step_x output_stride_x * number of elements along X processed per workitem(in bytes) 47 * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) 48 * @param[in] dst_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) 49 * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) 50 * @param[in] dst_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) 51 * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor 52 * @param[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p src_ptr 53 * @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes) 54 * @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes) 55 * @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor 56 * @param[in] std_ptr Pointer to the std tensor. Supported data types: same as @p src_ptr 57 * @param[in] std_stride_x Stride of the std tensor in X dimension (in bytes) 58 * @param[in] std_step_x std_stride_x * number of elements along X processed per workitem(in bytes) 59 * @param[in] std_offset_first_element_in_bytes The offset of the first element in the var source tensor 60 */ 61__kernel void normalize_planar_yuv_layer_nchw(TENSOR3D_DECLARATION(src), 62 TENSOR3D_DECLARATION(dst), 63 VECTOR_DECLARATION(mean), 64 VECTOR_DECLARATION(std)) 65{ 66 Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); 67 Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); 68 Vector mean = CONVERT_TO_VECTOR_STRUCT(mean); 69 Vector std = CONVERT_TO_VECTOR_STRUCT(std); 70 71 const uint current_slice = get_global_id(2) % NUM_CHANNELS; 72 73 const DATA_TYPE curr_mean = *((__global DATA_TYPE *)(mean.ptr + current_slice * sizeof(DATA_TYPE))); 74 const DATA_TYPE curr_std = *((__global DATA_TYPE *)(std.ptr + current_slice * sizeof(DATA_TYPE))); 75 76 TYPE data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)src.ptr); 77 TYPE res = (data - curr_mean) / curr_std; 78 79 VSTORE(VEC_SIZE) 80 (res, 0, (__global DATA_TYPE *)dst.ptr); 81} 82#endif // defined(DATA_TYPE) && defined(VEC_SIZE)