/* * Copyright (c) 2018 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 defined(DATA_TYPE) && defined(VEC_SIZE) #define TYPE VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) /** Apply normalize_planar_yuv layer on tensors with NCHW data layout. * * @note Data type should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE e.g. -DVEC_SIZE=8 * @note The depth of the input tensor should be given as a preprocessor argument using -DNUM_CHANNELS e.g. -DNUM_CHANNELS=8 * * @param[in] src_ptr Pointer to the first source tensor. Supported data types: F16/F32 * @param[in] src_stride_x Stride of the first source tensor in X dimension (in bytes) * @param[in] src_step_x input_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the first source tensor in Y dimension (in bytes) * @param[in] src_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the first source tensor in Z dimension (in bytes) * @param[in] src_step_z input_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 first 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 output_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 output_stride_y * number of elements along Y 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 output_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[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p src_ptr * @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes) * @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor * @param[in] std_ptr Pointer to the std tensor. Supported data types: same as @p src_ptr * @param[in] std_stride_x Stride of the std tensor in X dimension (in bytes) * @param[in] std_step_x std_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] std_offset_first_element_in_bytes The offset of the first element in the var source tensor */ __kernel void normalize_planar_yuv_layer_nchw(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), VECTOR_DECLARATION(mean), VECTOR_DECLARATION(std)) { Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); Vector mean = CONVERT_TO_VECTOR_STRUCT(mean); Vector std = CONVERT_TO_VECTOR_STRUCT(std); const uint current_slice = get_global_id(2) % NUM_CHANNELS; const DATA_TYPE curr_mean = *((__global DATA_TYPE *)(mean.ptr + current_slice * sizeof(DATA_TYPE))); const DATA_TYPE curr_std = *((__global DATA_TYPE *)(std.ptr + current_slice * sizeof(DATA_TYPE))); TYPE data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)src.ptr); TYPE res = (data - curr_mean) / curr_std; VSTORE(VEC_SIZE) (res, 0, (__global DATA_TYPE *)dst.ptr); } /** Apply normalize_planar_yuv layer on tensors with NHWC data layout. * * @note Data type should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE e.g. -DVEC_SIZE=8 * * @param[in] src_ptr Pointer to the first source tensor. Supported data types: F16/F32 * @param[in] src_stride_x Stride of the first source tensor in X dimension (in bytes) * @param[in] src_step_x input_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the first source tensor in Y dimension (in bytes) * @param[in] src_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the first source tensor in Z dimension (in bytes) * @param[in] src_step_z input_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 first 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 output_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 output_stride_y * number of elements along Y 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 output_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[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p src_ptr * @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes) * @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor * @param[in] std_ptr Pointer to the std tensor. Supported data types: same as @p src_ptr * @param[in] std_stride_x Stride of the std tensor in X dimension (in bytes) * @param[in] std_step_x std_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] std_offset_first_element_in_bytes The offset of the first element in the var source tensor */ __kernel void normalize_planar_yuv_layer_nhwc(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), VECTOR_DECLARATION(mean), VECTOR_DECLARATION(std)) { Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); Vector mean = CONVERT_TO_VECTOR_STRUCT(mean); Vector std = CONVERT_TO_VECTOR_STRUCT(std); const uint current_slice = get_global_id(0); const TYPE curr_mean = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(mean.ptr + current_slice * VEC_SIZE * sizeof(DATA_TYPE))); const TYPE curr_std = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(std.ptr + current_slice * VEC_SIZE * sizeof(DATA_TYPE))); TYPE data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)src.ptr); TYPE res = (data - curr_mean) / curr_std; VSTORE(VEC_SIZE) (res, 0, (__global DATA_TYPE *)dst.ptr); } #endif // defined(DATA_TYPE) && defined(VEC_SIZE)