| /* |
| * Copyright (c) 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 "helpers.h" |
| |
| #if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(EPSILON) && defined(WIDTH) |
| /** This function normalizes the input 2D tensor across the first dimension with respect to mean and standard deviation of the same dimension. |
| * |
| * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 |
| * @attention Data type should be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float |
| * @attention Width of the input tensor should be passed using the -DWIDTH compile flag, e.g. -DWIDTH=16 |
| * @attention Normalization epsilon parameter should be given as a preprocessor argument with -DEPSILON=value. e.g. -DEPSILON=0.001f |
| * |
| * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32 |
| * @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes) |
| * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) |
| * @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes) |
| * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) |
| * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor |
| * @param[out] output_ptr (Optional) Pointer to the destination tensor. Supported data types: same as @p input_ptr |
| * @param[in] output_stride_x (Optional) Stride of the destination tensor in X dimension (in bytes) |
| * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) |
| * @param[in] output_stride_y (Optional) Stride of the destination tensor in Y dimension (in bytes) |
| * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) |
| * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination tensor |
| */ |
| __kernel void mean_stddev_normalization( |
| IMAGE_DECLARATION(input) |
| #ifndef IN_PLACE |
| , |
| IMAGE_DECLARATION(output) |
| #endif /* IN_PLACE */ |
| ) |
| { |
| // Get pixels pointer |
| Image in = CONVERT_TO_IMAGE_STRUCT(input); |
| #ifdef IN_PLACE |
| Image out = in; |
| #else /* IN_PLACE */ |
| Image out = CONVERT_TO_IMAGE_STRUCT(output); |
| #endif /* IN_PLACE */ |
| |
| VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) |
| sum = 0.f; |
| VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) |
| sum_sq = 0.f; |
| // Calculate partial sum |
| int i = 0; |
| for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE) |
| { |
| // Load data |
| VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) |
| data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&in, i, 0)); |
| |
| sum += data; |
| sum_sq += data * data; |
| } |
| // Perform reduction |
| #if VEC_SIZE > 8 |
| sum.s01234567 += sum.s89abcdef; |
| sum_sq.s01234567 += sum_sq.s89abcdef; |
| #endif // VEC_SIZE > 8 |
| #if VEC_SIZE > 4 |
| sum.s0123 += sum.s4567; |
| sum_sq.s0123 += sum_sq.s4567; |
| #endif // VEC_SIZE > 4 |
| #if VEC_SIZE > 2 |
| sum.s01 += sum.s23; |
| sum_sq.s01 += sum_sq.s23; |
| #endif // VEC_SIZE > 2 |
| sum.s0 += sum.s1; |
| sum_sq.s0 += sum_sq.s1; |
| // Left-overs loop |
| for(; i < WIDTH; ++i) |
| { |
| DATA_TYPE data = *((__global DATA_TYPE *)offset(&in, i, 0)); |
| |
| sum.s0 += data; |
| sum_sq.s0 += data * data; |
| } |
| |
| DATA_TYPE mean = sum.s0 / WIDTH; |
| DATA_TYPE var = (sum_sq.s0 / WIDTH) - (mean * mean); |
| DATA_TYPE stddev_inv = 1.f / sqrt(var + EPSILON); |
| |
| i = 0; |
| for(; i <= (WIDTH - VEC_SIZE); i += VEC_SIZE) |
| { |
| VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) |
| data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)offset(&in, i, 0)); |
| |
| VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) |
| res = (data - mean) * stddev_inv; |
| VSTORE(VEC_SIZE) |
| (res, 0, (__global DATA_TYPE *)offset(&out, i, 0)); |
| } |
| for(; i < WIDTH; ++i) |
| { |
| DATA_TYPE data = *((__global DATA_TYPE *)offset(&in, i, 0)); |
| |
| *((__global DATA_TYPE *)offset(&out, i, 0)) = (data - mean) * stddev_inv; |
| } |
| } |
| #endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(EPSILON) && defined(WIDTH) */ |