/* * Copyright (c) 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 defined POOL_AVG #define POOL_OP(x, y) ((x) + (y)) #else #define POOL_OP(x, y) (fmax((x), (y))) #endif float calculate_avg_scale(const int pool_size, const int upper_bound_w, const int upper_bound_h, const int pad_x, const int pad_y, const int stride_x, const int stride_y) { int start_x = get_global_id(0) * stride_x - pad_x; int start_y = get_global_id(1) * stride_y - pad_y; int end_x = min(start_x + pool_size, upper_bound_w); int end_y = min(start_y + pool_size, upper_bound_h); return 1.f / ((end_y - start_y) * (end_x - start_x)); } /** Performs a pooling function of pool size equal to 2. * * @note Pooling stride must be passed using -DPOOL_STRIDE e.g -DPOOL_STRIDE=2. Supported strides are 1,2,3 * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32; * @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed. * * @param[in] input_ptr Pointer to the source image. Supported data types: F16, F32 * @param[in] input_stride_x Stride of the source image 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 source image 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_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image * @param[out] output_ptr Pointer to the destination image. Supported data types: F16, F32 * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image * @param[in] max_dims The maximum index that can be accessed in x and y dimension (width + pad) * @param[in] strides The pooling operation strides in each dimension * @param[in] paddings The pooling operation paddings in each dimension */ __kernel void pooling_layer_2( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output) #ifdef POOL_AVG , int2 max_dims, int2 strides, int2 paddings #endif ) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); // Load data VEC_DATA_TYPE(DATA_TYPE, 2) data0 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); VEC_DATA_TYPE(DATA_TYPE, 2) data1 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); // Perform calculations data0 = POOL_OP(data0, data1); DATA_TYPE res = POOL_OP(data0.s0, data0.s1); // Divide by 4 in case of average pooling #ifdef POOL_AVG res *= calculate_avg_scale(2, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y); #endif // Store result *(__global DATA_TYPE *)output.ptr = res; } /** Performs a pooling function of pool size equal to 3. * * @note Pooling stride must be passed using -DPOOL_STRIDE e.g -DPOOL_STRIDE=2. Supported strides are 1,2,3 * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32; * @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed. * * @param[in] input_ptr Pointer to the source image. Supported data types: F16, F32 * @param[in] input_stride_x Stride of the source image 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 source image 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_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image * @param[out] output_ptr Pointer to the destination image. Supported data types: F16, F32 * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image * @param[in] max_dims The maximum index that can be accessed in x and y dimension (width + pad) * @param[in] strides The pooling operation strides in each dimension * @param[in] paddings The pooling operation paddings in each dimension */ __kernel void pooling_layer_3( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output) #ifdef POOL_AVG , int2 max_dims, int2 strides, int2 paddings #endif ) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); // Load data VEC_DATA_TYPE(DATA_TYPE, 3) data0 = vload3(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); VEC_DATA_TYPE(DATA_TYPE, 3) data1 = vload3(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); VEC_DATA_TYPE(DATA_TYPE, 3) data2 = vload3(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); // Perform calculations data0 = POOL_OP(data0, data1); data0 = POOL_OP(data0, data2); DATA_TYPE res = POOL_OP(POOL_OP(data0.s0, data0.s1), data0.s2); // Divide by 4 in case of average pooling #ifdef POOL_AVG res *= calculate_avg_scale(3, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y); #endif // Store result *(__global DATA_TYPE *)output.ptr = res; }