/* * Copyright (c) 2017-2019 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(POOL_AVG) || defined(POOL_L2) #define POOL_OP(x, y) ((x) + (y)) #else /* defined(POOL_AVG) || defined(POOL_L2) */ #define POOL_OP(x, y) (fmax((x), (y))) #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) #define POW2_OP(x, vec_size) ((x) * (x)) #else /* defined(POOL_L2) */ #define POW2_OP(x, vec_size) (x) #endif /* defined(POOL_L2) */ #define DIV_OP(x, y) (x * (1.f / y)) #define SQRT_OP(x) sqrt((x)) #define DIV_OP_NHWC(x, y) (x * (VEC_DATA_TYPE(float, 8))(1.f / y)) #if STRIDE_X == 1 #define POOLING3x3(res, input, output) POOLING3x3_STRIDE1(res, input, output) #elif STRIDE_X == 2 /* STRIDE_X == 1 */ #define POOLING3x3(res, input, output) POOLING3x3_STRIDE2(res, input, output) #elif STRIDE_X == 3 /* STRIDE_X not equals 1 or 2 */ #define POOLING3x3(res, input, output) POOLING3x3_STRIDE3(res, input, output) #endif /* STRIDE_X == 3 */ #define POOLING3x3_STRIDE1(res, input, output) \ ({ \ VEC_DATA_TYPE(DATA_TYPE, 4) \ data00 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \ VEC_DATA_TYPE(DATA_TYPE, 2) \ data01 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 4); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ data10 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \ VEC_DATA_TYPE(DATA_TYPE, 2) \ data11 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 4); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ data20 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \ VEC_DATA_TYPE(DATA_TYPE, 2) \ data21 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 4); \ data00 = POW2_OP(data00, 4); \ data01 = POW2_OP(data01, 2); \ data10 = POW2_OP(data10, 4); \ data11 = POW2_OP(data11, 2); \ data20 = POW2_OP(data20, 4); \ data21 = POW2_OP(data21, 2); \ \ VEC_DATA_TYPE(DATA_TYPE, 8) \ values00 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data00.s01212323); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ values01 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data01.s0, data00.s3, data01.s01); \ VEC_DATA_TYPE(DATA_TYPE, 8) \ values10 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data10.s01212323); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ values11 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data11.s0, data10.s3, data11.s01); \ VEC_DATA_TYPE(DATA_TYPE, 8) \ values20 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data20.s01212323); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ values21 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data21.s0, data20.s3, data21.s01); \ \ values00 = POOL_OP(values00, values10); \ values01 = POOL_OP(values01, values11); \ values00 = POOL_OP(values00, values20); \ values01 = POOL_OP(values01, values21); \ \ res = POOL_OP((VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s147, values01.s2)); \ res = POOL_OP(res, (VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s25, values01.s03)); \ }) #define POOLING3x3_STRIDE2(res, input, output) \ ({ \ VEC_DATA_TYPE(DATA_TYPE, 8) \ data00 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \ DATA_TYPE data01 = *((__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8); \ VEC_DATA_TYPE(DATA_TYPE, 8) \ data10 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \ DATA_TYPE data11 = *((__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8); \ VEC_DATA_TYPE(DATA_TYPE, 8) \ data20 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \ DATA_TYPE data21 = *((__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 8); \ data00 = POW2_OP(data00, 8); \ data01 = POW2_OP(data01, 1); \ data10 = POW2_OP(data10, 8); \ data11 = POW2_OP(data11, 1); \ data20 = POW2_OP(data20, 8); \ data21 = POW2_OP(data21, 1); \ \ VEC_DATA_TYPE(DATA_TYPE, 8) \ values00 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data00.s01223445); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ values01 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data00.s667, data01); \ VEC_DATA_TYPE(DATA_TYPE, 8) \ values10 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data10.s01223445); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ values11 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data10.s667, data11); \ VEC_DATA_TYPE(DATA_TYPE, 8) \ values20 = (VEC_DATA_TYPE(DATA_TYPE, 8))(data20.s01223445); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ values21 = (VEC_DATA_TYPE(DATA_TYPE, 4))(data20.s667, data21); \ \ values00 = POOL_OP(values00, values10); \ values01 = POOL_OP(values01, values11); \ values00 = POOL_OP(values00, values20); \ values01 = POOL_OP(values01, values21); \ \ res = POOL_OP((VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s147, values01.s2)); \ res = POOL_OP(res, (VEC_DATA_TYPE(DATA_TYPE, 4))(values00.s25, values01.s03)); \ }) #define POOLING3x3_STRIDE3(res, input, output) \ ({ \ VEC_DATA_TYPE(DATA_TYPE, 8) \ data00 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ data01 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8); \ VEC_DATA_TYPE(DATA_TYPE, 8) \ data10 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ data11 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8); \ VEC_DATA_TYPE(DATA_TYPE, 8) \ data20 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \ VEC_DATA_TYPE(DATA_TYPE, 4) \ data21 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 8); \ data00 = POW2_OP(data00, 8); \ data01 = POW2_OP(data01, 4); \ data10 = POW2_OP(data10, 8); \ data11 = POW2_OP(data11, 4); \ data20 = POW2_OP(data20, 8); \ data21 = POW2_OP(data21, 4); \ \ data00 = POOL_OP(data00, data10); \ data01 = POOL_OP(data01, data11); \ data00 = POOL_OP(data00, data20); \ data01 = POOL_OP(data01, data21); \ \ res = POOL_OP((VEC_DATA_TYPE(DATA_TYPE, 4))(data00.s036, data01.s1), (VEC_DATA_TYPE(DATA_TYPE, 4))(data00.s147, data01.s2)); \ res = POOL_OP(res, (VEC_DATA_TYPE(DATA_TYPE, 4))(data00.s25, data01.s03)); \ }) DATA_TYPE calculate_avg_scale(const int pool_size_x, const int pool_size_y, 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; const int end_x = min(start_x + pool_size_x, upper_bound_w); const int end_y = min(start_y + pool_size_y, upper_bound_h); #if defined(EXCLUDE_PADDING) start_x = max(0, start_x); start_y = max(0, start_y); #endif /* defined(EXCLUDE_PADDING) */ return ((end_y - start_y) * (end_x - start_x)); } /** Performs a pooling function of pool size equal to 2. * * @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 the following information must be passed at compile time: * -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed. * -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad) * -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension * * @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: same as @p input_ptr * @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 */ __kernel void pooling_layer_2( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output)) { // 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)); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 = POW2_OP(data0, 2); data1 = POW2_OP(data1, 2); #endif /* defined(POOL_L2) */ // Perform calculations data0 = POOL_OP(data0, data1); DATA_TYPE res = POOL_OP(data0.s0, data0.s1); #if defined(POOL_AVG) || defined(POOL_L2) // Divide by pool region in case of average or l2 pooling res = DIV_OP(res, calculate_avg_scale(2, 2, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y)); #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) // Take square root of the result in L2 pooling res = SQRT_OP(res); #endif /* defined(POOL_L2) */ // Store result *(__global DATA_TYPE *)output.ptr = res; } /** Performs a pooling function of pool size equal to 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 the following information must be passed at compile time: * -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed. * -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad) * -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension * * @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: same as @p input_ptr * @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 */ __kernel void pooling_layer_3( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output)) { // 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)); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 = POW2_OP(data0, 3); data1 = POW2_OP(data1, 3); data2 = POW2_OP(data2, 3); #endif /* defined(POOL_L2) */ // 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); #if defined(POOL_AVG) || defined(POOL_L2) // Divide by pool region in case of average pooling res = DIV_OP(res, calculate_avg_scale(3, 3, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y)); #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) // Take square root of the result in L2 pooling res = SQRT_OP(res); #endif /* defined(POOL_L2) */ // Store result *(__global DATA_TYPE *)output.ptr = res; } #if defined(POOLING3x3) #define CONVERT_OP(data_type) convert_##data_type##4 #define CONVERT_VECTOR4(data_type) CONVERT_OP(data_type) VEC_DATA_TYPE(DATA_TYPE, 4) calculate_avg_scale4(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) { int4 start_x = ((int4)get_global_id(0) * 4 + (int4)(0, 1, 2, 3)) * (int4)stride_x - (int4)pad_x; int start_y = get_global_id(1) * stride_y - pad_y; const int4 end_x = min(start_x + (int4)pool_size, (int4)upper_bound_w); const int end_y = min(start_y + pool_size, upper_bound_h); #if defined(EXCLUDE_PADDING) start_x = max((int4)0, start_x); start_y = max(0, start_y); #endif /* defined(EXCLUDE_PADDING) */ return (VEC_DATA_TYPE(DATA_TYPE, 4))(1.f) / CONVERT_VECTOR4(DATA_TYPE)(((int4)(end_y - start_y)) * (end_x - start_x)); } /** Performs an optimized pooling function of pool size equal to 3 when the stride_x is less equal than 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 the following information must be passed at compile time: * -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed. * -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad) * -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension * * @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: same as @p input_ptr * @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 */ __kernel void pooling_layer_optimized_3( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output)) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); VEC_DATA_TYPE(DATA_TYPE, 4) res; // Perform pooling 3x3 for 4 output elements POOLING3x3(res, input, output); #if defined(POOL_AVG) || defined(POOL_L2) // Divide by pool region in case of average pooling res *= calculate_avg_scale4(3, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y); #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) // Take square root of the result in L2 pooling res = SQRT_OP(res); #endif /* defined(POOL_L2) */ vstore4(res, 0, (__global DATA_TYPE *)output.ptr); } #endif // defined(POOLING3x3) #if defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) // Set the initial value for the pooling operation accordingly with the data type #if defined(POOL_AVG) || defined(POOL_L2) #define INITIAL_VALUE 0 #else /* defined(POOL_AVG) || defined(POOL_L2) */ #if FP16 #define INITIAL_VALUE -HALF_MAX #else // FP16 #define INITIAL_VALUE -FLT_MAX #endif // FP16 #endif // POOL_AVG /** Performs a pooling function of pool size equal to N (NCHW) * * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32; * @note -DFP16 must be passed at compile time if half float data type is used * @note Pool sizes must be passed using -DPOOL_SIZE_X and -DPOOL_SIZE_Y e.g. -DPOOL_SIZE_X=13; * @note In case of average pooling the following information must be passed at compile time: * -DPOOL_AVG must be provided otherwise max pooling will be performed. * -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad) * -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension * * @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: same as @p input_ptr * @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 */ __kernel void pooling_layer_MxN_nchw( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output)) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); VEC_DATA_TYPE(DATA_TYPE, 8) vdata = INITIAL_VALUE; DATA_TYPE sdata = INITIAL_VALUE; // Load data for(int y = 0; y < POOL_SIZE_Y; y++) { int x = 0; for(; x <= ((int)POOL_SIZE_X - 8); x += 8) { VEC_DATA_TYPE(DATA_TYPE, 8) data0 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0)); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 *= data0; #endif /* defined(POOL_L2) */ vdata = POOL_OP(vdata, data0); } // Leftover for(; x < (int)POOL_SIZE_X; ++x) { DATA_TYPE data0 = *((__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0)); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 *= data0; #endif /* defined(POOL_L2) */ sdata = POOL_OP(sdata, data0); } } // Reduce result VEC_DATA_TYPE(DATA_TYPE, 4) reduce4 = POOL_OP(vdata.s0123, vdata.s4567); VEC_DATA_TYPE(DATA_TYPE, 2) reduce2 = POOL_OP(reduce4.s01, reduce4.s23); DATA_TYPE res = POOL_OP(reduce2.s0, reduce2.s1); res = POOL_OP(res, sdata); #if defined(POOL_AVG) || defined(POOL_L2) // Divide by pool region in case of average pooling res = DIV_OP(res, calculate_avg_scale(POOL_SIZE_X, POOL_SIZE_Y, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y)); #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) // Take square root of the result in L2 pooling res = SQRT_OP(res); #endif /* defined(POOL_L2) */ // Store result *(__global DATA_TYPE *)output.ptr = res; } #endif // defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) float calculate_avg_scale_nhwc(const int pool_size_x, const int pool_size_y, int upper_bound_w, 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(1) * stride_x - pad_x; #if defined(DST_DEPTH) int start_y = (get_global_id(2) % DST_DEPTH) * stride_y - pad_y; #else /* defined(DST_DEPTH) */ int start_y = get_global_id(2) * stride_y - pad_y; #endif /* defined(DST_DEPTH) */ #if !defined(EXCLUDE_PADDING) upper_bound_w += pad_x; upper_bound_h += pad_y; #endif /* defined(EXCLUDE_PADDING) */ const int end_x = min(start_x + pool_size_x, upper_bound_w); const int end_y = min(start_y + pool_size_y, upper_bound_h); #if defined(EXCLUDE_PADDING) start_x = max(0, start_x); start_y = max(0, start_y); #endif /* defined(EXCLUDE_PADDING) */ return ((end_y - start_y) * (end_x - start_x)); } /** Performs a pooling function of pool size equal to N (NHWC) * * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32 * @note -DFP16 must be passed at compile time if half float data type is used * @note Pool sizes must be passed using -DPOOL_SIZE_X and -DPOOL_SIZE_Y e.g. -DPOOL_SIZE_X=13; * @note Tensors width and height must be passed at compile time using -DMAX_WIDTH and -DMAX_HEIGHT * @note Strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * @note Pad values must be passed at compile time using -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension * @note In case of average pooling the following information must be passed at compile time: * -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_stride_w Stride of the source tensor in W dimension (in bytes) * @param[in] input_step_w input_stride_w * number of elements along W 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: same as @p input_ptr * @param[in] output_stride_x Stride of the destination tensor 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 tensor 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 destination 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_stride_w Stride of the destination tensor in W dimension (in bytes) * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image */ __kernel void pooling_layer_MxN_nhwc( TENSOR4D_DECLARATION(input), TENSOR4D_DECLARATION(output)) { // Get pixels pointer #if defined(DST_DEPTH) Tensor4D input = CONVERT_TO_TENSOR4D_STRUCT(input, DST_DEPTH); Tensor4D output = CONVERT_TO_TENSOR4D_STRUCT(output, DST_DEPTH); #else /* defined(DST_DEPTH) */ Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); #endif /* defined(DST_DEPTH) */ VEC_DATA_TYPE(float, 8) vdata = INITIAL_VALUE; const int idx_width = get_global_id(1) * STRIDE_X; #if defined(DST_DEPTH) const int idx_height = (get_global_id(2) % DST_DEPTH) * STRIDE_Y; #else /* defined(DST_DEPTH) */ const int idx_height = get_global_id(2) * STRIDE_Y; #endif /* defined(DST_DEPTH) */ for(int y = 0; y < POOL_SIZE_Y; ++y) { int y1 = select(y, PAD_Y - idx_height, y + idx_height - PAD_Y < 0 || y + idx_height - PAD_Y >= MAX_HEIGHT); for(int x = 0; x < POOL_SIZE_X; ++x) { int x1 = select(x, PAD_X - idx_width - 1, x + idx_width - PAD_X < 0 || x + idx_width - PAD_X >= MAX_WIDTH); x1 = select(x1, PAD_X - idx_width - 1, y != y1); #if defined(DST_DEPTH) VEC_DATA_TYPE(DATA_TYPE, 8) data0 = vload8(0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, x1 - PAD_X, y1 - PAD_Y, 0)); #else /* defined(DST_DEPTH) */ VEC_DATA_TYPE(DATA_TYPE, 8) data0 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, x1 - PAD_X, y1 - PAD_Y)); #endif /* defined(DST_DEPTH) */ #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 *= data0; #endif /* defined(POOL_L2) */ vdata = POOL_OP(vdata, CONVERT(data0, float8)); } } #if defined(POOL_AVG) || defined(POOL_L2) // Divide by pool region in case of average pooling vdata = DIV_OP_NHWC(vdata, calculate_avg_scale_nhwc(POOL_SIZE_X, POOL_SIZE_Y, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y)); #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) // Take square root of the result in L2 pooling vdata = SQRT_OP(vdata); #endif /* defined(POOL_L2) */ // Store result vstore8(CONVERT(vdata, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)output.ptr); }