| /* |
| * Copyright (c) 2017-2020 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" |
| #include "repeat.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)) |
| |
| #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 */ |
| |
| #if defined(FP_MIXED_PRECISION) |
| #define CONVERT_TO_ACC_DATA_TYPE(x, n) CONVERT(x, VEC_DATA_TYPE(ACC_DATA_TYPE, n)) |
| #define VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(n, offset, ptr) \ |
| CONVERT_TO_ACC_DATA_TYPE(vload##n(offset, ptr), n) |
| #else /* defined(FP_MIXED_PRECISION) */ |
| #define VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(n, offset, ptr) vload##n(offset, ptr) |
| #endif /* defined(FP_MIXED_PRECISION) */ |
| |
| #define POOLING3x3_STRIDE1(res, input, output) \ |
| ({ \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| data00 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 2) \ |
| data01 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 4); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| data10 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 2) \ |
| data11 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 4); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| data20 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 2) \ |
| data21 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 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(ACC_DATA_TYPE, 8) \ |
| values00 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data00.s01212323); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| values01 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data01.s0, data00.s3, data01.s01); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| values10 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data10.s01212323); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| values11 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data11.s0, data10.s3, data11.s01); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| values20 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data20.s01212323); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| values21 = (VEC_DATA_TYPE(ACC_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(ACC_DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s147, values01.s2)); \ |
| res = POOL_OP(res, (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s25, values01.s03)); \ |
| }) |
| |
| #define POOLING3x3_STRIDE2(res, input, output) \ |
| ({ \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| data00 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \ |
| ACC_DATA_TYPE data01 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8)); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| data10 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \ |
| ACC_DATA_TYPE data11 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8)); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| data20 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \ |
| ACC_DATA_TYPE data21 = (ACC_DATA_TYPE)(*((__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(ACC_DATA_TYPE, 8) \ |
| values00 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data00.s01223445); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| values01 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s667, data01); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| values10 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data10.s01223445); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| values11 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data10.s667, data11); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| values20 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data20.s01223445); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| values21 = (VEC_DATA_TYPE(ACC_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(ACC_DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s147, values01.s2)); \ |
| res = POOL_OP(res, (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s25, values01.s03)); \ |
| }) |
| |
| #define POOLING3x3_STRIDE3(res, input, output) \ |
| ({ \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| data00 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| data01 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| data10 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| data11 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ |
| data20 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ |
| data21 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 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(ACC_DATA_TYPE, 4))(data00.s036, data01.s1), (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s147, data01.s2)); \ |
| res = POOL_OP(res, (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s25, data01.s03)); \ |
| }) |
| |
| ACC_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 tensor. Supported data types: F16/F32 |
| * @param[in] input_stride_x Stride of the 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 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_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 tensor |
| * @param[out] output_ptr Pointer to the destination tensor. 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 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 tensor |
| */ |
| __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(ACC_DATA_TYPE, 2) |
| data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 2) |
| data1 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 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); |
| ACC_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 = (DATA_TYPE)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 tensor. Supported data types: F16/F32 |
| * @param[in] input_stride_x Stride of the 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 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_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 tensor |
| * @param[out] output_ptr Pointer to the destination tensor. 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 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 tensor |
| */ |
| __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(ACC_DATA_TYPE, 3) |
| data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(3, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 3) |
| data1 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(3, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 3) |
| data2 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(3, 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); |
| ACC_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 = (DATA_TYPE)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(ACC_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(ACC_DATA_TYPE, 4))(1.f) / CONVERT_VECTOR4(ACC_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 tensor. Supported data types: F16/F32 |
| * @param[in] input_stride_x Stride of the 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 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_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 tensor |
| * @param[out] output_ptr Pointer to the destination tensor. 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 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 tensor |
| */ |
| __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(ACC_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(CONVERT(res, VEC_DATA_TYPE(DATA_TYPE, 4)), 0, (__global DATA_TYPE *)output.ptr); |
| } |
| #endif // defined(POOLING3x3) |
| |
| #if defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) |
| |
| /** 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 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 |
| * @note The initial value for the pooling operation must be passed at compile time using -DINITIAL_VALUE e.g. -DINITIAL_VALUE=0 |
| * |
| * @param[in] input_ptr Pointer to the source tensor. Supported data types: F16/F32 |
| * @param[in] input_stride_x Stride of the 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 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_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 tensor |
| * @param[out] output_ptr Pointer to the destination tensor. 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 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 tensor |
| */ |
| __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(ACC_DATA_TYPE, 8) |
| vdata = INITIAL_VALUE; |
| ACC_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(ACC_DATA_TYPE, 8) |
| data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 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) |
| { |
| ACC_DATA_TYPE data0 = (ACC_DATA_TYPE)(*((__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(ACC_DATA_TYPE, 4) |
| reduce4 = POOL_OP(vdata.s0123, vdata.s4567); |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 2) |
| reduce2 = POOL_OP(reduce4.s01, reduce4.s23); |
| ACC_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 = (DATA_TYPE)res; |
| } |
| #endif // defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) |
| |
| #if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) |
| |
| inline void offset_no_padding_nchw(const Tensor3D *input, uint *offset_top, uint *offset_bottom) |
| { |
| const int pad_horiz = PAD_TENSOR_LEFT + PAD_TENSOR_RIGHT; |
| const int pad_vert = PAD_TENSOR_TOP + PAD_TENSOR_BOTTOM; |
| |
| const int x = get_global_id(0) * STRIDE_X; |
| const int y = get_global_id(1) * STRIDE_Y; |
| const int z = get_global_id(2); |
| |
| //x axis: width, y axis: height, z axis: component |
| const uint padded_offset = input->offset_first_element_in_bytes |
| + x * input->stride_x |
| + y * input->stride_y |
| + z * input->stride_z; |
| |
| const uint offset_base = padded_offset |
| - y * pad_horiz * sizeof(DATA_TYPE) /* Horizontal padding for each row */ |
| - PAD_TENSOR_TOP * input->stride_y /* top padding */ |
| - z * MAX_HEIGHT * pad_horiz * sizeof(DATA_TYPE) - z * pad_vert * input->stride_y /* Z plane padding */ |
| - PAD_TENSOR_LEFT * sizeof(DATA_TYPE); |
| |
| #if defined(TENSOR_CHANNEL) && defined(TENSOR_WIDTH) && defined(TENSOR_HEIGHT) |
| *offset_top = (uint)((offset_base / sizeof(DATA_TYPE)) % (TENSOR_CHANNEL * TENSOR_WIDTH * TENSOR_HEIGHT)); |
| #else /* defined(TENSOR_CHANNEL) && defined(TENSOR_WIDTH) && defined(TENSOR_HEIGHT) */ |
| *offset_top = (uint)(offset_base / sizeof(DATA_TYPE)); |
| #endif /* defined(TENSOR_CHANNEL) && defined(TENSOR_WIDTH) && defined(TENSOR_HEIGHT) */ |
| |
| *offset_bottom = *offset_top + input->stride_y / sizeof(DATA_TYPE) - pad_horiz; |
| |
| return; |
| } |
| |
| #endif //defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) |
| |
| /** Performs a MAX pooling of pool size equal to 2, and record max value indices for NCHW. |
| * |
| * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F32 |
| * @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 Pool 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 Tensor padding values must be passed at compile time using PAD_TENSOR_LEFT, PAD_TENSOR_RIGHT, PAD_TENSOR_TOP and PAD_TENSOR_BOTTOM |
| * |
| * @param[in] input_ptr Pointer to the source tensor. Supported data types: F32 |
| * @param[in] input_stride_x Stride of the 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 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_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 tensor |
| * @param[out] output_ptr Pointer to the destination tensor. 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 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 tensor |
| * @param[in] indices_ptr Pointer to the indices tensor. Supported data types: U32 |
| * @param[in] indices_stride_x Stride of the indices tensor in X dimension (in bytes) |
| * @param[in] indices_step_x indices_stride_x * number of elements along X processed per workitem(in bytes) |
| * @param[in] indices_stride_y Stride of the indices tensor in Y dimension (in bytes) |
| * @param[in] indices_step_y indices_stride_y * number of elements along Y processed per workitem(in bytes) |
| * @param[in] indices_stride_z Stride of the indices tensor in Z dimension (in bytes) |
| * @param[in] indices_step_z indices_stride_z * number of elements along Z processed per workitem(in bytes) |
| * @param[in] indices_offset_first_element_in_bytes The offset of the first element in the indices tensor |
| */ |
| __kernel void pooling_layer_2_nchw_indices_fp32( |
| TENSOR3D_DECLARATION(input), |
| TENSOR3D_DECLARATION(output), |
| TENSOR3D_DECLARATION(indices)) |
| { |
| // Get pixels pointer |
| Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); |
| Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); |
| Tensor3D indices = CONVERT_TO_TENSOR3D_STRUCT(indices); |
| |
| // Load data |
| float2 data0 = VLOAD(2)(0, (__global float *)tensor3D_offset(&input, 0, 0, 0)); |
| float2 data1 = VLOAD(2)(0, (__global float *)tensor3D_offset(&input, 0, 1, 0)); |
| |
| // Perform calculations |
| float data0_max = POOL_OP(data0.s0, data0.s1); |
| float data1_max = POOL_OP(data1.s0, data1.s1); |
| float res = POOL_OP(data0_max, data1_max); |
| // Store result |
| *(__global float *)output.ptr = res; |
| |
| #if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) |
| |
| uint offset_top = 0; |
| uint offset_bottom = 0; |
| |
| offset_no_padding_nchw(&input, &offset_top, &offset_bottom); |
| |
| uint index0 = select(offset_top + 1, offset_top, isgreaterequal(data0.s0, data0.s1)); |
| uint index1 = select(offset_bottom + 1, offset_bottom, isgreaterequal(data1.s0, data1.s1)); |
| uint index = select(index1, index0, isgreaterequal(data0_max, data1_max)); |
| |
| *(__global uint *)indices.ptr = index; |
| |
| #endif //defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) |
| } |
| |
| /** Performs a MAX pooling of pool size equal to 2, and record max value indices for NCHW. |
| * |
| * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F16 |
| * @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 Pool 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 Tensor padding values must be passed at compile time using PAD_TENSOR_LEFT, PAD_TENSOR_RIGHT, PAD_TENSOR_TOP and PAD_TENSOR_BOTTOM |
| * |
| * @param[in] input_ptr Pointer to the source tensor. Supported data types: F16 |
| * @param[in] input_stride_x Stride of the 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 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_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 tensor |
| * @param[out] output_ptr Pointer to the destination tensor. 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 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 tensor |
| * @param[in] indices_ptr Pointer to the indices tensor. Supported data types: U32 |
| * @param[in] indices_stride_x Stride of the indices tensor in X dimension (in bytes) |
| * @param[in] indices_step_x indices_stride_x * number of elements along X processed per workitem(in bytes) |
| * @param[in] indices_stride_y Stride of the indices tensor in Y dimension (in bytes) |
| * @param[in] indices_step_y indices_stride_y * number of elements along Y processed per workitem(in bytes) |
| * @param[in] indices_stride_z Stride of the indices tensor in Z dimension (in bytes) |
| * @param[in] indices_step_z indices_stride_z * number of elements along Z processed per workitem(in bytes) |
| * @param[in] indices_offset_first_element_in_bytes The offset of the first element in the indices tensor |
| */ |
| __kernel void pooling_layer_2_nchw_indices_fp16( |
| TENSOR3D_DECLARATION(input), |
| TENSOR3D_DECLARATION(output), |
| TENSOR3D_DECLARATION(indices)) |
| { |
| // Get pixels pointer |
| Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); |
| Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); |
| Tensor3D indices = CONVERT_TO_TENSOR3D_STRUCT(indices); |
| |
| // Load data |
| half2 data0 = VLOAD(2)(0, (__global half *)tensor3D_offset(&input, 0, 0, 0)); |
| half2 data1 = VLOAD(2)(0, (__global half *)tensor3D_offset(&input, 0, 1, 0)); |
| |
| // Perform calculations |
| half data0_max = POOL_OP(data0.s0, data0.s1); |
| half data1_max = POOL_OP(data1.s0, data1.s1); |
| half res = POOL_OP(data0_max, data1_max); |
| // Store result |
| *(__global half *)output.ptr = res; |
| |
| #if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) |
| |
| uint offset_top = 0; |
| uint offset_bottom = 0; |
| |
| offset_no_padding_nchw(&input, &offset_top, &offset_bottom); |
| |
| uint index0 = select(offset_top + 1, offset_top, isgreaterequal(data0.s0, data0.s1)); |
| uint index1 = select(offset_bottom + 1, offset_bottom, isgreaterequal(data1.s0, data1.s1)); |
| uint index = select(index1, index0, isgreaterequal(data0_max, data1_max)); |
| |
| *(__global uint *)indices.ptr = index; |
| |
| #endif //defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) |
| } |
| |
| #if defined(VEC_SIZE) && defined(VEC_SIZE_LEFTOVER) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_CHANNELS) && defined(DST_HEIGHT) && defined(DST_BATCH_SIZE) && defined(ACC_DATA_TYPE) |
| |
| #if defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) |
| /** Performs pooling layer of size equal to MxN. This OpenCL kernel can perform the following pooling types: |
| * -# max, -DPOOL_MAX must be passed at compile time |
| * -# average, -DPOOL_AVG must be passed at compile time. If padding has to be expluded, -DEXCLUDE_PADDING should be passed at compile time |
| * -# l2 normalisation, -DPOOL_L2 must be passed at compile time |
| * |
| * @note Datatype must be passed at compile type using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F32/F16 |
| * @note Accumulation data type must be passed at compile time using -DACC_DATA_TYPE e.g. -DACC_DATA_TYPE=float |
| * @note If -DFP_MIXED_PRECISION is passed at compile time, the kernel will use F32 for the partial result |
| * @note Pool size must be passed at compile time using -DPOOL_SIZE_X and -DPOOL_SIZE_Y. e.g. -DPOOL_SIZE_X=4, -DPOOL_SIZE_Y=4 |
| * @note Input tensor width and height must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT |
| * @note Output tensor height, channels and batch size must be passed at compile time using -DDST_HEIGHT, -DDST_CHANNELS and -DDST_BATCH_SIZE |
| * @note Pool 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 Pool pads must be passed at compile time using -DPAD_X and -DPAD_Y |
| * @note Vector size must be passed at compile time using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 |
| * @note Leftover vector size must be passed at compile time using -DVEC_SIZE_LEFTOVER. e.g. -DVEC_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VEC_SIZE |
| * @note The initial value for the pooling operation must be passed at compile time using -DINITIAL_VALUE e.g. -DINITIAL_VALUE=0 |
| * |
| * @param[in] input_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| * @param[in] input_stride_x Stride of the 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 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_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 tensor |
| * @param[out] output_ptr Pointer to the destination tensor. 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 tensor |
| */ |
| __kernel void pooling_layer_MxN_nhwc( |
| TENSOR4D_DECLARATION(input), |
| TENSOR4D_DECLARATION(output)) |
| { |
| // Note: If C is not multiple of VEC_SIZE, we shift back of VEC_SIZE_LEFTOVER elements to compute the leftover elements for get_global_id(0) == 0 |
| // Note: If C is less than VEC_SIZE, VEC_SIZE should be SHRINKED to the closest smaller VEC_SIZE. This operation is performed on the host side |
| int offset_c = max((int)(get_global_id(0) * VEC_SIZE - (VEC_SIZE - VEC_SIZE_LEFTOVER) % VEC_SIZE), 0) * sizeof(DATA_TYPE); |
| int idx_out_w = get_global_id(1); |
| #if DST_BATCH_SIZE != 1 |
| // If batch size != 1, the batch size dimension is collapsed over the height dimension |
| int idx_out_h = get_global_id(2) % DST_HEIGHT; |
| int idx_out_n = get_global_id(2) / DST_HEIGHT; |
| #else //DST_BATCH_SIZE != 1 |
| int idx_out_h = get_global_id(2); |
| int idx_out_n = 0; |
| #endif // DST_BATCH_SIZE != 1 |
| |
| int idx_in_w = idx_out_w * STRIDE_X - PAD_X; |
| int idx_in_h = idx_out_h * STRIDE_Y - PAD_Y; |
| |
| int pool_x_s = max((int)0, -idx_in_w); |
| int pool_x_e = min((int)POOL_SIZE_X, (int)SRC_WIDTH - idx_in_w); |
| int pool_y_s = max((int)0, -idx_in_h); |
| int pool_y_e = min((int)POOL_SIZE_Y, (int)SRC_HEIGHT - idx_in_h); |
| |
| __global unsigned char *in_base_ptr = input_ptr + input_offset_first_element_in_bytes + |
| offset_c + |
| idx_out_n * input_stride_w; |
| |
| __global unsigned char *out_base_ptr = output_ptr + output_offset_first_element_in_bytes + |
| offset_c + |
| idx_out_w * output_stride_y + |
| idx_out_h * output_stride_z + |
| idx_out_n * output_stride_w; |
| |
| #if ((defined(POOL_AVG) || defined(POOL_L2))) |
| #if defined(EXCLUDE_PADDING) |
| int filter_size = 0; |
| #else // defined(EXCLUDE_PADDING) |
| int filter_size = POOL_SIZE_X * POOL_SIZE_Y; |
| #endif // defined(EXCLUDE_PADDING) |
| #endif // ((defined(POOL_AVG) || defined(POOL_L2))) |
| |
| VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE) |
| res0 = INITIAL_VALUE; |
| |
| for(int y = pool_y_s; y < pool_y_e; ++y) |
| { |
| for(int x = pool_x_s; x < pool_x_e; ++x) |
| { |
| VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE) data0; |
| #if defined(FP_MIXED_PRECISION) |
| // In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE |
| data0 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + (x + idx_in_w) * input_stride_y + (y + idx_in_h) * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); |
| #else // defined(FP_MIXED_PRECISION) |
| data0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + (x + idx_in_w) * input_stride_y + (y + idx_in_h) * input_stride_z)); |
| #endif // defined(FP_MIXED_PRECISION) |
| |
| #if defined(POOL_L2) |
| // Raise to power of 2 for L2 Pooling |
| data0 *= data0; |
| #endif // defined(POOL_L2) |
| res0 = POOL_OP(res0, data0); |
| |
| #if ((defined(POOL_AVG) || defined(POOL_L2))) && defined(EXCLUDE_PADDING) |
| filter_size++; |
| #endif // ((defined(POOL_AVG) || defined(POOL_L2))) && defined(EXCLUDE_PADDING) |
| } |
| } |
| |
| #if defined(POOL_AVG) || defined(POOL_L2) |
| res0 /= (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))filter_size; |
| #endif // defined(POOL_AVG) || defined(POOL_L2) |
| |
| #if defined(POOL_L2) |
| // Take square root of the result in L2 pooling |
| res0 = SQRT_OP(res0); |
| #endif // defined(POOL_L2) |
| |
| // Store result |
| #if defined(FP_MIXED_PRECISION) |
| VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) res_converted0 = CONVERT(res0, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)); |
| STORE_VECTOR_SELECT(res_converted, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0); |
| #else // defined(FP_MIXED_PRECISION) |
| STORE_VECTOR_SELECT(res, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0); |
| #endif // defined(FP_MIXED_PRECISION) |
| } |
| #endif // defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) |
| |
| /** Performs pooling layer of size equal to 2. This OpenCL kernel can perform the following pooling types: |
| * -# max, -DPOOL_MAX must be passed at compile time |
| * -# max extracting the max index, -DPOOL_MAX and -DEXTRACT_MAX_INDEX must be passed at compile time |
| * -# average, -DPOOL_AVG must be passed at compile time. If padding has to be expluded, -DEXCLUDE_PADDING should be passed at compile time |
| * -# l2 normalisation, -DPOOL_L2 must be passed at compile time |
| * |
| * @note Datatype must be passed at compile type using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F32/F16 |
| * @note Accumulation data type must be passed at compile time using -DACC_DATA_TYPE e.g. -DACC_DATA_TYPE=float |
| * @note If -DFP_MIXED_PRECISION is passed at compile time, the kernel will use F32 for the partial result |
| * @note Input tensor width and height must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT |
| * @note Output tensor height, channels and batch size must be passed at compile time using -DDST_HEIGHT, -DDST_CHANNELS and -DDST_BATCH_SIZE |
| * @note Pool 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 Pool pads must be passed at compile time using -DPAD_X and -DPAD_Y |
| * @note Vector size must be passed at compile time using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 |
| * @note Leftover vector size must be passed at compile time using -DVEC_SIZE_LEFTOVER. e.g. -DVEC_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VEC_SIZE |
| * @note The initial value for the pooling operation must be passed at compile time using -DINITIAL_VALUE e.g. -DINITIAL_VALUE=0 |
| * |
| * @param[in] input_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| * @param[in] input_stride_x Stride of the 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 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_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 tensor |
| * @param[out] output_ptr Pointer to the destination tensor. 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 tensor |
| * @param[in] indices_ptr (Optional) Pointer to the indices tensor. Supported data types: U32 |
| * @param[in] indices_stride_x (Optional) Stride of the indices tensor in X dimension (in bytes) |
| * @param[in] indices_step_x (Optional) indices_stride_x * number of elements along X processed per workitem(in bytes) |
| * @param[in] indices_stride_y (Optional) Stride of the indices tensor in Y dimension (in bytes) |
| * @param[in] indices_step_y (Optional) indices_stride_y * number of elements along Y processed per workitem(in bytes) |
| * @param[in] indices_stride_z (Optional) Stride of the indices tensor in Z dimension (in bytes) |
| * @param[in] indices_step_z (Optional) indices_stride_z * number of elements along Z processed per workitem(in bytes) |
| * @param[in] indices_stride_w (Optional) Stride of the indices tensor in W dimension (in bytes) |
| * @param[in] indices_step_w (Optional) indices_stride_w * number of elements along W processed per workitem(in bytes) |
| * @param[in] indices_offset_first_element_in_bytes (Optional) The offset of the first element in the indices tensor |
| */ |
| __kernel void pooling_layer_2x2_nhwc( |
| TENSOR4D_DECLARATION(input), |
| TENSOR4D_DECLARATION(output) |
| #if defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX) |
| , |
| TENSOR4D_DECLARATION(indices) |
| #endif // defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX) |
| ) |
| { |
| // Note: If C is not multiple of VEC_SIZE, we shift back of VEC_SIZE_LEFTOVER elements to compute the leftover elements for get_global_id(0) == 0 |
| // Note: If C is less than VEC_SIZE, VEC_SIZE should be SHRINKED to the closest smaller VEC_SIZE. This operation is performed on the host side |
| int idx_out_c = max((int)(get_global_id(0) * VEC_SIZE - (VEC_SIZE - VEC_SIZE_LEFTOVER) % VEC_SIZE), 0); |
| int idx_out_w = get_global_id(1); |
| #if DST_BATCH_SIZE != 1 |
| // If batch size != 1, the batch size dimension is collapsed over the height dimension |
| int idx_out_h = get_global_id(2) % DST_HEIGHT; |
| int idx_out_n = get_global_id(2) / DST_HEIGHT; |
| #else //SRC_BATCH_SIZE != 1 |
| int idx_out_h = get_global_id(2); |
| int idx_out_n = 0; |
| #endif // SRC_BATCH_SIZE != 1 |
| |
| int idx_in_w = idx_out_w * STRIDE_X - PAD_X; |
| int idx_in_h = idx_out_h * STRIDE_Y - PAD_Y; |
| |
| __global unsigned char *in_base_ptr = input_ptr + input_offset_first_element_in_bytes + |
| idx_out_c * sizeof(DATA_TYPE) + |
| idx_out_n * input_stride_w; |
| |
| __global unsigned char *out_base_ptr = output_ptr + output_offset_first_element_in_bytes + |
| idx_out_c * sizeof(DATA_TYPE) + |
| idx_out_w * output_stride_y + |
| idx_out_h * output_stride_z + |
| idx_out_n * output_stride_w; |
| |
| int pool_x_s = max((int)0, -idx_in_w); |
| int pool_x_e = min((int)2, (int)SRC_WIDTH - idx_in_w); |
| int pool_y_s = max((int)0, -idx_in_h); |
| int pool_y_e = min((int)2, (int)SRC_HEIGHT - idx_in_h); |
| |
| int filter_size = (pool_x_e - pool_x_s) * (pool_y_e - pool_y_s); |
| |
| int x0 = pool_x_s + idx_in_w; |
| int y0 = pool_y_s + idx_in_h; |
| int x1 = pool_x_e - 1 + idx_in_w; |
| int y1 = pool_y_e - 1 + idx_in_h; |
| |
| REPEAT_VAR_INIT_TO_CONST(4, VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE), data, 0); |
| |
| #if defined(FP_MIXED_PRECISION) |
| // In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE |
| data0 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y0 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); |
| data1 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y0 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); |
| data2 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y1 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); |
| data3 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y1 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); |
| #else // defined(FP_MIXED_PRECISION) |
| data0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y0 * input_stride_z)); |
| data1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y0 * input_stride_z)); |
| data2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y1 * input_stride_z)); |
| data3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y1 * input_stride_z)); |
| #endif // defined(FP_MIXED_PRECISION) |
| |
| #if !defined(POOL_MAX) |
| if(filter_size != 4) |
| { |
| // Make invalid the values loaded if the x or y coordinate was clamped (out-of-bound) |
| data1 = select(data1, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))(pool_x_e == pool_x_s)); |
| data2 = select(data2, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))(pool_y_e == pool_y_s)); |
| data3 = select(data3, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))((pool_x_e == pool_x_s) || (pool_y_e == pool_y_s))); |
| } |
| #endif // !defined(POOL_MAX) |
| |
| #if defined(POOL_L2) |
| // Raise to power of 2 for L2 Pooling |
| data0 *= data0; |
| data1 *= data1; |
| data2 *= data2; |
| data3 *= data3; |
| #endif /* defined(POOL_L2) */ |
| |
| VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE) |
| res0 = data0; |
| res0 = POOL_OP(res0, data1); |
| res0 = POOL_OP(res0, data2); |
| res0 = POOL_OP(res0, data3); |
| |
| #if defined(POOL_AVG) || defined(POOL_L2) |
| #if defined(EXCLUDE_PADDING) |
| res0 /= (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))filter_size; |
| #else // !defined(EXCLUDE_PADDING) |
| res0 /= (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))4; |
| #endif // defined(EXCLUDE_PADDING) |
| #endif // defined(POOL_AVG) || defined(POOL_L2) |
| |
| #if defined(POOL_L2) |
| // Take square root of the result in L2 pooling |
| res0 = SQRT_OP(res0); |
| #endif // defined(POOL_L2) |
| |
| // Store result |
| #if defined(FP_MIXED_PRECISION) |
| VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) res_converted0 = CONVERT(res0, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)); |
| STORE_VECTOR_SELECT(res_converted, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0); |
| #else // defined(FP_MIXED_PRECISION) |
| STORE_VECTOR_SELECT(res, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0); |
| #endif // defined(FP_MIXED_PRECISION) |
| |
| #if defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX) |
| |
| // This part is used to return the index of the maximum value |
| // Note: DST_CHANNELS and DST_BATCH_SIZE can be used for either the input and output tensor |
| |
| // note: Batch dimension does not contribute in the offset contribution |
| VEC_DATA_TYPE(uint, VEC_SIZE) base_index = (uint)idx_out_c; |
| |
| base_index += VEC_OFFS(VEC_DATA_TYPE(uint, VEC_SIZE), VEC_SIZE); |
| |
| VEC_DATA_TYPE(uint, VEC_SIZE) index0 = base_index + (uint)x0 * DST_CHANNELS + (uint)y0 * (DST_CHANNELS * SRC_WIDTH); |
| VEC_DATA_TYPE(uint, VEC_SIZE) index1 = base_index + (uint)x1 * DST_CHANNELS + (uint)y0 * (DST_CHANNELS * SRC_WIDTH); |
| VEC_DATA_TYPE(uint, VEC_SIZE) index2 = base_index + (uint)x0 * DST_CHANNELS + (uint)y1 * (DST_CHANNELS * SRC_WIDTH); |
| VEC_DATA_TYPE(uint, VEC_SIZE) index3 = base_index + (uint)x1 * DST_CHANNELS + (uint)y1 * (DST_CHANNELS * SRC_WIDTH); |
| |
| index0 = select(index1, index0, CONVERT(isgreaterequal(data0, data1), VEC_DATA_TYPE(int, VEC_SIZE))); |
| index1 = select(index3, index2, CONVERT(isgreaterequal(data2, data3), VEC_DATA_TYPE(int, VEC_SIZE))); |
| index0 = select(index1, index0, CONVERT(isgreaterequal(max(data0, data1), max(data2, data3)), VEC_DATA_TYPE(int, VEC_SIZE))); |
| |
| __global unsigned char *idx_base_ptr = indices_ptr + indices_offset_first_element_in_bytes + |
| idx_out_c * sizeof(uint) + |
| idx_out_w * indices_stride_y + |
| idx_out_h * indices_stride_z + |
| idx_out_n * indices_stride_w; |
| |
| // Store result |
| STORE_VECTOR_SELECT(index, uint, idx_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, ((VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0)); |
| #endif // defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX) |
| } |
| #endif // defined(VEC_SIZE) && defined(VEC_SIZE_LEFTOVER) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_CHANNELS) && defined(DST_HEIGHT) && defined(DST_BATCH_SIZE) && defined(SELECT_DATA_TYPE) && defined(ACC_DATA_TYPE) |