| /* |
| * 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" |
| |
| #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(ACC_DATA_TYPE, 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 */ |
| |
| #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) |
| |
| ACC_DATA_TYPE 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 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. |
| * @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_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)) |
| { |
| // 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(ACC_DATA_TYPE, 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(ACC_DATA_TYPE, 8) |
| data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, x1 - PAD_X, y1 - PAD_Y, 0)); |
| #else /* defined(DST_DEPTH) */ |
| VEC_DATA_TYPE(ACC_DATA_TYPE, 8) |
| data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 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, VEC_DATA_TYPE(ACC_DATA_TYPE, 8))); |
| } |
| } |
| |
| #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); |
| } |
| |
| #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; |
| } |
| |
| inline void offset_no_padding_nhwc_3D(const Tensor3D *input, uint *offset_x0, uint *offset_x1, uint *offset_x2, uint *offset_x3) |
| { |
| const int pad_horiz = PAD_TENSOR_LEFT + PAD_TENSOR_RIGHT; |
| |
| const int x = get_global_id(0); |
| const int y = get_global_id(1) * STRIDE_X; |
| const int z = get_global_id(2) * STRIDE_Y; |
| |
| //x axis: component, y axis: width, z axis: height |
| const uint padded_offset = input->offset_first_element_in_bytes |
| + x * 8 * input->stride_x |
| + y * input->stride_y |
| + z * input->stride_z; |
| |
| const uint offset_base = padded_offset |
| - (z + 1) * PAD_TENSOR_TOP * input->stride_y /* Top padding for each z plane */ |
| - y * pad_horiz * sizeof(DATA_TYPE) /* Horizontal padding for each row */ |
| - z * MAX_WIDTH * pad_horiz * sizeof(DATA_TYPE) /* Horizontal padding for each z plane */ |
| - PAD_TENSOR_LEFT * sizeof(DATA_TYPE); |
| |
| *offset_x0 = (uint)offset_base / sizeof(DATA_TYPE); |
| *offset_x1 = *offset_x0 + input->stride_y / sizeof(DATA_TYPE) - pad_horiz; |
| *offset_x2 = *offset_x0 + input->stride_z / sizeof(DATA_TYPE) - pad_horiz * MAX_WIDTH - PAD_TENSOR_TOP * input->stride_y / sizeof(DATA_TYPE); |
| *offset_x3 = *offset_x2 + input->stride_y / sizeof(DATA_TYPE) - pad_horiz; |
| |
| return; |
| } |
| |
| #if defined(DST_DEPTH) |
| inline void offset_no_padding_nhwc_4D(const Tensor4D *input, uint *offset_x0, uint *offset_x1, uint *offset_x2, uint *offset_x3) |
| { |
| const int pad_horiz = PAD_TENSOR_LEFT + PAD_TENSOR_RIGHT; |
| const int z_max = get_global_size(2) / BATCH_SIZE; |
| |
| const int x = get_global_id(0); |
| const int y = get_global_id(1) * STRIDE_X; |
| const int z = (get_global_id(2) % z_max) * STRIDE_Y; |
| const int w = get_global_id(2) / z_max; |
| |
| const unsigned int padded_offset = input->offset_first_element_in_bytes |
| + x * 8 * input->stride_x |
| + y * input->stride_y |
| + z * input->stride_z; |
| |
| const unsigned int offset_base = padded_offset |
| - (z + 1) * PAD_TENSOR_TOP * input->stride_y /* Top padding for each z plane */ |
| - y * pad_horiz * sizeof(DATA_TYPE) /* Horizontal padding for each row */ |
| - z * MAX_WIDTH * pad_horiz * sizeof(DATA_TYPE) /* Horizontal padding for each z plane */ |
| - PAD_TENSOR_LEFT * sizeof(DATA_TYPE); |
| |
| *offset_x0 = (uint)offset_base / sizeof(DATA_TYPE); |
| *offset_x1 = *offset_x0 + input->stride_y / sizeof(DATA_TYPE) - pad_horiz; |
| *offset_x2 = *offset_x0 + input->stride_z / sizeof(DATA_TYPE) - pad_horiz * MAX_WIDTH - PAD_TENSOR_TOP * input->stride_y / sizeof(DATA_TYPE); |
| *offset_x3 = *offset_x2 + input->stride_y / sizeof(DATA_TYPE) - pad_horiz; |
| |
| return; |
| } |
| #endif //defined(DST_DEPTH) |
| |
| #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) |
| } |
| |
| /** Performs a MAX pooling of pool size equal to 2, and record max value indices for NHWC. |
| * |
| * @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_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 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_stride_w Stride of the indices tensor in W dimension (in bytes) |
| * @param[in] indices_step_w indices_stride_w * number of elements along W 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_nhwc_indices_fp32( |
| TENSOR4D_DECLARATION(input), |
| TENSOR4D_DECLARATION(output), |
| TENSOR4D_DECLARATION(indices)) |
| { |
| // 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); |
| Tensor4D indices = CONVERT_TO_TENSOR4D_STRUCT(indices, DST_DEPTH); |
| #else /* defined(DST_DEPTH) */ |
| Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); |
| Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); |
| Tensor3D indices = CONVERT_TO_TENSOR3D_STRUCT(indices); |
| #endif /* defined(DST_DEPTH) */ |
| |
| #if defined(DST_DEPTH) |
| // Load data |
| float8 data_top0 = VLOAD(8)(0, (__global float *)tensor4D_offset(&input, 0, 0, 0, 0)); |
| float8 data_top1 = VLOAD(8)(0, (__global float *)tensor4D_offset(&input, 0, 1, 0, 0)); |
| float8 data_bottom0 = VLOAD(8)(0, (__global float *)tensor4D_offset(&input, 0, 0, 1, 0)); |
| float8 data_bottom1 = VLOAD(8)(0, (__global float *)tensor4D_offset(&input, 0, 1, 1, 0)); |
| #else /* defined(DST_DEPTH) */ |
| // Load data |
| float8 data_top0 = VLOAD(8)(0, (__global float *)tensor3D_offset(&input, 0, 0, 0)); |
| float8 data_top1 = VLOAD(8)(0, (__global float *)tensor3D_offset(&input, 0, 1, 0)); |
| float8 data_bottom0 = VLOAD(8)(0, (__global float *)tensor3D_offset(&input, 0, 0, 1)); |
| float8 data_bottom1 = VLOAD(8)(0, (__global float *)tensor3D_offset(&input, 0, 1, 1)); |
| #endif /* defined(DST_DEPTH) */ |
| |
| float8 data_top_max = POOL_OP(data_top0, data_top1); |
| float8 data_bottom_max = POOL_OP(data_bottom0, data_bottom1); |
| float8 data_max = POOL_OP(data_top_max, data_bottom_max); |
| vstore8(data_max, 0, (__global float *)output.ptr); |
| |
| #if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) |
| |
| uint offset_x0 = 0; |
| uint offset_x1 = 0; |
| uint offset_x2 = 0; |
| uint offset_x3 = 0; |
| |
| #if defined(DST_DEPTH) |
| offset_no_padding_nhwc_4D(&input, &offset_x0, &offset_x1, &offset_x2, &offset_x3); |
| #else /* defined(DST_DEPTH) */ |
| offset_no_padding_nhwc_3D(&input, &offset_x0, &offset_x1, &offset_x2, &offset_x3); |
| #endif /* defined(DST_DEPTH) */ |
| |
| uint8 voffset_x0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3, offset_x0 + 4, offset_x0 + 5, offset_x0 + 6, offset_x0 + 7 }; |
| uint8 voffset_x1 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3, offset_x1 + 4, offset_x1 + 5, offset_x1 + 6, offset_x1 + 7 }; |
| uint8 voffset_x2 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3, offset_x2 + 4, offset_x2 + 5, offset_x2 + 6, offset_x2 + 7 }; |
| uint8 voffset_x3 = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3, offset_x3 + 4, offset_x3 + 5, offset_x3 + 6, offset_x3 + 7 }; |
| |
| uint8 index0 = select(voffset_x1, voffset_x0, isgreaterequal(data_top0, data_top1)); |
| uint8 index1 = select(voffset_x3, voffset_x2, isgreaterequal(data_bottom0, data_bottom1)); |
| uint8 index = select(index1, index0, isgreaterequal(data_top_max, data_bottom_max)); |
| vstore8(index, 0, (__global uint *)indices.ptr); |
| |
| #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 NHWC. |
| * |
| * @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_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 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_stride_w Stride of the indices tensor in W dimension (in bytes) |
| * @param[in] indices_step_w indices_stride_w * number of elements along W 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_nhwc_indices_fp16( |
| TENSOR4D_DECLARATION(input), |
| TENSOR4D_DECLARATION(output), |
| TENSOR4D_DECLARATION(indices)) |
| { |
| // 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); |
| Tensor4D indices = CONVERT_TO_TENSOR4D_STRUCT(indices, DST_DEPTH); |
| #else /* defined(DST_DEPTH) */ |
| Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); |
| Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); |
| Tensor3D indices = CONVERT_TO_TENSOR3D_STRUCT(indices); |
| #endif /* defined(DST_DEPTH) */ |
| |
| #if defined(DST_DEPTH) |
| // Load data |
| half8 data_top0 = VLOAD(8)(0, (__global half *)tensor4D_offset(&input, 0, 0, 0, 0)); |
| half8 data_top1 = VLOAD(8)(0, (__global half *)tensor4D_offset(&input, 0, 1, 0, 0)); |
| half8 data_bottom0 = VLOAD(8)(0, (__global half *)tensor4D_offset(&input, 0, 0, 1, 0)); |
| half8 data_bottom1 = VLOAD(8)(0, (__global half *)tensor4D_offset(&input, 0, 1, 1, 0)); |
| #else /* defined(DST_DEPTH) */ |
| // Load data |
| half8 data_top0 = VLOAD(8)(0, (__global half *)tensor3D_offset(&input, 0, 0, 0)); |
| half8 data_top1 = VLOAD(8)(0, (__global half *)tensor3D_offset(&input, 0, 1, 0)); |
| half8 data_bottom0 = VLOAD(8)(0, (__global half *)tensor3D_offset(&input, 0, 0, 1)); |
| half8 data_bottom1 = VLOAD(8)(0, (__global half *)tensor3D_offset(&input, 0, 1, 1)); |
| #endif /* defined(DST_DEPTH) */ |
| |
| half8 data_top_max = POOL_OP(data_top0, data_top1); |
| half8 data_bottom_max = POOL_OP(data_bottom0, data_bottom1); |
| half8 data_max = POOL_OP(data_top_max, data_bottom_max); |
| vstore8(data_max, 0, (__global half *)output.ptr); |
| |
| #if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) |
| |
| uint offset_x0_int = 0; |
| uint offset_x1_int = 0; |
| uint offset_x2_int = 0; |
| uint offset_x3_int = 0; |
| |
| #if defined(DST_DEPTH) |
| offset_no_padding_nhwc_4D(&input, &offset_x0_int, &offset_x1_int, &offset_x2_int, &offset_x3_int); |
| #else /* defined(DST_DEPTH) */ |
| offset_no_padding_nhwc_3D(&input, &offset_x0_int, &offset_x1_int, &offset_x2_int, &offset_x3_int); |
| #endif /* defined(DST_DEPTH) */ |
| |
| ushort offset_x0 = (ushort)offset_x0_int; |
| ushort offset_x1 = (ushort)offset_x1_int; |
| ushort offset_x2 = (ushort)offset_x2_int; |
| ushort offset_x3 = (ushort)offset_x3_int; |
| |
| ushort8 voffset_x0 = { offset_x0, offset_x0 + 1, offset_x0 + 2, offset_x0 + 3, offset_x0 + 4, offset_x0 + 5, offset_x0 + 6, offset_x0 + 7 }; |
| ushort8 voffset_x1 = { offset_x1, offset_x1 + 1, offset_x1 + 2, offset_x1 + 3, offset_x1 + 4, offset_x1 + 5, offset_x1 + 6, offset_x1 + 7 }; |
| ushort8 voffset_x2 = { offset_x2, offset_x2 + 1, offset_x2 + 2, offset_x2 + 3, offset_x2 + 4, offset_x2 + 5, offset_x2 + 6, offset_x2 + 7 }; |
| ushort8 voffset_x3 = { offset_x3, offset_x3 + 1, offset_x3 + 2, offset_x3 + 3, offset_x3 + 4, offset_x3 + 5, offset_x3 + 6, offset_x3 + 7 }; |
| |
| ushort8 index0 = select(voffset_x1, voffset_x0, isgreaterequal(data_top0, data_top1)); |
| ushort8 index1 = select(voffset_x3, voffset_x2, isgreaterequal(data_bottom0, data_bottom1)); |
| ushort8 index = select(index1, index0, isgreaterequal(data_top_max, data_bottom_max)); |
| vstore8(CONVERT(index, uint8), 0, (__global uint *)indices.ptr); |
| |
| #endif /* defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM */ |
| } |