1/* 2 * Copyright (c) 2017-2021 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24#include "helpers.h" 25#include "helpers_asymm.h" 26 27#if DATA_SIZE == 32 28#define VEC_SIZE 4 29#define VEC_MAX vec4_max 30#elif DATA_SIZE == 16 31#define VEC_SIZE 8 32#define VEC_MAX vec8_max 33#elif DATA_SIZE == 8 34#define VEC_SIZE 16 35#define VEC_MAX vec16_max 36#else /* DATA_SIZE not equals 8, 16, 32 */ 37#error "Unsupported data size" 38#endif /* DATA_SIZE == 32 */ 39 40// Define whether to use max (Quantized datatype) or fmax (Float) functions 41#if defined(OFFSET_OUT) && defined(SCALE_OUT) 42#define MAX(x, y) max(x, y) 43#else // !(defined(OFFSET_OUT) && defined(SCALE_OUT) 44#define MAX(x, y) fmax(x, y) 45#endif // defined(OFFSET_OUT) && defined(SCALE_OUT) 46 47inline DATA_TYPE vec4_max(VEC_DATA_TYPE(DATA_TYPE, 4) vec) 48{ 49 VEC_DATA_TYPE(DATA_TYPE, 2) 50 temp = MAX(vec.lo, vec.hi); 51 return MAX(temp.x, temp.y); 52} 53 54inline DATA_TYPE vec8_max(VEC_DATA_TYPE(DATA_TYPE, 8) vec) 55{ 56 VEC_DATA_TYPE(DATA_TYPE, 4) 57 temp = MAX(vec.lo, vec.hi); 58 return vec4_max(temp); 59} 60 61inline DATA_TYPE vec16_max(VEC_DATA_TYPE(DATA_TYPE, 16) vec) 62{ 63 VEC_DATA_TYPE(DATA_TYPE, 8) 64 temp = MAX(vec.lo, vec.hi); 65 return vec8_max(temp); 66} 67 68/** Performs a roi pooling on a single output pixel. 69 * 70 * @param[in] input Pointer to input Tensor3D struct. 71 * @param[in] region_start_x Start x index projected onto the input tensor. 72 * @param[in] region_end_x End x index projected onto the input tensor. 73 * @param[in] region_start_y Start y index projected onto the input tensor. 74 * @param[in] region_end_y End y index projected onto the input tensor. 75 * @param[in] pz z index of the input tensor. 76 * 77 * @return A max pooled value from the region specified in the input tensor. 78 */ 79inline DATA_TYPE roi_pool_1x1(const Tensor3D *input, int region_start_x, int region_end_x, int region_start_y, int region_end_y, int pz) 80{ 81 // Iterate through the pooling region 82 if((region_end_x <= region_start_x) || (region_end_y <= region_start_y)) 83 { 84 return (DATA_TYPE)0; 85 } 86 else 87 { 88 int num_iter = (int)((region_end_x - region_start_x) / VEC_SIZE); 89 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) 90 curr_max = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(MIN_VALUE); 91 92 for(int j = region_start_y; j < region_end_y; ++j) 93 { 94 int i = region_start_x; 95 for(; i < region_start_x + num_iter * VEC_SIZE; i += VEC_SIZE) 96 { 97 VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) 98 val = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor3D_offset(input, i, j, pz)); 99 curr_max = MAX(val, curr_max); 100 } 101 for(; i < region_end_x; ++i) 102 { 103 DATA_TYPE val = *(__global DATA_TYPE *)tensor3D_offset(input, i, j, pz); 104 curr_max = MAX(curr_max, val); 105 } 106 } 107 108 const DATA_TYPE temp = (DATA_TYPE)VEC_MAX(curr_max); 109 110#if defined(OFFSET_OUT) && defined(SCALE_OUT) 111 return QUANTIZE(temp, OFFSET_OUT, SCALE_OUT, DATA_TYPE, 1); 112#endif /* if quantized, requantize and return */ 113 114 return temp; 115 } 116} 117 118/** Performs a roi pooling function. 119 * 120 * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32, QASYMM8; 121 * @note Datasize must be passed using -DDATA_SIZE e.g. -DDATA_SIZE=32; 122 * @note Input dimensions must be passed using -DMAX_DIM_X, -DMAX_DIM_Y and -DMAX_DIM_Z; 123 * @note Pooled region dimensions must be passed using -DPOOLED_DIM_X and -DPOOLED_DIM_Y; 124 * @note Spatial scale must be passed using -DSPATIAL_SCALE; 125 * 126 * @param[in] input_ptr Pointer to the source image. Supported data types: F16, F32, QASYMM8 127 * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) 128 * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) 129 * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) 130 * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) 131 * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) 132 * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) 133 * @param[in] input_offset_first_element_in_bytes The offset of the first element in the pooled region of the source image as specifed by ROI 134 * @param[in] rois_ptr Pointer to the ROIs tensor. Layout: { batch_index, x1, y1, x2, y2 }. Supported data types: same as @p input_ptr 135 * @param[in] rois_stride_x Stride of the ROIs tensor in X dimension (in bytes) 136 * @param[in] rois_step_x Step of the ROIs tensor in X dimension (in bytes) 137 * @param[in] rois_stride_y Stride of the ROIs tensor in Y dimension (in bytes) 138 * @param[in] rois_step_y Step of the ROIs tensor in Y dimension (in bytes) 139 * @param[in] rois_offset_first_element_in_bytes The offset of the first element in the ROIs tensor 140 * @param[out] output_ptr Pointer to the destination image. Supported data types: same as input 141 * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) 142 * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) 143 * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) 144 * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) 145 * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) 146 * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) 147 * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image 148 * @param[in] input_stride_w Stride of the source image in W dimension (in bytes) 149 * @param[in] output_stride_w Stride of the destination image in W dimension (in bytes) 150 */ 151__kernel void roi_pooling_layer( 152 TENSOR3D_DECLARATION(input), 153 IMAGE_DECLARATION(rois), 154 TENSOR3D_DECLARATION(output), 155 unsigned int input_stride_w, unsigned int output_stride_w) 156{ 157 // Get pixels pointer 158 Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input); 159 Image rois = CONVERT_TO_IMAGE_STRUCT_NO_STEP(rois); 160 Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); 161 162 const int px = get_global_id(0); 163 const int py = get_global_id(1); 164 const int pw = get_global_id(2); 165 166 // Load roi parameters 167 // roi is laid out as follows { batch_index, x1, y1, x2, y2 } 168 const ushort roi_batch = (ushort) * ((__global ushort *)offset(&rois, 0, pw)); 169 const VEC_DATA_TYPE(ushort, 4) 170 roi = vload4(0, (__global ushort *)offset(&rois, 1, pw)); 171 const int2 roi_anchor = convert_int2_sat(round(convert_float2(roi.s01) * (float)SPATIAL_SCALE)); 172 const int2 roi_dims = convert_int2_sat(fmax(round(convert_float2(roi.s23 - roi.s01) * (float)SPATIAL_SCALE), 1.f)); 173 174 // Calculate pooled region start and end 175 const float2 spatial_indx = (float2)(px, py); 176 const float2 pooled_dims = (float2)(POOLED_DIM_X, POOLED_DIM_Y); 177 const int2 max_spatial_dims = (int2)(MAX_DIM_X, MAX_DIM_Y); 178 int2 region_start = convert_int2_sat(floor(spatial_indx / pooled_dims * convert_float2(roi_dims))) + roi_anchor; 179 int2 region_end = convert_int2_sat(floor((spatial_indx + 1) / pooled_dims * convert_float2(roi_dims))) + roi_anchor; 180 181 region_start = clamp(region_start, 0, max_spatial_dims); 182 region_end = clamp(region_end, 0, max_spatial_dims); 183 184 // Move input and output pointer across the fourth dimension 185 input.ptr += roi_batch * input_stride_w; 186 output.ptr += pw * output_stride_w; 187 188 for(int pz = 0; pz < MAX_DIM_Z; ++pz) 189 { 190 *(__global DATA_TYPE *)tensor3D_offset(&output, px, py, pz) = (__global DATA_TYPE)roi_pool_1x1(&input, 191 region_start.x, 192 region_end.x, 193 region_start.y, 194 region_end.y, pz); 195 } 196} 197