/* * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "helpers.h" #if DATA_SIZE == 32 #define VEC_SIZE 4 #define VEC_MAX vec4_max #elif DATA_SIZE == 16 #define VEC_SIZE 8 #define VEC_MAX vec8_max #else /* DATA_SIZE not equals 32 or 16 */ #error "Unsupported data size" #endif /* DATA_SIZE == 32 */ inline DATA_TYPE vec4_max(VEC_DATA_TYPE(DATA_TYPE, 4) vec) { VEC_DATA_TYPE(DATA_TYPE, 2) temp = fmax(vec.lo, vec.hi); return fmax(temp.x, temp.y); } inline DATA_TYPE vec8_max(VEC_DATA_TYPE(DATA_TYPE, 8) vec) { VEC_DATA_TYPE(DATA_TYPE, 4) temp = fmax(vec.lo, vec.hi); return vec4_max(temp); } /** Performs a roi pooling on a single output pixel. * * @param[in] input Pointer to input Tensor3D struct. * @param[in] region_start_x Start x index projected onto the input tensor. * @param[in] region_end_x End x index projected onto the input tensor. * @param[in] region_start_y Start y index projected onto the input tensor. * @param[in] region_end_y End y index projected onto the input tensor. * @param[in] pz z index of the input tensor. * * @return A max pooled value from the region specified in the input tensor. */ inline 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) { // Iterate through the pooling region if((region_end_x <= region_start_x) || (region_end_y <= region_start_y)) { return (DATA_TYPE)0; } else { int num_iter = (int)((region_end_x - region_start_x) / VEC_SIZE); VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) curr_max = (VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(-FLT_MAX); for(int j = region_start_y; j < region_end_y; ++j) { int i = region_start_x; for(; i < region_start_x + num_iter * VEC_SIZE; i += VEC_SIZE) { VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) val = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor3D_offset(input, i, j, pz)); curr_max = fmax(val, curr_max); } for(; i < region_end_x; ++i) { DATA_TYPE val = *(__global DATA_TYPE *)tensor3D_offset(input, i, j, pz); curr_max = fmax(curr_max, val); } } return (DATA_TYPE)VEC_MAX(curr_max); } } /** Performs a roi pooling function. * * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32; * @note Datasize must be passed using -DDATA_SIZE e.g. -DDATA_SIZE=32; * @note Input dimensions must be passed using -DMAX_DIM_X, -DMAX_DIM_Y and -DMAX_DIM_Z; * @note Pooled region dimensions must be passed using -DPOOLED_DIM_X and -DPOOLED_DIM_Y; * @note Spatial scale must be passed using -DSPATIAL_SCALE; * * @param[in] input_ptr Pointer to the source image. Supported data types: F16, F32 * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] input_offset_first_element_in_bytes The offset of the first element in the pooled region of the source image as specifed by ROI * @param[in] rois_ptr Pointer to the ROIs tensor. Layout: { batch_index, x1, y1, x2, y2 }. Supported data types: same as @p input_ptr * @param[in] rois_stride_x Stride of the ROIs tensor in X dimension (in bytes) * @param[in] rois_step_x Step of the ROIs tensor in X dimension (in bytes) * @param[in] rois_stride_y Stride of the ROIs tensor in Y dimension (in bytes) * @param[in] rois_step_y Step of the ROIs tensor in Y dimension (in bytes) * @param[in] rois_offset_first_element_in_bytes The offset of the first element in the ROIs tensor * @param[out] output_ptr Pointer to the destination image. Supported data types: F16, F32 * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] output_stride_z Stride of the 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_offset_first_element_in_bytes The offset of the first element in the destination image * @param[in] input_stride_w Stride of the source image in W dimension (in bytes) * @param[in] output_stride_w Stride of the destination image in W dimension (in bytes) */ __kernel void roi_pooling_layer( TENSOR3D_DECLARATION(input), IMAGE_DECLARATION(rois), TENSOR3D_DECLARATION(output), unsigned int input_stride_w, unsigned int output_stride_w) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input); Image rois = CONVERT_TO_IMAGE_STRUCT_NO_STEP(rois); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); const int px = get_global_id(0); const int py = get_global_id(1); const int pw = get_global_id(2); // Load roi parameters // roi is laid out as follows { batch_index, x1, y1, x2, y2 } const ushort roi_batch = (ushort) * ((__global DATA_TYPE *)offset(&rois, 0, pw)); const VEC_DATA_TYPE(DATA_TYPE, 4) roi = vload4(0, (__global DATA_TYPE *)offset(&rois, 1, pw)); const int2 roi_anchor = convert_int2_sat(round(convert_float2(roi.s01) * (float)SPATIAL_SCALE)); const int2 roi_dims = convert_int2_sat(fmax(round(convert_float2(roi.s23 - roi.s01) * (float)SPATIAL_SCALE), 1.f)); // Calculate pooled region start and end const float2 spatial_indx = (float2)(px, py); const float2 pooled_dims = (float2)(POOLED_DIM_X, POOLED_DIM_Y); const int2 max_spatial_dims = (int2)(MAX_DIM_X, MAX_DIM_Y); int2 region_start = convert_int2_sat(floor(spatial_indx / pooled_dims * convert_float2(roi_dims))) + roi_anchor; int2 region_end = convert_int2_sat(floor((spatial_indx + 1) / pooled_dims * convert_float2(roi_dims))) + roi_anchor; region_start = clamp(region_start, 0, max_spatial_dims); region_end = clamp(region_end, 0, max_spatial_dims); // Move input and output pointer across the fourth dimension input.ptr += roi_batch * input_stride_w; output.ptr += pw * output_stride_w; for(int pz = 0; pz < MAX_DIM_Z; ++pz) { *(__global DATA_TYPE *)tensor3D_offset(&output, px, py, pz) = (__global DATA_TYPE)roi_pool_1x1(&input, region_start.x, region_end.x, region_start.y, region_end.y, pz); } }