| /* |
| * Copyright (c) 2017-2021 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 "helpers_asymm.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 |
| #elif DATA_SIZE == 8 |
| #define VEC_SIZE 16 |
| #define VEC_MAX vec16_max |
| #else /* DATA_SIZE not equals 8, 16, 32 */ |
| #error "Unsupported data size" |
| #endif /* DATA_SIZE == 32 */ |
| |
| // Define whether to use max (Quantized datatype) or fmax (Float) functions |
| #if defined(OFFSET_OUT) && defined(SCALE_OUT) |
| #define MAX(x, y) max(x, y) |
| #else // !(defined(OFFSET_OUT) && defined(SCALE_OUT) |
| #define MAX(x, y) fmax(x, y) |
| #endif // defined(OFFSET_OUT) && defined(SCALE_OUT) |
| |
| inline DATA_TYPE vec4_max(VEC_DATA_TYPE(DATA_TYPE, 4) vec) |
| { |
| VEC_DATA_TYPE(DATA_TYPE, 2) |
| temp = MAX(vec.lo, vec.hi); |
| return MAX(temp.x, temp.y); |
| } |
| |
| inline DATA_TYPE vec8_max(VEC_DATA_TYPE(DATA_TYPE, 8) vec) |
| { |
| VEC_DATA_TYPE(DATA_TYPE, 4) |
| temp = MAX(vec.lo, vec.hi); |
| return vec4_max(temp); |
| } |
| |
| inline DATA_TYPE vec16_max(VEC_DATA_TYPE(DATA_TYPE, 16) vec) |
| { |
| VEC_DATA_TYPE(DATA_TYPE, 8) |
| temp = MAX(vec.lo, vec.hi); |
| return vec8_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))(MIN_VALUE); |
| |
| 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 = MAX(val, curr_max); |
| } |
| for(; i < region_end_x; ++i) |
| { |
| DATA_TYPE val = *(__global DATA_TYPE *)tensor3D_offset(input, i, j, pz); |
| curr_max = MAX(curr_max, val); |
| } |
| } |
| |
| const DATA_TYPE temp = (DATA_TYPE)VEC_MAX(curr_max); |
| |
| #if defined(OFFSET_OUT) && defined(SCALE_OUT) |
| return QUANTIZE(temp, OFFSET_OUT, SCALE_OUT, DATA_TYPE, 1); |
| #endif /* if quantized, requantize and return */ |
| |
| return temp; |
| } |
| } |
| |
| /** Performs a roi pooling function. |
| * |
| * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32, QASYMM8; |
| * @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, QASYMM8 |
| * @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: same as input |
| * @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 ushort *)offset(&rois, 0, pw)); |
| const VEC_DATA_TYPE(ushort, 4) |
| roi = vload4(0, (__global ushort *)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); |
| } |
| } |