/* * Copyright (c) 2018-2021 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" // This specifies the value to shift the result of roi_dims / pooled_dims before ceiling. // It is close to the epsilon machine (for a floating point system, x and x+EPS are the same number). #define EPS_GRID 0.00001f #if defined(DATA_TYPE) && defined(POOLED_DIM_X) && defined(POOLED_DIM_Y) && defined(MAX_DIM_X) && defined(MAX_DIM_Y) && defined(MAX_DIM_Z) && defined(SPATIAL_SCALE) // Check for compile time constants /** Performs a roi align 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 An average pooled value from the region specified in the input tensor. */ inline DATA_TYPE roi_align_1x1(const Tensor3D *input, float region_start_x, float bin_size_x, float grid_size_x, float region_end_x, float region_start_y, float bin_size_y, float grid_size_y, float region_end_y, int pz) { // Iterate through the pooling region float sum = 0; for(int iy = 0; iy < grid_size_y; ++iy) { for(int ix = 0; ix < grid_size_x; ++ix) { // Align the window in the middle of every bin const float y = region_start_y + (iy + 0.5f) * bin_size_y / (float)grid_size_y; const float x = region_start_x + (ix + 0.5f) * bin_size_x / (float)grid_size_x; // Interpolation in the unit square const int y_low = (int)y; const int x_low = (int)x; const int y_high = y_low + 1; const int x_high = x_low + 1; const float ly = y - y_low; const float lx = x - x_low; const float hy = 1.f - ly; const float hx = 1.f - lx; const float w1 = hy * hx; const float w2 = hy * lx; const float w3 = ly * hx; const float w4 = ly * lx; #if defined(NHWC) const DATA_TYPE data1 = *(__global DATA_TYPE *)tensor3D_offset(input, pz, x_low, y_low); const DATA_TYPE data2 = *(__global DATA_TYPE *)tensor3D_offset(input, pz, x_high, y_low); const DATA_TYPE data3 = *(__global DATA_TYPE *)tensor3D_offset(input, pz, x_low, y_high); const DATA_TYPE data4 = *(__global DATA_TYPE *)tensor3D_offset(input, pz, x_high, y_high); #else // !defined(NHWC) const DATA_TYPE data1 = *(__global DATA_TYPE *)tensor3D_offset(input, x_low, y_low, pz); const DATA_TYPE data2 = *(__global DATA_TYPE *)tensor3D_offset(input, x_high, y_low, pz); const DATA_TYPE data3 = *(__global DATA_TYPE *)tensor3D_offset(input, x_low, y_high, pz); const DATA_TYPE data4 = *(__global DATA_TYPE *)tensor3D_offset(input, x_high, y_high, pz); #endif // defined(NHWC) sum += w1 * data1 + w2 * data2 + w3 * data3 + w4 * data4; } } return (DATA_TYPE)(sum / (grid_size_x * grid_size_y)); } /** Performs a roi align 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; * @note Sampling ratio (i.e., the number of samples in each bin) may be passed using -DSAMPLING_RATIO. If not defined each roi * will have a default sampling ratio of roi_dims/pooling_dims * * @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 pooled region of the source tensor 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 tensor. Supported data types: 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_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes) * @param[in] output_stride_w Stride of the destination tensor in W dimension (in bytes) */ __kernel void roi_align_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); #if defined(NHWC) const int px = get_global_id(1); const int py = get_global_id(2); const int pw = get_global_id(0); #else // !defined(NHWC) const int px = get_global_id(0); const int py = get_global_id(1); const int pw = get_global_id(2); #endif // defined(NHWC) // 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 float2 roi_anchor = convert_float2(roi.s01) * convert_float(SPATIAL_SCALE); const float2 roi_dims = fmax(convert_float2(roi.s23 - roi.s01) * convert_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 float2 max_spatial_dims = (float2)(MAX_DIM_X, MAX_DIM_Y); const float2 bin_size = (float2)((roi_dims.s0 / (float)POOLED_DIM_X), (roi_dims.s1 / (float)POOLED_DIM_Y)); float2 region_start = spatial_indx * bin_size + roi_anchor; float2 region_end = (spatial_indx + 1) * bin_size + roi_anchor; region_start = clamp(region_start, 0, max_spatial_dims); region_end = clamp(region_end, 0, max_spatial_dims); #if defined(SAMPLING_RATIO) const float2 roi_bin_grid = SAMPLING_RATIO; #else // !defined(SAMPLING_RATIO) // Note that we subtract EPS_GRID before ceiling. This is to avoid situations where 1.000001 gets ceiled to 2. const float2 roi_bin_grid = ceil(bin_size - EPS_GRID); #endif // defined(SAMPLING_RATIO) // 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) { #if defined(NHWC) __global DATA_TYPE *_output_ptr = (__global DATA_TYPE *)tensor3D_offset(&output, pz, px, py); #else // !defined(NHWC) __global DATA_TYPE *_output_ptr = (__global DATA_TYPE *)tensor3D_offset(&output, px, py, pz); #endif // defined(NHWC) *_output_ptr = (__global DATA_TYPE)roi_align_1x1(&input, region_start.x, bin_size.x, roi_bin_grid.x, region_end.x, region_start.y, bin_size.y, roi_bin_grid.y, region_end.y, pz); } } #endif // Check for compile time constants