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+/*
+ * Copyright (c) 2017 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 array. Layout: {x, y, width, height, batch_indx}
+ * @param[in] rois_stride_x Stride of the rois array in X dimension (in bytes)
+ * @param[in] rois_step_x rois_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] rois_offset_first_element_in_bytes The offset of the first element in the rois array
+ * @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),
+ VECTOR_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);
+ Vector rois = CONVERT_TO_VECTOR_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:
+ // { x, y, width, height, batch_index }
+ const ushort8 roi = vload8(0, (__global ushort *)vector_offset(&rois, 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) * (float)SPATIAL_SCALE), 1.f));
+
+ // Determine pooled region in input image to pooled region in output image ratio
+ const float2 pool_region_ratio = convert_float2(roi_dims) / (float2)(POOLED_DIM_X, POOLED_DIM_Y);
+
+ // Calculate pooled region start and end
+ const float2 spatial_indx = (float2)(px, py);
+ const int2 max_spatial_dims = (int2)(MAX_DIM_X, MAX_DIM_Y);
+ int2 region_start = convert_int2_sat(floor(spatial_indx * pool_region_ratio)) + roi_anchor;
+ int2 region_end = convert_int2_sat(ceil((spatial_indx + 1) * pool_region_ratio)) + 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.s4 * 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);
+ }
+}