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/*
* 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);
}
}
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