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/*
* Copyright (c) 2019-2020 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_asymm.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) && defined(OFFSET_IN) && defined(OFFSET_OUT) && defined(SCALE_IN) && defined(SCALE_OUT) && defined(OFFSET_ROIS) && defined(SCALE_ROIS) // 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)
const float data1_f32 = DEQUANTIZE(data1, OFFSET_IN, SCALE_IN, DATA_TYPE, 1);
const float data2_f32 = DEQUANTIZE(data2, OFFSET_IN, SCALE_IN, DATA_TYPE, 1);
const float data3_f32 = DEQUANTIZE(data3, OFFSET_IN, SCALE_IN, DATA_TYPE, 1);
const float data4_f32 = DEQUANTIZE(data4, OFFSET_IN, SCALE_IN, DATA_TYPE, 1);
sum += w1 * data1_f32 + w2 * data2_f32 + w3 * data3_f32 + w4 * data4_f32;
}
}
const float res_f32 = sum / (grid_size_x * grid_size_y);
return QUANTIZE(res_f32, OFFSET_OUT, SCALE_OUT, DATA_TYPE, 1);
}
/** Performs a roi align function.
*
* @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=uchar
* @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: QASYMM8
* @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: QASYMM16 with 0.125f scale and 0 offset
* @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_quantized(
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 = *((__global ushort *)offset(&rois, 0, pw));
float4 roi = DEQUANTIZE(vload4(0, (__global ushort *)offset(&rois, 1, pw)), OFFSET_ROIS, SCALE_ROIS, ushort, 4);
float2 roi_anchor = roi.s01 * convert_float(SPATIAL_SCALE);
float2 roi_dims = fmax((roi.s23 - roi.s01) * convert_float(SPATIAL_SCALE), 1.f);
// Calculate pooled region start and end
float2 spatial_indx = (float2)(px, py);
float2 pooled_dims = (float2)(POOLED_DIM_X, POOLED_DIM_Y);
float2 max_spatial_dims = (float2)(MAX_DIM_X, MAX_DIM_Y);
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)
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.
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
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