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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 defined POOL_AVG
#define POOL_OP(x, y) ((x) + (y))
#else
#define POOL_OP(x, y) (fmax((x), (y)))
#endif
float calculate_avg_scale(const int pool_size, const int upper_bound_w, const int upper_bound_h,
const int pad_x, const int pad_y, const int stride_x, const int stride_y)
{
int start_x = get_global_id(0) * stride_x - pad_x;
int start_y = get_global_id(1) * stride_y - pad_y;
int end_x = min(start_x + pool_size, upper_bound_w);
int end_y = min(start_y + pool_size, upper_bound_h);
return 1.f / ((end_y - start_y) * (end_x - start_x));
}
/** Performs a pooling function of pool size equal to 2.
*
* @note Pooling stride must be passed using -DPOOL_STRIDE e.g -DPOOL_STRIDE=2. Supported strides are 1,2,3
* @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32;
* @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed.
*
* @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 source image
* @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 source 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] max_dims The maximum index that can be accessed in x and y dimension (width + pad)
* @param[in] strides The pooling operation strides in each dimension
* @param[in] paddings The pooling operation paddings in each dimension
*/
__kernel void pooling_layer_2(
TENSOR3D_DECLARATION(input),
TENSOR3D_DECLARATION(output)
#ifdef POOL_AVG
,
int2 max_dims, int2 strides, int2 paddings
#endif
)
{
// Get pixels pointer
Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
// Load data
VEC_DATA_TYPE(DATA_TYPE, 2)
data0 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0));
VEC_DATA_TYPE(DATA_TYPE, 2)
data1 = vload2(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0));
// Perform calculations
data0 = POOL_OP(data0, data1);
DATA_TYPE res = POOL_OP(data0.s0, data0.s1);
// Divide by 4 in case of average pooling
#ifdef POOL_AVG
res *= calculate_avg_scale(2, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y);
#endif
// Store result
*(__global DATA_TYPE *)output.ptr = res;
}
/** Performs a pooling function of pool size equal to 3.
*
* @note Pooling stride must be passed using -DPOOL_STRIDE e.g -DPOOL_STRIDE=2. Supported strides are 1,2,3
* @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32;
* @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed.
*
* @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 source image
* @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 source 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] max_dims The maximum index that can be accessed in x and y dimension (width + pad)
* @param[in] strides The pooling operation strides in each dimension
* @param[in] paddings The pooling operation paddings in each dimension
*/
__kernel void pooling_layer_3(
TENSOR3D_DECLARATION(input),
TENSOR3D_DECLARATION(output)
#ifdef POOL_AVG
,
int2 max_dims, int2 strides, int2 paddings
#endif
)
{
// Get pixels pointer
Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
// Load data
VEC_DATA_TYPE(DATA_TYPE, 3)
data0 = vload3(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0));
VEC_DATA_TYPE(DATA_TYPE, 3)
data1 = vload3(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0));
VEC_DATA_TYPE(DATA_TYPE, 3)
data2 = vload3(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0));
// Perform calculations
data0 = POOL_OP(data0, data1);
data0 = POOL_OP(data0, data2);
DATA_TYPE res = POOL_OP(POOL_OP(data0.s0, data0.s1), data0.s2);
// Divide by 4 in case of average pooling
#ifdef POOL_AVG
res *= calculate_avg_scale(3, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y);
#endif
// Store result
*(__global DATA_TYPE *)output.ptr = res;
}
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