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/*
* Copyright (c) 2017-2021 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"
#include "repeat.h"
#include "tile_helpers.h"
#if defined(POOL_AVG) || defined(POOL_L2)
#define POOL_OP(x, y) ((x) + (y))
#else /* defined(POOL_AVG) || defined(POOL_L2) */
#define POOL_OP(x, y) (fmax((x), (y)))
#endif /* defined(POOL_AVG) || defined(POOL_L2) */
#if defined(POOL_L2)
#define POW2_OP(x, vec_size) ((x) * (x))
#else /* defined(POOL_L2) */
#define POW2_OP(x, vec_size) (x)
#endif /* defined(POOL_L2) */
#define DIV_OP(x, y) (x * (1.f / y))
#define SQRT_OP(x) sqrt((x))
#if STRIDE_X == 1
#define POOLING3x3(res, input, output) POOLING3x3_STRIDE1(res, input, output)
#elif STRIDE_X == 2 /* STRIDE_X == 1 */
#define POOLING3x3(res, input, output) POOLING3x3_STRIDE2(res, input, output)
#elif STRIDE_X == 3 /* STRIDE_X not equals 1 or 2 */
#define POOLING3x3(res, input, output) POOLING3x3_STRIDE3(res, input, output)
#endif /* STRIDE_X == 3 */
#if defined(FP_MIXED_PRECISION)
#define CONVERT_TO_ACC_DATA_TYPE(x, n) CONVERT(x, VEC_DATA_TYPE(ACC_DATA_TYPE, n))
#define VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(n, offset, ptr) \
CONVERT_TO_ACC_DATA_TYPE(vload##n(offset, ptr), n)
#else /* defined(FP_MIXED_PRECISION) */
#define VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(n, offset, ptr) vload##n(offset, ptr)
#endif /* defined(FP_MIXED_PRECISION) */
#define POOLING3x3_STRIDE1(res, input, output) \
({ \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
data00 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 2) \
data01 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 4); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
data10 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 2) \
data11 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 4); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
data20 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 2) \
data21 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 4); \
data00 = POW2_OP(data00, 4); \
data01 = POW2_OP(data01, 2); \
data10 = POW2_OP(data10, 4); \
data11 = POW2_OP(data11, 2); \
data20 = POW2_OP(data20, 4); \
data21 = POW2_OP(data21, 2); \
\
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
values00 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data00.s01212323); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
values01 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data01.s0, data00.s3, data01.s01); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
values10 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data10.s01212323); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
values11 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data11.s0, data10.s3, data11.s01); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
values20 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data20.s01212323); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
values21 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data21.s0, data20.s3, data21.s01); \
\
values00 = POOL_OP(values00, values10); \
values01 = POOL_OP(values01, values11); \
values00 = POOL_OP(values00, values20); \
values01 = POOL_OP(values01, values21); \
\
res = POOL_OP((VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s147, values01.s2)); \
res = POOL_OP(res, (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s25, values01.s03)); \
})
#define POOLING3x3_STRIDE2(res, input, output) \
({ \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
data00 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \
ACC_DATA_TYPE data01 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8)); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
data10 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \
ACC_DATA_TYPE data11 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8)); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
data20 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \
ACC_DATA_TYPE data21 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 8)); \
data00 = POW2_OP(data00, 8); \
data01 = POW2_OP(data01, 1); \
data10 = POW2_OP(data10, 8); \
data11 = POW2_OP(data11, 1); \
data20 = POW2_OP(data20, 8); \
data21 = POW2_OP(data21, 1); \
\
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
values00 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data00.s01223445); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
values01 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s667, data01); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
values10 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data10.s01223445); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
values11 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data10.s667, data11); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
values20 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data20.s01223445); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
values21 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data20.s667, data21); \
\
values00 = POOL_OP(values00, values10); \
values01 = POOL_OP(values01, values11); \
values00 = POOL_OP(values00, values20); \
values01 = POOL_OP(values01, values21); \
\
res = POOL_OP((VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s147, values01.s2)); \
res = POOL_OP(res, (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s25, values01.s03)); \
})
#define POOLING3x3_STRIDE3(res, input, output) \
({ \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
data00 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
data01 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
data10 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
data11 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \
data20 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \
VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \
data21 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 8); \
data00 = POW2_OP(data00, 8); \
data01 = POW2_OP(data01, 4); \
data10 = POW2_OP(data10, 8); \
data11 = POW2_OP(data11, 4); \
data20 = POW2_OP(data20, 8); \
data21 = POW2_OP(data21, 4); \
\
data00 = POOL_OP(data00, data10); \
data01 = POOL_OP(data01, data11); \
data00 = POOL_OP(data00, data20); \
data01 = POOL_OP(data01, data21); \
\
res = POOL_OP((VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s036, data01.s1), (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s147, data01.s2)); \
res = POOL_OP(res, (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s25, data01.s03)); \
})
ACC_DATA_TYPE calculate_avg_scale(const int pool_size_x, const int pool_size_y, 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;
const int end_x = min(start_x + pool_size_x, upper_bound_w);
const int end_y = min(start_y + pool_size_y, upper_bound_h);
#if defined(EXCLUDE_PADDING)
start_x = max(0, start_x);
start_y = max(0, start_y);
#endif /* defined(EXCLUDE_PADDING) */
return ((end_y - start_y) * (end_x - start_x));
}
/** Performs a pooling function of pool size equal to 2.
*
* @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 the following information must be passed at compile time:
* -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed.
* -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
* -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
*
* @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 source tensor
* @param[out] output_ptr Pointer to the destination tensor. 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 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 tensor
*/
__kernel void pooling_layer_2(
TENSOR3D_DECLARATION(input),
TENSOR3D_DECLARATION(output))
{
// Get pixels pointer
Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
// Load data
VEC_DATA_TYPE(ACC_DATA_TYPE, 2)
data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0));
VEC_DATA_TYPE(ACC_DATA_TYPE, 2)
data1 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0));
#if defined(POOL_L2)
// Raise to power of 2 for L2 Pooling
data0 = POW2_OP(data0, 2);
data1 = POW2_OP(data1, 2);
#endif /* defined(POOL_L2) */
// Perform calculations
data0 = POOL_OP(data0, data1);
ACC_DATA_TYPE res = POOL_OP(data0.s0, data0.s1);
#if defined(POOL_AVG) || defined(POOL_L2)
// Divide by pool region in case of average or l2 pooling
res = DIV_OP(res, calculate_avg_scale(2, 2, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y));
#endif /* defined(POOL_AVG) || defined(POOL_L2) */
#if defined(POOL_L2)
// Take square root of the result in L2 pooling
res = SQRT_OP(res);
#endif /* defined(POOL_L2) */
// Store result
*(__global DATA_TYPE *)output.ptr = (DATA_TYPE)res;
}
/** Performs a pooling function of pool size equal to 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 the following information must be passed at compile time:
* -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed.
* -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
* -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
*
* @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 source tensor
* @param[out] output_ptr Pointer to the destination tensor. 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 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 tensor
*/
__kernel void pooling_layer_3(
TENSOR3D_DECLARATION(input),
TENSOR3D_DECLARATION(output))
{
// Get pixels pointer
Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
// Load data
VEC_DATA_TYPE(ACC_DATA_TYPE, 3)
data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(3, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0));
VEC_DATA_TYPE(ACC_DATA_TYPE, 3)
data1 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(3, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0));
VEC_DATA_TYPE(ACC_DATA_TYPE, 3)
data2 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(3, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0));
#if defined(POOL_L2)
// Raise to power of 2 for L2 Pooling
data0 = POW2_OP(data0, 3);
data1 = POW2_OP(data1, 3);
data2 = POW2_OP(data2, 3);
#endif /* defined(POOL_L2) */
// Perform calculations
data0 = POOL_OP(data0, data1);
data0 = POOL_OP(data0, data2);
ACC_DATA_TYPE res = POOL_OP(POOL_OP(data0.s0, data0.s1), data0.s2);
#if defined(POOL_AVG) || defined(POOL_L2)
// Divide by pool region in case of average pooling
res = DIV_OP(res, calculate_avg_scale(3, 3, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y));
#endif /* defined(POOL_AVG) || defined(POOL_L2) */
#if defined(POOL_L2)
// Take square root of the result in L2 pooling
res = SQRT_OP(res);
#endif /* defined(POOL_L2) */
// Store result
*(__global DATA_TYPE *)output.ptr = (DATA_TYPE)res;
}
#if defined(POOLING3x3)
#define CONVERT_OP(data_type) convert_##data_type##4
#define CONVERT_VECTOR4(data_type) CONVERT_OP(data_type)
VEC_DATA_TYPE(ACC_DATA_TYPE, 4)
calculate_avg_scale4(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)
{
int4 start_x = ((int4)get_global_id(0) * 4 + (int4)(0, 1, 2, 3)) * (int4)stride_x - (int4)pad_x;
int start_y = get_global_id(1) * stride_y - pad_y;
const int4 end_x = min(start_x + (int4)pool_size, (int4)upper_bound_w);
const int end_y = min(start_y + pool_size, upper_bound_h);
#if defined(EXCLUDE_PADDING)
start_x = max((int4)0, start_x);
start_y = max(0, start_y);
#endif /* defined(EXCLUDE_PADDING) */
return (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(1.f) / CONVERT_VECTOR4(ACC_DATA_TYPE)(((int4)(end_y - start_y)) * (end_x - start_x));
}
/** Performs an optimized pooling function of pool size equal to 3 when the stride_x is less equal than 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 the following information must be passed at compile time:
* -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed.
* -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
* -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
*
* @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 source tensor
* @param[out] output_ptr Pointer to the destination tensor. 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 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 tensor
*/
__kernel void pooling_layer_optimized_3(
TENSOR3D_DECLARATION(input),
TENSOR3D_DECLARATION(output))
{
// Get pixels pointer
Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
VEC_DATA_TYPE(ACC_DATA_TYPE, 4)
res;
// Perform pooling 3x3 for 4 output elements
POOLING3x3(res, input, output);
#if defined(POOL_AVG) || defined(POOL_L2)
// Divide by pool region in case of average pooling
res *= calculate_avg_scale4(3, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y);
#endif /* defined(POOL_AVG) || defined(POOL_L2) */
#if defined(POOL_L2)
// Take square root of the result in L2 pooling
res = SQRT_OP(res);
#endif /* defined(POOL_L2) */
vstore4(CONVERT(res, VEC_DATA_TYPE(DATA_TYPE, 4)), 0, (__global DATA_TYPE *)output.ptr);
}
#endif // defined(POOLING3x3)
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