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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)
#if defined(POOL_SIZE_X) && defined(POOL_SIZE_Y)
/** Performs a pooling function of pool size equal to N (NCHW)
*
* @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
* @note Pool sizes must be passed using -DPOOL_SIZE_X and -DPOOL_SIZE_Y e.g. -DPOOL_SIZE_X=13;
* @note In case of average pooling the following information must be passed at compile time:
* -DPOOL_AVG 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
* @note The initial value for the pooling operation must be passed at compile time using -DINITIAL_VALUE e.g. -DINITIAL_VALUE=0
*
* @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_MxN_nchw(
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, 8)
vdata = INITIAL_VALUE;
ACC_DATA_TYPE sdata = INITIAL_VALUE;
// Load data
for(int y = 0; y < POOL_SIZE_Y; y++)
{
int x = 0;
for(; x <= ((int)POOL_SIZE_X - 8); x += 8)
{
VEC_DATA_TYPE(ACC_DATA_TYPE, 8)
data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0));
#if defined(POOL_L2)
// Raise to power of 2 for L2 Pooling
data0 *= data0;
#endif /* defined(POOL_L2) */
vdata = POOL_OP(vdata, data0);
}
// Leftover
for(; x < (int)POOL_SIZE_X; ++x)
{
ACC_DATA_TYPE data0 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0)));
#if defined(POOL_L2)
// Raise to power of 2 for L2 Pooling
data0 *= data0;
#endif /* defined(POOL_L2) */
sdata = POOL_OP(sdata, data0);
}
}
// Reduce result
VEC_DATA_TYPE(ACC_DATA_TYPE, 4)
reduce4 = POOL_OP(vdata.s0123, vdata.s4567);
VEC_DATA_TYPE(ACC_DATA_TYPE, 2)
reduce2 = POOL_OP(reduce4.s01, reduce4.s23);
ACC_DATA_TYPE res = POOL_OP(reduce2.s0, reduce2.s1);
res = POOL_OP(res, sdata);
#if defined(POOL_AVG) || defined(POOL_L2)
// Divide by pool region in case of average pooling
res = DIV_OP(res, calculate_avg_scale(POOL_SIZE_X, POOL_SIZE_Y, 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;
}
#endif // defined(POOL_SIZE_X) && defined(POOL_SIZE_Y)
#if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM)
inline void offset_no_padding_nchw(const Tensor3D *input, uint *offset_top, uint *offset_bottom)
{
const int pad_horiz = PAD_TENSOR_LEFT + PAD_TENSOR_RIGHT;
const int pad_vert = PAD_TENSOR_TOP + PAD_TENSOR_BOTTOM;
const int x = get_global_id(0) * STRIDE_X;
const int y = get_global_id(1) * STRIDE_Y;
const int z = get_global_id(2);
//x axis: width, y axis: height, z axis: component
const uint padded_offset = input->offset_first_element_in_bytes
+ x * input->stride_x
+ y * input->stride_y
+ z * input->stride_z;
const uint offset_base = padded_offset
- y * pad_horiz * sizeof(DATA_TYPE) /* Horizontal padding for each row */
- PAD_TENSOR_TOP * input->stride_y /* top padding */
- z * MAX_HEIGHT * pad_horiz * sizeof(DATA_TYPE) - z * pad_vert * input->stride_y /* Z plane padding */
- PAD_TENSOR_LEFT * sizeof(DATA_TYPE);
#if defined(TENSOR_CHANNEL) && defined(TENSOR_WIDTH) && defined(TENSOR_HEIGHT)
*offset_top = (uint)((offset_base / sizeof(DATA_TYPE)) % (TENSOR_CHANNEL * TENSOR_WIDTH * TENSOR_HEIGHT));
#else /* defined(TENSOR_CHANNEL) && defined(TENSOR_WIDTH) && defined(TENSOR_HEIGHT) */
*offset_top = (uint)(offset_base / sizeof(DATA_TYPE));
#endif /* defined(TENSOR_CHANNEL) && defined(TENSOR_WIDTH) && defined(TENSOR_HEIGHT) */
*offset_bottom = *offset_top + input->stride_y / sizeof(DATA_TYPE) - pad_horiz;
return;
}
#endif //defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM)
/** Performs a MAX pooling of pool size equal to 2, and record max value indices for NCHW.
*
* @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F32
* @note Pool sizes must be passed using -DPOOL_SIZE_X and -DPOOL_SIZE_Y e.g. -DPOOL_SIZE_X=13;
* @note Tensors width and height must be passed at compile time using -DMAX_WIDTH and -DMAX_HEIGHT
* @note Pool strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* @note Tensor padding values must be passed at compile time using PAD_TENSOR_LEFT, PAD_TENSOR_RIGHT, PAD_TENSOR_TOP and PAD_TENSOR_BOTTOM
*
* @param[in] input_ptr Pointer to the source tensor. Supported data types: 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
* @param[in] indices_ptr Pointer to the indices tensor. Supported data types: U32
* @param[in] indices_stride_x Stride of the indices tensor in X dimension (in bytes)
* @param[in] indices_step_x indices_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] indices_stride_y Stride of the indices tensor in Y dimension (in bytes)
* @param[in] indices_step_y indices_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] indices_stride_z Stride of the indices tensor in Z dimension (in bytes)
* @param[in] indices_step_z indices_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] indices_offset_first_element_in_bytes The offset of the first element in the indices tensor
*/
__kernel void pooling_layer_2_nchw_indices_fp32(
TENSOR3D_DECLARATION(input),
TENSOR3D_DECLARATION(output),
TENSOR3D_DECLARATION(indices))
{
// Get pixels pointer
Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
Tensor3D indices = CONVERT_TO_TENSOR3D_STRUCT(indices);
// Load data
float2 data0 = VLOAD(2)(0, (__global float *)tensor3D_offset(&input, 0, 0, 0));
float2 data1 = VLOAD(2)(0, (__global float *)tensor3D_offset(&input, 0, 1, 0));
// Perform calculations
float data0_max = POOL_OP(data0.s0, data0.s1);
float data1_max = POOL_OP(data1.s0, data1.s1);
float res = POOL_OP(data0_max, data1_max);
// Store result
*(__global float *)output.ptr = res;
#if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM)
uint offset_top = 0;
uint offset_bottom = 0;
offset_no_padding_nchw(&input, &offset_top, &offset_bottom);
uint index0 = select(offset_top + 1, offset_top, isgreaterequal(data0.s0, data0.s1));
uint index1 = select(offset_bottom + 1, offset_bottom, isgreaterequal(data1.s0, data1.s1));
uint index = select(index1, index0, isgreaterequal(data0_max, data1_max));
*(__global uint *)indices.ptr = index;
#endif //defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM)
}
/** Performs a MAX pooling of pool size equal to 2, and record max value indices for NCHW.
*
* @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F16
* @note Pool sizes must be passed using -DPOOL_SIZE_X and -DPOOL_SIZE_Y e.g. -DPOOL_SIZE_X=13;
* @note Tensors width and height must be passed at compile time using -DMAX_WIDTH and -DMAX_HEIGHT
* @note Pool strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* @note Tensor padding values must be passed at compile time using PAD_TENSOR_LEFT, PAD_TENSOR_RIGHT, PAD_TENSOR_TOP and PAD_TENSOR_BOTTOM
*
* @param[in] input_ptr Pointer to the source tensor. Supported data types: F16
* @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
* @param[in] indices_ptr Pointer to the indices tensor. Supported data types: U32
* @param[in] indices_stride_x Stride of the indices tensor in X dimension (in bytes)
* @param[in] indices_step_x indices_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] indices_stride_y Stride of the indices tensor in Y dimension (in bytes)
* @param[in] indices_step_y indices_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] indices_stride_z Stride of the indices tensor in Z dimension (in bytes)
* @param[in] indices_step_z indices_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] indices_offset_first_element_in_bytes The offset of the first element in the indices tensor
*/
__kernel void pooling_layer_2_nchw_indices_fp16(
TENSOR3D_DECLARATION(input),
TENSOR3D_DECLARATION(output),
TENSOR3D_DECLARATION(indices))
{
// Get pixels pointer
Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
Tensor3D indices = CONVERT_TO_TENSOR3D_STRUCT(indices);
// Load data
half2 data0 = VLOAD(2)(0, (__global half *)tensor3D_offset(&input, 0, 0, 0));
half2 data1 = VLOAD(2)(0, (__global half *)tensor3D_offset(&input, 0, 1, 0));
// Perform calculations
half data0_max = POOL_OP(data0.s0, data0.s1);
half data1_max = POOL_OP(data1.s0, data1.s1);
half res = POOL_OP(data0_max, data1_max);
// Store result
*(__global half *)output.ptr = res;
#if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM)
uint offset_top = 0;
uint offset_bottom = 0;
offset_no_padding_nchw(&input, &offset_top, &offset_bottom);
uint index0 = select(offset_top + 1, offset_top, isgreaterequal(data0.s0, data0.s1));
uint index1 = select(offset_bottom + 1, offset_bottom, isgreaterequal(data1.s0, data1.s1));
uint index = select(index1, index0, isgreaterequal(data0_max, data1_max));
*(__global uint *)indices.ptr = index;
#endif //defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM)
}
#if defined(VEC_SIZE) && defined(VEC_SIZE_LEFTOVER) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_CHANNELS) && defined(DST_HEIGHT) && defined(DST_BATCH_SIZE) && defined(ACC_DATA_TYPE)
#if defined(POOL_SIZE_X) && defined(POOL_SIZE_Y)
/** Performs pooling layer of size equal to MxN. This OpenCL kernel can perform the following pooling types:
* -# max, -DPOOL_MAX must be passed at compile time
* -# average, -DPOOL_AVG must be passed at compile time. If padding has to be expluded, -DEXCLUDE_PADDING should be passed at compile time
* -# l2 normalisation, -DPOOL_L2 must be passed at compile time
*
* @note Datatype must be passed at compile type using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F32/F16
* @note Accumulation data type must be passed at compile time using -DACC_DATA_TYPE e.g. -DACC_DATA_TYPE=float
* @note If -DFP_MIXED_PRECISION is passed at compile time, the kernel will use F32 for the partial result
* @note Pool size must be passed at compile time using -DPOOL_SIZE_X and -DPOOL_SIZE_Y. e.g. -DPOOL_SIZE_X=4, -DPOOL_SIZE_Y=4
* @note Input tensor width and height must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT
* @note Output tensor height, channels and batch size must be passed at compile time using -DDST_HEIGHT, -DDST_CHANNELS and -DDST_BATCH_SIZE
* @note Pool strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* @note Pool pads must be passed at compile time using -DPAD_X and -DPAD_Y
* @note Vector size must be passed at compile time using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
* @note Leftover vector size must be passed at compile time using -DVEC_SIZE_LEFTOVER. e.g. -DVEC_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VEC_SIZE
* @note The initial value for the pooling operation must be passed at compile time using -DINITIAL_VALUE e.g. -DINITIAL_VALUE=0
*
* @param[in] input_ptr Pointer to the source tensor. Supported data types: F32/F16
* @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_stride_w Stride of the source tensor in W dimension (in bytes)
* @param[in] input_step_w input_stride_w * number of elements along W 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 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_stride_w Stride of the destination tensor in W dimension (in bytes)
* @param[in] output_step_w output_stride_w * number of elements along W 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_MxN_nhwc(
TENSOR4D_DECLARATION(input),
TENSOR4D_DECLARATION(output))
{
// Note: If C is not multiple of VEC_SIZE, we shift back of VEC_SIZE_LEFTOVER elements to compute the leftover elements for get_global_id(0) == 0
// Note: If C is less than VEC_SIZE, VEC_SIZE should be SHRINKED to the closest smaller VEC_SIZE. This operation is performed on the host side
int idx_out_c = GET_SPATIAL_IDX(0, VEC_SIZE, VEC_SIZE_LEFTOVER);
int idx_out_w = GET_SPATIAL_IDX(1, 1, 0);
#if DST_BATCH_SIZE != 1
// If batch size != 1, the batch size dimension is collapsed over the height dimension
int idx_out_h = GET_SPATIAL_IDX(2, 1, 0) % DST_HEIGHT;
int idx_out_n = GET_SPATIAL_IDX(2, 1, 0) / DST_HEIGHT;
#else //DST_BATCH_SIZE != 1
int idx_out_h = GET_SPATIAL_IDX(2, 1, 0);
int idx_out_n = 0;
#endif // DST_BATCH_SIZE != 1
__global unsigned char *in_base_ptr = input_ptr + input_offset_first_element_in_bytes + idx_out_c * sizeof(DATA_TYPE) + idx_out_n * input_stride_w;
__global unsigned char *out_base_ptr = output_ptr + output_offset_first_element_in_bytes + idx_out_c * sizeof(DATA_TYPE) + idx_out_w * output_stride_y + idx_out_h * output_stride_z + idx_out_n *
output_stride_w;
VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)
res0 = INITIAL_VALUE;
#if POOL_SIZE_X == SRC_WIDTH && POOL_SIZE_Y == SRC_HEIGHT && PAD_X == 0 && PAD_Y == 0
// Global pooling path
int filter_size = POOL_SIZE_X * POOL_SIZE_Y;
#pragma unroll 8
for(int y = 0; y < POOL_SIZE_X * POOL_SIZE_Y; ++y)
{
VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)
data0;
#if defined(FP_MIXED_PRECISION)
// In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE
data0 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE));
#else // defined(FP_MIXED_PRECISION)
data0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr));
#endif // defined(FP_MIXED_PRECISION)
#if defined(POOL_L2)
// Raise to power of 2 for L2 Pooling
data0 *= data0;
#endif // defined(POOL_L2)
res0 = POOL_OP(res0, data0);
in_base_ptr += input_stride_y;
}
#else // POOL_SIZE_X == SRC_WIDTH && POOL_SIZE_Y == SRC_HEIGHT && PAD_X == 0 && PAD_Y == 0
int idx_in_w = idx_out_w * STRIDE_X - PAD_X;
int idx_in_h = idx_out_h * STRIDE_Y - PAD_Y;
int pool_x_s = max((int)0, -idx_in_w);
int pool_x_e = min((int)POOL_SIZE_X, (int)SRC_WIDTH - idx_in_w);
int pool_y_s = max((int)0, -idx_in_h);
int pool_y_e = min((int)POOL_SIZE_Y, (int)SRC_HEIGHT - idx_in_h);
#if defined(EXCLUDE_PADDING)
int filter_size = (pool_y_e - pool_y_s) * (pool_x_e - pool_x_s);
#else // defined(EXCLUDE_PADDING)
int filter_size = POOL_SIZE_X * POOL_SIZE_Y;
#endif // defined(EXCLUDE_PADDING)
for(int y = pool_y_s; y < pool_y_e; ++y)
{
for(int x = pool_x_s; x < pool_x_e; ++x)
{
VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)
data0;
#if defined(FP_MIXED_PRECISION)
// In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE
data0 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + (x + idx_in_w) * input_stride_y + (y + idx_in_h) * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE));
#else // defined(FP_MIXED_PRECISION)
data0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + (x + idx_in_w) * input_stride_y + (y + idx_in_h) * input_stride_z));
#endif // defined(FP_MIXED_PRECISION)
#if defined(POOL_L2)
// Raise to power of 2 for L2 Pooling
data0 *= data0;
#endif // defined(POOL_L2)
res0 = POOL_OP(res0, data0);
}
}
#endif // POOL_SIZE_X == SRC_WIDTH && POOL_SIZE_Y == SRC_HEIGHT && PAD_X == 0 && PAD_Y == 0
#if defined(POOL_AVG) || defined(POOL_L2)
res0 /= (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))filter_size;
#endif // defined(POOL_AVG) || defined(POOL_L2)
#if defined(POOL_L2)
// Take square root of the result in L2 pooling
res0 = SQRT_OP(res0);
#endif // defined(POOL_L2)
// Store result
#if defined(FP_MIXED_PRECISION)
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
res_converted0 = CONVERT(res0, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE));
STORE_VECTOR_SELECT(res_converted, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0);
#else // defined(FP_MIXED_PRECISION)
STORE_VECTOR_SELECT(res, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0);
#endif // defined(FP_MIXED_PRECISION)
}
#endif // defined(POOL_SIZE_X) && defined(POOL_SIZE_Y)
#define SELECT_TYPE SELECT_VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)
/** Performs pooling layer of size equal to 2. This OpenCL kernel can perform the following pooling types:
* -# max, -DPOOL_MAX must be passed at compile time
* -# max extracting the max index, -DPOOL_MAX and -DEXTRACT_MAX_INDEX must be passed at compile time
* -# average, -DPOOL_AVG must be passed at compile time. If padding has to be expluded, -DEXCLUDE_PADDING should be passed at compile time
* -# l2 normalisation, -DPOOL_L2 must be passed at compile time
*
* @note Datatype must be passed at compile type using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F32/F16
* @note Accumulation data type must be passed at compile time using -DACC_DATA_TYPE e.g. -DACC_DATA_TYPE=float
* @note If -DFP_MIXED_PRECISION is passed at compile time, the kernel will use F32 for the partial result
* @note Input tensor width and height must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT
* @note Output tensor height, channels and batch size must be passed at compile time using -DDST_HEIGHT, -DDST_CHANNELS and -DDST_BATCH_SIZE
* @note Pool strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* @note Pool pads must be passed at compile time using -DPAD_X and -DPAD_Y
* @note Vector size must be passed at compile time using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
* @note Leftover vector size must be passed at compile time using -DVEC_SIZE_LEFTOVER. e.g. -DVEC_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VEC_SIZE
* @note The initial value for the pooling operation must be passed at compile time using -DINITIAL_VALUE e.g. -DINITIAL_VALUE=0
*
* @param[in] input_ptr Pointer to the source tensor. Supported data types: F32/F16
* @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_stride_w Stride of the source tensor in W dimension (in bytes)
* @param[in] input_step_w input_stride_w * number of elements along W 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 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_stride_w Stride of the destination tensor in W dimension (in bytes)
* @param[in] output_step_w output_stride_w * number of elements along W 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] indices_ptr (Optional) Pointer to the indices tensor. Supported data types: U32
* @param[in] indices_stride_x (Optional) Stride of the indices tensor in X dimension (in bytes)
* @param[in] indices_step_x (Optional) indices_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] indices_stride_y (Optional) Stride of the indices tensor in Y dimension (in bytes)
* @param[in] indices_step_y (Optional) indices_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] indices_stride_z (Optional) Stride of the indices tensor in Z dimension (in bytes)
* @param[in] indices_step_z (Optional) indices_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] indices_stride_w (Optional) Stride of the indices tensor in W dimension (in bytes)
* @param[in] indices_step_w (Optional) indices_stride_w * number of elements along W processed per workitem(in bytes)
* @param[in] indices_offset_first_element_in_bytes (Optional) The offset of the first element in the indices tensor
*/
__kernel void pooling_layer_2x2_nhwc(
TENSOR4D_DECLARATION(input),
TENSOR4D_DECLARATION(output)
#if defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX)
,
TENSOR4D_DECLARATION(indices)
#endif // defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX)
)
{
// Note: If C is not multiple of VEC_SIZE, we shift back of VEC_SIZE_LEFTOVER elements to compute the leftover elements for get_global_id(0) == 0
// Note: If C is less than VEC_SIZE, VEC_SIZE should be SHRINKED to the closest smaller VEC_SIZE. This operation is performed on the host side
int idx_out_c = max((int)(get_global_id(0) * VEC_SIZE - (VEC_SIZE - VEC_SIZE_LEFTOVER) % VEC_SIZE), 0);
int idx_out_w = get_global_id(1);
#if DST_BATCH_SIZE != 1
// If batch size != 1, the batch size dimension is collapsed over the height dimension
int idx_out_h = get_global_id(2) % DST_HEIGHT;
int idx_out_n = get_global_id(2) / DST_HEIGHT;
#else //SRC_BATCH_SIZE != 1
int idx_out_h = get_global_id(2);
int idx_out_n = 0;
#endif // SRC_BATCH_SIZE != 1
int idx_in_w = idx_out_w * STRIDE_X - PAD_X;
int idx_in_h = idx_out_h * STRIDE_Y - PAD_Y;
__global unsigned char *in_base_ptr = input_ptr + input_offset_first_element_in_bytes + idx_out_c * sizeof(DATA_TYPE) + idx_out_n * input_stride_w;
__global unsigned char *out_base_ptr = output_ptr + output_offset_first_element_in_bytes + idx_out_c * sizeof(DATA_TYPE) + idx_out_w * output_stride_y + idx_out_h * output_stride_z + idx_out_n *
output_stride_w;
int pool_x_s = max((int)0, -idx_in_w);
int pool_x_e = min((int)2, (int)SRC_WIDTH - idx_in_w);
int pool_y_s = max((int)0, -idx_in_h);
int pool_y_e = min((int)2, (int)SRC_HEIGHT - idx_in_h);
int filter_size = (pool_x_e - pool_x_s) * (pool_y_e - pool_y_s);
int x0 = pool_x_s + idx_in_w;
int y0 = pool_y_s + idx_in_h;
int x1 = pool_x_e - 1 + idx_in_w;
int y1 = pool_y_e - 1 + idx_in_h;
REPEAT_VAR_INIT_TO_CONST(4, VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE), data, 0);
#if defined(FP_MIXED_PRECISION)
// In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE
data0 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y0 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE));
data1 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y0 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE));
data2 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y1 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE));
data3 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y1 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE));
#else // defined(FP_MIXED_PRECISION)
data0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y0 * input_stride_z));
data1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y0 * input_stride_z));
data2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y1 * input_stride_z));
data3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y1 * input_stride_z));
#endif // defined(FP_MIXED_PRECISION)
#if !defined(POOL_MAX)
if(filter_size != 4)
{
SELECT_TYPE cond_w_s = (SELECT_TYPE)idx_in_w < (SELECT_TYPE)0;
SELECT_TYPE cond_w_e = (SELECT_TYPE)idx_in_w >= (SELECT_TYPE)(SRC_WIDTH - 1);
SELECT_TYPE cond_h_s = (SELECT_TYPE)idx_in_h < (SELECT_TYPE)0;
SELECT_TYPE cond_h_e = (SELECT_TYPE)idx_in_h >= (SELECT_TYPE)(SRC_HEIGHT - 1);
// Make invalid the values loaded if the x or y coordinate was clamped (out-of-bound)
data0 = select(data0, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_TYPE)(cond_w_s | cond_h_s));
data1 = select(data1, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_TYPE)(cond_w_e | cond_h_s));
data2 = select(data2, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_TYPE)(cond_w_s | cond_h_e));
data3 = select(data3, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_TYPE)(cond_w_e | cond_h_e));
}
#endif // !defined(POOL_MAX)
#if defined(POOL_L2)
// Raise to power of 2 for L2 Pooling
data0 *= data0;
data1 *= data1;
data2 *= data2;
data3 *= data3;
#endif /* defined(POOL_L2) */
VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)
res0 = data0;
res0 = POOL_OP(res0, data1);
res0 = POOL_OP(res0, data2);
res0 = POOL_OP(res0, data3);
#if defined(POOL_AVG) || defined(POOL_L2)
#if defined(EXCLUDE_PADDING)
res0 /= (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))filter_size;
#else // !defined(EXCLUDE_PADDING)
res0 /= (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))4;
#endif // defined(EXCLUDE_PADDING)
#endif // defined(POOL_AVG) || defined(POOL_L2)
#if defined(POOL_L2)
// Take square root of the result in L2 pooling
res0 = SQRT_OP(res0);
#endif // defined(POOL_L2)
// Store result
#if defined(FP_MIXED_PRECISION)
VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
res_converted0 = CONVERT(res0, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE));
STORE_VECTOR_SELECT(res_converted, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0);
#else // defined(FP_MIXED_PRECISION)
STORE_VECTOR_SELECT(res, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0);
#endif // defined(FP_MIXED_PRECISION)
#if defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX)
// This part is used to return the index of the maximum value
// Note: DST_CHANNELS and DST_BATCH_SIZE can be used for either the input and output tensor
// note: Batch dimension does not contribute in the offset contribution
VEC_DATA_TYPE(uint, VEC_SIZE)
base_index = (uint)idx_out_c;
base_index += VEC_OFFS(uint, VEC_SIZE);
VEC_DATA_TYPE(uint, VEC_SIZE)
index0 = base_index + (uint)x0 * DST_CHANNELS + (uint)y0 * (DST_CHANNELS * SRC_WIDTH);
VEC_DATA_TYPE(uint, VEC_SIZE)
index1 = base_index + (uint)x1 * DST_CHANNELS + (uint)y0 * (DST_CHANNELS * SRC_WIDTH);
VEC_DATA_TYPE(uint, VEC_SIZE)
index2 = base_index + (uint)x0 * DST_CHANNELS + (uint)y1 * (DST_CHANNELS * SRC_WIDTH);
VEC_DATA_TYPE(uint, VEC_SIZE)
index3 = base_index + (uint)x1 * DST_CHANNELS + (uint)y1 * (DST_CHANNELS * SRC_WIDTH);
index0 = select(index1, index0, CONVERT(isgreaterequal(data0, data1), VEC_DATA_TYPE(int, VEC_SIZE)));
index1 = select(index3, index2, CONVERT(isgreaterequal(data2, data3), VEC_DATA_TYPE(int, VEC_SIZE)));
index0 = select(index1, index0, CONVERT(isgreaterequal(max(data0, data1), max(data2, data3)), VEC_DATA_TYPE(int, VEC_SIZE)));
__global unsigned char *idx_base_ptr = indices_ptr + indices_offset_first_element_in_bytes + idx_out_c * sizeof(uint) + idx_out_w * indices_stride_y + idx_out_h * indices_stride_z + idx_out_n *
indices_stride_w;
// Store result
STORE_VECTOR_SELECT(index, uint, idx_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, ((VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0));
#endif // defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX)
}
#endif // defined(VEC_SIZE) && defined(VEC_SIZE_LEFTOVER) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_CHANNELS) && defined(DST_HEIGHT) && defined(DST_BATCH_SIZE) && defined(ACC_DATA_TYPE)
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