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/*
* Copyright (c) 2016, 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 STRIDE_X == 2
#define CONVOLVE1x3(left_pixel_position, left_coeff, middle_coeff, right_coeff) convolution1x3_stride2(left_pixel_position, left_coeff, middle_coeff, right_coeff)
#elif STRIDE_X == 1 /* STRIDE_X == 1 */
#define CONVOLVE1x3(left_pixel_position, left_coeff, middle_coeff, right_coeff) convolution1x3_stride1(left_pixel_position, left_coeff, middle_coeff, right_coeff)
#else /* STRIDE_X not equals 1 or 2 */
#error "STRIDE_X larger than 2 is not supported"
#endif /* STRIDE_X == 2 */
/** Compute a 1D horizontal convolution of size 3 with stride as 1.
*
* @param[in] left_pixel Pointer to the left pixel.
* @param[in] left_coeff Weight of the left pixel
* @param[in] middle_coeff Weight of the middle pixel
* @param[in] right_coeff Weight of the right pixel
*
* @return a convoluted values.
*/
inline VEC_DATA_TYPE(DATA_TYPE, 8) convolution1x3_stride1(__global const DATA_TYPE *left_pixel,
const DATA_TYPE left_coeff,
const DATA_TYPE middle_coeff,
const DATA_TYPE right_coeff)
{
VEC_DATA_TYPE(DATA_TYPE, 16)
temp = vload16(0, left_pixel);
VEC_DATA_TYPE(DATA_TYPE, 8)
left = temp.s01234567;
VEC_DATA_TYPE(DATA_TYPE, 8)
middle = temp.s12345678;
VEC_DATA_TYPE(DATA_TYPE, 8)
right = temp.s23456789;
return left * (VEC_DATA_TYPE(DATA_TYPE, 8))left_coeff + middle * (VEC_DATA_TYPE(DATA_TYPE, 8))middle_coeff + right * (VEC_DATA_TYPE(DATA_TYPE, 8))right_coeff;
}
/** Compute a 1D horizontal convolution of size 3 with stride as 2.
*
* @param[in] left_pixel Pointer to the left pixel.
* @param[in] left_coeff Weight of the left pixel
* @param[in] middle_coeff Weight of the middle pixel
* @param[in] right_coeff Weight of the right pixel
*
* @return a convoluted values.
*/
inline VEC_DATA_TYPE(DATA_TYPE, 8) convolution1x3_stride2(__global const DATA_TYPE *left_pixel,
const DATA_TYPE left_coeff,
const DATA_TYPE middle_coeff,
const DATA_TYPE right_coeff)
{
const int stride_size = 2;
VEC_DATA_TYPE(DATA_TYPE, 16)
temp1 = vload16(0, left_pixel);
VEC_DATA_TYPE(DATA_TYPE, 16)
temp2 = vload16(0, left_pixel + 8);
VEC_DATA_TYPE(DATA_TYPE, 8)
left = (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s0246, temp2.s0246);
VEC_DATA_TYPE(DATA_TYPE, 8)
middle = (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s1357, temp2.s1357);
VEC_DATA_TYPE(DATA_TYPE, 8)
right = (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s2468, temp2.s2468);
return left * (VEC_DATA_TYPE(DATA_TYPE, 8))left_coeff + middle * (VEC_DATA_TYPE(DATA_TYPE, 8))middle_coeff + right * (VEC_DATA_TYPE(DATA_TYPE, 8))right_coeff;
}
/** Apply a 3x3 2D convolution matrix on the input and return the result.
*
* Convolution matrix layout:
*
* [ mat0, mat1, mat2 ]\n
* [ mat3, mat4, mat5 ]\n
* [ mat6, mat7, mat8 ]\n
*
* @param[in] src A pointer to source Image structure
* @param[in] mat0 Coefficient from the convolution matrix
* @param[in] mat1 Coefficient from the convolution matrix
* @param[in] mat2 Coefficient from the convolution matrix
* @param[in] mat3 Coefficient from the convolution matrix
* @param[in] mat4 Coefficient from the convolution matrix
* @param[in] mat5 Coefficient from the convolution matrix
* @param[in] mat6 Coefficient from the convolution matrix
* @param[in] mat0 Coefficient from the convolution matrix
* @param[in] mat7 Coefficient from the convolution matrix
* @param[in] mat8 Coefficient from the convolution matrix
*
* @return convoluted values.
*/
inline VEC_DATA_TYPE(DATA_TYPE, 8) convolution3x3(
Image *src,
const DATA_TYPE mat0, const DATA_TYPE mat1, const DATA_TYPE mat2,
const DATA_TYPE mat3, const DATA_TYPE mat4, const DATA_TYPE mat5,
const DATA_TYPE mat6, const DATA_TYPE mat7, const DATA_TYPE mat8)
{
// Output pixels
VEC_DATA_TYPE(DATA_TYPE, 8)
pixels;
// Row 0
pixels = CONVOLVE1x3((__global DATA_TYPE *)offset(src, 0, 0), mat0, mat1, mat2);
// Row
pixels += CONVOLVE1x3((__global DATA_TYPE *)offset(src, 0, 1), mat3, mat4, mat5);
// Row 2
pixels += CONVOLVE1x3((__global DATA_TYPE *)offset(src, 0, 2), mat6, mat7, mat8);
return pixels;
}
/** This kernel performs a direct convolution to convolve the low three dimensions.
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The convolution stride x and stride y must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1, _DSTRIDE_Y=1
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
* @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
* @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
* @param[out] weights_ptr Pointer to the weights tensor. Supported data types: same as @p weights_ptr
* @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
* @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
* @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
* @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
* @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
* @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
* @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
* @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
* @param[in] weights_stride_w Stride of the weights tensor in W dimension
* @param[in] filter_depth The depth size of the filter
*/
__kernel void direct_convolution3x3(
TENSOR3D_DECLARATION(src),
TENSOR3D_DECLARATION(dst),
TENSOR3D_DECLARATION(weights),
#ifdef HAS_BIAS
VECTOR_DECLARATION(biases),
#endif /* defined(HAS_BIAS) */
unsigned int weights_stride_w,
unsigned int filter_depth)
{
Image src = CONVERT_TO_IMAGE_STRUCT(src);
Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
#ifdef HAS_BIAS
Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
#endif /* defined(HAS_BIAS) */
VEC_DATA_TYPE(DATA_TYPE, 8)
pixels = 0;
const uint z_index = get_global_id(2);
weights.ptr += z_index * weights_stride_w;
for(int d = 0; d < filter_depth; ++d)
{
VEC_DATA_TYPE(DATA_TYPE, 4)
weights_row1 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 0, 0));
VEC_DATA_TYPE(DATA_TYPE, 4)
weights_row2 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 1, 0));
VEC_DATA_TYPE(DATA_TYPE, 4)
weights_row3 = vload4(0, (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 2, 0));
pixels += convolution3x3(&src, weights_row1.s0,
weights_row1.s1,
weights_row1.s2,
weights_row2.s0,
weights_row2.s1,
weights_row2.s2,
weights_row3.s0,
weights_row3.s1,
weights_row3.s2);
src.ptr += src_stride_z;
weights.ptr += weights_stride_z;
}
#ifdef HAS_BIAS
pixels += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, z_index)));
#endif /* defined(HAS_BIAS) */
vstore8(pixels, 0, (__global DATA_TYPE *)dst.ptr);
}
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