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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 CONV_STRIDE_X == 1
#define convolution1x3 convolution1x3_stride_1
#elif CONV_STRIDE_X == 2
#define convolution1x3 convolution1x3_stride_2
#elif CONV_STRIDE_X == 3
#define convolution1x3 convolution1x3_stride_3
#else /* CONV_STRIDE_X */
#error "Stride not supported"
#endif /* CONV_STRIDE_X */
/** Compute a 1D horizontal convolution of size 3 and stride 1 for floating point type.
*
* @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 float2 containing 2 convoluted values.
*/
inline float2 convolution1x3_stride_1(__global const uchar *left_pixel,
const float left_coeff,
const float middle_coeff,
const float right_coeff)
{
float4 temp = vload4(0, (__global float *)left_pixel);
float2 left = CONVERT(temp.s01, float2);
float2 middle = CONVERT(temp.s12, float2);
float2 right = CONVERT(temp.s23, float2);
return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff;
}
/** Compute a 1D horizontal convolution of size 3 and stride 2 for floating point type.
*
* @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 float2 containing 2 convoluted values.
*/
inline float2 convolution1x3_stride_2(__global const uchar *left_pixel,
const float left_coeff,
const float middle_coeff,
const float right_coeff)
{
float4 temp0 = vload4(0, (__global float *)left_pixel);
float temp1 = *((__global float *)(left_pixel + 4 * sizeof(float)));
float2 left = CONVERT(temp0.s02, float2);
float2 middle = CONVERT(temp0.s13, float2);
float2 right = CONVERT((float2)(temp0.s2, temp1), float2);
return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff;
}
/** Compute a 1D horizontal convolution of size 3 and stride 3 for floating point type.
*
* @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 float2 containing 2 convoluted values.
*/
inline float2 convolution1x3_stride_3(__global const uchar *left_pixel,
const float left_coeff,
const float middle_coeff,
const float right_coeff)
{
float4 temp0 = vload4(0, (__global float *)left_pixel);
float2 temp1 = vload2(0, (__global float *)(left_pixel + 4 * sizeof(float)));
float2 left = CONVERT(temp0.s03, float2);
float2 middle = CONVERT((float2)(temp0.s1, temp1.s0), float2);
float2 right = CONVERT((float2)(temp0.s2, temp1.s1), float2);
return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff;
}
/** Apply a 3x3 convolution matrix to a single channel F32 input image 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 a float2 containing 2 convoluted values.
*/
inline float2 convolution3x3(
Image *src,
const float mat0, const float mat1, const float mat2,
const float mat3, const float mat4, const float mat5,
const float mat6, const float mat7, const float mat8)
{
float2 pixels;
pixels = convolution1x3(offset(src, 0, 0), mat0, mat1, mat2);
pixels += convolution1x3(offset(src, 0, 1), mat3, mat4, mat5);
pixels += convolution1x3(offset(src, 0, 2), mat6, mat7, mat8);
return pixels;
}
/** This function computes the horizontal integral of the image.
*
* @param[in] src_ptr Pointer to the source image. Supported data types: U8
* @param[in] src_stride_x Stride of the source image 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 image 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_offset_first_element_in_bytes The offset of the first element in the source image
* @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 Y processed per workitem(in bytes)
* @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F16/F32
* @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 Y 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 Y processed per workitem(in bytes)
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
* @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F16/F32
* @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 Y processed per workitem(in bytes)
* @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
*/
__kernel void depthwise_convolution_3x3(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), TENSOR3D_DECLARATION(weights))
{
Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src);
Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT(weights);
uchar3 offset = (uchar3)(0, 1, 2) * (uchar3)weights_stride_y;
float3 weights_values0 = vload3(0, (__global float *)(weights.ptr + offset.s0));
float3 weights_values1 = vload3(0, (__global float *)(weights.ptr + offset.s1));
float3 weights_values2 = vload3(0, (__global float *)(weights.ptr + offset.s2));
float2 pixels = convolution3x3(&src, weights_values0.s0, weights_values0.s1, weights_values0.s2,
weights_values1.s0, weights_values1.s1, weights_values1.s2,
weights_values2.s0, weights_values2.s1, weights_values2.s2);
vstore2(pixels, 0, (__global float *)dst.ptr);
}