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android / platform / external / ComputeLibrary / eb0170eb99a8d03384e87ff20a38f76ce16de252 / . / build / android-arm64v8a / src / core / CL / cl_kernels / im2col.clembed
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/*
* Copyright (c) 2018 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.
*/
/*
* Copyright (c) 2016-2020 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.
*/
#ifndef ARM_COMPUTE_HELPER_H
#define ARM_COMPUTE_HELPER_H
#if defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(cl_khr_fp16)
#if defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8)
#pragma OPENCL EXTENSION cl_arm_integer_dot_product_int8 : enable
#endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8)
#if defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8)
#pragma OPENCL EXTENSION cl_arm_integer_dot_product_accumulate_int8 : enable
#endif // defined(ARM_COMPUTE_OPENCL_DOT8_ACC_ENABLED) && defined(cl_arm_integer_dot_product_accumulate_int8)
#if defined(ARM_COMPUTE_DEBUG_ENABLED) && defined(cl_arm_printf)
#pragma OPENCL EXTENSION cl_arm_printf : enable
#endif // defined(ARM_COMPUTE_DEBUG_ENABLED) && defined(cl_arm_printf)
#define GPU_ARCH_MIDGARD 0x100
#define GPU_ARCH_BIFROST 0x200
/** Concatenate two inputs.
*
* @param[in] a The first input to be concatenated
* @param[in] b The second input to be concatenated
*
* @return The concatenated output
*/
#define CONCAT(a, b) a##b
/** Expand the given vector
*
* @param[in] x The vector to be expanded
*
* @return The expanded output
*/
#define EXPAND(x) x
/** Clamp the given value between an upper and lower bound.
*
* @param[in] x The value to be clamped
* @param[in] min_val The lower bound
* @param[in] max_val The upper bound
*
* @return The clamped value.
*/
#define CLAMP(x, min_val, max_val) min(max(x, min_val), max_val)
/** REVn reverses the given vector whose size is n.
* @name REVn
*
* @param[in] x The vector to be reversed
*
* @return The reversed vector
* @{
*/
#define REV1(x) ((x))
#define REV2(x) ((x).s10)
#define REV3(x) ((x).s210)
#define REV4(x) ((x).s3210)
#define REV8(x) ((x).s76543210)
#define REV16(x) ((x).sFEDCBA9876543210)
/** @} */ // end of group REVn
/** Reverse the given vector.
* @name REVERSE
*
* @param[in] x The vector to be reversed
* @param[in] s The size of the vector
*
* @return The reversed vector
* @{
*/
#define REVERSE_STR(x, s) REV##s((x))
#define REVERSE(x, s) REVERSE_STR(x, s)
/** @} */ // end of group REVERSE
/** Circular-right-shift (rotate-right) the vector of size s by the amount of n.
* @name ROTs_n
*
* @param[in] x The vector to be shifted
*
* @return The shifted vector
* @{
*/
#define ROT1_0(x) ((x))
#define ROT2_0(x) ((x))
#define ROT2_1(x) ((x).s10)
#define ROT3_0(x) ((x))
#define ROT3_1(x) ((x).s201)
#define ROT3_2(x) ((x).s120)
#define ROT4_0(x) ((x))
#define ROT4_1(x) ((x).s3012)
#define ROT4_2(x) ((x).s2301)
#define ROT4_3(x) ((x).s1230)
#define ROT8_0(x) ((x))
#define ROT8_1(x) ((x).s70123456)
#define ROT8_2(x) ((x).s67012345)
#define ROT8_3(x) ((x).s56701234)
#define ROT8_4(x) ((x).s45670123)
#define ROT8_5(x) ((x).s34567012)
#define ROT8_6(x) ((x).s23456701)
#define ROT8_7(x) ((x).s12345670)
#define ROT16_0(x) ((x))
#define ROT16_1(x) ((x).sF0123456789ABCDE)
#define ROT16_2(x) ((x).sEF0123456789ABCD)
#define ROT16_3(x) ((x).sDEF0123456789ABC)
#define ROT16_4(x) ((x).sCDEF0123456789AB)
#define ROT16_5(x) ((x).sBCDEF0123456789A)
#define ROT16_6(x) ((x).sABCDEF0123456789)
#define ROT16_7(x) ((x).s9ABCDEF012345678)
#define ROT16_8(x) ((x).s89ABCDEF01234567)
#define ROT16_9(x) ((x).s789ABCDEF0123456)
#define ROT16_10(x) ((x).s6789ABCDEF012345)
#define ROT16_11(x) ((x).s56789ABCDEF01234)
#define ROT16_12(x) ((x).s456789ABCDEF0123)
#define ROT16_13(x) ((x).s3456789ABCDEF012)
#define ROT16_14(x) ((x).s23456789ABCDEF01)
#define ROT16_15(x) ((x).s123456789ABCDEF0)
/** @} */ // end of group ROTs_n
/** Circular-right-shift (rotate-right) the given vector by the given amount.
* @name ROTATE
*
* @param[in] x The vector to be shifted
* @param[in] s The size of the vector
* @param[in] n The amount to be shifted
*
* @return The shifted vector
* @{
*/
#define ROTATE_STR(x, s, n) ROT##s##_##n(x)
#define ROTATE(x, s, n) ROTATE_STR(x, s, n)
/** @} */ // end of group ROTATE
/** Creates a vector of size n filled with offset values corresponding to the location of each element.
* @name V_OFFSn
*
* @param[in] dt The data type of the output vector
*
* @return The vector filled with offset values
* @{
*/
#define V_OFFS1(dt) (dt)(0)
#define V_OFFS2(dt) (dt)(0, 1)
#define V_OFFS3(dt) (dt)(0, 1, 3)
#define V_OFFS4(dt) (dt)(0, 1, 2, 3)
#define V_OFFS8(dt) (dt)(0, 1, 2, 3, 4, 5, 6, 7)
#define V_OFFS16(dt) (dt)(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
/** @} */ // end of group V_OFFSn
/** Create a vector filled with offset values corresponding to the location of each element.
* @name VEC_OFFS
*
* @param[in] dt The data type of the output vector
* @param[in] s The size of the output vector
*
* @return The vector filled with offset values
* @{
*/
#define VEC_OFFS_STR(dt, s) V_OFFS##s(dt)
#define VEC_OFFS(dt, s) VEC_OFFS_STR(dt, s)
/** @} */ // end of group VEC_OFFS
#define VLOAD_STR(size) vload##size
#define VLOAD(size) VLOAD_STR(size)
#define VSTORE_STR(size) vstore##size
#define VSTORE(size) VSTORE_STR(size)
#define float1 float
#define half1 half
#define char1 char
#define uchar1 uchar
#define short1 short
#define ushort1 ushort
#define int1 int
#define uint1 uint
#define long1 long
#define ulong1 ulong
#define double1 double
#define vload1(OFFSET, PTR) *(OFFSET + PTR)
#define vstore1(DATA, OFFSET, PTR) *(OFFSET + PTR) = DATA
// Convert built-in functions with _sat modifier are not supported in floating point so we create defines
// without _sat to overcome this issue
#define convert_float_sat convert_float
#define convert_float1_sat convert_float
#define convert_float2_sat convert_float2
#define convert_float3_sat convert_float3
#define convert_float4_sat convert_float4
#define convert_float8_sat convert_float8
#define convert_float16_sat convert_float16
#define convert_half_sat convert_float
#define convert_half1_sat convert_half
#define convert_half2_sat convert_half2
#define convert_half3_sat convert_half3
#define convert_half4_sat convert_half4
#define convert_half8_sat convert_half8
#define convert_half16_sat convert_half16
#define convert_float1 convert_float
#define convert_half1 convert_half
#define convert_char1 convert_char
#define convert_uchar1 convert_uchar
#define convert_short1 convert_short
#define convert_ushort1 convert_ushort
#define convert_int1 convert_int
#define convert_uint1 convert_uint
#define convert_long1 convert_long
#define convert_ulong1 convert_ulong
#define convert_double1 convert_double
#define convert_char1_sat convert_char_sat
#define convert_uchar1_sat convert_uchar_sat
#define convert_short1_sat convert_short_sat
#define convert_ushort1_sat convert_ushort_sat
#define convert_int1_sat convert_int_sat
#define convert_uint1_sat convert_uint_sat
#define convert_long1_sat convert_long_sat
#define convert_ulong1_sat convert_ulong_sat
#define convert_double1_sat convert_double_sat
#define VEC_DATA_TYPE_STR(type, size) type##size
#define VEC_DATA_TYPE(type, size) VEC_DATA_TYPE_STR(type, size)
#define CL_VEC_DATA_TYPE_STR(type, size) type##size
#define CL_VEC_DATA_TYPE(type, size) CL_VEC_DATA_TYPE_STR(type, size)
#define CONVERT_STR(x, type) (convert_##type((x)))
#define CONVERT(x, type) CONVERT_STR(x, type)
#define CONVERT_SAT_STR(x, type) (convert_##type##_sat((x)))
#define CONVERT_SAT(x, type) CONVERT_SAT_STR(x, type)
#define CONVERT_SAT_ROUND_STR(x, type, round) (convert_##type##_sat_##round((x)))
#define CONVERT_SAT_ROUND(x, type, round) CONVERT_SAT_ROUND_STR(x, type, round)
#define VECTOR_DECLARATION(name) \
__global uchar *name##_ptr, \
uint name##_stride_x, \
uint name##_step_x, \
uint name##_offset_first_element_in_bytes
#define IMAGE_DECLARATION(name) \
__global uchar *name##_ptr, \
uint name##_stride_x, \
uint name##_step_x, \
uint name##_stride_y, \
uint name##_step_y, \
uint name##_offset_first_element_in_bytes
#define TENSOR3D_DECLARATION(name) \
__global uchar *name##_ptr, \
uint name##_stride_x, \
uint name##_step_x, \
uint name##_stride_y, \
uint name##_step_y, \
uint name##_stride_z, \
uint name##_step_z, \
uint name##_offset_first_element_in_bytes
#define TENSOR4D_DECLARATION(name) \
__global uchar *name##_ptr, \
uint name##_stride_x, \
uint name##_step_x, \
uint name##_stride_y, \
uint name##_step_y, \
uint name##_stride_z, \
uint name##_step_z, \
uint name##_stride_w, \
uint name##_step_w, \
uint name##_offset_first_element_in_bytes
#define CONVERT_TO_VECTOR_STRUCT(name) \
update_vector_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, name##_step_x)
#define CONVERT_TO_VECTOR_STRUCT_NO_STEP(name) \
update_vector_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, 0)
#define CONVERT_TO_IMAGE_STRUCT(name) \
update_image_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, name##_step_x, name##_stride_y, name##_step_y)
#define CONVERT_TO_IMAGE_STRUCT_NO_STEP(name) \
update_image_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, 0, name##_stride_y, 0)
#define CONVERT_TENSOR3D_TO_IMAGE_STRUCT(name) \
update_image_from_tensor3D_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, name##_step_x, name##_stride_y, name##_step_y, name##_stride_z, name##_step_z)
#define CONVERT_TENSOR3D_TO_IMAGE_STRUCT_NO_STEP(name) \
update_image_from_tensor3D_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, 0, name##_stride_y, 0, name##_stride_z, name##_step_z)
#define CONVERT_TENSOR3D_TO_IMAGE_STRUCT(name) \
update_image_from_tensor3D_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, name##_step_x, name##_stride_y, name##_step_y, name##_stride_z, name##_step_z)
#define CONVERT_TO_TENSOR3D_STRUCT(name) \
update_tensor3D_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, name##_step_x, name##_stride_y, name##_step_y, \
name##_stride_z, name##_step_z)
#define CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(name) \
update_tensor3D_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, 0, name##_stride_y, 0, name##_stride_z, 0)
#define CONVERT_TO_TENSOR4D_STRUCT(name, mod_size) \
update_tensor4D_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, name##_step_x, name##_stride_y, name##_step_y, \
name##_stride_z, name##_step_z, name##_stride_w, name##_step_w, mod_size)
#define CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(name, mod_size) \
update_tensor4D_workitem_ptr(name##_ptr, name##_offset_first_element_in_bytes, name##_stride_x, 0, name##_stride_y, 0, name##_stride_z, 0, name##_stride_w, 0, mod_size)
/** Structure to hold Vector information */
typedef struct Vector
{
__global uchar *ptr; /**< Pointer to the starting postion of the buffer */
int offset_first_element_in_bytes; /**< The offset of the first element in the source image */
int stride_x; /**< Stride of the image in X dimension (in bytes) */
} Vector;
/** Structure to hold Image information */
typedef struct Image
{
__global uchar *ptr; /**< Pointer to the starting postion of the buffer */
int offset_first_element_in_bytes; /**< The offset of the first element in the source image */
int stride_x; /**< Stride of the image in X dimension (in bytes) */
int stride_y; /**< Stride of the image in Y dimension (in bytes) */
} Image;
/** Structure to hold 3D tensor information */
typedef struct Tensor3D
{
__global uchar *ptr; /**< Pointer to the starting postion of the buffer */
int offset_first_element_in_bytes; /**< The offset of the first element in the source image */
int stride_x; /**< Stride of the image in X dimension (in bytes) */
int stride_y; /**< Stride of the image in Y dimension (in bytes) */
int stride_z; /**< Stride of the image in Z dimension (in bytes) */
} Tensor3D;
/** Structure to hold 4D tensor information */
typedef struct Tensor4D
{
__global uchar *ptr; /**< Pointer to the starting postion of the buffer */
int offset_first_element_in_bytes; /**< The offset of the first element in the source image */
int stride_x; /**< Stride of the image in X dimension (in bytes) */
int stride_y; /**< Stride of the image in Y dimension (in bytes) */
int stride_z; /**< Stride of the image in Z dimension (in bytes) */
int stride_w; /**< Stride of the image in W dimension (in bytes) */
} Tensor4D;
/** Wrap vector information into an Vector structure, and make the pointer point at this workitem's data.
*
* @param[in] ptr Pointer to the starting postion of the buffer
* @param[in] offset_first_element_in_bytes The offset of the first element in the source vector
* @param[in] stride_x Stride of the vector in X dimension (in bytes)
* @param[in] step_x stride_x * number of elements along X processed per workitem(in bytes)
*
* @return An image object
*/
inline Vector update_vector_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x)
{
Vector vector =
{
.ptr = ptr,
.offset_first_element_in_bytes = offset_first_element_in_bytes,
.stride_x = stride_x,
};
vector.ptr += vector.offset_first_element_in_bytes + get_global_id(0) * step_x;
return vector;
}
/** Wrap image information into an Image structure, and make the pointer point at this workitem's data.
*
* @param[in] ptr Pointer to the starting postion of the buffer
* @param[in] offset_first_element_in_bytes The offset of the first element in the source image
* @param[in] stride_x Stride of the image in X dimension (in bytes)
* @param[in] step_x stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] stride_y Stride of the image in Y dimension (in bytes)
* @param[in] step_y stride_y * number of elements along Y processed per workitem(in bytes)
*
* @return An image object
*/
inline Image update_image_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x, uint stride_y, uint step_y)
{
Image img =
{
.ptr = ptr,
.offset_first_element_in_bytes = offset_first_element_in_bytes,
.stride_x = stride_x,
.stride_y = stride_y
};
img.ptr += img.offset_first_element_in_bytes + get_global_id(0) * step_x + get_global_id(1) * step_y;
return img;
}
/** Wrap 3D tensor information into an image structure, and make the pointer point at this workitem's data.
*
* @param[in] ptr Pointer to the starting postion of the buffer
* @param[in] offset_first_element_in_bytes The offset of the first element in the source image
* @param[in] stride_x Stride of the image in X dimension (in bytes)
* @param[in] step_x stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] stride_y Stride of the image in Y dimension (in bytes)
* @param[in] step_y stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] stride_z Stride of the image in Z dimension (in bytes)
* @param[in] step_z stride_z * number of elements along Z processed per workitem(in bytes)
*
* @return A 3D tensor object
*/
inline Image update_image_from_tensor3D_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x, uint stride_y, uint step_y, uint stride_z, uint step_z)
{
Image img =
{
.ptr = ptr,
.offset_first_element_in_bytes = offset_first_element_in_bytes,
.stride_x = stride_x,
.stride_y = stride_y
};
img.ptr += img.offset_first_element_in_bytes + get_global_id(0) * step_x + get_global_id(1) * step_y + get_global_id(2) * step_z;
return img;
}
/** Wrap 3D tensor information into an tensor structure, and make the pointer point at this workitem's data.
*
* @param[in] ptr Pointer to the starting postion of the buffer
* @param[in] offset_first_element_in_bytes The offset of the first element in the source image
* @param[in] stride_x Stride of the image in X dimension (in bytes)
* @param[in] step_x stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] stride_y Stride of the image in Y dimension (in bytes)
* @param[in] step_y stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] stride_z Stride of the image in Z dimension (in bytes)
* @param[in] step_z stride_z * number of elements along Z processed per workitem(in bytes)
*
* @return A 3D tensor object
*/
inline Tensor3D update_tensor3D_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x, uint stride_y, uint step_y, uint stride_z, uint step_z)
{
Tensor3D tensor =
{
.ptr = ptr,
.offset_first_element_in_bytes = offset_first_element_in_bytes,
.stride_x = stride_x,
.stride_y = stride_y,
.stride_z = stride_z
};
tensor.ptr += tensor.offset_first_element_in_bytes + get_global_id(0) * step_x + get_global_id(1) * step_y + get_global_id(2) * step_z;
return tensor;
}
inline Tensor4D update_tensor4D_workitem_ptr(__global uchar *ptr, uint offset_first_element_in_bytes, uint stride_x, uint step_x, uint stride_y, uint step_y, uint stride_z, uint step_z, uint stride_w,
uint step_w,
uint mod_size)
{
Tensor4D tensor =
{
.ptr = ptr,
.offset_first_element_in_bytes = offset_first_element_in_bytes,
.stride_x = stride_x,
.stride_y = stride_y,
.stride_z = stride_z,
.stride_w = stride_w
};
tensor.ptr += tensor.offset_first_element_in_bytes + get_global_id(0) * step_x + get_global_id(1) * step_y + (get_global_id(2) % mod_size) * step_z + (get_global_id(2) / mod_size) * step_w;
return tensor;
}
/** Get the pointer position of a Vector
*
* @param[in] vec Pointer to the starting position of the buffer
* @param[in] x Relative X position
*/
inline __global const uchar *vector_offset(const Vector *vec, int x)
{
return vec->ptr + x * vec->stride_x;
}
/** Get the pointer position of a Image
*
* @param[in] img Pointer to the starting position of the buffer
* @param[in] x Relative X position
* @param[in] y Relative Y position
*/
inline __global uchar *offset(const Image *img, int x, int y)
{
return img->ptr + x * img->stride_x + y * img->stride_y;
}
/** Get the pointer position of a Tensor3D
*
* @param[in] tensor Pointer to the starting position of the buffer
* @param[in] x Relative X position
* @param[in] y Relative Y position
* @param[in] z Relative Z position
*/
inline __global const uchar *tensor3D_offset(const Tensor3D *tensor, int x, int y, int z)
{
return tensor->ptr + x * tensor->stride_x + y * tensor->stride_y + z * tensor->stride_z;
}
/** Get the pointer position of a Tensor4D
*
* @param[in] tensor Pointer to the starting position of the buffer
* @param[in] x Relative X position
* @param[in] y Relative Y position
* @param[in] z Relative Z position
* @param[in] w Relative W position
*/
inline __global const uchar *tensor4D_offset(const Tensor4D *tensor, int x, int y, int z, int w)
{
return tensor->ptr + x * tensor->stride_x + y * tensor->stride_y + z * tensor->stride_z + w * tensor->stride_w;
}
#endif // _HELPER_H
#if defined(DATA_TYPE) && defined(ELEMENT_SIZE)
#if ELEMENT_SIZE == 1
#define COND_DATA_TYPE char
#elif ELEMENT_SIZE == 2
#define COND_DATA_TYPE short
#elif ELEMENT_SIZE == 4
#define COND_DATA_TYPE int
#else // ELEMENT_SIZE
#error "Element size not support"
#endif // ELEMENT_SIZE
#if defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(SRC_DEPTH)
/** This opencl kernel performs im2col when the kernel size is 1x1, the stride_x = 1 and the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
* @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -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.
* @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/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 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 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[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
__kernel void im2col1x1_stridex1_nchw(
TENSOR3D_DECLARATION(src),
#if defined(NUM_GROUPS)
TENSOR3D_DECLARATION(dst),
#else // defined(NUM_GROUPS)
IMAGE_DECLARATION(dst),
#endif // defined(NUM_GROUPS)
uint src_stride_w,
uint dst_stride_w)
{
const uint xc = get_global_id(0) * 4; // x coordinate in the convolved tensor
const uint yc = get_global_id(1); // y coordinate in the convolved tensor
const uint ch = get_global_id(2) % SRC_DEPTH; // input feature map
const uint batch = get_global_id(2) / SRC_DEPTH; // batch size
// Clamp xc
// The strategy clamps at "xc" as it will be a valid value for sure
uint4 xc_clamped = xc + (uint4)(0, 1, 2, 3);
// Check which values are valid
const VEC_DATA_TYPE(COND_DATA_TYPE, 4) cond0 = CONVERT((xc_clamped < SRC_WIDTH), VEC_DATA_TYPE(COND_DATA_TYPE, 4));
xc_clamped = select((uint4)xc, xc_clamped, convert_int4(cond0));
// Calculate input indices
const uint xi = xc;
const uint yi = yc * STRIDE_Y;
// Calculate output indices
#if defined(NUM_GROUPS)
const uint xo = ch % (SRC_DEPTH / NUM_GROUPS);
const uint zo = ch / (SRC_DEPTH / NUM_GROUPS);
#else // defined(NUM_GROUPS)
const uint xo = ch;
#endif // defined(NUM_GROUPS)
const uint4 yo = xc_clamped + yc * CONVOLVED_WIDTH; // Index of the convolution
// Get input and output address
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_x + yi * src_stride_y + ch * src_stride_z + batch * src_stride_w;
#if defined(NUM_GROUPS)
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + zo * dst_stride_z + batch * dst_stride_w;
#else // defined(NUM_GROUPS)
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + batch * dst_stride_w;
#endif // defined(NUM_GROUPS)
VEC_DATA_TYPE(DATA_TYPE, 4)
data = vload4(0, (__global DATA_TYPE *)input_ptr);
// If out-of-bound, overwrite with the first element
data = select((VEC_DATA_TYPE(DATA_TYPE, 4))data.s0, data, cond0);
*(__global DATA_TYPE *)(output_ptr + yo.s0 * dst_stride_y) = data.s0;
*(__global DATA_TYPE *)(output_ptr + yo.s1 * dst_stride_y) = data.s1;
*(__global DATA_TYPE *)(output_ptr + yo.s2 * dst_stride_y) = data.s2;
*(__global DATA_TYPE *)(output_ptr + yo.s3 * dst_stride_y) = data.s3;
#ifdef HAS_BIAS
#if defined(NUM_GROUPS)
if(xo == (SRC_DEPTH / NUM_GROUPS - 1))
#else // defined(NUM_GROUPS)
if(ch == (SRC_DEPTH - 1))
#endif // defined(NUM_GROUPS)
{
*((__global DATA_TYPE *)(output_ptr + yo.s0 * dst_stride_y) + 1) = 1.0f;
*((__global DATA_TYPE *)(output_ptr + yo.s1 * dst_stride_y) + 1) = 1.0f;
*((__global DATA_TYPE *)(output_ptr + yo.s2 * dst_stride_y) + 1) = 1.0f;
*((__global DATA_TYPE *)(output_ptr + yo.s3 * dst_stride_y) + 1) = 1.0f;
}
#endif // HAS_BIAS
}
#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(SRC_DEPTH)
#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
#if defined(DILATION_X) && defined(DILATION_Y)
/** This opencl kernel performs a generic im2col implementation when the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
* @note The kernel width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DSRC_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DSRC_DEPTH=64
* @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
* @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
* @note The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_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.
* @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/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 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 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[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
__kernel void im2col_generic_nchw(
TENSOR3D_DECLARATION(src),
#if defined(NUM_GROUPS)
TENSOR3D_DECLARATION(dst),
#else // defined(NUM_GROUPS)
IMAGE_DECLARATION(dst),
#endif // defined(NUM_GROUPS)
uint src_stride_w,
uint dst_stride_w)
{
const int xc = get_global_id(0); // x coordinate in the convolved tensor
const int yc = get_global_id(1); // y coordinate in the convolved tensor
const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X - PAD_LEFT;
const int yi = yc * STRIDE_Y - PAD_TOP;
// Calculate output indices
#if defined(NUM_GROUPS)
const int xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * KERNEL_WIDTH * KERNEL_HEIGHT;
const int zo = ch / (SRC_DEPTH / NUM_GROUPS);
#else // defined(NUM_GROUPS)
const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT;
#endif // defined(NUM_GROUPS)
const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w;
#if defined(NUM_GROUPS)
__global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w)) + xo;
#else // defined(NUM_GROUPS)
__global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo;
#endif // defined(NUM_GROUPS)
// Linearize convolution elements
for(int yk = 0; yk < KERNEL_HEIGHT; ++yk)
{
int y = yi + yk * DILATION_Y;
for(int xk = 0; xk < KERNEL_WIDTH; ++xk, ++output_ptr)
{
int x = xi + xk * DILATION_X;
#if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
*output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
#else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT)
{
*output_ptr = PAD_VALUE;
}
else
{
*output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
}
#endif // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
}
}
#ifdef HAS_BIAS
#if defined(NUM_GROUPS)
if((xo / (KERNEL_WIDTH * KERNEL_HEIGHT)) == (SRC_DEPTH / NUM_GROUPS - 1))
#else // defined(NUM_GROUPS)
if(ch == (SRC_DEPTH - 1))
#endif // defined(NUM_GROUPS)
{
*output_ptr = 1.0f;
}
#endif // HAS_BIAS
}
#endif // defined(DILATION_X) && defined(DILATION_Y)
/** This opencl kernel performs im2col when the kernel size is 3x3 and the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
* @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
* @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -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: QASYMM8/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 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 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[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
__kernel void im2col3x3_nchw(
TENSOR3D_DECLARATION(src),
#if defined(NUM_GROUPS)
TENSOR3D_DECLARATION(dst),
#else // defined(NUM_GROUPS)
IMAGE_DECLARATION(dst),
#endif // defined(NUM_GROUPS)
uint src_stride_w,
uint dst_stride_w)
{
const int xc = get_global_id(0); // x coordinate in the convolved tensor
const int yc = get_global_id(1); // y coordinate in the convolved tensor
const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X - PAD_LEFT;
const int yi = yc * STRIDE_Y - PAD_TOP;
// Calculate output indices
#if defined(NUM_GROUPS)
const int xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * 9; // 3x3
const int zo = ch / (SRC_DEPTH / NUM_GROUPS);
#else // defined(NUM_GROUPS)
const int xo = ch * 9; // 3x3
#endif // defined(NUM_GROUPS)
const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
// Get input and output address
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * (int)src_stride_x + yi * (int)src_stride_y + ch * src_stride_z + batch * src_stride_w;
#if defined(NUM_GROUPS)
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w;
#else // defined(NUM_GROUPS)
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;
#endif // defined(NUM_GROUPS)
VEC_DATA_TYPE(DATA_TYPE, 3)
row0 = vload3(0, (__global DATA_TYPE *)(input_ptr + 0 * src_stride_y));
VEC_DATA_TYPE(DATA_TYPE, 3)
row1 = vload3(0, (__global DATA_TYPE *)(input_ptr + 1 * src_stride_y));
VEC_DATA_TYPE(DATA_TYPE, 3)
row2 = vload3(0, (__global DATA_TYPE *)(input_ptr + 2 * src_stride_y));
#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
// Put 0 if the value is out-of-bound
int3 x = (int3)xi + (int3)(0, 1, 2);
int3 y = (int3)yi + (int3)(0, 1, 2);
VEC_DATA_TYPE(COND_DATA_TYPE, 3)
cond0 = CONVERT((x >= (int3)0 && x < (int3)SRC_WIDTH && (int3)(y.s0 >= 0 && y.s0 < SRC_HEIGHT)), VEC_DATA_TYPE(COND_DATA_TYPE, 3));
VEC_DATA_TYPE(COND_DATA_TYPE, 3)
cond1 = CONVERT((x >= (int3)0 && x < (int3)SRC_WIDTH && (int3)(y.s1 >= 0 && y.s1 < SRC_HEIGHT)), VEC_DATA_TYPE(COND_DATA_TYPE, 3));
VEC_DATA_TYPE(COND_DATA_TYPE, 3)
cond2 = CONVERT((x >= (int3)0 && x < (int3)SRC_WIDTH && (int3)(y.s2 >= 0 && y.s2 < SRC_HEIGHT)), VEC_DATA_TYPE(COND_DATA_TYPE, 3));
row0 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row0, cond0);
row1 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row1, cond1);
row2 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row2, cond2);
#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row0.s012, row1.s012, row2.s01), 0, (__global DATA_TYPE *)output_ptr);
*((__global DATA_TYPE *)output_ptr + 8) = row2.s2;
#ifdef HAS_BIAS
#if defined(NUM_GROUPS)
if((xo / 9) == (SRC_DEPTH / NUM_GROUPS - 1))
#else // defined(NUM_GROUPS)
if(ch == (SRC_DEPTH - 1))
#endif // defined(NUM_GROUPS)
{
*((__global DATA_TYPE *)output_ptr + 9) = 1.0f;
}
#endif // HAS_BIAS
}
/** This opencl kernel performs im2col when the kernel size is 5x5 and the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
* @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
* @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -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.
* @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/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 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 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[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
__kernel void im2col5x5_nchw(
TENSOR3D_DECLARATION(src),
#if defined(NUM_GROUPS)
TENSOR3D_DECLARATION(dst),
#else // defined(NUM_GROUPS)
IMAGE_DECLARATION(dst),
#endif // defined(NUM_GROUPS)
uint src_stride_w,
uint dst_stride_w)
{
const int xc = get_global_id(0); // x coordinate in the convolved tensor
const int yc = get_global_id(1); // y coordinate in the convolved tensor
const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X - PAD_LEFT;
const int yi = yc * STRIDE_Y - PAD_TOP;
// Calculate output indices
#if defined(NUM_GROUPS)
const int xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * 25; // 5x5
const int zo = ch / (SRC_DEPTH / NUM_GROUPS);
#else // defined(NUM_GROUPS)
const int xo = ch * 25; // 5x5
#endif // defined(NUM_GROUPS)
const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
// Put 0 if the value is out-of-bound
int4 x0 = (int4)xi + (int4)(0, 1, 2, 3);
int4 y0 = (int4)yi + (int4)(0, 1, 2, 3);
int x1 = xi + 4;
int y1 = yi + 4;
// Check if we could have out-of-bounds elements in the x direction
VEC_DATA_TYPE(COND_DATA_TYPE, 4)
x0_condition = CONVERT((x0 >= (int4)0 && x0 < (int4)SRC_WIDTH), VEC_DATA_TYPE(COND_DATA_TYPE, 4));
VEC_DATA_TYPE(COND_DATA_TYPE, 4)
y0_condition = CONVERT((y0 >= (int4)0 && y0 < (int4)SRC_HEIGHT), VEC_DATA_TYPE(COND_DATA_TYPE, 4));
COND_DATA_TYPE x1_condition = (COND_DATA_TYPE)(x1 >= 0 && x1 < SRC_WIDTH);
COND_DATA_TYPE y1_condition = (COND_DATA_TYPE)(y1 >= 0 && y1 < SRC_HEIGHT);
#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
// Get input and output address
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * (int)src_stride_x + yi * (int)src_stride_y + ch * src_stride_z + batch * src_stride_w;
#if defined(NUM_GROUPS)
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w;
#else // defined(NUM_GROUPS)
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;
#endif // defined(NUM_GROUPS)
{
VEC_DATA_TYPE(DATA_TYPE, 4)
row00 = vload4(0, (__global DATA_TYPE *)input_ptr);
DATA_TYPE
row01 = *((__global DATA_TYPE *)input_ptr + 4);
input_ptr += src_stride_y;
VEC_DATA_TYPE(DATA_TYPE, 4)
row10 = vload4(0, (__global DATA_TYPE *)input_ptr);
DATA_TYPE
row11 = *((__global DATA_TYPE *)input_ptr + 4);
#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
VEC_DATA_TYPE(COND_DATA_TYPE, 4)
cond00 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y0_condition.s0;
VEC_DATA_TYPE(COND_DATA_TYPE, 4)
cond10 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y0_condition.s1;
COND_DATA_TYPE cond01 = (COND_DATA_TYPE)(x1_condition && y0_condition.s0);
COND_DATA_TYPE cond11 = (COND_DATA_TYPE)(x1_condition && y0_condition.s1);
// Replace with 0 if the value is not valid
row00 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row00, cond00);
row10 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row10, cond10);
row01 = select((DATA_TYPE)PAD_VALUE, row01, cond01);
row11 = select((DATA_TYPE)PAD_VALUE, row11, cond11);
#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s0123, row01,
row10.s012),
0, (__global DATA_TYPE *)output_ptr);
vstore2((VEC_DATA_TYPE(DATA_TYPE, 2))(row10.s3, row11), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 10 * dst_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 4)
row00 = vload4(0, (__global DATA_TYPE *)input_ptr);
DATA_TYPE
row01 = *((__global DATA_TYPE *)input_ptr + 4);
input_ptr += src_stride_y;
VEC_DATA_TYPE(DATA_TYPE, 4)
row10 = vload4(0, (__global DATA_TYPE *)input_ptr);
DATA_TYPE
row11 = *((__global DATA_TYPE *)input_ptr + 4);
#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
VEC_DATA_TYPE(COND_DATA_TYPE, 4)
cond00 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y0_condition.s2;
VEC_DATA_TYPE(COND_DATA_TYPE, 4)
cond10 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y0_condition.s3;
COND_DATA_TYPE cond01 = (COND_DATA_TYPE)(x1_condition && y0_condition.s2);
COND_DATA_TYPE cond11 = (COND_DATA_TYPE)(x1_condition && y0_condition.s3);
// Replace with 0 if the value is not valid
row00 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row00, cond00);
row10 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row10, cond10);
row01 = select((DATA_TYPE)PAD_VALUE, row01, cond01);
row11 = select((DATA_TYPE)PAD_VALUE, row11, cond11);
#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s0123, row01,
row10.s012),
0, (__global DATA_TYPE *)output_ptr);
vstore2((VEC_DATA_TYPE(DATA_TYPE, 2))(row10.s3, row11), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 10 * dst_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 4)
row00 = vload4(0, (__global DATA_TYPE *)input_ptr);
DATA_TYPE
row01 = *((__global DATA_TYPE *)input_ptr + 4);
input_ptr += src_stride_y;
#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
VEC_DATA_TYPE(COND_DATA_TYPE, 4)
cond00 = x0_condition && (VEC_DATA_TYPE(COND_DATA_TYPE, 4))y1_condition;
COND_DATA_TYPE cond01 = (COND_DATA_TYPE)(x1_condition && y1_condition);
// Replace with 0 if the value is not valid
row00 = select((VEC_DATA_TYPE(DATA_TYPE, 4))PAD_VALUE, row00, cond00);
row01 = select((DATA_TYPE)PAD_VALUE, row01, cond01);
#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
vstore4(row00, 0, (__global DATA_TYPE *)output_ptr);
*((__global DATA_TYPE *)output_ptr + 4) = row01;
output_ptr += 5 * dst_stride_x;
}
#ifdef HAS_BIAS
#if defined(NUM_GROUPS)
if((xo / 25) == (SRC_DEPTH / NUM_GROUPS - 1))
#else // defined(NUM_GROUPS)
if(ch == (SRC_DEPTH - 1))
#endif // defined(NUM_GROUPS)
{
*((__global DATA_TYPE *)output_ptr) = 1.0f;
}
#endif // HAS_BIAS
}
#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH)
/** This opencl kernel performs im2col when the kernel size is 11x11, we do not have paddings and the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
* @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -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.
* @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/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 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 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[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
__kernel void im2col11x11_padx0_pady0_nchw(
TENSOR3D_DECLARATION(src),
#if defined(NUM_GROUPS)
TENSOR3D_DECLARATION(dst),
#else // defined(NUM_GROUPS)
IMAGE_DECLARATION(dst),
#endif // defined(NUM_GROUPS)
uint src_stride_w,
uint dst_stride_w)
{
const int xc = get_global_id(0); // x coordinate in the convolved tensor
const int yc = get_global_id(1); // y coordinate in the convolved tensor
const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X;
const int yi = yc * STRIDE_Y;
// Calculate output indices
#if defined(NUM_GROUPS)
const int xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * 121; // 11x11
const int zo = ch / (SRC_DEPTH / NUM_GROUPS);
#else // defined(NUM_GROUPS)
const int xo = ch * 121; // 11x11
#endif // defined(NUM_GROUPS)
const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
// Get input and output address
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_x + yi * src_stride_y + ch * src_stride_z + batch * src_stride_w;
#if defined(NUM_GROUPS)
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w;
#else // defined(NUM_GROUPS)
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;
#endif // defined(NUM_GROUPS)
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
input_ptr += src_stride_y;
output_ptr += 11 * src_stride_x;
}
{
VEC_DATA_TYPE(DATA_TYPE, 8)
row00 = vload8(0, (__global DATA_TYPE *)(input_ptr));
VEC_DATA_TYPE(DATA_TYPE, 3)
row01 = vload3(0, (__global DATA_TYPE *)(input_ptr) + 8);
vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row00.s01234567), 0, (__global DATA_TYPE *)output_ptr);
vstore3((VEC_DATA_TYPE(DATA_TYPE, 3))(row01.s012), 0, (__global DATA_TYPE *)output_ptr + 8);
output_ptr += 11 * src_stride_x;
}
#ifdef HAS_BIAS
#if defined(NUM_GROUPS)
if((xo / 121) == (SRC_DEPTH / NUM_GROUPS - 1))
#else // defined(NUM_GROUPS)
if(ch == (SRC_DEPTH - 1))
#endif // defined(NUM_GROUPS)
{
*((__global DATA_TYPE *)output_ptr) = 1.0f;
}
#endif // HAS_BIAS
}
#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH)
#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
/** This opencl kernel performs im2col when the kernel size is greater than 1x1, we do not have paddings and the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float.
* @note The vector size must be passed at compile time using -DVECTOR_SIZE e.g. -DVECTOR_SIZE=4.
* @note The width modulo vector size must be passed at compile time using -DWIDTH_MOD_VECTOR_SIZE e.g. -DWIDTH_MOD_VECTOR_SIZE=3.
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -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.
* @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: 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 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 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[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
__kernel void im2col_generic_padx0_pady0_nchw(
TENSOR3D_DECLARATION(src),
#if defined(NUM_GROUPS)
TENSOR3D_DECLARATION(dst),
#else // defined(NUM_GROUPS)
IMAGE_DECLARATION(dst),
#endif // defined(NUM_GROUPS)
uint src_stride_w,
uint dst_stride_w)
{
const int xc = get_global_id(0); // x coordinate in the convolved tensor
const int yc = get_global_id(1); // y coordinate in the convolved tensor
const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X;
const int yi = yc * STRIDE_Y;
// Calculate output indices
#if defined(NUM_GROUPS)
const int xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * KERNEL_WIDTH * KERNEL_HEIGHT;
const int zo = ch / (SRC_DEPTH / NUM_GROUPS);
#else // defined(NUM_GROUPS)
const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT;
#endif // defined(NUM_GROUPS)
const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w;
#if defined(NUM_GROUPS)
__global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w)) + xo;
#else // defined(NUM_GROUPS)
__global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo;
#endif // defined(NUM_GROUPS)
// Linearize convolution elements
for(int y = yi, y_e = yi + KERNEL_HEIGHT; y < y_e; ++y)
{
int last_x = 0;
for(int x = xi, x_e = xi + KERNEL_WIDTH; x + VECTOR_SIZE <= x_e; x += VECTOR_SIZE, output_ptr += VECTOR_SIZE)
{
VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)
row = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
VSTORE(VECTOR_SIZE)
(row, 0, output_ptr);
last_x = x;
}
// Copy the remainder of the row by doing VLOAD(WIDTH_MOD_VECTOR_SIZE) and VSTORE(WIDTH_MOD_VECTOR_SIZE).
// Note that x and output_ptr have already been incremented by VECTOR_SIZE by the loop just before exit.
#if WIDTH_MOD_VECTOR_SIZE == 1
*output_ptr = *((__global DATA_TYPE *)(input_ptr + (last_x + VECTOR_SIZE) * src_stride_x + y * src_stride_y));
#elif WIDTH_MOD_VECTOR_SIZE > 1
VEC_DATA_TYPE(DATA_TYPE, WIDTH_MOD_VECTOR_SIZE)
row = VLOAD(WIDTH_MOD_VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + (last_x + VECTOR_SIZE) * src_stride_x + y * src_stride_y));
VSTORE(WIDTH_MOD_VECTOR_SIZE)
(row, 0, output_ptr);
#endif /* WIDTH_MOD_VECTOR_SIZE */
output_ptr += WIDTH_MOD_VECTOR_SIZE;
} /* End of loop over KERNEL_HEIGHT */
#ifdef HAS_BIAS
#if defined(NUM_GROUPS)
if((xo / (KERNEL_WIDTH * KERNEL_HEIGHT)) == (SRC_DEPTH / NUM_GROUPS - 1))
#else // defined(NUM_GROUPS)
if(ch == (SRC_DEPTH - 1))
#endif // defined(NUM_GROUPS)
{
*output_ptr = 1.0f;
}
#endif // HAS_BIAS
}
#endif //defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) && defined(VECTOR_SIZE) && defined(LAST_ACCESSED)
#define VECTOR_N VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)
/** This kernel performs im2col when the kernel size is 3x3 and the data layout is NHWC
*
* @note This kernel computes VECTOR_SIZE elements
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
* @note The kernel depth must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -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: QASYMM8/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 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] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
__kernel void im2col3x3_nhwc(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
const int ch = min((int)(get_global_id(0) * VECTOR_SIZE), LAST_ACCESSED); // input feature map
const int yo = get_global_id(1);
const int batch = get_global_id(2); // batch size
// Calculate input indices
const int xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;
const int yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;
// Get input and output address
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + batch * (int)src_stride_w;
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + yo * (int)dst_stride_y + batch * (int)dst_stride_w;
int yi_coord = 0;
int3 offset = 0;
// Clamp xi
int3 xi_offset = ((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT);
#if PAD_TOP != 0 || PAD_BOTTOM != 0
#define CLAMP(x, min_val, max_val) min(max(x, min_val), max_val)
xi_offset = CLAMP(xi_offset, (int3)0, (int3)(SRC_WIDTH - 1));
#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
xi_offset *= (int3)src_stride_y;
// Out-of-bound condition for X
int3 x_cond = (((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT) < (int3)0) || (((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT) >= (int3)SRC_WIDTH);
// yi == 0
// Clamp yi
// yi_coord is casted to unsigned int in order to use just a min() operation
// A "-1" 32 bit signed variable converted to unsigned gives 4294967295
yi_coord = yi - (int)PAD_TOP;
// Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0
#if PAD_TOP != 0 || PAD_BOTTOM != 0
yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));
#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
// Compute offset
offset = xi_offset + (yi_coord * (int)src_stride_z);
// Load input values
VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
VECTOR_N values1 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
VECTOR_N values2 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
// Replace invalid values with PAD_VALUE
int y_cond = (int)((uint)(yi - (int)PAD_TOP) >= (uint)(SRC_HEIGHT));
values0 = select(values0, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s0));
values1 = select(values1, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s1));
values2 = select(values2, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s2));
#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
// yi == 1
// Clamp yi_coord (it can be negative if PAD_TOP > 1)
yi_coord = yi - (int)PAD_TOP + 1 * DILATION_Y;
// Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0
#if PAD_TOP != 0 || PAD_BOTTOM != 0
yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));
#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
// Compute offset
offset = xi_offset + (yi_coord * (int)src_stride_z);
// Load input values
VECTOR_N values3 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
VECTOR_N values4 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
VECTOR_N values5 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
// Replace invalid values with zeros
y_cond = (int)((uint)(yi - (int)PAD_TOP + 1 * DILATION_Y) >= (uint)(SRC_HEIGHT));
values3 = select(values3, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s0));
values4 = select(values4, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s1));
values5 = select(values5, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s2));
#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
// yi == 2
// Clamp yi_coord
yi_coord = yi - (int)PAD_TOP + 2 * DILATION_Y;
// Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0
#if PAD_TOP != 0 || PAD_BOTTOM != 0
yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));
#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
// Compute offset
offset = xi_offset + (yi_coord * (int)src_stride_z);
// Load input values
VECTOR_N values6 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
VECTOR_N values7 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
VECTOR_N values8 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
// Replace invalid values with PAD_VALUE
y_cond = (int)((uint)(yi - (int)PAD_TOP + 2 * DILATION_Y) >= (uint)(SRC_HEIGHT));
values6 = select(values6, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s0));
values7 = select(values7, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s1));
values8 = select(values8, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s2));
#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
// Store
VSTORE(VECTOR_SIZE)
(values0, 0, (__global DATA_TYPE *)(output_ptr) + 0 * SRC_DEPTH);
VSTORE(VECTOR_SIZE)
(values1, 0, (__global DATA_TYPE *)(output_ptr) + 1 * SRC_DEPTH);
VSTORE(VECTOR_SIZE)
(values2, 0, (__global DATA_TYPE *)(output_ptr) + 2 * SRC_DEPTH);
VSTORE(VECTOR_SIZE)
(values3, 0, (__global DATA_TYPE *)(output_ptr) + 3 * SRC_DEPTH);
VSTORE(VECTOR_SIZE)
(values4, 0, (__global DATA_TYPE *)(output_ptr) + 4 * SRC_DEPTH);
VSTORE(VECTOR_SIZE)
(values5, 0, (__global DATA_TYPE *)(output_ptr) + 5 * SRC_DEPTH);
VSTORE(VECTOR_SIZE)
(values6, 0, (__global DATA_TYPE *)(output_ptr) + 6 * SRC_DEPTH);
VSTORE(VECTOR_SIZE)
(values7, 0, (__global DATA_TYPE *)(output_ptr) + 7 * SRC_DEPTH);
VSTORE(VECTOR_SIZE)
(values8, 0, (__global DATA_TYPE *)(output_ptr) + 8 * SRC_DEPTH);
#ifdef HAS_BIAS
if((ch + VECTOR_SIZE) >= SRC_DEPTH)
{
*((__global DATA_TYPE *)(output_ptr) - ch + SRC_DEPTH * 9) = 1.0f;
}
#endif // HAS_BIAS
}
#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
#define IM2COL1x9(i) \
({ \
yi_coord = yi - (int)PAD_TOP + i * DILATION_Y; \
yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1)); \
\
offset0 = xi_offset0 + (yi_coord * (int)src_stride_z); \
offset1 = xi_offset1 + (yi_coord * (int)src_stride_z); \
\
VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s0)); \
VECTOR_N values1 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s1)); \
VECTOR_N values2 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s2)); \
VECTOR_N values3 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s3)); \
VECTOR_N values4 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s4)); \
VECTOR_N values5 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s5)); \
VECTOR_N values6 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s6)); \
VECTOR_N values7 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s7)); \
VECTOR_N values8 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset1)); \
\
int y_cond = (int)((uint)(yi - (int)PAD_TOP + i * DILATION_Y) >= (uint)(SRC_HEIGHT)); \
values0 = select(values0, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s0)); \
values1 = select(values1, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s1)); \
values2 = select(values2, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s2)); \
values3 = select(values3, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s3)); \
values4 = select(values4, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s4)); \
values5 = select(values5, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s5)); \
values6 = select(values6, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s6)); \
values7 = select(values7, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s7)); \
values8 = select(values8, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond1)); \
\
VSTORE(VECTOR_SIZE) \
(values0, 0, (__global DATA_TYPE *)(output_ptr) + (0 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values1, 0, (__global DATA_TYPE *)(output_ptr) + (1 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values2, 0, (__global DATA_TYPE *)(output_ptr) + (2 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values3, 0, (__global DATA_TYPE *)(output_ptr) + (3 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values4, 0, (__global DATA_TYPE *)(output_ptr) + (4 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values5, 0, (__global DATA_TYPE *)(output_ptr) + (5 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values6, 0, (__global DATA_TYPE *)(output_ptr) + (6 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values7, 0, (__global DATA_TYPE *)(output_ptr) + (7 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values8, 0, (__global DATA_TYPE *)(output_ptr) + (8 + i * 9) * SRC_DEPTH); \
})
#else // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
#define IM2COL1x9(i) \
({ \
yi_coord = yi - (int)PAD_TOP + i * DILATION_Y; \
yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1)); \
\
offset0 = xi_offset0 + (yi_coord * (int)src_stride_z); \
offset1 = xi_offset1 + (yi_coord * (int)src_stride_z); \
\
VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s0)); \
VECTOR_N values1 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s1)); \
VECTOR_N values2 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s2)); \
VECTOR_N values3 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s3)); \
VECTOR_N values4 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s4)); \
VECTOR_N values5 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s5)); \
VECTOR_N values6 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s6)); \
VECTOR_N values7 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s7)); \
VECTOR_N values8 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset1)); \
\
VSTORE(VECTOR_SIZE) \
(values0, 0, (__global DATA_TYPE *)(output_ptr) + (0 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values1, 0, (__global DATA_TYPE *)(output_ptr) + (1 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values2, 0, (__global DATA_TYPE *)(output_ptr) + (2 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values3, 0, (__global DATA_TYPE *)(output_ptr) + (3 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values4, 0, (__global DATA_TYPE *)(output_ptr) + (4 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values5, 0, (__global DATA_TYPE *)(output_ptr) + (5 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values6, 0, (__global DATA_TYPE *)(output_ptr) + (6 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values7, 0, (__global DATA_TYPE *)(output_ptr) + (7 + i * 9) * SRC_DEPTH); \
VSTORE(VECTOR_SIZE) \
(values8, 0, (__global DATA_TYPE *)(output_ptr) + (8 + i * 9) * SRC_DEPTH); \
})
#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
/** This kernel performs im2col when the kernel size is 9x9 and the data layout is NHWC
*
* @note This kernel computes VECTOR_SIZE elements
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
* @note The kernel depth must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -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: QASYMM8/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 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] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
__kernel void im2col9x9_nhwc(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
const int ch = min((int)(get_global_id(0) * VECTOR_SIZE), LAST_ACCESSED); // input feature map
const int yo = get_global_id(1);
const int batch = get_global_id(2); // batch size
// Calculate input indices
const int xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;
const int yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;
// Get input and output address
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + batch * (int)src_stride_w;
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + yo * (int)dst_stride_y + batch * (int)dst_stride_w;
int yi_coord = 0;
int8 offset0 = 0;
int offset1 = 0;
// Clamp xi
int8 xi_offset0 = ((int8)xi + (int8)(0, 1, 2, 3, 4, 5, 6, 7) * DILATION_X - (int8)PAD_LEFT);
int xi_offset1 = ((int)xi + (int)(8) * DILATION_X - (int)PAD_LEFT);
#if PAD_TOP != 0 || PAD_BOTTOM != 0
#define CLAMP(x, min_val, max_val) min(max(x, min_val), max_val)
xi_offset0 = CLAMP(xi_offset0, (int8)0, (int8)(SRC_WIDTH - 1));
xi_offset1 = CLAMP(xi_offset1, (int)0, (int)(SRC_WIDTH - 1));
#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
xi_offset0 *= (int8)src_stride_y;
xi_offset1 *= (int)src_stride_y;
// Out-of-bound condition for X
int8 x_cond0 = (((int8)xi + (int8)(0, 1, 2, 3, 4, 5, 6, 7) * DILATION_X - (int8)PAD_LEFT) < (int8)0) || (((int8)xi + (int8)(0, 1, 2, 3, 4, 5, 6, 7) * DILATION_X - (int8)PAD_LEFT) >= (int8)SRC_WIDTH);
int x_cond1 = (((int)xi + (int)(8) * DILATION_X - (int)PAD_LEFT) < (int)0) || (((int)xi + (int)(8) * DILATION_X - (int)PAD_LEFT) >= (int)SRC_WIDTH);
IM2COL1x9(0);
IM2COL1x9(1);
IM2COL1x9(2);
IM2COL1x9(3);
IM2COL1x9(4);
IM2COL1x9(5);
IM2COL1x9(6);
IM2COL1x9(7);
IM2COL1x9(8);
#ifdef HAS_BIAS
if((ch + VECTOR_SIZE) >= SRC_DEPTH)
{
*((__global DATA_TYPE *)(output_ptr) - ch + SRC_DEPTH * 81) = 1.0f;
}
#endif // HAS_BIAS
}
/** This opencl kernel performs a generic im2col implementation when the data layout is NHWC
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
* @note The kernel width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DSRC_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DSRC_DEPTH=64
* @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
* @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
* @note The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_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: QASYMM8/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 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] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
__kernel void im2col_generic_nhwc(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
const int ch = min((int)(get_global_id(0) * VECTOR_SIZE), LAST_ACCESSED); // input feature map
const int yo = get_global_id(1);
const int batch = get_global_id(2); // batch size
// Calculate input indices
const int xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;
const int yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;
// Get input and output address
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + batch * (int)src_stride_w;
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + yo * (int)dst_stride_y + batch * (int)dst_stride_w;
int i = 0;
for(int yk = 0; yk < KERNEL_HEIGHT; ++yk)
{
// Clamp yi_coord
int yi_coord = yi + yk * DILATION_Y - (int)PAD_TOP;
yi_coord = CLAMP(yi_coord, (int)0, (int)(SRC_HEIGHT - 1));
// Out-of-bound condition for Y
int y_border_condition = ((yi + yk * DILATION_Y - (int)PAD_TOP) < (int)0) || ((yi + yk * DILATION_Y - (int)PAD_TOP) >= (int)SRC_HEIGHT);
for(int xk = 0; xk < KERNEL_WIDTH; ++xk)
{
// Clamp xi_coord
int xi_coord = (xi + xk * DILATION_X - (int)PAD_LEFT);
xi_coord = CLAMP(xi_coord, (int)0, (int)(SRC_WIDTH - 1));
// Out-of-bound condition for X
int x_border_condition = ((xi + xk * DILATION_X - (int)PAD_LEFT) < (int)0) || ((xi + xk * DILATION_X - (int)PAD_LEFT) >= (int)SRC_WIDTH);
int offset = xi_coord * (int)src_stride_y + (yi_coord * (int)src_stride_z);
VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset));
// Replace with PAD_VALUE if the value is out-of-bound
values0 = select(values0, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))x_border_condition || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(y_border_condition));
// Store
VSTORE(VECTOR_SIZE)
(values0, 0, (__global DATA_TYPE *)(output_ptr) + i * (int)SRC_DEPTH);
i++;
}
}
#ifdef HAS_BIAS
if((ch + VECTOR_SIZE) >= SRC_DEPTH)
{
*((__global DATA_TYPE *)(output_ptr) - ch + SRC_DEPTH * KERNEL_WIDTH * KERNEL_HEIGHT) = 1.0f;
}
#endif // HAS_BIAS
}
#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) && defined(VECTOR_SIZE) && defined(LAST_ACCESSED)
#endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE)
)"