| /* |
| * Copyright (c) 2018-2020 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. |
| */ |
| #include "helpers.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_SIGNED/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_SIGNED/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_SIGNED/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_SIGNED/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_SIGNED/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: QASYMM8_SIGNED/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_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(BOUNDARY_VECTOR_SIZE) |
| |
| #define VECTOR_N VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) |
| #define COND_N VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE) |
| |
| /** Store a 1x9 row or a 3x3 block in a boundary-aware manner to avoid paddings in the channel dimension |
| * @name IM2COL1X9_NHWC_STORE |
| * |
| * @note To use this macro for a 3x3 block, @p ROW has to be 0 |
| * |
| * @param[in] VECTOR_SIZE The non-boundary vector width of @p DATA. Supported: 1(scalar), 2, 3, 4, 8, 16 |
| * @param[in] BOUNDARY_VECTOR_SIZE The boundary vector width of @p DATA. Supported: 1-16, but has to be <= @p size |
| * @param[in] DATA_TYPE Data type of @p DATA |
| * @param[in] SRC_DEPTH Input channel size / depth |
| * @param[in] DATA Value variable base name |
| * @param[in] ROW The row number to store. Supported: 0-8 |
| * @param[in] OUTPUT_PTR Output pointer |
| * @{ |
| */ |
| #if defined(VECTOR_SIZE) && defined(BOUNDARY_VECTOR_SIZE) && BOUNDARY_VECTOR_SIZE < VECTOR_SIZE |
| #define IM2COL1X9_NHWC_STORE(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, DATA, ROW, OUTPUT_PTR) \ |
| const bool at_channel_boundary = get_global_id(0) == 0; \ |
| if(at_channel_boundary) \ |
| { \ |
| IM2COL1X9_NHWC_STORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, DATA, ROW, OUTPUT_PTR) \ |
| } \ |
| else \ |
| { \ |
| IM2COL1X9_NHWC_STORE_NONPARTIAL(VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, DATA, ROW, OUTPUT_PTR) \ |
| } |
| #else // defined(VECTOR_SIZE) && defined(BOUNDARY_VECTOR_SIZE) && BOUNDARY_VECTOR_SIZE < VECTOR_SIZE |
| #define IM2COL1X9_NHWC_STORE(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, DATA, ROW, OUTPUT_PTR) \ |
| IM2COL1X9_NHWC_STORE_NONPARTIAL(VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, DATA, ROW, OUTPUT_PTR) |
| #endif // defined(VECTOR_SIZE) && defined(BOUNDARY_VECTOR_SIZE) && BOUNDARY_VECTOR_SIZE < VECTOR_SIZE |
| |
| #define IM2COL1X9_NHWC_STORE_NONPARTIAL(VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, DATA, ROW, OUTPUT_PTR) \ |
| VSTORE(VECTOR_SIZE) \ |
| (DATA##0, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (0 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE(VECTOR_SIZE) \ |
| (DATA##1, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (1 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE(VECTOR_SIZE) \ |
| (DATA##2, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (2 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE(VECTOR_SIZE) \ |
| (DATA##3, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (3 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE(VECTOR_SIZE) \ |
| (DATA##4, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (4 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE(VECTOR_SIZE) \ |
| (DATA##5, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (5 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE(VECTOR_SIZE) \ |
| (DATA##6, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (6 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE(VECTOR_SIZE) \ |
| (DATA##7, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (7 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE(VECTOR_SIZE) \ |
| (DATA##8, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (8 + ROW * 9) * SRC_DEPTH); |
| |
| #define IM2COL1X9_NHWC_STORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, DATA, ROW, OUTPUT_PTR) \ |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) \ |
| (DATA##0, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (0 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) \ |
| (DATA##1, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (1 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) \ |
| (DATA##2, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (2 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) \ |
| (DATA##3, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (3 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) \ |
| (DATA##4, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (4 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) \ |
| (DATA##5, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (5 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) \ |
| (DATA##6, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (6 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) \ |
| (DATA##7, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (7 + ROW * 9) * SRC_DEPTH); \ |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) \ |
| (DATA##8, 0, (__global DATA_TYPE *)(OUTPUT_PTR) + (8 + ROW * 9) * SRC_DEPTH); |
| /** @}*/ |
| |
| /** This kernel performs im2col when the kernel size is 3x3 and the data layout is NHWC |
| * |
| * @note This kernel computes VECTOR_SIZE elements |
| * @note This kernel stores VECTOR_SIZE or BOUNDARY_VECTOR_SIZE (if at boundary) elements |
| * @note The vector size must be passed at compile time using -DVECTOR_SIZE: e.g. -DVECTOR_SIZE=2 |
| * @note The boundary vector size must be passed at compile time using -DBOUNDARY_VECTOR_SIZE: e.g. -DBOUNDARY_VECTOR_SIZE=1 |
| * @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_SIGNED/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) |
| { |
| // input feature map, boundary-corrected (shift all non-boundary vectors by shift_amount) to avoid padding |
| const int shift_amount = (int)VECTOR_SIZE - (int)BOUNDARY_VECTOR_SIZE; |
| const int ch = max((int)(get_global_id(0) * VECTOR_SIZE) - shift_amount, 0); |
| 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_LEFT != 0 || PAD_RIGHT != 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_LEFT != 0 || PAD_RIGHT != 0 |
| // Multiply by src_stride_y as the width (X) dimension here is the second (y) dimension in src NHWC tensor |
| 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 |
| // This is a trick so that the values loaded in the padding areas are always from the last row (SRC_HEIGHT - 1), |
| // because of the negative yi_coord wrap-around, but it gets overwritten by PAD_VALUE immediately as the wrap-around |
| // also causes y_cond (y padding condition) to be satisfied |
| 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, (COND_N)((COND_N)y_cond || (COND_N)(x_cond.s0))); |
| values1 = select(values1, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond.s1))); |
| values2 = select(values2, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(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, (COND_N)((COND_N)y_cond || (COND_N)(x_cond.s0))); |
| values4 = select(values4, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond.s1))); |
| values5 = select(values5, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(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, (COND_N)((COND_N)y_cond || (COND_N)(x_cond.s0))); |
| values7 = select(values7, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond.s1))); |
| values8 = select(values8, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond.s2))); |
| #endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0 |
| |
| // Store in a boundary-aware way to avoid padding |
| IM2COL1X9_NHWC_STORE(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, values, 0, output_ptr) |
| |
| #ifdef HAS_BIAS |
| // We can use VECTOR_SIZE instead of BOUNDARY_VECTOR_SIZE even if it's at the boundary. This is because the bias is |
| // added at the end of the channel, while the boundary vec is at the beginning of the channel. |
| // The only case where the boundary vec is at the end of the channel is when there's only a single boundary vec in |
| // the whole channel dimension, but in that case VECTOR_SIZE is also equal to BOUNDARY_VECTOR_SIZE |
| // See the value of num_elems_processed_per_iteration in configure_opencl_kernel method in CLIm2ColKernel.cpp |
| 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, (COND_N)((COND_N)y_cond || (COND_N)(x_cond0.s0))); \ |
| values1 = select(values1, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond0.s1))); \ |
| values2 = select(values2, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond0.s2))); \ |
| values3 = select(values3, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond0.s3))); \ |
| values4 = select(values4, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond0.s4))); \ |
| values5 = select(values5, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond0.s5))); \ |
| values6 = select(values6, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond0.s6))); \ |
| values7 = select(values7, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond0.s7))); \ |
| values8 = select(values8, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)y_cond || (COND_N)(x_cond1))); \ |
| \ |
| IM2COL1X9_NHWC_STORE(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, values, i, output_ptr) \ |
| }) |
| #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)); \ |
| \ |
| IM2COL1X9_NHWC_STORE(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE, DATA_TYPE, SRC_DEPTH, values, i, output_ptr) \ |
| }) |
| #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 This kernel stores VECTOR_SIZE or BOUNDARY_VECTOR_SIZE (if at boundary) elements |
| * @note The vector size must be passed at compile time using -DVECTOR_SIZE: e.g. -DVECTOR_SIZE=2 |
| * @note The boundary vector size must be passed at compile time using -DBOUNDARY_VECTOR_SIZE: e.g. -DBOUNDARY_VECTOR_SIZE=1 |
| * @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_SIGNED/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) |
| { |
| // input feature map, boundary-corrected (shift all non-boundary vectors by shift_amount) to avoid padding |
| const int shift_amount = (int)VECTOR_SIZE - (int)BOUNDARY_VECTOR_SIZE; |
| const int ch = max((int)(get_global_id(0) * VECTOR_SIZE) - shift_amount, 0); |
| 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_LEFT != 0 || PAD_RIGHT != 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_LEFT != 0 || PAD_RIGHT != 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 |
| // We can use VECTOR_SIZE instead of BOUNDARY_VECTOR_SIZE even if it's at the boundary. This is because the bias is |
| // added at the end of the channel, while the boundary vec is at the beginning of the channel. |
| // The only case where the boundary vec is at the end of the channel is when there's only a single boundary vec in |
| // the whole channel dimension, but in that case VECTOR_SIZE is also equal to BOUNDARY_VECTOR_SIZE |
| // See the value of num_elems_processed_per_iteration in configure_opencl_kernel method in CLIm2ColKernel.cpp |
| 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 This kernel computes VECTOR_SIZE elements |
| * @note This kernel stores VECTOR_SIZE or BOUNDARY_VECTOR_SIZE (if at boundary) elements |
| * @note The vector size must be passed at compile time using -DVECTOR_SIZE: e.g. -DVECTOR_SIZE=2 |
| * @note The boundary vector size must be passed at compile time using -DBOUNDARY_VECTOR_SIZE: e.g. -DBOUNDARY_VECTOR_SIZE=1 |
| * @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_SIGNED/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) |
| { |
| // input feature map, boundary-corrected (shift all non-boundary vectors by shift_amount) to avoid padding |
| const int shift_amount = (int)VECTOR_SIZE - (int)BOUNDARY_VECTOR_SIZE; |
| const int ch = max((int)(get_global_id(0) * VECTOR_SIZE) - shift_amount, 0); |
| 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)); |
| |
| #if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 |
| // Replace with PAD_VALUE if the value is out-of-bound |
| values0 = select(values0, (VECTOR_N)PAD_VALUE, (COND_N)((COND_N)x_border_condition || (COND_N)(y_border_condition))); |
| #endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0 |
| |
| // Store in a boundary-aware way to avoid padding |
| #if BOUNDARY_VECTOR_SIZE != VECTOR_SIZE |
| const bool at_channel_boundary = get_global_id(0) == 0; |
| if(at_channel_boundary) |
| { |
| VSTORE_PARTIAL(VECTOR_SIZE, BOUNDARY_VECTOR_SIZE) |
| (values0, 0, (__global DATA_TYPE *)(output_ptr) + i * (int)SRC_DEPTH); |
| } |
| else // at_channel_boundary |
| #endif // BOUNDARY_VECTOR_SIZE != VECTOR_SIZE |
| { |
| VSTORE(VECTOR_SIZE) |
| (values0, 0, (__global DATA_TYPE *)(output_ptr) + i * (int)SRC_DEPTH); |
| } |
| i++; |
| } |
| } |
| |
| #ifdef HAS_BIAS |
| // We can use VECTOR_SIZE instead of BOUNDARY_VECTOR_SIZE even if it's at the boundary. This is because the bias is |
| // added at the end of the channel, while the boundary vec is at the beginning of the channel. |
| // The only case where the boundary vec is at the end of the channel is when there's only a single boundary vec in |
| // the whole channel dimension, but in that case VECTOR_SIZE is also equal to BOUNDARY_VECTOR_SIZE |
| // See the value of num_elems_processed_per_iteration in configure_opencl_kernel method in CLIm2ColKernel.cpp |
| 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(BOUNDARY_VECTOR_SIZE) |
| #endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE) |