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/*
* Copyright (c) 2019-2020 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(BLOCK_SHAPE) && defined(CHANNEL_SIZE)
/** Space to depth transformation. (NCHW)
*
* @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float
* @note The input tensor batch size must be passed at compile time using -DCHANNEL_SIZE. e.g. -DCHANNEL_SIZE=2
* @note The block shape must be passed at compile time using -DBLOCK_SHAPE. e.g. -DBLOCK_SHAPE=2
*
* @param[in] input_ptr Pointer to the source tensor. Supported data types: All
* @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes)
* @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor
* @param[in] batch_id The input tensor batch id
* @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr
* @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor
*/
__kernel void space_to_depth_nchw(
TENSOR4D_DECLARATION(input),
const int batch_id,
TENSOR3D_DECLARATION(output))
{
Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0);
Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(output);
const int r = (CHANNEL_SIZE / (BLOCK_SHAPE * BLOCK_SHAPE));
const int x = get_global_id(0);
const int y = get_global_id(1);
const int z = get_global_id(2) % r;
const int in_x = x * BLOCK_SHAPE + (get_global_id(2) / r) % BLOCK_SHAPE;
const int in_y = y * BLOCK_SHAPE + (get_global_id(2) / r) / BLOCK_SHAPE;
*((__global DATA_TYPE *)out.ptr) = *((__global DATA_TYPE *)tensor4D_offset(&in, in_x, in_y, z, batch_id));
}
/** Space to depth transformation. (NHWC)
*
* @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float
* @note The input tensor batch size must be passed at compile time using -DCHANNEL_SIZE. e.g. -DCHANNEL_SIZE=2
* @note The block shape must be passed at compile time using -DBLOCK_SHAPE. e.g. -DBLOCK_SHAPE=2
*
* @param[in] input_ptr Pointer to the source tensor. Supported data types: All
* @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes)
* @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor
* @param[in] batch_id The input tensor batch id
* @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr
* @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor
*/
__kernel void space_to_depth_nhwc(
TENSOR4D_DECLARATION(input),
const int batch_id,
TENSOR3D_DECLARATION(output))
{
Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(input, 0);
Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(output);
const int r = (CHANNEL_SIZE / (BLOCK_SHAPE * BLOCK_SHAPE));
const int x = get_global_id(1);
const int y = get_global_id(2);
const int z = get_global_id(0) % r;
const int in_x = x * BLOCK_SHAPE + (get_global_id(0) / r) % BLOCK_SHAPE;
const int in_y = y * BLOCK_SHAPE + (get_global_id(0) / r) / BLOCK_SHAPE;
*((__global DATA_TYPE *)out.ptr) = *((__global DATA_TYPE *)tensor4D_offset(&in, z, in_x, in_y, batch_id));
}
#endif // defined(DATA_TYPE) && defined(BLOCK_SHAPE) && defined(CHANNEL_SIZE)
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