| /* |
| * 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) |