You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
65 lines
2.7 KiB
65 lines
2.7 KiB
///
|
|
/// Copyright (c) 2021-2022 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.
|
|
///
|
|
|
|
namespace arm_compute
|
|
{
|
|
/**
|
|
@page data_layout_support Data Layout Support
|
|
|
|
@section data_layout_support_supported_data_layout Supported Data Layouts
|
|
|
|
With regard to convolution layers, Compute Library supports the following data layouts for input and output tensors:
|
|
|
|
- NHWC: The native layout of Compute Library that delivers the best performance where channels are in the fastest changing dimension
|
|
- NCHW: Legacy layout where width is in the fastest changing dimension
|
|
- NDHWC: New data layout for supporting 3D operators
|
|
|
|
, where N = batch, C = channel, H = height, W = width, D = depth.
|
|
|
|
Note: The right-most letter represents the fastest changing dimension, which is the "lower dimension".
|
|
The corresponding @ref TensorShape for each of the data layout would be initialized as:
|
|
|
|
- NHWC: TensorShape(C, W, H, N)
|
|
- NCHW: TensorShape(W, H, C, N)
|
|
- NDHWC: TensorShape(C, W, H, D, N)
|
|
|
|
For 2d Conv, the weight / filter tensors are arranged in 4 dimensions: Height (H), Width (W), Input channel (I), Output channel (O)
|
|
For 3d Conv, the additional Depth dimension means exactly the same as the Depth in the input / output layout.
|
|
|
|
The layout of weight tensors change with that of the input / output tensors, and the dimensions can be mapped as:
|
|
|
|
- Weight Height -> Height
|
|
- Weight Width -> Width
|
|
- Weight Input channel -> Channel
|
|
- Weight Output channel -> Batch
|
|
|
|
Therefore, the corresponding weight layouts for each input / output layout are:
|
|
|
|
- (input/output tensor) NHWC: (weight tensor) OHWI
|
|
- (input/output tensor) NCHW: (weight tensor) OIHW
|
|
- (input/output tensor) NDHWC: (weight tensor) ODHWI
|
|
|
|
*/
|
|
} // namespace
|