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diff --git a/docs/user_guide/data_layout.dox b/docs/user_guide/data_layout.dox new file mode 100644 index 0000000000..711b85f08c --- /dev/null +++ b/docs/user_guide/data_layout.dox @@ -0,0 +1,64 @@ +/// +/// 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 |