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Diffstat (limited to 'docs/user_guide/data_layout.dox')
| -rw-r--r-- | docs/user_guide/data_layout.dox | 31 |
1 files changed, 27 insertions, 4 deletions
diff --git a/docs/user_guide/data_layout.dox b/docs/user_guide/data_layout.dox index 97d3ea6262..711b85f08c 100644 --- a/docs/user_guide/data_layout.dox +++ b/docs/user_guide/data_layout.dox @@ -1,5 +1,5 @@ /// -/// Copyright (c) 2021 Arm Limited. +/// Copyright (c) 2021-2022 Arm Limited. /// /// SPDX-License-Identifier: MIT /// @@ -29,13 +29,36 @@ namespace arm_compute @section data_layout_support_supported_data_layout Supported Data Layouts -Compute Library supports the following data layouts and -the right-most letter represents the fastest changing dimension: +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. +, 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 |