| /// |
| /// Copyright (c) 2021-2022 Arm Limited. |
| /// |
| /// SPDX-License-Identifier: MIT |
| /// |
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| |
| 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 |