![]() ![]() Therefore, researchers can get results up to 1.5x faster than training without Tensor Cores while experiencing the benefits of mixed precision training. This model is trained with mixed precision using Tensor Cores on NVIDIA Volta, NVIDIA Turing, and the NVIDIA Ampere GPU architectures. In this case, the exploited symmetry is that these properties do not depend on the orientation or position of the molecules in space. This model enables you to predict quantum chemical properties of small organic molecules in the QM9 dataset. The spherical harmonics and Clebsch–Gordan coefficients, used to compute bases matrices, are computed with the e3nn library.The use of equivariant normalization between attention layers is an option ( -norm), off by default.The use of layer normalization in the fully connected radial profile layers is an option ( -use_layer_norm), off by default.Significantly reduced memory consumption.The QM9 dataset from DGL is used and automatically downloaded.Training and inference support for Mixed Precision.Training and inference support for multiple GPUs.The main differences between this implementation of SE(3)-Transformers and the official one are the following: Just like the official implementation, this implementation uses PyTorch and the Deep Graph Library (DGL). Iterative SE(3)-Transformers by Fabian B.Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds by Nathaniel Thomas, Tess Smidt, et al.Ī follow-up paper explains how this model can be used iteratively, for example, to predict or refine protein structures:.SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks (NeurIPS 2020) by Fabian B. ![]() The model is based on the following publications: This model is equivariant under continuous 3D roto-translations, meaning that when the inputs (graphs or sets of points) rotate in 3D space (or more generally experience a proper rigid transformation), the model outputs either stay invariant or transform with the input.Ī mathematical guarantee of equivariance is important to ensure stable and predictable performance in the presence of nuisance transformations of the data input and when the problem has some inherent symmetries we want to exploit. The SE(3)-Transformer is a Graph Neural Network using a variant of self-attention for 3D points and graphs processing. ![]()
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