From 56d87746678e0a78971fb0804c20f356c0f90e62 Mon Sep 17 00:00:00 2001 From: Phil Wang Date: Mon, 14 Mar 2022 07:52:40 -0700 Subject: [PATCH] readme --- README.md | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/README.md b/README.md index bae921d..1729ce6 100644 --- a/README.md +++ b/README.md @@ -173,6 +173,32 @@ mask = torch.ones_like(feats).bool() # (1, 1024) feats_out, coors_out = net(feats, coors, mask = mask) # (1, 1024, 32), (1, 1024, 3) ``` +## All parameters + +```python +import torch +from egnn_pytorch import EGNN + +model = EGNN( + dim = dim, # input dimension + edge_dim = 0, # dimension of the edges, if exists, should be > 0 + m_dim = 16, # hidden model dimension + fourier_features = 0, # number of fourier features for encoding of relative distance - defaults to none as in paper + num_nearest_neighbors = 0, # cap the number of neighbors doing message passing by relative distance + dropout = 0.0, # dropout + norm_feats = False, # whether to layernorm the features + norm_coors = False, # whether to normalize the coordinates, using a strategy from the SE(3) Transformers paper + update_feats = True, # whether to update features - you can build a layer that only updates one or the other + update_coors = True, # whether ot update coordinates + only_sparse_neighbors = False, # using this would only allow message passing along adjacent neighbors, using the adjacency matrix passed in + valid_radius = float('inf'), # the valid radius each node considers for message passing + m_pool_method = 'sum', # whether to mean or sum pool for output node representation + soft_edges = False, # extra GLU on the edges, purportedly helps stabilize the network in updated version of the paper + coor_weights_clamp_value = None # clamping of the coordinate updates, again, for stabilization purposes +) + +``` + ## Examples To run the protein backbone denoising example, first install `sidechainnet`