egnn pytorch
0.2.8
** تم اكتشاف خطأ في اختيار الجار في وجود الإخفاء. إذا قمت بإجراء أية تجارب قبل 0.1.12 تحتوي على إخفاء، فيرجى إعادة تشغيلها. **
تنفيذ الشبكات العصبية ذات الرسم البياني المتساوي E(n) في Pytorch. يمكن استخدامه في النهاية للنسخ المتماثل Alphafold2. استخدمت هذه التقنية ميزات ثابتة بسيطة، وانتهت بالتغلب على جميع الطرق السابقة (بما في ذلك SE3 Transformer وLie Conv) من حيث الدقة والأداء. SOTA في نماذج النظام الديناميكي، ومهام التنبؤ بالنشاط الجزيئي، وما إلى ذلك.
$ pip install egnn-pytorch import torch
from egnn_pytorch import EGNN
layer1 = EGNN ( dim = 512 )
layer2 = EGNN ( dim = 512 )
feats = torch . randn ( 1 , 16 , 512 )
coors = torch . randn ( 1 , 16 , 3 )
feats , coors = layer1 ( feats , coors )
feats , coors = layer2 ( feats , coors ) # (1, 16, 512), (1, 16, 3)مع الحواف
import torch
from egnn_pytorch import EGNN
layer1 = EGNN ( dim = 512 , edge_dim = 4 )
layer2 = EGNN ( dim = 512 , edge_dim = 4 )
feats = torch . randn ( 1 , 16 , 512 )
coors = torch . randn ( 1 , 16 , 3 )
edges = torch . randn ( 1 , 16 , 16 , 4 )
feats , coors = layer1 ( feats , coors , edges )
feats , coors = layer2 ( feats , coors , edges ) # (1, 16, 512), (1, 16, 3)شبكة EGNN كاملة
import torch
from egnn_pytorch import EGNN_Network
net = EGNN_Network (
num_tokens = 21 ,
num_positions = 1024 , # unless what you are passing in is an unordered set, set this to the maximum sequence length
dim = 32 ,
depth = 3 ,
num_nearest_neighbors = 8 ,
coor_weights_clamp_value = 2. # absolute clamped value for the coordinate weights, needed if you increase the num neareest neighbors
)
feats = torch . randint ( 0 , 21 , ( 1 , 1024 )) # (1, 1024)
coors = torch . randn ( 1 , 1024 , 3 ) # (1, 1024, 3)
mask = torch . ones_like ( feats ). bool () # (1, 1024)
feats_out , coors_out = net ( feats , coors , mask = mask ) # (1, 1024, 32), (1, 1024, 3)لا تهتم إلا بالجيران المتناثرين، الذين يتم إعطاؤهم للشبكة كمصفوفة مجاورة.
import torch
from egnn_pytorch import EGNN_Network
net = EGNN_Network (
num_tokens = 21 ,
dim = 32 ,
depth = 3 ,
only_sparse_neighbors = True
)
feats = torch . randint ( 0 , 21 , ( 1 , 1024 ))
coors = torch . randn ( 1 , 1024 , 3 )
mask = torch . ones_like ( feats ). bool ()
# naive adjacency matrix
# assuming the sequence is connected as a chain, with at most 2 neighbors - (1024, 1024)
i = torch . arange ( 1024 )
adj_mat = ( i [:, None ] >= ( i [ None , :] - 1 )) & ( i [:, None ] <= ( i [ None , :] + 1 ))
feats_out , coors_out = net ( feats , coors , mask = mask , adj_mat = adj_mat ) # (1, 1024, 32), (1, 1024, 3)يمكنك أيضًا جعل الشبكة تحدد تلقائيًا الأجهزة المجاورة من الترتيب N، وتمرير تضمين الجوار (اعتمادًا على الترتيب) لاستخدامه كحافة، مع وسيطتين إضافيتين للكلمات الرئيسية
import torch
from egnn_pytorch import EGNN_Network
net = EGNN_Network (
num_tokens = 21 ,
dim = 32 ,
depth = 3 ,
num_adj_degrees = 3 , # fetch up to 3rd degree neighbors
adj_dim = 8 , # pass an adjacency degree embedding to the EGNN layer, to be used in the edge MLP
only_sparse_neighbors = True
)
feats = torch . randint ( 0 , 21 , ( 1 , 1024 ))
coors = torch . randn ( 1 , 1024 , 3 )
mask = torch . ones_like ( feats ). bool ()
# naive adjacency matrix
# assuming the sequence is connected as a chain, with at most 2 neighbors - (1024, 1024)
i = torch . arange ( 1024 )
adj_mat = ( i [:, None ] >= ( i [ None , :] - 1 )) & ( i [:, None ] <= ( i [ None , :] + 1 ))
feats_out , coors_out = net ( feats , coors , mask = mask , adj_mat = adj_mat ) # (1, 1024, 32), (1, 1024, 3) إذا كنت بحاجة لتمرير في حواف مستمرة
import torch
from egnn_pytorch import EGNN_Network
net = EGNN_Network (
num_tokens = 21 ,
dim = 32 ,
depth = 3 ,
edge_dim = 4 ,
num_nearest_neighbors = 3
)
feats = torch . randint ( 0 , 21 , ( 1 , 1024 ))
coors = torch . randn ( 1 , 1024 , 3 )
mask = torch . ones_like ( feats ). bool ()
continuous_edges = torch . randn ( 1 , 1024 , 1024 , 4 )
# naive adjacency matrix
# assuming the sequence is connected as a chain, with at most 2 neighbors - (1024, 1024)
i = torch . arange ( 1024 )
adj_mat = ( i [:, None ] >= ( i [ None , :] - 1 )) & ( i [:, None ] <= ( i [ None , :] + 1 ))
feats_out , coors_out = net ( feats , coors , edges = continuous_edges , mask = mask , adj_mat = adj_mat ) # (1, 1024, 32), (1, 1024, 3) عانت البنية الأولية لـ EGNN من عدم الاستقرار عندما كان هناك عدد كبير من الجيران. ولحسن الحظ، يبدو أن هناك حلين يخففان من هذه المشكلة إلى حد كبير.
import torch
from egnn_pytorch import EGNN_Network
net = EGNN_Network (
num_tokens = 21 ,
dim = 32 ,
depth = 3 ,
num_nearest_neighbors = 32 ,
norm_coors = True , # normalize the relative coordinates
coor_weights_clamp_value = 2. # absolute clamped value for the coordinate weights, needed if you increase the num neareest neighbors
)
feats = torch . randint ( 0 , 21 , ( 1 , 1024 )) # (1, 1024)
coors = torch . randn ( 1 , 1024 , 3 ) # (1, 1024, 3)
mask = torch . ones_like ( feats ). bool () # (1, 1024)
feats_out , coors_out = net ( feats , coors , mask = mask ) # (1, 1024, 32), (1, 1024, 3) 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
) لتشغيل مثال تقليل الضوضاء في العمود الفقري للبروتين، قم أولاً بتثبيت sidechainnet
$ pip install sidechainnetثم
$ python denoise_sparse.pyتأكد من تثبيت pytorch الهندسي محليًا
$ python setup.py test @misc { satorras2021en ,
title = { E(n) Equivariant Graph Neural Networks } ,
author = { Victor Garcia Satorras and Emiel Hoogeboom and Max Welling } ,
year = { 2021 } ,
eprint = { 2102.09844 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.LG }
}
