affinity（model）
11140321 opened this issue · 0 comments
Hello, I would like to ask in the drug target affinity prediction model, you provide TransformerGVP (nn.Module) and ThreeD (nn.Module) : What is the difference? I see the GVP model used in the code, but it transforms first and then does three message passing aggregations, which doesn't match the description in the paper？
import numpy as np
import torch
import torch.nn as nn
from . import GVP, GVPConvLayer, LayerNorm, tuple_index
from torch.distributions import Categorical
from torch_scatter import scatter_mean
from performer_pytorch import Performer, PerformerLM
import torch_geometric
from torch_geometric.utils import to_dense_batch
from linear_attention_transformer import LinearAttentionTransformerLM, LinformerSettings
from performer_pytorch import PerformerLM
class TransformerGVP(nn.Module):
def init(self, node_in_dim, node_h_dim,
edge_in_dim, edge_h_dim,
seq_in=False, num_layers=3, drop_rate=0.1, attention_type = "performer"):
super().__init__()
if seq_in:
self.W_s = nn.Embedding(20, 64)
node_in_dim = (node_in_dim[0], node_in_dim[1])
self.W_v = nn.Sequential(
LayerNorm(node_in_dim), #对标量数据进行 Layer Normalization 操作，同时将矢量通道除以计算的 L2 范数。这有助于保持标量通道的分布稳定性，同时在矢量通道上进行归一化操作。
GVP(node_in_dim, node_h_dim, activations=(None, None))
)
self.W_e = nn.Sequential(
LayerNorm(edge_in_dim),
GVP(edge_in_dim, edge_h_dim, activations=(None, None))
)
self.W_in = nn.Sequential(
LayerNorm(node_h_dim),
GVP(node_h_dim, (node_h_dim[0], 0), vector_gate=True)
)
self.layers = nn.ModuleList(
GVPConvLayer(node_h_dim, edge_h_dim, drop_rate=drop_rate)
for _ in range(num_layers))
ns, _ = node_h_dim
self.W_out = nn.Sequential(
LayerNorm(node_h_dim),
GVP(node_h_dim, (ns, 0)))
self.attention_type = attention_type
if attention_type == "performer":
self.transformer = Performer(
dim = ns,
depth = 2,
heads = 4,
dim_head = ns // 4,
causal = False
)
else:
layer = nn.TransformerEncoderLayer(ns, 4, ns * 2, batch_first=True)
self.transformer = nn.TransformerEncoder(layer, 2)
self.final_readout = nn.Sequential(
nn.Linear(ns + ns, 128), nn.ReLU(), nn.Linear(128, 128)
)
self.seq_transformer = LinearAttentionTransformerLM(
num_tokens = 20,
dim = 128,
heads = 8,
depth = 2,
max_seq_len = 640,
return_embeddings=True,
linformer_settings = LinformerSettings(256))
def forward(self, h_V, edge_index, h_E, seq=None, batch=None):
'''
:param h_V: tuple (s, V) of node embeddings
:param edge_index: `torch.Tensor` of shape [2, num_edges]
:param h_E: tuple (s, V) of edge embeddings
:param seq: if not `None`, int `torch.Tensor` of shape [num_nodes]
to be embedded and appended to `h_V`
'''
if seq is not None:
#seq = self.W_s(seq)
seq, mask = to_dense_batch(seq, batch, max_num_nodes=640)
seq_emb = self.seq_transformer(seq)
seq_rep = torch.sum(seq_emb, dim = 1)
h_V = self.W_v(h_V) #h_V里面包含node-s和node_v
h_E = self.W_e(h_E) #里面包含edge-s和edge-v
h_t = self.W_in(h_V)
h_t, mask = to_dense_batch(h_t, batch)
h_t = self.transformer(h_t)
h_t = h_t[mask]
for layer in self.layers:
h_V = layer(h_V, edge_index, h_E)
out = self.W_out(h_V)
node_rep = torch.cat([h_t, out], dim = -1)
node_rep = self.final_readout(node_rep)
geo_rep = scatter_mean(node_rep, batch, dim = 0)
return torch.cat([geo_rep, seq_rep], dim = -1)
class ThreeD_Protein_Model(nn.Module):
def init(self, node_in_dim, node_h_dim,
edge_in_dim, edge_h_dim,
seq_in=False, num_layers=3, drop_rate=0.5, attention_type = "performer"):
super().__init__()
if seq_in:
self.W_s = nn.Embedding(20, 20)
node_in_dim = (node_in_dim[0], node_in_dim[1])
self.W_v = nn.Sequential(
LayerNorm(node_in_dim),
GVP(node_in_dim, node_h_dim, activations=(None, None))
)
self.W_e = nn.Sequential(
LayerNorm(edge_in_dim),
GVP(edge_in_dim, edge_h_dim, activations=(None, None))
)
self.layers = nn.ModuleList(
GVPConvLayer(node_h_dim, edge_h_dim, drop_rate=drop_rate)
for _ in range(num_layers))
ns, _ = node_h_dim
self.W_out = nn.Sequential(
LayerNorm(node_h_dim),
GVP(node_h_dim, (ns, 0), vector_gate=True))
self.attention_type = attention_type
if attention_type == "performer":
self.transformer = Performer(
dim = ns,
depth = 2,
heads = 4,
dim_head = ns // 4,
causal = False
)
else:
layer = nn.TransformerEncoderLayer(ns, 4, ns * 2, batch_first=True)
self.transformer = nn.TransformerEncoder(layer, 2)
self.seq_transformer = LinearAttentionTransformerLM(
num_tokens = 20,
dim = 128,
heads = 4,
depth = 4,
dim_head = 128 // 4,
max_seq_len = 640,
return_embeddings=True,
linformer_settings = LinformerSettings(256),
ff_dropout=drop_rate,
attn_dropout=drop_rate,
attn_layer_dropout=drop_rate)
self.skip_connection = nn.Sequential(nn.Linear(ns * 2, ns), nn.ReLU(), nn.Linear(ns, ns))
def forward(self, h_V, edge_index, h_E, seq=None, batch=None):
'''
:param h_V: tuple (s, V) of node embeddings
:param edge_index: `torch.Tensor` of shape [2, num_edges]
:param h_E: tuple (s, V) of edge embeddings
:param seq: if not `None`, int `torch.Tensor` of shape [num_nodes]
to be embedded and appended to `h_V`
'''
if seq is not None:
seq, mask = to_dense_batch(seq, batch, max_num_nodes=640)
seq_emb = self.seq_transformer(seq)
seq_rep = torch.mean(seq_emb, dim = 1)
h_V = self.W_v(h_V)
h_E = self.W_e(h_E)
for layer in self.layers:
h_V = layer(h_V, edge_index, h_E)
out = self.W_out(h_V)
x, mask = to_dense_batch(out, batch)
x_o = self.transformer(x)
x = torch.cat([x, x_o], dim = -1)
x = self.skip_connection(x)
geo_rep = x.mean(dim = 1)
if seq is not None:
z = torch.cat([geo_rep, seq_rep], dim = -1)
return z
return geo_rep