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import torch
from torch import nn
import torch.nn.functional as F
from config import CFG
import utils
import math
import numpy as np
from cliplayers import QuickGELU, Transformer as MSTsfmEncoder
from GNN import layers as gly
class MolGNNEncoder(nn.Module):
def __init__(self,
outdim,
n_feats=74, #330, # 74+256 morgan 256
n_filters_list=[256, 256, 256],
n_head=4,
mols=1,
adj_chans=6,
readout_layers=2,
bias=True):
super().__init__()
n_filters_list = [i for i in n_filters_list if i is not None]
lys = []
for i, nf in enumerate(n_filters_list):
if i == 0:
nf1 = n_feats
else:
nf1 = prevnf
prevnf = nf
ly = gly.GConvBlockNoGF(nf1, nf, mols, adj_chans, bias)
lys.append(ly)
self.block_layers = nn.ModuleList(lys)
self.attention_layer = gly.MultiHeadGlobalAttention(nf, n_head=n_head, concat=True, bias=bias)
self.readout_layers = nn.ModuleList([nn.Linear(nf*n_head, outdim, bias=bias)] + [nn.Linear(outdim, outdim) for _ in range(readout_layers-1)])
self.gelu = QuickGELU()
def forward(self, batch):
V = batch['V']
A = batch['A']
mol_size = batch['mol_size']
for ly in self.block_layers:
V = ly(V, A)
X = self.attention_layer(V, mol_size)
for ly in self.readout_layers:
X = self.gelu(ly(X))
return X
class ProjectionHead(nn.Module):
def __init__(self,
embedding_dim,
projection_dim,
cfg,
transformer=True,
lstm=False):
super().__init__()
self.projection = nn.Linear(embedding_dim, projection_dim)
self.gelu = nn.GELU() #QuickGELU()
self.transformer = None
if transformer:
self.transformer = MSTsfmEncoder(projection_dim, cfg.tsfm_layers, cfg.tsfm_heads)
self.lstm = None
if lstm:
self.lstm = nn.LSTM(input_size=projection_dim, hidden_size=projection_dim, num_layers=cfg.lstm_layers, batch_first=True)
self.dropout = nn.Dropout(cfg.dropout)
def forward(self, x):
projected = self.projection(x)
if self.transformer is None:
x = self.gelu(projected)
else:
x = self.transformer(projected)
if not self.lstm is None:
x, (_, _) = self.lstm(x)
x = self.dropout(x)
return x
# New name in paper is CMSSPModel
class FragSimiModel(nn.Module):
def __init__(
self,
cfg
):
super().__init__()
self.cfg = cfg
self.mol_gnn_encoder = None
mol_embedding_dim = cfg.mol_embedding_dim
if 'gnn' in self.cfg.mol_encoder:
self.mol_gnn_encoder = MolGNNEncoder(outdim=cfg.mol_embedding_dim,
n_filters_list=cfg.molgnn_n_filters_list,
n_head=cfg.molgnn_nhead,
readout_layers=cfg.molgnn_readout_layers)
if 'fp' in self.cfg.mol_encoder:
mol_embedding_dim = 2*cfg.mol_embedding_dim
if 'fm' in self.cfg.mol_encoder:
mol_embedding_dim += 10
self.ms_projection = ProjectionHead(cfg.ms_embedding_dim,
cfg.projection_dim,
cfg,
cfg.tsfm_in_ms,
cfg.lstm_in_ms)
self.mol_projection = ProjectionHead(mol_embedding_dim,
cfg.projection_dim,
cfg,
cfg.tsfm_in_mol,
cfg.lstm_in_mol)
def forward(self, batch):
ms_features = batch["ms_bins"]
mol_feat_list = []
if 'gnn' in self.cfg.mol_encoder:
mol_feat_list.append(self.mol_gnn_encoder(batch))
if 'fp' in self.cfg.mol_encoder:
mol_feat_list.append(batch["mol_fps"])
if 'fm' in self.cfg.mol_encoder:
mol_feat_list.append(batch["mol_fmvec"])
if len(mol_feat_list) > 1:
mol_features = torch.cat(mol_feat_list, dim=1)
else:
mol_features = mol_feat_list[0]
# Getting ms and mol Embeddings (with same dimension)
ms_embeddings = self.ms_projection(ms_features)
mol_embeddings = self.mol_projection(mol_features)
# Normalize the projected embeddings
mol_embeddings = F.normalize(mol_embeddings, p=2, dim=1)
ms_embeddings = F.normalize(ms_embeddings, p=2, dim=1)
return mol_embeddings, ms_embeddings
# Calculating the Loss
#logits = (mol_embeddings @ ms_embeddings.t())
#logit_scale = self.logit_scale.exp()
'''logits = mol_embeddings @ ms_embeddings.t()
ground_truth = torch.arange(ms_features.shape[0], dtype=torch.long, device=self.cfg.device)
ms_loss = loss_func(logits, ground_truth)
mol_loss = loss_func(logits.t(), ground_truth)
loss = (ms_loss + mol_loss) / 2.0 # shape: (batch_size)
return loss.mean()'''
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