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architecture_smiles.py
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import torch
from torch import nn
import pickle
from qm9 import utils as qm9_utils
from models.vit import ViT
from qm9.models import EGNN
device = torch.device("cuda")
dtype = torch.float32
from models.decoder import LatentToMol
def set_up_causal_mask(seq_len):
mask = (torch.triu(torch.ones(seq_len, seq_len)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
mask.requires_grad = False
return mask
import torch
from torch import nn
import pickle
from qm9 import utils as qm9_utils
from models.vit import ViT
from qm9.models import EGNN
device = torch.device("cuda")
dtype = torch.float32
from models.decoder import LatentToMol
def set_up_causal_mask(seq_len):
mask = (torch.triu(torch.ones(seq_len, seq_len)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
mask.requires_grad = False
return mask
class PositionalEncodings(nn.Module):
"""Attention is All You Need positional encoding layer"""
def __init__(self, seq_len, d_model, p_dropout):
"""Initializes the layer."""
super(PositionalEncodings, self).__init__()
token_positions = torch.arange(start=0, end=seq_len).view(-1, 1)
dim_positions = torch.arange(start=0, end=d_model).view(1, -1)
angles = token_positions / (10000 ** ((2 * dim_positions) / d_model))
encodings = torch.zeros(1, seq_len, d_model)
encodings[0, :, ::2] = torch.cos(angles[:, ::2])
encodings[0, :, 1::2] = torch.sin(angles[:, 1::2])
encodings.requires_grad = False
self.register_buffer("positional_encodings", encodings)
self.dropout = nn.Dropout(p_dropout)
def forward(self, x):
"""Performs forward pass of the module."""
x = x + self.positional_encodings
x = self.dropout(x)
return x
class bottle(nn.Module):
def __init__(self, seq_len, hidden_size):
super(bottle, self).__init__()
self.seq_len = seq_len
self.hidden_size = hidden_size
self.down = nn.Sequential(
nn.Linear(seq_len*hidden_size, 512),
nn.LeakyReLU(),
nn.Linear(512, hidden_size)
)
def forward(self, inp):
inp = inp.view(-1, self.seq_len * self.hidden_size)
embed = self.down(inp)
# out = self.up(embed)
return embed
class CLIP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.vocab = pickle.load(open(config['data']['vocab_path'], 'rb'))
self.temperature = config['train']['temperature']
self.max_charge = config['data']['max_charge']
self.num_species = config['data']['num_species']
self.embed = nn.Embedding(len(self.vocab), config['molecule_decoder']['hidden_size'], padding_idx=self.vocab.pad_index)
self.pe = PositionalEncodings(d_model=config['molecule_decoder']['hidden_size'], p_dropout=0.1, seq_len=config['data']['seq_len'])
transformer_encoder_layer = nn.TransformerEncoderLayer(d_model=config['molecule_decoder']['hidden_size'],
nhead=4,
dropout=0.1,
batch_first=True)
self.trfmencoder = nn.TransformerEncoder(encoder_layer=transformer_encoder_layer,
num_layers=3)
self.Spectra_Encoder = ViT(
patch_size = self.config['spectra_encoder']['patch_size'],
num_layers = self.config['spectra_encoder']['num_layers'],
h_dim = self.config['spectra_encoder']['h_dim'],
num_heads = self.config['spectra_encoder']['num_heads'],
output_size = self.config['spectra_encoder']['output_size'],
d_ff=self.config['spectra_encoder']['d_ff'],
max_time_steps=self.config['spectra_encoder']['max_time_steps'],
use_clf_token=self.config['spectra_encoder']['use_clf_token'],
dropout = self.config['spectra_encoder']['dropout'],
dropout_emb = self.config['spectra_encoder']['dropout_emb']
)
self.smiles_decoder = LatentToMol(
in_size=self.config['molecule_decoder']['latent_size'],
hidden_size=self.config['molecule_decoder']['hidden_size'],
n_layers=self.config['molecule_decoder']['n_layers'],
n_heads = self.config['molecule_decoder']['n_heads'],
seq_len=self.config['data']['seq_len'],
vocab = self.vocab)
self.bottle = bottle(config['data']['seq_len'], config['molecule_decoder']['hidden_size'])
self.logit_scale = nn.Parameter(torch.ones([]) * self.temperature)
def forward_mol(self, data):
smi = data['decoder_inp'].to(device)
smi = self.embed(smi)
# smi = self.res_block(smi)
smi = self.pe(smi)
mem = self.trfmencoder(smi)
mol_features = self.bottle(mem)
mol_features = mol_features / mol_features.norm(dim=1, keepdim=True)
return mol_features
def forward_spec(self, data):
spectra = data['IR'].to(device, dtype)
spectra = torch.unsqueeze(spectra, 1)
spectra = torch.unsqueeze(spectra, 1)
spectra_features = self.Spectra_Encoder(spectra)
spectra_features = spectra_features / spectra_features.norm(dim=1, keepdim=True)
return spectra_features
def forward_decoder(self, data, spec_latents):
smi = data['decoder_inp'].to(device)
tgt = data['decoder_tgt'].to(device)
tgt_padding_mask = data['tgt_padding_mask'].to(device)
tgt_mask = set_up_causal_mask(self.config['data']['seq_len']).to(device)
pred = self.smiles_decoder(spec_latents,
smi,
tgt_mask,
tgt_padding_mask)
return pred
def forward(self, data):
logits_scale = self.logit_scale.exp()
mol_latents = self.forward_mol(data)
spec_latents = self.forward_spec(data)
smile_preds = self.forward_decoder(data, spec_latents)
return mol_latents, spec_latents, smile_preds, logits_scale, data['index']