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"""Implementation details for PaccMann vanilla generator trained on polymer building blocks (catalysts/monomers)."""
import json
import os
from typing import List, Optional, Union
import torch
from rdkit import Chem, RDLogger
from paccmann_chemistry.models.vae import StackGRUDecoder, StackGRUEncoder, TeacherVAE
from paccmann_chemistry.utils import get_device
from paccmann_chemistry.utils.search import SamplingSearch
from pytoda.smiles.smiles_language import SMILESLanguage
from pytoda.smiles.transforms import Selfies, SMILESToTokenIndexes
from pytoda.transforms import Compose, ToTensor
from ....frameworks.torch import device_claim
RDLogger.DisableLog("rdApp.*")
[docs]class Generator:
[docs] def __init__(
self,
resources_path: str,
generated_length: int = 100,
batch_size: int = 32,
device: Optional[Union[torch.device, str]] = None,
):
"""Initialize the encoder/decoder generative model.
Args:
resources_path: directory where to find models and parameters.
generated_length: length of the generated molecule in tokens. Defaults to 100.
batch_size: size of the batch. Defaults to 1.
device: device where the inference is running either as a dedicated class or a string.
If not provided is inferred.
"""
self.device = device_claim(device)
self.generated_length = generated_length
self.batch_size = batch_size
self.resources_path = resources_path
self.load_pretrained_paccmann(
os.path.join(self.resources_path, "params.json"),
os.path.join(self.resources_path, "smiles_language.pkl"),
os.path.join(self.resources_path, "weights.pt"),
self.batch_size,
)
[docs] def load_pretrained_paccmann(
self, params_file: str, lang_file: str, weights_file: str, batch_size: int
) -> None:
"""Load a pretrained PaccMann model.
Args:
params_file: file for the parameters.
lang_file: language file.
weights_file: serialized weights file.
batch_size: size of the batch.
"""
params = dict()
with open(params_file, "r") as f:
params.update(json.load(f))
params["batch_mode"] = "Padded"
params["batch_size"] = batch_size
self.selfies = params.get("selfies", False)
self.device = get_device()
self.smiles_language = SMILESLanguage.load(lang_file)
self.gru_encoder = StackGRUEncoder(params).to(self.device)
self.gru_decoder = StackGRUDecoder(params).to(self.device)
self.gru_vae = TeacherVAE(self.gru_encoder, self.gru_decoder).to(self.device)
self.gru_vae.load_state_dict(torch.load(weights_file, map_location=self.device))
self.gru_vae.eval()
transforms = []
if self.selfies:
transforms += [Selfies()]
transforms += [SMILESToTokenIndexes(smiles_language=self.smiles_language)]
transforms += [ToTensor(device=self.device)]
self.transform = Compose(transforms)
[docs] def decode(
self, latent_z: torch.Tensor, search: SamplingSearch = SamplingSearch()
) -> List[int]:
"""Decodes a sequence of tokens given a position in the latent space.
Args:
latent_z: a batch size x latent size tensor.
search: defaults to sampling multinomial search.
Returns:
list of list of token indices.
"""
latent_z = latent_z.view(1, latent_z.shape[0], latent_z.shape[1]).float()
molecule_iter = self.gru_vae.generate(
latent_z,
prime_input=torch.tensor([self.smiles_language.start_index]).to(
self.device
),
end_token=torch.tensor([self.smiles_language.stop_index]).to(self.device),
generate_len=self.generated_length,
search=search,
)
return [
[self.smiles_language.start_index] + m.cpu().detach().tolist()
for m in molecule_iter
]
[docs] def sample(self) -> List[str]:
"""Sample random molecules.
Returns:
sampled molecule (SMILES).
"""
mol: List[str] = []
while len(mol) < 1:
indexes = self.decode(
torch.randn(
self.batch_size, self.gru_decoder.latent_dim, device=self.device
)
)
mol = [self.smiles_language.token_indexes_to_smiles(m) for m in indexes]
mol = [m for m in mol if Chem.MolFromSmiles(m) is not None and m != ""]
return mol