Source code for bio_embeddings.embed.cpcprot_embedder

from typing import List, Generator, Union

import numpy
import torch
from CPCProt import CPCProtModel, CPCProtEmbedding, CPCProtConfig
from CPCProt.model.cpcprot import DEFAULT_CONFIG
from CPCProt.tokenizer import Tokenizer
from numpy import ndarray

from bio_embeddings.embed import EmbedderInterface

[docs]class CPCProtEmbedder(EmbedderInterface): """CPCProt Embedder Lu, Amy X., et al. "Self-supervised contrastive learning of protein representations by mutual information maximization." bioRxiv (2020). """ name = "cpcprot" embedding_dimension = 512 number_of_layers = 1 necessary_files = ["model_file"]
[docs] def __init__(self, device: Union[None, str, torch.device] = None, **kwargs): super().__init__(device, **kwargs) self.tokenizer = Tokenizer(vocab="iupac") # If we don't do this here, CPCProtModel will end up on the gpu if one is # available, even if we passed the cpu as device. # Afaik this is the best way to derive from DEFAULT_CONFIG dict_cfg = DEFAULT_CONFIG.to_dict() dict_cfg["use_cuda"] = self._device.type == "cuda" raw_model = CPCProtModel(cfg=CPCProtConfig.from_dict(dict_cfg)).to(self._device) state_dict = dict( torch.load(self._options["model_file"], map_location=self._device) ) for i in list(state_dict.keys()): if i.startswith("module."): state_dict[i[7:]] = state_dict[i] del state_dict[i] raw_model.load_state_dict(state_dict) self._model = CPCProtEmbedding(
[docs] def embed(self, sequence: str) -> ndarray: [embedding] = self.embed_batch([sequence]) return embedding
[docs] def embed_batch(self, batch: List[str]) -> Generator[ndarray, None, None]: """See""" encoded = [numpy.array(self.tokenizer.encode(sequence)) for sequence in batch] # 11 is the minimum patch size, so we need to zero-pad shorter sequences pad_length = max(max([i.shape[0] for i in encoded]), 11) padded = [numpy.pad(i, (0, pad_length - i.shape[0])) for i in encoded] torch_inputs = torch.from_numpy(numpy.array(padded)) yield from self._model.get_z_mean(torch_inputs).detach().cpu().numpy()
[docs] @staticmethod def reduce_per_protein(embedding: ndarray) -> ndarray: return embedding