""" /* Copyright (c) 2023, thewall. All rights reserved. BSD 3-clause license: Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ """ import os import datasets import torch from torch import nn import torch.nn.functional as F import numpy as np import pandas as pd from typing import List from functools import partial DEEPBIND_MODEL_CONFIG = datasets.load_dataset(path="thewall/deepbindweight", split="all") SELEX_CONFIG = pd.read_excel(DEEPBIND_MODEL_CONFIG[0]['selex'], index_col=0) class DeepBind(nn.Module): ALPHABET = "ATGCN" ALPHABET_MAP = {key: i for i, key in enumerate(ALPHABET)} ALPHABET_MAP["U"] = 1 ALPHABET_COMPLEMENT = "TACGN" COMPLEMENT_ID_MAP = torch.IntTensor([1, 0, 3, 2, 4]) def __init__(self, reverse_complement=True, num_detectors=16, detector_len=24, has_avg_pooling=True, num_hidden=1, tokenizer=None): super(DeepBind, self).__init__() self.reverse_complement = reverse_complement self.num_detectors = num_detectors self.detector_len = detector_len self.has_avg_pooling = has_avg_pooling self.num_hidden = num_hidden self.build_embedding() self.detectors = nn.Conv1d(4, num_detectors, detector_len) if has_avg_pooling: self.avg_pool = nn.AvgPool1d(detector_len) self.max_pool = nn.MaxPool1d(detector_len) fcs = [nn.Linear(num_detectors * 2 if self.has_avg_pooling else num_detectors, num_hidden)] if num_hidden > 1: fcs.append(nn.ReLU()) fcs.append(nn.Linear(num_hidden, 1)) self.fc = nn.Sequential(*fcs) self.tokenizer = tokenizer if tokenizer is not None else self.get_tokenizer() @classmethod def get_tokenizer(cls): from tokenizers import Tokenizer, models, decoders tokenizer = Tokenizer(models.BPE(vocab=cls.ALPHABET_MAP, merges=[])) tokenizer.decoder = decoders.ByteLevel() return tokenizer @classmethod def complement_idxs_encode_batch(cls, idxs, reverse=False): return torch.stack(list(map(partial(cls.complement_idxs_encode, reverse=reverse), idxs))) @classmethod def complement_idxs_encode(cls, idxs, reverse=False): if reverse: idxs = reversed(idxs) return cls.COMPLEMENT_ID_MAP[idxs] def build_embedding(self): """ATGC->ACGT:0321""" embedding = torch.zeros(5, 4) embedding[0, 0] = 1 embedding[1, 3] = 1 embedding[2, 2] = 1 embedding[3, 1] = 1 embedding[-1] = 0.25 self.embedding = nn.Embedding.from_pretrained(embedding, freeze=True) return embedding @property def device(self): return self.detectors.bias.device def _load_detector(self, fobj): # dtype = functools.partial(lambda x:torch.Tensor(eval(x)) dtype = lambda x: torch.Tensor(eval(x)) weight1 = self._load_param(fobj, "detectors", dtype).reshape(self.detector_len, 4, self.num_detectors) biases1 = self._load_param(fobj, "thresholds", dtype) # Tx4xC->Cx4xT self.detectors.weight.data = weight1.permute(2, 1, 0).contiguous().to(device=self.detectors.weight.device) self.detectors.bias.data = biases1.to(device=self.detectors.bias.device) def _load_fc1(self, fobj): num_hidden1 = self.num_detectors * 2 if self.has_avg_pooling else self.num_detectors dtype = lambda x: torch.Tensor(np.array(eval(x))) weight1 = self._load_param(fobj, "weights1", dtype).reshape(num_hidden1, self.num_hidden) biases1 = self._load_param(fobj, "biases1", dtype) self.fc[0].weight.data = weight1.T.contiguous().to(device=self.fc[0].weight.device) self.fc[0].bias.data = biases1.to(device=self.fc[0].bias.device) def _load_fc2(self, fobj): dtype = lambda x: torch.Tensor(np.array(eval(x))) weight2 = self._load_param(fobj, "weights2", dtype) biases2 = self._load_param(fobj, "biases2", dtype) assert not (weight2 is None and self.num_hidden > 1) assert not (biases2 is None and self.num_hidden > 1) if self.num_hidden > 1: self.fc[2].weight.data = weight2.reshape(1, -1).to(device=self.fc[2].weight.device) self.fc[2].bias.data = biases2.to(device=self.fc[2].bias.device) @classmethod def _load_param(cls, fobj, param_name, dtype): line = fobj.readline().strip() tmp = line.split("=") assert tmp[0].strip() == param_name if len(tmp) > 1 and len(tmp[1].strip()) > 0: return dtype(tmp[1].strip()) @classmethod def load_model(cls, sra_id="ERR173157", file=None, ID=None): if file is None: if ID is None: data = SELEX_CONFIG ID = data.loc[sra_id]["ID"] file = os.path.join(DEEPBIND_MODEL_CONFIG['config'][0], f"{ID}.txt") keys = [("reverse_complement", lambda x: bool(eval(x))), ("num_detectors", int), ("detector_len", int), ("has_avg_pooling", lambda x: bool(eval(x))), ("num_hidden", int)] hparams = {} with open(file) as fobj: version = fobj.readline()[1:].strip() for key in keys: value = cls._load_param(fobj, key[0], key[1]) hparams[key[0]] = value if hparams['num_hidden'] == 0: hparams['num_hidden'] = 1 model = cls(**hparams) model._load_detector(fobj) model._load_fc1(fobj) model._load_fc2(fobj) print(f"load model from {file}") return model def inference(self, sequence: List[str], window_size=0, average_flag=False): if isinstance(sequence, str): sequence = [sequence] ans = [] self.tokenizer.no_padding() for seq in sequence: inputs = torch.IntTensor(self.tokenizer.encode(seq).ids).unsqueeze(0).to(device=self.device) score = self.test(inputs, window_size, average_flag).item() ans.append(score) return ans @torch.no_grad() def batch_inference(self, sequences: List[str], window_size=0, average_flag=False): if isinstance(sequences, str): sequences = [sequences] self.tokenizer.enable_padding() encodings = self.tokenizer.encode_batch(sequences) ids = torch.Tensor([encoding.ids for encoding in encodings]).to(device=self.device) mask = torch.BoolTensor([encoding.attention_mask for encoding in encodings]).to(device=self.device) seq_len = mask.sum(dim=1) score = self.batch_scan_model(ids, seq_len, window_size, average_flag) if self.reverse_complement: rev_seq = self.complement_idxs_encode_batch(ids.cpu().long(), reverse=True) rev_seq = torch.Tensor(rev_seq).to(device=self.device).float() rev_score = self.batch_scan_model(rev_seq, seq_len, window_size, average_flag) score = torch.stack([rev_score, score], dim=-1).max(dim=-1)[0] return score.cpu().tolist() def batch_scan_model(self, ids, seq_len, window_size: int = 0, average_flag: bool = False): if window_size < 1: window_size = int(self.detector_len * 1.5) scores = torch.zeros_like(seq_len).float() masked = seq_len <= window_size for idx in torch.where(masked)[0]: scores[idx] = self.forward(ids[idx:idx + 1, :seq_len[idx]].int()) if torch.all(masked): return scores fold_ids = F.unfold(ids[~masked].unsqueeze(1).unsqueeze(1), kernel_size=(1, window_size), stride=1) B, W, G = fold_ids.shape fold_ids = fold_ids.permute(0, 2, 1).reshape(-1, W) ans = self.forward(fold_ids.int()) ans = ans.reshape(B, G) if average_flag: valid_len = seq_len - window_size + 1 for idx, value in zip(torch.where(~masked)[0], ans): scores[idx] = value[:valid_len[idx]].mean() else: unvalid_mask = torch.arange(G).unsqueeze(0).to(seq_len.device) >= ( seq_len[~masked] - window_size + 1).unsqueeze(1) ans[unvalid_mask] = -torch.inf scores[~masked] = ans.max(dim=1)[0] return scores @torch.no_grad() def test(self, seq: torch.IntTensor, window_size=0, average_flag=False): score = self.scan_model(seq, window_size, average_flag) if self.reverse_complement: rev_seq = self.complement_idxs_encode_batch(seq.cpu().long(), reverse=True) rev_seq = torch.IntTensor(rev_seq).to(device=seq.device) rev_score = self.scan_model(rev_seq, window_size, average_flag) score = torch.cat([rev_score, score], dim=-1).max(dim=-1)[0] return score def scan_model(self, seq: torch.IntTensor, window_size: int = 0, average_flag: bool = False): seq_len = seq.shape[1] if window_size < 1: window_size = int(self.detector_len * 1.5) if seq_len <= window_size: return self.forward(seq) else: scores = [] for i in range(0, seq_len - window_size + 1): scores.append(self.forward(seq[:, i:i + window_size])) scores = torch.stack(scores, dim=-1) if average_flag: return scores.mean(dim=-1) else: return scores.max(dim=-1)[0] def forward(self, seq: torch.IntTensor): seq = F.pad(seq, (self.detector_len - 1, self.detector_len - 1), value=4) x = self.embedding(seq) x = x.permute(0, 2, 1) x = self.detectors(x) x = torch.relu(x) x = x.permute(0, 2, 1) if self.has_avg_pooling: x = torch.stack([torch.max(x, dim=1)[0], torch.mean(x, dim=1)], dim=-1) x = torch.flatten(x, 1) else: x = torch.max(x, dim=1)[0] x = x.squeeze(dim=-1) x = self.fc(x) return x if __name__ == "__main__": """ AGGUAAUAAUUUGCAUGAAAUAACUUGGAGAGGAUAGC AGACAGAGCUUCCAUCAGCGCUAGCAGCAGAGACCAUU GAGGTTACGCGGCAAGATAA TACCACTAGGGGGCGCCACC To generate 16 predictions (4 models, 4 sequences), run the deepbind executable as follows: % deepbind example.ids < example.seq D00210.001 D00120.001 D00410.003 D00328.003 7.451420 -0.166146 -0.408751 -0.026180 -0.155398 4.113817 0.516956 -0.248167 -0.140683 0.181295 5.885349 -0.026180 -0.174985 -0.152521 -0.379695 17.682623 """ sequences = ["AGGUAAUAAUUUGCAUGAAAUAACUUGGAGAGGAUAGC", "AGACAGAGCUUCCAUCAGCGCUAGCAGCAGAGACCAUU", "GAGGTTACGCGGCAAGATAA", "TACCACTAGGGGGCGCCACC"] model = DeepBind.load_model(ID='D00410.003') print(model.batch_inference(sequences)) import random import time from tqdm import tqdm sequences = ["".join([random.choice("ATGC") for _ in range(40)]) for i in range(1000)] def test_fn(sequences, fn): start_time = time.time() for start in tqdm(range(0, len(sequences), 256)): batch = sequences[start: min(start + 256, len(sequences))] fn(batch) print(time.time() - start_time) # test_fn(sequences, model.inference) # test_fn(sequences, model.batch_inference) model = model.cuda() test_fn(sequences, model.batch_inference) test_fn(sequences, model.inference) test_fn(sequences, model.batch_inference) test_fn(sequences, model.inference)