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Bertint V8 Inference — Standalone Prediction Pipeline
Architecture (Final — CrossAttn + MeanPool + Concat):
GLYCAN: WURCS -> BPE -> Bertose (frozen) -> [B, Lg, 768] -> proj -> [B, Lg, 512]
PROTEIN: ESM-C per-residue -> [B, Lp, 960] -> proj -> [B, Lp, 512]
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2x CrossAttentionBlock(d=512, 8H, FFN=1024)
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glycan_enriched -> mean(valid tokens) -> [B, 512]
protein_enriched -> mean(valid residues) -> [B, 512]
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concat -> [B, 1024] -> MLP -> score
"""
import argparse
import json
import logging
import sys
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import h5py
import numpy as np
import pandas as pd
import torch
from bertint_v8 import BertintV8, load_bertose_encoder
from dataset_v8 import load_bpe_tokenizer
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)
class BertintPredictor:
"""Standalone predictor for glycan-protein binding affinity."""
def __init__(self, checkpoint_path, bertose_checkpoint, vocab_path,
protein_emb_path, device="auto", max_protein_length=1024):
if device == "auto":
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
self.device = torch.device(device)
logger.info(f"Using device: {self.device}")
self.max_protein_length = max_protein_length
logger.info(f"Loading BPE tokenizer from {vocab_path}")
self.tokenizer = load_bpe_tokenizer(vocab_path)
logger.info(f"Loading Bertose from {bertose_checkpoint}")
bertose_config, seq_embeddings, seq_layers = load_bertose_encoder(
bertose_checkpoint, freeze_layers=12)
logger.info("Building Bertint V8 (CrossAttn + MeanPool + Concat)")
self.model = BertintV8(
seq_embeddings=seq_embeddings,
seq_layers=seq_layers,
glycan_dim=bertose_config.seq_hidden_size,
protein_dim=960, shared_dim=512, num_heads=8,
ffn_dim=1024, num_cross_layers=2, dropout=0.1,
swe_slices=512, swe_ref_points=64, separate_swe=False,
pooling_mode="mean", interaction_mode="concat",
use_cross_attention=True,
)
logger.info(f"Loading checkpoint from {checkpoint_path}")
state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
missing, unexpected = self.model.load_state_dict(state_dict, strict=False)
if missing:
logger.warning("Missing checkpoint tensors: %d", len(missing))
if unexpected:
logger.warning("Unexpected checkpoint tensors: %d", len(unexpected))
self.model.to(self.device)
self.model.eval()
logger.info("Model loaded and ready for inference")
logger.info(f"Loading protein embeddings from {protein_emb_path}")
self.protein_embs = {}
with h5py.File(protein_emb_path, "r") as f:
for key in f.keys():
emb = torch.from_numpy(f[key][:]).float()
if emb.dim() == 1:
emb = emb.unsqueeze(0)
if emb.shape[0] > max_protein_length:
emb = emb[:max_protein_length]
self.protein_embs[key.replace("|", "/")] = emb
logger.info(f" Loaded {len(self.protein_embs)} protein embeddings")
def _tokenize_glycan(self, wurcs):
tok = self.tokenizer.tokenize(wurcs, max_length=256)
return {k: torch.tensor(tok[k], dtype=torch.long)
for k in ["token_ids", "attention_mask", "branch_depths", "linkage_types"]}
@torch.no_grad()
def predict_single(self, wurcs, protein_id):
if protein_id not in self.protein_embs:
raise KeyError(f"Protein '{protein_id}' not found in embeddings.")
tokens = self._tokenize_glycan(wurcs)
protein_emb = self.protein_embs[protein_id]
batch = {
"token_ids": tokens["token_ids"].unsqueeze(0).to(self.device),
"attention_mask": tokens["attention_mask"].unsqueeze(0).float().to(self.device),
"branch_depths": tokens["branch_depths"].unsqueeze(0).to(self.device),
"linkage_types": tokens["linkage_types"].unsqueeze(0).to(self.device),
"protein_emb": protein_emb.unsqueeze(0).to(self.device),
"protein_mask": torch.ones(1, protein_emb.shape[0]).to(self.device),
}
score = self.model(
token_ids=batch["token_ids"], attention_mask=batch["attention_mask"],
branch_depths=batch["branch_depths"], linkage_types=batch["linkage_types"],
protein_emb=batch["protein_emb"], protein_mask=batch["protein_mask"],
)
return score.item()
@torch.no_grad()
def predict_batch(self, wurcs_list, protein_ids, batch_size=32):
all_scores = []
n = len(wurcs_list)
for start in range(0, n, batch_size):
end = min(start + batch_size, n)
tokenized = [self._tokenize_glycan(w) for w in wurcs_list[start:end]]
token_ids = torch.stack([t["token_ids"] for t in tokenized]).to(self.device)
attn_mask = torch.stack([t["attention_mask"] for t in tokenized]).float().to(self.device)
branch_d = torch.stack([t["branch_depths"] for t in tokenized]).to(self.device)
link_t = torch.stack([t["linkage_types"] for t in tokenized]).to(self.device)
missing = [pid for pid in protein_ids[start:end] if pid not in self.protein_embs]
if missing:
raise KeyError(f"{len(missing)} protein ids missing from embeddings, first: {missing[0]!r}")
protein_embs = [self.protein_embs[pid] for pid in protein_ids[start:end]]
max_len = max(e.shape[0] for e in protein_embs)
dim = protein_embs[0].shape[1]
prot_pad = torch.zeros(len(protein_embs), max_len, dim)
prot_mask = torch.zeros(len(protein_embs), max_len)
for i, emb in enumerate(protein_embs):
prot_pad[i, :emb.shape[0]] = emb
prot_mask[i, :emb.shape[0]] = 1.0
scores = self.model(
token_ids=token_ids, attention_mask=attn_mask,
branch_depths=branch_d, linkage_types=link_t,
protein_emb=prot_pad.to(self.device), protein_mask=prot_mask.to(self.device),
)
all_scores.extend(scores.cpu().tolist())
if (start // batch_size) % 10 == 0:
logger.info(f" Predicted {end}/{n} pairs ({100*end/n:.1f}%)")
return all_scores
def main():
parser = argparse.ArgumentParser(description="Bertint V8 Inference")
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--bertose_checkpoint", required=True)
parser.add_argument("--vocab_path", required=True)
parser.add_argument("--protein_emb_path", required=True)
parser.add_argument("--device", default="auto")
parser.add_argument("--wurcs", type=str, default=None)
parser.add_argument("--protein_id", type=str, default=None)
parser.add_argument("--input_csv", type=str, default=None)
parser.add_argument("--output_csv", type=str, default=None)
parser.add_argument("--batch_size", type=int, default=32)
args = parser.parse_args()
predictor = BertintPredictor(
checkpoint_path=args.checkpoint, bertose_checkpoint=args.bertose_checkpoint,
vocab_path=args.vocab_path, protein_emb_path=args.protein_emb_path,
device=args.device,
)
if args.wurcs and args.protein_id:
score = predictor.predict_single(args.wurcs, args.protein_id)
print(f"\nPrediction: {score:.4f} (0=no binding, 1=strong)")
elif args.input_csv:
df = pd.read_csv(args.input_csv)
scores = predictor.predict_batch(
df["glycan_wurcs"].tolist(), df["protein_id"].tolist(),
batch_size=args.batch_size)
df["predicted_score"] = scores
out = args.output_csv or args.input_csv.replace(".csv", "_predictions.csv")
df.to_csv(out, index=False)
logger.info(f"Saved {len(df)} predictions to {out}")
else:
parser.error("Provide --wurcs + --protein_id or --input_csv")
if __name__ == "__main__":
main()
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