Delete src/sampling/guided_generator.py

src/sampling/guided_generator.py

@@ -1,90 +0,0 @@
-#!/usr/bin/env python3
-
-import sys
-import os
-import torch
-import pandas as pd
-from tqdm import tqdm
-from datetime import datetime
-from omegaconf import OmegaConf
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-from src.lm.memdlm.diffusion_module import MembraneFlow
-from src.utils.model_utils import _print
-from src.sampling.guided_sampler import GuidedSampler
-from src.utils.generate_utils import (
-    mask_for_scaffold,
-    calc_blosum_score,
-    calc_ppl
-)
-
-config = OmegaConf.load("/home/a03-sgoel/MeMDLM_v2/src/configs/guidance.yaml")
-
-os.chdir(f'/home/a03-sgoel/MeMDLM_v2/results/infilling/guided/{config.lm.ft_evoflow}/test_set/')
-todays_date = datetime.today().strftime('%Y-%m-%d')
-csv_save_path = f'./{todays_date}_boltzmann-soft_new_clf_data_cleaned/'
-try: os.makedirs(csv_save_path, exist_ok=False)
-except FileExistsError: pass
-
-
-def main():
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    tokenizer = AutoTokenizer.from_pretrained(config.lm.pretrained_esm)
-    esm_model = AutoModelForMaskedLM.from_pretrained(config.lm.pretrained_esm).eval().to(device)
-
-    diffusion = MembraneFlow(config).to(device)
-    state_dict = diffusion.get_state_dict(f"/home/a03-sgoel/MeMDLM_v2/checkpoints/{config.lm.ft_evoflow}/best_model.ckpt")
-    diffusion.load_state_dict(state_dict)
-    diffusion.eval().to(device)
-
-    sampler = GuidedSampler(config, esm_model, tokenizer, diffusion, device)
-
-    df = pd.read_csv('/home/a03-sgoel/MeMDLM_v2/data/classifier/test.csv')
-    sequences = df['Sequence'].tolist()
-
-    gen_seqs, ppls, blosums = [], [], []
-
-
-    for seq in tqdm(sequences, desc='Infilling Sequences'):
-        masked_seq = mask_for_scaffold(seq, generate_type='uppercase', mask_token='<mask>')
-        tokens = tokenizer(masked_seq, return_tensors='pt')
-        input_ids, attn_masks = tokens['input_ids'].to(device), tokens['attention_mask'].to(device)
-        
-        soluble_idxs = [i for i in range(len(seq)) if seq[i].isupper()]
-        infilled_tokens = sampler.optimize_sequence(
-            input_ids=input_ids,
-            attn_masks=attn_masks,
-            soluble_indices=soluble_idxs,
-        )
-        infilled_seq = tokenizer.decode(infilled_tokens).replace(" ", "")[5:-5]
-        
-        bl = calc_blosum_score(seq.upper(), infilled_seq, soluble_idxs)
-        try:
-            ppl = calc_ppl(esm_model, tokenizer, infilled_seq, [i for i in range(len(seq))], model_type='esm')
-        except:
-            ppl = float('inf')
-
-        gen_seqs.append(infilled_seq)
-        ppls.append(ppl)
-        blosums.append(bl)
-
-        _print(seq)
-        _print(infilled_seq)
-        _print(ppl)
-        _print(bl)
-        _print('\n')
-
-
-    df['MeMDLM Sequence'] = gen_seqs
-    df['MeMDLM PPL'] = ppls
-    df['MeMDLM BLOSUM'] = blosums
-
-    _print(df)
-    df.to_csv(f'./{csv_save_path}/t=0.7_new-data-cleaned_infilled_seqs.csv', index=False)
-
-
-    
-if __name__ == "__main__":
-    main()
-