Delete moppit.py

moppit.py

@@ -1,90 +0,0 @@
-import yaml
-from tqdm import tqdm
-import torch
-from torch import nn
-from transformers import AutoTokenizer
-
-from models.peptide_classifiers import *
-
-from utils.parsing import parse_guidance_args
-args = parse_guidance_args()
-
-import pdb
-import random
-import inspect
-
-# MOO hyper-parameters
-step_size = 1 / 100
-n_samples = 1
-length = args.length
-target = args.target_protein
-motifs = args.motifs # args.motifs
-vocab_size = 24
-source_distribution = "uniform"
-device = 'cuda:0'
-
-tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
-target_sequence = tokenizer(target, return_tensors='pt')['input_ids'].to(device)
-motifs = parse_motifs(motifs).to(device)
-print(motifs)
-
-# Load Models
-solver = load_solver('./ckpt/peptide/cnn_epoch200_lr0.0001_embed512_hidden256_loss3.1051.ckpt', vocab_size, device)
-
-bindevaluator = load_bindevaluator('./classifier_ckpt/finetuned_BindEvaluator.ckpt', device)
-motif_model = MotifModel(bindevaluator, target_sequence, motifs, penalty=True)
-
-affinity_predictor = load_affinity_predictor('./classifier_ckpt/binding_affinity_unpooled.pt', device)
-affinity_model = AffinityModel(affinity_predictor, target_sequence)
-
-score_models = [motif_model, affinity_model]
-
-for i in range(args.n_batches):
-    if source_distribution == "uniform":
-        x_init = torch.randint(low=4, high=vocab_size, size=(n_samples, length), device=device)
-    elif source_distribution == "mask":
-        x_init = (torch.zeros(size=(n_samples, length), device=device) + 3).long()
-    else:
-        raise NotImplementedError
-
-    zeros = torch.zeros((n_samples, 1), dtype=x_init.dtype, device=x_init.device)
-    twos = torch.full((n_samples, 1), 2, dtype=x_init.dtype, device=x_init.device)
-    x_init = torch.cat([zeros, x_init, twos], dim=1)
-
-    x_1 = solver.multi_guidance_sample(args=args, x_init=x_init, 
-                                    step_size=step_size, 
-                                    verbose=True, 
-                                    time_grid=torch.tensor([0.0, 1.0-1e-3]),
-                                    score_models=score_models,
-                                    num_objectives=3,
-                                    weights=args.weights)
-
-    samples = x_1.tolist()
-    samples = [tokenizer.decode(seq).replace(' ', '')[5:-5] for seq in samples]
-    print(samples)
-
-    scores = []
-    for i, s in enumerate(score_models):
-        sig = inspect.signature(s.forward) if hasattr(s, 'forward') else inspect.signature(s)
-        if 't' in sig.parameters:
-            candidate_scores = s(x_1, 1)
-        else:
-            candidate_scores = s(x_1)
-
-        if isinstance(candidate_scores, tuple):
-            for score in candidate_scores:
-                scores.append(score.item())
-        else:
-            scores.append(candidate_scores.item())
-    print(scores)
-    
-    with open(args.output_file, 'a') as f:
-        f.write(samples[0])
-        for score in scores:
-            f.write(f",{score}")
-        f.write('\n')
-    # samples = x_1.tolist()
-    # sample = [tokenizer.decode(seq).replace(' ', '')[5:-5] for seq in samples][0]
-    # with open(f"/vast/home/c/chentong/MOG-DFM/samples/{name}.csv", "a") as f:
-    #     f.write(sample + ',' + str(score_list_0[-1]) + ',' + str(score_list_1[-1]) + '\n')
-