Update moppit.py

moppit.py

@@ -25,7 +25,7 @@ tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
 target_sequence = tokenizer(target, return_tensors='pt')['input_ids'].to(device)
 
 # Load Models
-solver = load_solver('/scratch/pranamlab/tong/checkpoints/MOG-DFM/ckpt/peptide/cnn_epoch200_lr0.0001_embed512_hidden256_loss3.1051.ckpt', vocab_size, device)
+solver = load_solver('./ckpt/peptide/cnn_epoch200_lr0.0001_embed512_hidden256_loss3.1051.ckpt', vocab_size, device)
 
 score_models = []
 if 'Hemolysis' in args.objectives:
@@ -41,11 +41,11 @@ if 'Half-Life' in args.objectives:
     halflife_model = HalfLifeModel(device=device)
     score_models.append(halflife_model)
 if 'Affinity' in args.objectives:
-    affinity_predictor = load_affinity_predictor('/scratch/pranamlab/tong/checkpoints/MOG-DFM/classifier_ckpt/binding_affinity_unpooled.pt', device)
+    affinity_predictor = load_affinity_predictor('./classifier_ckpt/binding_affinity_unpooled.pt', device)
     affinity_model = AffinityModel(affinity_predictor, target_sequence)
     score_models.append(affinity_model)
 if 'Motif' in args.objectives or 'Specificity' in args.objectives:
-    bindevaluator = load_bindevaluator('/scratch/pranamlab/tong/checkpoints/BindEvaluator/model_path/finetuned_BindEvaluator.ckpt', device)
+    bindevaluator = load_bindevaluator('./classifier_ckpt/finetuned_BindEvaluator.ckpt', device)
     if 'Specificity' in args.objectives:
         motif_penalty = True
     else: