Update trainer.py

trainer.py

@@ -422,7 +422,7 @@ class Trainer(object):
         
         ''' Loading the atom and bond decoders'''
         
-        with open("DrugGEN/data/decoders/" + dictionary_name + "_" + self.dataset_name + '.pkl', 'rb') as f:
+        with open("data/decoders/" + dictionary_name + "_" + self.dataset_name + '.pkl', 'rb') as f:
             
             return pickle.load(f)    
 
@@ -430,7 +430,7 @@ class Trainer(object):
         
         ''' Loading the atom and bond decoders'''
         
-        with open("DrugGEN/data/decoders/" + dictionary_name +"_" + self.drugs_name +'.pkl', 'rb') as f:
+        with open("data/decoders/" + dictionary_name +"_" + self.drugs_name +'.pkl', 'rb') as f:
             
             return pickle.load(f)    
  
@@ -531,15 +531,15 @@ class Trainer(object):
 
 
         # protein data
-        full_smiles = [line for line in open("DrugGEN/data/chembl_train.smi", 'r').read().splitlines()]
-        drug_smiles = [line for line in open("DrugGEN/data/akt_train.smi", 'r').read().splitlines()]
+        full_smiles = [line for line in open("data/chembl_train.smi", 'r').read().splitlines()]
+        drug_smiles = [line for line in open("data/akt_train.smi", 'r').read().splitlines()]
         
         drug_mols = [Chem.MolFromSmiles(smi) for smi in drug_smiles]
         drug_scaf = [MurckoScaffold.GetScaffoldForMol(x) for x in drug_mols]
         fps_r = [Chem.RDKFingerprint(x) for x in drug_scaf]
 
-        akt1_human_adj = torch.load("DrugGEN/data/akt/AKT1_human_adj.pt").reshape(1,-1).to(self.device).float() 
-        akt1_human_annot = torch.load("DrugGEN/data/akt/AKT1_human_annot.pt").reshape(1,-1).to(self.device).float() 
+        akt1_human_adj = torch.load("data/akt/AKT1_human_adj.pt").reshape(1,-1).to(self.device).float() 
+        akt1_human_annot = torch.load("data/akt/AKT1_human_annot.pt").reshape(1,-1).to(self.device).float() 
 
         if self.resume:
             self.restore_model(self.resume_epoch, self.resume_iter, self.resume_directory)
@@ -733,14 +733,14 @@ class Trainer(object):
             self.G2.load_state_dict(torch.load(G2_path, map_location=lambda storage, loc: storage))           
         
         
-        drug_smiles = [line for line in open("DrugGEN/data/akt_test.smi", 'r').read().splitlines()]
+        drug_smiles = [line for line in open("data/akt_test.smi", 'r').read().splitlines()]
         
         drug_mols = [Chem.MolFromSmiles(smi) for smi in drug_smiles]
         drug_scaf = [MurckoScaffold.GetScaffoldForMol(x) for x in drug_mols]
         fps_r = [Chem.RDKFingerprint(x) for x in drug_scaf]
 
-        akt1_human_adj = torch.load("DrugGEN/data/akt/AKT1_human_adj.pt").reshape(1,-1).to(self.device).float() 
-        akt1_human_annot = torch.load("DrugGEN/data/akt/AKT1_human_annot.pt").reshape(1,-1).to(self.device).float() 
+        akt1_human_adj = torch.load("data/akt/AKT1_human_adj.pt").reshape(1,-1).to(self.device).float() 
+        akt1_human_annot = torch.load("data/akt/AKT1_human_annot.pt").reshape(1,-1).to(self.device).float() 
         
         self.G.eval()
         #self.D.eval()
@@ -782,8 +782,8 @@ class Trainer(object):
         #metric_calc_mol = []
         metric_calc_dr = []
         date = time.time()
-        if not os.path.exists("DrugGEN/experiments/inference/{}".format(self.submodel)):
-            os.makedirs("DrugGEN/experiments/inference/{}".format(self.submodel))
+        if not os.path.exists("experiments/inference/{}".format(self.submodel)):
+            os.makedirs("experiments/inference/{}".format(self.submodel))
         with torch.inference_mode():
             
             dataloader_iterator = iter(self.inf_drugs_loader)
@@ -893,7 +893,7 @@ class Trainer(object):
                 inference_drugs = [Chem.MolToSmiles(line) for line in fake_mol_g if line is not None]   
                 inference_drugs = [None if x is None else max(x.split('.'), key=len) for x in inference_drugs]
 
-                with open("DrugGEN/experiments/inference/{}/inference_drugs.txt".format(self.submodel), "a") as f:
+                with open("experiments/inference/{}/inference_drugs.txt".format(self.submodel), "a") as f:
                     for molecules in inference_drugs:
                         
                         f.write(molecules)