reduce epoch time by loading file outside of the function call

predictor/const.py

 DRUGNAME_2_DRUGBANKID_FILE = os.path.join(DRUG_DATA_DIR, 'drugname2drugbankid.tsv')
 CELL_FEAT_FILE = os.path.join(CELL_DATA_DIR, 'cell_feat.npy')
 CELL2ID_FILE = os.path.join(CELL_DATA_DIR, 'cell2id.tsv')
-
+Drug2FP_FILE = os.path.join(PROJ_DIR, 'drug/data/drug2FP_synergy.csv')
 
 
 

predictor/model/models.py

 import torch.nn.functional as F
 import os
 import sys 
+import pandas as pd
 
 PROJ_DIR = os.path.dirname(os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..')))
 
 from drug.models import GCN
 from drug.datasets import DDInteractionDataset
 from model.utils import get_FP_by_negative_index
+from const import Drug2FP_FILE
 
 
 
         super(Connector, self).__init__()
         # self.ddiDataset = DDInteractionDataset(gpu_id = gpu_id)
         self.gcn = None
+        self.drug2FP_df = pd.read_csv(Drug2FP_FILE)
         
         #Cell line features
         # np.load('cell_feat.npy')
             drug2_feat = drug2_feat.cuda(self.gpu_id)
         for i, x in enumerate(drug1_idx):
             if x < 0:
-                drug1_feat[i] = get_FP_by_negative_index(x)
+                drug1_feat[i] = get_FP_by_negative_index(x,self.drug2FP_df)
         for i, x in enumerate(drug2_idx):
             if x < 0:
-                drug2_feat[i] = get_FP_by_negative_index(x)
+                drug2_feat[i] = get_FP_by_negative_index(x,self.drug2FP_df)
         feat = torch.cat([drug1_feat, drug2_feat, cell_feat], 1)
         return feat
 

predictor/model/utils.py

 import pandas as pd
 import numpy as np
 
-from const import SYNERGY_FILE, CELL_FEAT_FILE, CELL2ID_FILE, OUTPUT_DIR, DRUGNAME_2_DRUGBANKID_FILE, DRUGN2ID_FILE
+from const import DRUGNAME_2_DRUGBANKID_FILE, DRUGN2ID_FILE, Drug2FP_FILE
 
 def calc_stat(numbers):
     mu = sum(numbers) / len(numbers)
 
     return drug_index
 
-def get_FP_by_negative_index(index):
+def get_FP_by_negative_index(index, drug2FP_df = None):
 
     index = index.item()
 
-    project_path = os.path.dirname(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-    drug2FP_file = os.path.join(project_path, 'drug/data/drug2FP_synergy.csv')
-    drug2FP_df = pd.read_csv(drug2FP_file)
+    if drug2FP_df.empty:
+        print("load Drug2FP_FILE")
+        drug2FP_df = pd.read_csv(Drug2FP_FILE)
 
     array = np.array(list(drug2FP_df.iloc[-index])[1:])
     return torch.tensor(array, dtype=torch.float32)