update utils.py

utils.py

@@ -1,12 +1,10 @@
 import numpy as np
 import csv
-#from model import cumbersome_model2
-#from model import UNet_family
-#from model import UNet_attention
+from model import cumbersome_model2
+from model import UNet_family
+from model import UNet_attention
 from model import tf_model
 from model import tf_data
-#from opts import get_opts
-#from tools import pick_models
 
 import time
 import torch
@@ -15,8 +13,6 @@ import random
 import shutil
 from scipy.signal import decimate, resample_poly, firwin, lfilter
 
-from pathlib import Path
-
 
 os.environ["CUDA_VISIBLE_DEVICES"]="0"
 
@@ -118,8 +114,8 @@ def dataDelete(path):
     except OSError as e:
         print(e)
     else:
-        #pass
-        print("The directory is deleted successfully")
+        pass
+        #print("The directory is deleted successfully")
 
 
 def decode_data(data, std_num, mode=5):
@@ -183,18 +179,17 @@ def decode_data(data, std_num, mode=5):
     decode = np.array(decode.cpu()).astype(np.float64)
     return decode
 
-def preprocessing(filename, samplerate):
+def preprocessing(filepath, filename, samplerate):
     # establish temp folder
-    tmp_filepath = str(Path(str(filename)).parent)
     try:
-        os.mkdir(tmp_filepath+"/temp2/")
+        os.mkdir(filepath+"/temp2/")
     except OSError as e:
-        dataDelete(tmp_filepath+"/temp2/")
-        os.mkdir(tmp_filepath+"/temp2/")
+        dataDelete(filepath+"/temp2/")
+        os.mkdir(filepath+"/temp2/")
         print(e)
     
     # read data
-    signal = read_train_data(filename)
+    signal = read_train_data(filepath+'/'+filename)
     #print(signal.shape)
     # resample
     signal = resample(signal, samplerate)
@@ -203,7 +198,7 @@ def preprocessing(filename, samplerate):
     signal = FIR_filter(signal, 1, 50)
     #print(signal.shape)
     # cutting data
-    total_file_num = cut_data(tmp_filepath, signal)
+    total_file_num = cut_data(filepath, signal)
 
     return total_file_num