Final model

.gitignore

 *pycache*
 *.pdf
 *.csv
+*.ipynb
+*Logs*

1508.06576/requirements.txt

+matplotlib==3.5.1
+numpy==1.21.5
+Pillow==9.2.0
+torch==1.12.1+cpu
+torchvision==0.13.1+cpu

1703.10593/logger.py

+import logging
+import os
+#Author: @meetdoshi
+class Logger:
+    '''
+    Singleton logger class
+    '''
+    _instance = None
+    _logHandler = None
+    _formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
+    def __new__(cls,*args,**kwargs):
+        if not cls._instance:
+            os.system("rm -rf Logs/")
+            os.mkdir("Logs/")
+            logHandler = logging.FileHandler("Logs/style_transfer.log")
+            logHandler.setFormatter(cls._formatter)
+            cls._logHandler = logging.getLogger("Logs/style_transfer.log")
+            cls._logHandler.setLevel(logging.INFO)
+            cls._logHandler.addHandler(logHandler)
+            cls._instance = super(Logger, cls).__new__(cls,*args,**kwargs)
+        return cls._instance
+
+    def logger(self):
+        return self._logHandler
+
+'''
+#Demo use
+if __name__ == "__main__":
+    a = Logger()
+    b = Logger()
+    print(a is b)
+    INFO = a.logger()
+    ERROR = b.logger()
+    INFO.info("TEST")
+    ERROR.info("ERROR")
+'''

1703.10593/loss.py

+import numpy as np
+import torch
+from logger import Logger
+LOGGER = Logger().logger()
+
+class Loss:
+    @staticmethod
+    def adversarial_G():
+        '''
+        L_gan(G,Dy,X,Y) = 
+        '''
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