Loss and preprocessor

1508.06576/loss.py

+import numpy as np
+from logger import Logger
+LOGGER = Logger().logger()
+
+class Loss:
+    @staticmethod
+    def content_loss(F,P):
+        '''
+        Function to compute content loss between two feature representations at a particular layer
+        @params
+        F: 2D numpy array
+        P: 2D numpy array
+        '''
+        l2_norm_sq = None
+        try:
+            diff = F-P
+            l2_norm_sq = np.sum(diff*diff)
+        except Exception as e:
+            LOGGER.error("Error computing loss",e)
+        return l2_norm_sq/2.0
+
+    @staticmethod
+    def gram_matrix(F):
+        '''
+        Function to compute the gram matrix of a feature representation at a layer
+        '''
+
+    @staticmethod
+    def style_loss(F,A):
+        '''
+        Function to compute style loss between two feature representations at multiple layers
+        @params
+
+        '''
+
+    @staticmethod
+    def total_loss(alpha,beta,cont_fmap_real,cont_fmap_noise,style_fmap_real,style_fmap_noise):
+        '''
+        Function which computes total loss and returns it
+        @params
+
+        '''
+    
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1508.06576/preprocess.py

+from distutils.log import Log
+from logger import Logger
+LOGGER = Logger().logger()
+
+class Preprocessor:
+    @staticmethod
+    def process(img):
+        '''
+        Function to preprocess the image
+        @params
+        '''
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1508.06576/style_transfer.py

+import os
+import numpy as np
+import time 
+import torch
+import argparse
+from logger import Logger
+LOGGER = Logger().logger()
+LOGGER.info("Started Style Transfer")
+
+class StyleTransfer:
+    '''
+    Style Transfer Base Class
+    '''
+    def __init__(self) -> None:
+        pass
+
+    @staticmethod
+    def pipepline():
+        '''
+        Pipeline for style transfer
+        @params: None
+        '''
+
+    
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