content loss analysis

1508.06576/feature_maps.py

@@ -16,8 +16,8 @@ class FeatureMaps():
         super()
         try:
             self.model = torch.hub.load('pytorch/vision:v0.10.0',arch,pretrained=True)
-        except:
-            LOGGER.error("Could not load model")
+        except Exception as e:
+            LOGGER.error(f"Could not load model {e}")
         return
     
     def get_model(self):
@@ -36,8 +36,13 @@ class FeatureMaps():
             except:
                 LOGGER.error("Could not fetch layer "+str(layer))
         return weights
-
-    def get_fmaps(self,img,layer=[0,5,10,19,28]):
+    ''' 
+    0, 5, 10, 19, 28 - 1_1 2_1 3_1 4_1 5_1
+    21 - 4_2
+    7 ,12, 21 ,30 - 2_2 3_2 4_2 5_2
+    7,10,12,21,28,30 - mix
+    '''
+    def get_fmaps(self,img,layer=[7,10,12,21,28,300]):
         '''
         Function which will pass the image through the model and get the respective fmaps
         @params

1508.06576/image2gif.py

+import imageio
+import os
+
+
+
+fnames = []
+
+for img in os.listdir("styled_mix"):
+        fnames.append(os.path.join("styled_mix/", img))
+
+fnames.sort()
+
+with imageio.get_writer('content-mixed.gif', mode='I',duration = 0.9) as writer:
+    for fname in fnames:
+        image = imageio.imread(fname)
+        writer.append_data(image)
\ No newline at end of file

1508.06576/logger.py

 import logging
 import os
+from xml.dom.minidom import Identified
 #Author: @meetdoshi
 class Logger:
     '''
@@ -10,11 +11,12 @@ class Logger:
     _formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
     def __new__(cls,*args,**kwargs):
         if not cls._instance:
-            os.system("rm -rf Logs/")
+            identifier = 'content-reconstruction-mixed'
+            if not os.path.isdir("Logs/"):
                 os.mkdir("Logs/")
-            logHandler = logging.FileHandler("Logs/style_transfer.log")
+            logHandler = logging.FileHandler("Logs/style_transfer_"+identifier+".log")
             logHandler.setFormatter(cls._formatter)
-            cls._logHandler = logging.getLogger("Logs/style_transfer.log")
+            cls._logHandler = logging.getLogger("Logs/style_transfer_"+identifier+".log")
             cls._logHandler.setLevel(logging.INFO)
             cls._logHandler.addHandler(logHandler)
             cls._instance = super(Logger, cls).__new__(cls,*args,**kwargs)

1508.06576/optimizer.py

@@ -17,9 +17,9 @@ class Optimizer:
         '''
         LOGGER.info("Running gradient descent with the following parameters")
         epoch = 5000
-        learning_rate = 0.002
+        learning_rate = 0.1
         alpha = 1
-        beta = 0.01 
+        beta = 0
         LOGGER.info(f"{epoch},{learning_rate},{alpha},{beta}")
 
         optimizer=optim.Adam([content_img_clone],lr=learning_rate)
@@ -46,6 +46,9 @@ class Optimizer:
             optimizer.step()
             #plt.clf()
             #plt.plot(content_img_clone)
+            if(e%100==0):
+                learning_rate = max(learning_rate/2,0.001)
             if(e%10 == 0):
-                LOGGER.info(f"Epoch = {e} Total Loss = {total_loss} Style Loss = {total_cont_loss} Content Loss = {total_style_loss}")
-                save_image(content_img_clone,"styled.png")
+                LOGGER.info(f"Epoch = {e} Learning Rate = {learning_rate} Total Loss = {total_loss} Style Loss = {total_style_loss} Content Loss = {total_cont_loss}")
+                name = "styled_mix/styled_" + str(e) +".png"
\ No newline at end of file
+                save_image(content_img_clone,name)

1508.06576/preprocess.py

@@ -36,7 +36,10 @@ class Preprocessor:
         img: 3d numpy array
         '''
         #loader = transforms.Compose([transforms.ToTensor(),transforms.Resize([224,224]),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225],),])
-        loader = transforms.Compose([transforms.ToTensor(),transforms.Resize([224,224])])
+        loader = transforms.Compose([transforms.ToTensor(),transforms.Resize([224,224]),transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225],
+    ),])
         img = loader(img).unsqueeze(0)
         assert img.shape == (1,3,224,224)
         return img.to(device,torch.float)

1508.06576/style_transfer.py

@@ -33,13 +33,15 @@ class StyleTransfer:
 
         device = torch.device( "cuda" if (torch.cuda.is_available()) else 'cpu')
 
-        content_img_path = 'test/content.jpg'
+        content_img_path = 'test/content.jpeg'
+        random_img_path = 'test/random.jpeg'
         style_img_path = 'test/style.jpg'
 
         content_img = Preprocessor.process(content_img_path)
         style_img = Preprocessor.process(style_img_path)
+        random_img = Preprocessor.process(random_img_path)
 
-        content_img_clone = content_img.clone().requires_grad_(True)
+        content_img_clone = random_img.clone().requires_grad_(True)
 
         Optimizer.gradient_descent(content_img, style_img, content_img_clone)
 

[Meet Narendra]

Layer 300 incorrect. Change it to layer 30.