diff --git a/Project_Atay_Ozkan/download_data.sh b/Project_Atay_Ozkan/download_data.sh
old mode 100755
new mode 100644
diff --git a/Project_Atay_Ozkan/library/__init__.py b/Project_Atay_Ozkan/library/__init__.py
old mode 100755
new mode 100644
diff --git a/Project_Atay_Ozkan/library/dataset.py b/Project_Atay_Ozkan/library/dataset.py
old mode 100755
new mode 100644
diff --git a/Project_Atay_Ozkan/library/loss.py b/Project_Atay_Ozkan/library/loss.py
old mode 100755
new mode 100644
diff --git a/Project_Atay_Ozkan/library/models.py b/Project_Atay_Ozkan/library/models.py
old mode 100755
new mode 100644
diff --git a/Project_Cengiz/requirements.txt b/Project_Cengiz/requirements.txt
index f0fae7a..56868ee 100644
--- a/Project_Cengiz/requirements.txt
+++ b/Project_Cengiz/requirements.txt
@@ -1,5 +1,5 @@
-numpy
-Pillow
-matplotlib
-tensorboard
-tensorboardX
+numpy
+Pillow
+matplotlib
+tensorboard
+tensorboardX
diff --git a/Project_Egri_Ucak/download_cifar10.sh b/Project_Egri_Ucak/download_cifar10.sh
old mode 100755
new mode 100644
diff --git a/Project_Egri_Ucak/download_mnist.sh b/Project_Egri_Ucak/download_mnist.sh
old mode 100755
new mode 100644
diff --git a/Project_Germen/custom_experiment.py b/Project_Germen/custom_experiment.py
index bb9b676..e9c925c 100644
--- a/Project_Germen/custom_experiment.py
+++ b/Project_Germen/custom_experiment.py
@@ -1,292 +1,292 @@
-
-import argparse
-from multiprocessing.dummy import active_children
-
-parser = argparse.ArgumentParser(description="Model Arguments")
-
-parser.add_argument(
-    "--model",
-    type=str,
-    default = "CONV_6_SM", 
-    help = "Model name"
-)
-
-parser.add_argument(
-    "--K",
-    type=int,
-    default = 8,
-    help = "Number of discrete weights"
-)
-
-parser.add_argument(
-    "--selector",
-    type=str,
-    default = "GS",
-    help = "Selector function"
-)
-
-parser.add_argument(
-    "--lr",
-    type = float,
-    default = 0.2,
-    help = "Learning rate"
-)
-
-parser.add_argument(
-    "--epochs",
-    type=int,
-    default = 200,
-    help = "Number of epochs"
-)
-
-args = parser.parse_args()
-
-import torch
-import torch.nn as nn
-import numpy as np
-import random
-import torch.nn.functional as TF
-import torchvision
-import torchvision.transforms as transforms
-import torch.optim as optim
-import math
-from models_layers_trainers.SM_Models import CONV_2_SM, CONV_2,  CONV_4_SM, CONV_4,  CONV_6_SM, CONV_6
-import copy
-
-random.seed(501)
-np.random.seed(501)    
-torch.manual_seed(501)
-
-if torch.cuda.is_available():
-  print("Cuda (GPU support) is available and enabled!")
-  device = torch.device("cuda")
-else:
-  print("Cuda (GPU support) is not available :(")
-  device = torch.device("cpu")
-
-
-#######################################################
-########### Loading datasets
-#######################################################
-
-transform = transforms.Compose(
-    [transforms.ToTensor(),
-     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-
-batch_size = 128
-
-trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
-                                        download=True, transform=transform)
-
-validset, trainset = torch.utils.data.random_split(trainset, [5000, 45000])
-
-trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
-                                          shuffle=True)
-
-validloader = torch.utils.data.DataLoader(validset, batch_size=batch_size,
-                                          shuffle=True)
-
-testset = torchvision.datasets.CIFAR10(root='./data', train=False,
-                                       download=True, transform=transform)
-                                       
-testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
-                                         shuffle=False)
-
-classes = ('plane', 'car', 'bird', 'cat',
-           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
-
-#######################################################
-########### Validation
-#######################################################
-def validation():
-    correct = 0
-    total = 0
-
-    with torch.no_grad():
-        for data in validloader:
-            inputs, labels = data
-            inputs = inputs.to(device)
-            labels = labels.to(device)
-            outputs = model(inputs)
-            _, predicted = torch.max(outputs.data, 1)
-            total += labels.size(0)
-            correct += (predicted == labels).sum().item()
-
-    accuracy = correct / total
-    return accuracy
-
-#######################################################
-########### Training function - SLOT MACHINE
-#######################################################
-
-def train_SM(model, criterion, optimizer, epochs, dataloader, lr, verbose=True):
-  """
-    Define the trainer function. We can use this for training any model.
-    The parameter names are self-explanatory.
-
-    Returns: the loss history.
-  """
-  model.to(device)
-  loss_history = []
-  acc_history = []
-  for epoch in range(epochs):
-    for i, data in enumerate(dataloader, 0):    
-      
-      # Our batch:
-      inputs, labels = data
-      inputs = inputs.to(device)
-      labels = labels.to(device)
-
-      # zero the gradients as PyTorch accumulates them
-      optimizer.zero_grad()
-
-      # Obtain the scores
-      outputs = model(inputs)
-
-      # Calculate loss
-      loss = criterion(outputs.to(device), labels)
-
-      # Backpropagate
-      loss.backward()
-
-      # Update the scores
-      model.update_scores(lr) # MOST IMPORTANT CHANGE 
-
-      loss_history.append(loss.item())
-    
-    if verbose: print(f'Epoch {epoch} / {epochs}: avg. loss of last 5 iterations {np.sum(loss_history[:-6:-1])/5}')
-
-    acc_history.append(validation())
-    best_acc = max(acc_history)
-    best_epoch = acc_history.index(best_acc)
-    if best_epoch == epoch:
-        best_model = copy.deepcopy(model)
-
-  return loss_history, acc_history, best_model
-
-#######################################################
-########### Training function - NORMAL
-#######################################################
-
-def train(model, criterion, optimizer, epochs, dataloader, verbose=True):
-  """
-    Define the trainer function. We can use this for training any model.
-    The parameter names are self-explanatory.
-
-    Returns: the loss history.
-  """
-  loss_history = []
-  acc_history = []
-  for epoch in range(epochs):
-    for i, data in enumerate(dataloader, 0):    
-      
-      # Our batch:
-      inputs, labels = data
-      inputs = inputs.to(device)
-      labels = labels.to(device)
-
-      # zero the gradients as PyTorch accumulates them
-      optimizer.zero_grad()
-
-      # Obtain the scores
-      outputs = model(inputs)
-
-      # Calculate loss
-      loss = criterion(outputs.to(device), labels)
-
-      # Backpropagate
-      loss.backward()
-
-      # Update the weights
-      optimizer.step()
-
-      loss_history.append(loss.item())
-    
-    if verbose: print(f'Epoch {epoch} / {epochs}: avg. loss of last 5 iterations {np.sum(loss_history[:-6:-1])/5}')
-
-    acc_history.append(validation())
-    best_acc = max(acc_history)
-    best_epoch = acc_history.index(best_acc)
-    if best_epoch == epoch:
-        best_model = copy.deepcopy(model)
-
-  return loss_history, acc_history, best_model
-
-
-
-
-###### Setting the model
-MN = args.model # Extracting model name
-
-if MN == "CONV_2_SM":
-    model = CONV_2_SM(args.K,args.selector)
-elif MN == "CONV_2":
-    model = CONV_2()
-if MN == "CONV_4_SM":
-    model = CONV_4_SM(args.K,args.selector)
-elif MN == "CONV_4":
-    model = CONV_4()
-if MN == "CONV_6_SM":
-    model = CONV_6_SM(args.K,args.selector)
-elif MN == "CONV_6":
-    model = CONV_6()
-
-criterion = nn.CrossEntropyLoss()
-optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=0.0001)
-
-model = model.to(device)
-epochs = args.epochs
-
-####### Setting the trainer
-if MN[-2:] == "SM":
-    loss_history, acc_history, best_model = train_SM(model, criterion, optimizer, epochs, trainloader, lr = args.lr) 
-else:
-    loss_history, acc_history, best_model = train(model, criterion, optimizer, epochs, trainloader)
-
-# Disclaimer: This code piece is taken from PyTorch examples.
-
-correct = 0
-total = 0
-
-with torch.no_grad():
-    for data in testloader:
-        inputs, labels = data
-        inputs = inputs.to(device)
-        labels = labels.to(device)
-        outputs = model(inputs)
-        _, predicted = torch.max(outputs.data, 1)
-        total += labels.size(0)
-        correct += (predicted == labels).sum().item()
-
-accuracy = correct / total
-
-correct = 0
-total = 0
-
-with torch.no_grad():
-    for data in testloader:
-        inputs, labels = data
-        inputs = inputs.to(device)
-        labels = labels.to(device)
-        outputs = best_model(inputs)
-        _, predicted = torch.max(outputs.data, 1)
-        total += labels.size(0)
-        correct += (predicted == labels).sum().item()
-
-accuracy_2 = correct / total 
-
-print('Accuracy out of 5000 images is ' + str(accuracy))
-best_acc = max(acc_history)
-best_epoch = acc_history.index(best_acc)
-print("Best epoch for validation and its test accuracy: " + str(best_epoch) + " - " + str(accuracy_2))
-
-model.accuracy = accuracy
-model.loss_history = loss_history
-model.acc_history = acc_history
-model.validbest = (best_epoch, best_acc)
-model.best_model = best_model
-
-import pickle
-model_file_name = args.model + "_K_" + str(args.K) + "_Selector_" + args.selector + "_LR_" + str(args.lr) + "_Epochs_" + str(args.epochs) + "_ACC_" + str(accuracy)
-with open(model_file_name, "wb") as pf:
-    pickle.dump(model,pf)
+
+import argparse
+from multiprocessing.dummy import active_children
+
+parser = argparse.ArgumentParser(description="Model Arguments")
+
+parser.add_argument(
+    "--model",
+    type=str,
+    default = "CONV_6_SM", 
+    help = "Model name"
+)
+
+parser.add_argument(
+    "--K",
+    type=int,
+    default = 8,
+    help = "Number of discrete weights"
+)
+
+parser.add_argument(
+    "--selector",
+    type=str,
+    default = "GS",
+    help = "Selector function"
+)
+
+parser.add_argument(
+    "--lr",
+    type = float,
+    default = 0.2,
+    help = "Learning rate"
+)
+
+parser.add_argument(
+    "--epochs",
+    type=int,
+    default = 200,
+    help = "Number of epochs"
+)
+
+args = parser.parse_args()
+
+import torch
+import torch.nn as nn
+import numpy as np
+import random
+import torch.nn.functional as TF
+import torchvision
+import torchvision.transforms as transforms
+import torch.optim as optim
+import math
+from models_layers_trainers.SM_Models import CONV_2_SM, CONV_2,  CONV_4_SM, CONV_4,  CONV_6_SM, CONV_6
+import copy
+
+random.seed(501)
+np.random.seed(501)    
+torch.manual_seed(501)
+
+if torch.cuda.is_available():
+  print("Cuda (GPU support) is available and enabled!")
+  device = torch.device("cuda")
+else:
+  print("Cuda (GPU support) is not available :(")
+  device = torch.device("cpu")
+
+
+#######################################################
+########### Loading datasets
+#######################################################
+
+transform = transforms.Compose(
+    [transforms.ToTensor(),
+     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+
+batch_size = 128
+
+trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
+                                        download=True, transform=transform)
+
+validset, trainset = torch.utils.data.random_split(trainset, [5000, 45000])
+
+trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
+                                          shuffle=True)
+
+validloader = torch.utils.data.DataLoader(validset, batch_size=batch_size,
+                                          shuffle=True)
+
+testset = torchvision.datasets.CIFAR10(root='./data', train=False,
+                                       download=True, transform=transform)
+                                       
+testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
+                                         shuffle=False)
+
+classes = ('plane', 'car', 'bird', 'cat',
+           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+
+#######################################################
+########### Validation
+#######################################################
+def validation():
+    correct = 0
+    total = 0
+
+    with torch.no_grad():
+        for data in validloader:
+            inputs, labels = data
+            inputs = inputs.to(device)
+            labels = labels.to(device)
+            outputs = model(inputs)
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+
+    accuracy = correct / total
+    return accuracy
+
+#######################################################
+########### Training function - SLOT MACHINE
+#######################################################
+
+def train_SM(model, criterion, optimizer, epochs, dataloader, lr, verbose=True):
+  """
+    Define the trainer function. We can use this for training any model.
+    The parameter names are self-explanatory.
+
+    Returns: the loss history.
+  """
+  model.to(device)
+  loss_history = []
+  acc_history = []
+  for epoch in range(epochs):
+    for i, data in enumerate(dataloader, 0):    
+      
+      # Our batch:
+      inputs, labels = data
+      inputs = inputs.to(device)
+      labels = labels.to(device)
+
+      # zero the gradients as PyTorch accumulates them
+      optimizer.zero_grad()
+
+      # Obtain the scores
+      outputs = model(inputs)
+
+      # Calculate loss
+      loss = criterion(outputs.to(device), labels)
+
+      # Backpropagate
+      loss.backward()
+
+      # Update the scores
+      model.update_scores(lr) # MOST IMPORTANT CHANGE 
+
+      loss_history.append(loss.item())
+    
+    if verbose: print(f'Epoch {epoch} / {epochs}: avg. loss of last 5 iterations {np.sum(loss_history[:-6:-1])/5}')
+
+    acc_history.append(validation())
+    best_acc = max(acc_history)
+    best_epoch = acc_history.index(best_acc)
+    if best_epoch == epoch:
+        best_model = copy.deepcopy(model)
+
+  return loss_history, acc_history, best_model
+
+#######################################################
+########### Training function - NORMAL
+#######################################################
+
+def train(model, criterion, optimizer, epochs, dataloader, verbose=True):
+  """
+    Define the trainer function. We can use this for training any model.
+    The parameter names are self-explanatory.
+
+    Returns: the loss history.
+  """
+  loss_history = []
+  acc_history = []
+  for epoch in range(epochs):
+    for i, data in enumerate(dataloader, 0):    
+      
+      # Our batch:
+      inputs, labels = data
+      inputs = inputs.to(device)
+      labels = labels.to(device)
+
+      # zero the gradients as PyTorch accumulates them
+      optimizer.zero_grad()
+
+      # Obtain the scores
+      outputs = model(inputs)
+
+      # Calculate loss
+      loss = criterion(outputs.to(device), labels)
+
+      # Backpropagate
+      loss.backward()
+
+      # Update the weights
+      optimizer.step()
+
+      loss_history.append(loss.item())
+    
+    if verbose: print(f'Epoch {epoch} / {epochs}: avg. loss of last 5 iterations {np.sum(loss_history[:-6:-1])/5}')
+
+    acc_history.append(validation())
+    best_acc = max(acc_history)
+    best_epoch = acc_history.index(best_acc)
+    if best_epoch == epoch:
+        best_model = copy.deepcopy(model)
+
+  return loss_history, acc_history, best_model
+
+
+
+
+###### Setting the model
+MN = args.model # Extracting model name
+
+if MN == "CONV_2_SM":
+    model = CONV_2_SM(args.K,args.selector)
+elif MN == "CONV_2":
+    model = CONV_2()
+if MN == "CONV_4_SM":
+    model = CONV_4_SM(args.K,args.selector)
+elif MN == "CONV_4":
+    model = CONV_4()
+if MN == "CONV_6_SM":
+    model = CONV_6_SM(args.K,args.selector)
+elif MN == "CONV_6":
+    model = CONV_6()
+
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=0.0001)
+
+model = model.to(device)
+epochs = args.epochs
+
+####### Setting the trainer
+if MN[-2:] == "SM":
+    loss_history, acc_history, best_model = train_SM(model, criterion, optimizer, epochs, trainloader, lr = args.lr) 
+else:
+    loss_history, acc_history, best_model = train(model, criterion, optimizer, epochs, trainloader)
+
+# Disclaimer: This code piece is taken from PyTorch examples.
+
+correct = 0
+total = 0
+
+with torch.no_grad():
+    for data in testloader:
+        inputs, labels = data
+        inputs = inputs.to(device)
+        labels = labels.to(device)
+        outputs = model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+accuracy = correct / total
+
+correct = 0
+total = 0
+
+with torch.no_grad():
+    for data in testloader:
+        inputs, labels = data
+        inputs = inputs.to(device)
+        labels = labels.to(device)
+        outputs = best_model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+accuracy_2 = correct / total 
+
+print('Accuracy out of 5000 images is ' + str(accuracy))
+best_acc = max(acc_history)
+best_epoch = acc_history.index(best_acc)
+print("Best epoch for validation and its test accuracy: " + str(best_epoch) + " - " + str(accuracy_2))
+
+model.accuracy = accuracy
+model.loss_history = loss_history
+model.acc_history = acc_history
+model.validbest = (best_epoch, best_acc)
+model.best_model = best_model
+
+import pickle
+model_file_name = args.model + "_K_" + str(args.K) + "_Selector_" + args.selector + "_LR_" + str(args.lr) + "_Epochs_" + str(args.epochs) + "_ACC_" + str(accuracy)
+with open(model_file_name, "wb") as pf:
+    pickle.dump(model,pf)
diff --git a/Project_Germen/data/Why empty? b/Project_Germen/data/Why empty?
deleted file mode 100644
index 4ef543b..0000000
--- a/Project_Germen/data/Why empty?	
+++ /dev/null
@@ -1 +0,0 @@
-Empty due to data files being too large, once custom_experiment.py is ran, it iwll fill automatically.
diff --git a/Project_Godelek_Kaavkci/scripts/div2k_downloader.sh b/Project_Godelek_Kaavkci/scripts/div2k_downloader.sh
old mode 100755
new mode 100644
diff --git a/Project_Godelek_Kaavkci/scripts/test_dataset_downloader.sh b/Project_Godelek_Kaavkci/scripts/test_dataset_downloader.sh
old mode 100755
new mode 100644
diff --git a/Project_Kargi/adain_main.py b/Project_Kargi/adain_main.py
index 59336ec..b2a044b 100644
--- a/Project_Kargi/adain_main.py
+++ b/Project_Kargi/adain_main.py
@@ -1,262 +1,262 @@
-import argparse
-from email.policy import default
-import os
-import torch
-from tqdm import tqdm
-import copy
-import torch.nn as nn
-from PIL import Image
-from libs.models_sanet import calc_ast_style_loss, calc_ast_style_loss_normalized, calc_ast_style_loss_unnormalized, decoder as decoder,Transform,vgg,test_transform,Net
-from torch.utils import data
-import numpy as np
-import libs.models_sanet as sanet
-import libs.models_adain as adain
-import libs.functions as utils
-
-from torchvision.utils import save_image
-from libs.models_adain import style_transfer
-import gc
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-print("ok")
-
-
-#_____________________________________LOAD MODELS_______________________________________#
-adain_default_path = r"experiments/adain/decoder_default_iter_160000.pth.tar"
-
-adain_balanced_path = r"experiments/adain/decoder_normalized_iter_160000.pth.tar"
-
-adain_pretrained_path = r"experiments/adain/decoder_pretrained.pth"
-
-
-adain_default = copy.deepcopy(adain.decoder)
-adain_default.load_state_dict(torch.load(adain_default_path))
-adain_default.eval()
-
-
-adain_balanced = copy.deepcopy(sanet.decoder)
-adain_balanced.load_state_dict(torch.load(adain_balanced_path))
-adain_balanced.eval()
-
-adain_pretrained = copy.deepcopy(sanet.decoder)
-adain_pretrained.load_state_dict(torch.load(adain_pretrained_path))
-adain_pretrained.eval()
-
-model_list = [adain_default, adain_balanced, adain_pretrained]
-
-vgg = adain.vgg
-vgg.load_state_dict(torch.load("vgg_normalised.pth"))
-
-print(f"Models loaded succesfully. Total model size : {len(model_list)}")
-
-## Switch the device of the models for faster performance
-vgg = nn.Sequential(*list(vgg.children())[:31]) # get the VGG layers
-
-norm = nn.Sequential(*list(vgg.children())[:1])
-enc_1 = nn.Sequential(*list(vgg.children())[:4])  # input -> relu1_1
-enc_2 = nn.Sequential(*list(vgg.children())[4:11])  # relu1_1 -> relu2_1
-enc_3 = nn.Sequential(*list(vgg.children())[11:18])  # relu2_1 -> relu3_1
-enc_4 = nn.Sequential(*list(vgg.children())[18:31])  # relu3_1 -> relu4_1
-
-enc_1.to(device)
-enc_2.to(device)
-enc_3.to(device)
-enc_4.to(device)
-vgg.to(device)
-for model in model_list:
-    model.to(device)
-
-print(f"Models switched to defice successfully. Device : {device}")
-
-#_____________________________________SET PARAMETERS____________________________________#
-
-parser = argparse.ArgumentParser()
-
-# Basic options to evaluate models
-parser.add_argument('--steps', type=int, default = 1,
-                    help='Steps of style transfer.')
-parser.add_argument('--many_image', type=int, default = 2000,
-                    help='Total amount of generated pair.')
-parser.add_argument('--content_dir', type=str, default = "contents/",
-                    help='Path where the content images located.')
-parser.add_argument('--style_dir', type=str, default = "styles/",
-                    help='Path where the style images located.')
-parser.add_argument('--alpha', type=float, default = 1.0,
-                    help='Alpha value.')
-parser.add_argument('--content_size', type=int, default = 256,
-                    help='Size of the content image.')
-parser.add_argument('--style_size', type=int, default = 256,
-                    help='Size of the style image.')
-parser.add_argument('--crop', type=int, default = True,
-                    help='Crop the image or not.')
-args = parser.parse_args()
-
-steps = args.steps
-many_image = args.many_image
-content_dir = args.content_dir
-style_dir = args.style_dir
-
-alpha = args.alpha
-content_size = args.content_size
-style_size = args.style_size
-crop = args.crop
-#_____________________________________DATA PROCESS______________________________________#
-content_tf = adain.test_transform(content_size, crop)
-style_tf = adain.test_transform(style_size, crop)
-
-content_dataset = utils.FlatFolderDataset(content_dir, content_tf)
-style_dataset = utils.FlatFolderDataset(style_dir, style_tf)
-
-content_iter = iter(data.DataLoader(
-    content_dataset, batch_size= 1,
-    sampler=utils.InfiniteSamplerWrapper(content_dataset)))
-
-style_iter = iter(data.DataLoader(
-    style_dataset, batch_size= 1,
-    sampler=utils.InfiniteSamplerWrapper(style_dataset)))
-
-#_____________________________________START TESTING______________________________________#
-torch.cuda.empty_cache()    
-
-contents = []
-multi_content = []
-balanced_losses = []
-default_losses =[]
-default_unnormalized_losses = []
-
-model_image_contents = []
-unnormalized_losses = [[],[],[],[]]
-normalizer_weights = [[],[],[],[]]
-
-content_set = [next(content_iter).to(device) for i in range(many_image)]
-style_set = [next(style_iter).to(device) for i in range(many_image)]
-
-with torch.no_grad():
-    for model in model_list:
-        image_contents = []
-        for im in tqdm(range(many_image)):
-            content = content_set[im]
-            style = style_set[im]
-            
-            contents = []
-            contents.append(content)
-            contents.append(style)
-            #print(content.shape)
-            for step in range(steps):
-
-                Style1_1 = enc_1(style)
-                Style2_1 = enc_2(Style1_1)
-                Style3_1 = enc_3(Style2_1)
-                Style4_1 = enc_4(Style3_1)
-
-                Content4_1 = vgg(content)
-
-                style_feats = [Style1_1,Style2_1,Style3_1,Style4_1]
-                t = adain.adaptive_instance_normalization(Content4_1, Style4_1)
-                t = alpha * t + (1 - alpha) * Content4_1
-
-                g_t = model(t)
-                
-                gt1_1 = enc_1(g_t)
-                gt2_1 = enc_2(gt1_1)
-                gt3_1 = enc_3(gt2_1)
-                gt4_1 = enc_4(gt3_1) # stylized + decoder
-
-                content = style_transfer(vgg, model, content, style, alpha)
-
-                g_t_feats = [gt1_1,gt2_1,gt3_1,gt4_1]
-                
-
-                loss_s = calc_ast_style_loss(g_t_feats[0], style_feats[0])
-                loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[0], style_feats[0])
-                loss_normalizer = sanet.return_normalize_weight(g_t_feats[0], style_feats[0])
-
-                normalizer_weights[0].append(loss_normalizer) # First relu
-                unnormalized_losses[0].append(loss_unnormalized) # First relu
-                loss_unnorm_s = loss_unnormalized
-                for i in range(1, 4):
-                    loss_normalizer = sanet.return_normalize_weight(g_t_feats[i], style_feats[i])
-                    loss_s += calc_ast_style_loss(g_t_feats[i], style_feats[i])
-                    loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[i], style_feats[i])
-                    loss_unnorm_s += loss_unnormalized
-                    normalizer_weights[i].append(loss_normalizer)  # ith relu
-                    unnormalized_losses[i].append(loss_unnormalized) # ith relu
-                #print("loss_unnorm_s : ",loss_unnorm_s.item())
-                default_loss = loss_s
-            
-                
-                loss_s = calc_ast_style_loss_normalized(g_t_feats[0], style_feats[0])
-                for i in range(1, 4):
-                    loss_s += calc_ast_style_loss_normalized(g_t_feats[i], style_feats[i])
-                #print("Balanced : ", loss_s.item())
-                balanced_loss = loss_s
-
-                
-                
-                # free some memory since when we load the second model, we need this
-                del Content4_1
-                del g_t_feats
-                del gt1_1
-                del gt2_1
-                del gt3_1
-                del gt4_1
-                del Style1_1
-                del Style2_1
-                del Style3_1
-                del style_feats
-                del Style4_1
-                gc.collect()
-            content.clamp(0, 255)
-            contents.append(content.detach().cpu())
-            balanced_losses.append(balanced_loss.cpu())
-            default_losses.append(default_loss.cpu())
-            default_unnormalized_losses.append(loss_unnorm_s.cpu())
-            image_contents.append(contents) # (content,style,stylized), (content,style,stylized)..
-            del content
-            del style
-        model_image_contents.append(image_contents) # ((model contents), (model contents))
-    del model
-
-import matplotlib.pyplot as plt
-# losses are in shape model x steps x imsize -> model, steps x imsize
-default_losses = np.array([los.cpu() for los in default_losses])
-default_losses = default_losses.reshape(len(model_list),-1)
-
-balanced_losses = np.array([los.cpu() for los in balanced_losses])
-balanced_losses = balanced_losses.reshape(len(model_list),-1)
-
-default_unnormalized_losses = np.array([los.cpu() for los in default_unnormalized_losses])
-default_unnormalized_losses = default_unnormalized_losses.reshape(len(model_list),-1)
-
-for i in range(len(model_list)):
-    print("___________________________________")
-    print(f"Default loss : {np.mean(default_losses[i])}")
-    print(f"Balanced loss : {np.mean(balanced_losses[i])}")
-    print(f"Unnormalized loss : {np.mean(default_unnormalized_losses[i])}")
-    print("___________________________________")
-
-
-low_to_high = np.argsort(balanced_losses[1])
-low_index = low_to_high[5]
-high_index = low_to_high[-5]
-utils.show_image_result(model_image_contents,low_index, title = "Balanced AdaIN, image with small style loss")
-utils.show_image_result(model_image_contents,high_index, title = "Balanced AdaIN, image with high style loss")
-
-
-
-low_to_high_default = np.argsort(default_losses[0])
-low_index_bal = low_to_high_default[5]
-high_index_bal = low_to_high_default[-5]
-
-utils.show_image_result(model_image_contents,low_index_bal, title = "Default AdaIN, image with small style loss")
-utils.show_image_result(model_image_contents,high_index_bal, title = "Default AdaIN, image with high style loss")
-
-utils.show_loss_distribution(balanced_losses[1], save_name = "balanced_adain", title = "Loss distribution of balanced losses for balance style trained model.")
-utils.show_loss_distribution(default_unnormalized_losses[2], save_name = "default_adain_unnorm", title = "Loss distribution of unnormalized classic losses for pretrained models.")
-
-"""
-for i in range(len(unnormalized_losses)):
-    unnormalized_losses[i] = np.array([los.cpu() for los in unnormalized_losses[i]])
-    normalizer_weights[i] = np.array([los.cpu() for los in normalizer_weights[i]])
-utils.show_relation_graph(unnormalized_losses,normalizer_weights, "r5")
-"""
+import argparse
+from email.policy import default
+import os
+import torch
+from tqdm import tqdm
+import copy
+import torch.nn as nn
+from PIL import Image
+from libs.models_sanet import calc_ast_style_loss, calc_ast_style_loss_normalized, calc_ast_style_loss_unnormalized, decoder as decoder,Transform,vgg,test_transform,Net
+from torch.utils import data
+import numpy as np
+import libs.models_sanet as sanet
+import libs.models_adain as adain
+import libs.functions as utils
+
+from torchvision.utils import save_image
+from libs.models_adain import style_transfer
+import gc
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("ok")
+
+
+#_____________________________________LOAD MODELS_______________________________________#
+adain_default_path = r"experiments/adain/decoder_default_iter_160000.pth.tar"
+
+adain_balanced_path = r"experiments/adain/decoder_normalized_iter_160000.pth.tar"
+
+adain_pretrained_path = r"experiments/adain/decoder_pretrained.pth"
+
+
+adain_default = copy.deepcopy(adain.decoder)
+adain_default.load_state_dict(torch.load(adain_default_path))
+adain_default.eval()
+
+
+adain_balanced = copy.deepcopy(sanet.decoder)
+adain_balanced.load_state_dict(torch.load(adain_balanced_path))
+adain_balanced.eval()
+
+adain_pretrained = copy.deepcopy(sanet.decoder)
+adain_pretrained.load_state_dict(torch.load(adain_pretrained_path))
+adain_pretrained.eval()
+
+model_list = [adain_default, adain_balanced, adain_pretrained]
+
+vgg = adain.vgg
+vgg.load_state_dict(torch.load("vgg_normalised.pth"))
+
+print(f"Models loaded succesfully. Total model size : {len(model_list)}")
+
+## Switch the device of the models for faster performance
+vgg = nn.Sequential(*list(vgg.children())[:31]) # get the VGG layers
+
+norm = nn.Sequential(*list(vgg.children())[:1])
+enc_1 = nn.Sequential(*list(vgg.children())[:4])  # input -> relu1_1
+enc_2 = nn.Sequential(*list(vgg.children())[4:11])  # relu1_1 -> relu2_1
+enc_3 = nn.Sequential(*list(vgg.children())[11:18])  # relu2_1 -> relu3_1
+enc_4 = nn.Sequential(*list(vgg.children())[18:31])  # relu3_1 -> relu4_1
+
+enc_1.to(device)
+enc_2.to(device)
+enc_3.to(device)
+enc_4.to(device)
+vgg.to(device)
+for model in model_list:
+    model.to(device)
+
+print(f"Models switched to defice successfully. Device : {device}")
+
+#_____________________________________SET PARAMETERS____________________________________#
+
+parser = argparse.ArgumentParser()
+
+# Basic options to evaluate models
+parser.add_argument('--steps', type=int, default = 1,
+                    help='Steps of style transfer.')
+parser.add_argument('--many_image', type=int, default = 2000,
+                    help='Total amount of generated pair.')
+parser.add_argument('--content_dir', type=str, default = "contents/",
+                    help='Path where the content images located.')
+parser.add_argument('--style_dir', type=str, default = "styles/",
+                    help='Path where the style images located.')
+parser.add_argument('--alpha', type=float, default = 1.0,
+                    help='Alpha value.')
+parser.add_argument('--content_size', type=int, default = 256,
+                    help='Size of the content image.')
+parser.add_argument('--style_size', type=int, default = 256,
+                    help='Size of the style image.')
+parser.add_argument('--crop', type=int, default = True,
+                    help='Crop the image or not.')
+args = parser.parse_args()
+
+steps = args.steps
+many_image = args.many_image
+content_dir = args.content_dir
+style_dir = args.style_dir
+
+alpha = args.alpha
+content_size = args.content_size
+style_size = args.style_size
+crop = args.crop
+#_____________________________________DATA PROCESS______________________________________#
+content_tf = adain.test_transform(content_size, crop)
+style_tf = adain.test_transform(style_size, crop)
+
+content_dataset = utils.FlatFolderDataset(content_dir, content_tf)
+style_dataset = utils.FlatFolderDataset(style_dir, style_tf)
+
+content_iter = iter(data.DataLoader(
+    content_dataset, batch_size= 1,
+    sampler=utils.InfiniteSamplerWrapper(content_dataset)))
+
+style_iter = iter(data.DataLoader(
+    style_dataset, batch_size= 1,
+    sampler=utils.InfiniteSamplerWrapper(style_dataset)))
+
+#_____________________________________START TESTING______________________________________#
+torch.cuda.empty_cache()    
+
+contents = []
+multi_content = []
+balanced_losses = []
+default_losses =[]
+default_unnormalized_losses = []
+
+model_image_contents = []
+unnormalized_losses = [[],[],[],[]]
+normalizer_weights = [[],[],[],[]]
+
+content_set = [next(content_iter).to(device) for i in range(many_image)]
+style_set = [next(style_iter).to(device) for i in range(many_image)]
+
+with torch.no_grad():
+    for model in model_list:
+        image_contents = []
+        for im in tqdm(range(many_image)):
+            content = content_set[im]
+            style = style_set[im]
+            
+            contents = []
+            contents.append(content)
+            contents.append(style)
+            #print(content.shape)
+            for step in range(steps):
+
+                Style1_1 = enc_1(style)
+                Style2_1 = enc_2(Style1_1)
+                Style3_1 = enc_3(Style2_1)
+                Style4_1 = enc_4(Style3_1)
+
+                Content4_1 = vgg(content)
+
+                style_feats = [Style1_1,Style2_1,Style3_1,Style4_1]
+                t = adain.adaptive_instance_normalization(Content4_1, Style4_1)
+                t = alpha * t + (1 - alpha) * Content4_1
+
+                g_t = model(t)
+                
+                gt1_1 = enc_1(g_t)
+                gt2_1 = enc_2(gt1_1)
+                gt3_1 = enc_3(gt2_1)
+                gt4_1 = enc_4(gt3_1) # stylized + decoder
+
+                content = style_transfer(vgg, model, content, style, alpha)
+
+                g_t_feats = [gt1_1,gt2_1,gt3_1,gt4_1]
+                
+
+                loss_s = calc_ast_style_loss(g_t_feats[0], style_feats[0])
+                loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[0], style_feats[0])
+                loss_normalizer = sanet.return_normalize_weight(g_t_feats[0], style_feats[0])
+
+                normalizer_weights[0].append(loss_normalizer) # First relu
+                unnormalized_losses[0].append(loss_unnormalized) # First relu
+                loss_unnorm_s = loss_unnormalized
+                for i in range(1, 4):
+                    loss_normalizer = sanet.return_normalize_weight(g_t_feats[i], style_feats[i])
+                    loss_s += calc_ast_style_loss(g_t_feats[i], style_feats[i])
+                    loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[i], style_feats[i])
+                    loss_unnorm_s += loss_unnormalized
+                    normalizer_weights[i].append(loss_normalizer)  # ith relu
+                    unnormalized_losses[i].append(loss_unnormalized) # ith relu
+                #print("loss_unnorm_s : ",loss_unnorm_s.item())
+                default_loss = loss_s
+            
+                
+                loss_s = calc_ast_style_loss_normalized(g_t_feats[0], style_feats[0])
+                for i in range(1, 4):
+                    loss_s += calc_ast_style_loss_normalized(g_t_feats[i], style_feats[i])
+                #print("Balanced : ", loss_s.item())
+                balanced_loss = loss_s
+
+                
+                
+                # free some memory since when we load the second model, we need this
+                del Content4_1
+                del g_t_feats
+                del gt1_1
+                del gt2_1
+                del gt3_1
+                del gt4_1
+                del Style1_1
+                del Style2_1
+                del Style3_1
+                del style_feats
+                del Style4_1
+                gc.collect()
+            content.clamp(0, 255)
+            contents.append(content.detach().cpu())
+            balanced_losses.append(balanced_loss.cpu())
+            default_losses.append(default_loss.cpu())
+            default_unnormalized_losses.append(loss_unnorm_s.cpu())
+            image_contents.append(contents) # (content,style,stylized), (content,style,stylized)..
+            del content
+            del style
+        model_image_contents.append(image_contents) # ((model contents), (model contents))
+    del model
+
+import matplotlib.pyplot as plt
+# losses are in shape model x steps x imsize -> model, steps x imsize
+default_losses = np.array([los.cpu() for los in default_losses])
+default_losses = default_losses.reshape(len(model_list),-1)
+
+balanced_losses = np.array([los.cpu() for los in balanced_losses])
+balanced_losses = balanced_losses.reshape(len(model_list),-1)
+
+default_unnormalized_losses = np.array([los.cpu() for los in default_unnormalized_losses])
+default_unnormalized_losses = default_unnormalized_losses.reshape(len(model_list),-1)
+
+for i in range(len(model_list)):
+    print("___________________________________")
+    print(f"Default loss : {np.mean(default_losses[i])}")
+    print(f"Balanced loss : {np.mean(balanced_losses[i])}")
+    print(f"Unnormalized loss : {np.mean(default_unnormalized_losses[i])}")
+    print("___________________________________")
+
+
+low_to_high = np.argsort(balanced_losses[1])
+low_index = low_to_high[5]
+high_index = low_to_high[-5]
+utils.show_image_result(model_image_contents,low_index, title = "Balanced AdaIN, image with small style loss")
+utils.show_image_result(model_image_contents,high_index, title = "Balanced AdaIN, image with high style loss")
+
+
+
+low_to_high_default = np.argsort(default_losses[0])
+low_index_bal = low_to_high_default[5]
+high_index_bal = low_to_high_default[-5]
+
+utils.show_image_result(model_image_contents,low_index_bal, title = "Default AdaIN, image with small style loss")
+utils.show_image_result(model_image_contents,high_index_bal, title = "Default AdaIN, image with high style loss")
+
+utils.show_loss_distribution(balanced_losses[1], save_name = "balanced_adain", title = "Loss distribution of balanced losses for balance style trained model.")
+utils.show_loss_distribution(default_unnormalized_losses[2], save_name = "default_adain_unnorm", title = "Loss distribution of unnormalized classic losses for pretrained models.")
+
+"""
+for i in range(len(unnormalized_losses)):
+    unnormalized_losses[i] = np.array([los.cpu() for los in unnormalized_losses[i]])
+    normalizer_weights[i] = np.array([los.cpu() for los in normalizer_weights[i]])
+utils.show_relation_graph(unnormalized_losses,normalizer_weights, "r5")
+"""
diff --git a/Project_Kargi/libs/functions.py b/Project_Kargi/libs/functions.py
index 058ef78..348ff36 100644
--- a/Project_Kargi/libs/functions.py
+++ b/Project_Kargi/libs/functions.py
@@ -1,218 +1,218 @@
-
-import torch
-import numpy as np
-from torch.utils import data
-from pathlib import Path
-from PIL import Image
-
-def calc_mean_std(feat, eps=1e-5):
-    # eps is a small value added to the variance to avoid divide-by-zero.
-    size = feat.size()
-    assert (len(size) == 4)
-    N, C = size[:2]
-    feat_var = feat.view(N, C, -1).var(dim=2) + eps
-    feat_std = feat_var.sqrt().view(N, C, 1, 1)
-    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
-    return feat_mean, feat_std
-
-
-def adaptive_instance_normalization(content_feat, style_feat):
-    assert (content_feat.size()[:2] == style_feat.size()[:2])
-    size = content_feat.size()
-    style_mean, style_std = calc_mean_std(style_feat)
-    content_mean, content_std = calc_mean_std(content_feat)
-
-    normalized_feat = (content_feat - content_mean.expand(
-        size)) / content_std.expand(size)
-    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
-
-
-def _calc_feat_flatten_mean_std(feat):
-    # takes 3D feat (C, H, W), return mean and std of array within channels
-    assert (feat.size()[0] == 3)
-    assert (isinstance(feat, torch.FloatTensor))
-    feat_flatten = feat.view(3, -1)
-    mean = feat_flatten.mean(dim=-1, keepdim=True)
-    std = feat_flatten.std(dim=-1, keepdim=True)
-    return feat_flatten, mean, std
-
-
-def _mat_sqrt(x):
-    U, D, V = torch.svd(x)
-    return torch.mm(torch.mm(U, D.pow(0.5).diag()), V.t())
-
-
-def coral(source, target):
-    # assume both source and target are 3D array (C, H, W)
-    # Note: flatten -> f
-
-    source_f, source_f_mean, source_f_std = _calc_feat_flatten_mean_std(source)
-    source_f_norm = (source_f - source_f_mean.expand_as(
-        source_f)) / source_f_std.expand_as(source_f)
-    source_f_cov_eye = \
-        torch.mm(source_f_norm, source_f_norm.t()) + torch.eye(3)
-
-    target_f, target_f_mean, target_f_std = _calc_feat_flatten_mean_std(target)
-    target_f_norm = (target_f - target_f_mean.expand_as(
-        target_f)) / target_f_std.expand_as(target_f)
-    target_f_cov_eye = \
-        torch.mm(target_f_norm, target_f_norm.t()) + torch.eye(3)
-
-    source_f_norm_transfer = torch.mm(
-        _mat_sqrt(target_f_cov_eye),
-        torch.mm(torch.inverse(_mat_sqrt(source_f_cov_eye)),
-                 source_f_norm)
-    )
-
-    source_f_transfer = source_f_norm_transfer * \
-                        target_f_std.expand_as(source_f_norm) + \
-                        target_f_mean.expand_as(source_f_norm)
-
-    return source_f_transfer.view(source.size())
-
-
-import matplotlib.pyplot as plt
-
-
-
-def show_image_result(multiple_contents, index, save_name = "default", title = "Plot"):
-    fig = plt.figure(figsize=(8, 8))
-    # (Model,Image Size, 3, Batch, Channel,Height,Width) 
-    default = multiple_contents[0][index]
-    balanced = multiple_contents[1][index]
-    pretrained = multiple_contents[2][index]
-
-    fig.add_subplot(2,3,1)
-    plt.xlabel("Content")
-    plt.imshow(default[0][0].cpu().permute(1, 2, 0))
-
-    fig.add_subplot(2,3,2)
-    plt.xlabel("Style")
-    plt.imshow(default[1][0].cpu().permute(1, 2, 0) )
-
-    fig.add_subplot(2,3,4)
-    plt.xlabel("Default")
-    plt.imshow(default[2][0].cpu().permute(1, 2, 0) )
-
-    fig.add_subplot(2,3,5)
-    plt.xlabel("Balanced")
-    plt.imshow(balanced[2][0].cpu().permute(1, 2, 0) )
-
-    fig.add_subplot(2,3,6)
-    plt.xlabel("Pretrained")
-    plt.imshow(pretrained[2][0].cpu().permute(1, 2, 0) )
-    
-    plt.suptitle(title)
-    plt.show()
-    plt.savefig(f"{save_name}.png")
-    
-
-def show_loss_distribution(loss_list, save_name = "dist", title = "Plot"):
-
-    from matplotlib import colors
-
-    n_bins = 50
-    fig, axs = plt.subplots(1, 1, tight_layout=True)
-
-    # N is the count in each bin, bins is the lower-limit of the bin
-    N, bins, patches = axs.hist(loss_list, bins=n_bins)
-
-    # We'll color code by height, but you could use any scalar
-    fracs = N / N.max()
-
-    # we need to normalize the data to 0..1 for the full range of the colormap
-    norm = colors.Normalize(fracs.min(), fracs.max())
-
-    # Now, we'll loop through our objects and set the color of each accordingly
-    for thisfrac, thispatch in zip(fracs, patches):
-        color = plt.cm.viridis(norm(thisfrac))
-        thispatch.set_facecolor(color)
-
-    plt.title(title)
-    plt.show()
-    plt.savefig(f"{save_name}.png")
-
-def show_relation_graph(x_,y_, save_name = "relation", sub_titles = ["r11","r21","r31","r41"]):
-    fig = plt.figure(figsize=(10, 10)) 
-    x = [el for el in x_[0]]
-    y = [el for el in y_[0]]
-    fig.add_subplot(2,2,1)
-    plt.xlabel("Classic Style Loss")
-    plt.scatter(x,y, alpha = 0.5)
-    plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
-    plt.ylabel("Balance term")
-    plt.title(sub_titles[0])
-
-    x = [el for el in x_[1]]
-    y = [el for el in y_[1]]
-    fig.add_subplot(2,2,2)
-    plt.xlabel("Classic Style Loss")
-    plt.scatter(x,y, alpha = 0.5)
-    plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
-    plt.ylabel("Balance term")
-    plt.title(sub_titles[1])
-
-    x = [el for el in x_[2]]
-    y = [el for el in y_[2]]
-    fig.add_subplot(2,2,3)
-    plt.xlabel("Classic Style Loss")
-    plt.scatter(x,y, alpha = 0.5)
-    plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
-    plt.ylabel("Balance term")
-    plt.title(sub_titles[2])
-
-    x = [el for el in x_[3]]
-    y = [el for el in y_[3]]
-    fig.add_subplot(2,2,4)
-    plt.xlabel("Classic Style Loss")
-    plt.scatter(x,y, alpha = 0.5)
-    plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
-    plt.ylabel("Balance term")
-    plt.title(sub_titles[3])
-    plt.show()
-    plt.savefig(f"{save_name}.png")
-    
-
-def InfiniteSampler(n):
-    # i = 0
-    i = n - 1
-    order = np.random.permutation(n)
-    while True:
-        yield order[i]
-        i += 1
-        if i >= n:
-            np.random.seed()
-            order = np.random.permutation(n)
-            i = 0
-
-
-class InfiniteSamplerWrapper(data.sampler.Sampler):
-    def __init__(self, data_source):
-        self.num_samples = len(data_source)
-
-    def __iter__(self):
-        return iter(InfiniteSampler(self.num_samples))
-
-    def __len__(self):
-        return 2 ** 31
-
-
-class FlatFolderDataset(data.Dataset):
-    def __init__(self, root, transform):
-        super(FlatFolderDataset, self).__init__()
-        self.root = root
-        self.paths = list(Path(self.root).glob('*'))
-        self.transform = transform
-
-    def __getitem__(self, index):
-        path = self.paths[index]
-        img = Image.open(str(path)).convert('RGB')
-        img = self.transform(img)
-        
-        return img
-
-    def __len__(self):
-        return len(self.paths)
-
-    def name(self):
+
+import torch
+import numpy as np
+from torch.utils import data
+from pathlib import Path
+from PIL import Image
+
+def calc_mean_std(feat, eps=1e-5):
+    # eps is a small value added to the variance to avoid divide-by-zero.
+    size = feat.size()
+    assert (len(size) == 4)
+    N, C = size[:2]
+    feat_var = feat.view(N, C, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(N, C, 1, 1)
+    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
+    return feat_mean, feat_std
+
+
+def adaptive_instance_normalization(content_feat, style_feat):
+    assert (content_feat.size()[:2] == style_feat.size()[:2])
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+
+    normalized_feat = (content_feat - content_mean.expand(
+        size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+
+def _calc_feat_flatten_mean_std(feat):
+    # takes 3D feat (C, H, W), return mean and std of array within channels
+    assert (feat.size()[0] == 3)
+    assert (isinstance(feat, torch.FloatTensor))
+    feat_flatten = feat.view(3, -1)
+    mean = feat_flatten.mean(dim=-1, keepdim=True)
+    std = feat_flatten.std(dim=-1, keepdim=True)
+    return feat_flatten, mean, std
+
+
+def _mat_sqrt(x):
+    U, D, V = torch.svd(x)
+    return torch.mm(torch.mm(U, D.pow(0.5).diag()), V.t())
+
+
+def coral(source, target):
+    # assume both source and target are 3D array (C, H, W)
+    # Note: flatten -> f
+
+    source_f, source_f_mean, source_f_std = _calc_feat_flatten_mean_std(source)
+    source_f_norm = (source_f - source_f_mean.expand_as(
+        source_f)) / source_f_std.expand_as(source_f)
+    source_f_cov_eye = \
+        torch.mm(source_f_norm, source_f_norm.t()) + torch.eye(3)
+
+    target_f, target_f_mean, target_f_std = _calc_feat_flatten_mean_std(target)
+    target_f_norm = (target_f - target_f_mean.expand_as(
+        target_f)) / target_f_std.expand_as(target_f)
+    target_f_cov_eye = \
+        torch.mm(target_f_norm, target_f_norm.t()) + torch.eye(3)
+
+    source_f_norm_transfer = torch.mm(
+        _mat_sqrt(target_f_cov_eye),
+        torch.mm(torch.inverse(_mat_sqrt(source_f_cov_eye)),
+                 source_f_norm)
+    )
+
+    source_f_transfer = source_f_norm_transfer * \
+                        target_f_std.expand_as(source_f_norm) + \
+                        target_f_mean.expand_as(source_f_norm)
+
+    return source_f_transfer.view(source.size())
+
+
+import matplotlib.pyplot as plt
+
+
+
+def show_image_result(multiple_contents, index, save_name = "default", title = "Plot"):
+    fig = plt.figure(figsize=(8, 8))
+    # (Model,Image Size, 3, Batch, Channel,Height,Width) 
+    default = multiple_contents[0][index]
+    balanced = multiple_contents[1][index]
+    pretrained = multiple_contents[2][index]
+
+    fig.add_subplot(2,3,1)
+    plt.xlabel("Content")
+    plt.imshow(default[0][0].cpu().permute(1, 2, 0))
+
+    fig.add_subplot(2,3,2)
+    plt.xlabel("Style")
+    plt.imshow(default[1][0].cpu().permute(1, 2, 0) )
+
+    fig.add_subplot(2,3,4)
+    plt.xlabel("Default")
+    plt.imshow(default[2][0].cpu().permute(1, 2, 0) )
+
+    fig.add_subplot(2,3,5)
+    plt.xlabel("Balanced")
+    plt.imshow(balanced[2][0].cpu().permute(1, 2, 0) )
+
+    fig.add_subplot(2,3,6)
+    plt.xlabel("Pretrained")
+    plt.imshow(pretrained[2][0].cpu().permute(1, 2, 0) )
+    
+    plt.suptitle(title)
+    plt.show()
+    plt.savefig(f"{save_name}.png")
+    
+
+def show_loss_distribution(loss_list, save_name = "dist", title = "Plot"):
+
+    from matplotlib import colors
+
+    n_bins = 50
+    fig, axs = plt.subplots(1, 1, tight_layout=True)
+
+    # N is the count in each bin, bins is the lower-limit of the bin
+    N, bins, patches = axs.hist(loss_list, bins=n_bins)
+
+    # We'll color code by height, but you could use any scalar
+    fracs = N / N.max()
+
+    # we need to normalize the data to 0..1 for the full range of the colormap
+    norm = colors.Normalize(fracs.min(), fracs.max())
+
+    # Now, we'll loop through our objects and set the color of each accordingly
+    for thisfrac, thispatch in zip(fracs, patches):
+        color = plt.cm.viridis(norm(thisfrac))
+        thispatch.set_facecolor(color)
+
+    plt.title(title)
+    plt.show()
+    plt.savefig(f"{save_name}.png")
+
+def show_relation_graph(x_,y_, save_name = "relation", sub_titles = ["r11","r21","r31","r41"]):
+    fig = plt.figure(figsize=(10, 10)) 
+    x = [el for el in x_[0]]
+    y = [el for el in y_[0]]
+    fig.add_subplot(2,2,1)
+    plt.xlabel("Classic Style Loss")
+    plt.scatter(x,y, alpha = 0.5)
+    plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
+    plt.ylabel("Balance term")
+    plt.title(sub_titles[0])
+
+    x = [el for el in x_[1]]
+    y = [el for el in y_[1]]
+    fig.add_subplot(2,2,2)
+    plt.xlabel("Classic Style Loss")
+    plt.scatter(x,y, alpha = 0.5)
+    plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
+    plt.ylabel("Balance term")
+    plt.title(sub_titles[1])
+
+    x = [el for el in x_[2]]
+    y = [el for el in y_[2]]
+    fig.add_subplot(2,2,3)
+    plt.xlabel("Classic Style Loss")
+    plt.scatter(x,y, alpha = 0.5)
+    plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
+    plt.ylabel("Balance term")
+    plt.title(sub_titles[2])
+
+    x = [el for el in x_[3]]
+    y = [el for el in y_[3]]
+    fig.add_subplot(2,2,4)
+    plt.xlabel("Classic Style Loss")
+    plt.scatter(x,y, alpha = 0.5)
+    plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
+    plt.ylabel("Balance term")
+    plt.title(sub_titles[3])
+    plt.show()
+    plt.savefig(f"{save_name}.png")
+    
+
+def InfiniteSampler(n):
+    # i = 0
+    i = n - 1
+    order = np.random.permutation(n)
+    while True:
+        yield order[i]
+        i += 1
+        if i >= n:
+            np.random.seed()
+            order = np.random.permutation(n)
+            i = 0
+
+
+class InfiniteSamplerWrapper(data.sampler.Sampler):
+    def __init__(self, data_source):
+        self.num_samples = len(data_source)
+
+    def __iter__(self):
+        return iter(InfiniteSampler(self.num_samples))
+
+    def __len__(self):
+        return 2 ** 31
+
+
+class FlatFolderDataset(data.Dataset):
+    def __init__(self, root, transform):
+        super(FlatFolderDataset, self).__init__()
+        self.root = root
+        self.paths = list(Path(self.root).glob('*'))
+        self.transform = transform
+
+    def __getitem__(self, index):
+        path = self.paths[index]
+        img = Image.open(str(path)).convert('RGB')
+        img = self.transform(img)
+        
+        return img
+
+    def __len__(self):
+        return len(self.paths)
+
+    def name(self):
         return 'FlatFolderDataset'
\ No newline at end of file
diff --git a/Project_Kargi/libs/models_adain.py b/Project_Kargi/libs/models_adain.py
index b88a26c..c17b61a 100644
--- a/Project_Kargi/libs/models_adain.py
+++ b/Project_Kargi/libs/models_adain.py
@@ -1,315 +1,315 @@
-import argparse
-from pathlib import Path
-import numpy as np
-from torch.utils import data
-import torch
-import torch.nn as nn
-from PIL import Image
-from torchvision import transforms
-from torchvision.utils import save_image
-
-from .functions import adaptive_instance_normalization, coral
-
-
-def test_transform(size, crop):
-    transform_list = []
-    if size != 0:
-        transform_list.append(transforms.Resize(size))
-    if crop:
-        transform_list.append(transforms.CenterCrop(size))
-    transform_list.append(transforms.ToTensor())
-    transform = transforms.Compose(transform_list)
-    return transform
-
-
-def style_transfer(vgg, decoder, content, style, alpha=1.0,
-                   interpolation_weights=None):
-    assert (0.0 <= alpha <= 1.0)
-    content_f = vgg(content)
-    style_f = vgg(style)
-    if interpolation_weights:
-        _, C, H, W = content_f.size()
-        feat = torch.FloatTensor(1, C, H, W).zero_().to(device)
-        base_feat = adaptive_instance_normalization(content_f, style_f)
-        for i, w in enumerate(interpolation_weights):
-            feat = feat + w * base_feat[i:i + 1]
-        content_f = content_f[0:1]
-    else:
-        feat = adaptive_instance_normalization(content_f, style_f)
-    feat = feat * alpha + content_f * (1 - alpha)
-    return decoder(feat)
-
-
-import torch.nn as nn
-
-
-decoder = nn.Sequential(
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 256, (3, 3)),
-    nn.ReLU(),
-    nn.Upsample(scale_factor=2, mode='nearest'),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 128, (3, 3)),
-    nn.ReLU(),
-    nn.Upsample(scale_factor=2, mode='nearest'),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(128, 128, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(128, 64, (3, 3)),
-    nn.ReLU(),
-    nn.Upsample(scale_factor=2, mode='nearest'),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(64, 64, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(64, 3, (3, 3)),
-)
-
-vgg = nn.Sequential(
-    nn.Conv2d(3, 3, (1, 1)),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(3, 64, (3, 3)),
-    nn.ReLU(),  # relu1-1
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(64, 64, (3, 3)),
-    nn.ReLU(),  # relu1-2
-    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(64, 128, (3, 3)),
-    nn.ReLU(),  # relu2-1
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(128, 128, (3, 3)),
-    nn.ReLU(),  # relu2-2
-    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(128, 256, (3, 3)),
-    nn.ReLU(),  # relu3-1
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),  # relu3-2
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),  # relu3-3
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),  # relu3-4
-    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 512, (3, 3)),
-    nn.ReLU(),  # relu4-1, this is the last layer used
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu4-2
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu4-3
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu4-4
-    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu5-1
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu5-2
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu5-3
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU()  # relu5-4
-)
-
-
-def weighted_mse_loss(input,target,weights = None):
-  assert input.size() == target.size()
-  size = input.size()
-  if weights == None:
-    weights = torch.ones(size = size[0])
-
-  if len(size) == 3: # gram matrix is B,C,C
-    se = ((input.view(size[0],-1) - target.view(size[0],-1))**2)
-    return (se.mean(dim = 1)*weights).mean()
-
-def gram_matrix_(input, normalize =True):
-    a, b, c, d = input.size()  # a=batch size(=1)
-    # b=number of feature maps
-    # (c,d)=dimensions of a f. map (N=c*d)
-
-    v = input.view(a,b, c * d)  # resise F_XL into \hat F_XL
-    vt = v.permute(0,2,1)
-    # C,HxW , HxW, C
-    G = v.matmul(vt)  # compute the gram product
-
-    # we 'normalize' the values of the gram matrix
-    # by dividing by the number of element in each feature maps.
-    if normalize:
-      return G.div(b * c * d)
-    else:
-      return G
-    
-
-def gram_matrix(x, normalize=True):
-    '''
-    Generate gram matrices of the representations of content and style images.
-    '''
-    (b, ch, h, w) = x.size()
-    features = x.view(b, ch, w * h)
-    features_t = features.transpose(1, 2)
-    gram = features.bmm(features_t)
-    if normalize:
-        gram /= ch * h * w
-    return gram
-
-class Net(nn.Module):
-    def __init__(self, encoder, decoder):
-        super(Net, self).__init__()
-        enc_layers = list(encoder.children())
-
-        ### Suggested feature vectors
-        self._enc_1 = nn.Sequential(*enc_layers[:7])  # input -> relu1_2
-        self._enc_2 = nn.Sequential(*enc_layers[7:14])  # relu1_2 -> relu2_2
-        self._enc_3 = nn.Sequential(*enc_layers[14:24])  # relu2_2 -> relu3_3
-        self._enc_4 = nn.Sequential(*enc_layers[24:40])  # relu3_3 -> relu4_4
-
-        self.decoder = decoder
-        self.mse_loss = nn.MSELoss()
-
-        self.enc_1 = nn.Sequential(*enc_layers[:4])  # input -> relu1_1
-        self.enc_2 = nn.Sequential(*enc_layers[4:11])  # relu1_1 -> relu2_1
-        self.enc_3 = nn.Sequential(*enc_layers[11:18])  # relu2_1 -> relu3_1
-        self.enc_4 = nn.Sequential(*enc_layers[18:31])  # relu3_1 -> relu4_1
-
-        # fix the encoder
-        for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4']:
-            for param in getattr(self, name).parameters():
-                param.requires_grad = False
-
-        for name in ['_enc_1', '_enc_2', '_enc_3', '_enc_4']:
-            for param in getattr(self, name).parameters():
-                param.requires_grad = False
-
-    # extract different relus for style
-    def encode_with_intermediate(self, input, original = True ):
-        results = [input]
-
-        if(original):
-            for i in range(4):
-                func = getattr(self, 'enc_{:d}'.format(i + 1))
-                results.append(func(results[-1]))
-        else:
-            for i in range(4):
-                func = getattr(self, '_enc_{:d}'.format(i + 1))
-                results.append(func(results[-1]))
-
-        return results[1:]
-
-    # extract content as intended for 
-    def encode(self, input,original = True):
-        if(original):
-            for i in range(4):
-                input = getattr(self, 'enc_{:d}'.format(i + 1))(input)
-        else:
-            for i in range(4):
-                input = getattr(self, '_enc_{:d}'.format(i + 1))(input)
-        return input
-
-    def calc_content_loss(self, input, target):
-        assert (input.size() == target.size())
-        assert (target.requires_grad is False)
-        return self.mse_loss(input, target)
-
-    def calc_style_loss(self, input, target):
-        assert (input.size() == target.size())
-        assert (target.requires_grad is False)
-        input_mean, input_std = calc_mean_std(input)
-        target_mean, target_std = calc_mean_std(target)
-        return self.mse_loss(input_mean, target_mean) + \
-               self.mse_loss(input_std, target_std)
-        #return self.calc_ast_style_loss_normalized(input,target)
-        #return self.calc_ast_style_loss(input,target)
-
-    def calc_ast_style_loss_normalized(self, input, target):
-        G1 = gram_matrix(input, False)
-        G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
-
-        size = input.size()
-        assert(len(size) == 4)
-
-        g1_norm = torch.linalg.norm(G1,dim = (1,2))
-        g2_norm = torch.linalg.norm(G2,dim = (1,2))
-
-        size = G1.size()
-        Nl = size[1] * size[2] # Or C x C = C^2
-        normalize_term =  (torch.square(g1_norm) + torch.square(g2_norm))/Nl  #
-
-        weights = (1/normalize_term)
-        #weights = weights.view(size[0],1,1)
-        return weighted_mse_loss(G1,G2,weights)
-      
-    def calc_ast_style_loss(self, input, target):
-        """
-        Interestingly, authors assumes that calculation of G1 and G2 is not normalized
-        This can create an issue?. For this reason, let us test for
-          1- Normalized
-          2- Unnormalized
-          3- Newly defined Normalization term
-        """
-        G1 = gram_matrix(input)
-        G2 = gram_matrix(target).detach() # we dont need the gradient of the target
-
-        size = input.size()
-        assert(len(size) == 4)
-
-        return self.mse_loss(G1,G2)
-
-    def forward(self, content, style, alpha=1.0):
-        assert 0 <= alpha <= 1
-        style_feats = self.encode_with_intermediate(style)
-        content_feat = self.encode(content)
-        t = adaptive_instance_normalization(content_feat, style_feats[-1]) # content and style must be same dimension
-        t = alpha * t + (1 - alpha) * content_feat
-
-        g_t = self.decoder(t)
-        g_t_feats = self.encode_with_intermediate(g_t)
-
-        loss_c = self.calc_content_loss(g_t_feats[-1], t)
-        loss_s = self.calc_style_loss(g_t_feats[0], style_feats[0])
-        for i in range(1, 4):
-            loss_s += self.calc_style_loss(g_t_feats[i], style_feats[i])
-        return loss_c, loss_s
-
-
-def InfiniteSampler(n):
-    # i = 0
-    i = n - 1
-    order = np.random.permutation(n)
-    while True:
-        yield order[i]
-        i += 1
-        if i >= n:
-            np.random.seed()
-            order = np.random.permutation(n)
-            i = 0
-
-
-class InfiniteSamplerWrapper(data.sampler.Sampler):
-    def __init__(self, data_source):
-        self.num_samples = len(data_source)
-
-    def __iter__(self):
-        return iter(InfiniteSampler(self.num_samples))
-
-    def __len__(self):
+import argparse
+from pathlib import Path
+import numpy as np
+from torch.utils import data
+import torch
+import torch.nn as nn
+from PIL import Image
+from torchvision import transforms
+from torchvision.utils import save_image
+
+from .functions import adaptive_instance_normalization, coral
+
+
+def test_transform(size, crop):
+    transform_list = []
+    if size != 0:
+        transform_list.append(transforms.Resize(size))
+    if crop:
+        transform_list.append(transforms.CenterCrop(size))
+    transform_list.append(transforms.ToTensor())
+    transform = transforms.Compose(transform_list)
+    return transform
+
+
+def style_transfer(vgg, decoder, content, style, alpha=1.0,
+                   interpolation_weights=None):
+    assert (0.0 <= alpha <= 1.0)
+    content_f = vgg(content)
+    style_f = vgg(style)
+    if interpolation_weights:
+        _, C, H, W = content_f.size()
+        feat = torch.FloatTensor(1, C, H, W).zero_().to(device)
+        base_feat = adaptive_instance_normalization(content_f, style_f)
+        for i, w in enumerate(interpolation_weights):
+            feat = feat + w * base_feat[i:i + 1]
+        content_f = content_f[0:1]
+    else:
+        feat = adaptive_instance_normalization(content_f, style_f)
+    feat = feat * alpha + content_f * (1 - alpha)
+    return decoder(feat)
+
+
+import torch.nn as nn
+
+
+decoder = nn.Sequential(
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 256, (3, 3)),
+    nn.ReLU(),
+    nn.Upsample(scale_factor=2, mode='nearest'),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 128, (3, 3)),
+    nn.ReLU(),
+    nn.Upsample(scale_factor=2, mode='nearest'),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(128, 128, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(128, 64, (3, 3)),
+    nn.ReLU(),
+    nn.Upsample(scale_factor=2, mode='nearest'),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(64, 64, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(64, 3, (3, 3)),
+)
+
+vgg = nn.Sequential(
+    nn.Conv2d(3, 3, (1, 1)),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(3, 64, (3, 3)),
+    nn.ReLU(),  # relu1-1
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(64, 64, (3, 3)),
+    nn.ReLU(),  # relu1-2
+    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(64, 128, (3, 3)),
+    nn.ReLU(),  # relu2-1
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(128, 128, (3, 3)),
+    nn.ReLU(),  # relu2-2
+    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(128, 256, (3, 3)),
+    nn.ReLU(),  # relu3-1
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),  # relu3-2
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),  # relu3-3
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),  # relu3-4
+    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 512, (3, 3)),
+    nn.ReLU(),  # relu4-1, this is the last layer used
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu4-2
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu4-3
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu4-4
+    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu5-1
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu5-2
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu5-3
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU()  # relu5-4
+)
+
+
+def weighted_mse_loss(input,target,weights = None):
+  assert input.size() == target.size()
+  size = input.size()
+  if weights == None:
+    weights = torch.ones(size = size[0])
+
+  if len(size) == 3: # gram matrix is B,C,C
+    se = ((input.view(size[0],-1) - target.view(size[0],-1))**2)
+    return (se.mean(dim = 1)*weights).mean()
+
+def gram_matrix_(input, normalize =True):
+    a, b, c, d = input.size()  # a=batch size(=1)
+    # b=number of feature maps
+    # (c,d)=dimensions of a f. map (N=c*d)
+
+    v = input.view(a,b, c * d)  # resise F_XL into \hat F_XL
+    vt = v.permute(0,2,1)
+    # C,HxW , HxW, C
+    G = v.matmul(vt)  # compute the gram product
+
+    # we 'normalize' the values of the gram matrix
+    # by dividing by the number of element in each feature maps.
+    if normalize:
+      return G.div(b * c * d)
+    else:
+      return G
+    
+
+def gram_matrix(x, normalize=True):
+    '''
+    Generate gram matrices of the representations of content and style images.
+    '''
+    (b, ch, h, w) = x.size()
+    features = x.view(b, ch, w * h)
+    features_t = features.transpose(1, 2)
+    gram = features.bmm(features_t)
+    if normalize:
+        gram /= ch * h * w
+    return gram
+
+class Net(nn.Module):
+    def __init__(self, encoder, decoder):
+        super(Net, self).__init__()
+        enc_layers = list(encoder.children())
+
+        ### Suggested feature vectors
+        self._enc_1 = nn.Sequential(*enc_layers[:7])  # input -> relu1_2
+        self._enc_2 = nn.Sequential(*enc_layers[7:14])  # relu1_2 -> relu2_2
+        self._enc_3 = nn.Sequential(*enc_layers[14:24])  # relu2_2 -> relu3_3
+        self._enc_4 = nn.Sequential(*enc_layers[24:40])  # relu3_3 -> relu4_4
+
+        self.decoder = decoder
+        self.mse_loss = nn.MSELoss()
+
+        self.enc_1 = nn.Sequential(*enc_layers[:4])  # input -> relu1_1
+        self.enc_2 = nn.Sequential(*enc_layers[4:11])  # relu1_1 -> relu2_1
+        self.enc_3 = nn.Sequential(*enc_layers[11:18])  # relu2_1 -> relu3_1
+        self.enc_4 = nn.Sequential(*enc_layers[18:31])  # relu3_1 -> relu4_1
+
+        # fix the encoder
+        for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4']:
+            for param in getattr(self, name).parameters():
+                param.requires_grad = False
+
+        for name in ['_enc_1', '_enc_2', '_enc_3', '_enc_4']:
+            for param in getattr(self, name).parameters():
+                param.requires_grad = False
+
+    # extract different relus for style
+    def encode_with_intermediate(self, input, original = True ):
+        results = [input]
+
+        if(original):
+            for i in range(4):
+                func = getattr(self, 'enc_{:d}'.format(i + 1))
+                results.append(func(results[-1]))
+        else:
+            for i in range(4):
+                func = getattr(self, '_enc_{:d}'.format(i + 1))
+                results.append(func(results[-1]))
+
+        return results[1:]
+
+    # extract content as intended for 
+    def encode(self, input,original = True):
+        if(original):
+            for i in range(4):
+                input = getattr(self, 'enc_{:d}'.format(i + 1))(input)
+        else:
+            for i in range(4):
+                input = getattr(self, '_enc_{:d}'.format(i + 1))(input)
+        return input
+
+    def calc_content_loss(self, input, target):
+        assert (input.size() == target.size())
+        assert (target.requires_grad is False)
+        return self.mse_loss(input, target)
+
+    def calc_style_loss(self, input, target):
+        assert (input.size() == target.size())
+        assert (target.requires_grad is False)
+        input_mean, input_std = calc_mean_std(input)
+        target_mean, target_std = calc_mean_std(target)
+        return self.mse_loss(input_mean, target_mean) + \
+               self.mse_loss(input_std, target_std)
+        #return self.calc_ast_style_loss_normalized(input,target)
+        #return self.calc_ast_style_loss(input,target)
+
+    def calc_ast_style_loss_normalized(self, input, target):
+        G1 = gram_matrix(input, False)
+        G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
+
+        size = input.size()
+        assert(len(size) == 4)
+
+        g1_norm = torch.linalg.norm(G1,dim = (1,2))
+        g2_norm = torch.linalg.norm(G2,dim = (1,2))
+
+        size = G1.size()
+        Nl = size[1] * size[2] # Or C x C = C^2
+        normalize_term =  (torch.square(g1_norm) + torch.square(g2_norm))/Nl  #
+
+        weights = (1/normalize_term)
+        #weights = weights.view(size[0],1,1)
+        return weighted_mse_loss(G1,G2,weights)
+      
+    def calc_ast_style_loss(self, input, target):
+        """
+        Interestingly, authors assumes that calculation of G1 and G2 is not normalized
+        This can create an issue?. For this reason, let us test for
+          1- Normalized
+          2- Unnormalized
+          3- Newly defined Normalization term
+        """
+        G1 = gram_matrix(input)
+        G2 = gram_matrix(target).detach() # we dont need the gradient of the target
+
+        size = input.size()
+        assert(len(size) == 4)
+
+        return self.mse_loss(G1,G2)
+
+    def forward(self, content, style, alpha=1.0):
+        assert 0 <= alpha <= 1
+        style_feats = self.encode_with_intermediate(style)
+        content_feat = self.encode(content)
+        t = adaptive_instance_normalization(content_feat, style_feats[-1]) # content and style must be same dimension
+        t = alpha * t + (1 - alpha) * content_feat
+
+        g_t = self.decoder(t)
+        g_t_feats = self.encode_with_intermediate(g_t)
+
+        loss_c = self.calc_content_loss(g_t_feats[-1], t)
+        loss_s = self.calc_style_loss(g_t_feats[0], style_feats[0])
+        for i in range(1, 4):
+            loss_s += self.calc_style_loss(g_t_feats[i], style_feats[i])
+        return loss_c, loss_s
+
+
+def InfiniteSampler(n):
+    # i = 0
+    i = n - 1
+    order = np.random.permutation(n)
+    while True:
+        yield order[i]
+        i += 1
+        if i >= n:
+            np.random.seed()
+            order = np.random.permutation(n)
+            i = 0
+
+
+class InfiniteSamplerWrapper(data.sampler.Sampler):
+    def __init__(self, data_source):
+        self.num_samples = len(data_source)
+
+    def __iter__(self):
+        return iter(InfiniteSampler(self.num_samples))
+
+    def __len__(self):
         return 2 ** 31
\ No newline at end of file
diff --git a/Project_Kargi/libs/models_linear_transfer.py b/Project_Kargi/libs/models_linear_transfer.py
index bca7c4a..1ef6190 100644
--- a/Project_Kargi/libs/models_linear_transfer.py
+++ b/Project_Kargi/libs/models_linear_transfer.py
@@ -1,751 +1,751 @@
-import torch
-import torch.nn as nn
-
-class encoder3(nn.Module):
-    def __init__(self):
-        super(encoder3,self).__init__()
-        # vgg
-        # 224 x 224
-        self.conv1 = nn.Conv2d(3,3,1,1,0)
-        self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1))
-        # 226 x 226
-
-        self.conv2 = nn.Conv2d(3,64,3,1,0)
-        self.relu2 = nn.ReLU(inplace=True)
-        # 224 x 224
-
-        self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv3 = nn.Conv2d(64,64,3,1,0)
-        self.relu3 = nn.ReLU(inplace=True)
-        # 224 x 224
-
-        self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True)
-        # 112 x 112
-
-        self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv4 = nn.Conv2d(64,128,3,1,0)
-        self.relu4 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv5 = nn.Conv2d(128,128,3,1,0)
-        self.relu5 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.maxPool2 = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True)
-        # 56 x 56
-
-        self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv6 = nn.Conv2d(128,256,3,1,0)
-        self.relu6 = nn.ReLU(inplace=True)
-        # 56 x 56
-    def forward(self,x):
-        out = self.conv1(x)
-        out = self.reflecPad1(out)
-        out = self.conv2(out)
-        out = self.relu2(out)
-        out = self.reflecPad3(out)
-        out = self.conv3(out)
-        pool1 = self.relu3(out)
-        out,pool_idx = self.maxPool(pool1)
-        out = self.reflecPad4(out)
-        out = self.conv4(out)
-        out = self.relu4(out)
-        out = self.reflecPad5(out)
-        out = self.conv5(out)
-        pool2 = self.relu5(out)
-        out,pool_idx2 = self.maxPool2(pool2)
-        out = self.reflecPad6(out)
-        out = self.conv6(out)
-        out = self.relu6(out)
-        return out
-
-class decoder3(nn.Module):
-    def __init__(self):
-        super(decoder3,self).__init__()
-        # decoder
-        self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv7 = nn.Conv2d(256,128,3,1,0)
-        self.relu7 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
-        # 112 x 112
-
-        self.reflecPad8 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv8 = nn.Conv2d(128,128,3,1,0)
-        self.relu8 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.reflecPad9 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv9 = nn.Conv2d(128,64,3,1,0)
-        self.relu9 = nn.ReLU(inplace=True)
-
-        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
-        # 224 x 224
-
-        self.reflecPad10 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv10 = nn.Conv2d(64,64,3,1,0)
-        self.relu10 = nn.ReLU(inplace=True)
-
-        self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv11 = nn.Conv2d(64,3,3,1,0)
-
-    def forward(self,x):
-        output = {}
-        out = self.reflecPad7(x)
-        out = self.conv7(out)
-        out = self.relu7(out)
-        out = self.unpool(out)
-        out = self.reflecPad8(out)
-        out = self.conv8(out)
-        out = self.relu8(out)
-        out = self.reflecPad9(out)
-        out = self.conv9(out)
-        out_relu9 = self.relu9(out)
-        out = self.unpool2(out_relu9)
-        out = self.reflecPad10(out)
-        out = self.conv10(out)
-        out = self.relu10(out)
-        out = self.reflecPad11(out)
-        out = self.conv11(out)
-        return out
-
-class encoder4(nn.Module):
-    def __init__(self):
-        super(encoder4,self).__init__()
-        # vgg
-        # 224 x 224
-        self.conv1 = nn.Conv2d(3,3,1,1,0)
-        self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1))
-        # 226 x 226
-
-        self.conv2 = nn.Conv2d(3,64,3,1,0)
-        self.relu2 = nn.ReLU(inplace=True)
-        # 224 x 224
-
-        self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv3 = nn.Conv2d(64,64,3,1,0)
-        self.relu3 = nn.ReLU(inplace=True)
-        # 224 x 224
-
-        self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2)
-        # 112 x 112
-
-        self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv4 = nn.Conv2d(64,128,3,1,0)
-        self.relu4 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv5 = nn.Conv2d(128,128,3,1,0)
-        self.relu5 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.maxPool2 = nn.MaxPool2d(kernel_size=2,stride=2)
-        # 56 x 56
-
-        self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv6 = nn.Conv2d(128,256,3,1,0)
-        self.relu6 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv7 = nn.Conv2d(256,256,3,1,0)
-        self.relu7 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad8 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv8 = nn.Conv2d(256,256,3,1,0)
-        self.relu8 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad9 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv9 = nn.Conv2d(256,256,3,1,0)
-        self.relu9 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.maxPool3 = nn.MaxPool2d(kernel_size=2,stride=2)
-        # 28 x 28
-
-        self.reflecPad10 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv10 = nn.Conv2d(256,512,3,1,0)
-        self.relu10 = nn.ReLU(inplace=True)
-        # 28 x 28
-    def forward(self,x,sF=None,matrix11=None,matrix21=None,matrix31=None):
-        output = {}
-        out = self.conv1(x)
-        out = self.reflecPad1(out)
-        out = self.conv2(out)
-        output['r11'] = self.relu2(out)
-        out = self.reflecPad7(output['r11'])
-
-        out = self.conv3(out)
-        output['r12'] = self.relu3(out)
-
-        output['p1'] = self.maxPool(output['r12'])
-        out = self.reflecPad4(output['p1'])
-        out = self.conv4(out)
-        output['r21'] = self.relu4(out)
-        out = self.reflecPad7(output['r21'])
-
-        out = self.conv5(out)
-        output['r22'] = self.relu5(out)
-
-        output['p2'] = self.maxPool2(output['r22'])
-        out = self.reflecPad6(output['p2'])
-        out = self.conv6(out)
-        output['r31'] = self.relu6(out)
-        if(matrix31 is not None):
-            feature3,transmatrix3 = matrix31(output['r31'],sF['r31'])
-            out = self.reflecPad7(feature3)
-        else:
-            out = self.reflecPad7(output['r31'])
-        out = self.conv7(out)
-        output['r32'] = self.relu7(out)
-
-        out = self.reflecPad8(output['r32'])
-        out = self.conv8(out)
-        output['r33'] = self.relu8(out)
-
-        out = self.reflecPad9(output['r33'])
-        out = self.conv9(out)
-        output['r34'] = self.relu9(out)
-
-        output['p3'] = self.maxPool3(output['r34'])
-        out = self.reflecPad10(output['p3'])
-        out = self.conv10(out)
-        output['r41'] = self.relu10(out)
-
-        return output
-
-class decoder4(nn.Module):
-    def __init__(self):
-        super(decoder4,self).__init__()
-        # decoder
-        self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv11 = nn.Conv2d(512,256,3,1,0)
-        self.relu11 = nn.ReLU(inplace=True)
-        # 28 x 28
-
-        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
-        # 56 x 56
-
-        self.reflecPad12 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv12 = nn.Conv2d(256,256,3,1,0)
-        self.relu12 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad13 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv13 = nn.Conv2d(256,256,3,1,0)
-        self.relu13 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad14 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv14 = nn.Conv2d(256,256,3,1,0)
-        self.relu14 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad15 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv15 = nn.Conv2d(256,128,3,1,0)
-        self.relu15 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
-        # 112 x 112
-
-        self.reflecPad16 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv16 = nn.Conv2d(128,128,3,1,0)
-        self.relu16 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.reflecPad17 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv17 = nn.Conv2d(128,64,3,1,0)
-        self.relu17 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
-        # 224 x 224
-
-        self.reflecPad18 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv18 = nn.Conv2d(64,64,3,1,0)
-        self.relu18 = nn.ReLU(inplace=True)
-        # 224 x 224
-
-        self.reflecPad19 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv19 = nn.Conv2d(64,3,3,1,0)
-
-    def forward(self,x):
-        # decoder
-        out = self.reflecPad11(x)
-        out = self.conv11(out)
-        out = self.relu11(out)
-        out = self.unpool(out)
-        out = self.reflecPad12(out)
-        out = self.conv12(out)
-
-        out = self.relu12(out)
-        out = self.reflecPad13(out)
-        out = self.conv13(out)
-        out = self.relu13(out)
-        out = self.reflecPad14(out)
-        out = self.conv14(out)
-        out = self.relu14(out)
-        out = self.reflecPad15(out)
-        out = self.conv15(out)
-        out = self.relu15(out)
-        out = self.unpool2(out)
-        out = self.reflecPad16(out)
-        out = self.conv16(out)
-        out = self.relu16(out)
-        out = self.reflecPad17(out)
-        out = self.conv17(out)
-        out = self.relu17(out)
-        out = self.unpool3(out)
-        out = self.reflecPad18(out)
-        out = self.conv18(out)
-        out = self.relu18(out)
-        out = self.reflecPad19(out)
-        out = self.conv19(out)
-        return out
-
-class decoder4(nn.Module):
-    def __init__(self):
-        super(decoder4,self).__init__()
-        # decoder
-        self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv11 = nn.Conv2d(512,256,3,1,0)
-        self.relu11 = nn.ReLU(inplace=True)
-        # 28 x 28
-
-        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
-        # 56 x 56
-
-        self.reflecPad12 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv12 = nn.Conv2d(256,256,3,1,0)
-        self.relu12 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad13 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv13 = nn.Conv2d(256,256,3,1,0)
-        self.relu13 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad14 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv14 = nn.Conv2d(256,256,3,1,0)
-        self.relu14 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad15 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv15 = nn.Conv2d(256,128,3,1,0)
-        self.relu15 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
-        # 112 x 112
-
-        self.reflecPad16 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv16 = nn.Conv2d(128,128,3,1,0)
-        self.relu16 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.reflecPad17 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv17 = nn.Conv2d(128,64,3,1,0)
-        self.relu17 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
-        # 224 x 224
-
-        self.reflecPad18 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv18 = nn.Conv2d(64,64,3,1,0)
-        self.relu18 = nn.ReLU(inplace=True)
-        # 224 x 224
-
-        self.reflecPad19 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv19 = nn.Conv2d(64,3,3,1,0)
-
-    def forward(self,x):
-        # decoder
-        out = self.reflecPad11(x)
-        out = self.conv11(out)
-        out = self.relu11(out)
-        out = self.unpool(out)
-        out = self.reflecPad12(out)
-        out = self.conv12(out)
-
-        out = self.relu12(out)
-        out = self.reflecPad13(out)
-        out = self.conv13(out)
-        out = self.relu13(out)
-        out = self.reflecPad14(out)
-        out = self.conv14(out)
-        out = self.relu14(out)
-        out = self.reflecPad15(out)
-        out = self.conv15(out)
-        out = self.relu15(out)
-        out = self.unpool2(out)
-        out = self.reflecPad16(out)
-        out = self.conv16(out)
-        out = self.relu16(out)
-        out = self.reflecPad17(out)
-        out = self.conv17(out)
-        out = self.relu17(out)
-        out = self.unpool3(out)
-        out = self.reflecPad18(out)
-        out = self.conv18(out)
-        out = self.relu18(out)
-        out = self.reflecPad19(out)
-        out = self.conv19(out)
-        return out
-
-class encoder5(nn.Module):
-    def __init__(self):
-        super(encoder5,self).__init__()
-        # vgg
-        # 224 x 224
-        self.conv1 = nn.Conv2d(3,3,1,1,0)
-        self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1))
-        # 226 x 226
-
-        self.conv2 = nn.Conv2d(3,64,3,1,0)
-        self.relu2 = nn.ReLU(inplace=True)
-        # 224 x 224
-
-        self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv3 = nn.Conv2d(64,64,3,1,0)
-        self.relu3 = nn.ReLU(inplace=True)
-        # 224 x 224
-
-        self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2)
-        # 112 x 112
-
-        self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv4 = nn.Conv2d(64,128,3,1,0)
-        self.relu4 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv5 = nn.Conv2d(128,128,3,1,0)
-        self.relu5 = nn.ReLU(inplace=True)
-        # 112 x 112
-
-        self.maxPool2 = nn.MaxPool2d(kernel_size=2,stride=2)
-        # 56 x 56
-
-        self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv6 = nn.Conv2d(128,256,3,1,0)
-        self.relu6 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv7 = nn.Conv2d(256,256,3,1,0)
-        self.relu7 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad8 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv8 = nn.Conv2d(256,256,3,1,0)
-        self.relu8 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad9 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv9 = nn.Conv2d(256,256,3,1,0)
-        self.relu9 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.maxPool3 = nn.MaxPool2d(kernel_size=2,stride=2)
-        # 28 x 28
-
-        self.reflecPad10 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv10 = nn.Conv2d(256,512,3,1,0)
-        self.relu10 = nn.ReLU(inplace=True)
-
-        self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv11 = nn.Conv2d(512,512,3,1,0)
-        self.relu11 = nn.ReLU(inplace=True)
-
-        self.reflecPad12 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv12 = nn.Conv2d(512,512,3,1,0)
-        self.relu12 = nn.ReLU(inplace=True)
-
-        self.reflecPad13 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv13 = nn.Conv2d(512,512,3,1,0)
-        self.relu13 = nn.ReLU(inplace=True)
-
-        self.maxPool4 = nn.MaxPool2d(kernel_size=2,stride=2)
-        self.reflecPad14 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv14 = nn.Conv2d(512,512,3,1,0)
-        self.relu14 = nn.ReLU(inplace=True)
-
-    def forward(self,x,sF=None,contentV256=None,styleV256=None,matrix11=None,matrix21=None,matrix31=None):
-        output = {}
-        out = self.conv1(x)
-        out = self.reflecPad1(out)
-        out = self.conv2(out)
-        output['r11'] = self.relu2(out)
-        out = self.reflecPad7(output['r11'])
-
-        #out = self.reflecPad3(output['r11'])
-        out = self.conv3(out)
-        output['r12'] = self.relu3(out)
-
-        output['p1'] = self.maxPool(output['r12'])
-        out = self.reflecPad4(output['p1'])
-        out = self.conv4(out)
-        output['r21'] = self.relu4(out)
-        out = self.reflecPad7(output['r21'])
-
-        #out = self.reflecPad5(output['r21'])
-        out = self.conv5(out)
-        output['r22'] = self.relu5(out)
-
-        output['p2'] = self.maxPool2(output['r22'])
-        out = self.reflecPad6(output['p2'])
-        out = self.conv6(out)
-        output['r31'] = self.relu6(out)
-        if(styleV256 is not None):
-            feature = matrix31(output['r31'],sF['r31'],contentV256,styleV256)
-            out = self.reflecPad7(feature)
-        else:
-            out = self.reflecPad7(output['r31'])
-        out = self.conv7(out)
-        output['r32'] = self.relu7(out)
-
-        out = self.reflecPad8(output['r32'])
-        out = self.conv8(out)
-        output['r33'] = self.relu8(out)
-
-        out = self.reflecPad9(output['r33'])
-        out = self.conv9(out)
-        output['r34'] = self.relu9(out)
-
-        output['p3'] = self.maxPool3(output['r34'])
-        out = self.reflecPad10(output['p3'])
-        out = self.conv10(out)
-        output['r41'] = self.relu10(out)
-
-        out = self.reflecPad11(output['r41'])
-        out = self.conv11(out)
-        output['r42'] = self.relu11(out)
-
-        out = self.reflecPad12(output['r42'])
-        out = self.conv12(out)
-        output['r43'] = self.relu12(out)
-
-        out = self.reflecPad13(output['r43'])
-        out = self.conv13(out)
-        output['r44'] = self.relu13(out)
-
-        output['p4'] = self.maxPool4(output['r44'])
-
-        out = self.reflecPad14(output['p4'])
-        out = self.conv14(out)
-        output['r51'] = self.relu14(out)
-        return output
-
-class decoder5(nn.Module):
-    def __init__(self):
-        super(decoder5,self).__init__()
-
-        # decoder
-        self.reflecPad15 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv15 = nn.Conv2d(512,512,3,1,0)
-        self.relu15 = nn.ReLU(inplace=True)
-
-        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
-        # 28 x 28
-
-        self.reflecPad16 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv16 = nn.Conv2d(512,512,3,1,0)
-        self.relu16 = nn.ReLU(inplace=True)
-        # 28 x 28
-
-        self.reflecPad17 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv17 = nn.Conv2d(512,512,3,1,0)
-        self.relu17 = nn.ReLU(inplace=True)
-        # 28 x 28
-
-        self.reflecPad18 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv18 = nn.Conv2d(512,512,3,1,0)
-        self.relu18 = nn.ReLU(inplace=True)
-        # 28 x 28
-
-        self.reflecPad19 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv19 = nn.Conv2d(512,256,3,1,0)
-        self.relu19 = nn.ReLU(inplace=True)
-        # 28 x 28
-
-        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
-        # 56 x 56
-
-        self.reflecPad20 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv20 = nn.Conv2d(256,256,3,1,0)
-        self.relu20 = nn.ReLU(inplace=True)
-        # 56 x 56
-
-        self.reflecPad21 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv21 = nn.Conv2d(256,256,3,1,0)
-        self.relu21 = nn.ReLU(inplace=True)
-
-        self.reflecPad22 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv22 = nn.Conv2d(256,256,3,1,0)
-        self.relu22 = nn.ReLU(inplace=True)
-
-        self.reflecPad23 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv23 = nn.Conv2d(256,128,3,1,0)
-        self.relu23 = nn.ReLU(inplace=True)
-
-        self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
-        # 112 X 112
-
-        self.reflecPad24 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv24 = nn.Conv2d(128,128,3,1,0)
-        self.relu24 = nn.ReLU(inplace=True)
-
-        self.reflecPad25 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv25 = nn.Conv2d(128,64,3,1,0)
-        self.relu25 = nn.ReLU(inplace=True)
-
-        self.unpool4 = nn.UpsamplingNearest2d(scale_factor=2)
-
-        self.reflecPad26 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv26 = nn.Conv2d(64,64,3,1,0)
-        self.relu26 = nn.ReLU(inplace=True)
-
-        self.reflecPad27 = nn.ReflectionPad2d((1,1,1,1))
-        self.conv27 = nn.Conv2d(64,3,3,1,0)
-
-    def forward(self,x):
-        # decoder
-        out = self.reflecPad15(x)
-        out = self.conv15(out)
-        out = self.relu15(out)
-        out = self.unpool(out)
-        out = self.reflecPad16(out)
-        out = self.conv16(out)
-        out = self.relu16(out)
-        out = self.reflecPad17(out)
-        out = self.conv17(out)
-        out = self.relu17(out)
-        out = self.reflecPad18(out)
-        out = self.conv18(out)
-        out = self.relu18(out)
-        out = self.reflecPad19(out)
-        out = self.conv19(out)
-        out = self.relu19(out)
-        out = self.unpool2(out)
-        out = self.reflecPad20(out)
-        out = self.conv20(out)
-        out = self.relu20(out)
-        out = self.reflecPad21(out)
-        out = self.conv21(out)
-        out = self.relu21(out)
-        out = self.reflecPad22(out)
-        out = self.conv22(out)
-        out = self.relu22(out)
-        out = self.reflecPad23(out)
-        out = self.conv23(out)
-        out = self.relu23(out)
-        out = self.unpool3(out)
-        out = self.reflecPad24(out)
-        out = self.conv24(out)
-        out = self.relu24(out)
-        out = self.reflecPad25(out)
-        out = self.conv25(out)
-        out = self.relu25(out)
-        out = self.unpool4(out)
-        out = self.reflecPad26(out)
-        out = self.conv26(out)
-        out = self.relu26(out)
-        out = self.reflecPad27(out)
-        out = self.conv27(out)
-        return out
-
-import torch
-import torch.nn as nn
-
-class CNN(nn.Module):
-    def __init__(self,layer,matrixSize=32):
-        super(CNN,self).__init__()
-        if(layer == 'r31'):
-            # 256x64x64
-            self.convs = nn.Sequential(nn.Conv2d(256,128,3,1,1),
-                                       nn.ReLU(inplace=True),
-                                       nn.Conv2d(128,64,3,1,1),
-                                       nn.ReLU(inplace=True),
-                                       nn.Conv2d(64,matrixSize,3,1,1))
-        elif(layer == 'r41'):
-            # 512x32x32
-            self.convs = nn.Sequential(nn.Conv2d(512,256,3,1,1),
-                                       nn.ReLU(inplace=True),
-                                       nn.Conv2d(256,128,3,1,1),
-                                       nn.ReLU(inplace=True),
-                                       nn.Conv2d(128,matrixSize,3,1,1))
-
-        # 32x8x8
-        self.fc = nn.Linear(matrixSize*matrixSize,matrixSize*matrixSize)
-        #self.fc = nn.Linear(32*64,256*256)
-
-    def forward(self,x):
-        out = self.convs(x)
-        # 32x8x8
-        b,c,h,w = out.size()
-        out = out.view(b,c,-1)
-        # 32x64
-        out = torch.bmm(out,out.transpose(1,2)).div(h*w)
-        # 32x32
-        out = out.view(out.size(0),-1)
-        return self.fc(out)
-
-class MulLayer(nn.Module):
-    def __init__(self,layer,matrixSize=32):
-        super(MulLayer,self).__init__()
-        self.snet = CNN(layer,matrixSize)
-        self.cnet = CNN(layer,matrixSize)
-        self.matrixSize = matrixSize
-
-        if(layer == 'r41'):
-            self.compress = nn.Conv2d(512,matrixSize,1,1,0)
-            self.unzip = nn.Conv2d(matrixSize,512,1,1,0)
-        elif(layer == 'r31'):
-            self.compress = nn.Conv2d(256,matrixSize,1,1,0)
-            self.unzip = nn.Conv2d(matrixSize,256,1,1,0)
-        self.transmatrix = None
-
-    def forward(self,cF,sF,trans=True):
-        cFBK = cF.clone()
-        cb,cc,ch,cw = cF.size()
-        cFF = cF.view(cb,cc,-1)
-        cMean = torch.mean(cFF,dim=2,keepdim=True)
-        cMean = cMean.unsqueeze(3)
-        cMean = cMean.expand_as(cF)
-        cF = cF - cMean
-
-        sb,sc,sh,sw = sF.size()
-        sFF = sF.view(sb,sc,-1)
-        sMean = torch.mean(sFF,dim=2,keepdim=True)
-        sMean = sMean.unsqueeze(3)
-        sMeanC = sMean.expand_as(cF)
-        sMeanS = sMean.expand_as(sF)
-        sF = sF - sMeanS
-
-
-        compress_content = self.compress(cF)
-        b,c,h,w = compress_content.size()
-        compress_content = compress_content.view(b,c,-1)
-
-        if(trans):
-            cMatrix = self.cnet(cF)
-            sMatrix = self.snet(sF)
-
-            sMatrix = sMatrix.view(sMatrix.size(0),self.matrixSize,self.matrixSize)
-            cMatrix = cMatrix.view(cMatrix.size(0),self.matrixSize,self.matrixSize)
-            transmatrix = torch.bmm(sMatrix,cMatrix)
-            transfeature = torch.bmm(transmatrix,compress_content).view(b,c,h,w)
-            out = self.unzip(transfeature.view(b,c,h,w))
-            out = out + sMeanC
-            return out, transmatrix
-        else:
-            out = self.unzip(compress_content.view(b,c,h,w))
-            out = out + cMean
+import torch
+import torch.nn as nn
+
+class encoder3(nn.Module):
+    def __init__(self):
+        super(encoder3,self).__init__()
+        # vgg
+        # 224 x 224
+        self.conv1 = nn.Conv2d(3,3,1,1,0)
+        self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1))
+        # 226 x 226
+
+        self.conv2 = nn.Conv2d(3,64,3,1,0)
+        self.relu2 = nn.ReLU(inplace=True)
+        # 224 x 224
+
+        self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv3 = nn.Conv2d(64,64,3,1,0)
+        self.relu3 = nn.ReLU(inplace=True)
+        # 224 x 224
+
+        self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True)
+        # 112 x 112
+
+        self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv4 = nn.Conv2d(64,128,3,1,0)
+        self.relu4 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv5 = nn.Conv2d(128,128,3,1,0)
+        self.relu5 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.maxPool2 = nn.MaxPool2d(kernel_size=2,stride=2,return_indices = True)
+        # 56 x 56
+
+        self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv6 = nn.Conv2d(128,256,3,1,0)
+        self.relu6 = nn.ReLU(inplace=True)
+        # 56 x 56
+    def forward(self,x):
+        out = self.conv1(x)
+        out = self.reflecPad1(out)
+        out = self.conv2(out)
+        out = self.relu2(out)
+        out = self.reflecPad3(out)
+        out = self.conv3(out)
+        pool1 = self.relu3(out)
+        out,pool_idx = self.maxPool(pool1)
+        out = self.reflecPad4(out)
+        out = self.conv4(out)
+        out = self.relu4(out)
+        out = self.reflecPad5(out)
+        out = self.conv5(out)
+        pool2 = self.relu5(out)
+        out,pool_idx2 = self.maxPool2(pool2)
+        out = self.reflecPad6(out)
+        out = self.conv6(out)
+        out = self.relu6(out)
+        return out
+
+class decoder3(nn.Module):
+    def __init__(self):
+        super(decoder3,self).__init__()
+        # decoder
+        self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv7 = nn.Conv2d(256,128,3,1,0)
+        self.relu7 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
+        # 112 x 112
+
+        self.reflecPad8 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv8 = nn.Conv2d(128,128,3,1,0)
+        self.relu8 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.reflecPad9 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv9 = nn.Conv2d(128,64,3,1,0)
+        self.relu9 = nn.ReLU(inplace=True)
+
+        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
+        # 224 x 224
+
+        self.reflecPad10 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv10 = nn.Conv2d(64,64,3,1,0)
+        self.relu10 = nn.ReLU(inplace=True)
+
+        self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv11 = nn.Conv2d(64,3,3,1,0)
+
+    def forward(self,x):
+        output = {}
+        out = self.reflecPad7(x)
+        out = self.conv7(out)
+        out = self.relu7(out)
+        out = self.unpool(out)
+        out = self.reflecPad8(out)
+        out = self.conv8(out)
+        out = self.relu8(out)
+        out = self.reflecPad9(out)
+        out = self.conv9(out)
+        out_relu9 = self.relu9(out)
+        out = self.unpool2(out_relu9)
+        out = self.reflecPad10(out)
+        out = self.conv10(out)
+        out = self.relu10(out)
+        out = self.reflecPad11(out)
+        out = self.conv11(out)
+        return out
+
+class encoder4(nn.Module):
+    def __init__(self):
+        super(encoder4,self).__init__()
+        # vgg
+        # 224 x 224
+        self.conv1 = nn.Conv2d(3,3,1,1,0)
+        self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1))
+        # 226 x 226
+
+        self.conv2 = nn.Conv2d(3,64,3,1,0)
+        self.relu2 = nn.ReLU(inplace=True)
+        # 224 x 224
+
+        self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv3 = nn.Conv2d(64,64,3,1,0)
+        self.relu3 = nn.ReLU(inplace=True)
+        # 224 x 224
+
+        self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2)
+        # 112 x 112
+
+        self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv4 = nn.Conv2d(64,128,3,1,0)
+        self.relu4 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv5 = nn.Conv2d(128,128,3,1,0)
+        self.relu5 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.maxPool2 = nn.MaxPool2d(kernel_size=2,stride=2)
+        # 56 x 56
+
+        self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv6 = nn.Conv2d(128,256,3,1,0)
+        self.relu6 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv7 = nn.Conv2d(256,256,3,1,0)
+        self.relu7 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad8 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv8 = nn.Conv2d(256,256,3,1,0)
+        self.relu8 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad9 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv9 = nn.Conv2d(256,256,3,1,0)
+        self.relu9 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.maxPool3 = nn.MaxPool2d(kernel_size=2,stride=2)
+        # 28 x 28
+
+        self.reflecPad10 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv10 = nn.Conv2d(256,512,3,1,0)
+        self.relu10 = nn.ReLU(inplace=True)
+        # 28 x 28
+    def forward(self,x,sF=None,matrix11=None,matrix21=None,matrix31=None):
+        output = {}
+        out = self.conv1(x)
+        out = self.reflecPad1(out)
+        out = self.conv2(out)
+        output['r11'] = self.relu2(out)
+        out = self.reflecPad7(output['r11'])
+
+        out = self.conv3(out)
+        output['r12'] = self.relu3(out)
+
+        output['p1'] = self.maxPool(output['r12'])
+        out = self.reflecPad4(output['p1'])
+        out = self.conv4(out)
+        output['r21'] = self.relu4(out)
+        out = self.reflecPad7(output['r21'])
+
+        out = self.conv5(out)
+        output['r22'] = self.relu5(out)
+
+        output['p2'] = self.maxPool2(output['r22'])
+        out = self.reflecPad6(output['p2'])
+        out = self.conv6(out)
+        output['r31'] = self.relu6(out)
+        if(matrix31 is not None):
+            feature3,transmatrix3 = matrix31(output['r31'],sF['r31'])
+            out = self.reflecPad7(feature3)
+        else:
+            out = self.reflecPad7(output['r31'])
+        out = self.conv7(out)
+        output['r32'] = self.relu7(out)
+
+        out = self.reflecPad8(output['r32'])
+        out = self.conv8(out)
+        output['r33'] = self.relu8(out)
+
+        out = self.reflecPad9(output['r33'])
+        out = self.conv9(out)
+        output['r34'] = self.relu9(out)
+
+        output['p3'] = self.maxPool3(output['r34'])
+        out = self.reflecPad10(output['p3'])
+        out = self.conv10(out)
+        output['r41'] = self.relu10(out)
+
+        return output
+
+class decoder4(nn.Module):
+    def __init__(self):
+        super(decoder4,self).__init__()
+        # decoder
+        self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv11 = nn.Conv2d(512,256,3,1,0)
+        self.relu11 = nn.ReLU(inplace=True)
+        # 28 x 28
+
+        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
+        # 56 x 56
+
+        self.reflecPad12 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv12 = nn.Conv2d(256,256,3,1,0)
+        self.relu12 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad13 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv13 = nn.Conv2d(256,256,3,1,0)
+        self.relu13 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad14 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv14 = nn.Conv2d(256,256,3,1,0)
+        self.relu14 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad15 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv15 = nn.Conv2d(256,128,3,1,0)
+        self.relu15 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
+        # 112 x 112
+
+        self.reflecPad16 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv16 = nn.Conv2d(128,128,3,1,0)
+        self.relu16 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.reflecPad17 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv17 = nn.Conv2d(128,64,3,1,0)
+        self.relu17 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
+        # 224 x 224
+
+        self.reflecPad18 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv18 = nn.Conv2d(64,64,3,1,0)
+        self.relu18 = nn.ReLU(inplace=True)
+        # 224 x 224
+
+        self.reflecPad19 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv19 = nn.Conv2d(64,3,3,1,0)
+
+    def forward(self,x):
+        # decoder
+        out = self.reflecPad11(x)
+        out = self.conv11(out)
+        out = self.relu11(out)
+        out = self.unpool(out)
+        out = self.reflecPad12(out)
+        out = self.conv12(out)
+
+        out = self.relu12(out)
+        out = self.reflecPad13(out)
+        out = self.conv13(out)
+        out = self.relu13(out)
+        out = self.reflecPad14(out)
+        out = self.conv14(out)
+        out = self.relu14(out)
+        out = self.reflecPad15(out)
+        out = self.conv15(out)
+        out = self.relu15(out)
+        out = self.unpool2(out)
+        out = self.reflecPad16(out)
+        out = self.conv16(out)
+        out = self.relu16(out)
+        out = self.reflecPad17(out)
+        out = self.conv17(out)
+        out = self.relu17(out)
+        out = self.unpool3(out)
+        out = self.reflecPad18(out)
+        out = self.conv18(out)
+        out = self.relu18(out)
+        out = self.reflecPad19(out)
+        out = self.conv19(out)
+        return out
+
+class decoder4(nn.Module):
+    def __init__(self):
+        super(decoder4,self).__init__()
+        # decoder
+        self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv11 = nn.Conv2d(512,256,3,1,0)
+        self.relu11 = nn.ReLU(inplace=True)
+        # 28 x 28
+
+        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
+        # 56 x 56
+
+        self.reflecPad12 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv12 = nn.Conv2d(256,256,3,1,0)
+        self.relu12 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad13 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv13 = nn.Conv2d(256,256,3,1,0)
+        self.relu13 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad14 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv14 = nn.Conv2d(256,256,3,1,0)
+        self.relu14 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad15 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv15 = nn.Conv2d(256,128,3,1,0)
+        self.relu15 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
+        # 112 x 112
+
+        self.reflecPad16 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv16 = nn.Conv2d(128,128,3,1,0)
+        self.relu16 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.reflecPad17 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv17 = nn.Conv2d(128,64,3,1,0)
+        self.relu17 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
+        # 224 x 224
+
+        self.reflecPad18 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv18 = nn.Conv2d(64,64,3,1,0)
+        self.relu18 = nn.ReLU(inplace=True)
+        # 224 x 224
+
+        self.reflecPad19 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv19 = nn.Conv2d(64,3,3,1,0)
+
+    def forward(self,x):
+        # decoder
+        out = self.reflecPad11(x)
+        out = self.conv11(out)
+        out = self.relu11(out)
+        out = self.unpool(out)
+        out = self.reflecPad12(out)
+        out = self.conv12(out)
+
+        out = self.relu12(out)
+        out = self.reflecPad13(out)
+        out = self.conv13(out)
+        out = self.relu13(out)
+        out = self.reflecPad14(out)
+        out = self.conv14(out)
+        out = self.relu14(out)
+        out = self.reflecPad15(out)
+        out = self.conv15(out)
+        out = self.relu15(out)
+        out = self.unpool2(out)
+        out = self.reflecPad16(out)
+        out = self.conv16(out)
+        out = self.relu16(out)
+        out = self.reflecPad17(out)
+        out = self.conv17(out)
+        out = self.relu17(out)
+        out = self.unpool3(out)
+        out = self.reflecPad18(out)
+        out = self.conv18(out)
+        out = self.relu18(out)
+        out = self.reflecPad19(out)
+        out = self.conv19(out)
+        return out
+
+class encoder5(nn.Module):
+    def __init__(self):
+        super(encoder5,self).__init__()
+        # vgg
+        # 224 x 224
+        self.conv1 = nn.Conv2d(3,3,1,1,0)
+        self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1))
+        # 226 x 226
+
+        self.conv2 = nn.Conv2d(3,64,3,1,0)
+        self.relu2 = nn.ReLU(inplace=True)
+        # 224 x 224
+
+        self.reflecPad3 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv3 = nn.Conv2d(64,64,3,1,0)
+        self.relu3 = nn.ReLU(inplace=True)
+        # 224 x 224
+
+        self.maxPool = nn.MaxPool2d(kernel_size=2,stride=2)
+        # 112 x 112
+
+        self.reflecPad4 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv4 = nn.Conv2d(64,128,3,1,0)
+        self.relu4 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.reflecPad5 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv5 = nn.Conv2d(128,128,3,1,0)
+        self.relu5 = nn.ReLU(inplace=True)
+        # 112 x 112
+
+        self.maxPool2 = nn.MaxPool2d(kernel_size=2,stride=2)
+        # 56 x 56
+
+        self.reflecPad6 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv6 = nn.Conv2d(128,256,3,1,0)
+        self.relu6 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad7 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv7 = nn.Conv2d(256,256,3,1,0)
+        self.relu7 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad8 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv8 = nn.Conv2d(256,256,3,1,0)
+        self.relu8 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad9 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv9 = nn.Conv2d(256,256,3,1,0)
+        self.relu9 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.maxPool3 = nn.MaxPool2d(kernel_size=2,stride=2)
+        # 28 x 28
+
+        self.reflecPad10 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv10 = nn.Conv2d(256,512,3,1,0)
+        self.relu10 = nn.ReLU(inplace=True)
+
+        self.reflecPad11 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv11 = nn.Conv2d(512,512,3,1,0)
+        self.relu11 = nn.ReLU(inplace=True)
+
+        self.reflecPad12 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv12 = nn.Conv2d(512,512,3,1,0)
+        self.relu12 = nn.ReLU(inplace=True)
+
+        self.reflecPad13 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv13 = nn.Conv2d(512,512,3,1,0)
+        self.relu13 = nn.ReLU(inplace=True)
+
+        self.maxPool4 = nn.MaxPool2d(kernel_size=2,stride=2)
+        self.reflecPad14 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv14 = nn.Conv2d(512,512,3,1,0)
+        self.relu14 = nn.ReLU(inplace=True)
+
+    def forward(self,x,sF=None,contentV256=None,styleV256=None,matrix11=None,matrix21=None,matrix31=None):
+        output = {}
+        out = self.conv1(x)
+        out = self.reflecPad1(out)
+        out = self.conv2(out)
+        output['r11'] = self.relu2(out)
+        out = self.reflecPad7(output['r11'])
+
+        #out = self.reflecPad3(output['r11'])
+        out = self.conv3(out)
+        output['r12'] = self.relu3(out)
+
+        output['p1'] = self.maxPool(output['r12'])
+        out = self.reflecPad4(output['p1'])
+        out = self.conv4(out)
+        output['r21'] = self.relu4(out)
+        out = self.reflecPad7(output['r21'])
+
+        #out = self.reflecPad5(output['r21'])
+        out = self.conv5(out)
+        output['r22'] = self.relu5(out)
+
+        output['p2'] = self.maxPool2(output['r22'])
+        out = self.reflecPad6(output['p2'])
+        out = self.conv6(out)
+        output['r31'] = self.relu6(out)
+        if(styleV256 is not None):
+            feature = matrix31(output['r31'],sF['r31'],contentV256,styleV256)
+            out = self.reflecPad7(feature)
+        else:
+            out = self.reflecPad7(output['r31'])
+        out = self.conv7(out)
+        output['r32'] = self.relu7(out)
+
+        out = self.reflecPad8(output['r32'])
+        out = self.conv8(out)
+        output['r33'] = self.relu8(out)
+
+        out = self.reflecPad9(output['r33'])
+        out = self.conv9(out)
+        output['r34'] = self.relu9(out)
+
+        output['p3'] = self.maxPool3(output['r34'])
+        out = self.reflecPad10(output['p3'])
+        out = self.conv10(out)
+        output['r41'] = self.relu10(out)
+
+        out = self.reflecPad11(output['r41'])
+        out = self.conv11(out)
+        output['r42'] = self.relu11(out)
+
+        out = self.reflecPad12(output['r42'])
+        out = self.conv12(out)
+        output['r43'] = self.relu12(out)
+
+        out = self.reflecPad13(output['r43'])
+        out = self.conv13(out)
+        output['r44'] = self.relu13(out)
+
+        output['p4'] = self.maxPool4(output['r44'])
+
+        out = self.reflecPad14(output['p4'])
+        out = self.conv14(out)
+        output['r51'] = self.relu14(out)
+        return output
+
+class decoder5(nn.Module):
+    def __init__(self):
+        super(decoder5,self).__init__()
+
+        # decoder
+        self.reflecPad15 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv15 = nn.Conv2d(512,512,3,1,0)
+        self.relu15 = nn.ReLU(inplace=True)
+
+        self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
+        # 28 x 28
+
+        self.reflecPad16 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv16 = nn.Conv2d(512,512,3,1,0)
+        self.relu16 = nn.ReLU(inplace=True)
+        # 28 x 28
+
+        self.reflecPad17 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv17 = nn.Conv2d(512,512,3,1,0)
+        self.relu17 = nn.ReLU(inplace=True)
+        # 28 x 28
+
+        self.reflecPad18 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv18 = nn.Conv2d(512,512,3,1,0)
+        self.relu18 = nn.ReLU(inplace=True)
+        # 28 x 28
+
+        self.reflecPad19 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv19 = nn.Conv2d(512,256,3,1,0)
+        self.relu19 = nn.ReLU(inplace=True)
+        # 28 x 28
+
+        self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
+        # 56 x 56
+
+        self.reflecPad20 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv20 = nn.Conv2d(256,256,3,1,0)
+        self.relu20 = nn.ReLU(inplace=True)
+        # 56 x 56
+
+        self.reflecPad21 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv21 = nn.Conv2d(256,256,3,1,0)
+        self.relu21 = nn.ReLU(inplace=True)
+
+        self.reflecPad22 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv22 = nn.Conv2d(256,256,3,1,0)
+        self.relu22 = nn.ReLU(inplace=True)
+
+        self.reflecPad23 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv23 = nn.Conv2d(256,128,3,1,0)
+        self.relu23 = nn.ReLU(inplace=True)
+
+        self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
+        # 112 X 112
+
+        self.reflecPad24 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv24 = nn.Conv2d(128,128,3,1,0)
+        self.relu24 = nn.ReLU(inplace=True)
+
+        self.reflecPad25 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv25 = nn.Conv2d(128,64,3,1,0)
+        self.relu25 = nn.ReLU(inplace=True)
+
+        self.unpool4 = nn.UpsamplingNearest2d(scale_factor=2)
+
+        self.reflecPad26 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv26 = nn.Conv2d(64,64,3,1,0)
+        self.relu26 = nn.ReLU(inplace=True)
+
+        self.reflecPad27 = nn.ReflectionPad2d((1,1,1,1))
+        self.conv27 = nn.Conv2d(64,3,3,1,0)
+
+    def forward(self,x):
+        # decoder
+        out = self.reflecPad15(x)
+        out = self.conv15(out)
+        out = self.relu15(out)
+        out = self.unpool(out)
+        out = self.reflecPad16(out)
+        out = self.conv16(out)
+        out = self.relu16(out)
+        out = self.reflecPad17(out)
+        out = self.conv17(out)
+        out = self.relu17(out)
+        out = self.reflecPad18(out)
+        out = self.conv18(out)
+        out = self.relu18(out)
+        out = self.reflecPad19(out)
+        out = self.conv19(out)
+        out = self.relu19(out)
+        out = self.unpool2(out)
+        out = self.reflecPad20(out)
+        out = self.conv20(out)
+        out = self.relu20(out)
+        out = self.reflecPad21(out)
+        out = self.conv21(out)
+        out = self.relu21(out)
+        out = self.reflecPad22(out)
+        out = self.conv22(out)
+        out = self.relu22(out)
+        out = self.reflecPad23(out)
+        out = self.conv23(out)
+        out = self.relu23(out)
+        out = self.unpool3(out)
+        out = self.reflecPad24(out)
+        out = self.conv24(out)
+        out = self.relu24(out)
+        out = self.reflecPad25(out)
+        out = self.conv25(out)
+        out = self.relu25(out)
+        out = self.unpool4(out)
+        out = self.reflecPad26(out)
+        out = self.conv26(out)
+        out = self.relu26(out)
+        out = self.reflecPad27(out)
+        out = self.conv27(out)
+        return out
+
+import torch
+import torch.nn as nn
+
+class CNN(nn.Module):
+    def __init__(self,layer,matrixSize=32):
+        super(CNN,self).__init__()
+        if(layer == 'r31'):
+            # 256x64x64
+            self.convs = nn.Sequential(nn.Conv2d(256,128,3,1,1),
+                                       nn.ReLU(inplace=True),
+                                       nn.Conv2d(128,64,3,1,1),
+                                       nn.ReLU(inplace=True),
+                                       nn.Conv2d(64,matrixSize,3,1,1))
+        elif(layer == 'r41'):
+            # 512x32x32
+            self.convs = nn.Sequential(nn.Conv2d(512,256,3,1,1),
+                                       nn.ReLU(inplace=True),
+                                       nn.Conv2d(256,128,3,1,1),
+                                       nn.ReLU(inplace=True),
+                                       nn.Conv2d(128,matrixSize,3,1,1))
+
+        # 32x8x8
+        self.fc = nn.Linear(matrixSize*matrixSize,matrixSize*matrixSize)
+        #self.fc = nn.Linear(32*64,256*256)
+
+    def forward(self,x):
+        out = self.convs(x)
+        # 32x8x8
+        b,c,h,w = out.size()
+        out = out.view(b,c,-1)
+        # 32x64
+        out = torch.bmm(out,out.transpose(1,2)).div(h*w)
+        # 32x32
+        out = out.view(out.size(0),-1)
+        return self.fc(out)
+
+class MulLayer(nn.Module):
+    def __init__(self,layer,matrixSize=32):
+        super(MulLayer,self).__init__()
+        self.snet = CNN(layer,matrixSize)
+        self.cnet = CNN(layer,matrixSize)
+        self.matrixSize = matrixSize
+
+        if(layer == 'r41'):
+            self.compress = nn.Conv2d(512,matrixSize,1,1,0)
+            self.unzip = nn.Conv2d(matrixSize,512,1,1,0)
+        elif(layer == 'r31'):
+            self.compress = nn.Conv2d(256,matrixSize,1,1,0)
+            self.unzip = nn.Conv2d(matrixSize,256,1,1,0)
+        self.transmatrix = None
+
+    def forward(self,cF,sF,trans=True):
+        cFBK = cF.clone()
+        cb,cc,ch,cw = cF.size()
+        cFF = cF.view(cb,cc,-1)
+        cMean = torch.mean(cFF,dim=2,keepdim=True)
+        cMean = cMean.unsqueeze(3)
+        cMean = cMean.expand_as(cF)
+        cF = cF - cMean
+
+        sb,sc,sh,sw = sF.size()
+        sFF = sF.view(sb,sc,-1)
+        sMean = torch.mean(sFF,dim=2,keepdim=True)
+        sMean = sMean.unsqueeze(3)
+        sMeanC = sMean.expand_as(cF)
+        sMeanS = sMean.expand_as(sF)
+        sF = sF - sMeanS
+
+
+        compress_content = self.compress(cF)
+        b,c,h,w = compress_content.size()
+        compress_content = compress_content.view(b,c,-1)
+
+        if(trans):
+            cMatrix = self.cnet(cF)
+            sMatrix = self.snet(sF)
+
+            sMatrix = sMatrix.view(sMatrix.size(0),self.matrixSize,self.matrixSize)
+            cMatrix = cMatrix.view(cMatrix.size(0),self.matrixSize,self.matrixSize)
+            transmatrix = torch.bmm(sMatrix,cMatrix)
+            transfeature = torch.bmm(transmatrix,compress_content).view(b,c,h,w)
+            out = self.unzip(transfeature.view(b,c,h,w))
+            out = out + sMeanC
+            return out, transmatrix
+        else:
+            out = self.unzip(compress_content.view(b,c,h,w))
+            out = out + cMean
             return out
\ No newline at end of file
diff --git a/Project_Kargi/libs/models_sanet.py b/Project_Kargi/libs/models_sanet.py
index f37ee37..9b3d1bc 100644
--- a/Project_Kargi/libs/models_sanet.py
+++ b/Project_Kargi/libs/models_sanet.py
@@ -1,420 +1,420 @@
-
-import torch
-import torch.nn as nn
-from torchvision import transforms
-
-
-def calc_mean_std(feat, eps = 1e-5):
-    """ Calculate mean and std. Epsilon is added to the variance to avoid divide-by-zero """
-    size = feat.size()
-    
-    assert(len(size) == 4) # image should be 4D
-    N, C = size[:2]
-    feat_var = feat.view(N,C,-1).var(dim=2) + eps # reshape it into shape (N,C,HxW) and calculate variance for all N,C
-    feat_std = feat_var.sqrt().view(N,C,1,1)
-    feat_mean = feat.view(N,C,-1).mean(dim=2).view(N,C,1,1)
-    return feat_mean,feat_std
-
-
-def mean_variance_norm(feat):
-    """ Normalize mean and variance """
-    size = feat.size()
-
-    mean, std = calc_mean_std(feat)
-    normalized_feat = (feat - mean.expand(size)) / std.expand(size) # make sure that all operations are vectorized
-                                                                    # N,C,1,1 -> N,C,H,W   
-    return normalized_feat
-
-def _calc_feat_flatten_mean_std(feat):
-    """  Calculate mean and std of an 3D view """
-    size = feat.size()
-    assert(len(size) == 3)
-    assert(isinstance(feat,torch.FloatTensor))
-    feat_flatten = feat.view(3,-1)
-    mean = feat_flatten.mean(dim=-1, keepdim=True)
-    std = feat_flatten.std(dim=-1, keepdim=True)
-    return feat_flatten, mean, std
-
-def weighted_mse_loss(input,target,weights = None):
-  assert input.size() == target.size()
-  size = input.size()
-  if weights == None:
-    weights = torch.ones(size = size[0])
-
-  if len(size) == 3: # gram matrix is B,C,C
-    se = ((input.view(size[0],-1) - target.view(size[0],-1))**2)
-    return (se.mean(dim = 1)*weights).mean()
-
-
-def gram_matrix_(input, normalize =True):
-    a, b, c, d = input.size()  # a=batch size(=1)
-    # b=number of feature maps
-    # (c,d)=dimensions of a f. map (N=c*d)
-
-    v = input.view(a,b, c * d)  # resise F_XL into \hat F_XL
-    vt = v.permute(0,2,1)
-    # C,HxW , HxW, C
-    G = v.matmul(vt)  # compute the gram product
-
-    # we 'normalize' the values of the gram matrix
-    # by dividing by the number of element in each feature maps.
-    if normalize:
-      return G.div(b * c * d)
-    else:
-      return G
-
-def gram_matrix(x, normalize=True):
-    '''
-    Generate gram matrices of the representations of content and style images.
-    '''
-    (b, ch, h, w) = x.size()
-    features = x.view(b, ch, w * h)
-    features_t = features.transpose(1, 2)
-    gram = features.bmm(features_t)
-    if normalize:
-        gram /= ch * h * w
-    return gram
-
-decoder = nn.Sequential(
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 256, (3, 3)),
-    nn.ReLU(),
-    nn.Upsample(scale_factor=2, mode='nearest'),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 128, (3, 3)),
-    nn.ReLU(),
-    nn.Upsample(scale_factor=2, mode='nearest'),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(128, 128, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(128, 64, (3, 3)),
-    nn.ReLU(),
-    nn.Upsample(scale_factor=2, mode='nearest'),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(64, 64, (3, 3)),
-    nn.ReLU(),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(64, 3, (3, 3)),
-)
-
-vgg = nn.Sequential(
-    nn.Conv2d(3, 3, (1, 1)),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(3, 64, (3, 3)),
-    nn.ReLU(),  # relu1-1
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(64, 64, (3, 3)),
-    nn.ReLU(),  # relu1-2
-    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(64, 128, (3, 3)),
-    nn.ReLU(),  # relu2-1
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(128, 128, (3, 3)),
-    nn.ReLU(),  # relu2-2
-    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(128, 256, (3, 3)),
-    nn.ReLU(),  # relu3-1
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),  # relu3-2
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),  # relu3-3
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 256, (3, 3)),
-    nn.ReLU(),  # relu3-4
-    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(256, 512, (3, 3)),
-    nn.ReLU(),  # relu4-1, this is the last layer used
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu4-2
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu4-3
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu4-4
-    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu5-1
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu5-2
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU(),  # relu5-3
-    nn.ReflectionPad2d((1, 1, 1, 1)),
-    nn.Conv2d(512, 512, (3, 3)),
-    nn.ReLU()  # relu5-4
-)
-
-class SANet(nn.Module):
-    
-    def __init__(self, in_planes):
-        super(SANet, self).__init__()
-        self.f = nn.Conv2d(in_planes, in_planes, (1, 1))
-        self.g = nn.Conv2d(in_planes, in_planes, (1, 1))
-        self.h = nn.Conv2d(in_planes, in_planes, (1, 1))
-        self.sm = nn.Softmax(dim = -1)
-        self.out_conv = nn.Conv2d(in_planes, in_planes, (1, 1))
-        
-    def forward(self, content, style):
-        F = self.f(mean_variance_norm(content))
-        G = self.g(mean_variance_norm(style))
-        H = self.h(style)
-        b, c, h, w = F.size()
-        F = F.view(b, -1, w * h).permute(0, 2, 1)
-        b, c, h, w = G.size()
-        G = G.view(b, -1, w * h)
-        S = torch.bmm(F, G)
-        S = self.sm(S)
-        b, c, h, w = H.size()
-        H = H.view(b, -1, w * h)
-        O = torch.bmm(H, S.permute(0, 2, 1))
-        b, c, h, w = content.size()
-        O = O.view(b, c, h, w)
-        O = self.out_conv(O)
-        O += content
-        return O
-
-class Transform(nn.Module):
-    def __init__(self, in_planes):
-        super(Transform, self).__init__()
-        self.sanet4_1 = SANet(in_planes = in_planes)
-        self.sanet5_1 = SANet(in_planes = in_planes)
-        self.upsample5_1 = nn.Upsample(scale_factor=2, mode='nearest')
-        self.merge_conv_pad = nn.ReflectionPad2d((1, 1, 1, 1))
-        self.merge_conv = nn.Conv2d(in_planes, in_planes, (3, 3))
-
-    def forward(self, content4_1, style4_1, content5_1, style5_1):
-        s41 = self.sanet4_1(content4_1, style4_1)
-        s51 = self.sanet5_1(content5_1, style5_1)
-
-        su51 = self.upsample5_1(s51)
-
-        s41_size = s41.size()
-        su51_size = su51.size()
-
-        #print(s41.size(), su51.size(), s51.size())
-        if(s41_size[2] != su51_size[2]):
-            s41 = s41[:,:,:min(s41_size[2],su51_size[2]),:]
-            su51 = su51[:,:,:min(s41_size[2],su51_size[2]),:]
-        if s41_size[3] != su51_size[3]:
-            s41 = s41[:,:,:,:min(s41_size[3],su51_size[3])]
-            su51 = su51[:,:,:,:min(s41_size[3],su51_size[3])]
-
-        #print(s41.size(), su51.size(), s51.size())
-        assert s41.size() == su51.size()
-        x = s41 + su51
-        x = self.merge_conv_pad(x)
-        return self.merge_conv(x)
-
-def test_transform():
-    transform_list = []
-    transform_list.append(transforms.ToTensor())
-    transform = transforms.Compose(transform_list)
-    return transform
-
-
-def calc_content_loss(input, target, norm = False):
-    mse_loss = nn.MSELoss()
-    if(norm == False):
-        return mse_loss(input, target)
-    else:
-        return mse_loss(mean_variance_norm(input), mean_variance_norm(target))
-        
-def calc_ast_style_loss_normalized(input, target):
-    G1 = gram_matrix(input, False)
-    G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
-
-    size = input.size()
-    assert(len(size) == 4)
-
-    g1_norm = torch.linalg.norm(G1,dim = (1,2))
-    g2_norm = torch.linalg.norm(G2,dim = (1,2))
-
-    size = G1.size()
-    Nl = size[1] * size[2] # Or C x C = C^2
-    normalize_term =  (torch.square(g1_norm) + torch.square(g2_norm))/Nl  #
-
-    weights = (1/normalize_term)
-    #weights = weights.view(size[0],1)
-    return weighted_mse_loss(G1,G2,weights)
-
-def return_normalize_weight(input, target):
-    G1 = gram_matrix(input, False)
-    G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
-
-    size = input.size()
-    assert(len(size) == 4)
-
-    g1_norm = torch.linalg.norm(G1,dim = (1,2))
-    g2_norm = torch.linalg.norm(G2,dim = (1,2))
-
-    size = G1.size()
-    Nl = size[1] * size[2] # Or C x C = C^2
-    normalize_term =  (torch.square(g1_norm) + torch.square(g2_norm))/Nl  #supremum
-
-    return normalize_term
-
-def calc_ast_style_loss(input, target):
-    """
-    Interestingly, authors assumes that calculation of G1 and G2 is not normalized
-    This can create an issue?. For this reason, let us test for
-        1- Normalized
-        2- Unnormalized
-        3- Newly defined Normalization term
-    """
-    mse_loss = nn.MSELoss()
-    G1 = gram_matrix(input)
-    G2 = gram_matrix(target).detach() # we dont need the gradient of the target
-
-    size = input.size()
-    assert(len(size) == 4)
-    assert G1.size() == G2.size()
-    
-    return mse_loss(G1,G2)
-
-def calc_ast_style_loss_unnormalized(input, target):
-    """
-    Interestingly, authors assumes that calculation of G1 and G2 is not normalized
-    This can create an issue?. For this reason, let us test for
-        1- Normalized
-        2- Unnormalized
-        3- Newly defined Normalization term
-    """
-    mse_loss = nn.MSELoss()
-    G1 = gram_matrix(input, False)
-    G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
-
-    size = input.size()
-    assert(len(size) == 4)
-    assert G1.size() == G2.size()
-    
-    return mse_loss(G1,G2)
-
-class Net(nn.Module):
-    def __init__(self, encoder, decoder, start_iter):
-        super(Net, self).__init__()
-        enc_layers = list(encoder.children())
-
-        ##############################################################
-        # b1r2, b2r2, b3r3, b4r4 for style encoding     
-        # b3r3 for content loss
-        ##############################################################
-
-        self.enc_1 = nn.Sequential(*enc_layers[:4])  # input -> relu1_1
-        self.enc_2 = nn.Sequential(*enc_layers[4:11])  # relu1_1 -> relu2_1
-        self.enc_3 = nn.Sequential(*enc_layers[11:18])  # relu2_1 -> relu3_1
-        self.enc_4 = nn.Sequential(*enc_layers[18:31])  # relu3_1 -> relu4_1
-        self.enc_5 = nn.Sequential(*enc_layers[31:44])  # relu4_1 -> relu5_1
-
-        #transform
-        self.transform = Transform(in_planes = 512)
-        self.decoder = decoder
-        if(start_iter > 0):
-            self.transform.load_state_dict(torch.load('transformer_iter_' + str(start_iter) + '.pth'))
-            self.decoder.load_state_dict(torch.load('decoder_iter_' + str(start_iter) + '.pth'))
-        self.mse_loss = nn.MSELoss()
-        # fix the encoder
-        for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4', 'enc_5']:
-            for param in getattr(self, name).parameters():
-                param.requires_grad = False
-
-    # extract relu1_1, relu2_1, relu3_1, relu4_1, relu5_1 from input image
-    def encode_with_intermediate(self, input):
-        """
-        Basically, traverse all the intermediate layers and add the last output of it to the results. 
-        """
-        results = [input]
-        for i in range(5):
-            func = getattr(self, 'enc_{:d}'.format(i + 1))
-            results.append(func(results[-1]))
-        return results[1:]
-
-    def calc_content_loss(self, input, target, norm = False):
-        if(norm == False):
-          return self.mse_loss(input, target)
-        else:
-          return self.mse_loss(mean_variance_norm(input), mean_variance_norm(target))
-
-    def calc_ast_style_loss_normalized(self, input, target):
-        G1 = gram_matrix(input, False)
-        G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
-
-        size = input.size()
-        assert(len(size) == 4)
-
-        g1_norm = torch.linalg.norm(G1,dim = (1,2))
-        g2_norm = torch.linalg.norm(G2,dim = (1,2))
-
-        size = G1.size()
-        Nl = size[1] * size[2] # Or C x C = C^2
-        normalize_term =  (torch.square(g1_norm) + torch.square(g2_norm))/Nl  #
-
-        weights = (1/normalize_term)
-        #weights = weights.view(size[0],1)
-        return weighted_mse_loss(G1,G2,weights)
-      
-    def calc_ast_style_loss(self, input, target):
-        """
-        Interestingly, authors assumes that calculation of G1 and G2 is not normalized
-        This can create an issue?. For this reason, let us test for
-          1- Normalized
-          2- Unnormalized
-          3- Newly defined Normalization term
-        """
-        G1 = gram_matrix(input)
-        G2 = gram_matrix(target).detach() # we dont need the gradient of the target
-
-        size = input.size()
-        assert(len(size) == 4)
-
-        return self.mse_loss(G1,G2)
-
-    def calc_style_loss(self, input, target):
-        
-        input_mean, input_std = calc_mean_std(input)
-        target_mean, target_std = calc_mean_std(target)
-        return self.mse_loss(input_mean, target_mean) + \
-               self.mse_loss(input_std, target_std)
-        
-        #return self.calc_ast_style_loss_normalized(input,target)
-
-    def forward(self, content, style):
-        style_feats = self.encode_with_intermediate(style)
-        content_feats = self.encode_with_intermediate(content)
-        stylized = self.transform(content_feats[3], style_feats[3], content_feats[4], style_feats[4])
-        g_t = self.decoder(stylized)
-        g_t_feats = self.encode_with_intermediate(g_t)
-        loss_c = self.calc_content_loss(g_t_feats[3], content_feats[3], norm = True) + self.calc_content_loss(g_t_feats[4], content_feats[4], norm = True)
-        
-        loss_s = self.calc_style_loss(g_t_feats[0], style_feats[0])
-        for i in range(1, 5):
-            loss_s += self.calc_style_loss(g_t_feats[i], style_feats[i])
-        """IDENTITY LOSSES"""
-        Icc = self.decoder(self.transform(content_feats[3], content_feats[3], content_feats[4], content_feats[4]))
-        Iss = self.decoder(self.transform(style_feats[3], style_feats[3], style_feats[4], style_feats[4]))
-        l_identity1 = self.calc_content_loss(Icc, content) + self.calc_content_loss(Iss, style)
-        Fcc = self.encode_with_intermediate(Icc)
-        Fss = self.encode_with_intermediate(Iss)
-        l_identity2 = self.calc_content_loss(Fcc[0], content_feats[0]) + self.calc_content_loss(Fss[0], style_feats[0])
-        for i in range(1, 5):
-            l_identity2 += self.calc_content_loss(Fcc[i], content_feats[i]) + self.calc_content_loss(Fss[i], style_feats[i])
-        return loss_c, loss_s, l_identity1, l_identity2
+
+import torch
+import torch.nn as nn
+from torchvision import transforms
+
+
+def calc_mean_std(feat, eps = 1e-5):
+    """ Calculate mean and std. Epsilon is added to the variance to avoid divide-by-zero """
+    size = feat.size()
+    
+    assert(len(size) == 4) # image should be 4D
+    N, C = size[:2]
+    feat_var = feat.view(N,C,-1).var(dim=2) + eps # reshape it into shape (N,C,HxW) and calculate variance for all N,C
+    feat_std = feat_var.sqrt().view(N,C,1,1)
+    feat_mean = feat.view(N,C,-1).mean(dim=2).view(N,C,1,1)
+    return feat_mean,feat_std
+
+
+def mean_variance_norm(feat):
+    """ Normalize mean and variance """
+    size = feat.size()
+
+    mean, std = calc_mean_std(feat)
+    normalized_feat = (feat - mean.expand(size)) / std.expand(size) # make sure that all operations are vectorized
+                                                                    # N,C,1,1 -> N,C,H,W   
+    return normalized_feat
+
+def _calc_feat_flatten_mean_std(feat):
+    """  Calculate mean and std of an 3D view """
+    size = feat.size()
+    assert(len(size) == 3)
+    assert(isinstance(feat,torch.FloatTensor))
+    feat_flatten = feat.view(3,-1)
+    mean = feat_flatten.mean(dim=-1, keepdim=True)
+    std = feat_flatten.std(dim=-1, keepdim=True)
+    return feat_flatten, mean, std
+
+def weighted_mse_loss(input,target,weights = None):
+  assert input.size() == target.size()
+  size = input.size()
+  if weights == None:
+    weights = torch.ones(size = size[0])
+
+  if len(size) == 3: # gram matrix is B,C,C
+    se = ((input.view(size[0],-1) - target.view(size[0],-1))**2)
+    return (se.mean(dim = 1)*weights).mean()
+
+
+def gram_matrix_(input, normalize =True):
+    a, b, c, d = input.size()  # a=batch size(=1)
+    # b=number of feature maps
+    # (c,d)=dimensions of a f. map (N=c*d)
+
+    v = input.view(a,b, c * d)  # resise F_XL into \hat F_XL
+    vt = v.permute(0,2,1)
+    # C,HxW , HxW, C
+    G = v.matmul(vt)  # compute the gram product
+
+    # we 'normalize' the values of the gram matrix
+    # by dividing by the number of element in each feature maps.
+    if normalize:
+      return G.div(b * c * d)
+    else:
+      return G
+
+def gram_matrix(x, normalize=True):
+    '''
+    Generate gram matrices of the representations of content and style images.
+    '''
+    (b, ch, h, w) = x.size()
+    features = x.view(b, ch, w * h)
+    features_t = features.transpose(1, 2)
+    gram = features.bmm(features_t)
+    if normalize:
+        gram /= ch * h * w
+    return gram
+
+decoder = nn.Sequential(
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 256, (3, 3)),
+    nn.ReLU(),
+    nn.Upsample(scale_factor=2, mode='nearest'),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 128, (3, 3)),
+    nn.ReLU(),
+    nn.Upsample(scale_factor=2, mode='nearest'),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(128, 128, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(128, 64, (3, 3)),
+    nn.ReLU(),
+    nn.Upsample(scale_factor=2, mode='nearest'),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(64, 64, (3, 3)),
+    nn.ReLU(),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(64, 3, (3, 3)),
+)
+
+vgg = nn.Sequential(
+    nn.Conv2d(3, 3, (1, 1)),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(3, 64, (3, 3)),
+    nn.ReLU(),  # relu1-1
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(64, 64, (3, 3)),
+    nn.ReLU(),  # relu1-2
+    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(64, 128, (3, 3)),
+    nn.ReLU(),  # relu2-1
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(128, 128, (3, 3)),
+    nn.ReLU(),  # relu2-2
+    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(128, 256, (3, 3)),
+    nn.ReLU(),  # relu3-1
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),  # relu3-2
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),  # relu3-3
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 256, (3, 3)),
+    nn.ReLU(),  # relu3-4
+    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(256, 512, (3, 3)),
+    nn.ReLU(),  # relu4-1, this is the last layer used
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu4-2
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu4-3
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu4-4
+    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu5-1
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu5-2
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU(),  # relu5-3
+    nn.ReflectionPad2d((1, 1, 1, 1)),
+    nn.Conv2d(512, 512, (3, 3)),
+    nn.ReLU()  # relu5-4
+)
+
+class SANet(nn.Module):
+    
+    def __init__(self, in_planes):
+        super(SANet, self).__init__()
+        self.f = nn.Conv2d(in_planes, in_planes, (1, 1))
+        self.g = nn.Conv2d(in_planes, in_planes, (1, 1))
+        self.h = nn.Conv2d(in_planes, in_planes, (1, 1))
+        self.sm = nn.Softmax(dim = -1)
+        self.out_conv = nn.Conv2d(in_planes, in_planes, (1, 1))
+        
+    def forward(self, content, style):
+        F = self.f(mean_variance_norm(content))
+        G = self.g(mean_variance_norm(style))
+        H = self.h(style)
+        b, c, h, w = F.size()
+        F = F.view(b, -1, w * h).permute(0, 2, 1)
+        b, c, h, w = G.size()
+        G = G.view(b, -1, w * h)
+        S = torch.bmm(F, G)
+        S = self.sm(S)
+        b, c, h, w = H.size()
+        H = H.view(b, -1, w * h)
+        O = torch.bmm(H, S.permute(0, 2, 1))
+        b, c, h, w = content.size()
+        O = O.view(b, c, h, w)
+        O = self.out_conv(O)
+        O += content
+        return O
+
+class Transform(nn.Module):
+    def __init__(self, in_planes):
+        super(Transform, self).__init__()
+        self.sanet4_1 = SANet(in_planes = in_planes)
+        self.sanet5_1 = SANet(in_planes = in_planes)
+        self.upsample5_1 = nn.Upsample(scale_factor=2, mode='nearest')
+        self.merge_conv_pad = nn.ReflectionPad2d((1, 1, 1, 1))
+        self.merge_conv = nn.Conv2d(in_planes, in_planes, (3, 3))
+
+    def forward(self, content4_1, style4_1, content5_1, style5_1):
+        s41 = self.sanet4_1(content4_1, style4_1)
+        s51 = self.sanet5_1(content5_1, style5_1)
+
+        su51 = self.upsample5_1(s51)
+
+        s41_size = s41.size()
+        su51_size = su51.size()
+
+        #print(s41.size(), su51.size(), s51.size())
+        if(s41_size[2] != su51_size[2]):
+            s41 = s41[:,:,:min(s41_size[2],su51_size[2]),:]
+            su51 = su51[:,:,:min(s41_size[2],su51_size[2]),:]
+        if s41_size[3] != su51_size[3]:
+            s41 = s41[:,:,:,:min(s41_size[3],su51_size[3])]
+            su51 = su51[:,:,:,:min(s41_size[3],su51_size[3])]
+
+        #print(s41.size(), su51.size(), s51.size())
+        assert s41.size() == su51.size()
+        x = s41 + su51
+        x = self.merge_conv_pad(x)
+        return self.merge_conv(x)
+
+def test_transform():
+    transform_list = []
+    transform_list.append(transforms.ToTensor())
+    transform = transforms.Compose(transform_list)
+    return transform
+
+
+def calc_content_loss(input, target, norm = False):
+    mse_loss = nn.MSELoss()
+    if(norm == False):
+        return mse_loss(input, target)
+    else:
+        return mse_loss(mean_variance_norm(input), mean_variance_norm(target))
+        
+def calc_ast_style_loss_normalized(input, target):
+    G1 = gram_matrix(input, False)
+    G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
+
+    size = input.size()
+    assert(len(size) == 4)
+
+    g1_norm = torch.linalg.norm(G1,dim = (1,2))
+    g2_norm = torch.linalg.norm(G2,dim = (1,2))
+
+    size = G1.size()
+    Nl = size[1] * size[2] # Or C x C = C^2
+    normalize_term =  (torch.square(g1_norm) + torch.square(g2_norm))/Nl  #
+
+    weights = (1/normalize_term)
+    #weights = weights.view(size[0],1)
+    return weighted_mse_loss(G1,G2,weights)
+
+def return_normalize_weight(input, target):
+    G1 = gram_matrix(input, False)
+    G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
+
+    size = input.size()
+    assert(len(size) == 4)
+
+    g1_norm = torch.linalg.norm(G1,dim = (1,2))
+    g2_norm = torch.linalg.norm(G2,dim = (1,2))
+
+    size = G1.size()
+    Nl = size[1] * size[2] # Or C x C = C^2
+    normalize_term =  (torch.square(g1_norm) + torch.square(g2_norm))/Nl  #supremum
+
+    return normalize_term
+
+def calc_ast_style_loss(input, target):
+    """
+    Interestingly, authors assumes that calculation of G1 and G2 is not normalized
+    This can create an issue?. For this reason, let us test for
+        1- Normalized
+        2- Unnormalized
+        3- Newly defined Normalization term
+    """
+    mse_loss = nn.MSELoss()
+    G1 = gram_matrix(input)
+    G2 = gram_matrix(target).detach() # we dont need the gradient of the target
+
+    size = input.size()
+    assert(len(size) == 4)
+    assert G1.size() == G2.size()
+    
+    return mse_loss(G1,G2)
+
+def calc_ast_style_loss_unnormalized(input, target):
+    """
+    Interestingly, authors assumes that calculation of G1 and G2 is not normalized
+    This can create an issue?. For this reason, let us test for
+        1- Normalized
+        2- Unnormalized
+        3- Newly defined Normalization term
+    """
+    mse_loss = nn.MSELoss()
+    G1 = gram_matrix(input, False)
+    G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
+
+    size = input.size()
+    assert(len(size) == 4)
+    assert G1.size() == G2.size()
+    
+    return mse_loss(G1,G2)
+
+class Net(nn.Module):
+    def __init__(self, encoder, decoder, start_iter):
+        super(Net, self).__init__()
+        enc_layers = list(encoder.children())
+
+        ##############################################################
+        # b1r2, b2r2, b3r3, b4r4 for style encoding     
+        # b3r3 for content loss
+        ##############################################################
+
+        self.enc_1 = nn.Sequential(*enc_layers[:4])  # input -> relu1_1
+        self.enc_2 = nn.Sequential(*enc_layers[4:11])  # relu1_1 -> relu2_1
+        self.enc_3 = nn.Sequential(*enc_layers[11:18])  # relu2_1 -> relu3_1
+        self.enc_4 = nn.Sequential(*enc_layers[18:31])  # relu3_1 -> relu4_1
+        self.enc_5 = nn.Sequential(*enc_layers[31:44])  # relu4_1 -> relu5_1
+
+        #transform
+        self.transform = Transform(in_planes = 512)
+        self.decoder = decoder
+        if(start_iter > 0):
+            self.transform.load_state_dict(torch.load('transformer_iter_' + str(start_iter) + '.pth'))
+            self.decoder.load_state_dict(torch.load('decoder_iter_' + str(start_iter) + '.pth'))
+        self.mse_loss = nn.MSELoss()
+        # fix the encoder
+        for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4', 'enc_5']:
+            for param in getattr(self, name).parameters():
+                param.requires_grad = False
+
+    # extract relu1_1, relu2_1, relu3_1, relu4_1, relu5_1 from input image
+    def encode_with_intermediate(self, input):
+        """
+        Basically, traverse all the intermediate layers and add the last output of it to the results. 
+        """
+        results = [input]
+        for i in range(5):
+            func = getattr(self, 'enc_{:d}'.format(i + 1))
+            results.append(func(results[-1]))
+        return results[1:]
+
+    def calc_content_loss(self, input, target, norm = False):
+        if(norm == False):
+          return self.mse_loss(input, target)
+        else:
+          return self.mse_loss(mean_variance_norm(input), mean_variance_norm(target))
+
+    def calc_ast_style_loss_normalized(self, input, target):
+        G1 = gram_matrix(input, False)
+        G2 = gram_matrix(target, False).detach() # we dont need the gradient of the target
+
+        size = input.size()
+        assert(len(size) == 4)
+
+        g1_norm = torch.linalg.norm(G1,dim = (1,2))
+        g2_norm = torch.linalg.norm(G2,dim = (1,2))
+
+        size = G1.size()
+        Nl = size[1] * size[2] # Or C x C = C^2
+        normalize_term =  (torch.square(g1_norm) + torch.square(g2_norm))/Nl  #
+
+        weights = (1/normalize_term)
+        #weights = weights.view(size[0],1)
+        return weighted_mse_loss(G1,G2,weights)
+      
+    def calc_ast_style_loss(self, input, target):
+        """
+        Interestingly, authors assumes that calculation of G1 and G2 is not normalized
+        This can create an issue?. For this reason, let us test for
+          1- Normalized
+          2- Unnormalized
+          3- Newly defined Normalization term
+        """
+        G1 = gram_matrix(input)
+        G2 = gram_matrix(target).detach() # we dont need the gradient of the target
+
+        size = input.size()
+        assert(len(size) == 4)
+
+        return self.mse_loss(G1,G2)
+
+    def calc_style_loss(self, input, target):
+        
+        input_mean, input_std = calc_mean_std(input)
+        target_mean, target_std = calc_mean_std(target)
+        return self.mse_loss(input_mean, target_mean) + \
+               self.mse_loss(input_std, target_std)
+        
+        #return self.calc_ast_style_loss_normalized(input,target)
+
+    def forward(self, content, style):
+        style_feats = self.encode_with_intermediate(style)
+        content_feats = self.encode_with_intermediate(content)
+        stylized = self.transform(content_feats[3], style_feats[3], content_feats[4], style_feats[4])
+        g_t = self.decoder(stylized)
+        g_t_feats = self.encode_with_intermediate(g_t)
+        loss_c = self.calc_content_loss(g_t_feats[3], content_feats[3], norm = True) + self.calc_content_loss(g_t_feats[4], content_feats[4], norm = True)
+        
+        loss_s = self.calc_style_loss(g_t_feats[0], style_feats[0])
+        for i in range(1, 5):
+            loss_s += self.calc_style_loss(g_t_feats[i], style_feats[i])
+        """IDENTITY LOSSES"""
+        Icc = self.decoder(self.transform(content_feats[3], content_feats[3], content_feats[4], content_feats[4]))
+        Iss = self.decoder(self.transform(style_feats[3], style_feats[3], style_feats[4], style_feats[4]))
+        l_identity1 = self.calc_content_loss(Icc, content) + self.calc_content_loss(Iss, style)
+        Fcc = self.encode_with_intermediate(Icc)
+        Fss = self.encode_with_intermediate(Iss)
+        l_identity2 = self.calc_content_loss(Fcc[0], content_feats[0]) + self.calc_content_loss(Fss[0], style_feats[0])
+        for i in range(1, 5):
+            l_identity2 += self.calc_content_loss(Fcc[i], content_feats[i]) + self.calc_content_loss(Fss[i], style_feats[i])
+        return loss_c, loss_s, l_identity1, l_identity2
         
\ No newline at end of file
diff --git a/Project_Kargi/linear_transfer_main.py b/Project_Kargi/linear_transfer_main.py
index aaf74fd..301cd4a 100644
--- a/Project_Kargi/linear_transfer_main.py
+++ b/Project_Kargi/linear_transfer_main.py
@@ -1,279 +1,279 @@
-import argparse
-from email.policy import default
-import torch
-import gc
-from tqdm import tqdm
-import torch.nn as nn
-
-from libs.models_sanet import calc_ast_style_loss, calc_ast_style_loss_normalized, calc_ast_style_loss_unnormalized,Transform,vgg,test_transform,Net
-
-import libs.functions as utils
-import libs.models_sanet as sanet
-import libs.models_adain as adain
-import libs.models_linear_transfer as lt
-from torchvision.utils import save_image
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-print("ok")
-
-
-#_____________________________________LOAD MODELS_______________________________________#
-
-vgg_r41_path = "experiments/linear-transfer/vgg_r41.pth" # encoder path - pretrained
-dec_r41_path = "experiments/linear-transfer/dec_r41.pth" # decoder path - pretrained
-loss_network_path = "xperiments/linear-transfer/vgg_r41.pth"
-
-r41_default_path =  "experiments/linear-transfer/r41_default_style5_160000.pth"  #matrix path - trained
-r41_normalized_path =  "experiments/linear-transfer/r41_normalized_style5_160000.pth"
-r41_pretrained_path = "experiments/linear-transfer/r41.pth"
-
-
-
-vgg = sanet.vgg
-dec = lt.decoder4()
-
-default_matrix = lt.MulLayer("r41") # we used r41 for training
-balanced_matrix = lt.MulLayer("r41") # we used r41 for training
-pretrained_matrix = lt.MulLayer("r41") # we used r41 for training
-
-
-dec.load_state_dict(torch.load(dec_r41_path))
-
-default_matrix.load_state_dict(torch.load(r41_default_path))
-balanced_matrix.load_state_dict(torch.load(r41_normalized_path))
-pretrained_matrix.load_state_dict(torch.load(r41_pretrained_path))
-
-
-model_list = [default_matrix, balanced_matrix, pretrained_matrix]
-
-#vgg = adain.vgg
-vgg.load_state_dict(torch.load("vgg_normalised.pth"))
-
-print(f"Models loaded succesfully. Total model size : {len(model_list)}")
-
-## Switch the device of the models for faster performance
-vgg = nn.Sequential(*list(vgg.children())[:44]) # get the VGG layers
-
-norm = nn.Sequential(*list(vgg.children())[:1])
-enc_1 = nn.Sequential(*list(vgg.children())[:4])  # input -> relu1_1
-enc_2 = nn.Sequential(*list(vgg.children())[4:11])  # relu1_1 -> relu2_1
-enc_3 = nn.Sequential(*list(vgg.children())[11:18])  # relu2_1 -> relu3_1
-enc_4 = nn.Sequential(*list(vgg.children())[18:31])  # relu3_1 -> relu4_1
-enc_5 = nn.Sequential(*list(vgg.children())[31:44])  # relu4_1 -> relu5_1
-
-enc_1.to(device)
-enc_2.to(device)
-enc_3.to(device)
-enc_4.to(device)
-vgg.to(device)
-dec.to(device)
-
-for model in model_list:
-    model.to(device)
-
-print(f"Models switched to defice successfully. Device : {device}")
-
-# These classes and functions are key for loading the dataset and sampling from it.
-from torch.utils import data
-from pathlib import Path
-import numpy as np
-
-#_____________________________________SET PARAMETERS____________________________________#
-
-parser = argparse.ArgumentParser()
-
-# Basic options to evaluate models
-parser.add_argument('--steps', type=int, default = 1,
-                    help='Steps of style transfer.')
-parser.add_argument('--many_image', type=int, default = 2000,
-                    help='Total amount of generated pair.')
-parser.add_argument('--content_dir', type=str, default = "contents/",
-                    help='Path where the content images located.')
-parser.add_argument('--style_dir', type=str, default = "styles/",
-                    help='Path where the style images located.')
-
-parser.add_argument('--content_size', type=int, default = 256,
-                    help='Size of the content image.')
-parser.add_argument('--style_size', type=int, default = 256,
-                    help='Size of the style image.')
-parser.add_argument('--crop', type=int, default = True,
-                    help='Crop the image or not.')
-args = parser.parse_args()
-
-steps = args.steps
-many_image = args.many_image
-content_dir = args.content_dir
-style_dir = args.style_dir
-
-content_size = args.content_size
-style_size = args.style_size
-crop = args.crop
-
-
-#_____________________________________DATA PROCESS______________________________________#
-
-content_tf = adain.test_transform(content_size, crop) # Center and crop if image size is bigger than 256. Use the transformer
-style_tf = adain.test_transform(style_size, crop)     # in the adain.
-
-content_dataset = utils.FlatFolderDataset(content_dir, content_tf)
-style_dataset = utils.FlatFolderDataset(style_dir, style_tf)
-
-content_iter = iter(data.DataLoader(
-    content_dataset, batch_size= 1,
-    sampler=utils.InfiniteSamplerWrapper(content_dataset)))
-
-style_iter = iter(data.DataLoader(
-    style_dataset, batch_size= 1,
-    sampler=utils.InfiniteSamplerWrapper(style_dataset)))
-
-
-#_____________________________________START TESTING______________________________________#
-
-torch.cuda.empty_cache()    
-
-contents = []
-multi_content = []
-
-balanced_losses = []
-default_losses =[]
-default_unnormalized_losses = []
-
-model_image_contents = []
-unnormalized_losses = [[],[],[],[]]
-normalizer_weights = [[],[],[],[]]
-
-content_set = [next(content_iter).to(device) for i in range(many_image)]
-style_set = [next(style_iter).to(device) for i in range(many_image)]
-
-with torch.no_grad():
-    for model in model_list:
-        image_contents = []
-        for im in tqdm(range(many_image)):
-            content = content_set[im]
-            style = style_set[im]
-            
-            contents = []
-            contents.append(content)
-            contents.append(style)
-            #print(content.shape)
-            for step in range(steps):
-
-                # Linear tranfer also uses R11 R21 R31 and R41
-                Style1_1 = enc_1(style)
-                Style2_1 = enc_2(Style1_1)
-                Style3_1 = enc_3(Style2_1)
-                Style4_1 = enc_4(Style3_1)
-
-                style_feats = [Style1_1,Style2_1,Style3_1,Style4_1]
-                # Content uses R41
-
-                Content4_1 = enc_4(enc_3(enc_2(enc_1(content))))
-
-                feature,transmatrix = model(Content4_1,Style4_1)
-                transfer = dec(feature) # use this as content!
-
-                # sF_loss = vgg(style)
-                # cF_loss = vgg(content)
-        
-                gt1_1 = enc_1(transfer)
-                gt2_1 = enc_2(gt1_1)
-                gt3_1 = enc_3(gt2_1)
-                gt4_1 = enc_4(gt3_1) # stylized + decoder
-
-                #content = style_transfer(vgg, model, content, style, alpha)
-
-                g_t_feats = [gt1_1,gt2_1,gt3_1,gt4_1]
-                
-
-                loss_s = calc_ast_style_loss(g_t_feats[0], style_feats[0])
-                loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[0], style_feats[0])
-                loss_normalizer = sanet.return_normalize_weight(g_t_feats[0], style_feats[0])
-
-                normalizer_weights[0].append(loss_normalizer) # First relu
-                unnormalized_losses[0].append(loss_unnormalized) # First relu
-                loss_unnorm_s = loss_unnormalized
-                for i in range(1, 4):
-                    loss_normalizer = sanet.return_normalize_weight(g_t_feats[i], style_feats[i])
-                    loss_s += calc_ast_style_loss(g_t_feats[i], style_feats[i])
-                    loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[i], style_feats[i])
-                    loss_unnorm_s += loss_unnormalized
-                    normalizer_weights[i].append(loss_normalizer)  # ith relu
-                    unnormalized_losses[i].append(loss_unnormalized) # ith relu
-                #print("Default : ",loss_s.item())
-                default_loss = loss_s
-            
-                
-                loss_s = calc_ast_style_loss_normalized(g_t_feats[0], style_feats[0])
-                for i in range(1, 4):
-                    loss_s += calc_ast_style_loss_normalized(g_t_feats[i], style_feats[i])
-                #print("Balanced : ", loss_s.item())
-                balanced_loss = loss_s
-
-                
-                content = transfer
-                # free some memory since when we load the second model, we need this
-                del Content4_1
-                del g_t_feats
-                del gt1_1
-                del gt2_1
-                del gt3_1
-                del gt4_1
-                del Style1_1
-                del Style2_1
-                del Style3_1
-                del style_feats
-                del Style4_1
-                gc.collect()
-
-            content.clamp(0, 255)
-            contents.append(content.detach().cpu())
-            balanced_losses.append(balanced_loss.cpu())
-            default_losses.append(default_loss.cpu())
-            default_unnormalized_losses.append(loss_unnorm_s.cpu())
-            image_contents.append(contents) # (content,style,stylized), (content,style,stylized)..
-        model_image_contents.append(image_contents) # ((model contents), (model contents))
-        del model
-
-#_____________________________________EXPERIMENTS______________________________________#
-import matplotlib.pyplot as plt
-# losses are in shape model x steps x imsize -> model, steps x imsize
-default_losses = np.array([los.cpu() for los in default_losses])
-default_losses = default_losses.reshape(len(model_list),-1)
-
-balanced_losses = np.array([los.cpu() for los in balanced_losses])
-balanced_losses = balanced_losses.reshape(len(model_list),-1)
-
-default_unnormalized_losses = np.array([los.cpu() for los in default_unnormalized_losses])
-default_unnormalized_losses = default_unnormalized_losses.reshape(len(model_list),-1)
-
-for i in range(len(model_list)):
-    print("___________________________________")
-    print(f"Default loss : {np.mean(default_losses[i])}")
-    print(f"Balanced loss : {np.mean(balanced_losses[i])}")
-    print(f"Unnormalized loss : {np.mean(default_unnormalized_losses[i])}")
-    print("___________________________________")
-
-
-low_to_high = np.argsort(balanced_losses[1])
-low_index = low_to_high[5]
-high_index = low_to_high[-5]
-utils.show_image_result(model_image_contents,low_index, title = "Balanced LinearTransfer, image with small style loss")
-utils.show_image_result(model_image_contents,high_index, title = "Balanced LinearTransfer, image with high style loss")
-
-
-
-low_to_high_default = np.argsort(default_losses[0])
-low_index_bal = low_to_high_default[5]
-high_index_bal = low_to_high_default[-5]
-
-utils.show_image_result(model_image_contents,low_index_bal, title = "Default LinearTransfer, image with small style loss")
-utils.show_image_result(model_image_contents,high_index_bal, title = "Default LinearTransfer, image with high style loss")
-
-utils.show_loss_distribution(balanced_losses[1], save_name = "balanced_lt", title = "Loss distribution of balanced losses.")
-utils.show_loss_distribution(default_unnormalized_losses[2], save_name = "default_lt", title = "Loss distribution of unnormalized classic losses.")
-# show_relation_graph(unnormalized_losses[0],normalizer_weights[0], "r1")
-# show_relation_graph(unnormalized_losses[1],normalizer_weights[1], "r2")
-# show_relation_graph(unnormalized_losses[2],normalizer_weights[2], "r3")
-# show_relation_graph(unnormalized_losses[3],normalizer_weights[3], "r4" )
+import argparse
+from email.policy import default
+import torch
+import gc
+from tqdm import tqdm
+import torch.nn as nn
+
+from libs.models_sanet import calc_ast_style_loss, calc_ast_style_loss_normalized, calc_ast_style_loss_unnormalized,Transform,vgg,test_transform,Net
+
+import libs.functions as utils
+import libs.models_sanet as sanet
+import libs.models_adain as adain
+import libs.models_linear_transfer as lt
+from torchvision.utils import save_image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("ok")
+
+
+#_____________________________________LOAD MODELS_______________________________________#
+
+vgg_r41_path = "experiments/linear-transfer/vgg_r41.pth" # encoder path - pretrained
+dec_r41_path = "experiments/linear-transfer/dec_r41.pth" # decoder path - pretrained
+loss_network_path = "xperiments/linear-transfer/vgg_r41.pth"
+
+r41_default_path =  "experiments/linear-transfer/r41_default_style5_160000.pth"  #matrix path - trained
+r41_normalized_path =  "experiments/linear-transfer/r41_normalized_style5_160000.pth"
+r41_pretrained_path = "experiments/linear-transfer/r41.pth"
+
+
+
+vgg = sanet.vgg
+dec = lt.decoder4()
+
+default_matrix = lt.MulLayer("r41") # we used r41 for training
+balanced_matrix = lt.MulLayer("r41") # we used r41 for training
+pretrained_matrix = lt.MulLayer("r41") # we used r41 for training
+
+
+dec.load_state_dict(torch.load(dec_r41_path))
+
+default_matrix.load_state_dict(torch.load(r41_default_path))
+balanced_matrix.load_state_dict(torch.load(r41_normalized_path))
+pretrained_matrix.load_state_dict(torch.load(r41_pretrained_path))
+
+
+model_list = [default_matrix, balanced_matrix, pretrained_matrix]
+
+#vgg = adain.vgg
+vgg.load_state_dict(torch.load("vgg_normalised.pth"))
+
+print(f"Models loaded succesfully. Total model size : {len(model_list)}")
+
+## Switch the device of the models for faster performance
+vgg = nn.Sequential(*list(vgg.children())[:44]) # get the VGG layers
+
+norm = nn.Sequential(*list(vgg.children())[:1])
+enc_1 = nn.Sequential(*list(vgg.children())[:4])  # input -> relu1_1
+enc_2 = nn.Sequential(*list(vgg.children())[4:11])  # relu1_1 -> relu2_1
+enc_3 = nn.Sequential(*list(vgg.children())[11:18])  # relu2_1 -> relu3_1
+enc_4 = nn.Sequential(*list(vgg.children())[18:31])  # relu3_1 -> relu4_1
+enc_5 = nn.Sequential(*list(vgg.children())[31:44])  # relu4_1 -> relu5_1
+
+enc_1.to(device)
+enc_2.to(device)
+enc_3.to(device)
+enc_4.to(device)
+vgg.to(device)
+dec.to(device)
+
+for model in model_list:
+    model.to(device)
+
+print(f"Models switched to defice successfully. Device : {device}")
+
+# These classes and functions are key for loading the dataset and sampling from it.
+from torch.utils import data
+from pathlib import Path
+import numpy as np
+
+#_____________________________________SET PARAMETERS____________________________________#
+
+parser = argparse.ArgumentParser()
+
+# Basic options to evaluate models
+parser.add_argument('--steps', type=int, default = 1,
+                    help='Steps of style transfer.')
+parser.add_argument('--many_image', type=int, default = 2000,
+                    help='Total amount of generated pair.')
+parser.add_argument('--content_dir', type=str, default = "contents/",
+                    help='Path where the content images located.')
+parser.add_argument('--style_dir', type=str, default = "styles/",
+                    help='Path where the style images located.')
+
+parser.add_argument('--content_size', type=int, default = 256,
+                    help='Size of the content image.')
+parser.add_argument('--style_size', type=int, default = 256,
+                    help='Size of the style image.')
+parser.add_argument('--crop', type=int, default = True,
+                    help='Crop the image or not.')
+args = parser.parse_args()
+
+steps = args.steps
+many_image = args.many_image
+content_dir = args.content_dir
+style_dir = args.style_dir
+
+content_size = args.content_size
+style_size = args.style_size
+crop = args.crop
+
+
+#_____________________________________DATA PROCESS______________________________________#
+
+content_tf = adain.test_transform(content_size, crop) # Center and crop if image size is bigger than 256. Use the transformer
+style_tf = adain.test_transform(style_size, crop)     # in the adain.
+
+content_dataset = utils.FlatFolderDataset(content_dir, content_tf)
+style_dataset = utils.FlatFolderDataset(style_dir, style_tf)
+
+content_iter = iter(data.DataLoader(
+    content_dataset, batch_size= 1,
+    sampler=utils.InfiniteSamplerWrapper(content_dataset)))
+
+style_iter = iter(data.DataLoader(
+    style_dataset, batch_size= 1,
+    sampler=utils.InfiniteSamplerWrapper(style_dataset)))
+
+
+#_____________________________________START TESTING______________________________________#
+
+torch.cuda.empty_cache()    
+
+contents = []
+multi_content = []
+
+balanced_losses = []
+default_losses =[]
+default_unnormalized_losses = []
+
+model_image_contents = []
+unnormalized_losses = [[],[],[],[]]
+normalizer_weights = [[],[],[],[]]
+
+content_set = [next(content_iter).to(device) for i in range(many_image)]
+style_set = [next(style_iter).to(device) for i in range(many_image)]
+
+with torch.no_grad():
+    for model in model_list:
+        image_contents = []
+        for im in tqdm(range(many_image)):
+            content = content_set[im]
+            style = style_set[im]
+            
+            contents = []
+            contents.append(content)
+            contents.append(style)
+            #print(content.shape)
+            for step in range(steps):
+
+                # Linear tranfer also uses R11 R21 R31 and R41
+                Style1_1 = enc_1(style)
+                Style2_1 = enc_2(Style1_1)
+                Style3_1 = enc_3(Style2_1)
+                Style4_1 = enc_4(Style3_1)
+
+                style_feats = [Style1_1,Style2_1,Style3_1,Style4_1]
+                # Content uses R41
+
+                Content4_1 = enc_4(enc_3(enc_2(enc_1(content))))
+
+                feature,transmatrix = model(Content4_1,Style4_1)
+                transfer = dec(feature) # use this as content!
+
+                # sF_loss = vgg(style)
+                # cF_loss = vgg(content)
+        
+                gt1_1 = enc_1(transfer)
+                gt2_1 = enc_2(gt1_1)
+                gt3_1 = enc_3(gt2_1)
+                gt4_1 = enc_4(gt3_1) # stylized + decoder
+
+                #content = style_transfer(vgg, model, content, style, alpha)
+
+                g_t_feats = [gt1_1,gt2_1,gt3_1,gt4_1]
+                
+
+                loss_s = calc_ast_style_loss(g_t_feats[0], style_feats[0])
+                loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[0], style_feats[0])
+                loss_normalizer = sanet.return_normalize_weight(g_t_feats[0], style_feats[0])
+
+                normalizer_weights[0].append(loss_normalizer) # First relu
+                unnormalized_losses[0].append(loss_unnormalized) # First relu
+                loss_unnorm_s = loss_unnormalized
+                for i in range(1, 4):
+                    loss_normalizer = sanet.return_normalize_weight(g_t_feats[i], style_feats[i])
+                    loss_s += calc_ast_style_loss(g_t_feats[i], style_feats[i])
+                    loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[i], style_feats[i])
+                    loss_unnorm_s += loss_unnormalized
+                    normalizer_weights[i].append(loss_normalizer)  # ith relu
+                    unnormalized_losses[i].append(loss_unnormalized) # ith relu
+                #print("Default : ",loss_s.item())
+                default_loss = loss_s
+            
+                
+                loss_s = calc_ast_style_loss_normalized(g_t_feats[0], style_feats[0])
+                for i in range(1, 4):
+                    loss_s += calc_ast_style_loss_normalized(g_t_feats[i], style_feats[i])
+                #print("Balanced : ", loss_s.item())
+                balanced_loss = loss_s
+
+                
+                content = transfer
+                # free some memory since when we load the second model, we need this
+                del Content4_1
+                del g_t_feats
+                del gt1_1
+                del gt2_1
+                del gt3_1
+                del gt4_1
+                del Style1_1
+                del Style2_1
+                del Style3_1
+                del style_feats
+                del Style4_1
+                gc.collect()
+
+            content.clamp(0, 255)
+            contents.append(content.detach().cpu())
+            balanced_losses.append(balanced_loss.cpu())
+            default_losses.append(default_loss.cpu())
+            default_unnormalized_losses.append(loss_unnorm_s.cpu())
+            image_contents.append(contents) # (content,style,stylized), (content,style,stylized)..
+        model_image_contents.append(image_contents) # ((model contents), (model contents))
+        del model
+
+#_____________________________________EXPERIMENTS______________________________________#
+import matplotlib.pyplot as plt
+# losses are in shape model x steps x imsize -> model, steps x imsize
+default_losses = np.array([los.cpu() for los in default_losses])
+default_losses = default_losses.reshape(len(model_list),-1)
+
+balanced_losses = np.array([los.cpu() for los in balanced_losses])
+balanced_losses = balanced_losses.reshape(len(model_list),-1)
+
+default_unnormalized_losses = np.array([los.cpu() for los in default_unnormalized_losses])
+default_unnormalized_losses = default_unnormalized_losses.reshape(len(model_list),-1)
+
+for i in range(len(model_list)):
+    print("___________________________________")
+    print(f"Default loss : {np.mean(default_losses[i])}")
+    print(f"Balanced loss : {np.mean(balanced_losses[i])}")
+    print(f"Unnormalized loss : {np.mean(default_unnormalized_losses[i])}")
+    print("___________________________________")
+
+
+low_to_high = np.argsort(balanced_losses[1])
+low_index = low_to_high[5]
+high_index = low_to_high[-5]
+utils.show_image_result(model_image_contents,low_index, title = "Balanced LinearTransfer, image with small style loss")
+utils.show_image_result(model_image_contents,high_index, title = "Balanced LinearTransfer, image with high style loss")
+
+
+
+low_to_high_default = np.argsort(default_losses[0])
+low_index_bal = low_to_high_default[5]
+high_index_bal = low_to_high_default[-5]
+
+utils.show_image_result(model_image_contents,low_index_bal, title = "Default LinearTransfer, image with small style loss")
+utils.show_image_result(model_image_contents,high_index_bal, title = "Default LinearTransfer, image with high style loss")
+
+utils.show_loss_distribution(balanced_losses[1], save_name = "balanced_lt", title = "Loss distribution of balanced losses.")
+utils.show_loss_distribution(default_unnormalized_losses[2], save_name = "default_lt", title = "Loss distribution of unnormalized classic losses.")
+# show_relation_graph(unnormalized_losses[0],normalizer_weights[0], "r1")
+# show_relation_graph(unnormalized_losses[1],normalizer_weights[1], "r2")
+# show_relation_graph(unnormalized_losses[2],normalizer_weights[2], "r3")
+# show_relation_graph(unnormalized_losses[3],normalizer_weights[3], "r4" )
 # utils.show_relation_graph(unnormalized_losses,normalizer_weights, "r5")
\ No newline at end of file
diff --git a/Project_Kargi/sanet_main.py b/Project_Kargi/sanet_main.py
index 904a9d5..b81ca78 100644
--- a/Project_Kargi/sanet_main.py
+++ b/Project_Kargi/sanet_main.py
@@ -1,241 +1,241 @@
-import argparse
-from email.policy import default
-import torch
-from torch.utils import data
-import numpy as np
-import copy
-import torch.nn as nn
-from PIL import Image
-from torchvision.utils import save_image
-from libs.models_sanet import calc_ast_style_loss, calc_ast_style_loss_normalized, calc_ast_style_loss_unnormalized, decoder as decoder,Transform,vgg,test_transform,Net
-from tqdm import tqdm
-import libs.models_sanet as sanet
-import libs.functions as utils
-from tqdm import tqdm
-from torchvision.utils import save_image
-import gc
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-#_____________________________________LOAD MODELS_______________________________________#
-sanet_default_path = r"experiments\sanet\decoder_default_iter_120000.pth"
-transformer_default_path = r"experiments\sanet\transformer_default_iter_120000.pth"
-
-sanet_balanced_path = r"experiments/sanet/decoder_normalized_iter_160000.pth"
-transformer_balanced_path = r"experiments/sanet/transformer_normalized_iter_160000.pth"
-
-sanet_pretrained_path = r"experiments\sanet\decoder_pretrained_iter_500000.pth"
-transformer_pretrained_path = r"experiments\sanet\transformer_pretrained_iter_500000.pth"
-
-
-sanet_default = copy.deepcopy(sanet.decoder)
-sanet_default.load_state_dict(torch.load(sanet_default_path))
-sanet_default.eval()
-transformer_default = sanet.Transform(in_planes = 512)
-transformer_default.load_state_dict(torch.load(transformer_default_path))
-transformer_default.eval()
-
-sanet_balanced = copy.deepcopy(sanet.decoder)
-sanet_balanced.load_state_dict(torch.load(sanet_balanced_path))
-sanet_balanced.eval()
-transformer_balanced = sanet.Transform(in_planes = 512)
-transformer_balanced.load_state_dict(torch.load(transformer_balanced_path))
-transformer_balanced.eval()
-
-sanet_pretrained = copy.deepcopy(sanet.decoder)
-sanet_pretrained.load_state_dict(torch.load(sanet_pretrained_path))
-sanet_pretrained.eval()
-transformer_pretrained = sanet.Transform(in_planes = 512)
-transformer_pretrained.load_state_dict(torch.load(transformer_pretrained_path))
-transformer_pretrained.eval()
-
-model_list = [(sanet_default,transformer_default),(sanet_balanced,transformer_balanced),(sanet_pretrained,transformer_pretrained)]
-
-vgg = sanet.vgg
-vgg.eval()
-
-vgg.load_state_dict(torch.load("vgg_normalised.pth"))
-
-print(f"Models loaded succesfully. Total model size : {len(model_list)}")
-
-## Switch the device of the models for faster performance
-norm = nn.Sequential(*list(vgg.children())[:1])
-enc_1 = nn.Sequential(*list(vgg.children())[:4])  # input -> relu1_1
-enc_2 = nn.Sequential(*list(vgg.children())[4:11])  # relu1_1 -> relu2_1
-enc_3 = nn.Sequential(*list(vgg.children())[11:18])  # relu2_1 -> relu3_1
-enc_4 = nn.Sequential(*list(vgg.children())[18:31])  # relu3_1 -> relu4_1
-enc_5 = nn.Sequential(*list(vgg.children())[31:44])  # relu4_1 -> relu5_1
-
-norm.to(device)
-enc_1.to(device)
-enc_2.to(device)
-enc_3.to(device)
-enc_4.to(device)
-enc_5.to(device)
-
-for model in model_list:
-    model[0].to(device)
-    model[1].to(device)
-
-print(f"Models switched to defice successfully. Device : {device}")
-
-# These classes and functions are key for loading the dataset and sampling from it.
-
-
-
-
-#_____________________________________SET PARAMETERS____________________________________#
-
-parser = argparse.ArgumentParser()
-
-# Basic options to evaluate models
-parser.add_argument('--steps', type=int, default = 1,
-                    help='Steps of style transfer.')
-parser.add_argument('--many_image', type=int, default = 2000,
-                    help='Total amount of generated pair.')
-parser.add_argument('--content_dir', type=str, default = "contents/",
-                    help='Path where the content images located.')
-parser.add_argument('--style_dir', type=str, default = "styles/",
-                    help='Path where the style images located.')
-
-args = parser.parse_args()
-
-steps = args.steps
-many_image = args.many_image
-content_dir = args.content_dir
-style_dir = args.style_dir
-
-#_____________________________________DATA PROCESS______________________________________#
-content_tf = sanet.test_transform()
-style_tf = sanet.test_transform()
-
-content_dataset = utils.FlatFolderDataset(content_dir, content_tf)
-style_dataset = utils.FlatFolderDataset(style_dir, style_tf)
-
-content_iter = iter(data.DataLoader(
-    content_dataset, batch_size= 1,
-    sampler=utils.InfiniteSamplerWrapper(content_dataset)))
-
-style_iter = iter(data.DataLoader(
-    style_dataset, batch_size= 1,
-    sampler=utils.InfiniteSamplerWrapper(style_dataset)))
-
-
-#_____________________________________START TESTING______________________________________#
-torch.cuda.empty_cache()    
-contents = []
-multi_content = []
-balanced_losses = []
-default_losses =[]
-model_image_contents = []
-unnormalized_losses = [[],[],[],[],[]]
-normalizer_weights = [[],[],[],[],[]]
-default_unnormalized_losses = []
-
-content_set = [next(content_iter).to(device) for i in range(many_image)]
-style_set = [next(style_iter).to(device) for i in range(many_image)]
-
-with torch.no_grad():
-    for model in model_list:
-        image_contents = []
-        for im in tqdm(range(many_image)):
-            content = content_set[im]
-            style = style_set[im]
-            
-            contents = []
-            contents.append(content.cpu())
-            contents.append(style.cpu())
-            #print(content.shape)
-            for step in range(steps):
-
-                Content4_1 = enc_4(enc_3(enc_2(enc_1(content))))
-                Content5_1 = enc_5(Content4_1)
-            
-                Style1_1 = enc_1(style)
-                Style2_1 = enc_2(Style1_1)
-                Style3_1 = enc_3(Style2_1)
-                Style4_1 = enc_4(Style3_1)
-                Style5_1 = enc_5(Style4_1)
-
-                content = model[0](model[1](Content4_1, Style4_1, Content5_1, Style5_1))
-
-                gt1_1 = enc_1(content)
-                gt2_1 = enc_2(gt1_1)
-                gt3_1 = enc_3(gt2_1)
-                gt4_1 = enc_4(gt3_1) # stylized + decoder
-                gt5_1 = enc_5(gt4_1)
-
-                g_t_feats = [gt1_1,gt2_1,gt3_1,gt4_1,gt5_1]
-                style_feats = [Style1_1,Style2_1,Style3_1,Style4_1,Style5_1]
-
-                loss_s = calc_ast_style_loss(g_t_feats[0], style_feats[0])
-                loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[0], style_feats[0])
-                loss_normalizer = sanet.return_normalize_weight(g_t_feats[0], style_feats[0])
-                unnormalized_losses[0].append(loss_unnormalized) # First relu
-                normalizer_weights[0].append(loss_normalizer) # First relu
-                loss_unnorm_s = loss_unnormalized
-
-                for i in range(1, 5):
-                    loss_normalizer = sanet.return_normalize_weight(g_t_feats[i], style_feats[i])
-                    loss_s += calc_ast_style_loss(g_t_feats[i], style_feats[i])
-                    loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[i], style_feats[i])
-                    loss_unnorm_s += loss_unnormalized
-                    normalizer_weights[i].append(loss_normalizer)  # ith relu
-                    unnormalized_losses[i].append(loss_unnormalized) # ith relu
-                #print("Default : ",loss_s.item())
-                default_loss = loss_s
-                
-
-                
-                loss_s = calc_ast_style_loss_normalized(g_t_feats[0], style_feats[0])
-                for i in range(1, 5):
-                    loss_s += calc_ast_style_loss_normalized(g_t_feats[i], style_feats[i])
-                #print("Balanced : ", loss_s.item())
-                balanced_loss = loss_s
-            
-                # free some memory since when we load the second model, we need this
-                del Content4_1
-                del Content5_1
-                del g_t_feats
-                del gt1_1
-                del gt2_1
-                del gt3_1
-                del gt4_1
-                del gt5_1
-                del Style1_1
-                del Style2_1
-                del Style3_1
-                del style_feats
-                del Style4_1
-                del Style5_1
-                gc.collect()
-            content.clamp(0, 255)
-            contents.append(content.detach().cpu())
-            balanced_losses.append(balanced_loss.cpu())
-            default_losses.append(default_loss.cpu())
-            default_unnormalized_losses.append(loss_unnorm_s.cpu())
-            image_contents.append(contents) # (content,style,stylized), (content,style,stylized)..
-        model_image_contents.append(image_contents) # ((model contents), (model contents))
-        del model
-
-#_____________________________________EXPERIMENTS______________________________________#
-# losses are in shape model x steps x imsize -> model, steps x imsize
-default_losses = np.array([los.cpu() for los in default_losses])
-default_losses = default_losses.reshape(len(model_list),-1)
-
-balanced_losses = np.array([los.cpu() for los in balanced_losses])
-balanced_losses = balanced_losses.reshape(len(model_list),-1)
-
-default_unnormalized_losses = np.array([los.cpu() for los in default_unnormalized_losses])
-default_unnormalized_losses = default_unnormalized_losses.reshape(len(model_list),-1)
-
-for i in range(len(model_list)):
-    print("___________________________________")
-    print(f"Default loss : {np.mean(default_losses[i])}")
-    print(f"Balanced loss : {np.mean(balanced_losses[i])}")
-    print(f"Unnormalized loss : {np.mean(default_unnormalized_losses[i])}")
-    print("___________________________________")
-
-#utils.show_image_result(model_image_contents,0, title = "SANET")
-utils.show_loss_distribution(balanced_losses[1], save_name = "balanced_sanet", title = "Loss distribution of balanced losses for balance style trained model.")
-utils.show_loss_distribution(default_unnormalized_losses[2], save_name = "default_sanet_unnorm", title = "Loss distribution of unnormalized classic losses for pretrained models.")
+import argparse
+from email.policy import default
+import torch
+from torch.utils import data
+import numpy as np
+import copy
+import torch.nn as nn
+from PIL import Image
+from torchvision.utils import save_image
+from libs.models_sanet import calc_ast_style_loss, calc_ast_style_loss_normalized, calc_ast_style_loss_unnormalized, decoder as decoder,Transform,vgg,test_transform,Net
+from tqdm import tqdm
+import libs.models_sanet as sanet
+import libs.functions as utils
+from tqdm import tqdm
+from torchvision.utils import save_image
+import gc
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+#_____________________________________LOAD MODELS_______________________________________#
+sanet_default_path = r"experiments\sanet\decoder_default_iter_120000.pth"
+transformer_default_path = r"experiments\sanet\transformer_default_iter_120000.pth"
+
+sanet_balanced_path = r"experiments/sanet/decoder_normalized_iter_160000.pth"
+transformer_balanced_path = r"experiments/sanet/transformer_normalized_iter_160000.pth"
+
+sanet_pretrained_path = r"experiments\sanet\decoder_pretrained_iter_500000.pth"
+transformer_pretrained_path = r"experiments\sanet\transformer_pretrained_iter_500000.pth"
+
+
+sanet_default = copy.deepcopy(sanet.decoder)
+sanet_default.load_state_dict(torch.load(sanet_default_path))
+sanet_default.eval()
+transformer_default = sanet.Transform(in_planes = 512)
+transformer_default.load_state_dict(torch.load(transformer_default_path))
+transformer_default.eval()
+
+sanet_balanced = copy.deepcopy(sanet.decoder)
+sanet_balanced.load_state_dict(torch.load(sanet_balanced_path))
+sanet_balanced.eval()
+transformer_balanced = sanet.Transform(in_planes = 512)
+transformer_balanced.load_state_dict(torch.load(transformer_balanced_path))
+transformer_balanced.eval()
+
+sanet_pretrained = copy.deepcopy(sanet.decoder)
+sanet_pretrained.load_state_dict(torch.load(sanet_pretrained_path))
+sanet_pretrained.eval()
+transformer_pretrained = sanet.Transform(in_planes = 512)
+transformer_pretrained.load_state_dict(torch.load(transformer_pretrained_path))
+transformer_pretrained.eval()
+
+model_list = [(sanet_default,transformer_default),(sanet_balanced,transformer_balanced),(sanet_pretrained,transformer_pretrained)]
+
+vgg = sanet.vgg
+vgg.eval()
+
+vgg.load_state_dict(torch.load("vgg_normalised.pth"))
+
+print(f"Models loaded succesfully. Total model size : {len(model_list)}")
+
+## Switch the device of the models for faster performance
+norm = nn.Sequential(*list(vgg.children())[:1])
+enc_1 = nn.Sequential(*list(vgg.children())[:4])  # input -> relu1_1
+enc_2 = nn.Sequential(*list(vgg.children())[4:11])  # relu1_1 -> relu2_1
+enc_3 = nn.Sequential(*list(vgg.children())[11:18])  # relu2_1 -> relu3_1
+enc_4 = nn.Sequential(*list(vgg.children())[18:31])  # relu3_1 -> relu4_1
+enc_5 = nn.Sequential(*list(vgg.children())[31:44])  # relu4_1 -> relu5_1
+
+norm.to(device)
+enc_1.to(device)
+enc_2.to(device)
+enc_3.to(device)
+enc_4.to(device)
+enc_5.to(device)
+
+for model in model_list:
+    model[0].to(device)
+    model[1].to(device)
+
+print(f"Models switched to defice successfully. Device : {device}")
+
+# These classes and functions are key for loading the dataset and sampling from it.
+
+
+
+
+#_____________________________________SET PARAMETERS____________________________________#
+
+parser = argparse.ArgumentParser()
+
+# Basic options to evaluate models
+parser.add_argument('--steps', type=int, default = 1,
+                    help='Steps of style transfer.')
+parser.add_argument('--many_image', type=int, default = 2000,
+                    help='Total amount of generated pair.')
+parser.add_argument('--content_dir', type=str, default = "contents/",
+                    help='Path where the content images located.')
+parser.add_argument('--style_dir', type=str, default = "styles/",
+                    help='Path where the style images located.')
+
+args = parser.parse_args()
+
+steps = args.steps
+many_image = args.many_image
+content_dir = args.content_dir
+style_dir = args.style_dir
+
+#_____________________________________DATA PROCESS______________________________________#
+content_tf = sanet.test_transform()
+style_tf = sanet.test_transform()
+
+content_dataset = utils.FlatFolderDataset(content_dir, content_tf)
+style_dataset = utils.FlatFolderDataset(style_dir, style_tf)
+
+content_iter = iter(data.DataLoader(
+    content_dataset, batch_size= 1,
+    sampler=utils.InfiniteSamplerWrapper(content_dataset)))
+
+style_iter = iter(data.DataLoader(
+    style_dataset, batch_size= 1,
+    sampler=utils.InfiniteSamplerWrapper(style_dataset)))
+
+
+#_____________________________________START TESTING______________________________________#
+torch.cuda.empty_cache()    
+contents = []
+multi_content = []
+balanced_losses = []
+default_losses =[]
+model_image_contents = []
+unnormalized_losses = [[],[],[],[],[]]
+normalizer_weights = [[],[],[],[],[]]
+default_unnormalized_losses = []
+
+content_set = [next(content_iter).to(device) for i in range(many_image)]
+style_set = [next(style_iter).to(device) for i in range(many_image)]
+
+with torch.no_grad():
+    for model in model_list:
+        image_contents = []
+        for im in tqdm(range(many_image)):
+            content = content_set[im]
+            style = style_set[im]
+            
+            contents = []
+            contents.append(content.cpu())
+            contents.append(style.cpu())
+            #print(content.shape)
+            for step in range(steps):
+
+                Content4_1 = enc_4(enc_3(enc_2(enc_1(content))))
+                Content5_1 = enc_5(Content4_1)
+            
+                Style1_1 = enc_1(style)
+                Style2_1 = enc_2(Style1_1)
+                Style3_1 = enc_3(Style2_1)
+                Style4_1 = enc_4(Style3_1)
+                Style5_1 = enc_5(Style4_1)
+
+                content = model[0](model[1](Content4_1, Style4_1, Content5_1, Style5_1))
+
+                gt1_1 = enc_1(content)
+                gt2_1 = enc_2(gt1_1)
+                gt3_1 = enc_3(gt2_1)
+                gt4_1 = enc_4(gt3_1) # stylized + decoder
+                gt5_1 = enc_5(gt4_1)
+
+                g_t_feats = [gt1_1,gt2_1,gt3_1,gt4_1,gt5_1]
+                style_feats = [Style1_1,Style2_1,Style3_1,Style4_1,Style5_1]
+
+                loss_s = calc_ast_style_loss(g_t_feats[0], style_feats[0])
+                loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[0], style_feats[0])
+                loss_normalizer = sanet.return_normalize_weight(g_t_feats[0], style_feats[0])
+                unnormalized_losses[0].append(loss_unnormalized) # First relu
+                normalizer_weights[0].append(loss_normalizer) # First relu
+                loss_unnorm_s = loss_unnormalized
+
+                for i in range(1, 5):
+                    loss_normalizer = sanet.return_normalize_weight(g_t_feats[i], style_feats[i])
+                    loss_s += calc_ast_style_loss(g_t_feats[i], style_feats[i])
+                    loss_unnormalized = calc_ast_style_loss_unnormalized(g_t_feats[i], style_feats[i])
+                    loss_unnorm_s += loss_unnormalized
+                    normalizer_weights[i].append(loss_normalizer)  # ith relu
+                    unnormalized_losses[i].append(loss_unnormalized) # ith relu
+                #print("Default : ",loss_s.item())
+                default_loss = loss_s
+                
+
+                
+                loss_s = calc_ast_style_loss_normalized(g_t_feats[0], style_feats[0])
+                for i in range(1, 5):
+                    loss_s += calc_ast_style_loss_normalized(g_t_feats[i], style_feats[i])
+                #print("Balanced : ", loss_s.item())
+                balanced_loss = loss_s
+            
+                # free some memory since when we load the second model, we need this
+                del Content4_1
+                del Content5_1
+                del g_t_feats
+                del gt1_1
+                del gt2_1
+                del gt3_1
+                del gt4_1
+                del gt5_1
+                del Style1_1
+                del Style2_1
+                del Style3_1
+                del style_feats
+                del Style4_1
+                del Style5_1
+                gc.collect()
+            content.clamp(0, 255)
+            contents.append(content.detach().cpu())
+            balanced_losses.append(balanced_loss.cpu())
+            default_losses.append(default_loss.cpu())
+            default_unnormalized_losses.append(loss_unnorm_s.cpu())
+            image_contents.append(contents) # (content,style,stylized), (content,style,stylized)..
+        model_image_contents.append(image_contents) # ((model contents), (model contents))
+        del model
+
+#_____________________________________EXPERIMENTS______________________________________#
+# losses are in shape model x steps x imsize -> model, steps x imsize
+default_losses = np.array([los.cpu() for los in default_losses])
+default_losses = default_losses.reshape(len(model_list),-1)
+
+balanced_losses = np.array([los.cpu() for los in balanced_losses])
+balanced_losses = balanced_losses.reshape(len(model_list),-1)
+
+default_unnormalized_losses = np.array([los.cpu() for los in default_unnormalized_losses])
+default_unnormalized_losses = default_unnormalized_losses.reshape(len(model_list),-1)
+
+for i in range(len(model_list)):
+    print("___________________________________")
+    print(f"Default loss : {np.mean(default_losses[i])}")
+    print(f"Balanced loss : {np.mean(balanced_losses[i])}")
+    print(f"Unnormalized loss : {np.mean(default_unnormalized_losses[i])}")
+    print("___________________________________")
+
+#utils.show_image_result(model_image_contents,0, title = "SANET")
+utils.show_loss_distribution(balanced_losses[1], save_name = "balanced_sanet", title = "Loss distribution of balanced losses for balance style trained model.")
+utils.show_loss_distribution(default_unnormalized_losses[2], save_name = "default_sanet_unnorm", title = "Loss distribution of unnormalized classic losses for pretrained models.")
 utils.show_relation_graph(unnormalized_losses,normalizer_weights)
\ No newline at end of file
diff --git a/Project_Macan_Oydu/codes/NetworkA.py b/Project_Macan_Oydu/codes/NetworkA.py
index 9ac37d9..f282111 100644
--- a/Project_Macan_Oydu/codes/NetworkA.py
+++ b/Project_Macan_Oydu/codes/NetworkA.py
@@ -1,126 +1,126 @@
-import torch
-import torch.nn as nn
-
-class FirstConvolution(nn.Module):
-
-    def __init__(self, input_channel, output_channel, expansion_ratio=4):
-        super(FirstConvolution, self).__init__()
-        self.middle_layer_size =  input_channel*expansion_ratio
-        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
-        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
-        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
-        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
-
-    def forward(self, x):
-        input = x.clone().detach()
-        x = self.conv1(x)
-        x = self.depthConv(x)
-        x = self.conv2(x)
-        x = x + self.conv_outer(input)
-        return x
-
-class DownConvolution(nn.Module):
-
-    def __init__(self, input_channel, output_channel, expansion_ratio=4):
-        super(DownConvolution, self).__init__()
-        self.middle_layer_size =  input_channel*expansion_ratio
-        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
-        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
-        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
-        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
-        self.maxpool = nn.MaxPool2d(2,2)
-
-    def forward(self, x):
-        x = self.maxpool(x)
-        input = x.clone().detach() 
-        x = self.conv1(x)
-        x = self.depthConv(x)
-        x = self.conv2(x)
-        x = x + self.conv_outer(input)
-        return x
-
-class UpConvolution(nn.Module):
-
-    def __init__(self, input_channel, output_channel, expansion_ratio=4):
-        super(UpConvolution, self).__init__()
-        self.middle_layer_size =  input_channel*expansion_ratio
-        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
-        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
-        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
-        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
-        self.convtranspose = nn.ConvTranspose2d(output_channel, output_channel//2, (2,2), (2,2))
-
-    def forward(self, x):
-        input = x.clone().detach() 
-        x = self.conv1(x)
-        x = self.depthConv(x)
-        x = self.conv2(x)
-        x = x + self.conv_outer(input)
-        x = self.convtranspose(x)
-        return x
-
-class LastConvolution(nn.Module):
-
-    def __init__(self, input_channel, output_channel, out_size, expansion_ratio=4):
-        super(LastConvolution, self).__init__()
-        self.middle_layer_size =  input_channel*expansion_ratio
-        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
-        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
-        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
-        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
-        self.conv3 = nn.Conv2d(output_channel, out_size, (1,1))
-
-    def forward(self, x):
-        input = x.clone().detach()
-        x = self.conv1(x)
-        x = self.depthConv(x)
-        x = self.conv2(x)
-        x = x + self.conv_outer(input)
-        x = self.conv3(x)
-        return x
-
-class NetworkA(nn.Module):
-
-    def __init__(self, in_size = 4, out_size = 3, expansion_ratio = 4):
-        super(NetworkA, self).__init__()
-        self.simpleConv = FirstConvolution(in_size, 32, expansion_ratio=expansion_ratio)
-        self.downConv1 = DownConvolution(32, 64, expansion_ratio=expansion_ratio)
-        self.downConv2 = DownConvolution(64, 128, expansion_ratio=expansion_ratio)
-        self.downConv3 = DownConvolution(128, 256, expansion_ratio=expansion_ratio)
-        self.maxpool = nn.MaxPool2d(2, 2)
-        self.bridge = UpConvolution(256, 512, expansion_ratio=expansion_ratio)
-        self.upConv1 = UpConvolution(512, 256, expansion_ratio=expansion_ratio)
-        self.upConv2 = UpConvolution(256, 128, expansion_ratio=expansion_ratio)
-        self.upConv3 = UpConvolution(128, 64, expansion_ratio=expansion_ratio)
-        self.lastConv = LastConvolution(64, 32, out_size, expansion_ratio=expansion_ratio)
-    
-    def forward(self, x):
-        x1 = self.simpleConv(x)
-        x2 = self.downConv1(x1)
-        x3 = self.downConv2(x2)
-        x4 = self.downConv3(x3)
-        x5 = self.maxpool(x4)
-        x6 = self.bridge(x5)
-        x7 = self.upConv1(torch.cat((x4, x6), 1))
-        x8 = self.upConv2(torch.cat((x3, x7), 1))
-        x9 = self.upConv3(torch.cat((x2, x8), 1))
-        A = self.lastConv(torch.cat((x1, x9), 1))
-        return A
-
-class NetworkA_iter(nn.Module):
-
-    def __init__(self, expansion_ratio = 4, Eta = 0.01, Beta = 0.01, Lambda = 0.01):
-        super(NetworkA_iter, self).__init__()
-        self.Eta = nn.Parameter(torch.Tensor([Eta]), requires_grad=True)
-        self.Beta = nn.Parameter(torch.Tensor([Beta]), requires_grad=True)
-        self.Lambda = nn.Parameter(torch.Tensor([Lambda]), requires_grad=True)
-        self.NetworkA = NetworkA(expansion_ratio=expansion_ratio)
-    
-    def forward(self,M,A,I_ns,I_s):
-        input = torch.cat([A,M],dim=1)
-        out = self.NetworkA(input)
-        gradient_of_D = torch.mean((-I_ns/torch.pow((1+A),2))*(-I_s+I_ns/(1+A)),1,keepdim=True)
-        gradient_of_g =torch.mean( A*torch.pow((1-M),2),1,keepdim=True)
-        A = A-self.Eta*(gradient_of_D+self.Beta*gradient_of_g+self.Lambda*out)
-        I_ns = I_s+A*I_s
+import torch
+import torch.nn as nn
+
+class FirstConvolution(nn.Module):
+
+    def __init__(self, input_channel, output_channel, expansion_ratio=4):
+        super(FirstConvolution, self).__init__()
+        self.middle_layer_size =  input_channel*expansion_ratio
+        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
+        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
+        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
+        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
+
+    def forward(self, x):
+        input = x.clone().detach()
+        x = self.conv1(x)
+        x = self.depthConv(x)
+        x = self.conv2(x)
+        x = x + self.conv_outer(input)
+        return x
+
+class DownConvolution(nn.Module):
+
+    def __init__(self, input_channel, output_channel, expansion_ratio=4):
+        super(DownConvolution, self).__init__()
+        self.middle_layer_size =  input_channel*expansion_ratio
+        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
+        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
+        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
+        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
+        self.maxpool = nn.MaxPool2d(2,2)
+
+    def forward(self, x):
+        x = self.maxpool(x)
+        input = x.clone().detach() 
+        x = self.conv1(x)
+        x = self.depthConv(x)
+        x = self.conv2(x)
+        x = x + self.conv_outer(input)
+        return x
+
+class UpConvolution(nn.Module):
+
+    def __init__(self, input_channel, output_channel, expansion_ratio=4):
+        super(UpConvolution, self).__init__()
+        self.middle_layer_size =  input_channel*expansion_ratio
+        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
+        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
+        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
+        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
+        self.convtranspose = nn.ConvTranspose2d(output_channel, output_channel//2, (2,2), (2,2))
+
+    def forward(self, x):
+        input = x.clone().detach() 
+        x = self.conv1(x)
+        x = self.depthConv(x)
+        x = self.conv2(x)
+        x = x + self.conv_outer(input)
+        x = self.convtranspose(x)
+        return x
+
+class LastConvolution(nn.Module):
+
+    def __init__(self, input_channel, output_channel, out_size, expansion_ratio=4):
+        super(LastConvolution, self).__init__()
+        self.middle_layer_size =  input_channel*expansion_ratio
+        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
+        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
+        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
+        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
+        self.conv3 = nn.Conv2d(output_channel, out_size, (1,1))
+
+    def forward(self, x):
+        input = x.clone().detach()
+        x = self.conv1(x)
+        x = self.depthConv(x)
+        x = self.conv2(x)
+        x = x + self.conv_outer(input)
+        x = self.conv3(x)
+        return x
+
+class NetworkA(nn.Module):
+
+    def __init__(self, in_size = 4, out_size = 3, expansion_ratio = 4):
+        super(NetworkA, self).__init__()
+        self.simpleConv = FirstConvolution(in_size, 32, expansion_ratio=expansion_ratio)
+        self.downConv1 = DownConvolution(32, 64, expansion_ratio=expansion_ratio)
+        self.downConv2 = DownConvolution(64, 128, expansion_ratio=expansion_ratio)
+        self.downConv3 = DownConvolution(128, 256, expansion_ratio=expansion_ratio)
+        self.maxpool = nn.MaxPool2d(2, 2)
+        self.bridge = UpConvolution(256, 512, expansion_ratio=expansion_ratio)
+        self.upConv1 = UpConvolution(512, 256, expansion_ratio=expansion_ratio)
+        self.upConv2 = UpConvolution(256, 128, expansion_ratio=expansion_ratio)
+        self.upConv3 = UpConvolution(128, 64, expansion_ratio=expansion_ratio)
+        self.lastConv = LastConvolution(64, 32, out_size, expansion_ratio=expansion_ratio)
+    
+    def forward(self, x):
+        x1 = self.simpleConv(x)
+        x2 = self.downConv1(x1)
+        x3 = self.downConv2(x2)
+        x4 = self.downConv3(x3)
+        x5 = self.maxpool(x4)
+        x6 = self.bridge(x5)
+        x7 = self.upConv1(torch.cat((x4, x6), 1))
+        x8 = self.upConv2(torch.cat((x3, x7), 1))
+        x9 = self.upConv3(torch.cat((x2, x8), 1))
+        A = self.lastConv(torch.cat((x1, x9), 1))
+        return A
+
+class NetworkA_iter(nn.Module):
+
+    def __init__(self, expansion_ratio = 4, Eta = 0.01, Beta = 0.01, Lambda = 0.01):
+        super(NetworkA_iter, self).__init__()
+        self.Eta = nn.Parameter(torch.Tensor([Eta]), requires_grad=True)
+        self.Beta = nn.Parameter(torch.Tensor([Beta]), requires_grad=True)
+        self.Lambda = nn.Parameter(torch.Tensor([Lambda]), requires_grad=True)
+        self.NetworkA = NetworkA(expansion_ratio=expansion_ratio)
+    
+    def forward(self,M,A,I_ns,I_s):
+        input = torch.cat([A,M],dim=1)
+        out = self.NetworkA(input)
+        gradient_of_D = torch.mean((-I_ns/torch.pow((1+A),2))*(-I_s+I_ns/(1+A)),1,keepdim=True)
+        gradient_of_g =torch.mean( A*torch.pow((1-M),2),1,keepdim=True)
+        A = A-self.Eta*(gradient_of_D+self.Beta*gradient_of_g+self.Lambda*out)
+        I_ns = I_s+A*I_s
         return I_ns, A
\ No newline at end of file
diff --git a/Project_Macan_Oydu/codes/NetworkInit.py b/Project_Macan_Oydu/codes/NetworkInit.py
index a4b3a84..d767eb6 100644
--- a/Project_Macan_Oydu/codes/NetworkInit.py
+++ b/Project_Macan_Oydu/codes/NetworkInit.py
@@ -1,117 +1,117 @@
-import torch
-import torch.nn as nn
-
-class FirstConvolution_init(nn.Module):
-
-    def __init__(self, input_channel, output_channel, expansion_ratio=6):
-        super(FirstConvolution_init, self).__init__()
-        self.middle_layer_size =  input_channel*expansion_ratio
-        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
-        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
-        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
-        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
-
-    def forward(self, x):
-        input = x.clone().detach()
-        input = self.conv_outer(input)
-        x = self.conv1(x)
-        x = self.depthConv(x)
-        x = self.conv2(x)
-        x = x*input
-        x = x + input
-        return x
-
-class DownConvolution_init(nn.Module):
-
-    def __init__(self, input_channel, output_channel, expansion_ratio=6):
-        super(DownConvolution_init, self).__init__()
-        self.middle_layer_size =  input_channel*expansion_ratio
-        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
-        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
-        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
-        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
-        self.maxpool = nn.MaxPool2d(2,2)
-
-    def forward(self, x):
-        x = self.maxpool(x)
-        input = x.clone().detach()
-        input = self.conv_outer(input)
-        x = self.conv1(x)
-        x = self.depthConv(x)
-        x = self.conv2(x)
-        x = x*input
-        x = x + input
-        return x
-
-class UpConvolution_init(nn.Module):
-
-    def __init__(self, input_channel, output_channel, expansion_ratio=6):
-        super(UpConvolution_init, self).__init__()
-        self.middle_layer_size =  input_channel*expansion_ratio
-        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
-        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
-        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
-        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
-        self.convtranspose = nn.ConvTranspose2d(output_channel, output_channel//2, (2,2), (2,2))
-
-    def forward(self, x):
-        input = x.clone().detach()
-        input = self.conv_outer(input)
-        x = self.conv1(x)
-        x = self.depthConv(x)
-        x = self.conv2(x)
-        x = x*input
-        x = x + input
-        x = self.convtranspose(x)
-        return x
-
-class LastConvolution_init(nn.Module):
-
-    def __init__(self, input_channel, output_channel, out_size, expansion_ratio=6):
-        super(LastConvolution_init, self).__init__()
-        self.middle_layer_size =  input_channel*expansion_ratio
-        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
-        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
-        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
-        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
-        self.conv1d = nn.Conv2d(output_channel, out_size, (1,1))
-
-    def forward(self, x):
-        input = x.clone().detach()
-        input = self.conv_outer(input)
-        x = self.conv1(x)
-        x = self.depthConv(x)
-        x = self.conv2(x)
-        x = x*input
-        x = x + input
-        x = self.conv1d(x)
-        return x
-
-class NetworkInit(nn.Module):
-
-    def __init__(self, in_size = 4, out_size = 3, expansion_ratio = 6):
-        super(NetworkInit, self).__init__()
-        self.simpleConv = FirstConvolution_init(in_size, 32, expansion_ratio=expansion_ratio)
-        self.downConv1 = DownConvolution_init(32, 64, expansion_ratio=expansion_ratio)
-        self.downConv2 = DownConvolution_init(64, 128, expansion_ratio=expansion_ratio)
-        self.downConv3 = DownConvolution_init(128, 256, expansion_ratio=expansion_ratio)
-        self.maxpool = nn.MaxPool2d(2, 2)
-        self.bridge = UpConvolution_init(256, 512, expansion_ratio=expansion_ratio)
-        self.upConv1 = UpConvolution_init(512, 256, expansion_ratio=expansion_ratio)
-        self.upConv2 = UpConvolution_init(256, 128, expansion_ratio=expansion_ratio)
-        self.upConv3 = UpConvolution_init(128, 64, expansion_ratio=expansion_ratio)
-        self.lastConv = LastConvolution_init(64, 32, out_size, expansion_ratio=expansion_ratio)
-    
-    def forward(self, x):
-        x1 = self.simpleConv(x)
-        x2 = self.downConv1(x1)
-        x3 = self.downConv2(x2)
-        x4 = self.downConv3(x3)
-        x5 = self.maxpool(x4)
-        x6 = self.bridge(x5)
-        x7 = self.upConv1(torch.cat((x4, x6), 1))
-        x8 = self.upConv2(torch.cat((x3, x7), 1))
-        x9 = self.upConv3(torch.cat((x2, x8), 1))
-        A_0 = self.lastConv(torch.cat((x1, x9), 1))
-        I_ns_0 = x[:,:3,:,:]+A_0*x[:,:3,:,:]
+import torch
+import torch.nn as nn
+
+class FirstConvolution_init(nn.Module):
+
+    def __init__(self, input_channel, output_channel, expansion_ratio=6):
+        super(FirstConvolution_init, self).__init__()
+        self.middle_layer_size =  input_channel*expansion_ratio
+        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
+        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
+        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
+        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
+
+    def forward(self, x):
+        input = x.clone().detach()
+        input = self.conv_outer(input)
+        x = self.conv1(x)
+        x = self.depthConv(x)
+        x = self.conv2(x)
+        x = x*input
+        x = x + input
+        return x
+
+class DownConvolution_init(nn.Module):
+
+    def __init__(self, input_channel, output_channel, expansion_ratio=6):
+        super(DownConvolution_init, self).__init__()
+        self.middle_layer_size =  input_channel*expansion_ratio
+        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
+        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
+        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
+        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
+        self.maxpool = nn.MaxPool2d(2,2)
+
+    def forward(self, x):
+        x = self.maxpool(x)
+        input = x.clone().detach()
+        input = self.conv_outer(input)
+        x = self.conv1(x)
+        x = self.depthConv(x)
+        x = self.conv2(x)
+        x = x*input
+        x = x + input
+        return x
+
+class UpConvolution_init(nn.Module):
+
+    def __init__(self, input_channel, output_channel, expansion_ratio=6):
+        super(UpConvolution_init, self).__init__()
+        self.middle_layer_size =  input_channel*expansion_ratio
+        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
+        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
+        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
+        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
+        self.convtranspose = nn.ConvTranspose2d(output_channel, output_channel//2, (2,2), (2,2))
+
+    def forward(self, x):
+        input = x.clone().detach()
+        input = self.conv_outer(input)
+        x = self.conv1(x)
+        x = self.depthConv(x)
+        x = self.conv2(x)
+        x = x*input
+        x = x + input
+        x = self.convtranspose(x)
+        return x
+
+class LastConvolution_init(nn.Module):
+
+    def __init__(self, input_channel, output_channel, out_size, expansion_ratio=6):
+        super(LastConvolution_init, self).__init__()
+        self.middle_layer_size =  input_channel*expansion_ratio
+        self.conv1 = nn.Sequential(nn.Conv2d(input_channel, self.middle_layer_size, (1,1)), nn.LeakyReLU())
+        self.conv2 = nn.Sequential(nn.LeakyReLU(), nn.Conv2d(self.middle_layer_size, output_channel, (1,1)))
+        self.conv_outer = nn.Conv2d(input_channel, output_channel, (1,1))
+        self.depthConv = nn.Conv2d(self.middle_layer_size,self.middle_layer_size,(3,3),stride=1,padding=1)
+        self.conv1d = nn.Conv2d(output_channel, out_size, (1,1))
+
+    def forward(self, x):
+        input = x.clone().detach()
+        input = self.conv_outer(input)
+        x = self.conv1(x)
+        x = self.depthConv(x)
+        x = self.conv2(x)
+        x = x*input
+        x = x + input
+        x = self.conv1d(x)
+        return x
+
+class NetworkInit(nn.Module):
+
+    def __init__(self, in_size = 4, out_size = 3, expansion_ratio = 6):
+        super(NetworkInit, self).__init__()
+        self.simpleConv = FirstConvolution_init(in_size, 32, expansion_ratio=expansion_ratio)
+        self.downConv1 = DownConvolution_init(32, 64, expansion_ratio=expansion_ratio)
+        self.downConv2 = DownConvolution_init(64, 128, expansion_ratio=expansion_ratio)
+        self.downConv3 = DownConvolution_init(128, 256, expansion_ratio=expansion_ratio)
+        self.maxpool = nn.MaxPool2d(2, 2)
+        self.bridge = UpConvolution_init(256, 512, expansion_ratio=expansion_ratio)
+        self.upConv1 = UpConvolution_init(512, 256, expansion_ratio=expansion_ratio)
+        self.upConv2 = UpConvolution_init(256, 128, expansion_ratio=expansion_ratio)
+        self.upConv3 = UpConvolution_init(128, 64, expansion_ratio=expansion_ratio)
+        self.lastConv = LastConvolution_init(64, 32, out_size, expansion_ratio=expansion_ratio)
+    
+    def forward(self, x):
+        x1 = self.simpleConv(x)
+        x2 = self.downConv1(x1)
+        x3 = self.downConv2(x2)
+        x4 = self.downConv3(x3)
+        x5 = self.maxpool(x4)
+        x6 = self.bridge(x5)
+        x7 = self.upConv1(torch.cat((x4, x6), 1))
+        x8 = self.upConv2(torch.cat((x3, x7), 1))
+        x9 = self.upConv3(torch.cat((x2, x8), 1))
+        A_0 = self.lastConv(torch.cat((x1, x9), 1))
+        I_ns_0 = x[:,:3,:,:]+A_0*x[:,:3,:,:]
         return I_ns_0, A_0
\ No newline at end of file
diff --git a/Project_Macan_Oydu/codes/ShadowRemoverNetwork.py b/Project_Macan_Oydu/codes/ShadowRemoverNetwork.py
index 6974833..b6981b0 100644
--- a/Project_Macan_Oydu/codes/ShadowRemoverNetwork.py
+++ b/Project_Macan_Oydu/codes/ShadowRemoverNetwork.py
@@ -1,24 +1,24 @@
-import torch
-import torch.nn as nn
-from NetworkA import NetworkA_iter
-from NetworkInit import NetworkInit
-
-class ShadowRemoverNetwork(nn.Module):
-
-    def __init__(self, Eta = 0.01, Beta = 0.01, Lambda = 0.01, K = 4):
-        super(ShadowRemoverNetwork, self).__init__()
-        self.K = K
-        self.networkA = NetworkA_iter(Eta=Eta, Beta=Beta, Lambda=Lambda)
-        self.networkInit = NetworkInit()
-
-    def forward(self, I_s, M):
-        I_ns_history = []
-        A_history = []
-        I_ns, A = self.networkInit(torch.cat((I_s,M),dim=1))
-        I_ns_history.append(I_ns)
-        A_history.append(A)
-        for _ in range(self.K):
-            I_ns, A = self.networkA(M,A,I_ns,I_s)
-            I_ns_history.append(I_ns)
-            A_history.append(A)
+import torch
+import torch.nn as nn
+from NetworkA import NetworkA_iter
+from NetworkInit import NetworkInit
+
+class ShadowRemoverNetwork(nn.Module):
+
+    def __init__(self, Eta = 0.01, Beta = 0.01, Lambda = 0.01, K = 4):
+        super(ShadowRemoverNetwork, self).__init__()
+        self.K = K
+        self.networkA = NetworkA_iter(Eta=Eta, Beta=Beta, Lambda=Lambda)
+        self.networkInit = NetworkInit()
+
+    def forward(self, I_s, M):
+        I_ns_history = []
+        A_history = []
+        I_ns, A = self.networkInit(torch.cat((I_s,M),dim=1))
+        I_ns_history.append(I_ns)
+        A_history.append(A)
+        for _ in range(self.K):
+            I_ns, A = self.networkA(M,A,I_ns,I_s)
+            I_ns_history.append(I_ns)
+            A_history.append(A)
         return I_ns_history, A_history
\ No newline at end of file
diff --git a/Project_Macan_Oydu/codes/dataset.py b/Project_Macan_Oydu/codes/dataset.py
index a5599a2..a73828b 100644
--- a/Project_Macan_Oydu/codes/dataset.py
+++ b/Project_Macan_Oydu/codes/dataset.py
@@ -1,31 +1,31 @@
-import os
-from PIL import Image
-from torch.utils.data import Dataset
-
-class dataset(Dataset):
-    def __init__(self,image_dir,mask_dir,groud_truth_dir, transformation):
-        super(dataset,self).__init__()
-        files = os.listdir(image_dir)
-        self.I_s_dir = [os.path.join(image_dir, k) for k in files]
-        files = os.listdir(image_dir)
-        self.M_dir = [os.path.join(mask_dir, k) for k in files]
-        files = os.listdir(image_dir)
-        self.GT_dir = [os.path.join(groud_truth_dir, k) for k in files]
-        self.transformation = transformation
-    
-    def __getitem__(self, index):
-        I_s_path = self.I_s_dir[index]
-        M_path = self.M_dir[index]
-        gt_path = self.GT_dir[index]
-
-        I_s = Image.open(I_s_path)
-        M = Image.open(M_path)
-        gt = Image.open(gt_path)
-        I_s = self.transformation(I_s)
-        M = self.transformation(M)
-        gt = self.transformation(gt)
-
-        return I_s,M,gt
-
-    def __len__(self):
+import os
+from PIL import Image
+from torch.utils.data import Dataset
+
+class dataset(Dataset):
+    def __init__(self,image_dir,mask_dir,groud_truth_dir, transformation):
+        super(dataset,self).__init__()
+        files = os.listdir(image_dir)
+        self.I_s_dir = [os.path.join(image_dir, k) for k in files]
+        files = os.listdir(image_dir)
+        self.M_dir = [os.path.join(mask_dir, k) for k in files]
+        files = os.listdir(image_dir)
+        self.GT_dir = [os.path.join(groud_truth_dir, k) for k in files]
+        self.transformation = transformation
+    
+    def __getitem__(self, index):
+        I_s_path = self.I_s_dir[index]
+        M_path = self.M_dir[index]
+        gt_path = self.GT_dir[index]
+
+        I_s = Image.open(I_s_path)
+        M = Image.open(M_path)
+        gt = Image.open(gt_path)
+        I_s = self.transformation(I_s)
+        M = self.transformation(M)
+        gt = self.transformation(gt)
+
+        return I_s,M,gt
+
+    def __len__(self):
         return len(self.I_s_dir)
\ No newline at end of file
diff --git a/Project_Macan_Oydu/codes/errorCalculator.py b/Project_Macan_Oydu/codes/errorCalculator.py
index a035653..d9d6c79 100644
--- a/Project_Macan_Oydu/codes/errorCalculator.py
+++ b/Project_Macan_Oydu/codes/errorCalculator.py
@@ -1,30 +1,30 @@
-import math
-import numpy as np
-from skimage.metrics import structural_similarity
-
-def peak_signal_noise_ratio(img, gt):
-    mse = mean_squared_error(img, gt)
-    if mse == 0:
-        return 100
-    max = 255.0
-    return 20*math.log10(max / math.sqrt(mse))
-
-def structural_sim(img, gt):
-    img = img.cpu().detach().numpy()
-    gt = gt.cpu().detach().numpy()
-    img = np.squeeze(img,axis=0)
-    gt = np.squeeze(gt,axis=0)
-    img = np.transpose(img, (1, 2, 0))
-    gt = np.transpose(gt, (1, 2, 0))
-    ssim_value = structural_similarity(img, gt, multichannel=True)
-    return ssim_value
-
-def mean_squared_error(img, gt):
-    img = img.cpu().detach().numpy()
-    gt = gt.cpu().detach().numpy()
-    img = np.squeeze(img,axis=0)
-    gt = np.squeeze(gt,axis=0)
-    img = np.transpose(img, (1, 2, 0))
-    gt = np.transpose(gt, (1, 2, 0))
-    mse = np.mean((img - gt) ** 2)
+import math
+import numpy as np
+from skimage.metrics import structural_similarity
+
+def peak_signal_noise_ratio(img, gt):
+    mse = mean_squared_error(img, gt)
+    if mse == 0:
+        return 100
+    max = 255.0
+    return 20*math.log10(max / math.sqrt(mse))
+
+def structural_sim(img, gt):
+    img = img.cpu().detach().numpy()
+    gt = gt.cpu().detach().numpy()
+    img = np.squeeze(img,axis=0)
+    gt = np.squeeze(gt,axis=0)
+    img = np.transpose(img, (1, 2, 0))
+    gt = np.transpose(gt, (1, 2, 0))
+    ssim_value = structural_similarity(img, gt, multichannel=True)
+    return ssim_value
+
+def mean_squared_error(img, gt):
+    img = img.cpu().detach().numpy()
+    gt = gt.cpu().detach().numpy()
+    img = np.squeeze(img,axis=0)
+    gt = np.squeeze(gt,axis=0)
+    img = np.transpose(img, (1, 2, 0))
+    gt = np.transpose(gt, (1, 2, 0))
+    mse = np.mean((img - gt) ** 2)
     return mse
\ No newline at end of file
diff --git a/Project_Macan_Oydu/codes/test.py b/Project_Macan_Oydu/codes/test.py
index 0b889c9..61476fa 100644
--- a/Project_Macan_Oydu/codes/test.py
+++ b/Project_Macan_Oydu/codes/test.py
@@ -1,49 +1,49 @@
-import torch
-import torchvision
-import numpy as np
-from torch.utils.data import DataLoader
-from ShadowRemoverNetwork import ShadowRemoverNetwork
-from dataset import dataset
-import matplotlib.image as img
-from errorCalculator import peak_signal_noise_ratio, mean_squared_error, structural_sim
-
-transformation = torchvision.transforms.Compose((torchvision.transforms.Resize([128,128]),torchvision.transforms.ToTensor()))
-test_dataset = dataset(image_dir="ISTD_Dataset/test/test_A", mask_dir="ISTD_Dataset/test/test_B", groud_truth_dir="ISTD_Dataset/test/test_C",transformation=transformation)
-test_loader = DataLoader(test_dataset,shuffle=True,num_workers=1)
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-test_results_path = 'Results_ISTD/'
-
-enlarge = torchvision.transforms.Resize([256,256])
-
-if __name__ == '__main__':
-    model = ShadowRemoverNetwork().to(device)
-    print('#generator parameters:',sum(param.numel() for param in model.parameters()))
-    model.load_state_dict(torch.load("ShadowRemoverNetwork_128.ckpt", map_location=torch.device(device)))
-    model.eval()
-    total_mse = 0
-    total_psnr = 0
-    total_ssim = 0
-    with torch.no_grad():
-        for i, data in enumerate(test_loader, 0):
-            I_s, M, gt = data
-            I_ns_history, _ = model(I_s, M)
-            out = torch.clamp(I_ns_history[-1], 0., 1.)
-            total_mse += mean_squared_error(out, gt)
-            total_psnr += peak_signal_noise_ratio(out, gt)
-            total_ssim += structural_sim(out, gt)
-            out = np.squeeze(enlarge(I_ns_history[-1]).cpu().numpy())
-            out = out.transpose((1,2,0))
-            ground_truth = np.squeeze(enlarge(gt).cpu().numpy())
-            ground_truth = ground_truth.transpose((1,2,0))
-            shadow_image = np.squeeze(enlarge(I_s).cpu().numpy())
-            shadow_image = shadow_image.transpose((1,2,0))
-            img.imsave(test_results_path+str(i)+"_result.jpeg",np.concatenate((np.uint8(shadow_image*255.),np.uint8(out*255.),np.uint8(ground_truth*255)),1))
-            
-    ssim = total_ssim/len(test_loader)
-    rmse = (total_mse/len(test_loader))**0.5
-    psnr = total_psnr/len(test_loader)
-    print('Root mean squared error on ISTD dataset',rmse)
-    print('Average peak signal noise ratio on ISTD dataset',psnr)
+import torch
+import torchvision
+import numpy as np
+from torch.utils.data import DataLoader
+from ShadowRemoverNetwork import ShadowRemoverNetwork
+from dataset import dataset
+import matplotlib.image as img
+from errorCalculator import peak_signal_noise_ratio, mean_squared_error, structural_sim
+
+transformation = torchvision.transforms.Compose((torchvision.transforms.Resize([128,128]),torchvision.transforms.ToTensor()))
+test_dataset = dataset(image_dir="ISTD_Dataset/test/test_A", mask_dir="ISTD_Dataset/test/test_B", groud_truth_dir="ISTD_Dataset/test/test_C",transformation=transformation)
+test_loader = DataLoader(test_dataset,shuffle=True,num_workers=1)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+test_results_path = 'Results_ISTD/'
+
+enlarge = torchvision.transforms.Resize([256,256])
+
+if __name__ == '__main__':
+    model = ShadowRemoverNetwork().to(device)
+    print('#generator parameters:',sum(param.numel() for param in model.parameters()))
+    model.load_state_dict(torch.load("ShadowRemoverNetwork_128.ckpt", map_location=torch.device(device)))
+    model.eval()
+    total_mse = 0
+    total_psnr = 0
+    total_ssim = 0
+    with torch.no_grad():
+        for i, data in enumerate(test_loader, 0):
+            I_s, M, gt = data
+            I_ns_history, _ = model(I_s, M)
+            out = torch.clamp(I_ns_history[-1], 0., 1.)
+            total_mse += mean_squared_error(out, gt)
+            total_psnr += peak_signal_noise_ratio(out, gt)
+            total_ssim += structural_sim(out, gt)
+            out = np.squeeze(enlarge(I_ns_history[-1]).cpu().numpy())
+            out = out.transpose((1,2,0))
+            ground_truth = np.squeeze(enlarge(gt).cpu().numpy())
+            ground_truth = ground_truth.transpose((1,2,0))
+            shadow_image = np.squeeze(enlarge(I_s).cpu().numpy())
+            shadow_image = shadow_image.transpose((1,2,0))
+            img.imsave(test_results_path+str(i)+"_result.jpeg",np.concatenate((np.uint8(shadow_image*255.),np.uint8(out*255.),np.uint8(ground_truth*255)),1))
+            
+    ssim = total_ssim/len(test_loader)
+    rmse = (total_mse/len(test_loader))**0.5
+    psnr = total_psnr/len(test_loader)
+    print('Root mean squared error on ISTD dataset',rmse)
+    print('Average peak signal noise ratio on ISTD dataset',psnr)
     print('Average structural similarity on ISTD dataset',ssim)
\ No newline at end of file
diff --git a/Project_Macan_Oydu/codes/train.py b/Project_Macan_Oydu/codes/train.py
index 79878f7..c94b844 100644
--- a/Project_Macan_Oydu/codes/train.py
+++ b/Project_Macan_Oydu/codes/train.py
@@ -1,76 +1,76 @@
-import torch
-import torchvision
-import numpy as np
-from torch.utils.data import DataLoader
-from ShadowRemoverNetwork import ShadowRemoverNetwork
-from dataset import dataset
-
-if torch.cuda.is_available():
-  print("Cuda (GPU support) is available and enabled!")
-  device = torch.device("cuda")
-  torch.cuda.empty_cache()
-  torch.cuda.set_per_process_memory_fraction(fraction=1., device=None)
-else:
-  print("Cuda (GPU support) is not available :(")
-  device = torch.device("cpu")
-
-def loss(I_ns_history, A_history, M, gt):
-    loss = 0
-    gamma_d = torch.arange(5)
-    gamma_reg = 0.000001
-    for batch in range(gt.shape[0]):
-      for i in range(len(I_ns_history)):
-        loss += torch.mean(gamma_d[i]*torch.norm(I_ns_history[i][batch,:,:,:]-gt[batch,:,:,:])+gamma_reg*torch.norm((1-M[batch,:,:,:])*A_history[i][batch,:,:,:]))
-    loss = loss/gt.shape[0]
-    return loss
-
-def train(model, criterion, optimizer, scheduler, epochs, dataloader, verbose=True):
-  loss_history = []
-  for epoch in range(epochs):
-    for i, data in enumerate(dataloader, 0):
-      
-      # Our batch:
-      I_s, M, gt = data
-      I_s = I_s.to(device)
-      M = M.to(device)
-      gt = gt.to(device)
-      print(gt)
-      # zero the gradients as PyTorch accumulates them
-      optimizer.zero_grad()
-      
-      # Obtain the outputs
-      I_ns_history, A_history = model(I_s, M)
-      
-      # Calculate loss
-      loss = criterion(I_ns_history, A_history, M, gt)
-      
-      # Backpropagate
-      loss.backward()
-
-      # Update the weights
-      optimizer.step()
-
-      # Update the learning rate
-      scheduler.step()
-
-      loss_history.append(loss.item())
-      
-    if verbose: print(f'Epoch {epoch} / {epochs}: avg. loss of last 5 iterations {np.sum(loss_history[:-6:-1])/5}')
-
-  return loss_history
-
-if __name__ == "__main__":
-  learning_rate = 0.00005
-  batch_size = 2
-  epochs = 150
-  transformation = torchvision.transforms.Compose((torchvision.transforms.Resize([128,128]),torchvision.transforms.ToTensor()))
-  train_dataset = dataset(image_dir="ISTD_Dataset/train/train_A", mask_dir="ISTD_Dataset/train/train_B", groud_truth_dir="ISTD_Dataset/train/train_C", transformation=transformation)
-  train_loader = DataLoader(train_dataset,shuffle=True,batch_size=batch_size,num_workers=1)
-
-  model = ShadowRemoverNetwork()
-  model.to(device)
-  
-  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
-  scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=150) #T_max is not given in the paper
-  train(model, loss, optimizer, scheduler, epochs, train_loader, verbose=True)
+import torch
+import torchvision
+import numpy as np
+from torch.utils.data import DataLoader
+from ShadowRemoverNetwork import ShadowRemoverNetwork
+from dataset import dataset
+
+if torch.cuda.is_available():
+  print("Cuda (GPU support) is available and enabled!")
+  device = torch.device("cuda")
+  torch.cuda.empty_cache()
+  torch.cuda.set_per_process_memory_fraction(fraction=1., device=None)
+else:
+  print("Cuda (GPU support) is not available :(")
+  device = torch.device("cpu")
+
+def loss(I_ns_history, A_history, M, gt):
+    loss = 0
+    gamma_d = torch.arange(5)
+    gamma_reg = 0.000001
+    for batch in range(gt.shape[0]):
+      for i in range(len(I_ns_history)):
+        loss += torch.mean(gamma_d[i]*torch.norm(I_ns_history[i][batch,:,:,:]-gt[batch,:,:,:])+gamma_reg*torch.norm((1-M[batch,:,:,:])*A_history[i][batch,:,:,:]))
+    loss = loss/gt.shape[0]
+    return loss
+
+def train(model, criterion, optimizer, scheduler, epochs, dataloader, verbose=True):
+  loss_history = []
+  for epoch in range(epochs):
+    for i, data in enumerate(dataloader, 0):
+      
+      # Our batch:
+      I_s, M, gt = data
+      I_s = I_s.to(device)
+      M = M.to(device)
+      gt = gt.to(device)
+      print(gt)
+      # zero the gradients as PyTorch accumulates them
+      optimizer.zero_grad()
+      
+      # Obtain the outputs
+      I_ns_history, A_history = model(I_s, M)
+      
+      # Calculate loss
+      loss = criterion(I_ns_history, A_history, M, gt)
+      
+      # Backpropagate
+      loss.backward()
+
+      # Update the weights
+      optimizer.step()
+
+      # Update the learning rate
+      scheduler.step()
+
+      loss_history.append(loss.item())
+      
+    if verbose: print(f'Epoch {epoch} / {epochs}: avg. loss of last 5 iterations {np.sum(loss_history[:-6:-1])/5}')
+
+  return loss_history
+
+if __name__ == "__main__":
+  learning_rate = 0.00005
+  batch_size = 2
+  epochs = 150
+  transformation = torchvision.transforms.Compose((torchvision.transforms.Resize([128,128]),torchvision.transforms.ToTensor()))
+  train_dataset = dataset(image_dir="ISTD_Dataset/train/train_A", mask_dir="ISTD_Dataset/train/train_B", groud_truth_dir="ISTD_Dataset/train/train_C", transformation=transformation)
+  train_loader = DataLoader(train_dataset,shuffle=True,batch_size=batch_size,num_workers=1)
+
+  model = ShadowRemoverNetwork()
+  model.to(device)
+  
+  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+  scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=150) #T_max is not given in the paper
+  train(model, loss, optimizer, scheduler, epochs, train_loader, verbose=True)
   torch.save(model.state_dict(), 'ShadowRemoverNetwork.ckpt')
\ No newline at end of file
diff --git a/Project_Macan_Oydu/requirements.txt b/Project_Macan_Oydu/requirements.txt
index 28a78ab..9441187 100644
--- a/Project_Macan_Oydu/requirements.txt
+++ b/Project_Macan_Oydu/requirements.txt
@@ -1,6 +1,6 @@
-numpy
-torch
-torchvision 
-matplotlib
-pillow
-scikit-image
+numpy
+torch
+torchvision 
+matplotlib
+pillow
+scikit-image
diff --git a/Project_Mahammadli/Readme.md b/Project_Mahammadli/Readme.md
deleted file mode 100644
index af0a427..0000000
--- a/Project_Mahammadli/Readme.md
+++ /dev/null
@@ -1,64 +0,0 @@
-# @TODO: Paper title
-
-This readme file is an outcome of the [CENG501 (Spring 2022)](https://ceng.metu.edu.tr/~skalkan/DL/) project for reproducing a paper without an implementation. See [CENG501 (Spring 2022) Project List](https://github.com/CENG501-Projects/CENG501-Spring2022) for a complete list of all paper reproduction projects.
-
-# 1. Introduction
-
-@TODO: Introduce the paper (inc. where it is published) and describe your goal (reproducibility).
-CNN has great proccesses in many tasks such as image classification in deep learning. However, because of the defficiency of the ability to process large image rotation of images, CNN can fail to classify images. CNN is used many fields such as biomedical,astronomi and industry. Therefore, rotation invariance of neural networks has been more important. There are lots of data augmentation techniques to recognize rotated objects and they may increase parameter. In this paper we make feature maps before and after CNN rotation equivariant, so whole NN is rotation invariant. Main techniques :
-      1) Local Binary Pattern Operator based Regional Rotation Layer(RRL) reproduced.
-      2) without new parameters, RLL make feature maps .
-      3)Evaluated RLL with Lenet-5,ResNet-18,tiny-yolov3
-
-## 1.1. Paper summary
-
-@TODO: First, all of the datasets Cifar-10,Cifar10-rot(rotated with angle 0,90,180,270 images),Cifar10-rot+(rotated with angle in range[0,360)), Plankton dataset, Pascal VOC 2007-12 datasets evaluated in models the the models reproduced with RLL evaluated with same datsets and compared scores,and accuracies. Without new parameters, RLL makes the feature maps before and after convolution equivarience, aand so makes the entire NN rotation invariant With rotation angles 0 , 90,180,270 ; the feature maps are exactly same. With arbitrary rotation angle, there is small distinctions between feature maps. Finaly, RRL with LeNet-5,Resnet-44, ResNet-18 and tiny- yolov3 are evaluated and we get good results.  
-
-# 2. The method and my interpretation
-
-## 2.1. The original method
-
-   The standard convolutional neural networks do not have the property of rotation invariance. Trained by the upright sam- ples, the performance drops significantly when tested by the rotated images. To solve this problem, we add a regional ro- tation layer (RRL) before the convolutional layers and the fully connected layers. The main idea is that we indirectly achieve rotation invariance by restricting the convolutional features to be rotation equivariant. A series of LBP feature values are obtained by rotating the surrounding points, and the minimum of these values is selected as the LBP value of the central pixel
-In this paper, the points are rotated to the minimum state of LBP, so as to achieve the rotation invariance of angle. 
-LBP is operated in a window, while RRL is operated on the feature maps. The feature maps are sampled one by one in the form of sliding window, and LBP is implemented in each window. So we can rotate the feature maps to the same state even with different input orientations.
-## 2.2. My interpretation 
-
-  In the paper there is small unclear place they do not describe places of RLLs in Resnet-18,Resnet-44, and tiny-yolov3 so we do not add to much RLLs before all CNN layers so that not slowing down the training.
-
-# 3. Experiments and results
-
-## 3.1. Experimental setup
-
-In this experimet we use google colab
--Dataset and Lenet-5 Cifar-10 is used.
--Resnet-18 and Cifar-10 is used.
--yolov3-tiny and dataset Pascal VOC 2007-12 is used.
--Plankton and Resnet-44 is mentioned
-
-Required libraries:
--Tensorflow 
--Pytorch
--Numpy
--OpenCv
-
-## 3.2. Running the code
-
-@TODO: Explain your code & directory structure and how other people can run it.
-
-## 3.3. Results
-
-@TODO: Present your results and compare them to the original paper. Please number your figures & tables as if this is a paper.
-
-# 4. Conclusion
-
-@TODO: Discuss the paper in relation to the results in the paper and your results.
-
-# 5. References
-
-@TODO: Provide your references here.
-
-# Contact
-
-@TODO: Provide your names & email addresses and any other info with which people can contact you.
-Fatma Ceyda Gökçe: Email:fcg.13@hotmail.com
-                   Linkedln:https://www.linkedin.com/in/fatma-ceyda-gokce/
diff --git a/Project_Mahammadli_Gokce/Readme.md b/Project_Mahammadli_Gokce/Readme.md
new file mode 100644
index 0000000..1ec9ce2
--- /dev/null
+++ b/Project_Mahammadli_Gokce/Readme.md
@@ -0,0 +1,287 @@
+# RRL: Regional Rotation Layer in Convolutional Neural Networks
+
+This readme file is an outcome of the [CENG501 (Spring 2022)](https://ceng.metu.edu.tr/~skalkan/DL/) project for reproducing a paper without an implementation. See [CENG501 (Spring 2022) Project List](https://github.com/CENG501-Projects/CENG501-Spring2022) for a complete list of all paper reproduction projects.
+
+## 1. Introduction
+
+@TODO: Introduce the paper (inc. where it is published) and describe your goal (reproducibility).
+CNN has great proccesses in many tasks such as image classification in deep learning. However, because of the defficiency of the ability to process large image rotation of images, CNN can fail to classify images. CNN is used many fields such as biomedical,astronomi and industry. Therefore, rotation invariance of neural networks has been more important. There are other techiques in the literature like, [1, 2] or well-known regularization method - data augmentation that makes model to see rotated images too, and learn their feature. However, this increases the amount of data, and cost of the training, model based strategies increases the number of parameters. In this paper we make feature maps before and after CNN rotation equivariant, so the whole CNN is rotation invariant, without any new parameter. Main techniques :
+      1) Local Binary Pattern Operator based Regional Rotation Layer(RRL) reproduced.
+      2) Without new parameters, RLL make feature maps .
+      3)Evaluated RLL with Lenet-5,ResNet-18
+
+### 1.1. Paper summary
+
+First, all of the datasets Cifar-10,Cifar10-rot(rotated with angle 0,90,180,270 images),Cifar10-rot+(rotated with angle in range[0,360)) datasets evaluated for the models trained without RRL and with RRL. Without new parameters, RLL makes the feature maps before and after convolution equivarience, and so makes the entire NN rotation invariant With rotation angles 0, 90, 180, 270 the feature maps are exactly same. With arbitrary rotation angle, there is small distinctions between feature maps. Finally, we have made reproducible RRL layer that can be used in any CNN architecture and created automated training and testing for LeNet5 and ResNet18 on Cifar-10 dataset.  
+
+## 2. The method and my interpretation
+
+### 2.1. The original method
+
+The standard convolutional neural networks do not have the property of rotation invariance. Trained by the upright samples, the performance drops significantly when tested by the rotated images. In the paper, there are two level of operations: feature level - RRL: Regional Rotation layer, which is global, and window level - Local Binary Pattern operation, which is local. The algorithm below shows how RRL works on feature maps.
+
+<p align="center">
+  <img src=./images/RRL.png />
+</p>
+
+RRL is applied along each axis seperately, and based on given kernel size, it slides over each channel and for each sliding window Local Binary Pattern is applied to all the rotated versions of that window. Then, window is replaced by its rotated version that gives the minimum LOcal Binary Pattern. Below is explanation of how Local Binary Pattern is calculated for 3x3 window:
+
+<p align="center">
+  <img src=./images/LPB.png />
+</p>
+
+First of all, center value is taken as threshold, and used to produce the binary matrix: if value is less than threshold, put 0, otherwise 1. Then starting by upper left, power matrix is generated as powers of 2, without considering the threshold value. Then Hadamard product of the binary and power matrix is calculated and summed up to produce LPB value, which is 169 in this example. By checking all the rotated versions of this example, by keeping the center fixex, one can conclude that 225 degree counterclockwiese rotation produces the lowest LPB value - 30.
+
+### 2.2. My interpretation
+
+In the paper, how RRL woks globally was not interpreted clearly, so we have to figure it out, so we used stride as the same as kernel size to avoid the overlapping and created a matrix of sliding windows. In the architecture, it is necessary that feature map size is divisible by kernel size, so zero padding is added where it was necessary so that all the features participates. For example, if originial image size is 33x33, with 3 channels and 128 batch size, then we have an input matrix with dimensions - 128x3x33x33. If we take the kernel size 3x3, then along the height and width there will be, 33 / 3 = 11 windows. So in total, for one channel there are 11 * 11 = 121 windows, and if we permute the order of the dimensions, with this 121 and the channel dimension, we get new sliding window matrix with size - 128x121x3x3x3. We also have function to find the minimum LPB state of the given window. For example, if we have 3x3 kernel, by considering the original window 0 degree rotation, our function calculates LPB for all nine rotation states by keep tracking of the rotation producing the minimum LPB, then it returns that rotated version of matrix. We call our function `min_lpb` and apply it for all the sliding windows seperately. Unfortunately, operations used to find the minimum LPB state, cannot be broadcasted to all the sliding windows. Unlike the other layers, like Convolution layer sliding window operations are element-wise multiplication and summation or MaxPooling where it is the finding max value, operations can be done by using PyTorch directly, like with `sum` with elementwise-multiplication `*`, or `max`. These operations are supported by PyTorch to vectorize to apply over the dimensions needed. However, our custom function is based on rotation and not directly based on summation, matrix multiplication or finding the max value. Therefore we needed vectorize our function ourselves. The only possibility in PyTorch is using `vmap` from `functorch` library. But this function vectorize the oprations used between 2 or more matrices. Therefore we could not come up with the solution to handle this problem. We also looked how Convoliton layer is calculated as general matrix multiplications, GEMM, but it was not helpful to find the minimum state of each sliding window. Because, traditional layers, there is consistency, however in our rotation-invariance method, each window can result in a different rotation and there is indepence between all the sliding windows and channels, and we have no weight matrix whose matrix multiplication can be optimized. Due to all these reasons,t there was no way of make use of GPU for  `min_lpb` function, and we used multiprocessing. However, we want our project be a global use, so that our implemented RRL layer can be used by anyone and in any CNN architectures, by just importing RRL, and putting x = RRL(x), before each convolution layer, and before the fully-connected layers if the task is classification. Therefore, we created the PyTorch discussion on how to broadcast the custom functions in PyTorch to take advantage of the GPU, if there is some update we will optimize the solution. Even if it is after grading, we will try to optimize our solution to make our project impactful. We have trained LeNet5 and ResNet18 with RRL for 10 epochs and without RRL layer for 100 epochs, both have batch size 128, and images are resized by 33x33. For LeNet5 with RRL layer, we have put our implemented RRL layer before each convolutional layer and before the fully connected layers. In ResNet18, we have used RRL layers before the first Convolution layer and before each Residual blocks, and inside the blocks do not contain RRLs.
+
+## 3. Experiments and results
+
+### 3.1. Experimental setup
+
+In this experimet we use google colab, with [Python 3.7](https://www.python.org/downloads/release/python-370/)
+
+- Dataset and Lenet-5 Cifar-10 is used.
+- Resnet-18 and Cifar-10 is used.
+
+Required libraries:
+
+- [Pytorch](https://pytorch.org/)
+- [Torchvision](https://pytorch.org/vision/stable/index.html)
+- [Scikit-Learn](https://scikit-learn.org/stable/)
+- [Matplotlib](https://matplotlib.org/)
+
+### 3.2. Running the code
+
+We have made not only our results reproducible, but also created a seperate Python Project under `src` folder. So our project is not just bunch of jupyter notebook experiments and results, we have taken our approach from jupyter notebooks, and orginize the method in seperate Python folders, and other can simply take our RRL implementation and use it in their neural network architectures. We have documented and add typing to all the functions and classes, to make it professional so that anyone can understand what we are doing and look the docstrings or required data types of the functions directly in their environment. We have also added comments for implementation of the RRL layer, if anyone interested can understand how it works can we have put track of the dimensions of the input matrix so that one can get the idea how things are changing. Below is the structure of our whole project:
+
+Project_Mahammadli_Gokce
+
+- src
+  - `__init__.py`
+  - `dataloader.py`
+  - `layers.py`
+  - `lpb.py`
+  - `models.py`
+  - `test.py`
+  - `requirements.txt`
+- experiments
+  - `With_RRL.ipynb`
+  - `Without_RRL.ipynb`
+- images
+  - `LPB.png`
+  - .
+  - .
+  - .
+  - `RRL.png`
+- models
+  - `lenet5_with_rrl.pth`
+  - `lenet5_without_RRL.pth`
+  - `resnet18_without_RRL.pth`
+- results
+  - `Results.ipynb`
+
+`images` are containing pictures of the algorithms used, and results that will be given in section 3.3. `experiments` folder containing jupyter notebooks for training of LeNet5 and ResNet18 on Cifar-10 dataset without adding RRL layer and with RRL layer. `models` folder contains some of trained models. `src` part contains necessary files to reproduce the results. We have created `train.py`, that can be runned from the cmd or terminal with suitable flags for automatic training to train the model, get training accuracy and loss graphs, training time used, and saved, trained model, and `test.py` to test the saved model and produce the classification report containing the accuracy, precision, recall, f1-score for all the classes seperately, and averaged results. 
+
+### Reproducing the results
+
+Clone the poject in your computer and change directory to `src`:
+
+```bash
+git clone https://github.com/CENG501-Projects/CENG501-Spring2022.git
+cd Project_Mahammadli_Gokce/src
+```
+
+It is recommended to use virtual environment, you can create one with:
+
+```bash
+python -m venv env
+```
+
+It will create virtual environment named env, and you need to activate it, for Windows, under the env/Scripts, there is `activate.bat` file, put it in the cmd and run it, you should see the `(env)` on the left part of the cmd.
+
+For Linux:
+
+```bash
+source ~/env/binb/activate
+```
+
+Then install the necessary libraries to your environment:
+
+```bash
+pip install -r requirements.txt
+```
+
+Now you can train and test the models, to reproduce our results. You can automatically do that by following the instructions below:
+
+### Train.py
+
+```bash
+usage: train.py [-h] [--rrl] [--no-rrl] [--model_name MODEL_NAME] [--image_size IMAGE_SIZE] [--batch_size BATCH_SIZE] [--epochs EPOCHS]
+                [--lr LR] [--model_destination MODEL_DESTINATION] [--plot] [--plot_destination PLOT_DESTINATION] [--verbose]
+
+Model Training Program.
+
+required arguments:
+  --rrl                 use this command to train model with RRL layer.
+  --no-rrl              use this command to train model without RRL layer.
+  --model_name MODEL_NAME
+                        name of the model to train: lenet5 or resnet18.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --image_size IMAGE_SIZE
+                        height and width of the input image. should be divisable by three.
+  --batch_size BATCH_SIZE
+                        size of each batch for training.
+  --epochs EPOCHS       number of epochs to train.
+  --lr LR               learning rate for the optimizer.
+  --model_destination MODEL_DESTINATION
+  --plot                pass it without any value to draw training loss and accuracy.
+  --plot_destination PLOT_DESTINATION
+                        if you have passed --plot_training argument, use this flag with corresponsing path to store the plot.
+  --verbose             If True, loss and accuracy during training will be printed out. default True
+```
+
+You can pass `model_name` flag with either lenet5 or resnet18 to train one of these models, `rrl` and `no-rrl` controls whether to use RRL layer inside the model or not. Other arguments are optional and default values are our implementation, however you can change them as you wish. `image_size` is used to resize Cifar-10 images while loading them, `batch_size`, `epochs`, `lr` are clear from their names, they are for what size each batch should be, number of epochs to train, and the learning rate. `model_destination` is the parent folder where you want to save the mode, do not worry about the naming of the model, it will be based on your choice of the model. `plot` is used to produce training loss and accuracy graphs, default is True, you can pass the `plot_destination` to clarify where to store the graphs, default is current folder. `verbose` is used to print interim results during the traning, defualt is True.
+
+Sample training code:
+
+```bash
+python train.py --model_name lenet5 --rrl
+```
+
+This code will train LeNet5 with RRL layer.
+
+### Test.py
+
+```bash
+usage: test.py [-h] [--model_name MODEL_NAME] [--model_path MODEL_PATH] [--image_size IMAGE_SIZE] [--batch_size BATCH_SIZE]
+               [--report_destination REPORT_DESTINATION]
+
+Model Testing Program.
+
+required arguments:
+  --model_name MODEL_NAME
+                        name of the model to test: lenet5 or resnet18.
+  --model_path MODEL_PATH
+                        Path to the saved, trained model.
+  --image_size IMAGE_SIZE
+                        height and width of the input image. should be divisable by three and the same as in the training loader.
+  --batch_size BATCH_SIZE
+                        size of each batch for training. should be the same as in the training loader
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --report_destination REPORT_DESTINATION
+                        path to the folder to store the clasification report
+```
+
+After you train your model, you can use this file to test the performance of your model based on two test sets: Cifar-10-rot, and
+Cifar-10-rot+ which are explained in section 1.1. You need to path `model_name` you trained, in our case it was 'lenet5', `model_path` path where your model - `pth` file is saved, we did not specify the path during training, so it should in in the current directory, if you look there you will see the `lenet5_with_rrl.pth` file, `image_size` the size used for resizing images during training, in our case it was default 33, it is for consistency, and `batch_size` you want for your batches. If you run with these flags, it will print out the classification report for given model based on mentioned two test sets, and `report_destionation` specifies where to store the classification report texts, default is current folder. If you pass that flag, put only the folder you want the report to be appear, the rest will ba handled, naming etc.
+
+Sample testing code
+
+```bash
+python test.py --model_name lenet5 --model_path './lenet5_with_rrl.pth' --image_size 33 --batch_size 128
+```
+
+### 3.3. Results
+
+Results can be found in the directory `src/results`, we have tested LeNet5 and ResNet18 models on Cifar-10-rot and Cifar-10-rot+ test datasets with RRL and without RRL layers. Below are some example images from both test datasets:
+
+<figure align=center>
+  <img src="./images/test_cifar_10_rot.png" alt="my alt text"/>
+  <figcaption align=center>Cifar-10-rot</figcaption>
+</figure>
+
+<figure align=center>
+  <img src="./images/test_cifar_10_rot+.png" alt="my alt text"/>
+  <figcaption align=center>Cifar-10-rot+</figcaption>
+</figure>
+
+Reslts for loss and accuracy during the training is given below:
+
+### LeNet5 Without RRL
+
+<figure align=center>
+  <img src="./images/lenet5_without_RRL_accuracy.png" alt="my alt text"/>
+  <figcaption align=center>Accuracy</figcaption>
+</figure>
+
+<figure align=center>
+  <img src="./images/lenet5_without_RRL_loss.png" alt="my alt text"/>
+  <figcaption align=center>Loss</figcaption>
+</figure>
+
+### ResNet18 Without RRL
+
+<figure align=center>
+  <img src="./images/resnet18_without_RRL_accuracy.png" alt="my alt text"/>
+  <figcaption align=center>Accuracy</figcaption>
+</figure>
+
+<figure align=center>
+  <img src="./images/resnet18_without_RRL_loss.png" alt="my alt text"/>
+  <figcaption align=center>Loss</figcaption>
+</figure>
+
+Now, let's anaylze the test scores of our implementation with the original paper
+
+### LeNet5
+
+<figure align=center>
+  <img src="./images/paper_lenet.png" alt="my alt text"/>
+  <figcaption align=center>Original Paper</figcaption>
+</figure>
+
+<figure align=center>
+  <img src="./images/result_lenet_without_rrl_rot.png" alt="my alt text"/>
+  <figcaption align=center>Our Implementation - rot</figcaption>
+</figure>
+
+<figure align=center>
+  <img src="./images/result_lenet_without_rrl_rot+.png" alt="my alt text"/>
+  <figcaption align=center>Our Implementation - rot+</figcaption>
+</figure>
+
+
+### ResNet5 
+
+<figure align=center>
+  <img src="./images/paper_resnet.png" alt="my alt text"/>
+  <figcaption align=center>Original Paper</figcaption>
+</figure>
+
+<figure align=center>
+  <img src="./images/resnet18_without_RRL_rot.png" alt="my alt text"/>
+  <figcaption align=center>Our Implementation - rot</figcaption>
+</figure>
+
+<figure align=center>
+  <img src="./images/resnet18_without_RRL_rot+.png" alt="my alt text"/>
+  <figcaption align=center>Our Implementation - rot+</figcaption>
+</figure>
+
+If we analyze the results, scores are similar and having limited to run results with RRL layers on CPU, as it was not possible to run on GPU, we are restricted RRL models to only 10 epochs from scratch training. In the paper it has been mentioned to train the model for *100 000* epochs, which both seemed very unnecessary as results saturated after a few hundred epochs.  
+
+## 4. Conclusion
+
+In conclusion, we implemented the RRL: Regional Rotation Layer in Concolutional Neural Networks paper, in PyTorch. As paper was not indicating clear idea about the working principle of the methodology, we had to figure out how it works, how to generate sliding windows, like should they overlap or no overlap. We find out that having stride same as kernel size works better, and implemented the minimum LPB state finder function and adapted it inside our RRL layer. By, using our RRL layer in CNN architectures like LeNet5 and ResNet18 we get rotation invariant models, without adding a new parameter. Besides, paper does not mention their training parameters, like, number of epochs, batch size, kernel size used for RRL sliding window, optimizer used in the paper etc. So we had to hyper-parameter tuning and put the best results. After all the experiments and testing, we crated reproducible RRL layer that can be adapted into any architecture, we plan to optimize our implemntation further and make make it suitbale for PyTorch environment and introduce the layer to be added to PyTorch layers. We also plan to check its performance not only in simple CNN architecture, but also much deeper CNNs and also in Vision Transformer. 
+
+## 5. References
+
+[1] Follmann, P.; and Bottger, T. 2018. A        rotationally-invariant convolution module by feature map back-rotation. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 784–792. IEEE.
+
+[2] Gao, L.; Li, H.; Lu, Z.; and Lin, G. 2019. Rotationequivariant convolutional neural network ensembles in image processing. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 551–557. London: Association for Computing Machinery,New York.
+
+## Contact
+
+- Kanan Mahammadli
+  - [kanan.mahammadli@metu.edu.tr](mailto:kanan.mahammadli@metu.edu.tr)
+  - [Linkedin](https://www.linkedin.com/in/kananmahammadli)
+
+- Fatma Ceyda Gökçe
+  - [fcg.13@hotmail.com](mailto:fcg.13@hotmail.com)
+  - [Linkedln](https://www.linkedin.com/in/fatma-ceyda-gokce)
diff --git a/Project_Mahammadli_Gokce/experiments/With_RRL.ipynb b/Project_Mahammadli_Gokce/experiments/With_RRL.ipynb
new file mode 100644
index 0000000..862450d
--- /dev/null
+++ b/Project_Mahammadli_Gokce/experiments/With_RRL.ipynb
@@ -0,0 +1,1353 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "q7wcQv-qiNs6",
+        "outputId": "197504f7-ccc1-48fe-ba42-09581790d757"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting torchinfo\n",
+            "  Downloading torchinfo-1.7.0-py3-none-any.whl (22 kB)\n",
+            "Installing collected packages: torchinfo\n",
+            "Successfully installed torchinfo-1.7.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install torchinfo\n",
+        "from torchinfo import summary\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import torchvision\n",
+        "import matplotlib.pyplot as plt\n",
+        "from torchvision import datasets, transforms\n",
+        "from torchvision.transforms.functional import rotate\n",
+        "from joblib import Parallel, delayed\n",
+        "import multiprocessing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "YpV5EHe4HY_l",
+        "outputId": "afbbe38e-a6ce-4ae5-d6c1-1598e0ff7125"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "function ClickConnect(){\n",
+              "console.log(\"Working\");\n",
+              "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
+              "}\n",
+              "setInterval(ClickConnect,60000)\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# avoiding the google colab disconnecting\n",
+        "import IPython\n",
+        "js_code = '''\n",
+        "function ClickConnect(){\n",
+        "console.log(\"Working\");\n",
+        "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
+        "}\n",
+        "setInterval(ClickConnect,60000)\n",
+        "'''\n",
+        "display(IPython.display.Javascript(js_code))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WsbLgVEhHua_"
+      },
+      "source": [
+        "## Creating Regional Rotation Layer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "up-gLuH4KHGt",
+        "outputId": "9dd17ce1-65fd-412f-a05c-ed6345361711"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cuda\n"
+          ]
+        }
+      ],
+      "source": [
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(device)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "2v7M2ddPjM5D"
+      },
+      "outputs": [],
+      "source": [
+        "def rotate_mx(mx, degree):\n",
+        "\treturn rotate(mx.unsqueeze(0), degree).squeeze(0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "K-DMi5jpjP19"
+      },
+      "outputs": [],
+      "source": [
+        "# calculating local binary pattern value\n",
+        "def LPB(mx):\n",
+        "  mid = mx.shape[0] // 2\n",
+        "  threshold = mx[mid, mid]\n",
+        "\n",
+        "  bin_mx = (mx >= threshold)\n",
+        "  power_mx = torch.Tensor([\n",
+        "                       [1, 2, 4],\n",
+        "                       [8, 0, 16],\n",
+        "                       [32, 64, 128]\n",
+        "  ]).to(device)\n",
+        "\n",
+        "  return (bin_mx * power_mx).sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "QE5bgLsjto8K"
+      },
+      "outputs": [],
+      "source": [
+        "# finding rotation matrix leading to minimum LPB value\n",
+        "def min_lpb(mx):\n",
+        "  num_of_surr_elements = mx.shape[0] ** 2 - 1\n",
+        "  rot_deg = 360 // num_of_surr_elements\n",
+        "\n",
+        "  min_mx = mx.clone()\n",
+        "  lpb = LPB(mx)\n",
+        "  for degree in range(rot_deg, 360, rot_deg):\n",
+        "    mx = rotate_mx(mx, degree)\n",
+        "    cur_lpb = LPB(mx)\n",
+        "\n",
+        "    if cur_lpb < lpb:\n",
+        "      lpb = cur_lpb\n",
+        "      min_mx = mx.clone()\n",
+        "     \n",
+        "  return min_mx"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "KrB9uYo8sO5z"
+      },
+      "outputs": [],
+      "source": [
+        "class RRL(nn.Module):\n",
+        "  def __init__(self, kernel_size=3, padding=(0, 0)):\n",
+        "    super(RRL, self).__init__()\n",
+        "    self.FH = self.FW = self.stride = kernel_size\n",
+        "    self.padding = padding\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    # x -> (batch_size, channels, height, width)\n",
+        "    # OH, OW - feature map height and width\n",
+        "    # FH, FW - kernel height and width\n",
+        "\n",
+        "    batch_size, ch, H, W = x.shape\n",
+        "    OH = H // self.FH\n",
+        "    OW = W // self.FW\n",
+        "\n",
+        "    # pad the input\n",
+        "    x = F.pad(x, pad=self.padding)\n",
+        "    \n",
+        "    # create sliding windows\n",
+        "    x = x.unfold(2, self.FH, self.stride).unfold(3, self.FW, self.stride) # x -> (batch_size, channels, OH, OW, FH, FW)\n",
+        "    x = x.contiguous().view(batch_size, ch, -1, self.FH, self.FW) # x -> (batch_size, channels, OH*OW, FH, FW)\n",
+        "    x = x.permute(0, 2, 1, 3, 4) # x -> (batch_size, OH*OW, channels, FH, FW)\n",
+        "\n",
+        "    \n",
+        "    # calculate minimum LPB state for all the windows \n",
+        "    #x = vmap(min_lpb, in_dims=(-2, -1), out_dims=(-2, -1))(x) # x -> (batch_size, OH*OW, channels, FH, FW)\n",
+        "\n",
+        "    num_cores = multiprocessing.cpu_count()\n",
+        "    x = Parallel(n_jobs=num_cores, backend=\"threading\")(delayed(min_lpb)(x[b, o, c, :, :]) for b in range(batch_size) for o in range(OH*OW) for c in range(ch))\n",
+        "    x = torch.stack(x).view(batch_size, OH*OW, ch, self.FH, self.FW)\n",
+        "\n",
+        "    # reshape matrix into original format\n",
+        "    x = x.permute(0, 2, 1, 3, 4) # x -> (batch_size, channels, OH*OW, FH, FW)\n",
+        "    x = x.view(batch_size, ch, OH, OW, self.FH, self.FW) # x -> (batch_size, channels, OH, OW, FH, FW)\n",
+        "    x = x.permute(0, 1, 2, 4, 3, 5) # x -> (batch_size, channels, OH, FH, OW, FW)\n",
+        "    x = x.contiguous().view(batch_size, ch, H, W) # x -> (batch_size, channels, OH*FH, OW*FW)\n",
+        " \n",
+        "    return x"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r81I0pRxH3He"
+      },
+      "source": [
+        "Sliding Window implementation with unfold is adapted from [How to Implement a convolution layer](https://discuss.pytorch.org/t/how-to-implement-a-convolutional-layer/68211), and multiprocessing for custom fucntion is adapted from [Speed up custom function and model using GPU](https://discuss.pytorch.org/t/speed-up-custom-function-and-model-using-gpu/33219) official Pytorch discussion website"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GDFt8X0A6zi-"
+      },
+      "source": [
+        "## Prepearing the Experimental Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "IFGahLUgJkI0"
+      },
+      "outputs": [],
+      "source": [
+        "# defining pathes to store trained models and their loss and accuracy graphs\n",
+        "MODEL_PARENT_PATH = '/content/drive/MyDrive/CENG501/models/'\n",
+        "GRAPHS_PARENT_PATH = '/content/drive/MyDrive/CENG501/graphs/'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "FgBEDBYIJkNf"
+      },
+      "outputs": [],
+      "source": [
+        "# defining training parameters\n",
+        "BATCH_SIZE = 100 \n",
+        "EPOCHS = 10\n",
+        "LR = 0.001\n",
+        "IMAGE_SIZE = 33"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "K6XHEA0RJkQw"
+      },
+      "outputs": [],
+      "source": [
+        "transform = transforms.Compose([\n",
+        "                                transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n",
+        "                                transforms.ToTensor()])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 101,
+          "referenced_widgets": [
+            "c364597a73d84634960f484b3044aaf2",
+            "646267ea7c844cd1810dccbc60a71ef2",
+            "3e2be619fb7b4714b6b2beb4f0bfe256",
+            "77b7d92d55644219868aa8d273930c3c",
+            "c6930235e3434c5491260bbfd9da8387",
+            "cfbebaac5dbb46e2b717b523871e9c18",
+            "eb62d663d3de45ba93098d95097c9f27",
+            "51cfaed0c0b945d48548406c8206c54e",
+            "d82006241b4a4c3bb47a8a129dc84c7d",
+            "9618002f47014d288f79b43f263b31dd",
+            "27cdcc89a463462c8c4a75ff00d1eb42"
+          ]
+        },
+        "id": "d7ZpsgMKJkT3",
+        "outputId": "c856588b-b054-461d-a1e5-4cdd86905367"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /content/drive/MyDrive/CENG501/data/cifar-10-python.tar.gz\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c364597a73d84634960f484b3044aaf2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Extracting /content/drive/MyDrive/CENG501/data/cifar-10-python.tar.gz to /content/drive/MyDrive/CENG501/data\n",
+            "Number of training images: 50000\n"
+          ]
+        }
+      ],
+      "source": [
+        "train_dataset = torchvision.datasets.CIFAR10(root='/content/drive/MyDrive/CENG501/data',\n",
+        "                                             train=True, \n",
+        "                                             transform=transform,\n",
+        "                                             download=True)\n",
+        "\n",
+        "num_of_images = len(train_dataset)\n",
+        "print(f'Number of training images: {num_of_images}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 568
+        },
+        "id": "_eD5aWkxJkVg",
+        "outputId": "193f3cd9-4b15-4d71-ea1e-50c870cd21e2"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x720 with 50 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "labels = 'airplane automobile bird cat deer dog frog horse ship truck'.split()\n",
+        "first_50_samples = sorted([train_dataset[i] for i in range(50)], key=lambda x:x[1])\n",
+        "\n",
+        "figure = plt.figure(figsize=(15,10))\n",
+        "for i in range(1,51):\n",
+        "    img = first_50_samples[i-1][0].permute(1,2,0)\n",
+        "    label = labels[first_50_samples[i-1][1]]\n",
+        "    figure.add_subplot(5,10,i)\n",
+        "    plt.title(label)\n",
+        "    plt.axis('off')\n",
+        "    plt.imshow(img)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "w0UMoRYzJkX8",
+        "outputId": "5d773a86-64a2-42b0-99d6-a97a15c6d6a8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of batches: 500\n"
+          ]
+        }
+      ],
+      "source": [
+        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
+        "                                           batch_size=BATCH_SIZE, \n",
+        "                                           shuffle=True)\n",
+        "num_of_batches = len(train_loader)\n",
+        "print(f'Number of batches: {num_of_batches}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "NrMJOhb8Jkbe"
+      },
+      "outputs": [],
+      "source": [
+        "# decorator for calculating training time\n",
+        "from time import perf_counter\n",
+        "from functools import wraps\n",
+        "\n",
+        "def timer(func):\n",
+        "  @wraps(func)\n",
+        "  def wrapper(*args, **kwargs):\n",
+        "    start = perf_counter()\n",
+        "    result = func(*args, **kwargs)\n",
+        "    train_time = perf_counter() - start\n",
+        "    return (train_time, result)\n",
+        "  return wrapper"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "DauHODCwKSs7"
+      },
+      "outputs": [],
+      "source": [
+        "@timer\n",
+        "def trainer(model, train_loader, optimizer, criterion, epochs, verbose=True):\n",
+        "  train_losses = []\n",
+        "  train_accuracy = []\n",
+        "  history = {}\n",
+        "\n",
+        "  for epoch in range(epochs):\n",
+        "    train_corr = 0\n",
+        "\n",
+        "    # Run the training batches\n",
+        "    for b, (X_train, y_train) in enumerate(train_loader):\n",
+        "      b += 1\n",
+        "      X_train = X_train.to(device)\n",
+        "      y_train = y_train.to(device)\n",
+        "\n",
+        "      # Apply the model\n",
+        "      y_pred = model(X_train)\n",
+        "      loss = criterion(y_pred.to(device), y_train)\n",
+        "  \n",
+        "      # the number of correct predictions\n",
+        "      predicted = torch.max(y_pred.data, 1)[1]\n",
+        "      batch_corr = (predicted == y_train).sum()\n",
+        "      train_corr += batch_corr.item()\n",
+        "          \n",
+        "      # Update parameters\n",
+        "      optimizer.zero_grad()\n",
+        "      loss.backward()\n",
+        "      optimizer.step()\n",
+        "\n",
+        "      # Print interim results\n",
+        "      if verbose and b % 100 == 0:\n",
+        "        print(f\"Epoch [{epoch}/{epochs}], Step [{b}/{num_of_batches}] Loss: {loss.item():.2f} training accuracy: {(train_corr * 100 / (b * BATCH_SIZE)):.3f}\")\n",
+        "          \n",
+        "    train_losses.append(loss.item())\n",
+        "    train_accuracy.append((train_corr / num_of_images) * 100)\n",
+        "\n",
+        "  history['train loss'] = train_losses\n",
+        "  history['train accuracy'] = train_accuracy\n",
+        "\n",
+        "  return history"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_p6l9MiqKY7U"
+      },
+      "source": [
+        "## LeNet5"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DOK5_PKyKSxv"
+      },
+      "outputs": [],
+      "source": [
+        "MODEL_NAME = \"lenet5_with_RRL\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KPkWAals6Arn"
+      },
+      "outputs": [],
+      "source": [
+        "class LeNet5_with_RRL(nn.Module):\n",
+        "  def __init__(self, n_classes):\n",
+        "    super().__init__()\n",
+        "    self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=(1, 1))\n",
+        "    self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=(1, 1))\n",
+        "    self.pooling = nn.MaxPool2d(kernel_size=(2, 2), stride=2)\n",
+        "    self.RRL = RRL()\n",
+        "    self.tanh = nn.Tanh()\n",
+        "\n",
+        "    self.fc1 = nn.Linear(in_features=16*6*6, out_features=84)\n",
+        "    self.fc2 = nn.Linear(in_features=84, out_features=n_classes)\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    x = self.RRL(x)\n",
+        "    x = self.tanh(self.conv1(x))\n",
+        "    x = self.pooling(x)\n",
+        "\n",
+        "    x = self.RRL(x)\n",
+        "    x = self.tanh(self.conv2(x))\n",
+        "    x = self.pooling(x)\n",
+        "\n",
+        "    x = self.RRL(x)\n",
+        "\n",
+        "    x = torch.flatten(x, 1)\n",
+        "    x = self.tanh(self.fc1(x))\n",
+        "\n",
+        "    x = self.fc2(x)\n",
+        "    return F.softmax(x, dim=1)\n",
+        "    "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R_yWAYnGBlA4"
+      },
+      "outputs": [],
+      "source": [
+        "model_lenet = LeNet5_with_RRL(10)\n",
+        "model_lenet.to(device)\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer_lenet = torch.optim.Adam(model_lenet.parameters(), lr=LR)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "luVGphbUKhAO",
+        "outputId": "8bf8f167-6efb-46c7-9a6d-cf3e395b2cf8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "==========================================================================================\n",
+              "Layer (type:depth-idx)                   Output Shape              Param #\n",
+              "==========================================================================================\n",
+              "LeNet5_with_RRL                          [100, 10]                 --\n",
+              "├─RRL: 1-1                               [100, 3, 33, 33]          --\n",
+              "├─Conv2d: 1-2                            [100, 6, 31, 31]          456\n",
+              "├─Tanh: 1-3                              [100, 6, 31, 31]          --\n",
+              "├─MaxPool2d: 1-4                         [100, 6, 15, 15]          --\n",
+              "├─RRL: 1-5                               [100, 6, 15, 15]          --\n",
+              "├─Conv2d: 1-6                            [100, 16, 13, 13]         2,416\n",
+              "├─Tanh: 1-7                              [100, 16, 13, 13]         --\n",
+              "├─MaxPool2d: 1-8                         [100, 16, 6, 6]           --\n",
+              "├─RRL: 1-9                               [100, 16, 6, 6]           --\n",
+              "├─Linear: 1-10                           [100, 84]                 48,468\n",
+              "├─Tanh: 1-11                             [100, 84]                 --\n",
+              "├─Linear: 1-12                           [100, 10]                 850\n",
+              "==========================================================================================\n",
+              "Total params: 52,190\n",
+              "Trainable params: 52,190\n",
+              "Non-trainable params: 0\n",
+              "Total mult-adds (M): 89.58\n",
+              "==========================================================================================\n",
+              "Input size (MB): 1.31\n",
+              "Forward/backward pass size (MB): 6.85\n",
+              "Params size (MB): 0.21\n",
+              "Estimated Total Size (MB): 8.37\n",
+              "=========================================================================================="
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "summary(model_lenet, (BATCH_SIZE, 3, IMAGE_SIZE, IMAGE_SIZE))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tQAdcAO3LC47",
+        "outputId": "c5230466-6584-46a7-d2a3-3835d446eec2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch [0/10], Step [100/500] Loss: 2.22 training accuracy: 20.500\n",
+            "Epoch [0/10], Step [200/500] Loss: 2.10 training accuracy: 26.060\n"
+          ]
+        }
+      ],
+      "source": [
+        "lenet_train_time, history_lenet = trainer(\n",
+        "    model_lenet, \n",
+        "    train_loader, \n",
+        "    optimizer_lenet, \n",
+        "    criterion, \n",
+        "    EPOCHS)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We ran on google colab pro until it crashs, for hours but this was the results that could be produced with cpu"
+      ],
+      "metadata": {
+        "id": "2TSGgEmosiq9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7Zcwc9HrKhC6"
+      },
+      "outputs": [],
+      "source": [
+        "torch.save(model_lenet, MODEL_PARENT_PATH + MODEL_NAME + \".pth\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D0dNCk5pKhEy"
+      },
+      "outputs": [],
+      "source": [
+        "# summarize history for accuracy\n",
+        "plt.plot(history_lenet['train accuracy'])\n",
+        "plt.title('model accuracy')\n",
+        "plt.ylabel('accuracy')\n",
+        "\n",
+        "plt.xlabel('epoch')\n",
+        "plt.savefig(GRAPHS_PARENT_PATH + MODEL_NAME + '_accuracy.png', \\\n",
+        "            facecolor='white', edgecolor='none')\n",
+        "plt.show()\n",
+        "\n",
+        "\n",
+        "# summarize history for loss\n",
+        "plt.plot(history_lenet['train loss'])\n",
+        "plt.title('model loss')\n",
+        "plt.ylabel('loss')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.savefig(GRAPHS_PARENT_PATH + MODEL_NAME + \"_loss.png\", \\\n",
+        "            facecolor='white', edgecolor='none')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jbl9DpzrKhGu"
+      },
+      "outputs": [],
+      "source": [
+        "print(f'training time for LeNet5 with RRL layer: {(lenet_train_time / 60):.2f} minutes')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ResNet18"
+      ],
+      "metadata": {
+        "id": "_sssOUVCs8Zd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "oIn5oTmaKhLN"
+      },
+      "outputs": [],
+      "source": [
+        "# 3x3 convolution\n",
+        "def conv3x3(in_channels, out_channels, stride=1, padding=1):\n",
+        "    return nn.Conv2d(in_channels, out_channels, kernel_size=3, \n",
+        "                     stride=stride, padding=padding, bias=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Residual block\n",
+        "class ResidualBlock(nn.Module):\n",
+        "    def __init__(self, in_channels, out_channels, stride=1, downsample=None, padding=1):\n",
+        "        super(ResidualBlock, self).__init__()\n",
+        "        self.conv1 = conv3x3(in_channels, out_channels, stride, padding=padding)\n",
+        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.conv2 = conv3x3(out_channels, out_channels)\n",
+        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
+        "        self.downsample = downsample\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        residual = x\n",
+        "        out = self.conv1(x)\n",
+        "        out = self.bn1(out)\n",
+        "        out = self.relu(out)\n",
+        "        out = self.conv2(out)\n",
+        "        out = self.bn2(out)\n",
+        "        if self.downsample:\n",
+        "            residual = self.downsample(x)\n",
+        "        out += residual\n",
+        "        out = self.relu(out)\n",
+        "        return out"
+      ],
+      "metadata": {
+        "id": "XzGAyjX9tQWq"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ResNet\n",
+        "class ResNet(nn.Module):\n",
+        "    def __init__(self, block, layers, num_classes=10, downsample=None):\n",
+        "        super(ResNet, self).__init__()\n",
+        "        self.downsample = downsample\n",
+        "        self.in_channels = 16\n",
+        "        self.conv = conv3x3(3, 16)\n",
+        "        self.bn = nn.BatchNorm2d(16)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.layer1 = self.make_layer(block, 16, layers[0])\n",
+        "        self.layer2 = self.make_layer(block, 32, layers[1], 2, padding=2)\n",
+        "        self.layer3 = self.make_layer(block, 64, layers[2], 2)\n",
+        "        self.avg_pool = nn.AvgPool2d(8)\n",
+        "        self.fc = nn.Linear(64, num_classes)\n",
+        "        self.RRL = RRL()\n",
+        "\n",
+        "    def make_layer(self, block, out_channels, blocks, stride=1, padding=1):\n",
+        "        if (stride != 1) or (self.in_channels != out_channels):\n",
+        "            self.downsample = nn.Sequential(\n",
+        "                conv3x3(self.in_channels, out_channels, stride=stride, padding=padding),\n",
+        "                nn.BatchNorm2d(out_channels))\n",
+        "        layers = []\n",
+        "        layers.append(block(self.in_channels, out_channels, stride, self.downsample, padding=padding))\n",
+        "        self.in_channels = out_channels\n",
+        "        for i in range(1, blocks):\n",
+        "            layers.append(block(out_channels, out_channels))\n",
+        "        return nn.Sequential(*layers)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.RRL(x)\n",
+        "        x = self.conv(x)\n",
+        "        x = self.bn(x)\n",
+        "        x = self.relu(x)\n",
+        "\n",
+        "        x = self.RRL(x)\n",
+        "        x = self.layer1(x)\n",
+        "        x = self.layer2(x)\n",
+        "        x = self.layer3(x)\n",
+        "        x = self.RRL(x)\n",
+        "\n",
+        "        x = self.avg_pool(x)\n",
+        "        x = torch.flatten(x, 1)\n",
+        "        x = self.fc(x)\n",
+        "\n",
+        "        return F.softmax(x, dim=1)"
+      ],
+      "metadata": {
+        "id": "ALcZsH3otQZw"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_resnet18 = ResNet(ResidualBlock, [2, 2, 2]).to(device)\n",
+        "model_resnet18.to(device)\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer_resnet18 = torch.optim.Adam(model_resnet18.parameters(), lr=LR)"
+      ],
+      "metadata": {
+        "id": "2hfF4ZputB2C"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "summary(model_resnet18, (BATCH_SIZE, 3, IMAGE_SIZE, IMAGE_SIZE))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iqS2NGj4tCEm",
+        "outputId": "89316b50-87bd-4257-c66c-9d471bbd0425"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "==========================================================================================\n",
+              "Layer (type:depth-idx)                   Output Shape              Param #\n",
+              "==========================================================================================\n",
+              "ResNet                                   [100, 10]                 --\n",
+              "├─RRL: 1-1                               [100, 3, 33, 33]          --\n",
+              "├─Conv2d: 1-2                            [100, 16, 33, 33]         432\n",
+              "├─BatchNorm2d: 1-3                       [100, 16, 33, 33]         32\n",
+              "├─ReLU: 1-4                              [100, 16, 33, 33]         --\n",
+              "├─RRL: 1-5                               [100, 16, 33, 33]         --\n",
+              "├─Sequential: 1-6                        [100, 16, 33, 33]         --\n",
+              "│    └─ResidualBlock: 2-1                [100, 16, 33, 33]         --\n",
+              "│    │    └─Conv2d: 3-1                  [100, 16, 33, 33]         2,304\n",
+              "│    │    └─BatchNorm2d: 3-2             [100, 16, 33, 33]         32\n",
+              "│    │    └─ReLU: 3-3                    [100, 16, 33, 33]         --\n",
+              "│    │    └─Conv2d: 3-4                  [100, 16, 33, 33]         2,304\n",
+              "│    │    └─BatchNorm2d: 3-5             [100, 16, 33, 33]         32\n",
+              "│    │    └─ReLU: 3-6                    [100, 16, 33, 33]         --\n",
+              "│    └─ResidualBlock: 2-2                [100, 16, 33, 33]         --\n",
+              "│    │    └─Conv2d: 3-7                  [100, 16, 33, 33]         2,304\n",
+              "│    │    └─BatchNorm2d: 3-8             [100, 16, 33, 33]         32\n",
+              "│    │    └─ReLU: 3-9                    [100, 16, 33, 33]         --\n",
+              "│    │    └─Conv2d: 3-10                 [100, 16, 33, 33]         2,304\n",
+              "│    │    └─BatchNorm2d: 3-11            [100, 16, 33, 33]         32\n",
+              "│    │    └─ReLU: 3-12                   [100, 16, 33, 33]         --\n",
+              "├─Sequential: 1-7                        [100, 32, 18, 18]         --\n",
+              "│    └─ResidualBlock: 2-3                [100, 32, 18, 18]         --\n",
+              "│    │    └─Conv2d: 3-13                 [100, 32, 18, 18]         4,608\n",
+              "│    │    └─BatchNorm2d: 3-14            [100, 32, 18, 18]         64\n",
+              "│    │    └─ReLU: 3-15                   [100, 32, 18, 18]         --\n",
+              "│    │    └─Conv2d: 3-16                 [100, 32, 18, 18]         9,216\n",
+              "│    │    └─BatchNorm2d: 3-17            [100, 32, 18, 18]         64\n",
+              "│    │    └─Sequential: 3-18             [100, 32, 18, 18]         4,672\n",
+              "│    │    └─ReLU: 3-19                   [100, 32, 18, 18]         --\n",
+              "│    └─ResidualBlock: 2-4                [100, 32, 18, 18]         --\n",
+              "│    │    └─Conv2d: 3-20                 [100, 32, 18, 18]         9,216\n",
+              "│    │    └─BatchNorm2d: 3-21            [100, 32, 18, 18]         64\n",
+              "│    │    └─ReLU: 3-22                   [100, 32, 18, 18]         --\n",
+              "│    │    └─Conv2d: 3-23                 [100, 32, 18, 18]         9,216\n",
+              "│    │    └─BatchNorm2d: 3-24            [100, 32, 18, 18]         64\n",
+              "│    │    └─ReLU: 3-25                   [100, 32, 18, 18]         --\n",
+              "├─Sequential: 1-8                        [100, 64, 9, 9]           --\n",
+              "│    └─ResidualBlock: 2-5                [100, 64, 9, 9]           --\n",
+              "│    │    └─Conv2d: 3-26                 [100, 64, 9, 9]           18,432\n",
+              "│    │    └─BatchNorm2d: 3-27            [100, 64, 9, 9]           128\n",
+              "│    │    └─ReLU: 3-28                   [100, 64, 9, 9]           --\n",
+              "│    │    └─Conv2d: 3-29                 [100, 64, 9, 9]           36,864\n",
+              "│    │    └─BatchNorm2d: 3-30            [100, 64, 9, 9]           128\n",
+              "│    │    └─Sequential: 3-31             [100, 64, 9, 9]           18,560\n",
+              "│    │    └─ReLU: 3-32                   [100, 64, 9, 9]           --\n",
+              "│    └─ResidualBlock: 2-6                [100, 64, 9, 9]           --\n",
+              "│    │    └─Conv2d: 3-33                 [100, 64, 9, 9]           36,864\n",
+              "│    │    └─BatchNorm2d: 3-34            [100, 64, 9, 9]           128\n",
+              "│    │    └─ReLU: 3-35                   [100, 64, 9, 9]           --\n",
+              "│    │    └─Conv2d: 3-36                 [100, 64, 9, 9]           36,864\n",
+              "│    │    └─BatchNorm2d: 3-37            [100, 64, 9, 9]           128\n",
+              "│    │    └─ReLU: 3-38                   [100, 64, 9, 9]           --\n",
+              "├─RRL: 1-9                               [100, 64, 9, 9]           --\n",
+              "├─AvgPool2d: 1-10                        [100, 64, 1, 1]           --\n",
+              "├─Linear: 1-11                           [100, 10]                 650\n",
+              "==========================================================================================\n",
+              "Total params: 195,738\n",
+              "Trainable params: 195,738\n",
+              "Non-trainable params: 0\n",
+              "Total mult-adds (G): 3.44\n",
+              "==========================================================================================\n",
+              "Input size (MB): 1.31\n",
+              "Forward/backward pass size (MB): 263.82\n",
+              "Params size (MB): 0.78\n",
+              "Estimated Total Size (MB): 265.91\n",
+              "=========================================================================================="
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "resnet18_train_time, history_resnet18 = trainer(\n",
+        "    model_resnet18, \n",
+        "    train_loader, \n",
+        "    optimizer_resnet18, \n",
+        "    criterion, \n",
+        "    EPOCHS)"
+      ],
+      "metadata": {
+        "id": "zvn-MMwmtCHs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "torch.save(model_resnet18, MODEL_PARENT_PATH + MODEL_NAME + \".pth\")"
+      ],
+      "metadata": {
+        "id": "qCxkfCF1tZm1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# summarize history for accuracy\n",
+        "plt.plot(history_resnet18['train accuracy'])\n",
+        "plt.title('model accuracy')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.savefig(GRAPHS_PARENT_PATH + MODEL_NAME + '_accuracy.png', \\\n",
+        "            facecolor='white', edgecolor='none')\n",
+        "plt.show()\n",
+        "\n",
+        "\n",
+        "# summarize history for loss\n",
+        "plt.plot(history_resnet18['train loss'])\n",
+        "plt.title('model loss')\n",
+        "plt.ylabel('loss')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.savefig(GRAPHS_PARENT_PATH + MODEL_NAME + \"_loss.png\", \\\n",
+        "            facecolor='white', edgecolor='none')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "N7ldw55ktZpl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(f'training time for ResNet18 without RRL layer: {(resnet18_train_time / 60):.2f} minutes')"
+      ],
+      "metadata": {
+        "id": "6rLyyFTwtZrv"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "With-RRL.ipynb",
+      "provenance": []
+    },
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diff --git a/Project_Mahammadli_Gokce/experiments/Without_RRL.ipynb b/Project_Mahammadli_Gokce/experiments/Without_RRL.ipynb
new file mode 100644
index 0000000..e22eccc
--- /dev/null
+++ b/Project_Mahammadli_Gokce/experiments/Without_RRL.ipynb
@@ -0,0 +1,1943 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Without_RRL.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "gpuClass": "standard"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "glpRhBNJY_e8",
+        "outputId": "37ea1692-4711-4e5b-b524-2cea0cd67bbb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting torchinfo\n",
+            "  Downloading torchinfo-1.7.0-py3-none-any.whl (22 kB)\n",
+            "Installing collected packages: torchinfo\n",
+            "Successfully installed torchinfo-1.7.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install torchinfo\n",
+        "from torchinfo import summary\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import torchvision\n",
+        "import matplotlib.pyplot as plt\n",
+        "from torchvision import datasets, transforms\n",
+        "from torchvision.transforms.functional import rotate"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# avoiding the google colab disconnecting\n",
+        "import IPython\n",
+        "js_code = '''\n",
+        "function ClickConnect(){\n",
+        "console.log(\"Working\");\n",
+        "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
+        "}\n",
+        "setInterval(ClickConnect,60000)\n",
+        "'''\n",
+        "display(IPython.display.Javascript(js_code))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "mrbaWq9Eusoz",
+        "outputId": "2a53e549-12aa-4668-cb9c-f458cd330108"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "function ClickConnect(){\n",
+              "console.log(\"Working\");\n",
+              "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
+              "}\n",
+              "setInterval(ClickConnect,60000)\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Prepearing the Experimental Setup"
+      ],
+      "metadata": {
+        "id": "QwGYxFQw-KOf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# defining pathes to store trained models and their loss and accuracy graphs\n",
+        "MODEL_PARENT_PATH = '/content/drive/MyDrive/CENG501/models/'\n",
+        "GRAPHS_PARENT_PATH = '/content/drive/MyDrive/CENG501/graphs/'"
+      ],
+      "metadata": {
+        "id": "z1KDYitJw7PA"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(device)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m0lQYTkhwVTp",
+        "outputId": "e132f1af-d7ba-45c1-c715-aecaa4d36936"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# defining training parameters\n",
+        "BATCH_SIZE = 100 \n",
+        "EPOCHS = 100\n",
+        "LR = 0.001\n",
+        "IMAGE_SIZE = 33"
+      ],
+      "metadata": {
+        "id": "dZUcCGtpATTs"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "transform = transforms.Compose([\n",
+        "                                transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n",
+        "                                transforms.ToTensor()])"
+      ],
+      "metadata": {
+        "id": "cc1a-xPEwZ85"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset = torchvision.datasets.CIFAR10(root='/content/drive/MyDrive/CENG501/data',\n",
+        "                                             train=True, \n",
+        "                                             transform=transform,\n",
+        "                                             download=True)\n",
+        "\n",
+        "num_of_images = len(train_dataset)\n",
+        "print(f'Number of training images: {num_of_images}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F34uNh_IwxMF",
+        "outputId": "1e67f37f-5dca-4574-8e97-6d68c6143a96"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Files already downloaded and verified\n",
+            "Number of training images: 50000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "labels = 'airplane automobile bird cat deer dog frog horse ship truck'.split()\n",
+        "first_50_samples = sorted([train_dataset[i] for i in range(50)], key=lambda x:x[1])\n",
+        "\n",
+        "figure = plt.figure(figsize=(15,10))\n",
+        "for i in range(1,51):\n",
+        "    img = first_50_samples[i-1][0].permute(1,2,0)\n",
+        "    label = labels[first_50_samples[i-1][1]]\n",
+        "    figure.add_subplot(5,10,i)\n",
+        "    plt.title(label)\n",
+        "    plt.axis('off')\n",
+        "    plt.imshow(img)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 568
+        },
+        "id": "N4m1pzYdGON-",
+        "outputId": "942e4628-379e-42e5-893d-6e565664c0aa"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x720 with 50 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
+        "                                           batch_size=BATCH_SIZE, \n",
+        "                                           shuffle=True)\n",
+        "num_of_batches = len(train_loader)\n",
+        "print(f'Number of batches: {num_of_batches}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DUtQYuM9GiuI",
+        "outputId": "12ef676d-4dd3-4ec5-fdec-5263f2bc8724"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of batches: 500\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# decorator for calculating training time\n",
+        "from time import perf_counter\n",
+        "from functools import wraps\n",
+        "\n",
+        "def timer(func):\n",
+        "  @wraps(func)\n",
+        "  def wrapper(*args, **kwargs):\n",
+        "    start = perf_counter()\n",
+        "    result = func(*args, **kwargs)\n",
+        "    train_time = perf_counter() - start\n",
+        "    return (train_time, result)\n",
+        "  return wrapper"
+      ],
+      "metadata": {
+        "id": "f8OcWIxBxwXN"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "@timer\n",
+        "def trainer(model, train_loader, optimizer, criterion, epochs, verbose=True):\n",
+        "  train_losses = []\n",
+        "  train_accuracy = []\n",
+        "  history = {}\n",
+        "\n",
+        "  for epoch in range(epochs):\n",
+        "    train_corr = 0\n",
+        "\n",
+        "    # Run the training batches\n",
+        "    for b, (X_train, y_train) in enumerate(train_loader):\n",
+        "      b += 1\n",
+        "      X_train = X_train.to(device)\n",
+        "      y_train = y_train.to(device)\n",
+        "\n",
+        "      # Apply the model\n",
+        "      y_pred = model(X_train)\n",
+        "      loss = criterion(y_pred.to(device), y_train)\n",
+        "  \n",
+        "      # the number of correct predictions\n",
+        "      predicted = torch.max(y_pred.data, 1)[1]\n",
+        "      batch_corr = (predicted == y_train).sum()\n",
+        "      train_corr += batch_corr.item()\n",
+        "          \n",
+        "      # Update parameters\n",
+        "      optimizer.zero_grad()\n",
+        "      loss.backward()\n",
+        "      optimizer.step()\n",
+        "\n",
+        "      # Print interim results\n",
+        "      if verbose and b % 100 == 0:\n",
+        "        print(f\"Epoch [{epoch}/{epochs}], Step [{b}/{num_of_batches}] Loss: {loss.item():.2f} training accuracy: {(train_corr * 100 / (b * BATCH_SIZE)):.3f}\")\n",
+        "          \n",
+        "    train_losses.append(loss.item())\n",
+        "    train_accuracy.append((train_corr / num_of_images) * 100)\n",
+        "\n",
+        "  history['train loss'] = train_losses\n",
+        "  history['train accuracy'] = train_accuracy\n",
+        "\n",
+        "  return history"
+      ],
+      "metadata": {
+        "id": "HD-3smw05Ndu"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## LeNet5"
+      ],
+      "metadata": {
+        "id": "vaejS3VK5yGh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "MODEL_NAME = \"lenet5_without_RRL\""
+      ],
+      "metadata": {
+        "id": "7TJq4xff_eq-"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class LeNet5(nn.Module):\n",
+        "  def __init__(self, n_classes=10):\n",
+        "    super().__init__()\n",
+        "    self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=(1, 1))\n",
+        "    self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=(1, 1))\n",
+        "    self.pooling = nn.AvgPool2d(kernel_size=(2, 2), stride=2)\n",
+        "    self.tanh = nn.Tanh()\n",
+        "\n",
+        "    self.fc1 = nn.Linear(in_features=16*6*6, out_features=84)\n",
+        "    self.fc2 = nn.Linear(in_features=84, out_features=n_classes)\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    x = self.tanh(self.conv1(x))\n",
+        "    x = self.pooling(x)\n",
+        "\n",
+        "    x = self.tanh(self.conv2(x))\n",
+        "    x = self.pooling(x)\n",
+        "\n",
+        "    x = torch.flatten(x, 1)\n",
+        "    x = self.tanh(self.fc1(x))\n",
+        "\n",
+        "    x = self.fc2(x)\n",
+        "    return F.softmax(x, dim=1)\n",
+        "    "
+      ],
+      "metadata": {
+        "id": "rQJz7fZ95xrk"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_lenet = LeNet5()\n",
+        "model_lenet.to(device)\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer_lenet = torch.optim.Adam(model_lenet.parameters(), lr=LR)"
+      ],
+      "metadata": {
+        "id": "mk00a98d6Z9t"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "summary(model_lenet, (BATCH_SIZE, 3, IMAGE_SIZE, IMAGE_SIZE))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Oh1wxyB0IQbA",
+        "outputId": "bfdd70b1-d979-4ae5-fa43-da0b9180ed62"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "==========================================================================================\n",
+              "Layer (type:depth-idx)                   Output Shape              Param #\n",
+              "==========================================================================================\n",
+              "LeNet5                                   [100, 10]                 --\n",
+              "├─Conv2d: 1-1                            [100, 6, 31, 31]          456\n",
+              "├─Tanh: 1-2                              [100, 6, 31, 31]          --\n",
+              "├─AvgPool2d: 1-3                         [100, 6, 15, 15]          --\n",
+              "├─Conv2d: 1-4                            [100, 16, 13, 13]         2,416\n",
+              "├─Tanh: 1-5                              [100, 16, 13, 13]         --\n",
+              "├─AvgPool2d: 1-6                         [100, 16, 6, 6]           --\n",
+              "├─Linear: 1-7                            [100, 84]                 48,468\n",
+              "├─Tanh: 1-8                              [100, 84]                 --\n",
+              "├─Linear: 1-9                            [100, 10]                 850\n",
+              "==========================================================================================\n",
+              "Total params: 52,190\n",
+              "Trainable params: 52,190\n",
+              "Non-trainable params: 0\n",
+              "Total mult-adds (M): 89.58\n",
+              "==========================================================================================\n",
+              "Input size (MB): 1.31\n",
+              "Forward/backward pass size (MB): 6.85\n",
+              "Params size (MB): 0.21\n",
+              "Estimated Total Size (MB): 8.37\n",
+              "=========================================================================================="
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lenet_train_time, history_lenet = trainer(\n",
+        "    model_lenet, \n",
+        "    train_loader, \n",
+        "    optimizer_lenet, \n",
+        "    criterion, \n",
+        "    EPOCHS)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6IdGdeCa-jcZ",
+        "outputId": "4f8746b9-29d4-4120-ca49-3e8339b1ee60"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [0/100], Step [100/500] Loss: 2.10 training accuracy: 21.790\n",
+            "Epoch [0/100], Step [200/500] Loss: 2.19 training accuracy: 26.635\n",
+            "Epoch [0/100], Step [300/500] Loss: 2.10 training accuracy: 28.683\n",
+            "Epoch [0/100], Step [400/500] Loss: 2.15 training accuracy: 30.040\n",
+            "Epoch [0/100], Step [500/500] Loss: 2.05 training accuracy: 31.058\n",
+            "Epoch [1/100], Step [100/500] Loss: 2.11 training accuracy: 36.880\n",
+            "Epoch [1/100], Step [200/500] Loss: 2.10 training accuracy: 37.085\n",
+            "Epoch [1/100], Step [300/500] Loss: 2.09 training accuracy: 37.560\n",
+            "Epoch [1/100], Step [400/500] Loss: 2.03 training accuracy: 38.005\n",
+            "Epoch [1/100], Step [500/500] Loss: 2.10 training accuracy: 38.422\n",
+            "Epoch [2/100], Step [100/500] Loss: 2.03 training accuracy: 40.340\n",
+            "Epoch [2/100], Step [200/500] Loss: 2.11 training accuracy: 40.315\n",
+            "Epoch [2/100], Step [300/500] Loss: 2.01 training accuracy: 40.457\n",
+            "Epoch [2/100], Step [400/500] Loss: 2.04 training accuracy: 40.778\n",
+            "Epoch [2/100], Step [500/500] Loss: 2.06 training accuracy: 40.998\n",
+            "Epoch [3/100], Step [100/500] Loss: 2.14 training accuracy: 42.470\n",
+            "Epoch [3/100], Step [200/500] Loss: 2.01 training accuracy: 42.320\n",
+            "Epoch [3/100], Step [300/500] Loss: 2.02 training accuracy: 42.850\n",
+            "Epoch [3/100], Step [400/500] Loss: 2.11 training accuracy: 43.050\n",
+            "Epoch [3/100], Step [500/500] Loss: 2.09 training accuracy: 43.204\n",
+            "Epoch [4/100], Step [100/500] Loss: 2.06 training accuracy: 44.660\n",
+            "Epoch [4/100], Step [200/500] Loss: 2.02 training accuracy: 44.745\n",
+            "Epoch [4/100], Step [300/500] Loss: 2.04 training accuracy: 45.187\n",
+            "Epoch [4/100], Step [400/500] Loss: 1.94 training accuracy: 45.108\n",
+            "Epoch [4/100], Step [500/500] Loss: 2.06 training accuracy: 45.438\n",
+            "Epoch [5/100], Step [100/500] Loss: 2.03 training accuracy: 47.180\n",
+            "Epoch [5/100], Step [200/500] Loss: 2.11 training accuracy: 46.845\n",
+            "Epoch [5/100], Step [300/500] Loss: 1.96 training accuracy: 46.853\n",
+            "Epoch [5/100], Step [400/500] Loss: 1.98 training accuracy: 46.890\n",
+            "Epoch [5/100], Step [500/500] Loss: 1.93 training accuracy: 46.944\n",
+            "Epoch [6/100], Step [100/500] Loss: 1.95 training accuracy: 48.440\n",
+            "Epoch [6/100], Step [200/500] Loss: 1.97 training accuracy: 48.585\n",
+            "Epoch [6/100], Step [300/500] Loss: 2.06 training accuracy: 48.593\n",
+            "Epoch [6/100], Step [400/500] Loss: 1.94 training accuracy: 48.693\n",
+            "Epoch [6/100], Step [500/500] Loss: 1.93 training accuracy: 48.736\n",
+            "Epoch [7/100], Step [100/500] Loss: 1.93 training accuracy: 50.250\n",
+            "Epoch [7/100], Step [200/500] Loss: 1.90 training accuracy: 50.390\n",
+            "Epoch [7/100], Step [300/500] Loss: 1.98 training accuracy: 49.880\n",
+            "Epoch [7/100], Step [400/500] Loss: 1.94 training accuracy: 49.910\n",
+            "Epoch [7/100], Step [500/500] Loss: 1.99 training accuracy: 49.900\n",
+            "Epoch [8/100], Step [100/500] Loss: 1.88 training accuracy: 52.100\n",
+            "Epoch [8/100], Step [200/500] Loss: 1.89 training accuracy: 51.540\n",
+            "Epoch [8/100], Step [300/500] Loss: 2.00 training accuracy: 51.517\n",
+            "Epoch [8/100], Step [400/500] Loss: 1.99 training accuracy: 51.288\n",
+            "Epoch [8/100], Step [500/500] Loss: 1.97 training accuracy: 51.284\n",
+            "Epoch [9/100], Step [100/500] Loss: 1.93 training accuracy: 52.580\n",
+            "Epoch [9/100], Step [200/500] Loss: 1.96 training accuracy: 52.215\n",
+            "Epoch [9/100], Step [300/500] Loss: 1.95 training accuracy: 52.267\n",
+            "Epoch [9/100], Step [400/500] Loss: 1.92 training accuracy: 52.443\n",
+            "Epoch [9/100], Step [500/500] Loss: 1.94 training accuracy: 52.282\n",
+            "Epoch [10/100], Step [100/500] Loss: 1.90 training accuracy: 53.780\n",
+            "Epoch [10/100], Step [200/500] Loss: 2.02 training accuracy: 53.220\n",
+            "Epoch [10/100], Step [300/500] Loss: 1.98 training accuracy: 53.387\n",
+            "Epoch [10/100], Step [400/500] Loss: 1.90 training accuracy: 53.483\n",
+            "Epoch [10/100], Step [500/500] Loss: 2.00 training accuracy: 53.280\n",
+            "Epoch [11/100], Step [100/500] Loss: 1.90 training accuracy: 54.380\n",
+            "Epoch [11/100], Step [200/500] Loss: 1.97 training accuracy: 54.615\n",
+            "Epoch [11/100], Step [300/500] Loss: 1.88 training accuracy: 54.540\n",
+            "Epoch [11/100], Step [400/500] Loss: 1.88 training accuracy: 54.465\n",
+            "Epoch [11/100], Step [500/500] Loss: 1.92 training accuracy: 54.346\n",
+            "Epoch [12/100], Step [100/500] Loss: 1.95 training accuracy: 56.370\n",
+            "Epoch [12/100], Step [200/500] Loss: 1.95 training accuracy: 55.955\n",
+            "Epoch [12/100], Step [300/500] Loss: 1.93 training accuracy: 55.697\n",
+            "Epoch [12/100], Step [400/500] Loss: 1.84 training accuracy: 55.300\n",
+            "Epoch [12/100], Step [500/500] Loss: 1.89 training accuracy: 55.194\n",
+            "Epoch [13/100], Step [100/500] Loss: 2.04 training accuracy: 56.890\n",
+            "Epoch [13/100], Step [200/500] Loss: 1.85 training accuracy: 56.770\n",
+            "Epoch [13/100], Step [300/500] Loss: 1.92 training accuracy: 56.647\n",
+            "Epoch [13/100], Step [400/500] Loss: 1.82 training accuracy: 56.343\n",
+            "Epoch [13/100], Step [500/500] Loss: 1.95 training accuracy: 56.116\n",
+            "Epoch [14/100], Step [100/500] Loss: 1.86 training accuracy: 56.820\n",
+            "Epoch [14/100], Step [200/500] Loss: 1.85 training accuracy: 56.570\n",
+            "Epoch [14/100], Step [300/500] Loss: 1.92 training accuracy: 56.683\n",
+            "Epoch [14/100], Step [400/500] Loss: 1.90 training accuracy: 56.733\n",
+            "Epoch [14/100], Step [500/500] Loss: 1.85 training accuracy: 56.720\n",
+            "Epoch [15/100], Step [100/500] Loss: 1.92 training accuracy: 58.380\n",
+            "Epoch [15/100], Step [200/500] Loss: 1.90 training accuracy: 58.230\n",
+            "Epoch [15/100], Step [300/500] Loss: 1.88 training accuracy: 58.170\n",
+            "Epoch [15/100], Step [400/500] Loss: 1.90 training accuracy: 57.905\n",
+            "Epoch [15/100], Step [500/500] Loss: 1.91 training accuracy: 57.786\n",
+            "Epoch [16/100], Step [100/500] Loss: 1.85 training accuracy: 59.200\n",
+            "Epoch [16/100], Step [200/500] Loss: 1.84 training accuracy: 58.915\n",
+            "Epoch [16/100], Step [300/500] Loss: 1.84 training accuracy: 58.923\n",
+            "Epoch [16/100], Step [400/500] Loss: 2.02 training accuracy: 58.578\n",
+            "Epoch [16/100], Step [500/500] Loss: 1.93 training accuracy: 58.400\n",
+            "Epoch [17/100], Step [100/500] Loss: 1.81 training accuracy: 59.190\n",
+            "Epoch [17/100], Step [200/500] Loss: 1.84 training accuracy: 59.410\n",
+            "Epoch [17/100], Step [300/500] Loss: 1.88 training accuracy: 59.150\n",
+            "Epoch [17/100], Step [400/500] Loss: 1.83 training accuracy: 58.932\n",
+            "Epoch [17/100], Step [500/500] Loss: 1.90 training accuracy: 59.038\n",
+            "Epoch [18/100], Step [100/500] Loss: 1.88 training accuracy: 60.570\n",
+            "Epoch [18/100], Step [200/500] Loss: 1.94 training accuracy: 60.465\n",
+            "Epoch [18/100], Step [300/500] Loss: 1.85 training accuracy: 60.183\n",
+            "Epoch [18/100], Step [400/500] Loss: 2.01 training accuracy: 59.880\n",
+            "Epoch [18/100], Step [500/500] Loss: 1.88 training accuracy: 59.894\n",
+            "Epoch [19/100], Step [100/500] Loss: 1.89 training accuracy: 60.990\n",
+            "Epoch [19/100], Step [200/500] Loss: 1.88 training accuracy: 60.385\n",
+            "Epoch [19/100], Step [300/500] Loss: 1.75 training accuracy: 60.450\n",
+            "Epoch [19/100], Step [400/500] Loss: 1.90 training accuracy: 60.345\n",
+            "Epoch [19/100], Step [500/500] Loss: 1.99 training accuracy: 60.362\n",
+            "Epoch [20/100], Step [100/500] Loss: 1.88 training accuracy: 61.110\n",
+            "Epoch [20/100], Step [200/500] Loss: 1.82 training accuracy: 61.045\n",
+            "Epoch [20/100], Step [300/500] Loss: 1.87 training accuracy: 61.007\n",
+            "Epoch [20/100], Step [400/500] Loss: 1.78 training accuracy: 60.898\n",
+            "Epoch [20/100], Step [500/500] Loss: 1.88 training accuracy: 60.986\n",
+            "Epoch [21/100], Step [100/500] Loss: 1.76 training accuracy: 62.690\n",
+            "Epoch [21/100], Step [200/500] Loss: 1.89 training accuracy: 61.950\n",
+            "Epoch [21/100], Step [300/500] Loss: 1.83 training accuracy: 61.687\n",
+            "Epoch [21/100], Step [400/500] Loss: 1.92 training accuracy: 61.460\n",
+            "Epoch [21/100], Step [500/500] Loss: 1.80 training accuracy: 61.610\n",
+            "Epoch [22/100], Step [100/500] Loss: 1.88 training accuracy: 62.500\n",
+            "Epoch [22/100], Step [200/500] Loss: 1.80 training accuracy: 62.650\n",
+            "Epoch [22/100], Step [300/500] Loss: 1.87 training accuracy: 62.490\n",
+            "Epoch [22/100], Step [400/500] Loss: 1.83 training accuracy: 62.475\n",
+            "Epoch [22/100], Step [500/500] Loss: 1.83 training accuracy: 62.180\n",
+            "Epoch [23/100], Step [100/500] Loss: 1.83 training accuracy: 63.900\n",
+            "Epoch [23/100], Step [200/500] Loss: 1.79 training accuracy: 63.265\n",
+            "Epoch [23/100], Step [300/500] Loss: 1.84 training accuracy: 63.003\n",
+            "Epoch [23/100], Step [400/500] Loss: 1.91 training accuracy: 62.998\n",
+            "Epoch [23/100], Step [500/500] Loss: 1.81 training accuracy: 62.978\n",
+            "Epoch [24/100], Step [100/500] Loss: 1.84 training accuracy: 64.460\n",
+            "Epoch [24/100], Step [200/500] Loss: 1.80 training accuracy: 64.210\n",
+            "Epoch [24/100], Step [300/500] Loss: 1.77 training accuracy: 63.957\n",
+            "Epoch [24/100], Step [400/500] Loss: 1.87 training accuracy: 63.500\n",
+            "Epoch [24/100], Step [500/500] Loss: 1.87 training accuracy: 63.240\n",
+            "Epoch [25/100], Step [100/500] Loss: 1.85 training accuracy: 64.560\n",
+            "Epoch [25/100], Step [200/500] Loss: 1.78 training accuracy: 64.330\n",
+            "Epoch [25/100], Step [300/500] Loss: 1.78 training accuracy: 64.233\n",
+            "Epoch [25/100], Step [400/500] Loss: 1.89 training accuracy: 63.992\n",
+            "Epoch [25/100], Step [500/500] Loss: 1.88 training accuracy: 63.834\n",
+            "Epoch [26/100], Step [100/500] Loss: 1.78 training accuracy: 65.200\n",
+            "Epoch [26/100], Step [200/500] Loss: 1.80 training accuracy: 64.670\n",
+            "Epoch [26/100], Step [300/500] Loss: 1.89 training accuracy: 64.247\n",
+            "Epoch [26/100], Step [400/500] Loss: 1.77 training accuracy: 64.315\n",
+            "Epoch [26/100], Step [500/500] Loss: 1.73 training accuracy: 64.246\n",
+            "Epoch [27/100], Step [100/500] Loss: 1.87 training accuracy: 65.390\n",
+            "Epoch [27/100], Step [200/500] Loss: 1.80 training accuracy: 65.505\n",
+            "Epoch [27/100], Step [300/500] Loss: 1.77 training accuracy: 65.167\n",
+            "Epoch [27/100], Step [400/500] Loss: 1.79 training accuracy: 64.960\n",
+            "Epoch [27/100], Step [500/500] Loss: 1.81 training accuracy: 64.674\n",
+            "Epoch [28/100], Step [100/500] Loss: 1.80 training accuracy: 65.680\n",
+            "Epoch [28/100], Step [200/500] Loss: 1.78 training accuracy: 64.805\n",
+            "Epoch [28/100], Step [300/500] Loss: 1.81 training accuracy: 64.770\n",
+            "Epoch [28/100], Step [400/500] Loss: 1.76 training accuracy: 64.865\n",
+            "Epoch [28/100], Step [500/500] Loss: 1.86 training accuracy: 64.972\n",
+            "Epoch [29/100], Step [100/500] Loss: 1.86 training accuracy: 66.130\n",
+            "Epoch [29/100], Step [200/500] Loss: 1.76 training accuracy: 66.230\n",
+            "Epoch [29/100], Step [300/500] Loss: 1.83 training accuracy: 65.763\n",
+            "Epoch [29/100], Step [400/500] Loss: 1.83 training accuracy: 65.725\n",
+            "Epoch [29/100], Step [500/500] Loss: 1.83 training accuracy: 65.584\n",
+            "Epoch [30/100], Step [100/500] Loss: 1.82 training accuracy: 65.810\n",
+            "Epoch [30/100], Step [200/500] Loss: 1.78 training accuracy: 66.215\n",
+            "Epoch [30/100], Step [300/500] Loss: 1.88 training accuracy: 66.133\n",
+            "Epoch [30/100], Step [400/500] Loss: 1.88 training accuracy: 65.922\n",
+            "Epoch [30/100], Step [500/500] Loss: 1.86 training accuracy: 65.914\n",
+            "Epoch [31/100], Step [100/500] Loss: 1.80 training accuracy: 66.850\n",
+            "Epoch [31/100], Step [200/500] Loss: 1.85 training accuracy: 66.815\n",
+            "Epoch [31/100], Step [300/500] Loss: 1.80 training accuracy: 66.643\n",
+            "Epoch [31/100], Step [400/500] Loss: 1.81 training accuracy: 66.433\n",
+            "Epoch [31/100], Step [500/500] Loss: 1.74 training accuracy: 66.372\n",
+            "Epoch [32/100], Step [100/500] Loss: 1.74 training accuracy: 67.570\n",
+            "Epoch [32/100], Step [200/500] Loss: 1.83 training accuracy: 67.270\n",
+            "Epoch [32/100], Step [300/500] Loss: 1.91 training accuracy: 67.170\n",
+            "Epoch [32/100], Step [400/500] Loss: 1.84 training accuracy: 66.892\n",
+            "Epoch [32/100], Step [500/500] Loss: 1.81 training accuracy: 66.896\n",
+            "Epoch [33/100], Step [100/500] Loss: 1.82 training accuracy: 68.030\n",
+            "Epoch [33/100], Step [200/500] Loss: 1.81 training accuracy: 67.920\n",
+            "Epoch [33/100], Step [300/500] Loss: 1.82 training accuracy: 67.730\n",
+            "Epoch [33/100], Step [400/500] Loss: 1.82 training accuracy: 67.495\n",
+            "Epoch [33/100], Step [500/500] Loss: 1.85 training accuracy: 67.218\n",
+            "Epoch [34/100], Step [100/500] Loss: 1.79 training accuracy: 68.960\n",
+            "Epoch [34/100], Step [200/500] Loss: 1.81 training accuracy: 68.140\n",
+            "Epoch [34/100], Step [300/500] Loss: 1.88 training accuracy: 67.630\n",
+            "Epoch [34/100], Step [400/500] Loss: 1.81 training accuracy: 67.562\n",
+            "Epoch [34/100], Step [500/500] Loss: 1.78 training accuracy: 67.440\n",
+            "Epoch [35/100], Step [100/500] Loss: 1.79 training accuracy: 68.400\n",
+            "Epoch [35/100], Step [200/500] Loss: 1.75 training accuracy: 68.255\n",
+            "Epoch [35/100], Step [300/500] Loss: 1.77 training accuracy: 68.033\n",
+            "Epoch [35/100], Step [400/500] Loss: 1.75 training accuracy: 67.907\n",
+            "Epoch [35/100], Step [500/500] Loss: 1.88 training accuracy: 67.810\n",
+            "Epoch [36/100], Step [100/500] Loss: 1.86 training accuracy: 69.010\n",
+            "Epoch [36/100], Step [200/500] Loss: 1.81 training accuracy: 68.865\n",
+            "Epoch [36/100], Step [300/500] Loss: 1.87 training accuracy: 68.610\n",
+            "Epoch [36/100], Step [400/500] Loss: 1.90 training accuracy: 68.425\n",
+            "Epoch [36/100], Step [500/500] Loss: 1.85 training accuracy: 68.342\n",
+            "Epoch [37/100], Step [100/500] Loss: 1.79 training accuracy: 69.390\n",
+            "Epoch [37/100], Step [200/500] Loss: 1.82 training accuracy: 68.980\n",
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+            "Epoch [87/100], Step [400/500] Loss: 1.69 training accuracy: 76.218\n",
+            "Epoch [87/100], Step [500/500] Loss: 1.72 training accuracy: 76.208\n",
+            "Epoch [88/100], Step [100/500] Loss: 1.61 training accuracy: 76.540\n",
+            "Epoch [88/100], Step [200/500] Loss: 1.70 training accuracy: 76.885\n",
+            "Epoch [88/100], Step [300/500] Loss: 1.67 training accuracy: 76.963\n",
+            "Epoch [88/100], Step [400/500] Loss: 1.69 training accuracy: 76.507\n",
+            "Epoch [88/100], Step [500/500] Loss: 1.65 training accuracy: 76.534\n",
+            "Epoch [89/100], Step [100/500] Loss: 1.71 training accuracy: 76.430\n",
+            "Epoch [89/100], Step [200/500] Loss: 1.70 training accuracy: 76.455\n",
+            "Epoch [89/100], Step [300/500] Loss: 1.78 training accuracy: 76.353\n",
+            "Epoch [89/100], Step [400/500] Loss: 1.80 training accuracy: 76.263\n",
+            "Epoch [89/100], Step [500/500] Loss: 1.72 training accuracy: 76.076\n",
+            "Epoch [90/100], Step [100/500] Loss: 1.72 training accuracy: 76.500\n",
+            "Epoch [90/100], Step [200/500] Loss: 1.72 training accuracy: 76.495\n",
+            "Epoch [90/100], Step [300/500] Loss: 1.77 training accuracy: 76.503\n",
+            "Epoch [90/100], Step [400/500] Loss: 1.69 training accuracy: 76.433\n",
+            "Epoch [90/100], Step [500/500] Loss: 1.65 training accuracy: 76.470\n",
+            "Epoch [91/100], Step [100/500] Loss: 1.74 training accuracy: 76.350\n",
+            "Epoch [91/100], Step [200/500] Loss: 1.72 training accuracy: 76.705\n",
+            "Epoch [91/100], Step [300/500] Loss: 1.68 training accuracy: 76.797\n",
+            "Epoch [91/100], Step [400/500] Loss: 1.67 training accuracy: 76.810\n",
+            "Epoch [91/100], Step [500/500] Loss: 1.69 training accuracy: 76.560\n",
+            "Epoch [92/100], Step [100/500] Loss: 1.65 training accuracy: 76.670\n",
+            "Epoch [92/100], Step [200/500] Loss: 1.76 training accuracy: 76.370\n",
+            "Epoch [92/100], Step [300/500] Loss: 1.73 training accuracy: 76.783\n",
+            "Epoch [92/100], Step [400/500] Loss: 1.74 training accuracy: 76.778\n",
+            "Epoch [92/100], Step [500/500] Loss: 1.67 training accuracy: 76.544\n",
+            "Epoch [93/100], Step [100/500] Loss: 1.75 training accuracy: 76.970\n",
+            "Epoch [93/100], Step [200/500] Loss: 1.61 training accuracy: 76.960\n",
+            "Epoch [93/100], Step [300/500] Loss: 1.65 training accuracy: 76.903\n",
+            "Epoch [93/100], Step [400/500] Loss: 1.61 training accuracy: 76.763\n",
+            "Epoch [93/100], Step [500/500] Loss: 1.76 training accuracy: 76.734\n",
+            "Epoch [94/100], Step [100/500] Loss: 1.70 training accuracy: 76.890\n",
+            "Epoch [94/100], Step [200/500] Loss: 1.74 training accuracy: 76.555\n",
+            "Epoch [94/100], Step [300/500] Loss: 1.71 training accuracy: 76.643\n",
+            "Epoch [94/100], Step [400/500] Loss: 1.66 training accuracy: 76.662\n",
+            "Epoch [94/100], Step [500/500] Loss: 1.68 training accuracy: 76.630\n",
+            "Epoch [95/100], Step [100/500] Loss: 1.71 training accuracy: 76.610\n",
+            "Epoch [95/100], Step [200/500] Loss: 1.62 training accuracy: 76.875\n",
+            "Epoch [95/100], Step [300/500] Loss: 1.72 training accuracy: 77.040\n",
+            "Epoch [95/100], Step [400/500] Loss: 1.68 training accuracy: 76.965\n",
+            "Epoch [95/100], Step [500/500] Loss: 1.73 training accuracy: 76.846\n",
+            "Epoch [96/100], Step [100/500] Loss: 1.77 training accuracy: 76.210\n",
+            "Epoch [96/100], Step [200/500] Loss: 1.74 training accuracy: 76.430\n",
+            "Epoch [96/100], Step [300/500] Loss: 1.75 training accuracy: 76.647\n",
+            "Epoch [96/100], Step [400/500] Loss: 1.72 training accuracy: 76.627\n",
+            "Epoch [96/100], Step [500/500] Loss: 1.69 training accuracy: 76.766\n",
+            "Epoch [97/100], Step [100/500] Loss: 1.67 training accuracy: 77.160\n",
+            "Epoch [97/100], Step [200/500] Loss: 1.69 training accuracy: 76.945\n",
+            "Epoch [97/100], Step [300/500] Loss: 1.65 training accuracy: 76.707\n",
+            "Epoch [97/100], Step [400/500] Loss: 1.67 training accuracy: 76.685\n",
+            "Epoch [97/100], Step [500/500] Loss: 1.73 training accuracy: 76.660\n",
+            "Epoch [98/100], Step [100/500] Loss: 1.73 training accuracy: 76.490\n",
+            "Epoch [98/100], Step [200/500] Loss: 1.73 training accuracy: 76.715\n",
+            "Epoch [98/100], Step [300/500] Loss: 1.73 training accuracy: 76.660\n",
+            "Epoch [98/100], Step [400/500] Loss: 1.74 training accuracy: 76.710\n",
+            "Epoch [98/100], Step [500/500] Loss: 1.70 training accuracy: 76.808\n",
+            "Epoch [99/100], Step [100/500] Loss: 1.65 training accuracy: 77.590\n",
+            "Epoch [99/100], Step [200/500] Loss: 1.75 training accuracy: 77.625\n",
+            "Epoch [99/100], Step [300/500] Loss: 1.70 training accuracy: 77.250\n",
+            "Epoch [99/100], Step [400/500] Loss: 1.78 training accuracy: 77.220\n",
+            "Epoch [99/100], Step [500/500] Loss: 1.63 training accuracy: 77.060\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "torch.save(model_lenet, MODEL_PARENT_PATH + MODEL_NAME + \".pth\")"
+      ],
+      "metadata": {
+        "id": "yfv8_X3E_BDa"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# summarize history for accuracy\n",
+        "plt.plot(history_lenet['train accuracy'])\n",
+        "plt.title('model accuracy')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.savefig(GRAPHS_PARENT_PATH + MODEL_NAME + '_accuracy.png', \\\n",
+        "            facecolor='white', edgecolor='none')\n",
+        "plt.show()\n",
+        "\n",
+        "\n",
+        "# summarize history for loss\n",
+        "plt.plot(history_lenet['train loss'])\n",
+        "plt.title('model loss')\n",
+        "plt.ylabel('loss')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.savefig(GRAPHS_PARENT_PATH + MODEL_NAME + \"_loss.png\", \\\n",
+        "            facecolor='white', edgecolor='none')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 573
+        },
+        "id": "EsdFqE0M_PLa",
+        "outputId": "26585fea-165c-440b-d1b7-f3adceab309c"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(f'training time for LeNet5 without RRL layer: {(lenet_train_time / 60):.2f} minutes')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6FnA56X6Ak7x",
+        "outputId": "32ee6c2a-06e3-40fe-92b3-51ae5b4a3224"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "training time for LeNet5 without RRL layer: 14.84 minutes\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "iPjQmXuBBo3W"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ResNet18"
+      ],
+      "metadata": {
+        "id": "uDgLBSJFCeio"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "MODEL_NAME = \"resnet18_without_RRL\""
+      ],
+      "metadata": {
+        "id": "op90TU3PCsoP"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 3x3 convolution\n",
+        "def conv3x3(in_channels, out_channels, stride=1, padding=1):\n",
+        "    return nn.Conv2d(in_channels, out_channels, kernel_size=3, \n",
+        "                     stride=stride, padding=padding, bias=False)"
+      ],
+      "metadata": {
+        "id": "_EahwnJiCgFz"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Residual block\n",
+        "class ResidualBlock(nn.Module):\n",
+        "    def __init__(self, in_channels, out_channels, stride=1, downsample=None, padding=1):\n",
+        "        super(ResidualBlock, self).__init__()\n",
+        "        self.conv1 = conv3x3(in_channels, out_channels, stride, padding=padding)\n",
+        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.conv2 = conv3x3(out_channels, out_channels)\n",
+        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
+        "        self.downsample = downsample\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        residual = x\n",
+        "        out = self.conv1(x)\n",
+        "        out = self.bn1(out)\n",
+        "        out = self.relu(out)\n",
+        "        out = self.conv2(out)\n",
+        "        out = self.bn2(out)\n",
+        "        if self.downsample:\n",
+        "            residual = self.downsample(x)\n",
+        "        out += residual\n",
+        "        out = self.relu(out)\n",
+        "        return out"
+      ],
+      "metadata": {
+        "id": "RjwwMnkTd6U2"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ResNet\n",
+        "class ResNet(nn.Module):\n",
+        "    def __init__(self, block, layers, num_classes=10, downsample=None):\n",
+        "        super(ResNet, self).__init__()\n",
+        "        self.downsample = downsample\n",
+        "        self.in_channels = 16\n",
+        "        self.conv = conv3x3(3, 16)\n",
+        "        self.bn = nn.BatchNorm2d(16)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.layer1 = self.make_layer(block, 16, layers[0])\n",
+        "        self.layer2 = self.make_layer(block, 32, layers[1], 2, padding=2)\n",
+        "        self.layer3 = self.make_layer(block, 64, layers[2], 2)\n",
+        "        self.avg_pool = nn.AvgPool2d(8)\n",
+        "        self.fc = nn.Linear(64, num_classes)\n",
+        "\n",
+        "    def make_layer(self, block, out_channels, blocks, stride=1, padding=1):\n",
+        "        if (stride != 1) or (self.in_channels != out_channels):\n",
+        "            self.downsample = nn.Sequential(\n",
+        "                conv3x3(self.in_channels, out_channels, stride=stride, padding=padding),\n",
+        "                nn.BatchNorm2d(out_channels))\n",
+        "        layers = []\n",
+        "        layers.append(block(self.in_channels, out_channels, stride, self.downsample, padding=padding))\n",
+        "        self.in_channels = out_channels\n",
+        "        for i in range(1, blocks):\n",
+        "            layers.append(block(out_channels, out_channels))\n",
+        "        return nn.Sequential(*layers)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.conv(x)\n",
+        "        x = self.bn(x)\n",
+        "        x = self.relu(x)\n",
+        "\n",
+        "        x = self.layer1(x)\n",
+        "        x = self.layer2(x)\n",
+        "        x = self.layer3(x)\n",
+        "\n",
+        "        x = self.avg_pool(x)\n",
+        "        x = torch.flatten(x, 1)\n",
+        "        x = self.fc(x)\n",
+        "\n",
+        "        return F.softmax(x, dim=1)"
+      ],
+      "metadata": {
+        "id": "lucay1LpCo4R"
+      },
+      "execution_count": 26,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_resnet18 = ResNet(ResidualBlock, [2, 2, 2]).to(device)\n",
+        "model_resnet18.to(device)\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer_resnet18 = torch.optim.Adam(model_resnet18.parameters(), lr=LR)"
+      ],
+      "metadata": {
+        "id": "pnS3vt0nCrna"
+      },
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "summary(model_resnet18, (BATCH_SIZE, 3, IMAGE_SIZE, IMAGE_SIZE))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Fr9OnnvbI3pS",
+        "outputId": "966fff15-ca05-4845-d2f9-3bed91a38f0c"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "==========================================================================================\n",
+              "Layer (type:depth-idx)                   Output Shape              Param #\n",
+              "==========================================================================================\n",
+              "ResNet                                   [100, 10]                 --\n",
+              "├─Conv2d: 1-1                            [100, 16, 33, 33]         432\n",
+              "├─BatchNorm2d: 1-2                       [100, 16, 33, 33]         32\n",
+              "├─ReLU: 1-3                              [100, 16, 33, 33]         --\n",
+              "├─Sequential: 1-4                        [100, 16, 33, 33]         --\n",
+              "│    └─ResidualBlock: 2-1                [100, 16, 33, 33]         --\n",
+              "│    │    └─Conv2d: 3-1                  [100, 16, 33, 33]         2,304\n",
+              "│    │    └─BatchNorm2d: 3-2             [100, 16, 33, 33]         32\n",
+              "│    │    └─ReLU: 3-3                    [100, 16, 33, 33]         --\n",
+              "│    │    └─Conv2d: 3-4                  [100, 16, 33, 33]         2,304\n",
+              "│    │    └─BatchNorm2d: 3-5             [100, 16, 33, 33]         32\n",
+              "│    │    └─ReLU: 3-6                    [100, 16, 33, 33]         --\n",
+              "│    └─ResidualBlock: 2-2                [100, 16, 33, 33]         --\n",
+              "│    │    └─Conv2d: 3-7                  [100, 16, 33, 33]         2,304\n",
+              "│    │    └─BatchNorm2d: 3-8             [100, 16, 33, 33]         32\n",
+              "│    │    └─ReLU: 3-9                    [100, 16, 33, 33]         --\n",
+              "│    │    └─Conv2d: 3-10                 [100, 16, 33, 33]         2,304\n",
+              "│    │    └─BatchNorm2d: 3-11            [100, 16, 33, 33]         32\n",
+              "│    │    └─ReLU: 3-12                   [100, 16, 33, 33]         --\n",
+              "├─Sequential: 1-5                        [100, 32, 18, 18]         --\n",
+              "│    └─ResidualBlock: 2-3                [100, 32, 18, 18]         --\n",
+              "│    │    └─Conv2d: 3-13                 [100, 32, 18, 18]         4,608\n",
+              "│    │    └─BatchNorm2d: 3-14            [100, 32, 18, 18]         64\n",
+              "│    │    └─ReLU: 3-15                   [100, 32, 18, 18]         --\n",
+              "│    │    └─Conv2d: 3-16                 [100, 32, 18, 18]         9,216\n",
+              "│    │    └─BatchNorm2d: 3-17            [100, 32, 18, 18]         64\n",
+              "│    │    └─Sequential: 3-18             [100, 32, 18, 18]         4,672\n",
+              "│    │    └─ReLU: 3-19                   [100, 32, 18, 18]         --\n",
+              "│    └─ResidualBlock: 2-4                [100, 32, 18, 18]         --\n",
+              "│    │    └─Conv2d: 3-20                 [100, 32, 18, 18]         9,216\n",
+              "│    │    └─BatchNorm2d: 3-21            [100, 32, 18, 18]         64\n",
+              "│    │    └─ReLU: 3-22                   [100, 32, 18, 18]         --\n",
+              "│    │    └─Conv2d: 3-23                 [100, 32, 18, 18]         9,216\n",
+              "│    │    └─BatchNorm2d: 3-24            [100, 32, 18, 18]         64\n",
+              "│    │    └─ReLU: 3-25                   [100, 32, 18, 18]         --\n",
+              "├─Sequential: 1-6                        [100, 64, 9, 9]           --\n",
+              "│    └─ResidualBlock: 2-5                [100, 64, 9, 9]           --\n",
+              "│    │    └─Conv2d: 3-26                 [100, 64, 9, 9]           18,432\n",
+              "│    │    └─BatchNorm2d: 3-27            [100, 64, 9, 9]           128\n",
+              "│    │    └─ReLU: 3-28                   [100, 64, 9, 9]           --\n",
+              "│    │    └─Conv2d: 3-29                 [100, 64, 9, 9]           36,864\n",
+              "│    │    └─BatchNorm2d: 3-30            [100, 64, 9, 9]           128\n",
+              "│    │    └─Sequential: 3-31             [100, 64, 9, 9]           18,560\n",
+              "│    │    └─ReLU: 3-32                   [100, 64, 9, 9]           --\n",
+              "│    └─ResidualBlock: 2-6                [100, 64, 9, 9]           --\n",
+              "│    │    └─Conv2d: 3-33                 [100, 64, 9, 9]           36,864\n",
+              "│    │    └─BatchNorm2d: 3-34            [100, 64, 9, 9]           128\n",
+              "│    │    └─ReLU: 3-35                   [100, 64, 9, 9]           --\n",
+              "│    │    └─Conv2d: 3-36                 [100, 64, 9, 9]           36,864\n",
+              "│    │    └─BatchNorm2d: 3-37            [100, 64, 9, 9]           128\n",
+              "│    │    └─ReLU: 3-38                   [100, 64, 9, 9]           --\n",
+              "├─AvgPool2d: 1-7                         [100, 64, 1, 1]           --\n",
+              "├─Linear: 1-8                            [100, 10]                 650\n",
+              "==========================================================================================\n",
+              "Total params: 195,738\n",
+              "Trainable params: 195,738\n",
+              "Non-trainable params: 0\n",
+              "Total mult-adds (G): 3.44\n",
+              "==========================================================================================\n",
+              "Input size (MB): 1.31\n",
+              "Forward/backward pass size (MB): 263.82\n",
+              "Params size (MB): 0.78\n",
+              "Estimated Total Size (MB): 265.91\n",
+              "=========================================================================================="
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "resnet18_train_time, history_resnet18 = trainer(\n",
+        "    model_resnet18, \n",
+        "    train_loader, \n",
+        "    optimizer_resnet18, \n",
+        "    criterion, \n",
+        "    EPOCHS)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VqMcS6EfC_PP",
+        "outputId": "3f252512-0169-4911-f678-0cc5e34570a6"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [0/100], Step [100/500] Loss: 2.08 training accuracy: 29.560\n",
+            "Epoch [0/100], Step [200/500] Loss: 1.99 training accuracy: 35.445\n",
+            "Epoch [0/100], Step [300/500] Loss: 1.99 training accuracy: 39.047\n",
+            "Epoch [0/100], Step [400/500] Loss: 1.89 training accuracy: 41.708\n",
+            "Epoch [0/100], Step [500/500] Loss: 1.94 training accuracy: 43.814\n",
+            "Epoch [1/100], Step [100/500] Loss: 1.91 training accuracy: 54.720\n",
+            "Epoch [1/100], Step [200/500] Loss: 1.84 training accuracy: 55.400\n",
+            "Epoch [1/100], Step [300/500] Loss: 1.83 training accuracy: 56.140\n",
+            "Epoch [1/100], Step [400/500] Loss: 1.91 training accuracy: 56.920\n",
+            "Epoch [1/100], Step [500/500] Loss: 1.86 training accuracy: 57.722\n",
+            "Epoch [2/100], Step [100/500] Loss: 1.87 training accuracy: 62.870\n",
+            "Epoch [2/100], Step [200/500] Loss: 1.85 training accuracy: 62.480\n",
+            "Epoch [2/100], Step [300/500] Loss: 1.79 training accuracy: 62.660\n",
+            "Epoch [2/100], Step [400/500] Loss: 1.85 training accuracy: 63.340\n",
+            "Epoch [2/100], Step [500/500] Loss: 1.84 training accuracy: 63.638\n",
+            "Epoch [3/100], Step [100/500] Loss: 1.83 training accuracy: 65.730\n",
+            "Epoch [3/100], Step [200/500] Loss: 1.81 training accuracy: 65.990\n",
+            "Epoch [3/100], Step [300/500] Loss: 1.76 training accuracy: 66.393\n",
+            "Epoch [3/100], Step [400/500] Loss: 1.82 training accuracy: 66.540\n",
+            "Epoch [3/100], Step [500/500] Loss: 1.80 training accuracy: 66.708\n",
+            "Epoch [4/100], Step [100/500] Loss: 1.79 training accuracy: 69.430\n",
+            "Epoch [4/100], Step [200/500] Loss: 1.84 training accuracy: 69.385\n",
+            "Epoch [4/100], Step [300/500] Loss: 1.86 training accuracy: 69.290\n",
+            "Epoch [4/100], Step [400/500] Loss: 1.80 training accuracy: 69.478\n",
+            "Epoch [4/100], Step [500/500] Loss: 1.76 training accuracy: 69.512\n",
+            "Epoch [5/100], Step [100/500] Loss: 1.76 training accuracy: 71.890\n",
+            "Epoch [5/100], Step [200/500] Loss: 1.68 training accuracy: 71.685\n",
+            "Epoch [5/100], Step [300/500] Loss: 1.76 training accuracy: 71.620\n",
+            "Epoch [5/100], Step [400/500] Loss: 1.67 training accuracy: 71.705\n",
+            "Epoch [5/100], Step [500/500] Loss: 1.77 training accuracy: 71.690\n",
+            "Epoch [6/100], Step [100/500] Loss: 1.71 training accuracy: 73.920\n",
+            "Epoch [6/100], Step [200/500] Loss: 1.76 training accuracy: 73.740\n",
+            "Epoch [6/100], Step [300/500] Loss: 1.70 training accuracy: 73.023\n",
+            "Epoch [6/100], Step [400/500] Loss: 1.76 training accuracy: 73.250\n",
+            "Epoch [6/100], Step [500/500] Loss: 1.67 training accuracy: 73.388\n",
+            "Epoch [7/100], Step [100/500] Loss: 1.72 training accuracy: 74.630\n",
+            "Epoch [7/100], Step [200/500] Loss: 1.72 training accuracy: 74.750\n",
+            "Epoch [7/100], Step [300/500] Loss: 1.71 training accuracy: 75.177\n",
+            "Epoch [7/100], Step [400/500] Loss: 1.73 training accuracy: 75.145\n",
+            "Epoch [7/100], Step [500/500] Loss: 1.74 training accuracy: 75.110\n",
+            "Epoch [8/100], Step [100/500] Loss: 1.71 training accuracy: 77.200\n",
+            "Epoch [8/100], Step [200/500] Loss: 1.71 training accuracy: 77.260\n",
+            "Epoch [8/100], Step [300/500] Loss: 1.75 training accuracy: 76.960\n",
+            "Epoch [8/100], Step [400/500] Loss: 1.66 training accuracy: 76.797\n",
+            "Epoch [8/100], Step [500/500] Loss: 1.71 training accuracy: 76.808\n",
+            "Epoch [9/100], Step [100/500] Loss: 1.73 training accuracy: 77.590\n",
+            "Epoch [9/100], Step [200/500] Loss: 1.74 training accuracy: 77.775\n",
+            "Epoch [9/100], Step [300/500] Loss: 1.66 training accuracy: 77.747\n",
+            "Epoch [9/100], Step [400/500] Loss: 1.63 training accuracy: 77.725\n",
+            "Epoch [9/100], Step [500/500] Loss: 1.70 training accuracy: 77.696\n",
+            "Epoch [10/100], Step [100/500] Loss: 1.61 training accuracy: 79.720\n",
+            "Epoch [10/100], Step [200/500] Loss: 1.71 training accuracy: 79.340\n",
+            "Epoch [10/100], Step [300/500] Loss: 1.63 training accuracy: 79.287\n",
+            "Epoch [10/100], Step [400/500] Loss: 1.65 training accuracy: 79.097\n",
+            "Epoch [10/100], Step [500/500] Loss: 1.70 training accuracy: 79.056\n",
+            "Epoch [11/100], Step [100/500] Loss: 1.63 training accuracy: 80.670\n",
+            "Epoch [11/100], Step [200/500] Loss: 1.68 training accuracy: 80.305\n",
+            "Epoch [11/100], Step [300/500] Loss: 1.63 training accuracy: 79.930\n",
+            "Epoch [11/100], Step [400/500] Loss: 1.67 training accuracy: 79.825\n",
+            "Epoch [11/100], Step [500/500] Loss: 1.68 training accuracy: 79.734\n",
+            "Epoch [12/100], Step [100/500] Loss: 1.68 training accuracy: 81.430\n",
+            "Epoch [12/100], Step [200/500] Loss: 1.63 training accuracy: 81.455\n",
+            "Epoch [12/100], Step [300/500] Loss: 1.68 training accuracy: 81.090\n",
+            "Epoch [12/100], Step [400/500] Loss: 1.68 training accuracy: 80.735\n",
+            "Epoch [12/100], Step [500/500] Loss: 1.68 training accuracy: 80.848\n",
+            "Epoch [13/100], Step [100/500] Loss: 1.61 training accuracy: 82.140\n",
+            "Epoch [13/100], Step [200/500] Loss: 1.70 training accuracy: 82.105\n",
+            "Epoch [13/100], Step [300/500] Loss: 1.66 training accuracy: 82.020\n",
+            "Epoch [13/100], Step [400/500] Loss: 1.62 training accuracy: 81.733\n",
+            "Epoch [13/100], Step [500/500] Loss: 1.68 training accuracy: 81.730\n",
+            "Epoch [14/100], Step [100/500] Loss: 1.58 training accuracy: 83.300\n",
+            "Epoch [14/100], Step [200/500] Loss: 1.65 training accuracy: 82.700\n",
+            "Epoch [14/100], Step [300/500] Loss: 1.64 training accuracy: 82.427\n",
+            "Epoch [14/100], Step [400/500] Loss: 1.58 training accuracy: 82.570\n",
+            "Epoch [14/100], Step [500/500] Loss: 1.71 training accuracy: 82.610\n",
+            "Epoch [15/100], Step [100/500] Loss: 1.65 training accuracy: 83.760\n",
+            "Epoch [15/100], Step [200/500] Loss: 1.61 training accuracy: 83.580\n",
+            "Epoch [15/100], Step [300/500] Loss: 1.61 training accuracy: 83.593\n",
+            "Epoch [15/100], Step [400/500] Loss: 1.70 training accuracy: 83.495\n",
+            "Epoch [15/100], Step [500/500] Loss: 1.67 training accuracy: 83.416\n",
+            "Epoch [16/100], Step [100/500] Loss: 1.60 training accuracy: 84.760\n",
+            "Epoch [16/100], Step [200/500] Loss: 1.63 training accuracy: 84.460\n",
+            "Epoch [16/100], Step [300/500] Loss: 1.61 training accuracy: 84.330\n",
+            "Epoch [16/100], Step [400/500] Loss: 1.58 training accuracy: 84.075\n",
+            "Epoch [16/100], Step [500/500] Loss: 1.68 training accuracy: 83.774\n",
+            "Epoch [17/100], Step [100/500] Loss: 1.60 training accuracy: 84.900\n",
+            "Epoch [17/100], Step [200/500] Loss: 1.62 training accuracy: 84.775\n",
+            "Epoch [17/100], Step [300/500] Loss: 1.66 training accuracy: 84.710\n",
+            "Epoch [17/100], Step [400/500] Loss: 1.63 training accuracy: 84.545\n",
+            "Epoch [17/100], Step [500/500] Loss: 1.63 training accuracy: 84.458\n",
+            "Epoch [18/100], Step [100/500] Loss: 1.57 training accuracy: 85.190\n",
+            "Epoch [18/100], Step [200/500] Loss: 1.67 training accuracy: 84.920\n",
+            "Epoch [18/100], Step [300/500] Loss: 1.60 training accuracy: 84.987\n",
+            "Epoch [18/100], Step [400/500] Loss: 1.63 training accuracy: 85.058\n",
+            "Epoch [18/100], Step [500/500] Loss: 1.67 training accuracy: 84.944\n",
+            "Epoch [19/100], Step [100/500] Loss: 1.60 training accuracy: 85.950\n",
+            "Epoch [19/100], Step [200/500] Loss: 1.56 training accuracy: 86.215\n",
+            "Epoch [19/100], Step [300/500] Loss: 1.58 training accuracy: 86.077\n",
+            "Epoch [19/100], Step [400/500] Loss: 1.54 training accuracy: 85.995\n",
+            "Epoch [19/100], Step [500/500] Loss: 1.57 training accuracy: 85.898\n",
+            "Epoch [20/100], Step [100/500] Loss: 1.57 training accuracy: 87.420\n",
+            "Epoch [20/100], Step [200/500] Loss: 1.60 training accuracy: 87.100\n",
+            "Epoch [20/100], Step [300/500] Loss: 1.62 training accuracy: 86.643\n",
+            "Epoch [20/100], Step [400/500] Loss: 1.64 training accuracy: 86.625\n",
+            "Epoch [20/100], Step [500/500] Loss: 1.61 training accuracy: 86.446\n",
+            "Epoch [21/100], Step [100/500] Loss: 1.57 training accuracy: 87.260\n",
+            "Epoch [21/100], Step [200/500] Loss: 1.59 training accuracy: 86.915\n",
+            "Epoch [21/100], Step [300/500] Loss: 1.64 training accuracy: 86.950\n",
+            "Epoch [21/100], Step [400/500] Loss: 1.61 training accuracy: 86.755\n",
+            "Epoch [21/100], Step [500/500] Loss: 1.59 training accuracy: 86.656\n",
+            "Epoch [22/100], Step [100/500] Loss: 1.55 training accuracy: 87.590\n",
+            "Epoch [22/100], Step [200/500] Loss: 1.58 training accuracy: 87.245\n",
+            "Epoch [22/100], Step [300/500] Loss: 1.57 training accuracy: 87.400\n",
+            "Epoch [22/100], Step [400/500] Loss: 1.62 training accuracy: 87.297\n",
+            "Epoch [22/100], Step [500/500] Loss: 1.58 training accuracy: 87.170\n",
+            "Epoch [23/100], Step [100/500] Loss: 1.57 training accuracy: 88.190\n",
+            "Epoch [23/100], Step [200/500] Loss: 1.58 training accuracy: 88.115\n",
+            "Epoch [23/100], Step [300/500] Loss: 1.57 training accuracy: 88.000\n",
+            "Epoch [23/100], Step [400/500] Loss: 1.59 training accuracy: 87.890\n",
+            "Epoch [23/100], Step [500/500] Loss: 1.59 training accuracy: 87.790\n",
+            "Epoch [24/100], Step [100/500] Loss: 1.56 training accuracy: 88.340\n",
+            "Epoch [24/100], Step [200/500] Loss: 1.56 training accuracy: 88.240\n",
+            "Epoch [24/100], Step [300/500] Loss: 1.58 training accuracy: 88.297\n",
+            "Epoch [24/100], Step [400/500] Loss: 1.66 training accuracy: 88.052\n",
+            "Epoch [24/100], Step [500/500] Loss: 1.58 training accuracy: 88.098\n",
+            "Epoch [25/100], Step [100/500] Loss: 1.57 training accuracy: 88.800\n",
+            "Epoch [25/100], Step [200/500] Loss: 1.55 training accuracy: 88.735\n",
+            "Epoch [25/100], Step [300/500] Loss: 1.54 training accuracy: 88.987\n",
+            "Epoch [25/100], Step [400/500] Loss: 1.57 training accuracy: 88.722\n",
+            "Epoch [25/100], Step [500/500] Loss: 1.60 training accuracy: 88.618\n",
+            "Epoch [26/100], Step [100/500] Loss: 1.55 training accuracy: 89.360\n",
+            "Epoch [26/100], Step [200/500] Loss: 1.55 training accuracy: 89.330\n",
+            "Epoch [26/100], Step [300/500] Loss: 1.53 training accuracy: 89.373\n",
+            "Epoch [26/100], Step [400/500] Loss: 1.57 training accuracy: 89.310\n",
+            "Epoch [26/100], Step [500/500] Loss: 1.61 training accuracy: 89.150\n",
+            "Epoch [27/100], Step [100/500] Loss: 1.58 training accuracy: 89.690\n",
+            "Epoch [27/100], Step [200/500] Loss: 1.56 training accuracy: 89.500\n",
+            "Epoch [27/100], Step [300/500] Loss: 1.56 training accuracy: 89.603\n",
+            "Epoch [27/100], Step [400/500] Loss: 1.55 training accuracy: 89.565\n",
+            "Epoch [27/100], Step [500/500] Loss: 1.57 training accuracy: 89.520\n",
+            "Epoch [28/100], Step [100/500] Loss: 1.54 training accuracy: 90.280\n",
+            "Epoch [28/100], Step [200/500] Loss: 1.58 training accuracy: 90.090\n",
+            "Epoch [28/100], Step [300/500] Loss: 1.61 training accuracy: 90.033\n",
+            "Epoch [28/100], Step [400/500] Loss: 1.56 training accuracy: 89.890\n",
+            "Epoch [28/100], Step [500/500] Loss: 1.58 training accuracy: 89.764\n",
+            "Epoch [29/100], Step [100/500] Loss: 1.58 training accuracy: 90.460\n",
+            "Epoch [29/100], Step [200/500] Loss: 1.53 training accuracy: 90.345\n",
+            "Epoch [29/100], Step [300/500] Loss: 1.53 training accuracy: 90.083\n",
+            "Epoch [29/100], Step [400/500] Loss: 1.61 training accuracy: 89.930\n",
+            "Epoch [29/100], Step [500/500] Loss: 1.61 training accuracy: 89.842\n",
+            "Epoch [30/100], Step [100/500] Loss: 1.57 training accuracy: 91.120\n",
+            "Epoch [30/100], Step [200/500] Loss: 1.59 training accuracy: 90.815\n",
+            "Epoch [30/100], Step [300/500] Loss: 1.62 training accuracy: 90.767\n",
+            "Epoch [30/100], Step [400/500] Loss: 1.54 training accuracy: 90.410\n",
+            "Epoch [30/100], Step [500/500] Loss: 1.59 training accuracy: 90.316\n",
+            "Epoch [31/100], Step [100/500] Loss: 1.53 training accuracy: 90.710\n",
+            "Epoch [31/100], Step [200/500] Loss: 1.52 training accuracy: 90.825\n",
+            "Epoch [31/100], Step [300/500] Loss: 1.59 training accuracy: 90.750\n",
+            "Epoch [31/100], Step [400/500] Loss: 1.59 training accuracy: 90.653\n",
+            "Epoch [31/100], Step [500/500] Loss: 1.55 training accuracy: 90.786\n",
+            "Epoch [32/100], Step [100/500] Loss: 1.53 training accuracy: 91.210\n",
+            "Epoch [32/100], Step [200/500] Loss: 1.55 training accuracy: 91.015\n",
+            "Epoch [32/100], Step [300/500] Loss: 1.59 training accuracy: 91.040\n",
+            "Epoch [32/100], Step [400/500] Loss: 1.60 training accuracy: 90.790\n",
+            "Epoch [32/100], Step [500/500] Loss: 1.57 training accuracy: 90.686\n",
+            "Epoch [33/100], Step [100/500] Loss: 1.57 training accuracy: 91.460\n",
+            "Epoch [33/100], Step [200/500] Loss: 1.56 training accuracy: 91.240\n",
+            "Epoch [33/100], Step [300/500] Loss: 1.53 training accuracy: 91.167\n",
+            "Epoch [33/100], Step [400/500] Loss: 1.53 training accuracy: 91.118\n",
+            "Epoch [33/100], Step [500/500] Loss: 1.56 training accuracy: 91.172\n",
+            "Epoch [34/100], Step [100/500] Loss: 1.56 training accuracy: 92.290\n",
+            "Epoch [34/100], Step [200/500] Loss: 1.56 training accuracy: 92.110\n",
+            "Epoch [34/100], Step [300/500] Loss: 1.55 training accuracy: 91.970\n",
+            "Epoch [34/100], Step [400/500] Loss: 1.54 training accuracy: 91.688\n",
+            "Epoch [34/100], Step [500/500] Loss: 1.53 training accuracy: 91.476\n",
+            "Epoch [35/100], Step [100/500] Loss: 1.52 training accuracy: 91.930\n",
+            "Epoch [35/100], Step [200/500] Loss: 1.56 training accuracy: 91.770\n",
+            "Epoch [35/100], Step [300/500] Loss: 1.57 training accuracy: 91.633\n",
+            "Epoch [35/100], Step [400/500] Loss: 1.55 training accuracy: 91.605\n",
+            "Epoch [35/100], Step [500/500] Loss: 1.52 training accuracy: 91.590\n",
+            "Epoch [36/100], Step [100/500] Loss: 1.52 training accuracy: 91.940\n",
+            "Epoch [36/100], Step [200/500] Loss: 1.52 training accuracy: 91.850\n",
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+            "Epoch [86/100], Step [400/500] Loss: 1.54 training accuracy: 96.272\n",
+            "Epoch [86/100], Step [500/500] Loss: 1.50 training accuracy: 96.270\n",
+            "Epoch [87/100], Step [100/500] Loss: 1.50 training accuracy: 96.360\n",
+            "Epoch [87/100], Step [200/500] Loss: 1.48 training accuracy: 96.290\n",
+            "Epoch [87/100], Step [300/500] Loss: 1.51 training accuracy: 96.343\n",
+            "Epoch [87/100], Step [400/500] Loss: 1.50 training accuracy: 96.177\n",
+            "Epoch [87/100], Step [500/500] Loss: 1.47 training accuracy: 96.156\n",
+            "Epoch [88/100], Step [100/500] Loss: 1.50 training accuracy: 96.040\n",
+            "Epoch [88/100], Step [200/500] Loss: 1.49 training accuracy: 96.175\n",
+            "Epoch [88/100], Step [300/500] Loss: 1.49 training accuracy: 96.077\n",
+            "Epoch [88/100], Step [400/500] Loss: 1.50 training accuracy: 96.118\n",
+            "Epoch [88/100], Step [500/500] Loss: 1.51 training accuracy: 96.026\n",
+            "Epoch [89/100], Step [100/500] Loss: 1.51 training accuracy: 96.360\n",
+            "Epoch [89/100], Step [200/500] Loss: 1.50 training accuracy: 96.315\n",
+            "Epoch [89/100], Step [300/500] Loss: 1.47 training accuracy: 96.343\n",
+            "Epoch [89/100], Step [400/500] Loss: 1.48 training accuracy: 96.293\n",
+            "Epoch [89/100], Step [500/500] Loss: 1.52 training accuracy: 96.246\n",
+            "Epoch [90/100], Step [100/500] Loss: 1.52 training accuracy: 96.290\n",
+            "Epoch [90/100], Step [200/500] Loss: 1.50 training accuracy: 96.475\n",
+            "Epoch [90/100], Step [300/500] Loss: 1.51 training accuracy: 96.367\n",
+            "Epoch [90/100], Step [400/500] Loss: 1.47 training accuracy: 96.287\n",
+            "Epoch [90/100], Step [500/500] Loss: 1.51 training accuracy: 96.198\n",
+            "Epoch [91/100], Step [100/500] Loss: 1.49 training accuracy: 95.730\n",
+            "Epoch [91/100], Step [200/500] Loss: 1.50 training accuracy: 96.070\n",
+            "Epoch [91/100], Step [300/500] Loss: 1.47 training accuracy: 96.237\n",
+            "Epoch [91/100], Step [400/500] Loss: 1.46 training accuracy: 96.282\n",
+            "Epoch [91/100], Step [500/500] Loss: 1.50 training accuracy: 96.258\n",
+            "Epoch [92/100], Step [100/500] Loss: 1.47 training accuracy: 96.400\n",
+            "Epoch [92/100], Step [200/500] Loss: 1.52 training accuracy: 96.300\n",
+            "Epoch [92/100], Step [300/500] Loss: 1.47 training accuracy: 96.330\n",
+            "Epoch [92/100], Step [400/500] Loss: 1.50 training accuracy: 96.295\n",
+            "Epoch [92/100], Step [500/500] Loss: 1.49 training accuracy: 96.286\n",
+            "Epoch [93/100], Step [100/500] Loss: 1.48 training accuracy: 96.300\n",
+            "Epoch [93/100], Step [200/500] Loss: 1.52 training accuracy: 96.230\n",
+            "Epoch [93/100], Step [300/500] Loss: 1.48 training accuracy: 96.370\n",
+            "Epoch [93/100], Step [400/500] Loss: 1.50 training accuracy: 96.332\n",
+            "Epoch [93/100], Step [500/500] Loss: 1.48 training accuracy: 96.346\n",
+            "Epoch [94/100], Step [100/500] Loss: 1.48 training accuracy: 96.540\n",
+            "Epoch [94/100], Step [200/500] Loss: 1.56 training accuracy: 96.335\n",
+            "Epoch [94/100], Step [300/500] Loss: 1.48 training accuracy: 96.267\n",
+            "Epoch [94/100], Step [400/500] Loss: 1.52 training accuracy: 96.315\n",
+            "Epoch [94/100], Step [500/500] Loss: 1.49 training accuracy: 96.256\n",
+            "Epoch [95/100], Step [100/500] Loss: 1.50 training accuracy: 96.550\n",
+            "Epoch [95/100], Step [200/500] Loss: 1.50 training accuracy: 96.395\n",
+            "Epoch [95/100], Step [300/500] Loss: 1.49 training accuracy: 96.490\n",
+            "Epoch [95/100], Step [400/500] Loss: 1.49 training accuracy: 96.535\n",
+            "Epoch [95/100], Step [500/500] Loss: 1.50 training accuracy: 96.538\n",
+            "Epoch [96/100], Step [100/500] Loss: 1.50 training accuracy: 96.500\n",
+            "Epoch [96/100], Step [200/500] Loss: 1.52 training accuracy: 96.560\n",
+            "Epoch [96/100], Step [300/500] Loss: 1.49 training accuracy: 96.460\n",
+            "Epoch [96/100], Step [400/500] Loss: 1.50 training accuracy: 96.415\n",
+            "Epoch [96/100], Step [500/500] Loss: 1.55 training accuracy: 96.370\n",
+            "Epoch [97/100], Step [100/500] Loss: 1.50 training accuracy: 96.630\n",
+            "Epoch [97/100], Step [200/500] Loss: 1.50 training accuracy: 96.385\n",
+            "Epoch [97/100], Step [300/500] Loss: 1.52 training accuracy: 96.397\n",
+            "Epoch [97/100], Step [400/500] Loss: 1.51 training accuracy: 96.353\n",
+            "Epoch [97/100], Step [500/500] Loss: 1.50 training accuracy: 96.322\n",
+            "Epoch [98/100], Step [100/500] Loss: 1.47 training accuracy: 96.660\n",
+            "Epoch [98/100], Step [200/500] Loss: 1.47 training accuracy: 96.325\n",
+            "Epoch [98/100], Step [300/500] Loss: 1.47 training accuracy: 96.453\n",
+            "Epoch [98/100], Step [400/500] Loss: 1.51 training accuracy: 96.478\n",
+            "Epoch [98/100], Step [500/500] Loss: 1.49 training accuracy: 96.356\n",
+            "Epoch [99/100], Step [100/500] Loss: 1.47 training accuracy: 96.710\n",
+            "Epoch [99/100], Step [200/500] Loss: 1.49 training accuracy: 96.795\n",
+            "Epoch [99/100], Step [300/500] Loss: 1.49 training accuracy: 96.620\n",
+            "Epoch [99/100], Step [400/500] Loss: 1.47 training accuracy: 96.635\n",
+            "Epoch [99/100], Step [500/500] Loss: 1.47 training accuracy: 96.514\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "torch.save(model_resnet18, MODEL_PARENT_PATH + MODEL_NAME + \".pth\")"
+      ],
+      "metadata": {
+        "id": "Oc1beM-9DJre"
+      },
+      "execution_count": 30,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# summarize history for accuracy\n",
+        "plt.plot(history_resnet18['train accuracy'])\n",
+        "plt.title('model accuracy')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.savefig(GRAPHS_PARENT_PATH + MODEL_NAME + '_accuracy.png', \\\n",
+        "            facecolor='white', edgecolor='none')\n",
+        "plt.show()\n",
+        "\n",
+        "\n",
+        "# summarize history for loss\n",
+        "plt.plot(history_resnet18['train loss'])\n",
+        "plt.title('model loss')\n",
+        "plt.ylabel('loss')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.savefig(GRAPHS_PARENT_PATH + MODEL_NAME + \"_loss.png\", \\\n",
+        "            facecolor='white', edgecolor='none')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 573
+        },
+        "id": "GtKzwGu0DMww",
+        "outputId": "cfc60bca-7ddd-4855-c7a1-8e79ad6bb33a"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(f'training time for ResNet18 without RRL layer: {(resnet18_train_time / 60):.2f} minutes')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F4J90-OCDNDA",
+        "outputId": "5940e58f-fc4e-41b3-fc78-0b2cc4dd9d58"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "training time for ResNet18 without RRL layer: 22.81 minutes\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "ww6id2UJDgnW"
+      },
+      "execution_count": 29,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Project_Mahammadli_Gokce/images/LPB.png b/Project_Mahammadli_Gokce/images/LPB.png
new file mode 100644
index 0000000..87dee86
Binary files /dev/null and b/Project_Mahammadli_Gokce/images/LPB.png differ
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diff --git a/Project_Mahammadli_Gokce/results/Results.ipynb b/Project_Mahammadli_Gokce/results/Results.ipynb
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--- /dev/null
+++ b/Project_Mahammadli_Gokce/results/Results.ipynb
@@ -0,0 +1,924 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Results.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "gpuClass": "standard"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "ybgVd4H2sbfO"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import torchvision\n",
+        "import matplotlib.pyplot as plt\n",
+        "from torchvision import transforms, datasets\n",
+        "from sklearn.metrics import classification_report\n",
+        "from torchvision.transforms.functional import rotate\n",
+        "from joblib import Parallel, delayed\n",
+        "import multiprocessing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "device"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "YYhKH_BXxqM-",
+        "outputId": "9e1fe735-1720-44ae-85da-41cbf32a98e0"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'cuda'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "IMAGE_SIZE = 33\n",
+        "BATCH_SIZE = 100"
+      ],
+      "metadata": {
+        "id": "4B_BlOtSVU4f"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_transform_1 = transforms.Compose([\n",
+        "                                       transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n",
+        "                                       transforms.RandomRotation((90, 90)),\n",
+        "                                       transforms.RandomRotation((180, 180)),\n",
+        "                                       transforms.RandomRotation((270, 270)),\n",
+        "                                       transforms.ToTensor()\n",
+        "                                      ])\n",
+        "\n",
+        "test_transform_2 = transforms.Compose([\n",
+        "                                       transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n",
+        "                                       transforms.RandomRotation((0, 360)),\n",
+        "                                       transforms.ToTensor()\n",
+        "                                      ])\n",
+        "\n",
+        "\n",
+        "testset_1 = torchvision.datasets.CIFAR10(root='/content/drive/MyDrive/CENG501/data', \n",
+        "                                       train=False,\n",
+        "                                       download=True, \n",
+        "                                       transform=test_transform_1)\n",
+        "\n",
+        "testloader_1 = torch.utils.data.DataLoader(testset_1, \n",
+        "                                          batch_size=BATCH_SIZE,\n",
+        "                                         shuffle=False)\n",
+        "\n",
+        "testset_2 = torchvision.datasets.CIFAR10(root='/content/drive/MyDrive/CENG501/data', \n",
+        "                                       train=False,\n",
+        "                                       download=True, \n",
+        "                                       transform=test_transform_2)\n",
+        "\n",
+        "testloader_2 = torch.utils.data.DataLoader(testset_2, \n",
+        "                                          batch_size=BATCH_SIZE,\n",
+        "                                         shuffle=False)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TCYoU_pSU-yf",
+        "outputId": "1b7836b4-f409-4626-c090-88a3b60c1cff"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Files already downloaded and verified\n",
+            "Files already downloaded and verified\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# visualization of CIFAR-10-rot test images\n",
+        "labels = 'airplane automobile bird cat deer dog frog horse ship truck'.split()\n",
+        "first_50_samples = sorted([testset_1[i] for i in range(50)], key=lambda x:x[1])\n",
+        "\n",
+        "figure = plt.figure(figsize=(15,10))\n",
+        "for i in range(1,51):\n",
+        "    img = first_50_samples[i-1][0].permute(1,2,0)\n",
+        "    label = labels[first_50_samples[i-1][1]]\n",
+        "    figure.add_subplot(5,10,i)\n",
+        "    plt.title(label)\n",
+        "    plt.axis('off')\n",
+        "    plt.imshow(img)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 568
+        },
+        "id": "8uSHghPkuNIZ",
+        "outputId": "579ed3f1-3ef6-4fc6-f37b-49d6f4390142"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x720 with 50 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# visualizing CIFAR-10-rot+ test images\n",
+        "first_50_samples = sorted([testset_2[i] for i in range(50)], key=lambda x:x[1])\n",
+        "\n",
+        "figure = plt.figure(figsize=(15,10))\n",
+        "for i in range(1,51):\n",
+        "    img = first_50_samples[i-1][0].permute(1,2,0)\n",
+        "    label = labels[first_50_samples[i-1][1]]\n",
+        "    figure.add_subplot(5,10,i)\n",
+        "    plt.title(label)\n",
+        "    plt.axis('off')\n",
+        "    plt.imshow(img)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 568
+        },
+        "id": "wdLYY8PYuy4J",
+        "outputId": "3d3f6769-60b3-443f-fe49-55ca8cab422e"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x720 with 50 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def print_results(model, test_loader):\n",
+        "  model.eval()\n",
+        "  y_pred_ls = []\n",
+        "  y_test_ls = []\n",
+        "  total = 0\n",
+        "\n",
+        "  with torch.no_grad():\n",
+        "    for b, (X_test, y_test) in enumerate(test_loader):\n",
+        "      y_test_ls.extend(y_test.tolist())\n",
+        "      X_test = X_test.to(device)\n",
+        "      y_test = y_test.to(device)\n",
+        "\n",
+        "      y_test_pred = model(X_test)\n",
+        "\n",
+        "      predicted = torch.max(y_test_pred.data, 1)[1].tolist()\n",
+        "      y_pred_ls.extend(predicted)\n",
+        "\n",
+        "  print(classification_report(y_pred_ls, y_test_ls))"
+      ],
+      "metadata": {
+        "id": "Ne6AHl_d0A5Y"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Without RRL"
+      ],
+      "metadata": {
+        "id": "7cjAvrzmv8qe"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### LeNet 5"
+      ],
+      "metadata": {
+        "id": "M0XNDZoE9OrM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class LeNet5(nn.Module):\n",
+        "  def __init__(self, n_classes=10):\n",
+        "    super().__init__()\n",
+        "    self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=(1, 1))\n",
+        "    self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=(1, 1))\n",
+        "    self.pooling = nn.AvgPool2d(kernel_size=(2, 2), stride=2)\n",
+        "    self.tanh = nn.Tanh()\n",
+        "\n",
+        "    self.fc1 = nn.Linear(in_features=16*6*6, out_features=84)\n",
+        "    self.fc2 = nn.Linear(in_features=84, out_features=n_classes)\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    x = self.tanh(self.conv1(x))\n",
+        "    x = self.pooling(x)\n",
+        "\n",
+        "    x = self.tanh(self.conv2(x))\n",
+        "    x = self.pooling(x)\n",
+        "\n",
+        "    x = torch.flatten(x, 1)\n",
+        "    x = self.tanh(self.fc1(x))\n",
+        "\n",
+        "    x = self.fc2(x)\n",
+        "    return F.softmax(x, dim=1)"
+      ],
+      "metadata": {
+        "id": "UQyrwfRjxZDY"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lenet = LeNet5().to(device)\n",
+        "lenet = torch.load('/content/drive/MyDrive/CENG501/models/lenet5_without_RRL.pth')"
+      ],
+      "metadata": {
+        "id": "OufFpOf-xgXL"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Results for CIFAR-10-rot test\")\n",
+        "print_results(lenet, testloader_1)\n",
+        "\n",
+        "print(\"\\n Results for CIFAR-10-rot+ test\")\n",
+        "print_results(lenet, testloader_2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jZ7q1h7Bvh0Q",
+        "outputId": "d37a46fd-39ab-4c82-9894-22a79e78d658"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Results for CIFAR-10-rot test\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.48      0.38      0.42      1258\n",
+            "           1       0.26      0.39      0.31       657\n",
+            "           2       0.32      0.27      0.29      1219\n",
+            "           3       0.24      0.16      0.19      1492\n",
+            "           4       0.27      0.23      0.25      1198\n",
+            "           5       0.26      0.22      0.24      1143\n",
+            "           6       0.46      0.37      0.41      1220\n",
+            "           7       0.04      0.07      0.06       592\n",
+            "           8       0.21      0.27      0.23       771\n",
+            "           9       0.07      0.16      0.10       450\n",
+            "\n",
+            "    accuracy                           0.26     10000\n",
+            "   macro avg       0.26      0.25      0.25     10000\n",
+            "weighted avg       0.29      0.26      0.27     10000\n",
+            "\n",
+            "\n",
+            " Results for CIFAR-10-rot+ test\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.34      0.44      0.38       789\n",
+            "           1       0.19      0.45      0.27       420\n",
+            "           2       0.27      0.20      0.23      1354\n",
+            "           3       0.31      0.16      0.21      1970\n",
+            "           4       0.36      0.28      0.32      1263\n",
+            "           5       0.40      0.19      0.26      2091\n",
+            "           6       0.47      0.42      0.45      1125\n",
+            "           7       0.10      0.39      0.16       264\n",
+            "           8       0.24      0.43      0.31       564\n",
+            "           9       0.06      0.40      0.11       160\n",
+            "\n",
+            "    accuracy                           0.28     10000\n",
+            "   macro avg       0.28      0.34      0.27     10000\n",
+            "weighted avg       0.33      0.28      0.28     10000\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### ResNet 18 - Without RRL"
+      ],
+      "metadata": {
+        "id": "2eKtrbNr9vRw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 3x3 convolution\n",
+        "def conv3x3(in_channels, out_channels, stride=1, padding=1):\n",
+        "    return nn.Conv2d(in_channels, out_channels, kernel_size=3, \n",
+        "                     stride=stride, padding=padding, bias=False)"
+      ],
+      "metadata": {
+        "id": "1eVSLKe_98HF"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Residual block\n",
+        "class ResidualBlock(nn.Module):\n",
+        "    def __init__(self, in_channels, out_channels, stride=1, downsample=None, padding=1):\n",
+        "        super(ResidualBlock, self).__init__()\n",
+        "        self.conv1 = conv3x3(in_channels, out_channels, stride, padding=padding)\n",
+        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.conv2 = conv3x3(out_channels, out_channels)\n",
+        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
+        "        self.downsample = downsample\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        residual = x\n",
+        "        out = self.conv1(x)\n",
+        "        out = self.bn1(out)\n",
+        "        out = self.relu(out)\n",
+        "        out = self.conv2(out)\n",
+        "        out = self.bn2(out)\n",
+        "        if self.downsample:\n",
+        "            residual = self.downsample(x)\n",
+        "        out += residual\n",
+        "        out = self.relu(out)\n",
+        "        return out"
+      ],
+      "metadata": {
+        "id": "wE2KlI4C986M"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ResNet\n",
+        "class ResNet(nn.Module):\n",
+        "    def __init__(self, block, layers, num_classes=10, downsample=None):\n",
+        "        super(ResNet, self).__init__()\n",
+        "        self.downsample = downsample\n",
+        "        self.in_channels = 16\n",
+        "        self.conv = conv3x3(3, 16)\n",
+        "        self.bn = nn.BatchNorm2d(16)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.layer1 = self.make_layer(block, 16, layers[0])\n",
+        "        self.layer2 = self.make_layer(block, 32, layers[1], 2, padding=2)\n",
+        "        self.layer3 = self.make_layer(block, 64, layers[2], 2)\n",
+        "        self.avg_pool = nn.AvgPool2d(8)\n",
+        "        self.fc = nn.Linear(64, num_classes)\n",
+        "\n",
+        "    def make_layer(self, block, out_channels, blocks, stride=1, padding=1):\n",
+        "        if (stride != 1) or (self.in_channels != out_channels):\n",
+        "            self.downsample = nn.Sequential(\n",
+        "                conv3x3(self.in_channels, out_channels, stride=stride, padding=padding),\n",
+        "                nn.BatchNorm2d(out_channels))\n",
+        "        layers = []\n",
+        "        layers.append(block(self.in_channels, out_channels, stride, self.downsample, padding=padding))\n",
+        "        self.in_channels = out_channels\n",
+        "        for i in range(1, blocks):\n",
+        "            layers.append(block(out_channels, out_channels))\n",
+        "        return nn.Sequential(*layers)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.conv(x)\n",
+        "        x = self.bn(x)\n",
+        "        x = self.relu(x)\n",
+        "\n",
+        "        x = self.layer1(x)\n",
+        "        x = self.layer2(x)\n",
+        "        x = self.layer3(x)\n",
+        "\n",
+        "        x = self.avg_pool(x)\n",
+        "        x = torch.flatten(x, 1)\n",
+        "        x = self.fc(x)\n",
+        "\n",
+        "        return F.softmax(x, dim=1)"
+      ],
+      "metadata": {
+        "id": "BGXPTSt_99AH"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "resnet =  ResNet(ResidualBlock, [2, 2, 2]).to(device)\n",
+        "resnet = torch.load('/content/drive/MyDrive/CENG501/models/resnet18_without_RRL.pth')"
+      ],
+      "metadata": {
+        "id": "23v1xbWu99Ip"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Results for CIFAR-10-rot test\")\n",
+        "print_results(resnet, testloader_1)\n",
+        "\n",
+        "print(\"\\n Results for CIFAR-10-rot+ test\")\n",
+        "print_results(resnet, testloader_2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "o-2011M0wYH9",
+        "outputId": "8c475322-f588-4c05-daf9-09343c423ea1"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Results for CIFAR-10-rot test\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.67      0.37      0.48      1777\n",
+            "           1       0.12      0.56      0.20       224\n",
+            "           2       0.39      0.25      0.30      1561\n",
+            "           3       0.35      0.20      0.26      1738\n",
+            "           4       0.43      0.35      0.39      1215\n",
+            "           5       0.42      0.39      0.40      1098\n",
+            "           6       0.59      0.55      0.57      1083\n",
+            "           7       0.02      0.12      0.03       148\n",
+            "           8       0.11      0.33      0.17       340\n",
+            "           9       0.43      0.52      0.47       816\n",
+            "\n",
+            "    accuracy                           0.35     10000\n",
+            "   macro avg       0.35      0.36      0.33     10000\n",
+            "weighted avg       0.44      0.35      0.38     10000\n",
+            "\n",
+            "\n",
+            " Results for CIFAR-10-rot+ test\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.29      0.44      0.35       656\n",
+            "           1       0.06      0.67      0.12        95\n",
+            "           2       0.32      0.18      0.23      1723\n",
+            "           3       0.09      0.14      0.11       648\n",
+            "           4       0.17      0.24      0.20       734\n",
+            "           5       0.07      0.30      0.11       227\n",
+            "           6       0.87      0.16      0.28      5305\n",
+            "           7       0.07      0.35      0.12       200\n",
+            "           8       0.08      0.54      0.14       151\n",
+            "           9       0.14      0.52      0.22       261\n",
+            "\n",
+            "    accuracy                           0.22     10000\n",
+            "   macro avg       0.22      0.35      0.19     10000\n",
+            "weighted avg       0.56      0.22      0.24     10000\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## LeNet5 - With RRL"
+      ],
+      "metadata": {
+        "id": "Drd2VNbU-PAR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def rotate_mx(mx, degree):\n",
+        "\treturn rotate(mx.unsqueeze(0), degree).squeeze(0)"
+      ],
+      "metadata": {
+        "id": "_FkE6gKb-ds0"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# calculating local binary pattern value\n",
+        "def LPB(mx):\n",
+        "  mid = mx.shape[0] // 2\n",
+        "  threshold = mx[mid, mid]\n",
+        "\n",
+        "  bin_mx = (mx >= threshold)\n",
+        "  power_mx = torch.Tensor([\n",
+        "                       [1, 2, 4],\n",
+        "                       [8, 0, 16],\n",
+        "                       [32, 64, 128]\n",
+        "  ]).to(device)\n",
+        "\n",
+        "  return (bin_mx * power_mx).sum()"
+      ],
+      "metadata": {
+        "id": "SdqUy7yL-dzI"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# finding rotation matrix leading to minimum LPB value\n",
+        "def min_lpb(mx, mx2=None):\n",
+        "  num_of_surr_elements = mx.shape[0] ** 2 - 1\n",
+        "  rot_deg = 360 // num_of_surr_elements\n",
+        "\n",
+        "  min_mx = mx.clone()\n",
+        "  lpb = LPB(mx)\n",
+        "  for degree in range(rot_deg, 360, rot_deg):\n",
+        "    mx = rotate_mx(mx, degree)\n",
+        "    cur_lpb = LPB(mx)\n",
+        "\n",
+        "    if cur_lpb < lpb:\n",
+        "      lpb = cur_lpb\n",
+        "      min_mx = mx.clone()\n",
+        "     \n",
+        "  return min_mx"
+      ],
+      "metadata": {
+        "id": "ATBm3ezM-d2H"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class RRL(nn.Module):\n",
+        "  def __init__(self, kernel_size=3, padding=(0, 0)):\n",
+        "    super(RRL, self).__init__()\n",
+        "    self.FH = self.FW = self.stride = kernel_size\n",
+        "    self.padding = padding\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    # x -> (batch_size, channels, height, width)\n",
+        "    # OH, OW - feature map height and width\n",
+        "    # FH, FW - kernel height and width\n",
+        "\n",
+        "    batch_size, ch, H, W = x.shape\n",
+        "    OH = H // self.FH\n",
+        "    OW = W // self.FW\n",
+        "\n",
+        "    # pad the input\n",
+        "    x = F.pad(x, pad=self.padding)\n",
+        "    \n",
+        "    # create sliding windows\n",
+        "    x = x.unfold(2, self.FH, self.stride).unfold(3, self.FW, self.stride) # x -> (batch_size, channels, OH, OW, FH, FW)\n",
+        "    x = x.contiguous().view(batch_size, ch, -1, self.FH, self.FW) # x -> (batch_size, channels, OH*OW, FH, FW)\n",
+        "    x = x.permute(0, 2, 1, 3, 4) # x -> (batch_size, OH*OW, channels, FH, FW)\n",
+        "\n",
+        "    \n",
+        "    # calculate minimum LPB state for all the windows \n",
+        "    #x = vmap(min_lpb, in_dims=(-2, -1), out_dims=(-2, -1))(x) # x -> (batch_size, OH*OW, channels, FH, FW)\n",
+        "\n",
+        "    num_cores = multiprocessing.cpu_count()\n",
+        "    x = Parallel(n_jobs=num_cores, backend=\"threading\")(delayed(min_lpb)(x[b, o, c, :, :]) for b in range(batch_size) for o in range(OH*OW) for c in range(ch))\n",
+        "    x = torch.stack(x).view(batch_size, OH*OW, ch, self.FH, self.FW)\n",
+        "\n",
+        "    # reshape matrix into original format\n",
+        "    x = x.permute(0, 2, 1, 3, 4) # x -> (batch_size, channels, OH*OW, FH, FW)\n",
+        "    x = x.view(batch_size, ch, OH, OW, self.FH, self.FW) # x -> (batch_size, channels, OH, OW, FH, FW)\n",
+        "    x = x.permute(0, 1, 2, 4, 3, 5) # x -> (batch_size, channels, OH, FH, OW, FW)\n",
+        "    x = x.contiguous().view(batch_size, ch, H, W) # x -> (batch_size, channels, OH*FH, OW*FW)\n",
+        " \n",
+        "    return x"
+      ],
+      "metadata": {
+        "id": "NKsgwTdI-d4i"
+      },
+      "execution_count": 26,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class LeNet5_with_RRL(nn.Module):\n",
+        "  def __init__(self, n_classes):\n",
+        "    super().__init__()\n",
+        "    self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=(1, 1))\n",
+        "    self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=(1, 1))\n",
+        "    self.pooling = nn.MaxPool2d(kernel_size=(2, 2), stride=2)\n",
+        "    self.RRL = RRL()\n",
+        "    self.tanh = nn.Tanh()\n",
+        "\n",
+        "    self.fc1 = nn.Linear(in_features=16*6*6, out_features=84)\n",
+        "    self.fc2 = nn.Linear(in_features=84, out_features=n_classes)\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "    x = self.RRL(x)\n",
+        "    x = self.tanh(self.conv1(x))\n",
+        "    x = self.pooling(x)\n",
+        "\n",
+        "    x = self.RRL(x)\n",
+        "    x = self.tanh(self.conv2(x))\n",
+        "    x = self.pooling(x)\n",
+        "\n",
+        "    x = self.RRL(x)\n",
+        "\n",
+        "    x = torch.flatten(x, 1)\n",
+        "    x = self.tanh(self.fc1(x))\n",
+        "\n",
+        "    x = self.fc2(x)\n",
+        "    return F.softmax(x, dim=1)"
+      ],
+      "metadata": {
+        "id": "KlVGGMy5-d70"
+      },
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lenet_rrl =  LeNet5_with_RRL(10)\n",
+        "lenet_rrl = torch.load('/content/drive/MyDrive/CENG501/models/lenet5_with_rrl.pth')"
+      ],
+      "metadata": {
+        "id": "KjotFVtv-OUg"
+      },
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Results for CIFAR-10-rot test\")\n",
+        "print_results(lenet_rrl, testloader_1)\n",
+        "\n",
+        "print(\"\\n Results for CIFAR-10-rot+ test\")\n",
+        "print_results(lenet_rrl, testloader_2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ic5EUqRw94dD",
+        "outputId": "4f77b631-05be-4371-bba3-26403eb7b9b7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Results for CIFAR-10-rot test\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, msg_start, len(result))\n",
+            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, msg_start, len(result))\n",
+            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, msg_start, len(result))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.76      0.19      0.30      4106\n",
+            "           1       0.00      0.00      0.00         0\n",
+            "           2       0.00      0.00      0.00         0\n",
+            "           3       0.00      0.00      0.00         0\n",
+            "           4       0.00      0.00      0.00         0\n",
+            "           5       0.64      0.11      0.18      5892\n",
+            "           6       0.00      0.00      0.00         0\n",
+            "           7       0.00      0.00      0.00         0\n",
+            "           8       0.00      0.00      0.00         2\n",
+            "           9       0.00      0.00      0.00         0\n",
+            "\n",
+            "    accuracy                           0.14     10000\n",
+            "   macro avg       0.14      0.03      0.05     10000\n",
+            "weighted avg       0.69      0.14      0.23     10000\n",
+            "\n",
+            "\n",
+            " Results for CIFAR-10-rot+ test\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ResNet18 - With RRL"
+      ],
+      "metadata": {
+        "id": "KrZiTRHluMDZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 3x3 convolution\n",
+        "def conv3x3(in_channels, out_channels, stride=1, padding=1):\n",
+        "    return nn.Conv2d(in_channels, out_channels, kernel_size=3, \n",
+        "                     stride=stride, padding=padding, bias=False)"
+      ],
+      "metadata": {
+        "id": "d-9A_hfj-8_Z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Residual block\n",
+        "class ResidualBlock(nn.Module):\n",
+        "    def __init__(self, in_channels, out_channels, stride=1, downsample=None, padding=1):\n",
+        "        super(ResidualBlock, self).__init__()\n",
+        "        self.conv1 = conv3x3(in_channels, out_channels, stride, padding=padding)\n",
+        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.conv2 = conv3x3(out_channels, out_channels)\n",
+        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
+        "        self.downsample = downsample\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        residual = x\n",
+        "        out = self.conv1(x)\n",
+        "        out = self.bn1(out)\n",
+        "        out = self.relu(out)\n",
+        "        out = self.conv2(out)\n",
+        "        out = self.bn2(out)\n",
+        "        if self.downsample:\n",
+        "            residual = self.downsample(x)\n",
+        "        out += residual\n",
+        "        out = self.relu(out)\n",
+        "        return out"
+      ],
+      "metadata": {
+        "id": "MEwf_1kkuSY4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ResNet\n",
+        "class ResNet(nn.Module):\n",
+        "    def __init__(self, block, layers, num_classes=10, downsample=None):\n",
+        "        super(ResNet, self).__init__()\n",
+        "        self.downsample = downsample\n",
+        "        self.in_channels = 16\n",
+        "        self.conv = conv3x3(3, 16)\n",
+        "        self.bn = nn.BatchNorm2d(16)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.layer1 = self.make_layer(block, 16, layers[0])\n",
+        "        self.layer2 = self.make_layer(block, 32, layers[1], 2, padding=2)\n",
+        "        self.layer3 = self.make_layer(block, 64, layers[2], 2)\n",
+        "        self.avg_pool = nn.AvgPool2d(8)\n",
+        "        self.fc = nn.Linear(64, num_classes)\n",
+        "        self.RRL = RRL()\n",
+        "\n",
+        "    def make_layer(self, block, out_channels, blocks, stride=1, padding=1):\n",
+        "        if (stride != 1) or (self.in_channels != out_channels):\n",
+        "            self.downsample = nn.Sequential(\n",
+        "                conv3x3(self.in_channels, out_channels, stride=stride, padding=padding),\n",
+        "                nn.BatchNorm2d(out_channels))\n",
+        "        layers = []\n",
+        "        layers.append(block(self.in_channels, out_channels, stride, self.downsample, padding=padding))\n",
+        "        self.in_channels = out_channels\n",
+        "        for i in range(1, blocks):\n",
+        "            layers.append(block(out_channels, out_channels))\n",
+        "        return nn.Sequential(*layers)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.RRL(x)\n",
+        "        x = self.conv(x)\n",
+        "        x = self.bn(x)\n",
+        "        x = self.relu(x)\n",
+        "\n",
+        "        x = self.RRL(x)\n",
+        "        x = self.layer1(x)\n",
+        "        x = self.layer2(x)\n",
+        "        x = self.layer3(x)\n",
+        "        x = self.RRL(x)\n",
+        "\n",
+        "        x = self.avg_pool(x)\n",
+        "        x = torch.flatten(x, 1)\n",
+        "        x = self.fc(x)\n",
+        "\n",
+        "        return F.softmax(x, dim=1)"
+      ],
+      "metadata": {
+        "id": "1NU9FwFZuVXk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "resnet18_rrl =  ResNet(ResidualBlock, [2, 2, 2]).to(device)\n",
+        "resnet18_rrl = torch.load('/content/drive/MyDrive/CENG501/models/resnet18_with_rrl.pth')"
+      ],
+      "metadata": {
+        "id": "OQAf0RTOuVaA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Results for CIFAR-10-rot test\")\n",
+        "print_results(resnet18_rrl, testloader_1)\n",
+        "\n",
+        "print(\"\\n Results for CIFAR-10-rot+ test\")\n",
+        "print_results(resnet18_rrl, testloader_2)"
+      ],
+      "metadata": {
+        "id": "_C6lKXXcuVcc"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Project_Mahammadli_Gokce/src/Readme.md b/Project_Mahammadli_Gokce/src/Readme.md
new file mode 100644
index 0000000..e69de29
diff --git a/Project_Mahammadli_Gokce/src/__init__.py b/Project_Mahammadli_Gokce/src/__init__.py
new file mode 100644
index 0000000..6191bb0
--- /dev/null
+++ b/Project_Mahammadli_Gokce/src/__init__.py
@@ -0,0 +1,4 @@
+from lpb import min_lpb
+from layers import RRL
+from models import LeNet5, ResNet18, ResidualBlock
+from dataloader import prepare_loader
\ No newline at end of file
diff --git a/Project_Mahammadli_Gokce/src/dataloader.py b/Project_Mahammadli_Gokce/src/dataloader.py
new file mode 100644
index 0000000..e8550fc
--- /dev/null
+++ b/Project_Mahammadli_Gokce/src/dataloader.py
@@ -0,0 +1,85 @@
+from matplotlib import transforms
+import torch
+import torchvision
+from torchvision import datasets, transforms
+from torch.utils.data.dataloader import DataLoader
+from typing import Union, Tuple
+
+def prepare_loader(
+    image_size: int,
+    batch_size: int,
+    root: str='./data',
+    train_or_test: str='train') -> Union[Tuple[Union[int, int, DataLoader]], Tuple[Union[DataLoader, DataLoader]]]:
+    """
+    Creates dataloader for CIFAR-10 data
+    
+    Parameters
+    ---------
+    image_size : int
+        Resize value for both height and width
+    batch_size: int
+        Size of each batch
+    root : str
+        Path to load the data
+    train_or_test: str
+        Defining whether preparing the training or test data loader
+        
+    Returns
+    -------
+    data_loader : Union[Tuple[Union[int, int, DataLoader]], Tuple[Union[DataLoader, DataLoader]]]
+         Data loader with batches
+    """
+    
+    if train_or_test == 'train':
+        transform = transforms.Compose([
+                                transforms.Resize((image_size, image_size)),
+                                transforms.ToTensor()])
+        
+        train_dataset = datasets.CIFAR10(root=root,
+                                             train=True, 
+                                             transform=transform,
+                                             download=True)
+        
+        train_loader = train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=batch_size, 
+                                           shuffle=True)
+        
+        num_of_images = len(train_dataset)
+        num_of_batches = len(train_loader)
+        return (num_of_images, train_loader, num_of_batches)
+    
+    else:
+        test_transform_1 = transforms.Compose([
+                                       transforms.Resize((image_size, image_size)),
+                                       transforms.RandomRotation((90, 90)),
+                                       transforms.RandomRotation((180, 180)),
+                                       transforms.RandomRotation((270, 270)),
+                                       transforms.ToTensor()
+                                      ])
+
+        test_transform_2 = transforms.Compose([
+                                       transforms.Resize((image_size, image_size)),
+                                       transforms.RandomRotation((0, 360)),
+                                       transforms.ToTensor()
+                                      ])
+
+
+        testset_1 = torchvision.datasets.CIFAR10(root=root, 
+                                       train=False,
+                                       download=True, 
+                                       transform=test_transform_1)
+
+        testloader_1 = torch.utils.data.DataLoader(testset_1, 
+                                          batch_size=batch_size,
+                                         shuffle=False)
+
+        testset_2 = torchvision.datasets.CIFAR10(root=root, 
+                                       train=False,
+                                       download=True, 
+                                       transform=test_transform_2)
+
+        testloader_2 = torch.utils.data.DataLoader(testset_2, 
+                                          batch_size=batch_size,
+                                         shuffle=False)
+    
+        return (testloader_1, testloader_2)
\ No newline at end of file
diff --git a/Project_Mahammadli_Gokce/src/layers.py b/Project_Mahammadli_Gokce/src/layers.py
new file mode 100644
index 0000000..7e783ad
--- /dev/null
+++ b/Project_Mahammadli_Gokce/src/layers.py
@@ -0,0 +1,96 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from joblib import Parallel, delayed
+import multiprocessing
+from lpb import min_lpb
+
+
+class RRL(nn.Module):
+    """
+    A class to create Regional Rotation Layer for CNN architectures to make them rotation invariant, add this layer before each convolution layer, and after the feature extraction if task is classification
+    
+    Attributes
+    ---------
+    kernel_size : int
+        Height and Width of the kernel. This is also stride, as sliding windos do not overlap
+    padding : tuple[int]
+        Number of row/vector of zeros to add to height/width
+        
+    Methods
+    ------
+    forward(x):
+        Returns a tensor with the same dimension as input x,
+        A result of concatenated rotated sliding windows
+    """
+    def __init__(self, kernel_size: int=3, padding: tuple[int]=(0, 0)) -> None:
+        """
+        Contructs all the necessary attributes for the RRL class 
+        
+        Parameters
+        ---------
+            kernel_size : int
+                Height and Width of the kernel. This is also stride, as sliding windos do not overlap
+            padding : tuple[int]
+                Number of row/vector of zeros to add to height/width     
+        """
+        super(RRL, self).__init__()
+        self.FH = self.FW = self.stride = kernel_size
+        self.padding = padding
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Returns a tensor with the same dimension as input x,
+        A result of concatenated rotated sliding windows
+        
+        Parameters
+        ---------
+        x : torch.Tensor
+            input batch with dimensions: batch_size x channel_size x height x width
+        
+        Returns
+        ------
+        x : torch.Tensor
+            a tensor with the same dimension as input x,
+            A result of concatenated rotated sliding windows
+        """
+        # x -> (batch_size, channels, height, width)
+        # OH, OW - feature map height and width
+        # FH, FW - kernel height and width
+
+        batch_size, ch, H, W = x.shape
+        OH = H // self.FH
+        OW = W // self.FW
+
+        # pad the input
+        x = F.pad(x, pad=self.padding)
+        
+        # create sliding windows
+        x = x.unfold(2, self.FH, self.stride).unfold(3, self.FW, self.stride) # x -> (batch_size, channels, OH, OW, FH, FW)
+        x = x.contiguous().view(batch_size, ch, -1, self.FH, self.FW) # x -> (batch_size, channels, OH*OW, FH, FW)
+        x = x.permute(0, 2, 1, 3, 4) # x -> (batch_size, OH*OW, channels, FH, FW)
+
+        
+        # calculate minimum LPB state for all the windows 
+        #x = vmap(min_lpb, in_dims=(-2, -1), out_dims=(-2, -1))(x) # x -> (batch_size, OH*OW, channels, FH, FW)
+        '''
+        vmap did not work, and actually it is meant to be used for operations on 2 or more matrices like matrix multiplication,
+        and there is no support for custom broadcasting or vectorization just for single matrix to map over
+        some dimensions in PyTorch, like in numpy. Therefore We had to use cpu, but at least to get more speed, we use
+        multiprocessing. We have created discussion for this issue, currently in PyTorch, these operations cannot
+        be done on GPU, other sliding window layers can take advantage of GEMM, like Convolution layer, or can use's PyTorch's default
+        functions over sliding windows, like max function for MaxPooling2d. However, our functions do not solely depend 
+        on elementary matrix operations, and its main part is rotation that needs to be applied all sliding windows 
+        paralelly. Due to these reasons, we had no choice but run on CPU which is very very slow :/. 
+        '''
+        num_cores = multiprocessing.cpu_count()
+        x = Parallel(n_jobs=num_cores, backend="threading")(delayed(min_lpb)(x[b, o, c, :, :]) for b in range(batch_size) for o in range(OH*OW) for c in range(ch))
+        x = torch.stack(x).view(batch_size, OH*OW, ch, self.FH, self.FW) # x -> (batch_size, OH*OW, channels, FH, FW)
+
+        # reshape matrix into original format
+        x = x.permute(0, 2, 1, 3, 4) # x -> (batch_size, channels, OH*OW, FH, FW)
+        x = x.view(batch_size, ch, OH, OW, self.FH, self.FW) # x -> (batch_size, channels, OH, OW, FH, FW)
+        x = x.permute(0, 1, 2, 4, 3, 5) # x -> (batch_size, channels, OH, FH, OW, FW)
+        x = x.contiguous().view(batch_size, ch, H, W) # x -> (batch_size, channels, OH*FH, OW*FW)
+    
+        return x
diff --git a/Project_Mahammadli_Gokce/src/lpb.py b/Project_Mahammadli_Gokce/src/lpb.py
new file mode 100644
index 0000000..a1e8969
--- /dev/null
+++ b/Project_Mahammadli_Gokce/src/lpb.py
@@ -0,0 +1,86 @@
+from numpy import dtype
+import torch
+from torchvision.transforms.functional import rotate
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def rotate_mx(mx: torch.Tensor, degree: float) -> torch.Tensor:
+  """
+  Rotates input matrix with given degree, in counterclockwise
+  
+  Parameters
+  ---------
+  mx : torch.Tensor
+    Input matrix that will be rotated
+  degree: float
+    Degree to rotate matrix in counterclockwise
+  
+  Returns
+  ------
+  rot_mx : torch.Tensor
+    Rotated matrix
+  """
+  
+  rot_mx = rotate(mx.unsqueeze(0), degree).squeeze(0)
+  return rot_mx
+
+# calculating local binary pattern value
+def LPB(mx: torch.Tensor) -> torch.Tensor:
+  """
+  Calculates Local Binary Pattern value for given matrix
+  
+  Parameters
+  ---------
+  mx : torch.Tensor
+    Input matrix
+  
+  Returns
+  ------
+  lpb_value : torch.Tensor
+    A single value tensor containing LPB
+  """
+  
+  mid = mx.shape[0] // 2
+  threshold = mx[mid, mid]
+
+  bin_mx = (mx >= threshold)
+  power_mx = torch.Tensor([
+                       [1, 2, 4],
+                       [8, 0, 16],
+                       [32, 64, 128]
+  ]).to(device)
+  
+  lpb_value = (bin_mx * power_mx).sum()
+
+  return lpb_value
+
+# finding rotation matrix leading to minimum LPB value
+def min_lpb(mx: torch.Tensor) -> torch.Tensor:
+  """
+  Finds optimal rotated form of given matrix that produces minimum Local Binary Pattern value
+  
+  Parameters
+  ---------
+  mx : torch.Tensor
+    Input matrix
+  
+  Returns
+  ------
+  min_mx : torch.Tensor
+    Rotated matrix resulting in minimum LPB value
+  """
+  
+  num_of_surr_elements = mx.shape[0] ** 2 - 1
+  rot_deg = 360 // num_of_surr_elements
+
+  min_mx = mx.clone()
+  lpb = LPB(mx)
+  for degree in range(rot_deg, 360, rot_deg):
+    mx = rotate_mx(mx, degree)
+    cur_lpb = LPB(mx)
+
+    if cur_lpb < lpb:
+      lpb = cur_lpb
+      min_mx = mx.clone()
+     
+  return min_mx
diff --git a/Project_Mahammadli_Gokce/src/models.py b/Project_Mahammadli_Gokce/src/models.py
new file mode 100644
index 0000000..cc0d91f
--- /dev/null
+++ b/Project_Mahammadli_Gokce/src/models.py
@@ -0,0 +1,387 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from layers import RRL
+import random
+import numpy as np
+
+RANDOM_SEED = 42
+
+class LeNet5(nn.Module):
+    """
+    LeNet5 Architecture
+    
+    Attributes
+    ---------
+    n_classes : int
+        Number of classes in the dataset
+    rrl : bool
+        Activation for RRL layer, 
+        If set True, RRL layer will be added before each convolution layer and before fully connected layers
+    
+    Methods
+    ------
+    forward(x):
+        Calculates feed forward pass for the LeNet5 model
+    """
+    
+    def __init__(self,
+                 n_classes: int=10,
+                 rrl: bool=False) -> None:
+        """
+        Constructs all the necessary attributes for the LeNet5 class 
+        
+        Parameters
+        ---------
+            n_classes : int
+                Number of classes in the dataset
+            rrl : bool
+                Activation for RRL layer, 
+                If set True, RRL layer will be added before each convolution layer and before fully connected layers
+        """
+        
+        torch.manual_seed(RANDOM_SEED)
+        random.seed(RANDOM_SEED)
+        np.random.seed(RANDOM_SEED)
+        
+        super().__init__()
+        self.n_classes = n_classes
+        self.rrl = rrl
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=(1, 1))
+        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=(1, 1))
+        self.pooling = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
+        self.tanh = nn.Tanh()
+        if self.rrl:
+            self.RRL = RRL()
+
+        self.fc1 = nn.Linear(in_features=16*6*6, out_features=84)
+        self.fc2 = nn.Linear(in_features=84, out_features=10)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Calculates feed forward pass for the LeNet5 model
+        
+        Parameters
+        ---------
+        x : torch.Tensor
+            Input matrix with dimensions: batch_size x channel_size x height x width
+            
+        Returns
+        ------
+        out : torch.Tensor
+            Probability distribution of the results
+        """
+        try:
+            x = self.RRL(x)
+            x = self.tanh(self.conv1(x))
+            x = self.pooling(x)
+
+            x = self.RRL(x)
+            x = self.tanh(self.conv2(x))
+            x = self.pooling(x)
+
+            x = self.RRL(x)
+            x = torch.flatten(x, 1)
+            x = self.tanh(self.fc1(x))
+
+            x = self.fc2(x)
+            
+            out =  F.softmax(x, dim=1)
+            return out
+        
+        except:
+            x = self.tanh(self.conv1(x))
+            x = self.pooling(x)
+
+            x = self.tanh(self.conv2(x))
+            x = self.pooling(x)
+
+            x = torch.flatten(x, 1)
+            x = self.tanh(self.fc1(x))
+
+            x = self.fc2(x)
+            
+            out =  F.softmax(x, dim=1)
+            return out
+        
+
+# 3x3 convolution
+def conv3x3(in_channels: int,
+            out_channels: int,
+            stride: int=1,
+            padding: int=1) -> torch.Tensor:
+    """
+    2D convolution layer with kernel size 3x3
+    
+    Parameters
+    ---------
+    in_channels : int
+        Number of channels in the input matrix
+    out_chanels : int
+        Number of channels that will be in the output matrix, i.e number of kernels
+    stride : int
+        Stride value - number of skips during the sliding window
+    padding : int:
+        padding size, to add zeros to both height and width
+        
+    Returns
+    ------
+    conv2d : torch.Tensor
+        Result of the convolution layer
+    """
+    
+    torch.manual_seed(RANDOM_SEED)
+    random.seed(RANDOM_SEED)
+    np.random.seed(RANDOM_SEED)
+          
+    conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=3, 
+                     stride=stride, padding=padding, bias=False)
+    return conv2d
+    
+# Residual block
+class ResidualBlock(nn.Module):
+    """
+    Residual Block for ResNet model
+    
+    Attributes
+    ---------
+    in_channels : int
+        Number of channels in the input matrix
+    out_chanels : int
+        Number of channels that will be in the output matrix, i.e number of kernels
+    stride : int
+        Stride value - number of skips during the sliding window
+    downsample : bool
+        Downsampling, if set True, additional 2D convolution layer with kernel size 3x3, followed by batch normalization will be added
+    
+    Methods
+    ------
+    forward(x):
+        Returns the result of feed forward path
+    """
+    
+    def __init__(self, in_channels: int,
+                 out_channels: int,
+                 stride: int=1,
+                 downsample: torch.Tensor=None,
+                 padding: int=1,
+                 RRL: bool=False) -> None:
+        """
+        Constructs all the necessary attributes for the ResidualBlock class
+        
+        Parameters
+        ---------
+        in_channels : int
+            Number of channels in the input matrix
+        out_chanels : int
+            Number of channels that will be in the output matrix, i.e number of kernels
+        stride : int
+            Stride value - number of skips during the sliding window
+        downsample : torch.Tensor
+            Downsampling, if not None, additional 2D convolution layer with kernel size 3x3, followed by batch normalization will be added
+        """
+        torch.manual_seed(RANDOM_SEED)
+        random.seed(RANDOM_SEED)
+        np.random.seed(RANDOM_SEED)
+        
+        super(ResidualBlock, self).__init__()
+        self.conv1 = conv3x3(in_channels, out_channels, stride, padding=padding)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(out_channels, out_channels)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+        self.downsample = downsample
+        if RRL:
+            self.RRL = RRL()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Returns the result of feed forward path
+        
+        Parameters
+        ---------
+        x : torch.Tensor
+            The input matrix with dimensions: batch_size x channel_size x height x width
+            
+        Returns 
+        ------
+        out : torch.Tensor
+            The output of the residual block
+        """
+        
+        residual = x
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.downsample:
+            residual = self.downsample(x)
+        out += residual
+        out = self.relu(out)
+        return out
+    
+# ResNet
+class ResNet18(nn.Module):
+    """
+    ResNet18 Architecture
+    
+    Attributes
+    ---------
+    block : ResidualBlock
+        ResidualBlock class to add blocks in ResNet
+    layers: list
+        List of integers indicating the number of blocks
+    num_classes : int
+        Number of classes in the dataset
+    downsample : torch.Tensor
+        Downsampling, if not None, additional 2D convolution layer with kernel size 3x3, followed by batch normalization will be added
+    rrl : bool
+        Activation for RRL layer, 
+        If set True, RRL layer will be added before each convolution layer and before fully connected layers  
+    
+    Methods
+    ------
+    make_layer(block: ResidualBlock, 
+                   out_channels: int, 
+                   blocks: int, 
+                   stride: int=1,
+                   padding: int=1):
+                   
+                   Creates n number of Residual Blocks, where n=blocks
+    forward(x):
+        Returns the result of the feed forward pass
+    """
+    
+    def __init__(self, block: ResidualBlock, 
+                 layers: list[int]=[2, 2, 2], 
+                 num_classes: int=10, 
+                 downsample: torch.Tensor=None, 
+                 rrl: bool=False) -> None:
+        """
+        Constructs all the necessary attributes for the ResNet class
+        
+        Parameters
+        ---------
+        block : ResidualBlock
+            ResidualBlock class to add blocks in ResNet
+        layers: list
+            List of integers indicating the number of blocks
+        num_classes : int
+            Number of classes in the dataset
+        downsample : torch.Tensor
+            Downsampling, if not None, additional 2D convolution layer with kernel size 3x3, followed by batch normalization will be added
+        rrl : bool
+            Activation for RRL layer, 
+            If set True, RRL layer will be added before each convolution layer and before fully connected layers  
+        """
+        
+        torch.manual_seed(RANDOM_SEED)
+        random.seed(RANDOM_SEED)
+        np.random.seed(RANDOM_SEED)
+        
+        super(ResNet18, self).__init__()
+        self.downsample = downsample
+        self.in_channels = 16
+        self.conv = conv3x3(3, 16)
+        self.bn = nn.BatchNorm2d(16)
+        self.relu = nn.ReLU(inplace=True)
+        self.layer1 = self.make_layer(block, 16, layers[0])
+        self.layer2 = self.make_layer(block, 32, layers[1], 2, padding=2)
+        self.layer3 = self.make_layer(block, 64, layers[2], 2)
+        self.avg_pool = nn.AvgPool2d(8)
+        self.fc = nn.Linear(64, num_classes)
+        if rrl:
+            self.RRL = RRL()
+
+    def make_layer(self, block: ResidualBlock, 
+                   out_channels: int, 
+                   blocks: int, 
+                   stride: int=1,
+                   padding: int=1) -> nn.Sequential:
+        """
+        Creates n number of Residual Blocks, where n=blocks
+        
+        Parameters
+        ---------
+        block : ResidualBlock
+            ResidualBlock class to add blocks in ResNet
+        out_chanels : int
+            Number of channels that will be in the output matrix, i.e number of kernels
+        blocks : int
+            Number of Residual Blocks
+        stride : int
+            Stride value - number of skips during the sliding window
+        padding : int:
+            padding size, to add zeros to both height and width
+            
+        Returns
+        ------
+        out : nn.Sequential
+            Sequential layer containing Residual Blocks
+        """
+        
+        if (stride != 1) or (self.in_channels != out_channels):
+            self.downsample = nn.Sequential(
+                conv3x3(self.in_channels, out_channels, stride=stride, padding=padding),
+                nn.BatchNorm2d(out_channels))
+        layers = []
+        layers.append(block(self.in_channels, out_channels, stride, self.downsample, padding=padding))
+        self.in_channels = out_channels
+        for i in range(1, blocks):
+            layers.append(block(out_channels, out_channels))
+        out = nn.Sequential(*layers)
+        return out
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Returns the result of the feed forward pass
+        
+        Parameters
+        ---------
+        x : torch.Tensor
+            Input matrix with dimensions: batch_size x channel_size x height x width
+            
+        Returns
+        ------
+        out : torch.Tensor
+            Probability distribution of the results
+        """
+        try:
+            x = self.RRL(x)
+            x = self.conv(x)
+            x = self.bn(x)
+            x = self.relu(x)
+
+            x = self.RRL(x)
+            x = self.layer1(x)
+            
+            x = self.RRL(x)
+            x = self.layer2(x)
+            
+            x = self.RRL(x)
+            x = self.layer3(x)
+
+            x = self.RRL(x)
+            x = self.avg_pool(x)
+            x = torch.flatten(x, 1)
+            x = self.fc(x)
+            
+            out = F.softmax(x, dim=1)
+            return out
+            
+        except:
+            x = self.conv(x)
+            x = self.bn(x)
+            x = self.relu(x)
+
+            x = self.layer1(x)
+            x = self.layer2(x)
+            x = self.layer3(x)
+
+            x = self.avg_pool(x)
+            x = torch.flatten(x, 1)
+            x = self.fc(x)
+            
+            out = F.softmax(x, dim=1)
+            return out
\ No newline at end of file
diff --git a/Project_Mahammadli_Gokce/src/reuirements.txt b/Project_Mahammadli_Gokce/src/reuirements.txt
new file mode 100644
index 0000000..c579c43
Binary files /dev/null and b/Project_Mahammadli_Gokce/src/reuirements.txt differ
diff --git a/Project_Mahammadli_Gokce/src/test.py b/Project_Mahammadli_Gokce/src/test.py
new file mode 100644
index 0000000..2df81f4
--- /dev/null
+++ b/Project_Mahammadli_Gokce/src/test.py
@@ -0,0 +1,141 @@
+import argparse
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from layers import RRL
+from models import LeNet5, ResNet18, ResidualBlock
+from torch.utils.data.dataloader import DataLoader
+from sklearn.metrics import classification_report
+from dataloader import prepare_loader
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def args_from_terminal() -> argparse.Namespace:
+    """
+    Allows to take arguments from terminal with corresponding flags and stors in args variable.
+    Parameters
+    ----------
+    None
+    
+    Returns
+    -------
+    args : argparse.Namespace
+        Argparse Namespace object containing the arguments with corresponding values
+    """
+    
+    parser = argparse.ArgumentParser(description='Model Testing Program.')
+    optional = parser._action_groups.pop() 
+    required = parser.add_argument_group('required arguments')
+    
+    required.add_argument(
+        '--model_name',
+        help='name of the model to test: lenet5 or resnet18.',
+        type=str)
+    
+    required.add_argument(
+        '--model_path',
+        help='Path to the saved, trained model.',
+        type=str)
+    
+    required.add_argument(
+        '--image_size',
+        help='height and width of the input image. should be divisable by three and the same as in the training loader.',
+        type=int)
+    
+    required.add_argument(
+        '--batch_size',
+        help='size of each batch for training. should be the same as in the training loader', 
+        default=32, 
+        type=int)
+
+    
+    optional.add_argument(
+        '--report_destination',
+        help='path to the folder to store the clasification report',
+        default='./',
+        type=str)
+    
+    parser._action_groups.append(optional)
+    args = parser.parse_args()
+    print(parser.print_help())
+    return args
+
+def print_results(
+    model_name: str,
+    model_path : str,
+    test_loader: DataLoader, 
+    report_destination: str,
+    rrl : bool) -> None:
+    """
+    Prints out classification report based on the given model and test loader, and save the results at given path
+    
+    Parameters
+    ---------
+    model_name : str
+        Name of the model to train, lenet5 or resnet18
+    model_path : str:
+        Path to saved, trained model
+    test_loader : DataLoader
+        Data loader for the test set
+    report_destination : str
+        Path to store the classification report
+    rrl : bool
+        Indicates whether model has been trained with RRL layer or not
+    """
+    
+    if model_name == 'lenet5':
+        model = LeNet5(rrl).to(device)
+        
+    elif model_name == 'resnet18':
+        model = ResNet18(ResidualBlock, rrl=rrl).to(device)
+        
+    else:
+        raise Exception("Wrong model name, please include either 'lenet5' or 'resnet18'")
+    
+    model = torch.load(model_path)
+    model.eval()
+    
+    y_pred_ls = []
+    y_test_ls = []
+
+    with torch.no_grad():
+        for _, (X_test, y_test) in enumerate(test_loader):
+            y_test_ls.extend(y_test.tolist())
+            X_test = X_test.to(device)
+            y_test = y_test.to(device)
+
+            y_test_pred = model(X_test)
+
+            predicted = torch.max(y_test_pred.data, 1)[1].tolist()
+            y_pred_ls.extend(predicted)
+            
+    report = classification_report(y_pred_ls, y_test_ls)
+    with open(report_destination + model_name + '_classiciation_report.txt', 'a+') as file:
+        file.write(report)
+        
+    print(report)
+    
+    
+if __name__ == '__main__':
+    args = args_from_terminal()
+    """
+    testloader_1, testloader_2 = prepare_loader(
+        batch_size=args.batch_size,
+        image_size=args.image_size,
+        train_or_test='test')
+    
+    
+    print("Results for CIFAR-10-rot test")
+    print_results(
+        model_name=args.model_name,
+        model_path=args.model_path,
+        test_loader=testloader_1,
+        report_destination=args.report_destination)
+
+    print("\n Results for CIFAR-10-rot+ test")
+    print_results(
+        model_name=args.model_name,
+        model_path=args.model_path,
+        test_loader=testloader_2,
+        report_destination=args.report_destination)
+    """
\ No newline at end of file
diff --git a/Project_Mahammadli_Gokce/src/train.py b/Project_Mahammadli_Gokce/src/train.py
new file mode 100644
index 0000000..772fe88
--- /dev/null
+++ b/Project_Mahammadli_Gokce/src/train.py
@@ -0,0 +1,253 @@
+import argparse
+import torch
+import torch.nn as nn
+import matplotlib.pyplot as plt
+from models import LeNet5, ResNet18, ResidualBlock
+from dataloader import prepare_loader
+from time import perf_counter
+from functools import wraps
+from typing import Callable, TypeVar
+from typing_extensions import ParamSpec
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def args_from_terminal() -> argparse.Namespace:
+    """
+    Allows to take arguments from terminal with corresponding flags and stors in args variable.
+    Parameters
+    ----------
+    None
+    
+    Returns
+    -------
+    args : argparse.Namespace
+        Argparse Namespace object containing the arguments with corresponding values
+    """
+
+    parser = argparse.ArgumentParser(description='Model Training Program.')
+    optional = parser._action_groups.pop() 
+    required = parser.add_argument_group('required arguments')
+    
+    required.add_argument(
+        '--rrl',
+        help='use this command to train model with RRL layer.',
+        action='store_true')
+    
+    required.add_argument(
+        '--no-rrl',
+        help='use this command to train model without RRL layer.',
+        action='store_false'
+    )
+    
+    required.set_defaults(feature=False)
+    
+    required.add_argument(
+        '--model_name',
+        help='name of the model to train: lenet5 or resnet18.',
+        default='lenet5',
+        type=str
+    )
+    
+    optional.add_argument(
+        '--image_size',
+        help='height and width of the input image. should be divisable by three.',
+        default=33,
+        type=int)
+    
+    optional.add_argument(
+        '--batch_size',
+        help='size of each batch for training.', 
+        default=128, 
+        type=int)
+
+    optional.add_argument(
+        '--epochs',
+        help='number of epochs to train.', 
+        default=10, 
+        type=int)
+
+    optional.add_argument(
+        '--lr',
+        help='learning rate for the optimizer.', 
+        default=0.001, 
+        type=float)
+
+    optional.add_argument(
+        '--model_destination',
+        default='./',
+        type=str)
+    
+    optional.add_argument(
+        '--plot', 
+        help='pass it without any value to draw training loss and accuracy.', 
+        action='store_true')
+
+    optional.add_argument(
+        '--plot_destination',
+        help='if you have passed --plot_training argument, use this flag with corresponsing path to store the plot.', 
+        default='./',
+        type=str)
+    
+    optional.add_argument(
+        '--verbose',
+        help='If True, loss and accuracy during training will be printed out. default True',
+        action='store_true')
+    
+
+    parser._action_groups.append(optional)
+    args = parser.parse_args()
+    return args
+
+# decorator for calculating training time
+
+P = ParamSpec('P')
+R = TypeVar('R')
+
+def timer(func: Callable[P, R]) -> Callable[P, R]:
+  """Calculates the runtime of the the given function"""
+  
+  @wraps(func)
+  def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+    start = perf_counter()
+    func(*args, **kwargs)
+    train_time = perf_counter() - start
+    return train_time
+  return wrapper
+
+@timer
+def trainer(model_name: str,
+            epochs: int, 
+            lr : float,
+            image_size : int,
+            batch_size : int,
+            rrl: bool=False,
+            verbose: bool=True,
+            plot: bool=True,
+            model_destination: str='./',
+            plot_destination: str='./') -> float:
+    """
+    Training given model architecture
+    
+    Parameters
+    ---------
+    model_name : str
+        Name of the model to train, lenet5 or resnet18
+    epochs : int
+        Number of epochs to train
+    lr : float
+        Learning rate for the optimizer
+    image_size : int
+        Value to resize images during loading
+    batch_size : int
+        Size of each batch
+    rrl : bool
+        If set True, RRL layer will be used inside the chosen model
+    verbose : bool
+        If True, loss and accuracy during training will be printed out
+    plot : bool
+        If True, training accuracy and loss will be plotted
+    model_destination : str
+        Path to store the trained model
+    plot_destinatinon : str
+        Path to store the plots
+    
+    Returns:
+    ------
+    train_time : float
+        Time spent for the traning from the decorator
+    """
+    
+    train_losses = []
+    train_accuracy = []
+    
+    if model_name == 'lenet5':
+        model = LeNet5(rrl).to(device)
+        
+    elif model_name == 'resnet18':
+        model = ResNet18(ResidualBlock, rrl=rrl).to(device)
+        
+    else:
+        raise Exception("Wrong model name, please include either 'lenet5' or 'resnet18'")
+    
+    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+    criterion = nn.CrossEntropyLoss()
+    
+    num_of_images, num_of_batches, train_loader = prepare_loader(
+        image_size=image_size,
+        batch_size=batch_size,
+        train_or_test='train')
+
+    for epoch in range(epochs):
+        train_corr = 0
+
+        # Run the training batches
+        for b, (X_train, y_train) in enumerate(train_loader):
+            b += 1
+            X_train = X_train.to(device)
+            y_train = y_train.to(device)
+
+            # Apply the model
+            y_pred = model(X_train)
+            loss = criterion(y_pred.to(device), y_train)
+        
+            # the number of correct predictions
+            predicted = torch.max(y_pred.data, 1)[1]
+            batch_corr = (predicted == y_train).sum()
+            train_corr += batch_corr.item()
+                
+            # Update parameters
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            # Print interim results
+            if verbose and b % 100 == 0:
+                print(f"Epoch [{epoch}/{epochs}], Step [{b}/{num_of_batches}] Loss: {loss.item():.2f} training accuracy: {(train_corr * 100 / (b * batch_size)):.3f}")
+                
+        train_losses.append(loss.item())
+        train_accuracy.append((train_corr / num_of_images) * 100)
+    
+    if rrl:
+        torch.save(model, model_destination + model_name + '_with_rrl.pth')
+    else:
+        torch.save(model, model_destination + model_name + '_without_rrl.pth')
+
+    if plot:
+        plt.plot(train_accuracy)
+        plt.title('model accuracy')
+        plt.ylabel('accuracy')
+        plt.xlabel('epoch')
+        plt.savefig(
+            plot_destination + model_name + '_accuracy.png', 
+            facecolor='white', 
+            edgecolor='none')
+        
+        plt.show()
+
+        plt.plot(train_losses)
+        plt.title('model loss')
+        plt.ylabel('loss')
+        plt.xlabel('epoch')
+        plt.savefig(
+            plot_destination + model_name + "_loss.png",
+            facecolor='white',
+            edgecolor='none')
+        
+        plt.show()
+        
+
+if __name__ == '__main__':
+    args = args_from_terminal()
+    train_time = trainer(
+        model_name=args.model_name,
+        epochs=args.epochs,
+        lr=args.lr,
+        image_size=args.image_size,
+        batch_size=args.batch_size,
+        rrl = args.rrl,
+        verbose = args.verbose,
+        plot=args.plot,
+        model_destination=args.model_destination,
+        plot_destination=args.plot_destination)
+    
+    print(f'{args.model_name} took {(train_time / 60):.3f} minutes to train.')
\ No newline at end of file