rebased on master

eval_model_ciedge.py

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+import os
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from torchvision import datasets, models
+import torch.nn as nn
+import torch.optim as optim
+from tqdm import tqdm # https://tqdm.github.io/
+
+# https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+
+# ======= Settings =======
+CIEDGE_DIR = "./CIEDGE"
+CIFAKE_DIR = "./CIFAKE"
+BATCH_SIZE = 16
+NUM_CLASSES = 2
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ======= Transforms =======
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Ensure fixed input size
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],  # ImageNet mean
+                         [0.229, 0.224, 0.225])  # ImageNet std
+])
+
+cifake_dataset = datasets.ImageFolder(os.path.join(CIFAKE_DIR, "test"), transform=transform)
+ciedge_dataset = datasets.ImageFolder(os.path.join(CIEDGE_DIR, "test"), transform=transform)
+cifake_loader = DataLoader(cifake_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
+ciedge_loader = DataLoader(ciedge_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
+
+# ======= Load Model =======
+cifake_model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)
+ciedge_model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)
+
+cifake_model.fc = nn.Linear(cifake_model.fc.in_features, NUM_CLASSES)
+ciedge_model.fc = nn.Linear(ciedge_model.fc.in_features, NUM_CLASSES)
+
+ciedge_state = torch.load("resnet18_ciedge.pth", map_location=torch.device('cpu'))
+cifake_state = torch.load("resnet18_cifake.pth", map_location=torch.device('cpu'))
+
+cifake_model.load_state_dict(cifake_state)
+ciedge_model.load_state_dict(ciedge_state)
+
+
+cifake_model = cifake_model.to(DEVICE)
+ciedge_model = ciedge_model.to(DEVICE)
+
+
+
+cifake_model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in cifake_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = cifake_model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        if total > 400:
+            print(f"CIFAKE model evaluating CIFAKE data: {100 * correct / total:.2f}%")
+            break
+
+
+
+cifake_model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in ciedge_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = cifake_model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        if total > 400:
+            print(f"CIFAKE model evaluating CIEDGE data: {100 * correct / total:.2f}%")
+            break
+
+
+
+ciedge_model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in cifake_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = ciedge_model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        if total > 400:
+            print(f"CIEDGE model evaluating CIFAKE data: {100 * correct / total:.2f}%")
+            break
+
+
+
+ciedge_model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in ciedge_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = ciedge_model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        if total > 400:
+            print(f"CIEDGE model evaluating CIEDGE data: {100 * correct / total:.2f}%")
+            break
+
+
+
+
+
+
+
+
+