rebased on master

train_model_ciedge.py

@@ -0,0 +1,85 @@
+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 =======
+DATA_DIR = "./CIEDGE"
+BATCH_SIZE = 16
+NUM_CLASSES = 2
+NUM_EPOCHS = 10
+LEARNING_RATE = 1e-4
+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
+])
+
+# ======= Load Data =======
+train_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, "train"), transform=transform)
+test_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, "test"), transform=transform)
+
+train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
+test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True)
+
+# ======= Load Model =======
+model = models.resnet18(pretrained=True)
+model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
+model = model.to(DEVICE)
+
+# ======= Loss and Optimizer =======
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
+
+# ======= Training Loop =======
+for epoch in range(NUM_EPOCHS):
+    model.train()
+    running_loss = 0.0
+    correct, total = 0, 0
+
+    loop = tqdm(train_loader, desc=f"Epoch {epoch+1}/{NUM_EPOCHS}")
+    for inputs, labels in loop:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()  # Ensure tensor layout for MIOpen
+
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+        running_loss += loss.item()
+        _, predicted = torch.max(outputs, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+        loop.set_postfix(loss=loss.item(), acc=100. * correct / total)
+
+# ======= Evaluation =======
+model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in test_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+print(f"Test Accuracy: {100 * correct / total:.2f}%")
+
+# ======= Save Model =======
+torch.save(model.state_dict(), "resnet18_ciedge.pth")
+#