program

Signed-off-by: 葛峻恺 <202115006@mail.sdu.edu.cn>

utils/train_val_lr.py

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+import os
+import sys
+import torch
+from torchsummary import summary
+from torchvision.utils import make_grid, save_image
+
+# Add parent directory to path for config import
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from config import Network_train_Config as cfg
+
+# Training function
+def train(train_loader, model, loss_func, optimizer, epoch):
+    model.train()
+    total_loss = 0
+    batch_count = 0
+
+    for data, label in train_loader:
+        data = data.cuda()
+        label = label.cuda()
+
+        output = model(data.type(torch.cuda.FloatTensor))
+        loss = loss_func(output.float(), label.float())
+
+        total_loss += loss.item()
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        batch_count += 1
+
+    avg_loss = total_loss / batch_count
+    print(f"Epoch {epoch}: Training Loss = {avg_loss:.6f}")
+    return avg_loss
+
+# Validation function
+def validate(val_loader, model, loss_func):
+    model.eval()
+    total_loss = 0
+    batch_count = 0
+
+    with torch.no_grad():
+        for data, label in val_loader:
+            data = data.cuda()
+            label = label.cuda()
+
+            output = model(data.type(torch.cuda.FloatTensor))
+            loss = loss_func(output.float(), label.float())
+
+            total_loss += loss.item()
+            batch_count += 1
+
+    avg_loss = total_loss / batch_count
+    return avg_loss
+
+# Learning rate scheduler
+def adjust_learning_rate(optimizer, epoch, start_lr):
+    """Exponentially decays learning rate based on epoch and configured decay rate."""
+    lr = start_lr * (cfg.lr_decrease_rate ** epoch)
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+    return lr
+
+
+