gpr-sidl-inv/utils/train_val_lr.py

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