train/train.py

#%%
import torch
import torch.nn as nn
import torch.optim as optim

from model import *
from dataset import NosePointDataset

image_size = (64, 64)
batch_size = 32
num_epochs = 1000
lr = 1e-3
val_split = 0.2
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

dataset = NosePointDataset(image_size=image_size)

train, val = torch.utils.data.random_split(dataset, [int(len(dataset) * (1 - val_split)), len(dataset) - int(len(dataset) * (1 - val_split))])
train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True)
val_loader = torch.utils.data.DataLoader(val, batch_size=batch_size, shuffle=False)

# model = NosePointRegressor(input_channels=3).to(device)
model = ResNetNoseRegressor(pretrained=True).to(device)
# criterion = nn.MSELoss()
criterion = nn.SmoothL1Loss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# %%
import matplotlib.pyplot as plt
from tqdm import tqdm

save_path = "best_model.pth"
plot_path = "loss_plot.png"

train_losses = []
val_losses = []
best_val_loss = float('inf')

# ===== Training Loop =====
for epoch in range(num_epochs):
    model.train()
    train_loss = 0.0

    for images, targets in tqdm(train_loader):
        images, targets = images.to(device), targets.to(device)

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        train_loss += loss.item() * images.size(0)
    train_loss /= len(train_loader.dataset)

    model.eval()
    val_loss = 0.0
    with torch.no_grad():
        for images, targets in val_loader:
            images, targets = images.to(device), targets.to(device)
            outputs = model(images)
            loss = criterion(outputs, targets)
            val_loss += loss.item() * images.size(0)
    val_loss /= len(val_loader.dataset)

    # Logging
    train_losses.append(train_loss)
    val_losses.append(val_loss)
    print(f"[Epoch {epoch+1}/{num_epochs}] Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f}")

    # Save best model
    if val_loss < best_val_loss:
        best_val_loss = val_loss
        torch.save(model.state_dict(), save_path)
        print("✅ Saved best model.")

    # Save plot
    plt.figure(figsize=(6, 4))
    plt.plot(range(1, len(train_losses)+1), train_losses, label="Train Loss")
    plt.plot(range(1, len(val_losses)+1), val_losses, label="Val Loss")
    plt.xlabel("Epoch")
    plt.ylabel("Loss")
    plt.title("Training vs Validation Loss")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.savefig(plot_path)
    plt.close()

print("✅ Training complete.")
# %%