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#%%
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.")
# %%
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