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import time
import torch
from tqdm import tqdm
from torch.cuda.amp import autocast, GradScaler
# =====================================================================
# 1. Epoch Level Training & Validation Loops
# =====================================================================
def train_epoch_standard(model, loader, optimizer, criterion, device, scaler=None):
"""
Trains standard models (Classification/Segmentation) for one epoch.
Supports Automatic Mixed Precision (AMP) when scaler is provided.
"""
model.train()
running_loss = 0.0
correct = 0
total = 0
for x, y in tqdm(loader, desc="Training Batches", leave=False):
# MedMNIST targets are often squeezed to shape (batch_size, 1), we need shape (batch_size,)
if len(y.shape) == 2 and y.shape[1] == 1:
y = y.squeeze(1)
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
if scaler is not None:
with autocast():
outputs = model(x)
loss = criterion(outputs, y)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
outputs = model(x)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()
running_loss += loss.item() * x.size(0)
# Classification metric collection
if len(outputs.shape) > 1 and outputs.shape[1] > 1:
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == y.data).item()
total += y.size(0)
epoch_loss = running_loss / len(loader.dataset)
epoch_acc = (correct / total) if total > 0 else 0.0
return epoch_loss, epoch_acc
@torch.no_grad()
def val_epoch_standard(model, loader, criterion, device):
"""
Validates standard models (Classification/Segmentation) for one epoch.
"""
model.eval()
running_loss = 0.0
correct = 0
total = 0
for x, y in tqdm(loader, desc="Validation Batches", leave=False):
if len(y.shape) == 2 and y.shape[1] == 1:
y = y.squeeze(1)
x, y = x.to(device), y.to(device)
outputs = model(x)
loss = criterion(outputs, y)
running_loss += loss.item() * x.size(0)
if len(outputs.shape) > 1 and outputs.shape[1] > 1:
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == y.data).item()
total += y.size(0)
epoch_loss = running_loss / len(loader.dataset)
epoch_acc = (correct / total) if total > 0 else 0.0
return epoch_loss, epoch_acc
def train_epoch_detector(model, loader, optimizer, device, scaler=None):
"""
Trains Faster R-CNN detection model for one epoch.
In PyTorch torchvision, Faster R-CNN outputs a loss dict when in training mode.
"""
model.train()
running_loss = 0.0
for images, targets in tqdm(loader, desc="Detection Batches", leave=False):
# Images: list of Tensors
# Targets: list of dicts: {"boxes": Tensor, "labels": Tensor}
images = [img.to(device) for img in images]
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
optimizer.zero_grad()
if scaler is not None:
with autocast():
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
scaler.scale(losses).backward()
scaler.step(optimizer)
scaler.update()
else:
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
losses.backward()
optimizer.step()
running_loss += losses.item() * len(images)
epoch_loss = running_loss / len(loader.dataset)
return epoch_loss, 0.0
# =====================================================================
# 2. Complete Training Runner with Checkpointing & Early Stopping
# =====================================================================
def fit_model(
model,
train_loader,
val_loader,
optimizer,
criterion,
epochs,
device,
checkpoint_path,
scheduler=None,
early_stopping_patience=5,
task_type="classification"
):
"""
General runner function to train and validate a model.
"""
best_val_loss = float("inf")
patience_counter = 0
history = {
"train_loss": [],
"train_acc": [],
"val_loss": [],
"val_acc": [],
"epoch_time": []
}
# Enable mixed precision scaler if running on CUDA
scaler = GradScaler() if device == "cuda" else None
print(f"[Training] Starting training on device: {device} (Task: {task_type})")
for epoch in range(1, epochs + 1):
start_time = time.time()
# 1. Run training epoch
if task_type == "detection":
train_loss, train_acc = train_epoch_detector(model, train_loader, optimizer, device, scaler)
val_loss, val_acc = 0.0, 0.0 # Detection model doesn't compute validation loss this way natively
else:
train_loss, train_acc = train_epoch_standard(model, train_loader, optimizer, criterion, device, scaler)
val_loss, val_acc = val_epoch_standard(model, val_loader, criterion, device)
epoch_time = time.time() - start_time
# Log metrics
history["train_loss"].append(train_loss)
history["train_acc"].append(train_acc)
history["val_loss"].append(val_loss)
history["val_acc"].append(val_acc)
history["epoch_time"].append(epoch_time)
# Print summary
if task_type == "detection":
print(f"Epoch {epoch}/{epochs} | Train Loss: {train_loss:.4f} | Time: {epoch_time:.1f}s")
else:
print(f"Epoch {epoch}/{epochs} | Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.4f} | Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.4f} | Time: {epoch_time:.1f}s")
# Update Scheduler
if scheduler is not None:
# Check if ReduceLROnPlateau or standard scheduler
if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
scheduler.step(val_loss if task_type != "detection" else train_loss)
else:
scheduler.step()
# 2. Checkpoint Saving & Early Stopping
if task_type == "detection":
# For detection, we save based on train loss improvement
metric_to_check = train_loss
else:
metric_to_check = val_loss
if metric_to_check < best_val_loss:
best_val_loss = metric_to_check
patience_counter = 0
# Save checkpoint
torch.save(model.state_dict(), checkpoint_path)
print(f" => State saved! New best loss: {best_val_loss:.4f}")
else:
patience_counter += 1
if patience_counter >= early_stopping_patience:
print(f"[Training] Early stopping triggered. Validation performance has not improved for {early_stopping_patience} epochs.")
break
# 3. Restore Best Checkpoint
if os.path.exists(checkpoint_path):
print(f"[Training] Restoring best checkpoint from: {checkpoint_path}")
model.load_state_dict(torch.load(checkpoint_path, map_location=device))
else:
print("[Training Warning] No checkpoint saved, returning current model state.")
return history
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