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import sys
import argparse
import time
import yaml
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
# Ensure repo root is on sys.path (fixes ModuleNotFoundError for src/)
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from src.data_loader import get_eurosat_dataloaders
from src.cnn_model import GreeneryClassifier
def load_config(config_path="config/config.yaml"):
with open(config_path, "r") as f:
return yaml.safe_load(f)
def gpu_handshake():
"""
Strict GPU verification. Prints the GPU name on success.
Raises RuntimeError and halts execution if no CUDA GPU is found.
"""
if not torch.cuda.is_available():
raise RuntimeError(
"❌ FATAL: No CUDA-capable GPU detected!\n"
" This training script requires an NVIDIA GPU with CUDA support.\n"
" Please verify:\n"
" 1. Your NVIDIA drivers are installed (nvidia-smi)\n"
" 2. You installed the CUDA version of PyTorch (torch+cu...)\n"
" 3. Your GPU is visible to the system\n"
" Aborting to prevent silent CPU fallback."
)
gpu_name = torch.cuda.get_device_name(0)
vram_gb = torch.cuda.get_device_properties(0).total_memory / (1024**3)
print(f"🚀 Training on: {gpu_name} ({vram_gb:.1f} GB VRAM)")
print(f" CUDA Version: {torch.version.cuda}")
print(f" PyTorch Version: {torch.__version__}")
return torch.device("cuda")
def train(config, args):
device = gpu_handshake()
train_loader, val_loader, classes = get_eurosat_dataloaders(
data_dir=config["paths"]["eurosat_dir"],
batch_size=config["training"]["batch_size"],
)
print(
f"📊 Dataset loaded: {len(train_loader.dataset)} train / {len(val_loader.dataset)} val samples"
)
model = GreeneryClassifier(
num_classes=config["model"]["num_classes"], pretrained=True
)
model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=config["training"]["learning_rate"])
# TensorBoard Setup
log_dir = config["paths"].get("output_logs", "outputs/logs")
os.makedirs(log_dir, exist_ok=True)
writer = SummaryWriter(log_dir=log_dir)
print(f"📈 TensorBoard logs → {os.path.abspath(log_dir)}")
# AMP Setup (Automatic Mixed Precision — leverages Tensor Cores on RTX cards)
scaler = torch.amp.GradScaler("cuda")
best_val_acc = 0.0
start_epoch = 0
checkpoint_path = os.path.join(
config["paths"]["output_models"], "training_checkpoint.pth"
)
best_model_path = os.path.join(
config["paths"]["output_models"], "resnet50_eurosat.pth"
)
# Resume from checkpoint if enabled and a checkpoint exists
if config["training"].get("resume_checkpoint", False) and os.path.exists(
checkpoint_path
):
print(f"🔄 Resuming from checkpoint: {checkpoint_path}")
checkpoint = torch.load(checkpoint_path, map_location=device)
model.load_state_dict(checkpoint["model_state_dict"])
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
scaler.load_state_dict(checkpoint["scaler_state_dict"])
start_epoch = checkpoint["epoch"] + 1
best_val_acc = checkpoint["best_val_acc"]
print(
f" ↳ Resumed at epoch {start_epoch}/{config['training']['epochs']} | Best Val Acc so far: {best_val_acc:.2f}%"
)
if args.dry_run:
print("✅ Dry run completed successfully. Models and DataLoaders initialized.")
writer.close()
return
epochs = config["training"]["epochs"]
training_start = time.time()
print(f"\n{'='*60}")
print(
f" TRAINING START — {epochs} epochs, batch_size={config['training']['batch_size']}"
)
print(
f" AMP: Enabled | Optimizer: Adam | LR: {config['training']['learning_rate']}"
)
print(f"{'='*60}\n")
for epoch in range(start_epoch, epochs):
epoch_start = time.time()
model.train()
running_loss = 0.0
# Training loop with tqdm progress bar
train_bar = tqdm(
train_loader,
desc=f"Epoch {epoch+1}/{epochs} [Train]",
unit="batch",
leave=True,
ncols=100,
)
for inputs, labels in train_bar:
inputs, labels = inputs.to(device, non_blocking=True), labels.to(
device, non_blocking=True
)
optimizer.zero_grad()
# AMP Autocast — forward pass in float16 for speed
with torch.autocast(device_type="cuda"):
outputs = model(inputs)
loss = criterion(outputs, labels)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
running_loss += loss.item()
train_bar.set_postfix(loss=f"{loss.item():.4f}")
# Validation loop with tqdm progress bar
model.eval()
correct = 0
total = 0
val_loss = 0.0
with torch.no_grad():
val_bar = tqdm(
val_loader,
desc=f"Epoch {epoch+1}/{epochs} [Val] ",
unit="batch",
leave=True,
ncols=100,
)
for inputs, labels in val_bar:
inputs, labels = inputs.to(device, non_blocking=True), labels.to(
device, non_blocking=True
)
with torch.autocast(device_type="cuda"):
outputs = model(inputs)
loss = criterion(outputs, labels)
val_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
train_loss = running_loss / len(train_loader)
val_loss_avg = val_loss / len(val_loader)
val_acc = 100 * correct / total
epoch_time = time.time() - epoch_start
print(
f" ✦ Epoch {epoch+1}/{epochs} — "
f"Train Loss: {train_loss:.4f} | Val Loss: {val_loss_avg:.4f} | "
f"Val Acc: {val_acc:.2f}% | Time: {epoch_time:.1f}s"
)
# Log to TensorBoard
writer.add_scalar("Loss/Train", train_loss, epoch)
writer.add_scalar("Loss/Validation", val_loss_avg, epoch)
writer.add_scalar("Accuracy/Validation", val_acc, epoch)
writer.add_scalar("Time/Epoch_Seconds", epoch_time, epoch)
# Save checkpoint every epoch (for crash recovery)
os.makedirs(os.path.dirname(checkpoint_path), exist_ok=True)
torch.save(
{
"epoch": epoch,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"scaler_state_dict": scaler.state_dict(),
"best_val_acc": best_val_acc,
},
checkpoint_path,
)
# Save best model
if val_acc > best_val_acc:
best_val_acc = val_acc
torch.save(model.state_dict(), best_model_path)
print(
f" 🏆 New best model saved! Val Acc: {val_acc:.2f}% → {best_model_path}"
)
total_time = time.time() - training_start
print(f"\n{'='*60}")
print(f" ✅ TRAINING COMPLETE")
print(f" Best Validation Accuracy: {best_val_acc:.2f}%")
print(f" Total Training Time: {total_time/60:.1f} minutes")
print(f" Best Model: {os.path.abspath(best_model_path)}")
print(f" TensorBoard Logs: {os.path.abspath(log_dir)}")
print(f"{'='*60}\n")
writer.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Train CNN Classifier on EuroSAT")
parser.add_argument("--config", default="config/config.yaml", help="Path to config")
parser.add_argument(
"--dry-run", action="store_true", help="Initialize but do not train"
)
args = parser.parse_args()
config = load_config(args.config)
train(config, args)
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