File size: 5,072 Bytes
a3b520a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | """Training script for the kick drum VAE."""
import sys
from pathlib import Path
import torch
from torch.utils.data import DataLoader, Dataset, random_split
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent))
from models.autoencoder import KickVAE
from training.config import AutoencoderConfig
from training.losses import vae_loss
class MelDataset(Dataset):
"""Dataset of preprocessed mel spectrogram tensors."""
def __init__(self, data_dir: Path) -> None:
self.files = sorted(
f for f in data_dir.glob("*.pt") if not f.name.startswith("._")
)
if not self.files:
raise FileNotFoundError(
f"No .pt files found in {data_dir}"
)
def __len__(self) -> int:
return len(self.files)
def __getitem__(self, idx: int) -> torch.Tensor:
return torch.load(self.files[idx], weights_only=False)
def train(cfg: AutoencoderConfig | None = None) -> None:
"""Run VAE training."""
if cfg is None:
cfg = AutoencoderConfig()
device = torch.device(
"cuda" if torch.cuda.is_available()
else "mps" if torch.backends.mps.is_available()
else "cpu"
)
print(f"Using device: {device}")
# Data
dataset = MelDataset(cfg.data_dir)
val_size = int(len(dataset) * cfg.val_split)
train_size = len(dataset) - val_size
train_set, val_set = random_split(
dataset, [train_size, val_size],
generator=torch.Generator().manual_seed(42),
)
train_loader = DataLoader(
train_set,
batch_size=cfg.batch_size,
shuffle=True,
num_workers=cfg.num_workers,
pin_memory=True,
)
val_loader = DataLoader(
val_set,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=cfg.num_workers,
pin_memory=True,
)
print(f"Train: {train_size}, Val: {val_size}")
# Model
model = KickVAE(latent_dim=cfg.latent_dim).to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate)
scaler = torch.amp.GradScaler(enabled=cfg.use_amp and device.type == "cuda")
# Logging
cfg.log_dir.mkdir(parents=True, exist_ok=True)
cfg.checkpoint_dir.mkdir(parents=True, exist_ok=True)
writer = SummaryWriter(cfg.log_dir)
global_step = 0
for epoch in range(cfg.epochs):
model.train()
kl_weight = cfg.kl_weight_at_epoch(epoch)
epoch_metrics: dict[str, float] = {}
epoch_count = 0
pbar = tqdm(train_loader, desc=f"Epoch {epoch+1}/{cfg.epochs}")
for batch in pbar:
batch = batch.to(device)
with torch.amp.autocast(
device_type=device.type,
enabled=cfg.use_amp and device.type == "cuda",
):
recon, mu, logvar = model(batch)
loss, metrics = vae_loss(
recon, batch, mu, logvar, kl_weight
)
optimizer.zero_grad()
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
# Accumulate metrics
for k, v in metrics.items():
epoch_metrics[k] = epoch_metrics.get(k, 0.0) + v
epoch_count += 1
global_step += 1
pbar.set_postfix(loss=f"{metrics['total']:.4f}")
# Log epoch averages
for k, v in epoch_metrics.items():
writer.add_scalar(f"train/{k}", v / epoch_count, epoch)
writer.add_scalar("train/kl_weight", kl_weight, epoch)
# Validation
model.eval()
val_metrics: dict[str, float] = {}
val_count = 0
with torch.no_grad():
for batch in val_loader:
batch = batch.to(device)
recon, mu, logvar = model(batch)
_, metrics = vae_loss(
recon, batch, mu, logvar, kl_weight
)
for k, v in metrics.items():
val_metrics[k] = val_metrics.get(k, 0.0) + v
val_count += 1
avg_val_loss = val_metrics.get("total", 0.0) / max(val_count, 1)
for k, v in val_metrics.items():
writer.add_scalar(f"val/{k}", v / val_count, epoch)
print(
f"Epoch {epoch+1}: "
f"train={epoch_metrics['total']/epoch_count:.4f} "
f"val={avg_val_loss:.4f} "
f"kl_w={kl_weight:.6f}"
)
# Checkpoint
if (epoch + 1) % cfg.checkpoint_every == 0:
path = cfg.checkpoint_dir / f"vae_epoch_{epoch+1}.pt"
torch.save({
"epoch": epoch + 1,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"config": cfg,
}, path)
print(f"Saved checkpoint: {path}")
writer.close()
print("Training complete.")
if __name__ == "__main__":
train()
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