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import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
import argparse
import os
from .model import ODCNN
from .data import BeatTrackingDataset
from .utils import MultiViewSpectrogram
from ..data.load import ds
def train(target_type: str, output_dir: str):
# Note: Paper uses SGD with Momentum, Dropout, and ReLU
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
BATCH_SIZE = 512
EPOCHS = 50
LR = 0.05
MOMENTUM = 0.9
NUM_WORKERS = 4
print(f"--- Training Model for target: {target_type} ---")
print(f"Output directory: {output_dir}")
# Create output directory
os.makedirs(output_dir, exist_ok=True)
# TensorBoard writer
writer = SummaryWriter(log_dir=os.path.join(output_dir, "logs"))
# Data - use existing train/test splits
train_dataset = BeatTrackingDataset(ds["train"], target_type=target_type)
val_dataset = BeatTrackingDataset(ds["test"], target_type=target_type)
train_loader = DataLoader(
train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=NUM_WORKERS,
pin_memory=True,
prefetch_factor=4,
persistent_workers=True,
)
val_loader = DataLoader(
val_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=NUM_WORKERS,
pin_memory=True,
prefetch_factor=4,
persistent_workers=True,
)
print(f"Train samples: {len(train_dataset)}, Val samples: {len(val_dataset)}")
# Model
model = ODCNN(dropout_rate=0.5).to(DEVICE)
# GPU Spectrogram Preprocessor
preprocessor = MultiViewSpectrogram(sample_rate=16000, hop_length=160).to(DEVICE)
# Optimizer
optimizer = optim.SGD(model.parameters(), lr=LR, momentum=MOMENTUM)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)
criterion = nn.BCELoss() # Binary Cross Entropy
best_val_loss = float("inf")
global_step = 0
for epoch in range(EPOCHS):
# Training
model.train()
total_train_loss = 0
for waveform, y in tqdm(
train_loader,
desc=f"[{target_type}] Epoch {epoch + 1}/{EPOCHS} Train",
leave=False,
):
waveform, y = waveform.to(DEVICE), y.to(DEVICE)
# Compute spectrogram on GPU
with torch.no_grad():
spec = preprocessor(waveform) # (B, 3, 80, T)
# Normalize
mean = spec.mean(dim=(2, 3), keepdim=True)
std = spec.std(dim=(2, 3), keepdim=True) + 1e-6
spec = (spec - mean) / std
# Extract center context (T should be ~15 frames)
x = spec[:, :, :, 7:22] # center 15 frames
optimizer.zero_grad()
output = model(x)
loss = criterion(output, y)
loss.backward()
optimizer.step()
total_train_loss += loss.item()
global_step += 1
# Log batch loss
writer.add_scalar("train/batch_loss", loss.item(), global_step)
avg_train_loss = total_train_loss / len(train_loader)
# Validation
model.eval()
total_val_loss = 0
with torch.no_grad():
for waveform, y in tqdm(
val_loader,
desc=f"[{target_type}] Epoch {epoch + 1}/{EPOCHS} Val",
leave=False,
):
waveform, y = waveform.to(DEVICE), y.to(DEVICE)
# Compute spectrogram on GPU
spec = preprocessor(waveform) # (B, 3, 80, T)
# Normalize
mean = spec.mean(dim=(2, 3), keepdim=True)
std = spec.std(dim=(2, 3), keepdim=True) + 1e-6
spec = (spec - mean) / std
# Extract center context
x = spec[:, :, :, 7:22]
output = model(x)
loss = criterion(output, y)
total_val_loss += loss.item()
avg_val_loss = total_val_loss / len(val_loader)
# Log epoch metrics
writer.add_scalar("train/epoch_loss", avg_train_loss, epoch)
writer.add_scalar("val/loss", avg_val_loss, epoch)
writer.add_scalar("train/learning_rate", scheduler.get_last_lr()[0], epoch)
# Step the scheduler
scheduler.step()
print(
f"[{target_type}] Epoch {epoch + 1}/{EPOCHS} - "
f"Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}"
)
# Save best model
if avg_val_loss < best_val_loss:
best_val_loss = avg_val_loss
model.save_pretrained(output_dir)
print(f" -> Saved best model (val_loss: {best_val_loss:.4f})")
writer.close()
# Save final model
final_dir = os.path.join(output_dir, "final")
model.save_pretrained(final_dir)
print(f"Saved final model to {final_dir}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--target",
type=str,
choices=["beats", "downbeats"],
default=None,
help="Train a model for 'beats' or 'downbeats'. If not specified, trains both.",
)
parser.add_argument(
"--output-dir",
type=str,
default="outputs/baseline1",
help="Directory to save model and logs",
)
args = parser.parse_args()
# Determine which targets to train
targets = [args.target] if args.target else ["beats", "downbeats"]
for target in targets:
output_dir = os.path.join(args.output_dir, target)
train(target, output_dir)
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