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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 ResNet
from .data import BeatTrackingDataset
from ..baseline1.utils import MultiViewSpectrogram
from ..data.load import ds
def train(target_type: str, output_dir: str):
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
BATCH_SIZE = 128 # Reduced batch size due to larger context
EPOCHS = 3
LR = 0.001 # Adjusted LR for Adam (ResNet usually prefers Adam/AdamW)
NUM_WORKERS = 4
CONTEXT_FRAMES = 50 # +/- 50 frames -> 101 frames total
PATIENCE = 5 # Early stopping patience
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
train_dataset = BeatTrackingDataset(
ds["train"], target_type=target_type, context_frames=CONTEXT_FRAMES
)
val_dataset = BeatTrackingDataset(
ds["test"], target_type=target_type, context_frames=CONTEXT_FRAMES
)
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 = ResNet(dropout_rate=0.5).to(DEVICE)
# GPU Spectrogram Preprocessor
preprocessor = MultiViewSpectrogram(sample_rate=16000, hop_length=160).to(DEVICE)
# Optimizer - Using AdamW for ResNet
optimizer = optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)
criterion = nn.BCELoss() # Binary Cross Entropy
best_val_loss = float("inf")
patience_counter = 0
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_raw)
# Normalize
mean = spec.mean(dim=(2, 3), keepdim=True)
std = spec.std(dim=(2, 3), keepdim=True) + 1e-6
spec = (spec - mean) / std
T_curr = spec.shape[-1]
target_T = CONTEXT_FRAMES * 2 + 1
if T_curr > target_T:
start = (T_curr - target_T) // 2
x = spec[:, :, :, start : start + target_T]
elif T_curr < target_T:
# This shouldn't happen if dataset is correct, but just in case pad
pad = target_T - T_curr
x = torch.nn.functional.pad(spec, (0, pad))
else:
x = spec
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
T_curr = spec.shape[-1]
target_T = CONTEXT_FRAMES * 2 + 1
if T_curr > target_T:
start = (T_curr - target_T) // 2
x = spec[:, :, :, start : start + target_T]
else:
pad = target_T - T_curr
x = torch.nn.functional.pad(spec, (0, pad))
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
patience_counter = 0
model.save_pretrained(output_dir)
print(f" -> Saved best model (val_loss: {best_val_loss:.4f})")
else:
patience_counter += 1
print(f" -> No improvement (patience: {patience_counter}/{PATIENCE})")
if patience_counter >= PATIENCE:
print("Early stopping triggered.")
break
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/baseline2",
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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