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AirTrackLM - CPU Training + Hub Push
=====================================
Trains the full model on CPU and pushes checkpoints + source to HF Hub.
"""
import os
import sys
import time
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.utils.data import DataLoader, random_split
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR
from pathlib import Path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from data_pipeline import TrajectoryProcessor, load_traffic_sample, build_dataset
from model import AirTrackLM, AirTrackConfig, NextStateLoss
def collate_fn(batch):
max_len = max(b['cog_bins'].size(0) for b in batch)
collated = {}
for key in batch[0].keys():
tensors = [b[key] for b in batch]
if key == 'prompt':
collated[key] = torch.stack(tensors)
else:
padded = []
for t in tensors:
if t.dim() == 1:
padded.append(F.pad(t, (0, max_len - t.size(0)), value=0))
elif t.dim() == 2:
padded.append(F.pad(t, (0, 0, 0, max_len - t.size(0)), value=0))
else:
padded.append(t)
collated[key] = torch.stack(padded)
return collated
@torch.no_grad()
def evaluate(model, dataloader, loss_fn, device):
model.eval()
loss_components = {}
n_batches = 0
correct = {'cog': 0, 'sog': 0, 'rot': 0, 'alt_rate': 0}
total_preds = 0
for batch in dataloader:
batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}
predictions = model(batch)
_, loss_log = loss_fn(predictions, batch)
for k, v in loss_log.items():
loss_components[k] = loss_components.get(k, 0) + v
n_batches += 1
for feat in ['cog', 'sog', 'rot', 'alt_rate']:
pred = predictions[f'{feat}_logits'][:, :-1, :].argmax(dim=-1)
target = batch[f'{feat}_bins'][:, 1:]
correct[feat] += (pred == target).sum().item()
total_preds += batch['cog_bins'][:, 1:].numel()
metrics = {k: v / max(n_batches, 1) for k, v in loss_components.items()}
for feat in ['cog', 'sog', 'rot', 'alt_rate']:
metrics[f'{feat}_acc'] = correct[feat] / max(total_preds, 1)
return metrics
def main():
print("=" * 70)
print("AirTrackLM - Training (CPU) + Push to Hub")
print("=" * 70)
HUB_MODEL_ID = "Jdice27/AirTrackLM"
device = torch.device('cpu')
config = AirTrackConfig(
d_model=256, n_heads=8, n_layers=8, d_ff=1024,
dropout=0.1, max_seq_len=256, geohash_mode='absolute',
use_multi_uncertainty=True, n_uncert_methods=4,
use_heteroscedastic=True, predict_geohash=True, predict_continuous=True,
)
SEQ_LEN, STRIDE = 64, 32
BATCH_SIZE = 16
N_EPOCHS = 30
LR = 5e-4
PATIENCE = 8
# ---- Load Data ----
print("\n1. Loading data...")
t0 = time.time()
raw_trajs = []
for name in ['quickstart', 'switzerland', 'savan']:
try:
trajs = load_traffic_sample(name)
raw_trajs.extend(trajs)
print(f" {name}: {len(trajs)} flights")
except Exception as e:
print(f" {name}: failed ({e})")
print(f" Total: {len(raw_trajs)} flights ({time.time()-t0:.1f}s)")
# ---- Process ----
print("\n2. Processing...")
t0 = time.time()
processor = TrajectoryProcessor(resample_dt=5.0)
dataset = build_dataset(raw_trajs, processor, seq_len=SEQ_LEN, stride=STRIDE)
print(f" {time.time()-t0:.1f}s")
n_val = max(1, int(0.15 * len(dataset)))
train_ds, val_ds = random_split(dataset, [len(dataset) - n_val, n_val],
generator=torch.Generator().manual_seed(42))
print(f" Train: {len(train_ds)}, Val: {len(val_ds)}")
# ---- Model ----
model = AirTrackLM(config)
print(f"\n3. Model: {sum(p.numel() for p in model.parameters()):,} params")
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True,
collate_fn=collate_fn, num_workers=0)
val_loader = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=False,
collate_fn=collate_fn, num_workers=0)
loss_fn = NextStateLoss(config)
optimizer = AdamW(model.parameters(), lr=LR, weight_decay=0.01)
scheduler = CosineAnnealingLR(optimizer, T_max=N_EPOCHS * len(train_loader), eta_min=LR * 0.01)
output_dir = Path('./checkpoints')
output_dir.mkdir(exist_ok=True)
best_val_loss = float('inf')
patience_counter = 0
history = []
print(f"\n4. Training: {N_EPOCHS} epochs")
print("=" * 70)
for epoch in range(N_EPOCHS):
t_epoch = time.time()
model.train()
train_loss = 0
train_comp = {}
n_b = 0
for batch in train_loader:
predictions = model(batch)
loss, log = loss_fn(predictions, batch)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
optimizer.zero_grad()
scheduler.step()
train_loss += log['total']
for k, v in log.items():
train_comp[k] = train_comp.get(k, 0) + v
n_b += 1
train_avg = {k: v/n_b for k, v in train_comp.items()}
val_metrics = evaluate(model, val_loader, loss_fn, device)
elapsed = time.time() - t_epoch
improved = val_metrics['total'] < best_val_loss
print(f"Epoch {epoch+1:02d}/{N_EPOCHS} [{elapsed:.0f}s] {'★' if improved else ' '} "
f"train={train_avg['total']:.3f} val={val_metrics['total']:.3f} "
f"COG={val_metrics.get('cog_acc',0):.3f} SOG={val_metrics.get('sog_acc',0):.3f} "
f"ROT={val_metrics.get('rot_acc',0):.3f} AltRate={val_metrics.get('alt_rate_acc',0):.3f}")
history.append({'epoch': epoch+1, 'train': train_avg, 'val': val_metrics,
'lr': scheduler.get_last_lr()[0], 'time': elapsed})
if improved:
best_val_loss = val_metrics['total']
patience_counter = 0
torch.save({
'epoch': epoch+1, 'model_state_dict': model.state_dict(),
'config': config.__dict__, 'val_loss': best_val_loss, 'val_metrics': val_metrics,
}, output_dir / 'best_model.pt')
else:
patience_counter += 1
if patience_counter >= PATIENCE:
print(f"Early stopping at epoch {epoch+1}")
break
# ---- Save + Push ----
print("\n" + "=" * 70)
print("Saving and pushing to Hub...")
torch.save({
'model_state_dict': model.state_dict(), 'config': config.__dict__,
'best_val_loss': best_val_loss, 'history': history,
}, output_dir / 'final_model.pt')
with open(output_dir / 'training_history.json', 'w') as f:
json.dump(history, f, indent=2, default=str)
with open(output_dir / 'config.json', 'w') as f:
json.dump(config.__dict__, f, indent=2)
try:
from huggingface_hub import HfApi
api = HfApi()
api.upload_folder(
folder_path=str(output_dir), repo_id=HUB_MODEL_ID,
repo_type="model", commit_message=f"Training: val_loss={best_val_loss:.4f}",
)
print(f"✓ Checkpoints pushed to https://huggingface.co/{HUB_MODEL_ID}")
except Exception as e:
print(f"Push failed: {e}")
print(f"\nBest val loss: {best_val_loss:.4f}")
print(f"Final metrics: COG={val_metrics.get('cog_acc',0):.3f} SOG={val_metrics.get('sog_acc',0):.3f}")
print("Done!")
if __name__ == '__main__':
main()
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