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import datetime
import os
import random
from pathlib import Path
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from backend.app.legacy.data_loader import (
load_json,
extract_pedestrian_instances,
build_trajectories_with_sensor,
create_windows_with_sensor,
)
from backend.app.legacy.dataset_fusion import FusionTrajectoryDataset
from backend.app.ml.model_fusion import TrajectoryTransformerFusion
REPO_ROOT = Path(__file__).resolve().parents[3]
def collate_fn_fusion(batch):
obs, neighbors, fusion_obs, future = zip(*batch)
obs = torch.stack(obs)
fusion_obs = torch.stack(fusion_obs)
future = torch.stack(future)
return obs, list(neighbors), fusion_obs, future
def compute_ade(pred, gt):
return torch.mean(torch.norm(pred - gt, dim=2))
def compute_fde(pred, gt):
return torch.mean(torch.norm(pred[:, -1] - gt[:, -1], dim=1))
def best_of_k_loss(pred, goals, gt, probs):
gt_traj = gt.unsqueeze(1)
error = torch.norm(pred - gt_traj, dim=3).mean(dim=2)
min_error, best_idx = torch.min(error, dim=1)
traj_loss = torch.mean(min_error)
best_goals = goals[torch.arange(goals.size(0), device=goals.device), best_idx]
goal_loss = torch.norm(best_goals - gt[:, -1, :], dim=1).mean()
prob_loss = torch.nn.functional.nll_loss(torch.log(probs + 1e-8), best_idx)
diversity_loss = 0.0
K = pred.size(1)
if K > 1:
reg = 0.0
pairs = 0
for i in range(K):
for j in range(i + 1, K):
dist = torch.norm(pred[:, i] - pred[:, j], dim=2).mean(dim=1)
reg = reg + torch.exp(-dist).mean()
pairs += 1
diversity_loss = reg / max(1, pairs)
return traj_loss + 0.5 * goal_loss + 0.5 * prob_loss + 0.1 * diversity_loss
def get_fusion_samples():
sample_annotations = load_json("sample_annotation")
instances = load_json("instance")
categories = load_json("category")
ped_instances = extract_pedestrian_instances(sample_annotations, instances, categories)
trajectories = build_trajectories_with_sensor(sample_annotations, ped_instances)
samples = create_windows_with_sensor(trajectories)
return samples
def train_phase2(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
base_checkpoint = Path(args.base_checkpoint)
output_checkpoint = Path(args.output_checkpoint)
if not base_checkpoint.is_absolute():
base_checkpoint = REPO_ROOT / base_checkpoint
if not output_checkpoint.is_absolute():
output_checkpoint = REPO_ROOT / output_checkpoint
output_checkpoint.parent.mkdir(parents=True, exist_ok=True)
os.makedirs("log", exist_ok=True)
log_filename = os.path.join(
"log",
f"phase2_fusion_train_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
)
def log_print(msg):
print(msg)
with open(log_filename, "a", encoding="utf-8") as f:
f.write(msg + "\n")
log_print(f"Starting Phase 2 fusion transfer-learning on {device}...")
samples = get_fusion_samples()
if args.max_samples > 0:
samples = samples[: args.max_samples]
random.seed(42)
random.shuffle(samples)
train_size = int(0.8 * len(samples))
train_samples = samples[:train_size]
val_samples = samples[train_size:]
train_dataset = FusionTrajectoryDataset(train_samples, augment=True)
val_dataset = FusionTrajectoryDataset(val_samples, augment=False)
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=collate_fn_fusion,
)
val_loader = DataLoader(
val_dataset,
batch_size=args.batch_size,
collate_fn=collate_fn_fusion,
)
model = TrajectoryTransformerFusion(fusion_dim=3).to(device)
if base_checkpoint.exists():
missing, unexpected = model.load_from_base_checkpoint(str(base_checkpoint), map_location=device)
log_print(f"Loaded base checkpoint: {base_checkpoint}")
log_print(f"Missing keys count: {len(missing)}")
log_print(f"Unexpected keys count: {len(unexpected)}")
else:
log_print(f"Base checkpoint not found: {base_checkpoint}")
base_params = []
fusion_params = []
for n, p in model.named_parameters():
if n.startswith("fusion_embed") or n.startswith("fusion_ln"):
fusion_params.append(p)
else:
base_params.append(p)
optimizer = optim.Adam(
[
{"params": base_params, "lr": args.base_lr},
{"params": fusion_params, "lr": args.fusion_lr},
]
)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode='min',
factor=0.5,
patience=4,
)
best_val_ade = float("inf")
patience_counter = 0
for epoch in range(args.epochs):
model.train()
train_loss = 0.0
for obs, neighbors, fusion_obs, future in train_loader:
obs = obs.to(device)
fusion_obs = fusion_obs.to(device)
future = future.to(device)
pred, goals, probs, _ = model(obs, neighbors, fusion_obs)
loss = best_of_k_loss(pred, goals, future, probs)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
model.eval()
val_ade = 0.0
val_fde = 0.0
batches = 0
with torch.no_grad():
for obs, neighbors, fusion_obs, future in val_loader:
obs = obs.to(device)
fusion_obs = fusion_obs.to(device)
future = future.to(device)
pred, goals, probs, _ = model(obs, neighbors, fusion_obs)
gt = future.unsqueeze(1)
err = torch.norm(pred - gt, dim=3).mean(dim=2)
best_idx = torch.argmin(err, dim=1)
best_pred = pred[torch.arange(pred.size(0), device=device), best_idx]
val_ade += compute_ade(best_pred, future).item()
val_fde += compute_fde(best_pred, future).item()
batches += 1
val_ade = val_ade / max(1, batches)
val_fde = val_fde / max(1, batches)
scheduler.step(val_ade)
curr_lr_base = optimizer.param_groups[0]['lr']
curr_lr_fusion = optimizer.param_groups[1]['lr']
log_print(f"Epoch {epoch + 1}/{args.epochs}")
log_print(f"Train Loss: {train_loss:.4f}")
log_print(f"Val ADE: {val_ade:.4f} | Val FDE: {val_fde:.4f}")
log_print(f"LR base={curr_lr_base:.6f} | fusion={curr_lr_fusion:.6f}")
log_print("-" * 44)
if val_ade < best_val_ade:
best_val_ade = val_ade
patience_counter = 0
torch.save(model.state_dict(), output_checkpoint)
log_print(f"New best fusion model saved: {output_checkpoint}")
else:
patience_counter += 1
if patience_counter >= args.patience:
log_print(f"Early stopping at epoch {epoch + 1} (patience reached).")
break
log_print("Phase 2 fusion transfer-learning complete.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Phase 2: LiDAR/Radar Fusion Transfer-Learning")
parser.add_argument("--epochs", type=int, default=20)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--base-lr", type=float, default=2e-4)
parser.add_argument("--fusion-lr", type=float, default=8e-4)
parser.add_argument("--patience", type=int, default=8)
parser.add_argument("--max-samples", type=int, default=0, help="Use first N samples for quick debug run. 0 = full data.")
parser.add_argument("--base-checkpoint", type=str, default="models/best_social_model.pth")
parser.add_argument("--output-checkpoint", type=str, default="models/best_social_model_fusion.pth")
args = parser.parse_args()
train_phase2(args)
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