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98075af | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | import torch
from torch.utils.data import DataLoader, random_split
import torch.optim as optim
import os
import datetime
from pathlib import Path
from backend.app.legacy.dataset import TrajectoryDataset
from backend.app.ml.model import TrajectoryTransformer
from backend.app.legacy.data_loader import (
load_json, extract_pedestrian_instances,
build_trajectories, create_windows
)
REPO_ROOT = Path(__file__).resolve().parents[3]
MODEL_DIR = REPO_ROOT / "models"
# ----------------------------
# CUSTOM COLLATE (IMPORTANT)
# ----------------------------
def collate_fn(batch):
obs, neighbors, future = zip(*batch)
obs = torch.stack(obs)
future = torch.stack(future)
return obs, list(neighbors), future
# ----------------------------
# LOAD DATA
# ----------------------------
def get_data():
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(sample_annotations, ped_instances)
samples = create_windows(trajectories)
return samples
# ----------------------------
# METRICS
# ----------------------------
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))
# ----------------------------
# LOSS
# ----------------------------
def best_of_k_loss(pred, goals, gt, probs):
gt_traj = gt.unsqueeze(1) # (B, 1, 6, 2)
gt_goal = gt[:, -1, :].unsqueeze(1) # (B, 1, 2)
# Error calculation over the entire path
error = torch.norm(pred - gt_traj, dim=3).mean(dim=2) # (B, K)
min_error, best_idx = torch.min(error, dim=1)
traj_loss = torch.mean(min_error)
# Goal Loss: force the network to explicitly predict accurate endpoints!
best_goals = goals[torch.arange(goals.size(0)), best_idx] # (B, 2)
goal_loss = torch.norm(best_goals - gt[:, -1, :], dim=1).mean()
prob_loss = torch.nn.functional.cross_entropy(probs, best_idx)
# -----------------------------
# DIVERSITY REGULARIZATION
# -----------------------------
diversity_loss = 0
K = pred.size(1)
if K > 1:
for i in range(K):
for j in range(i + 1, K):
dist = torch.norm(pred[:, i] - pred[:, j], dim=2).mean(dim=1)
diversity_loss += torch.exp(-dist).mean()
diversity_loss /= (K * (K - 1) / 2)
return traj_loss + 0.5 * goal_loss + 0.5 * prob_loss + 0.1 * diversity_loss
# ----------------------------
# TRAIN
# ----------------------------
def train():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
os.makedirs("log", exist_ok=True)
MODEL_DIR.mkdir(parents=True, exist_ok=True)
log_filename = os.path.join("log", f"train_log_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.txt")
best_model_path = MODEL_DIR / "best_social_model.pth"
def log_print(msg):
print(msg)
with open(log_filename, "a") as f:
f.write(msg + "\n")
import random
log_print(f"Starting training on {device}...")
samples = get_data()
# Deterministic split as promised
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 = TrajectoryDataset(train_samples, augment=True)
val_dataset = TrajectoryDataset(val_samples, augment=False)
train_loader = DataLoader(
train_dataset, batch_size=64, shuffle=True, collate_fn=collate_fn
)
val_loader = DataLoader(
val_dataset, batch_size=64, collate_fn=collate_fn
)
model = TrajectoryTransformer().to(device)
optimizer = optim.Adam(model.parameters(), lr=0.001)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=5)
best_ade = float("inf")
patience_counter = 0
max_patience = 15
for epoch in range(100): # Increased to 100 max epochs with early stopping
model.train()
total_loss = 0
for obs, neighbors, future in train_loader:
obs, future = obs.to(device), future.to(device)
pred, goals, probs, _ = model(obs, neighbors)
loss = best_of_k_loss(pred, goals, future, probs)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
# ---------------- VALIDATION ----------------
model.eval()
ade, fde = 0, 0
with torch.no_grad():
for obs, neighbors, future in val_loader:
obs, future = obs.to(device), future.to(device)
pred, goals, probs, _ = model(obs, neighbors)
gt = future.unsqueeze(1)
error = torch.norm(pred - gt, dim=3).mean(dim=2)
best_idx = torch.argmin(error, dim=1)
best_pred = pred[torch.arange(pred.size(0)), best_idx]
ade += compute_ade(best_pred, future).item()
fde += compute_fde(best_pred, future).item()
log_print(f"Epoch {epoch+1}")
log_print(f"Train Loss: {total_loss:.4f}")
log_print(f"ADE: {ade:.4f}, FDE: {fde:.4f}")
log_print("-" * 40)
# Save best model
if ade < best_ade:
log_print(f"New best model found! ADE improved from {best_ade:.4f} to {ade:.4f}")
best_ade = ade
torch.save(model.state_dict(), best_model_path)
patience_counter = 0
else:
patience_counter += 1
# Update Learning Rate
scheduler.step(ade)
current_lr = optimizer.param_groups[0]['lr']
log_print(f"Current Learning Rate: {current_lr}")
if patience_counter >= max_patience:
log_print(f"Early stopping triggered! No improvement for {max_patience} epochs.")
break
log_print("Training complete!")
if __name__ == "__main__":
train() |