""" main_sdd_mid_graphv5_sigma.py — MID + V6 graph + learned σ on SDD. SDD format: per-pedestrian (past[8,2], future[12,2], neighbors[20,N,2]). Combine target + neighbors into variable-A scene, batch_size=1. Graph skipped when A<2 (46% of samples have no neighbors). Eval only on target agent (index 0). Coordinates in pixels → TRAJ_SCALE=100. Usage: python main_sdd_mid_graphv5_sigma.py --gpu 0 """ import os, sys, time, pickle, logging, argparse import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import Dataset, DataLoader from torch.utils.tensorboard import SummaryWriter # tbX-broken from tqdm.auto import tqdm MOFLOW_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'MoFlow')) sys.path.insert(0, MOFLOW_ROOT) from models.graph_interaction_nba_v6 import FutureInteractionGraphV6 from models.context_encoder.mtr_encoder import SinusoidalPosEmb from models.diffusion import VarianceSchedule from models.common import PositionalEncoding, ConcatSquashLinear OBS_LEN = 8 PRED_LEN = 12 TRAJ_SCALE = 100.0 K_EVAL = 20 HORIZONS = {'1.6s': 4, '3.2s': 8, '4.8s': 12} DATA_ROOT = '/mnt/jaewoo4tb/srtp/MoFlow/data/sdd/original' class SDDDataset(Dataset): def __init__(self, split='train'): super().__init__() path = os.path.join(DATA_ROOT, f'sdd_{split}.pkl') with open(path, 'rb') as f: raw = pickle.load(f) self.scenes = [] for past, fut, neigh in raw: past = past.astype(np.float32) fut = fut.astype(np.float32) neigh = neigh.astype(np.float32) N = neigh.shape[1] traj_target = np.concatenate([past, fut], axis=0)[None] if N > 0: traj_neigh = neigh.transpose(1, 0, 2) traj_all = np.concatenate([traj_target, traj_neigh], axis=0) else: traj_all = traj_target self.scenes.append(torch.from_numpy(traj_all)) a = np.array([len(x) for x in self.scenes]) print(f'[SDDDataset] {split}: {len(self.scenes)} samples, ' f'A min/mean/max = {a.min()}/{a.mean():.1f}/{a.max()}') def __len__(self): return len(self.scenes) def __getitem__(self, i): x = self.scenes[i] return x[:, :OBS_LEN], x[:, OBS_LEN:] def collate_bs1(batch): assert len(batch) == 1 return batch[0] def preprocess_scene(pre, fut, device): pre = pre.to(device); fut = fut.to(device) last_obs = pre[:, -1:, :] rel = (pre - last_obs) / TRAJ_SCALE vel = torch.cat([rel[:, 1:] - rel[:, :-1], torch.zeros_like(rel[:, :1])], dim=1) past_6ch = torch.cat([rel, rel, vel], dim=-1) fut_rel = ((fut - last_obs) / TRAJ_SCALE).contiguous() A = pre.size(0) mask = torch.zeros(A, A, device=device) return past_6ch, fut_rel, mask, last_obs class _STEncoder(nn.Module): def __init__(self, in_channels=6, hidden=256): super().__init__() self.conv = nn.Conv1d(in_channels, 32, kernel_size=3, padding=1) self.relu = nn.ReLU() self.gru = nn.GRU(32, hidden, num_layers=1, batch_first=True) nn.init.kaiming_normal_(self.conv.weight) nn.init.kaiming_normal_(self.gru.weight_ih_l0) nn.init.kaiming_normal_(self.gru.weight_hh_l0) nn.init.zeros_(self.conv.bias) nn.init.zeros_(self.gru.bias_ih_l0) nn.init.zeros_(self.gru.bias_hh_l0) def forward(self, x): h = self.relu(self.conv(x.transpose(1, 2))) _, s = self.gru(h.transpose(1, 2)) return s.squeeze(0) class _SocialTransformer(nn.Module): def __init__(self, past_len=OBS_LEN, hidden=256): super().__init__() self.proj = nn.Linear(past_len * 6, hidden, bias=False) layer = nn.TransformerEncoderLayer( d_model=hidden, nhead=2, dim_feedforward=hidden, batch_first=False) self.encoder = nn.TransformerEncoder(layer, num_layers=2) def forward(self, x_flat, mask): h = self.proj(x_flat).unsqueeze(1) h = h + self.encoder(h, mask) return h.squeeze(1) class SDDEncoder(nn.Module): def __init__(self, encoder_dim=256, past_len=OBS_LEN): super().__init__() self.ego_encoder = _STEncoder(6, 256) self.social_encoder = _SocialTransformer(past_len=past_len, hidden=256) self.fusion = nn.Linear(512, encoder_dim) def forward(self, past_6ch, mask): ego = self.ego_encoder(past_6ch) soc = self.social_encoder(past_6ch.reshape(past_6ch.size(0), -1), mask) return self.fusion(torch.cat([ego, soc], dim=-1)) def _rebuild_graph_for_A(graph, A, max_top_n, device): graph.num_agents = A graph._E0 = A * (A - 1) graph.top_n = max(1, min(max_top_n, A - 1)) src, dst = [], [] for i in range(A): for j in range(A): if i != j: src.append(j); dst.append(i) graph._single_edge_index = torch.tensor([src, dst], dtype=torch.long, device=device) class GraphDenoiserNet(nn.Module): def __init__(self, context_dim=256, tf_layer=3, T=PRED_LEN, max_agents=16, graph_hidden=128, top_n_neighbors=5, rel_traj_hidden=32, y0_score_dim=32, num_gnn_layers=2, graph_dropout=0.1): super().__init__() self.T, self.D = T, graph_hidden self.max_top_n = top_n_neighbors hid = 2 * context_dim ctx = context_dim + 3 self.pos_emb = PositionalEncoding(d_model=hid, dropout=0.1, max_len=24) self.concat1 = ConcatSquashLinear(2, hid, ctx) layer = nn.TransformerEncoderLayer( d_model=hid, nhead=4, dim_feedforward=4 * context_dim) self.transformer_encoder = nn.TransformerEncoder(layer, num_layers=tf_layer) self.concat3 = ConcatSquashLinear(hid, context_dim, ctx) self.concat4 = ConcatSquashLinear(context_dim, context_dim // 2, ctx) self.out_linear = ConcatSquashLinear(context_dim // 2, 2, ctx) self.node_pool_query = nn.Parameter(torch.randn(1, 1, hid) * 0.02) self.node_proj = nn.Sequential( nn.Linear(hid, graph_hidden), nn.ReLU(inplace=True)) self.time_mlp = nn.Sequential( SinusoidalPosEmb(graph_hidden), nn.Linear(graph_hidden, graph_hidden), nn.ReLU(), nn.Linear(graph_hidden, graph_hidden)) self.future_graph = FutureInteractionGraphV6( embed_dim=graph_hidden, future_steps=T, num_agents=max_agents, num_heads=4, dropout=graph_dropout, num_gnn_layers=num_gnn_layers, time_dim=graph_hidden, top_n_neighbors=min(top_n_neighbors, max_agents - 1), rel_traj_hidden=rel_traj_hidden, y0_score_dim=y0_score_dim) self.graph_out_proj = nn.Linear(graph_hidden, hid) nn.init.xavier_uniform_(self.graph_out_proj.weight, gain=0.1) nn.init.zeros_(self.graph_out_proj.bias) self.graph_gate = nn.Parameter(torch.tensor(0.1)) self.logvar_head = nn.Sequential( nn.Linear(hid, hid // 2), nn.ReLU(inplace=True), nn.Linear(hid // 2, 1)) def _build_ctx_emb(self, beta, context): N = beta.size(0) beta_v = beta.view(N, 1, 1) ctx_v = context.view(N, 1, -1) time_emb = torch.cat([beta_v, torch.sin(beta_v), torch.cos(beta_v)], dim=-1) return torch.cat([time_emb, ctx_v], dim=-1) def _encode(self, x_t, ctx_emb): h = self.concat1(ctx_emb, x_t) h = self.pos_emb(h.permute(1, 0, 2)) return self.transformer_encoder(h).permute(1, 0, 2) def _decode(self, trans, ctx_emb): h = self.concat3(ctx_emb, trans) h = self.concat4(ctx_emb, h) return self.out_linear(ctx_emb, h) def forward(self, x_t, beta, context, A, tau, y_0_for_graph=None, skip_graph=False): N, T, _ = x_t.shape D = self.D ctx_emb = self._build_ctx_emb(beta, context) trans = self._encode(x_t, ctx_emb) logvar = self.logvar_head(trans).squeeze(-1).clamp(min=-5, max=5) if (not skip_graph) and (y_0_for_graph is not None) and A >= 2: _rebuild_graph_for_A(self.future_graph, A, self.max_top_n, x_t.device) y_abs = y_0_for_graph.view(1, 1, A, T, 2) sigma_agent = logvar.view(1, 1, A, T) attn = (self.node_pool_query * trans).sum(-1, keepdim=True).softmax(dim=1) node = (trans * attn).sum(dim=1) y_emb = self.node_proj(node).view(1, 1, A, D) beta_scene = beta.view(1, A)[:, 0] t_emb = self.time_mlp(beta_scene) y_emb_out = self.future_graph( y_emb, y_abs, t_emb, tau, sigma_agent=sigma_agent) graph_out = self.graph_out_proj(y_emb_out.squeeze(1).reshape(N, D)) trans = trans + self.graph_gate * graph_out.unsqueeze(1) return self._decode(trans, ctx_emb), logvar class DiffusionTrajGraph(nn.Module): def __init__(self, net, var_sched, train_mode='two_pass', uncertainty_weight=0.01): super().__init__() self.net = net; self.var_sched = var_sched self.train_mode = train_mode self.uncertainty_weight = uncertainty_weight def get_loss(self, x_0, context, A, last_obs): device = x_0.device N = A t_scene = self.var_sched.uniform_sample_t(1) t = [t_scene[0]] * A alpha_bar = self.var_sched.alpha_bars[t].to(device) beta = self.var_sched.betas[t].to(device) c0 = alpha_bar.sqrt().view(N, 1, 1) c1 = (1 - alpha_bar).sqrt().view(N, 1, 1) e_rand = torch.randn_like(x_0) x_t = c0 * x_0 + c1 * e_rand tau = torch.tensor([t_scene[0] / self.var_sched.num_steps], dtype=torch.float32, device=device) if self.train_mode == 'gt': y_0_abs = x_0 * TRAJ_SCALE + last_obs eps_pred, logvar = self.net(x_t, beta, context, A, tau, y_0_for_graph=y_0_abs) else: with torch.no_grad(): eps_geom, _ = self.net(x_t, beta, context, A, tau, skip_graph=True) x_0_geom = (x_t - c1 * eps_geom) / c0 y_0_abs = x_0_geom * TRAJ_SCALE + last_obs eps_pred, logvar = self.net(x_t, beta, context, A, tau, y_0_for_graph=y_0_abs) mse = F.mse_loss(eps_pred, e_rand) eps_err_sq = (eps_pred.detach() - e_rand).pow(2).mean(dim=-1) nll = 0.5 * (logvar + eps_err_sq / logvar.exp()).mean() return mse + self.uncertainty_weight * nll @torch.no_grad() def sample(self, num_points, context, A, last_obs, sample=K_EVAL, bestof=True, sampling='ddim', step=10): device, N = context.device, A stride = self.var_sched.num_steps // step out = [] for _ in range(sample): x_t = torch.randn(N, num_points, 2, device=device) if bestof \ else torch.zeros(N, num_points, 2, device=device) y_0_prev = None for t in range(self.var_sched.num_steps, 0, -stride): alpha_bar = self.var_sched.alpha_bars[t].to(device) alpha_bar_prev = self.var_sched.alpha_bars[t - stride].to(device) beta_batch = self.var_sched.betas[t].to(device).expand(N) tau = torch.full((1,), t / self.var_sched.num_steps, dtype=torch.float32, device=device) e_theta, _ = self.net(x_t, beta_batch, context, A, tau, y_0_for_graph=y_0_prev, skip_graph=(y_0_prev is None)) x_0_pred = (x_t - (1 - alpha_bar).sqrt() * e_theta) / alpha_bar.sqrt() y_0_prev = x_0_pred * TRAJ_SCALE + last_obs if sampling == 'ddim': x_t = alpha_bar_prev.sqrt() * x_0_pred \ + (1 - alpha_bar_prev).sqrt() * e_theta else: sigma = self.var_sched.get_sigmas(t, flexibility=0.0) z = torch.randn_like(x_t) if t > stride else torch.zeros_like(x_t) c0_ = 1.0 / self.var_sched.alphas[t].sqrt() c1_ = (1 - self.var_sched.alphas[t]) / (1 - alpha_bar).sqrt() x_t = c0_ * (x_t - c1_ * e_theta) + sigma * z out.append(x_t) return torch.stack(out) class Trainer: def __init__(self, args): self.args = args self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self._build_dirs(); self._build_data() self._build_model(); self._build_optimizer() def _build_dirs(self): self.exp_dir = os.path.join('experiments', self.args.exp_name) os.makedirs(self.exp_dir, exist_ok=True) self.tb_log = SummaryWriter(log_dir=self.exp_dir) log_path = os.path.join(self.exp_dir, f'sdd_{time.strftime("%Y-%m-%d-%H-%M")}.log') self.log = logging.getLogger(self.args.exp_name) self.log.setLevel(logging.INFO) self.log.addHandler(logging.FileHandler(log_path)) self.log.addHandler(logging.StreamHandler(sys.stdout)) self.log.info(f'Args: {self.args}') def _build_data(self): train_dset = SDDDataset(split='train') test_dset = SDDDataset(split='test') self.train_loader = DataLoader(train_dset, batch_size=1, shuffle=True, num_workers=2, collate_fn=collate_bs1) self.test_loader = DataLoader(test_dset, batch_size=1, shuffle=False, num_workers=2, collate_fn=collate_bs1) self.log.info(f'Train={len(train_dset)} Test={len(test_dset)}') def _build_model(self): self.encoder = SDDEncoder(encoder_dim=self.args.encoder_dim, past_len=OBS_LEN).to(self.device) net = GraphDenoiserNet( context_dim=self.args.encoder_dim, tf_layer=self.args.tf_layer, T=PRED_LEN, max_agents=self.args.max_agents, graph_hidden=self.args.graph_hidden, top_n_neighbors=self.args.top_n_neighbors, rel_traj_hidden=self.args.rel_traj_hidden, y0_score_dim=self.args.y0_score_dim, num_gnn_layers=self.args.graph_gnn_layers, graph_dropout=self.args.graph_dropout) self.diffusion = DiffusionTrajGraph( net=net, var_sched=VarianceSchedule(num_steps=100, beta_T=5e-2, mode='linear'), train_mode=self.args.train_mode, uncertainty_weight=self.args.uncertainty_weight).to(self.device) n_enc = sum(p.numel() for p in self.encoder.parameters()) n_diff = sum(p.numel() for p in self.diffusion.parameters()) self.log.info(f'Encoder: {n_enc:,} Diffusion: {n_diff:,}') def _build_optimizer(self): net = self.diffusion.net graph_names = {'future_graph', 'node_proj', 'time_mlp', 'graph_out_proj', 'graph_gate', 'node_pool_query'} graph_params, other_params = [], [] for n, p in net.named_parameters(): (graph_params if any(k in n for k in graph_names) else other_params).append(p) self.optimizer = torch.optim.Adam([ {'params': list(self.encoder.parameters()) + other_params, 'lr': self.args.lr}, {'params': graph_params, 'lr': self.args.lr * self.args.graph_lr_mult}, ]) self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( self.optimizer, T_max=self.args.epochs, eta_min=1e-6) def train(self): best_ade = float('inf') accum = self.args.grad_accum for epoch in range(1, self.args.epochs + 1): self.encoder.train(); self.diffusion.train() total, count = 0.0, 0 self.optimizer.zero_grad() for i, (pre, fut) in enumerate(tqdm(self.train_loader, ncols=90, desc=f'E{epoch}')): A = pre.size(0) if A < 1: continue past_6ch, fut_rel, mask, last_obs = preprocess_scene(pre, fut, self.device) context = self.encoder(past_6ch, mask) loss = self.diffusion.get_loss(fut_rel, context, A=A, last_obs=last_obs) (loss / accum).backward() if (i + 1) % accum == 0: nn.utils.clip_grad_norm_( list(self.encoder.parameters()) + list(self.diffusion.parameters()), 1.0) self.optimizer.step(); self.optimizer.zero_grad() total += loss.item(); count += 1 self.optimizer.step(); self.optimizer.zero_grad() self.scheduler.step() avg = total / max(count, 1) self.tb_log.add_scalar('loss/train', avg, epoch) self.log.info(f'Epoch {epoch} train_loss={avg:.4f} ' f'lr={self.scheduler.get_last_lr()[0]:.6f}') if epoch % self.args.eval_every == 0: m = self.evaluate() for k, v in m.items(): self.tb_log.add_scalar(f'metric/{k}', v, epoch) self.log.info('Epoch %d ' % epoch + ' '.join( f'ADE({h})={m[f"ADE_{h}"]:.4f}/FDE={m[f"FDE_{h}"]:.4f}' for h in HORIZONS)) ade = m['ADE_4.8s'] if ade < best_ade: best_ade = ade torch.save({'encoder': self.encoder.state_dict(), 'diffusion': self.diffusion.state_dict(), 'epoch': epoch, 'metrics': m}, os.path.join(self.exp_dir, 'best.pt')) self.log.info(f' ** New best ADE(4.8s)={ade:.4f}') @torch.no_grad() def evaluate(self): """SDD eval: only the TARGET agent (index 0) counts.""" self.encoder.eval(); self.diffusion.eval() sums = {f'{k}_{h}': 0.0 for h in HORIZONS for k in ('ADE', 'FDE')} n_target = 0 for pre, fut in tqdm(self.test_loader, ncols=90, desc='Eval'): A = pre.size(0) if A < 1: continue past_6ch, _, mask, last_obs = preprocess_scene(pre, fut, self.device) context = self.encoder(past_6ch, mask) pred_rel = self.diffusion.sample( num_points=PRED_LEN, context=context, A=A, last_obs=last_obs, sample=K_EVAL, bestof=True, sampling=self.args.sampling, step=self.args.sampling_step) pred_abs = pred_rel * TRAJ_SCALE + last_obs.unsqueeze(0) fut_abs = fut.to(self.device) dist = (pred_abs[:, 0] - fut_abs[0].unsqueeze(0)).norm(dim=-1) for h, end in HORIZONS.items(): sums[f'ADE_{h}'] += dist[:, :end].mean(dim=-1).min().item() sums[f'FDE_{h}'] += dist[:, end - 1].min().item() n_target += 1 return {k: v / n_target for k, v in sums.items()} def parse_args(): p = argparse.ArgumentParser() p.add_argument('--exp_name', type=str, default='mid_sdd_graphv5_sigma') p.add_argument('--gpu', type=int, default=0) p.add_argument('--epochs', type=int, default=100) p.add_argument('--grad_accum', type=int, default=32) p.add_argument('--lr', type=float, default=1e-3) p.add_argument('--graph_lr_mult', type=float, default=1.0) p.add_argument('--eval_every', type=int, default=1) p.add_argument('--encoder_dim', type=int, default=256) p.add_argument('--tf_layer', type=int, default=3) p.add_argument('--max_agents', type=int, default=16) p.add_argument('--graph_hidden', type=int, default=128) p.add_argument('--graph_gnn_layers', type=int, default=2) p.add_argument('--graph_dropout', type=float, default=0.1) p.add_argument('--top_n_neighbors', type=int, default=5) p.add_argument('--rel_traj_hidden', type=int, default=32) p.add_argument('--y0_score_dim', type=int, default=32) p.add_argument('--train_mode', type=str, default='two_pass', choices=['gt', 'two_pass']) p.add_argument('--sampling', type=str, default='ddim') p.add_argument('--sampling_step', type=int, default=10) p.add_argument('--uncertainty_weight', type=float, default=0.01) return p.parse_args() if __name__ == '__main__': args = parse_args() Trainer(args).train()