MID/main_sdd_mid.py

"""
main_sdd_mid.py — MID baseline on SDD (Stanford Drone Dataset).

Uses MoFlow's original pkl at `MoFlow/data/sdd/original/sdd_{train,test}.pkl`.
Each sample is a tuple (past[8,2], future[12,2], neighbors[20,N,2]) with variable N.
We combine target + neighbors into a scene of A=1+N agents.
batch_size=1 (variable A), gradient accumulation.

Standard SDD: 8 past + 12 future frames.  Coordinates in pixels.
We normalize per-agent last-obs-relative and divide by TRAJ_SCALE=100
(rough pixel scale) to keep values small.

Usage:
    python main_sdd_mid.py --gpu 0
"""

import os, sys, time, pickle, logging, argparse
import numpy as np
import torch
import torch.nn as nn
from torch import optim
from torch.utils.data import Dataset, DataLoader
from torch.utils.tensorboard import SummaryWriter  # tbX-broken
from tqdm.auto import tqdm

from models.diffusion import DiffusionTraj, VarianceSchedule, TransformerConcatLinear


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):
    """Per-pedestrian dataset: each item is target + neighbors as a scene."""
    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)       # [8, 2]
            fut   = fut.astype(np.float32)         # [12, 2]
            neigh = neigh.astype(np.float32)       # [20, N, 2]
            N = neigh.shape[1]
            traj_target = np.concatenate([past, fut], axis=0)[None]  # [1, 20, 2]
            if N > 0:
                traj_neigh = neigh.transpose(1, 0, 2)  # [N, 20, 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, 20, 2]
        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))


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 = TransformerConcatLinear(point_dim=2, context_dim=self.args.encoder_dim,
                                      tf_layer=self.args.tf_layer, residual=False)
        self.diffusion = DiffusionTraj(
            net=net,
            var_sched=VarianceSchedule(num_steps=100, beta_T=5e-2, mode='linear'),
        ).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):
        params = list(self.encoder.parameters()) + list(self.diffusion.parameters())
        self.optimizer = optim.Adam(params, lr=self.args.lr)
        self.scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=0.98)

    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, _ = preprocess_scene(pre, fut, self.device)
                context = self.encoder(past_6ch, mask)
                loss = self.diffusion.get_loss(fut_rel, context)
                (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}')

            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(
                    f'Epoch {epoch}  ADE(4.8s)={m["ADE_4.8s"]:.4f}  FDE(4.8s)={m["FDE_4.8s"]:.4f}'
                    f'  ADE(1.6s)={m["ADE_1.6s"]:.4f}  ADE(3.2s)={m["ADE_3.2s"]:.4f}')
                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 toward ADE/FDE."""
        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, 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)
            # Only target agent (index 0)
            dist = (pred_abs[:, 0] - fut_abs[0].unsqueeze(0)).norm(dim=-1)  # [K, T]
            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_baseline')
    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('--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('--sampling',       type=str, default='ddim')
    p.add_argument('--sampling_step',  type=int, default=10)
    return p.parse_args()


if __name__ == '__main__':
    args = parse_args()
    Trainer(args).train()