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Stage-2 LED trainer for SDD. Variable-A scenes, batch_size=1, grad_accum.
Optional graph module (--use_graph --use_v6_graph).
Eval: only target agent (index 0) counts toward ADE/FDE.
"""
import os, time, torch, random, numpy as np
import torch.nn as nn
from utils.config import Config
from utils.utils import print_log
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from data.dataloader_sdd import SDDDataset, sdd_seq_collate
from models.model_led_initializer import LEDInitializer as InitializationModel
from models.model_diffusion import TransformerDenoisingModel as CoreDenoisingModel
NUM_Tau = 5
class Trainer:
def __init__(self, config):
if torch.cuda.is_available():
torch.cuda.set_device(config.gpu)
self.device = torch.device('cuda') if config.cuda else torch.device('cpu')
self.cfg = Config(config.cfg, config.info)
self.use_graph = bool(getattr(config, 'use_graph', False))
self.use_v6_graph = bool(getattr(config, 'use_v6_graph', False))
self.residual_on = getattr(config, 'residual_on', 'y0')
self.grad_accum = getattr(config, 'grad_accum', 16)
train_dset = SDDDataset(obs_len=self.cfg.past_frames,
pred_len=self.cfg.future_frames, split='train')
test_dset = SDDDataset(obs_len=self.cfg.past_frames,
pred_len=self.cfg.future_frames, split='test')
self.train_loader = DataLoader(train_dset, batch_size=1, shuffle=True,
num_workers=2, collate_fn=sdd_seq_collate)
self.test_loader = DataLoader(test_dset, batch_size=1, shuffle=False,
num_workers=2, collate_fn=sdd_seq_collate)
self.traj_mean = torch.FloatTensor(self.cfg.traj_mean).cuda().unsqueeze(0).unsqueeze(0).unsqueeze(0)
self.traj_scale = float(self.cfg.traj_scale)
self.n_steps = self.cfg.diffusion.steps
self.betas = self._make_beta_schedule(
self.cfg.diffusion.beta_schedule, self.n_steps,
self.cfg.diffusion.beta_start, self.cfg.diffusion.beta_end).cuda()
self.alphas = 1 - self.betas
self.alphas_prod = torch.cumprod(self.alphas, 0)
self.alphas_bar_sqrt = torch.sqrt(self.alphas_prod)
self.one_minus_alphas_bar_sqrt = torch.sqrt(1 - self.alphas_prod)
self.model = CoreDenoisingModel(past_len=self.cfg.past_frames).cuda()
ckpt_path = self.cfg.pretrained_core_denoising_model
if not os.path.isfile(ckpt_path):
raise FileNotFoundError(f'Missing pretrained denoiser: {ckpt_path}')
self.model.load_state_dict(torch.load(ckpt_path, map_location='cpu')['model_dict'])
self.model_initializer = InitializationModel(
t_h=self.cfg.past_frames, d_h=6,
t_f=self.cfg.future_frames, d_f=2, k_pred=20).cuda()
params = list(self.model_initializer.parameters())
self.interaction_graph = None
if self.use_graph:
from models.future_interaction_graph_v6 import FutureInteractionGraphV6Wrapper
self.interaction_graph = FutureInteractionGraphV6Wrapper(
num_agents=64, future_steps=self.cfg.future_frames,
past_steps=self.cfg.past_frames, past_channels=6,
node_dim=128, top_n=5, num_denoise_steps=NUM_Tau).cuda()
params += list(self.interaction_graph.parameters())
self.opt = torch.optim.AdamW(params, lr=config.learning_rate)
self.scheduler = torch.optim.lr_scheduler.StepLR(
self.opt, step_size=self.cfg.decay_step, gamma=self.cfg.decay_gamma)
self.log = open(os.path.join(self.cfg.log_dir, 'log.txt'), 'a+')
self.tb = SummaryWriter(log_dir=os.path.join(self.cfg.log_dir, 'tb'))
self.global_step = 0
self._print_param(self.model, 'Core Denoiser')
self._print_param(self.model_initializer, 'Initializer')
if self.interaction_graph:
self._print_param(self.interaction_graph, 'Graph')
T = self.cfg.future_frames
self.temporal_reweight = torch.FloatTensor(
[(T + 1) - i for i in range(1, T + 1)]).cuda().unsqueeze(0).unsqueeze(0) / (T / 2)
def _print_param(self, m, name):
t = sum(p.numel() for p in m.parameters())
tr = sum(p.numel() for p in m.parameters() if p.requires_grad)
print_log(f'[{name}] {tr}/{t}', self.log)
def _make_beta_schedule(self, schedule, n, start, end):
if schedule == 'linear': return torch.linspace(start, end, n)
return torch.linspace(start, end, n)
def _extract(self, a, t, x):
out = torch.gather(a, 0, t.to(a.device))
return out.reshape(t.shape[0], *([1] * (len(x.shape) - 1)))
def p_sample_accelerate(self, x, mask, cur_y, t, sigma=None):
t_tensor = torch.tensor([int(t)]).cuda()
eps_factor = ((1 - self._extract(self.alphas, t_tensor, cur_y))
/ self._extract(self.one_minus_alphas_bar_sqrt, t_tensor, cur_y))
beta = self._extract(self.betas, t_tensor.repeat(x.shape[0]), cur_y)
eps_theta = self.model.generate_accelerate(cur_y, beta, x, mask)
if self.interaction_graph is not None:
abs_t = self._extract(self.alphas_bar_sqrt, t_tensor, cur_y)
am1_t = self._extract(self.one_minus_alphas_bar_sqrt, t_tensor, cur_y)
y0_hat = (cur_y - am1_t * eps_theta) / abs_t
delta = self.interaction_graph(
y0_hat, x, int(t), sigma=sigma, A_override=x.size(0))
eps_theta = eps_theta - (abs_t / am1_t) * delta
mean = (1 / self._extract(self.alphas, t_tensor, cur_y).sqrt()) \
* (cur_y - eps_factor * eps_theta)
z = torch.randn_like(cur_y)
sigma_t = self._extract(self.betas, t_tensor, cur_y).sqrt()
return mean + sigma_t * z * 0.00001
def p_sample_loop_accelerate(self, x, mask, loc, sigma=None):
cur_y = loc[:, :10]
for i in reversed(range(NUM_Tau)):
cur_y = self.p_sample_accelerate(x, mask, cur_y, i, sigma=sigma)
cur_y_ = loc[:, 10:]
for i in reversed(range(NUM_Tau)):
cur_y_ = self.p_sample_accelerate(x, mask, cur_y_, i, sigma=sigma)
return torch.cat((cur_y_, cur_y), dim=1)
def data_preprocess(self, data):
pre = data['pre_motion_3D'].cuda()
fut = data['fut_motion_3D'].cuda()
A = pre.size(1)
initial_pos = pre[:, :, -1:]
past_abs = ((pre - self.traj_mean) / self.traj_scale).contiguous().view(-1, self.cfg.past_frames, 2)
past_rel = ((pre - initial_pos) / self.traj_scale).contiguous().view(-1, self.cfg.past_frames, 2)
past_vel = torch.cat([past_rel[:, 1:] - past_rel[:, :-1],
torch.zeros_like(past_rel[:, -1:])], dim=1)
past_traj = torch.cat([past_abs, past_rel, past_vel], dim=-1)
fut_traj = ((fut - initial_pos) / self.traj_scale).contiguous().view(-1, self.cfg.future_frames, 2)
mask = torch.ones(A, A).cuda()
return A, mask, past_traj, fut_traj
def fit(self):
for epoch in range(self.cfg.num_epochs):
lt, ld, lu = self._train_epoch(epoch)
print_log(f'[{time.strftime("%Y-%m-%d %H:%M:%S")}] Epoch: {epoch}\t'
f'Loss: {lt:.6f}\tDist: {ld:.6f}\tUnc: {lu:.6f}', self.log)
self.tb.add_scalar('train/loss', lt, epoch)
self.tb.add_scalar('train/loss_dist', ld, epoch)
if (epoch + 1) % self.cfg.test_interval == 0:
perf, n = self._test_epoch()
# MID protocol: scale normalized ADE/FDE by 50 to report in pixels
ade_px = perf['ADE'] / n * 50.0
fde_px = perf['FDE'] / n * 50.0
print_log(f'Epoch {epoch} Best Of 20: ADE: {ade_px:.4f} FDE: {fde_px:.4f}', self.log)
self.tb.add_scalar('val/ADE_px', ade_px, epoch)
self.tb.add_scalar('val/FDE_px', fde_px, epoch)
cp = {'model_initializer_dict': self.model_initializer.state_dict()}
if self.interaction_graph:
cp['interaction_graph_dict'] = self.interaction_graph.state_dict()
torch.save(cp, self.cfg.model_path % (epoch + 1))
self.scheduler.step()
self.tb.flush(); self.tb.close()
def _train_epoch(self, epoch):
self.model.train(); self.model_initializer.train()
if self.interaction_graph: self.interaction_graph.train()
lt, ld, lu, cnt = 0, 0, 0, 0
self.opt.zero_grad()
for i, data in enumerate(self.train_loader):
A, mask, past, fut = self.data_preprocess(data)
sp, me, ve = self.model_initializer(past, mask)
ve = ve.clamp(min=-5, max=5)
sp = torch.exp(ve / 2)[..., None, None] * sp \
/ (sp.std(dim=1).mean(dim=(1, 2))[:, None, None, None] + 1e-6)
loc = sp + me[:, None]
sigma_in = ve if self.use_v6_graph else None
gen = self.p_sample_loop_accelerate(past, mask, loc, sigma=sigma_in)
loss_d = ((gen - fut.unsqueeze(1)).norm(p=2, dim=-1)
* self.temporal_reweight).mean(dim=-1).min(dim=1)[0].mean()
loss_u = (torch.exp(-ve)
* (gen - fut.unsqueeze(1)).norm(p=2, dim=-1).mean(dim=(1, 2))
+ ve).mean()
loss = loss_d * 50 + loss_u
(loss / self.grad_accum).backward()
if (i + 1) % self.grad_accum == 0:
params = list(self.model_initializer.parameters())
if self.interaction_graph: params += list(self.interaction_graph.parameters())
nn.utils.clip_grad_norm_(params, 1.0)
self.opt.step(); self.opt.zero_grad()
lt += loss.item(); ld += loss_d.item() * 50; lu += loss_u.item(); cnt += 1
self.global_step += 1
self.opt.step(); self.opt.zero_grad()
return lt / cnt, ld / cnt, lu / cnt
def _test_epoch(self):
"""MID-style SDD protocol:
per-pedestrian full-horizon ADE (mean L2 over 12 future frames) and
FDE (L2 at final frame), best_of_20 per pedestrian, then average
across all evaluated pedestrians. Coordinates are already in
MID's ÷50 mean-centered space, so the final ADE/FDE is multiplied
by 50 to report in pixels.
Each scene in the SDD dataloader corresponds to one target
pedestrian (index 0) + its neighbors; we evaluate only the target
per scene so each pedestrian is counted exactly once (matches
MID's get_timesteps_data qualification intent).
"""
T = self.cfg.future_frames
perf = {'ADE': 0.0, 'FDE': 0.0}
n = 0
np.random.seed(0); random.seed(0)
torch.manual_seed(0); torch.cuda.manual_seed_all(0)
self.model_initializer.eval()
if self.interaction_graph: self.interaction_graph.eval()
with torch.no_grad():
for data in self.test_loader:
A, mask, past, fut = self.data_preprocess(data)
sp, me, ve = self.model_initializer(past, mask)
ve = ve.clamp(min=-5, max=5)
sp = torch.exp(ve / 2)[..., None, None] * sp \
/ (sp.std(dim=1).mean(dim=(1, 2))[:, None, None, None] + 1e-6)
loc = sp + me[:, None]
sigma_in = ve if self.use_v6_graph else None
pred = self.p_sample_loop_accelerate(past, mask, loc, sigma=sigma_in)
# MID protocol: only target agent (index 0) per scene
pred_0 = pred[0:1] # [1, 20, T, 2]
fut_0 = fut[0:1] # [1, T, 2]
dist = torch.norm(fut_0.unsqueeze(1) - pred_0, dim=-1) * self.traj_scale # [1, 20, T]
# best_of_20 per pedestrian, then full-horizon ADE / final FDE
ade_per_ped = dist.mean(dim=-1).min(dim=-1)[0] # [1]
fde_per_ped = dist[:, :, -1].min(dim=-1)[0] # [1]
perf['ADE'] += ade_per_ped.sum().item()
perf['FDE'] += fde_per_ped.sum().item()
n += 1
return perf, n
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