File size: 23,628 Bytes
d4cbafd | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 |
import os
import copy
import math
import os
import pickle
import numpy as np
import matplotlib.pyplot as plt
from glob import glob
from pathlib import Path
import torch
import torch.nn as nn
from einops import rearrange, reduce
from accelerate import Accelerator
from ema_pytorch import EMA
from tqdm.auto import tqdm
from utils.utils import set_random_seed
from utils.normalization import unnormalize_min_max, unnormalize_sqrt
from .denoising_model_trainers import exists, default, identity, has_int_squareroot, cycle, build_optimizer, build_scheduler
class IMLETrainer(object):
def __init__(
self,
cfg,
imle_generator,
train_loader,
test_loader,
val_loader=None,
tb_log=None,
logger=None,
gradient_accumulate_every=1,
ema_decay=0.995,
ema_update_every=1,
save_samples=False,
*awgs, **kwargs
):
super().__init__()
# init
self.cfg = cfg
self.imle_model = imle_generator
self.train_loader = train_loader
self.test_loader = test_loader
self.val_loader = default(val_loader, test_loader)
self.tb_log = tb_log
self.logger = logger
self.gradient_accumulate_every = gradient_accumulate_every
self.ema_decay = ema_decay
self.ema_update_every = ema_update_every
# config fields
self.save_dir = Path(cfg.cfg_dir)
# sampling and training hyperparameters
self.save_and_sample_every = cfg.checkpt_freq * len(train_loader)
self.gradient_accumulate_every = gradient_accumulate_every
self.train_num_steps = cfg.OPTIMIZATION.NUM_EPOCHS * len(train_loader)
self.save_samples = save_samples
assert self.cfg.latent_tau == 0
# accelerator
self.accelerator = Accelerator(
split_batches = True,
mixed_precision = 'no'
)
# EMA model
if self.accelerator.is_main_process:
self.ema = EMA(imle_generator, beta=ema_decay, update_every=ema_update_every)
self.ema.to(self.device)
# optimizer
self.opt = build_optimizer(self.imle_model, self.cfg.OPTIMIZATION)
self.scheduler = build_scheduler(self.opt, self.cfg.OPTIMIZATION, len(self.train_loader))
# prepare model, dataloader, optimizer with accelerator
self.imle_model, self.opt = self.accelerator.prepare(self.imle_model, self.opt)
# datasets and dataloaders
train_dl_ = self.accelerator.prepare(train_loader)
self.train_loader = train_dl_
self.dl = cycle(train_dl_)
self.test_loader = self.accelerator.prepare(test_loader)
val_loader = default(val_loader, test_loader)
self.val_loader = self.accelerator.prepare(val_loader)
# set counters and training states
self.step = 0
self.best_ade_min = float('inf')
if self.cfg.get('data_norm', None) == 'sqrt':
self.sqrt_a_ = torch.tensor([self.cfg.sqrt_x_a, self.cfg.sqrt_y_a], device=self.device)
self.sqrt_b_ = torch.tensor([self.cfg.sqrt_x_b, self.cfg.sqrt_y_b], device=self.device)
# print the number of model parameters
self.print_model_params(self.imle_model, 'Stage Two Model')
def print_model_params(self, model: nn.Module, name: str):
total_num = sum(p.numel() for p in model.parameters())
trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
self.logger.info(f"[{name}] Trainable/Total: {trainable_num}/{total_num}")
@property
def device(self):
return self.cfg.device
def save_ckpt(self, ckpt_name):
if not self.accelerator.is_local_main_process:
return
data = {
'step': self.step,
'model': self.accelerator.get_state_dict(self.imle_model),
'opt': self.opt.state_dict(),
'ema': self.ema.state_dict(),
'scheduler': self.scheduler.state_dict(),
'scaler': self.accelerator.scaler.state_dict() if exists(self.accelerator.scaler) else None,
}
torch.save(data, os.path.join(self.cfg.model_dir, f'{ckpt_name}.pt'))
def save_last_ckpt(self):
data = {
'step': self.step,
'model': self.accelerator.get_state_dict(self.imle_model),
'opt': self.opt.state_dict(),
'ema': self.ema.state_dict(),
'scheduler': self.scheduler.state_dict(),
}
torch.save(data, os.path.join(self.cfg.model_dir, 'checkpoint_last.pt'))
def load(self, ckpt_name):
accelerator = self.accelerator
data = torch.load(os.path.join(self.cfg.model_dir, f'{ckpt_name}.pt'), map_location=self.device, weights_only=True)
model = self.accelerator.unwrap_model(self.imle_model)
model.load_state_dict(data['model'])
self.step = data['step']
self.opt.load_state_dict(data['opt'])
if self.accelerator.is_main_process:
# pass
self.ema.load_state_dict(data["ema"])
if 'version' in data:
print(f"loading from version {data['version']}")
if exists(self.accelerator.scaler) and exists(data['scaler']):
self.accelerator.scaler.load_state_dict(data['scaler'])
def train(self):
"""
Training loop
"""
# init
accelerator = self.accelerator
self.logger.info('training start')
iter_per_epoch = self.train_num_steps // self.cfg.OPTIMIZATION.NUM_EPOCHS
with tqdm(initial = self.step, total = self.train_num_steps, disable = not accelerator.is_main_process) as pbar:
while self.step < self.train_num_steps:
# init per-iteration variables
total_loss = 0.
self.imle_model.train()
self.ema.ema_model.train()
for _ in range(self.gradient_accumulate_every):
data = {k : v.to(self.device) for k, v in next(self.dl).items()}
log_dict = {'cur_epoch': self.step // iter_per_epoch}
if self.cfg.get('perturb_ctx', 0.0):
# used in SDD dataset
bs = data['past_traj'].shape[0]
scale_ = torch.randn((bs), device=self.device) * self.cfg.perturb_ctx + 1
data['past_traj_original_scale'] = data['past_traj_original_scale'] * scale_[:, None, None, None]
# compute the loss
with self.accelerator.autocast():
loss, loss_chamfer, loss_gt = self.imle_model(data)
loss = loss / self.gradient_accumulate_every
total_loss += loss.item()
self.accelerator.backward(loss)
# log to tensorboard
if self.tb_log is not None:
self.tb_log.add_scalar('train/loss_total', loss.item(), self.step)
self.tb_log.add_scalar('train/loss_chamfer', loss_chamfer.item(), self.step)
self.tb_log.add_scalar('train/loss_gt', loss_gt.item(), self.step)
self.tb_log.add_scalar('train/learning_rate', self.opt.param_groups[0]["lr"], self.step)
pbar.set_description(f'total loss: {total_loss:.4f}, chamfer loss: {loss_chamfer.item():.4f}, gt loss: {loss_gt.item():.4f}, lr: {self.opt.param_groups[0]["lr"]:.6f}')
accelerator.wait_for_everyone()
accelerator.clip_grad_norm_(self.imle_model.parameters(), self.cfg.OPTIMIZATION.GRAD_NORM_CLIP)
self.opt.step()
self.opt.zero_grad()
accelerator.wait_for_everyone()
if accelerator.is_main_process:
self.ema.update()
# checkpt test and save the best validation model
if (self.step + 1) >= self.save_and_sample_every and (self.step + 1) % self.save_and_sample_every == 0:
fut_traj_gt, performance, n_samples = self.eval_dataloader(testing_mode=False, training_err_check=False)
# update the best model
if performance['ADE_min'][3] < self.best_ade_min:
self.best_ade_min = performance['ADE_min'][3]
self.logger.info(f'Current best ADE_MIN: {self.best_ade_min/n_samples}')
self.save_ckpt('checkpoint_best')
# save the model and remove the old models
cur_epoch = self.step // iter_per_epoch
ckpt_list = glob(os.path.join(self.cfg.model_dir, 'checkpoint_epoch_*.pt*'))
ckpt_list.sort(key=os.path.getmtime)
if ckpt_list.__len__() >= self.cfg.max_num_ckpts:
for cur_file_idx in range(0, len(ckpt_list) - self.cfg.max_num_ckpts + 1):
os.remove(ckpt_list[cur_file_idx])
self.save_ckpt('checkpoint_epoch_%d' % cur_epoch)
self.step += 1
pbar.update(1)
self.scheduler.step()
# end of one training iteration
# end of training loop
self.save_last_ckpt()
self.logger.info('training complete')
def compute_ADE_FDE(self, distances, end_frame):
'''
Helper function to compute ADE and FDE
distances: [b*num_agents, k_preds, future_frames] or [b*num_agents, timestamps, k_preds, future_frames]
ade_frames: int
fde_frame: int
'''
ade_best = (distances[..., :end_frame]).mean(dim=-1).min(dim=-1).values.sum(dim=0)
fde_best = (distances[..., end_frame-1]).min(dim=-1).values.sum(dim=0)
ade_avg = (distances[..., :end_frame]).mean(dim=-1).mean(dim=-1).sum(dim=0)
fde_avg = (distances[..., end_frame-1]).mean(dim=-1).sum(dim=0)
return ade_best, fde_best, ade_avg, fde_avg
### TODO: add the eval of JADE/JFDE
### Based on https://arxiv.org/abs/2305.06292 Joint metric for ADE and FDE
def compute_JADE_JFDE(self, distances, end_frame):
'''
Helper function to compute JADE and JFDE
distances: [b*num_agents, k_preds, future_frames] or [b*num_agents, timestamps, k_preds, future_frames]
ade_frames: int
fde_frame: int
'''
jade_best = (distances[..., :end_frame]).mean(dim=-1).sum(dim=0).min(dim=-1).values
jfde_best = (distances[..., end_frame-1]).sum(dim=0).min(dim=-1).values
jade_avg = (distances[..., :end_frame]).mean(dim=-1).sum(dim=0).mean(dim=0)
jfde_avg = (distances[..., end_frame-1]).sum(dim=0).mean(dim=-1)
return jade_best, jfde_best, jade_avg, jfde_avg
def compute_avar_fvar(self, pred_trajs, end_frame):
'''
Helper function to compute AVar and FVar
distances: [b*num_agents, k_preds, future_frames] or [b*num_agents, timestamps, k_preds, future_frames]
ade_frames: int
fde_frame: int
'''
a_var = pred_trajs[..., :end_frame,:].var(dim=(1,3)).mean(dim=1).sum()
f_var = pred_trajs[..., end_frame-1,:].var(dim=(1,2)).sum()
return a_var, f_var
def compute_MASD(self, pred_trajs, end_frame):
'''
Helper function to compute MASD
predictions: [b*num_agents,k_preds, future_frames, dim]
ade_frames: int
fde_frame: int
'''
# Reshape for pairwise computation: (B, T, N, D)
predictions = pred_trajs[:, :, :end_frame, :].permute(0, 2, 1, 3) # Shape: (B, T, N, D)
# Compute pairwise L2 distances among N samples at each (B, T)
pairwise_distances = torch.cdist(predictions, predictions, p=2) # Shape: (B, T, N, N)
# Get the maximum squared distance among all pairs (excluding diagonal)
max_squared_distance = pairwise_distances.max(dim=-1)[0].max(dim=-1)[0] # Shape: (B, T)
# Compute the final MASD metric
masd = max_squared_distance.mean(dim=-1).sum()
return masd
@torch.no_grad()
def test(self, mode, eval_on_train=False):
# init
self.logger.info(f'testing start with the {mode} ckpt')
set_random_seed(42)
if mode == 'last':
ckpt_states = torch.load(os.path.join(self.cfg.model_dir, 'checkpoint_last.pt'), map_location=self.device, weights_only=True)
else:
ckpt_states = torch.load(os.path.join(self.cfg.model_dir, 'checkpoint_best.pt'), map_location=self.device, weights_only=True)
self.imle_model = self.accelerator.unwrap_model(self.imle_model)
self.imle_model.load_state_dict(ckpt_states['model'])
if self.accelerator.is_main_process:
self.ema.load_state_dict(ckpt_states["ema"])
# testing_mode=False, training_err_check=False
if eval_on_train:
fut_traj_gt, _, _ = self.eval_dataloader(training_err_check=True)
else:
fut_traj_gt, _, _ = self.eval_dataloader(testing_mode=True)
self.logger.info(f'testing complete with the {mode} ckpt')
def sample_from_imle(self, data):
"""
Return the samples from denoising model in normal scale
"""
pred_traj = self.imle_model(data, num_to_gen=1)
pred_traj = pred_traj.squeeze(1)
if self.cfg.dataset == 'nba':
assert list(pred_traj.shape[2:]) == [self.cfg.agents, 40]
elif self.cfg.dataset in ['eth_ucy', 'sdd']:
assert list(pred_traj.shape[2:]) == [self.cfg.agents, 24]
pred_traj = rearrange(pred_traj, 'b k a (f d) -> (b a) k f d', f=self.cfg.future_frames)[...,0:2] # [B, k_preds, 11, 40] -> [B * 11, k_preds, 20, 2]
if self.cfg.get('data_norm', None) == 'min_max':
pred_traj = unnormalize_min_max(pred_traj, self.cfg.fut_traj_min, self.cfg.fut_traj_max, -1, 1)
elif self.cfg.get('data_norm', None) == 'sqrt':
pred_traj = unnormalize_sqrt(pred_traj, self.sqrt_a_, self.sqrt_b_)
return pred_traj
def save_latent_states(self, y_pred_data_ls, x_data_ls, file_name):
self.logger.info("Begin to save the denoising samples...")
if self.cfg.dataset in ['nba', 'sdd', 'eth_ucy']:
keys_to_save = ['past_traj', 'fut_traj', 'past_traj_original_scale', 'fut_traj_original_scale', 'fut_traj_vel']
else:
raise NotImplementedError(f'Dataset [{self.cfg.dataset}] is not implemented yet.')
states_to_save = {k: [] for k in keys_to_save}
states_to_save['y_pred_data'] = []
for i_batch, (y_pred_data, x_data) in enumerate(zip(y_pred_data_ls, x_data_ls)):
y_pred_data = y_pred_data.detach().cpu().numpy()
states_to_save['y_pred_data'].append(y_pred_data)
for key in keys_to_save:
x_data_val_ = x_data[key].detach().cpu().numpy()
assert len(y_pred_data) == len(x_data_val_)
states_to_save[key].append(x_data_val_)
for key in states_to_save:
states_to_save[key] = np.concatenate(states_to_save[key], axis=0)
# clean up the cfg and remove any path related fields
cfg_ = copy.deepcopy(self.cfg.yml_dict)
def _remove_path_fields(cfg):
for k in list(cfg.keys()):
if 'path' in k or 'dir' in k:
cfg.pop(k)
elif isinstance(cfg[k], dict):
_remove_path_fields(cfg[k])
else:
try:
if os.path.isdir(cfg[k]) or os.path.isfile(cfg[k]):
cfg.pop(k)
except:
pass
_remove_path_fields(cfg_)
num_datapoints = len(states_to_save['y_pred_data'])
meta_data = {'cfg': cfg_, 'size': num_datapoints}
states_to_save['meta_data'] = meta_data
# save_path = os.path.join(self.cfg.sample_dir, f'{file_name}.npz')
# np.savez_compressed(save_path, **states_to_save)
save_path = os.path.join(self.cfg.sample_dir, f'{file_name}.pkl')
self.logger.info("Saving the IMLE samples to {}".format(save_path))
pickle.dump(states_to_save, open(save_path, 'wb'))
def eval_dataloader(self, testing_mode=False, training_err_check=False):
"""
General API to evaluate the dataloader/dataset
"""
### turn on the eval mode
self.imle_model.eval()
self.ema.ema_model.eval()
self.logger.info(f'Record the statistics of samples from the denoising model')
if testing_mode:
self.logger.info(f'Start recording test set ADE/FDE...')
status = 'test'
dl = self.test_loader
elif training_err_check:
self.logger.info(f'Start recording training set ADE/FDE...')
status = 'train'
dl = self.train_loader
else:
self.logger.info(f'Start recording validation set ADE/FDE...')
status = 'val'
dl = self.val_loader
### setup the performance dict
performance = {'FDE_min': [0,0,0,0], 'ADE_min': [0,0,0,0], 'FDE_avg': [0,0,0,0], 'ADE_avg': [0,0,0,0], 'A_var': [0,0,0,0], 'F_var': [0,0,0,0], 'MASD': [0,0,0,0]}
performance_joint = {'JFDE_min': [0,0,0,0], 'JADE_min': [0,0,0,0], 'JFDE_avg': [0,0,0,0], 'JADE_avg': [0,0,0,0]}
num_trajs = 0
### record running time
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
for i_batch, data in enumerate(dl):
bs = int(data['batch_size'])
data = {k : v.to(self.device) for k, v in data.items()}
pred_traj = self.sample_from_imle(data)
fut_traj = rearrange(data['fut_traj_original_scale'], 'b a f d -> (b a) f d') # [B, A, T, F] -> [B * A, T, F]
fut_traj_gt = fut_traj.unsqueeze(1).repeat(1, self.cfg.denoising_head_preds, 1, 1) # [B * A, K, T, F]
distances = (fut_traj_gt - pred_traj).norm(p=2, dim=-1) # [B * A, K, T]
if self.cfg.dataset == 'nba':
freq = 5
factor_time = 1
elif self.cfg.dataset == 'eth_ucy':
freq = 3
factor_time = 1.2
elif self.cfg.dataset == 'sdd':
freq = 3
factor_time = 1.2
for time in range(1, 5):
ade, fde, ade_avg, fde_avg = self.compute_ADE_FDE(distances, int(time * freq))
jade, jfde, jade_avg, jfde_avg = self.compute_JADE_JFDE(distances, int(time * freq))
a_var, f_var = self.compute_avar_fvar(pred_traj, int(time * freq))
masd = self.compute_MASD(pred_traj, int(time * freq))
performance_joint['JADE_min'][time - 1] += jade.item()
performance_joint['JFDE_min'][time - 1] += jfde.item()
performance_joint['JADE_avg'][time - 1] += jade_avg.item()
performance_joint['JFDE_avg'][time - 1] += jfde_avg.item()
performance['ADE_min'][time - 1] += ade.item()
performance['FDE_min'][time - 1] += fde.item()
performance['ADE_avg'][time - 1] += ade_avg.item()
performance['FDE_avg'][time - 1] += fde_avg.item()
performance['A_var'][time - 1] += a_var.item()
performance['F_var'][time - 1] += f_var.item()
performance['MASD'][time - 1] += masd.item()
num_trajs += fut_traj.shape[0]
# save the imle samples
if self.save_samples:
pred_traj = rearrange(pred_traj, '(b a) k f d -> b k a f d', b=bs) # [B, K, A, T, F]
num_datapoints = len(pred_traj)
y_pred_data_ls = [pred_traj]
x_data_ls = [data]
solver_tag = self.cfg.get('solver_tag', '')
save_name = f'imle_samples_{status}_batch_{i_batch}_{num_datapoints}_{solver_tag}'
self.save_latent_states(y_pred_data_ls, x_data_ls, save_name)
y_pred_data_ls, x_data_ls = [], []
end.record()
torch.cuda.synchronize()
self.logger.info(f'Time elapsed: {start.elapsed_time(end):.5f} ms')
self.logger.info(f'Time elapsed per scene: {start.elapsed_time(end)/len(dl.dataset):.5f} ms')
self.logger.info(f'Number of scenes: {len(dl.dataset)}')
cur_epoch = self.step // (self.train_num_steps // self.cfg.OPTIMIZATION.NUM_EPOCHS)
if not testing_mode:
self.logger.info(f'{self.step}/{self.train_num_steps}, running inference on {num_trajs} agents (trajectories)')
for time in range(4):
if self.tb_log:
self.tb_log.add_scalar(f'eval_{status}/ADE_min_{(time+1)*factor_time:.1f}s', performance['ADE_min'][time]/num_trajs, cur_epoch)
self.tb_log.add_scalar(f'eval_{status}/FDE_min_{(time+1)*factor_time:.1f}s', performance['FDE_min'][time]/num_trajs, cur_epoch)
self.tb_log.add_scalar(f'eval_{status}/ADE_avg_{(time+1)*factor_time:.1f}s', performance['ADE_avg'][time]/num_trajs, cur_epoch)
self.tb_log.add_scalar(f'eval_{status}/FDE_avg_{(time+1)*factor_time:.1f}s', performance['FDE_avg'][time]/num_trajs, cur_epoch)
self.tb_log.add_scalar(f'eval_{status}/JADE_min_{(time+1)*factor_time:.1f}s', performance_joint['JADE_min'][time]/num_trajs, cur_epoch)
self.tb_log.add_scalar(f'eval_{status}/JFDE_min_{(time+1)*factor_time:.1f}s', performance_joint['JFDE_min'][time]/num_trajs, cur_epoch)
# print out the performance
for time in range(4):
self.logger.info('--ADE_min({:.1f}s): {:.7f}\t--FDE_min({:.1f}s): {:.7f}'.format(
time+1, performance['ADE_min'][time]/num_trajs, (time+1)*factor_time, performance['FDE_min'][time]/num_trajs))
for time in range(4):
self.logger.info('--ADE_avg({:.1f}s): {:.7f}\t--FDE_avg({:.1f}s): {:.7f}'.format(
time+1, performance['ADE_avg'][time]/num_trajs, (time+1)*factor_time, performance['FDE_avg'][time]/num_trajs))
for time in range(4):
self.logger.info('--AVar({:.1f}s): {:.7f}\t--FVar({:.1f}s): {:.7f}'.format(
time+1, performance['A_var'][time]/num_trajs, time+1, performance['F_var'][time]/num_trajs))
for time in range(4):
self.logger.info('--MASD({:.1f}s): {:.7f}'.format(
time+1, performance['MASD'][time]/num_trajs))
# print out the joint performance
for time in range(4):
self.logger.info('--JADE_min({:.1f}s): {:.7f}\t--JFDE_min({:.1f}s): {:.7f}'.format(
time+1, performance_joint['JADE_min'][time]/num_trajs, (time+1)*factor_time, performance_joint['JFDE_min'][time]/num_trajs))
for time in range(4):
self.logger.info('--JADE_avg({:.1f}s): {:.7f}\t--JFDE_avg({:.1f}s): {:.7f}'.format(
time+1, performance_joint['JADE_avg'][time]/num_trajs, (time+1)*factor_time, performance_joint['JFDE_avg'][time]/num_trajs))
return fut_traj_gt, performance, num_trajs
|