def test(args, epch, test_loader, model, warmup, writer):
test_loss = 0
kld_loss = 0
nll_loss = 0
total_traj = 0
ade = 0
fde = 0
model.eval()
with torch.no_grad():
for batch_idx, data in enumerate(test_loader):
data = [tensor.cuda() for tensor in data]
(obs_traj, pred_traj_gt, obs_traj_rel, pred_traj_gt_rel,
non_linear_ped, loss_mask, seq_start_end) = data
kld, nll, h = model(obs_traj_rel)
loss = (warmup[epch - 1] * kld) + nll
test_loss += loss.item()
kld_loss += kld.item()
nll_loss += nll.item()
if batch_idx % args.print_every_batch == 0:
writer.add_scalar('test/loss_items', loss.item(),
epch) if not DEBUG else None
# predict trajectories from latest h
samples_rel = model.sample(args.pred_len, h)
samples = relative_to_abs(samples_rel, obs_traj[-1])
total_traj += samples.shape[1] # num_seqs
ade += average_displacement_error(samples, pred_traj_gt)
fde += final_displacement_error(samples[-1, :, :],
pred_traj_gt[-1, :, :])
mean_loss = loss / len(test_loader.dataset)
writer.add_scalar('test/Loss', mean_loss, epch) if not DEBUG else None
mean_kld_loss = kld_loss / len(test_loader.dataset)
mean_nll_loss = nll_loss / len(test_loader.dataset)
writer.add_scalar('test/KLD_loss', mean_kld_loss,
epch) if not DEBUG else None
writer.add_scalar('test/NLL_loss', mean_nll_loss,
epch) if not DEBUG else None
# ADE
ade_val = ade / (total_traj * args.pred_len)
writer.add_scalar('test/ADE', ade_val, epch) if not DEBUG else None
# FDE
fde_val = fde / total_traj
writer.add_scalar('test/FDE', fde_val, epch) if not DEBUG else None
# plotting
if DEBUG:
obs = obs_traj.cpu().numpy()
pred = samples.cpu().numpy()
pred_gt = pred_traj_gt.cpu().numpy()
plot_traj(obs, pred_gt, pred, seq_start_end, writer, epch)
def test(args, epch, test_loader, model, warmup, writer):
test_loss = 0
kld_loss = 0
nll_loss = 0
cross_entropy_loss = 0
total_traj = 0
ade = 0
fde = 0
model.eval()
with torch.no_grad():
for batch_idx, data in enumerate(test_loader):
data = [tensor.cuda() for tensor in data]
(obs_traj, pred_traj_gt, obs_traj_rel, pred_traj_rel_gt,
obs_goals, pred_goals_gt, seq_start_end) = data
# goals one-hot encoding
obs_goals_ohe = to_goals_one_hot(obs_goals, args.g_dim).cuda()
# adj matrix for current batch
if args.adjacency_type == 0:
adj_out = compute_adjs(args, seq_start_end).cuda()
elif args.adjacency_type == 1:
adj_out = compute_adjs_distsim(args, seq_start_end, obs_traj.detach().cpu(),
pred_traj_gt.detach().cpu()).cuda()
elif args.adjacency_type == 2:
adj_out = compute_adjs_knnsim(args, seq_start_end, obs_traj.detach().cpu(),
pred_traj_gt.detach().cpu()).cuda()
kld, nll, ce, h = model(obs_traj, obs_traj_rel, obs_goals_ohe, seq_start_end, adj_out)
loss = nll + (warmup[epch - 1] * kld) + (ce * args.CE_weight)
test_loss += loss.item()
kld_loss += kld.item()
nll_loss += nll.item()
cross_entropy_loss += ce.item()
if batch_idx % args.print_every_batch == 0:
writer.add_scalar('test/loss_items', loss.item(), epch) if not DEBUG else None
# predict trajectories from latest h; samples_rel shape=(pred_seq_len, n_agents, batch, xy)
samples_rel = model.sample(args.pred_len, h, obs_traj[-1], obs_goals_ohe[-1], seq_start_end)
samples = relative_to_abs(samples_rel, obs_traj[-1])
total_traj += samples.shape[1] # num_seqs
ade += average_displacement_error(samples, pred_traj_gt)
fde += final_displacement_error(samples[-1, :, :], pred_traj_gt[-1, :, :])
mean_loss = loss / len(test_loader.dataset)
writer.add_scalar('test/Loss', mean_loss, epch) if not DEBUG else None
mean_kld_loss = kld_loss / len(test_loader.dataset)
mean_nll_loss = nll_loss / len(test_loader.dataset)
mean_cross_entropy_loss = cross_entropy_loss / len(test_loader.dataset)
writer.add_scalar('test/KLD_loss', mean_kld_loss, epch) if not DEBUG else None
writer.add_scalar('test/NLL_loss', mean_nll_loss, epch) if not DEBUG else None
writer.add_scalar('test/CE_loss', mean_cross_entropy_loss, epch) if not DEBUG else None
# ADE
ade_val = ade / (total_traj * args.pred_len)
writer.add_scalar('test/ADE', ade_val, epch) if not DEBUG else None
# FDE
fde_val = fde / total_traj
writer.add_scalar('test/FDE', fde_val, epch) if not DEBUG else None
# plotting
if not DEBUG:
obs = obs_traj.cpu().numpy()
pred = samples.cpu().numpy()
pred_gt = pred_traj_gt.cpu().numpy()
plot_traj(obs, pred_gt, pred, seq_start_end, writer, epch)