def evaluate(args, loader, generator, num_samples):
ade_outer, fde_outer = [], []
total_traj = 0
with torch.no_grad():
for batch in loader:
batch = [tensor.cuda() for tensor in batch]
(obs_traj, pred_traj_gt, obs_traj_rel, pred_traj_gt_rel,
non_linear_ped, loss_mask, seq_start_end) = batch
ade, fde = [], []
total_traj += pred_traj_gt.size(1)
for _ in range(num_samples):
pred_traj_fake_rel = generator(
obs_traj, obs_traj_rel, seq_start_end
)
pred_traj_fake = relative_to_abs(
pred_traj_fake_rel, obs_traj[-1]
)
ade.append(displacement_error(
pred_traj_fake, pred_traj_gt, mode='raw'
))
fde.append(final_displacement_error(
pred_traj_fake[-1], pred_traj_gt[-1], mode='raw'
))
ade_sum = evaluate_helper(ade, seq_start_end)
fde_sum = evaluate_helper(fde, seq_start_end)
ade_outer.append(ade_sum)
fde_outer.append(fde_sum)
ade = sum(ade_outer) / (total_traj * args.pred_len)
fde = sum(fde_outer) / (total_traj)
return ade, fde
def evaluate(args, loader, generator, num_samples):
ade_outer, fde_outer = [], []
total_traj = 0
trajs = []
with torch.no_grad():
for batch in loader:
# batch = [tensor.cuda() for tensor in batch]
(obs_traj, pred_traj_gt, obs_traj_rel, pred_traj_gt_rel,
non_linear_ped, loss_mask, seq_start_end) = batch
obs_traj = obs_traj.float()
obs_traj_rel = obs_traj_rel.float()
pred_traj_gt = pred_traj_gt.double()
ade, fde = [], []
total_traj += pred_traj_gt.size(1)
for _ in range(num_samples):
pred_traj_fake_rel = generator(obs_traj, obs_traj_rel,
seq_start_end)
pred_traj_fake = relative_to_abs(pred_traj_fake_rel,
obs_traj[-1])
pred_traj_fake = pred_traj_fake.double()
trajs.append([
obs_traj.cpu().numpy(),
pred_traj_fake.cpu().numpy(),
pred_traj_gt.cpu().numpy(),
seq_start_end.cpu().numpy()
])
ade_traj = displacement_error(pred_traj_fake,
pred_traj_gt,
mode='sum')
fde_traj = final_displacement_error(pred_traj_fake[-1],
pred_traj_gt[-1],
mode='sum')
# ade.append(displacement_error(
# pred_traj_fake, pred_traj_gt, mode='sum'
# ))
# fde.append(final_displacement_error(
# pred_traj_fake[-1], pred_traj_gt[-1], mode='sum'
# ))
# ade_sum = evaluate_helper(ade, seq_start_end)
# fde_sum = evaluate_helper(fde, seq_start_end)
# ade_outer.append(ade_sum)
# fde_outer.append(fde_sum)
ade_outer.append(ade_traj)
fde_outer.append(fde_traj)
ade = sum(ade_outer) / (total_traj * args.pred_len)
fde = sum(fde_outer) / (total_traj)
return ade, fde, trajs
def cal_ade(pred_traj_gt, pred_traj_fake, linear_ped, non_linear_ped):
ade = displacement_error(pred_traj_fake, pred_traj_gt)
ade_l = displacement_error(pred_traj_fake, pred_traj_gt, linear_ped)
ade_nl = displacement_error(pred_traj_fake, pred_traj_gt, non_linear_ped)
return ade, ade_l, ade_nl
def cal_ade(pred_traj_gt, pred_traj_fake):
ade = displacement_error(pred_traj_fake, pred_traj_gt)
return ade