def evaluate(data_loader, model_pos, device):
batch_time = AverageMeter()
data_time = AverageMeter()
epoch_loss_3d_pos = AverageMeter()
epoch_loss_3d_pos_procrustes = AverageMeter()
# Switch to evaluate mode
torch.set_grad_enabled(False)
model_pos.eval()
end = time.time()
bar = Bar('Eval ', max=len(data_loader))
for i, (targets_3d, inputs_2d, _) in enumerate(data_loader):
# Measure data loading time
data_time.update(time.time() - end)
num_poses = targets_3d.size(0)
inputs_2d = inputs_2d.to(device)
outputs_3d = model_pos(inputs_2d.view(num_poses,
-1)).view(num_poses, -1,
3).cpu()
outputs_3d = torch.cat(
[torch.zeros(num_poses, 1, outputs_3d.size(2)), outputs_3d],
1) # Pad hip joint (0,0,0)
epoch_loss_3d_pos.update(
mpjpe(outputs_3d, targets_3d).item() * 1000.0, num_poses)
epoch_loss_3d_pos_procrustes.update(
p_mpjpe(outputs_3d.numpy(), targets_3d.numpy()).item() * 1000.0,
num_poses)
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
bar.suffix = '({batch}/{size}) Data: {data:.6f}s | Batch: {bt:.3f}s | Total: {ttl:} | ETA: {eta:} ' \
'| MPJPE: {e1: .4f} | P-MPJPE: {e2: .4f}' \
.format(batch=i + 1, size=len(data_loader), data=data_time.avg, bt=batch_time.avg,
ttl=bar.elapsed_td, eta=bar.eta_td, e1=epoch_loss_3d_pos.avg, e2=epoch_loss_3d_pos_procrustes.avg)
bar.next()
bar.finish()
return epoch_loss_3d_pos.avg, epoch_loss_3d_pos_procrustes.avg
def evaluate(data_loader, model_pos, device):
batch_time = AverageMeter()
data_time = AverageMeter()
epoch_loss_3d_pos = AverageMeter()
epoch_loss_3d_pos_procrustes = AverageMeter()
# Switch to evaluate mode
torch.set_grad_enabled(False)
model_pos.eval()
end = time.time()
bar = Bar('Eval ', max=len(data_loader))
for i, (targets_3d, inputs_2d, feature_mutual) in enumerate(data_loader):
# Measure data loading time
data_time.update(time.time() - end)
num_poses = targets_3d.size(1)
inputs_2d, feature_mutual = inputs_2d.to(device), feature_mutual.to(
device)
outputs_3d = model_pos([inputs_2d, feature_mutual]).cpu()
#outputs_3d[:, :, :] -= outputs_3d[:, :1, :] # Zero-centre the root (hip)
epoch_loss_3d_pos.update(
mpjpe(outputs_3d, targets_3d).item() * 1000.0, num_poses)
epoch_loss_3d_pos_procrustes.update(
p_mpjpe(outputs_3d.numpy(), targets_3d.numpy()).item() * 1000.0,
num_poses)
# Measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
bar.suffix = '({batch}/{size}) Data: {data:.6f}s | Batch: {bt:.3f}s | Total: {ttl:} | ETA: {eta:} ' \
'| MPJPE: {e1: .4f} | P-MPJPE: {e2: .4f}' \
.format(batch=i + 1, size=len(data_loader), data=data_time.val, bt=batch_time.avg,
ttl=bar.elapsed_td, eta=bar.eta_td, e1=epoch_loss_3d_pos.avg, e2=epoch_loss_3d_pos_procrustes.avg)
bar.next()
bar.finish()
return epoch_loss_3d_pos.avg, epoch_loss_3d_pos_procrustes.avg
def evaluate(test_generator, action=None, return_predictions=False):
epoch_loss_3d_pos = 0
epoch_loss_3d_pos_procrustes = 0
epoch_loss_3d_pos_scale = 0
epoch_loss_3d_vel = 0
with torch.no_grad():
model_pos.eval()
N = 0
for _, batch, batch_2d in test_generator.next_epoch():
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_2d = inputs_2d.cuda()
# Positional model
predicted_3d_pos = model_pos(inputs_2d)
# Test-time augmentation (if enabled)
if test_generator.augment_enabled():
# Undo flipping and take average with non-flipped version
predicted_3d_pos[1, :, :, 0] *= -1
predicted_3d_pos[1, :, joints_left +
joints_right] = predicted_3d_pos[
1, :, joints_right + joints_left]
predicted_3d_pos = torch.mean(predicted_3d_pos,
dim=0,
keepdim=True)
if return_predictions:
return predicted_3d_pos.squeeze(0).cpu().numpy()
inputs_3d = torch.from_numpy(batch.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_3d[:, :, 0] = 0
if test_generator.augment_enabled():
inputs_3d = inputs_3d[:1]
error = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_pos_scale += inputs_3d.shape[0] * inputs_3d.shape[
1] * n_mpjpe(predicted_3d_pos, inputs_3d).item()
epoch_loss_3d_pos += inputs_3d.shape[0] * inputs_3d.shape[
1] * error.item()
N += inputs_3d.shape[0] * inputs_3d.shape[1]
inputs = inputs_3d.cpu().numpy().reshape(-1, inputs_3d.shape[-2],
inputs_3d.shape[-1])
predicted_3d_pos = predicted_3d_pos.cpu().numpy().reshape(
-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
epoch_loss_3d_pos_procrustes += inputs_3d.shape[
0] * inputs_3d.shape[1] * p_mpjpe(predicted_3d_pos, inputs)
# Compute velocity error
epoch_loss_3d_vel += inputs_3d.shape[0] * inputs_3d.shape[
1] * mean_velocity_error(predicted_3d_pos, inputs)
if action is None:
print('----------')
else:
print('----' + action + '----')
e1 = (epoch_loss_3d_pos / N) * 1000
e2 = (epoch_loss_3d_pos_procrustes / N) * 1000
e3 = (epoch_loss_3d_pos_scale / N) * 1000
ev = (epoch_loss_3d_vel / N) * 1000
print('Test time augmentation:', test_generator.augment_enabled())
print('Protocol #1 Error (MPJPE):', e1, 'mm')
print('Protocol #2 Error (P-MPJPE):', e2, 'mm')
print('Protocol #3 Error (N-MPJPE):', e3, 'mm')
print('Velocity Error (MPJVE):', ev, 'mm')
print('----------')
return e1, e2, e3, ev
