# Dataset if cfg['data']['input_type'] == 'img': cfg['model']['encoder_kwargs'].update({'pretrained': ''}) if args.subject_idx >= 0 and args.sequence_idx >= 0: dataset = config.get_dataset('test', cfg, subject_idx=args.subject_idx, sequence_idx=args.sequence_idx) else: dataset = config.get_dataset('test', cfg) # Model model = config.get_model(cfg, device=device, dataset=dataset) checkpoint_io = CheckpointIO(out_dir, model=model) checkpoint_io.load(cfg['test']['model_file']) # Generator generator = config.get_generator(model, cfg, device=device) # Determine what to generate generate_mesh = cfg['generation']['generate_mesh'] generate_pointcloud = cfg['generation']['generate_pointcloud'] if generate_mesh and not hasattr(generator, 'generate_mesh'): generate_mesh = False print('Warning: generator does not support mesh generation.') if generate_pointcloud and not hasattr(generator, 'generate_pointcloud'): generate_pointcloud = False
batch_size=12, shuffle=True, collate_fn=data.collate_remove_none, worker_init_fn=data.worker_init_fn) data_vis = next(iter(vis_loader)) # Model model = config.get_model(cfg, device=device, dataset=train_dataset) # Intialize training npoints = 1000 optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) # optimizer = optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) trainer = config.get_trainer(model, optimizer, cfg, device=device) checkpoint_io = CheckpointIO(out_dir, model=model, optimizer=optimizer) try: load_dict = checkpoint_io.load('model.pt') except FileExistsError: load_dict = dict() epoch_it = load_dict.get('epoch_it', -1) it = load_dict.get('it', -1) metric_val_best = load_dict.get('loss_val_best', -model_selection_sign * np.inf) # Hack because of previous bug in code # TODO: remove, because shouldn't be necessary if metric_val_best == np.inf or metric_val_best == -np.inf: metric_val_best = -model_selection_sign * np.inf # TODO: remove this switch
def run(pointcloud_path, out_dir, decoder_type='siren', resume=True, **kwargs): """ test_implicit_siren_noisy_wNormals """ device = torch.device('cuda:0') if not os.path.exists(out_dir): os.makedirs(out_dir) # data points, normals = np.split( read_ply(pointcloud_path).astype('float32'), (3,), axis=1) pmax, pmin = points.max(axis=0), points.min(axis=0) scale = (pmax - pmin).max() pcenter = (pmax + pmin) /2 points = (points - pcenter) / scale * 1.5 scale_mat = scale_mat_inv = np.identity(4) scale_mat[[0,1,2], [0,1,2]] = 1/scale * 1.5 scale_mat[[0,1,2], [3,3,3]] = - pcenter / scale * 1.5 scale_mat_inv = np.linalg.inv(scale_mat) normals = normals @ np.linalg.inv(scale_mat[:3, :3].T) object_bounding_sphere = np.linalg.norm(points, axis=1).max() pcl = trimesh.Trimesh( vertices=points, vertex_normals=normals, process=False) pcl.export(os.path.join(out_dir, "input_pcl.ply"), vertex_normal=True) assert(np.abs(points).max() < 1) dataset = torch.utils.data.TensorDataset( torch.from_numpy(points), torch.from_numpy(normals)) data_loader = torch.utils.data.DataLoader( dataset, batch_size=6000, num_workers=1, shuffle=True, collate_fn=tolerating_collate, ) gt_surface_pts_all = torch.from_numpy(points).unsqueeze(0).float() gt_surface_normals_all = torch.from_numpy(normals).unsqueeze(0).float() gt_surface_normals_all = F.normalize(gt_surface_normals_all, dim=-1) if kwargs['use_off_normal_loss']: # subsample from pointset sub_idx = torch.randperm(gt_surface_normals_all.shape[1])[:20000] gt_surface_pts_sub = torch.index_select(gt_surface_pts_all, 1, sub_idx).to(device=device) gt_surface_normals_sub = torch.index_select(gt_surface_normals_all, 1, sub_idx).to(device=device) from DSS.core.cloud import denoise_normals gt_surface_normals_sub = denoise_normals(gt_surface_pts_sub, gt_surface_normals_sub, neighborhood_size=30) if decoder_type == 'siren': decoder_params = { 'dim': 3, "out_dims": {'sdf': 1}, "c_dim": 0, "hidden_size": 256, 'n_layers': 3, "first_omega_0": 30, "hidden_omega_0": 30, "outermost_linear": True, } decoder = Siren(**decoder_params) # pretrained_model_file = os.path.join('data', 'trained_model', 'siren_l{}_c{}_o{}.pt'.format( # decoder_params['n_layers'], decoder_params['hidden_size'], decoder_params['first_omega_0'])) # loaded_state_dict = torch.load(pretrained_model_file) # decoder.load_state_dict(loaded_state_dict) elif decoder_type == 'sdf': decoder_params = { 'dim': 3, "out_dims": {'sdf': 1}, "c_dim": 0, "hidden_size": 512, 'n_layers': 8, 'bias': 1.0, } decoder = SDF(**decoder_params) else: raise ValueError print(decoder) decoder = decoder.to(device) # training total_iter = 30000 optimizer = torch.optim.Adam(decoder.parameters(), lr=1e-4) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [10000, 20000], gamma=0.5) shape = Shape(gt_surface_pts_all.cuda(), n_points=gt_surface_pts_all.shape[1]//8, normals=gt_surface_normals_all.cuda()) # initialize siren with sphere_initialization checkpoint_io = CheckpointIO(out_dir, model=decoder, optimizer=optimizer) load_dict = dict() if resume: models_avail = [f for f in os.listdir(out_dir) if f[-3:] == '.pt'] if len(models_avail) > 0: models_avail.sort() load_dict = checkpoint_io.load(models_avail[-1]) it = load_dict.get('it', 0) if it > 0: try: iso_point_files = [f for f in os.listdir(out_dir) if f[-7:] == 'iso.ply'] iso_point_iters = [int(os.path.basename(f[:-len('_iso.ply')])) for f in iso_point_files] iso_point_iters = np.array(iso_point_iters) idx = np.argmax(iso_point_iters[(iso_point_iters - it)<=0]) iso_point_file = np.array(iso_point_files)[(iso_point_iters - it)<=0][idx] iso_points = torch.from_numpy(read_ply(os.path.join(out_dir, iso_point_file))[...,:3]) shape.points = iso_points.to(device=shape.points.device).view(1, -1 ,3) print('Loaded iso-points from %s' % iso_point_file) except Exception as e: pass # loss eikonal_loss = NormalLengthLoss(reduction='mean') # start training # save_ply(os.path.join(out_dir, 'in_iso_points.ply'), (to_homogen(shape.points).cpu().detach().numpy() @ scale_mat_inv.T)[...,:3].reshape(-1,3)) save_ply(os.path.join(out_dir, 'in_iso_points.ply'), shape.points.cpu().view(-1,3)) # autograd.set_detect_anomaly(True) iso_points = shape.points iso_points_normal = None while True: if (it > total_iter): checkpoint_io.save('model_{:04d}.pt'.format(it), it=it) mesh = get_surface_high_res_mesh( lambda x: decoder(x).sdf.squeeze(), resolution=512) mesh.apply_transform(scale_mat_inv) mesh.export(os.path.join(out_dir, "final.ply")) break for batch in data_loader: gt_surface_pts, gt_surface_normals = batch gt_surface_pts.unsqueeze_(0) gt_surface_normals.unsqueeze_(0) gt_surface_pts = gt_surface_pts.to(device=device).detach() gt_surface_normals = gt_surface_normals.to( device=device).detach() optimizer.zero_grad() decoder.train() loss = defaultdict(float) lambda_surface_sdf = 1e3 lambda_surface_normal = 1e2 if kwargs['warm_up'] >= 0 and it >= kwargs['warm_up']: lambda_surface_sdf = kwargs['lambda_surface_sdf'] lambda_surface_normal = kwargs['lambda_surface_normal'] # debug if (it - kwargs['warm_up']) % 1000 == 0: # generate iso surface with torch.autograd.no_grad(): box_size = (object_bounding_sphere * 2 + 0.2, ) * 3 imgs = plot_cuts(lambda x: decoder(x).sdf.squeeze().detach(), box_size=box_size, max_n_eval_pts=10000, thres=0.0, imgs_per_cut=1, save_path=os.path.join(out_dir, '%010d_iso.html' % it)) mesh = get_surface_high_res_mesh( lambda x: decoder(x).sdf.squeeze(), resolution=200) mesh.apply_transform(scale_mat_inv) mesh.export(os.path.join(out_dir, '%010d_mesh.ply' % it)) if it % 2000 == 0: checkpoint_io.save('model.pt', it=it) pred_surface_grad = gradient(gt_surface_pts.clone(), lambda x: decoder(x).sdf) # every once in a while update shape and points # sample points in space and on the shape # use iso points to weigh data points loss weights = 1.0 if kwargs['warm_up'] >= 0 and it >= kwargs['warm_up']: if it == kwargs['warm_up'] or kwargs['resample_every'] > 0 and (it - kwargs['warm_up']) % kwargs['resample_every'] == 0: # if shape.points.shape[1]/iso_points.shape[1] < 1.0: # idx = fps(iso_points.view(-1,3), torch.zeros(iso_points.shape[1], dtype=torch.long, device=iso_points.device), shape.points.shape[1]/iso_points.shape[1]) # iso_points = iso_points.view(-1,3)[idx].view(1,-1,3) iso_points = shape.get_iso_points(iso_points+0.1*(torch.rand_like(iso_points)-0.5), decoder, ear=kwargs['ear'], outlier_tolerance=kwargs['outlier_tolerance']) # iso_points = shape.get_iso_points(shape.points, decoder, ear=kwargs['ear'], outlier_tolerance=kwargs['outlier_tolerance']) iso_points_normal = estimate_pointcloud_normals(iso_points.view(1,-1,3), 8, False) if kwargs['denoise_normal']: iso_points_normal = denoise_normals(iso_points, iso_points_normal, num_points=None) iso_points_normal = iso_points_normal.view_as(iso_points) elif iso_points_normal is None: iso_points_normal = estimate_pointcloud_normals(iso_points.view(1,-1,3), 8, False) # iso_points = resample_uniformly(iso_points.view(1,-1,3)) # TODO: use gradient from network or neighborhood? iso_points_g = gradient(iso_points.clone(), lambda x: decoder(x).sdf) if it == kwargs['warm_up'] or kwargs['resample_every'] > 0 and (it - kwargs['warm_up']) % kwargs['resample_every'] == 0: # save_ply(os.path.join(out_dir, '%010d_iso.ply' % it), (to_homogen(iso_points).cpu().detach().numpy() @ scale_mat_inv.T)[...,:3].reshape(-1,3), normals=iso_points_g.view(-1,3).detach().cpu()) save_ply(os.path.join(out_dir, '%010d_iso.ply' % it), iso_points.cpu().detach().view(-1,3), normals=iso_points_g.view(-1,3).detach().cpu()) if kwargs['weight_mode'] == 1: weights = get_iso_bilateral_weights(gt_surface_pts, gt_surface_normals, iso_points, iso_points_g).detach() elif kwargs['weight_mode'] == 2: weights = get_laplacian_weights(gt_surface_pts, gt_surface_normals, iso_points, iso_points_g).detach() elif kwargs['weight_mode'] == 3: weights = get_heat_kernel_weights(gt_surface_pts, gt_surface_normals, iso_points, iso_points_g).detach() if (it - kwargs['warm_up']) % 1000 == 0 and kwargs['weight_mode'] != -1: print("min {:.4g}, max {:.4g}, std {:.4g}, mean {:.4g}".format(weights.min(), weights.max(), weights.std(), weights.mean())) colors = scaler_to_color(1-weights.view(-1).cpu().numpy(), cmap='Reds') save_ply(os.path.join(out_dir, '%010d_batch_weight.ply' % it), (to_homogen(gt_surface_pts).cpu().detach().numpy() @ scale_mat_inv.T)[...,:3].reshape(-1,3), colors=colors) sample_idx = torch.randperm(iso_points.shape[1])[:min(10000, iso_points.shape[1])] iso_points_sampled = iso_points.detach()[:, sample_idx, :] # iso_points_sampled = iso_points.detach() iso_points_sdf = decoder(iso_points_sampled.detach()).sdf loss_iso_points_sdf = iso_points_sdf.abs().mean()* kwargs['lambda_iso_sdf'] * iso_points_sdf.nelement() / (iso_points_sdf.nelement()+8000) loss['loss_sdf_iso'] = loss_iso_points_sdf.detach() loss['loss'] += loss_iso_points_sdf # TODO: predict iso_normals from local_frame iso_normals_sampled = iso_points_normal.detach()[:, sample_idx, :] iso_g_sampled = iso_points_g[:, sample_idx, :] loss_normals = torch.mean((1 - F.cosine_similarity(iso_normals_sampled, iso_g_sampled, dim=-1).abs())) * kwargs['lambda_iso_normal'] * iso_points_sdf.nelement() / (iso_points_sdf.nelement()+8000) # loss_normals = torch.mean((1 - F.cosine_similarity(iso_points_normal, iso_points_g, dim=-1).abs())) * kwargs['lambda_iso_normal'] loss['loss_normal_iso'] = loss_normals.detach() loss['loss'] += loss_normals idx = torch.randperm(gt_surface_pts.shape[1]).to(device=gt_surface_pts.device)[:(gt_surface_pts.shape[1]//2)] tmp = torch.index_select(gt_surface_pts, 1, idx) space_pts = torch.cat( [torch.rand_like(tmp) * 2 - 1, torch.randn_like(tmp, device=tmp.device, dtype=tmp.dtype) * 0.1+tmp], dim=1) space_pts.requires_grad_(True) pred_space_sdf = decoder(space_pts).sdf pred_space_grad = torch.autograd.grad(pred_space_sdf, [space_pts], [ torch.ones_like(pred_space_sdf)], create_graph=True)[0] # 1. eikonal term loss_eikonal = (eikonal_loss(pred_surface_grad) + eikonal_loss(pred_space_grad)) * kwargs['lambda_eikonal'] loss['loss_eikonal'] = loss_eikonal.detach() loss['loss'] += loss_eikonal # 2. SDF loss # loss on iso points pred_surface_sdf = decoder(gt_surface_pts).sdf loss_sdf = torch.mean(weights * pred_surface_sdf.abs()) * lambda_surface_sdf if kwargs['warm_up'] >= 0 and it >= kwargs['warm_up'] and kwargs['lambda_iso_sdf'] != 0: # loss_sdf = 0.5 * loss_sdf loss_sdf = loss_sdf * pred_surface_sdf.nelement() / (pred_surface_sdf.nelement() + iso_points_sdf.nelement()) if kwargs['use_sal_loss'] and iso_points is not None: dists, idxs, _ = knn_points(space_pts.view(1,-1,3), iso_points.view(1,-1,3).detach(), K=1) dists = dists.view_as(pred_space_sdf) idxs = idxs.view_as(pred_space_sdf) loss_inter = ((eps_sqrt(dists).sqrt() - pred_space_sdf.abs())**2).mean() * kwargs['lambda_inter_sal'] else: alpha = (it / total_iter + 1)*100 loss_inter = torch.exp(-alpha * pred_space_sdf.abs()).mean() * kwargs['lambda_inter_sdf'] loss_sald = torch.tensor(0.0).cuda() if kwargs['use_off_normal_loss'] and it < 1000: dists, idxs, _ = knn_points(space_pts.view(1,-1,3), gt_surface_pts_sub.view(1,-1,3).cuda(), K=1) knn_normal = knn_gather(gt_surface_normals_sub.cuda().view(1,-1,3), idxs).view(1,-1,3) direction_correctness = -F.cosine_similarity(knn_normal, pred_space_grad, dim=-1) direction_correctness[direction_correctness < 0] = 0 loss_sald = torch.mean(direction_correctness*torch.exp(-2*dists)) * 2 # 3. normal direction loss_normals = torch.mean( weights * (1 - F.cosine_similarity(gt_surface_normals, pred_surface_grad, dim=-1))) * lambda_surface_normal if kwargs['warm_up'] >= 0 and it >= kwargs['warm_up'] and kwargs['lambda_iso_normal'] != 0: # loss_normals = 0.5 * loss_normals loss_normals = loss_normals * gt_surface_normals.nelement() / (gt_surface_normals.nelement() + iso_normals_sampled.nelement()) loss['loss_sdf'] = loss_sdf.detach() loss['loss_inter'] = loss_inter.detach() loss['loss_normals'] = loss_normals.detach() loss['loss_sald'] = loss_sald loss['loss'] += loss_sdf loss['loss'] += loss_inter loss['loss'] += loss_sald loss['loss'] += loss_normals loss['loss'].backward() torch.nn.utils.clip_grad_norm_(decoder.parameters(), max_norm=1.) optimizer.step() scheduler.step() if it % 20 == 0: print("iter {:05d} {}".format(it, ', '.join( ['{}: {}'.format(k, v.item()) for k, v in loss.items()]))) it += 1
# Model model = config.get_model(cfg, device=device, dataset=train_dataset) # Get optimizer and trainer optimizer = optim.Adam(model.parameters(), lr=lr) trainer = config.get_trainer(model, optimizer, cfg, device=device) # Load pre-trained model is existing kwargs = { 'model': model, 'optimizer': optimizer, } checkpoint_io = CheckpointIO( out_dir, initialize_from=cfg['model']['initialize_from'], initialization_file_name=cfg['model']['initialization_file_name'], **kwargs) try: load_dict = checkpoint_io.load('model.pt') except FileExistsError: load_dict = dict() epoch_it = load_dict.get('epoch_it', -1) it = load_dict.get('it', -1) metric_val_best = load_dict.get('loss_val_best', -model_selection_sign * np.inf) if metric_val_best == np.inf or metric_val_best == -np.inf: metric_val_best = -model_selection_sign * np.inf print('Current best validation metric (%s): %.8f' %
fields = get_fields() train_dataset = data.Shapes3dDataset_AllImgs(dataset_folder, fields, split=None) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=4, shuffle=False, collate_fn=data.collate_remove_none, worker_init_fn=data.worker_init_fn) # Model model = config.get_model(cfg, device=device, dataset=train_dataset) checkpoint_io = CheckpointIO(cfg['training']['out_dir'], model=model) try: load_dict = checkpoint_io.load('model_best.pt', strict=True) except FileExistsError: load_dict = dict() it = 0 batch_count = len(train_loader) t0 = time.time() if 'absolute_depth' in cfg['data']: absolute_depth = cfg['data']['absolute_depth'] else: absolute_depth = True print('absolute_depth:', absolute_depth)