def main(): parser = argparse.ArgumentParser() parser.add_argument('--filelist', '-t', help='Path to training set ground truth (.txt)', required=True) parser.add_argument('--filelist_val', '-v', help='Path to validation set ground truth (.txt)', required=True) parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load') parser.add_argument( '--save_folder', '-s', help='Path to folder for saving check points and summary', required=True) parser.add_argument('--model', '-m', help='Model to use', required=True) parser.add_argument('--setting', '-x', help='Setting to use', required=True) parser.add_argument( '--epochs', help='Number of training epochs (default defined in setting)', type=int) parser.add_argument('--batch_size', help='Batch size (default defined in setting)', type=int) parser.add_argument( '--log', help= 'Log to FILE in save folder; use - for stdout (default is log.txt)', metavar='FILE', default='log.txt') parser.add_argument('--no_timestamp_folder', help='Dont save to timestamp folder', action='store_true') parser.add_argument('--no_code_backup', help='Dont backup code', action='store_true') args = parser.parse_args() if not args.no_timestamp_folder: time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S') root_folder = os.path.join( args.save_folder, '%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid())) else: root_folder = args.save_folder if not os.path.exists(root_folder): os.makedirs(root_folder) if args.log != '-': sys.stdout = open(os.path.join(root_folder, args.log), 'w') #print('PID:', os.getpid()) #print(args) model = importlib.import_module(args.model) setting_path = os.path.join(os.path.dirname(__file__), args.model) sys.path.append(setting_path) setting = importlib.import_module(args.setting) num_epochs = args.epochs or setting.num_epochs batch_size = args.batch_size or setting.batch_size sample_num = setting.sample_num step_val = setting.step_val label_weights_list = setting.label_weights rotation_range = setting.rotation_range rotation_range_val = setting.rotation_range_val scaling_range = setting.scaling_range scaling_range_val = setting.scaling_range_val jitter = setting.jitter jitter_val = setting.jitter_val # Prepare inputs #print('{}-Preparing datasets...'.format(datetime.now())) is_list_of_h5_list = not data_utils.is_h5_list(args.filelist) if is_list_of_h5_list: seg_list = data_utils.load_seg_list(args.filelist) seg_list_idx = 0 filelist_train = seg_list[seg_list_idx] seg_list_idx = seg_list_idx + 1 else: filelist_train = args.filelist data_train, _, data_num_train, label_train, _ = data_utils.load_seg( filelist_train) data_val, _, data_num_val, label_val, _ = data_utils.load_seg( args.filelist_val) # shuffle # data_num_train is the number of points in each point cloud data_train, data_num_train, label_train = \ data_utils.grouped_shuffle([data_train, data_num_train, label_train]) num_train = data_train.shape[0] point_num = data_train.shape[1] num_val = data_val.shape[0] #print('{}-{:d}/{:d} training/validation samples.'.format(datetime.now(), num_train, num_val)) batch_num = (num_train * num_epochs + batch_size - 1) // batch_size #print('{}-{:d} training batches.'.format(datetime.now(), batch_num)) batch_num_val = math.ceil(num_val / batch_size) #print('{}-{:d} testing batches per test.'.format(datetime.now(), batch_num_val)) ###################################################################### # Placeholders indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices") xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms") rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations") jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range") global_step = tf.Variable(0, trainable=False, name='global_step') is_training = tf.placeholder(tf.bool, name='is_training') pts_fts = tf.placeholder(tf.float32, shape=(None, point_num, setting.data_dim), name='pts_fts') labels_seg = tf.placeholder(tf.int64, shape=(None, point_num), name='labels_seg') labels_weights = tf.placeholder(tf.float32, shape=(None, point_num), name='labels_weights') ###################################################################### pts_fts_sampled = tf.gather_nd(pts_fts, indices=indices, name='pts_fts_sampled') features_augmented = None if setting.data_dim > 3: points_sampled, features_sampled = tf.split( pts_fts_sampled, [3, setting.data_dim - 3], axis=-1, name='split_points_features') if setting.use_extra_features: if setting.with_normal_feature: if setting.data_dim < 6: print('Only 3D normals are supported!') exit() elif setting.data_dim == 6: features_augmented = pf.augment(features_sampled, rotations) else: normals, rest = tf.split(features_sampled, [3, setting.data_dim - 6]) normals_augmented = pf.augment(normals, rotations) features_augmented = tf.concat([normals_augmented, rest], axis=-1) else: features_augmented = features_sampled else: points_sampled = pts_fts_sampled points_augmented = pf.augment(points_sampled, xforms, jitter_range) labels_sampled = tf.gather_nd(labels_seg, indices=indices, name='labels_sampled') labels_weights_sampled = tf.gather_nd(labels_weights, indices=indices, name='labels_weight_sampled') net = model.Net(points_augmented, features_augmented, is_training, setting) logits = net.logits probs = tf.nn.softmax(logits, name='probs') predictions = tf.argmax(probs, axis=-1, name='predictions') loss_op = tf.losses.sparse_softmax_cross_entropy( labels=labels_sampled, logits=logits, weights=labels_weights_sampled) with tf.name_scope('metrics'): loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op) t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy( labels_sampled, predictions, weights=labels_weights_sampled) t_1_per_class_acc_op, t_1_per_class_acc_update_op = \ tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class, weights=labels_weights_sampled) reset_metrics_op = tf.variables_initializer([ var for var in tf.local_variables() if var.name.split('/')[0] == 'metrics' ]) _ = tf.summary.scalar('loss/train', tensor=loss_mean_op, collections=['train']) _ = tf.summary.scalar('t_1_acc/train', tensor=t_1_acc_op, collections=['train']) _ = tf.summary.scalar('t_1_per_class_acc/train', tensor=t_1_per_class_acc_op, collections=['train']) _ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val']) _ = tf.summary.scalar('t_1_acc/val', tensor=t_1_acc_op, collections=['val']) _ = tf.summary.scalar('t_1_per_class_acc/val', tensor=t_1_per_class_acc_op, collections=['val']) lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps, setting.decay_rate, staircase=True) lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min) _ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train']) reg_loss = setting.weight_decay * tf.losses.get_regularization_loss() if setting.optimizer == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op, epsilon=setting.epsilon) elif setting.optimizer == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=setting.momentum, use_nesterov=True) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) saver = tf.train.Saver(max_to_keep=None) # backup all code if not args.no_code_backup: code_folder = os.path.abspath(os.path.dirname(__file__)) shutil.copytree( code_folder, os.path.join(root_folder, os.path.basename(code_folder))) folder_ckpt = os.path.join(root_folder, 'ckpts') if not os.path.exists(folder_ckpt): os.makedirs(folder_ckpt) folder_summary = os.path.join(root_folder, 'summary') if not os.path.exists(folder_summary): os.makedirs(folder_summary) parameter_num = np.sum( [np.prod(v.shape.as_list()) for v in tf.trainable_variables()]) print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num)) with tf.Session() as sess: summaries_op = tf.summary.merge_all('train') summaries_val_op = tf.summary.merge_all('val') summary_writer = tf.summary.FileWriter(folder_summary, sess.graph) sess.run(init_op) # Load the model if args.load_ckpt is not None: saver.restore(sess, args.load_ckpt) print('{}-Checkpoint loaded from {}!'.format( datetime.now(), args.load_ckpt)) else: latest_ckpt = tf.train.latest_checkpoint(folder_ckpt) if latest_ckpt: print('{}-Found checkpoint {}'.format(datetime.now(), latest_ckpt)) saver.restore(sess, latest_ckpt) print('{}-Checkpoint loaded from {} (Iter {})'.format( datetime.now(), latest_ckpt, sess.run(global_step))) for batch_idx_train in tqdm(range(batch_num)): ###################################################################### # Validation if (batch_idx_train % step_val == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \ or batch_idx_train == batch_num - 1: filename_ckpt = os.path.join(folder_ckpt, 'iter') saver.save(sess, filename_ckpt, global_step=global_step) print('{}-Checkpoint saved to {}!'.format( datetime.now(), filename_ckpt)) sess.run(reset_metrics_op) for batch_val_idx in range(batch_num_val): start_idx = batch_size * batch_val_idx end_idx = min(start_idx + batch_size, num_val) batch_size_val = end_idx - start_idx points_batch = data_val[start_idx:end_idx, ...] points_num_batch = data_num_val[start_idx:end_idx, ...] labels_batch = label_val[start_idx:end_idx, ...] weights_batch = np.array(label_weights_list)[labels_batch] xforms_np, rotations_np = pf.get_xforms( batch_size_val, rotation_range=rotation_range_val, scaling_range=scaling_range_val, order=setting.rotation_order) sess.run( [ loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op ], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_val, sample_num, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter_val]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: False, }) loss_val, t_1_acc_val, t_1_per_class_acc_val, summaries_val, step = sess.run( [ loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, summaries_val_op, global_step ]) summary_writer.add_summary(summaries_val, step) print( '{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}' .format(datetime.now(), loss_val, t_1_acc_val, t_1_per_class_acc_val)) sys.stdout.flush() ###################################################################### ###################################################################### # Training start_idx = (batch_size * batch_idx_train) % num_train end_idx = min(start_idx + batch_size, num_train) batch_size_train = end_idx - start_idx points_batch = data_train[start_idx:end_idx, ...] points_num_batch = data_num_train[start_idx:end_idx, ...] labels_batch = label_train[start_idx:end_idx, ...] weights_batch = np.array(label_weights_list)[labels_batch] if start_idx + batch_size_train == num_train: if is_list_of_h5_list: filelist_train_prev = seg_list[(seg_list_idx - 1) % len(seg_list)] filelist_train = seg_list[seg_list_idx % len(seg_list)] if filelist_train != filelist_train_prev: data_train, _, data_num_train, label_train, _ = data_utils.load_seg( filelist_train) num_train = data_train.shape[0] seg_list_idx = seg_list_idx + 1 data_train, data_num_train, label_train = \ data_utils.grouped_shuffle([data_train, data_num_train, label_train]) offset = int( random.gauss(0, sample_num * setting.sample_num_variance)) offset = max(offset, -sample_num * setting.sample_num_clip) offset = min(offset, sample_num * setting.sample_num_clip) sample_num_train = sample_num + offset xforms_np, rotations_np = pf.get_xforms( batch_size_train, rotation_range=rotation_range, scaling_range=scaling_range, order=setting.rotation_order) sess.run(reset_metrics_op) sess.run( [ train_op, loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op ], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: True, }) if batch_idx_train % 10 == 0: loss, t_1_acc, t_1_per_class_acc, summaries, step = sess.run([ loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, summaries_op, global_step ]) summary_writer.add_summary(summaries, step) print( '{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}' .format(datetime.now(), step, loss, t_1_acc, t_1_per_class_acc)) sys.stdout.flush() ###################################################################### print('{}-Done!'.format(datetime.now()))
def main(): parser = argparse.ArgumentParser() parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load') parser.add_argument('--save_folder', '-s', default='log/seg', help='Path to folder for saving check points and summary') parser.add_argument('--model', '-m', default='shellconv', help='Model to use') parser.add_argument('--setting', '-x', default='seg_s3dis', help='Setting to use') parser.add_argument('--log', help='Log to FILE in save folder; use - for stdout (default is log.txt)', metavar='FILE', default='log.txt') args = parser.parse_args() time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S') root_folder = os.path.join(args.save_folder, '%s_%s_%s' % (args.model, args.setting, time_string)) if not os.path.exists(root_folder): os.makedirs(root_folder) global LOG_FOUT if args.log != '-': LOG_FOUT = open(os.path.join(root_folder, args.log), 'w') model = importlib.import_module(args.model) setting_path = os.path.join(os.path.dirname(__file__), args.model) sys.path.append(setting_path) setting = importlib.import_module(args.setting) num_epochs = setting.num_epochs batch_size = setting.batch_size sample_num = setting.sample_num step_val = setting.step_val label_weights_list = setting.label_weights rotation_range = setting.rotation_range rotation_range_val = setting.rotation_range_val scaling_range = setting.scaling_range scaling_range_val = setting.scaling_range_val jitter = setting.jitter jitter_val = setting.jitter_val is_list_of_h5_list = data_utils.is_h5_list(setting.filelist) if is_list_of_h5_list: seg_list = [setting.filelist] # for train else: seg_list = data_utils.load_seg_list(setting.filelist) # for train data_val, _, data_num_val, label_val, _ = data_utils.load_seg(setting.filelist_val) if data_val.shape[-1] > 3: data_val = data_val[:,:,:3] # only use the xyz coordinates point_num = data_val.shape[1] num_val = data_val.shape[0] batch_num_val = num_val // batch_size ###################################################################### # Placeholders indices = tf.placeholder(tf.int32, shape=(None, sample_num, 2), name="indices") xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms") rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations") jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range") global_step = tf.Variable(0, trainable=False, name='global_step') is_training = tf.placeholder(tf.bool, name='is_training') pts_fts = tf.placeholder(tf.float32, shape=(None, point_num, setting.data_dim), name='pts_fts') labels_seg = tf.placeholder(tf.int64, shape=(None, point_num), name='labels_seg') labels_weights = tf.placeholder(tf.float32, shape=(None, point_num), name='labels_weights') ###################################################################### points_sampled = tf.gather_nd(pts_fts, indices=indices, name='pts_fts_sampled') points_augmented = pf.augment(points_sampled, xforms, jitter_range) labels_sampled = tf.gather_nd(labels_seg, indices=indices, name='labels_sampled') labels_weights_sampled = tf.gather_nd(labels_weights, indices=indices, name='labels_weight_sampled') bn_decay_exp_op = tf.train.exponential_decay(0.5, global_step, setting.decay_steps, 0.5, staircase=True) bn_decay_op = tf.minimum(0.99, 1 - bn_decay_exp_op) logits_op = model.get_model(points_augmented, is_training, setting.sconv_params, setting.sdconv_params, setting.fc_params, sampling=setting.sampling, weight_decay=setting.weight_decay, bn_decay = bn_decay_op, part_num=setting.num_class) predictions = tf.argmax(logits_op, axis=-1, name='predictions') loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_sampled, logits=logits_op, weights=labels_weights_sampled) with tf.name_scope('metrics'): loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op) t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(labels_sampled, predictions, weights=labels_weights_sampled) t_1_per_class_acc_op, t_1_per_class_acc_update_op = \ tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class, weights=labels_weights_sampled) reset_metrics_op = tf.variables_initializer([var for var in tf.local_variables() if var.name.split('/')[0] == 'metrics']) _ = tf.summary.scalar('loss/train', tensor=loss_mean_op, collections=['train']) _ = tf.summary.scalar('t_1_acc/train', tensor=t_1_acc_op, collections=['train']) _ = tf.summary.scalar('t_1_per_class_acc/train', tensor=t_1_per_class_acc_op, collections=['train']) _ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val']) _ = tf.summary.scalar('t_1_acc/val', tensor=t_1_acc_op, collections=['val']) _ = tf.summary.scalar('t_1_per_class_acc/val', tensor=t_1_per_class_acc_op, collections=['val']) lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps, setting.decay_rate, staircase=True) lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min) _ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train']) reg_loss = setting.weight_decay * tf.losses.get_regularization_loss() if setting.optimizer == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op) elif setting.optimizer == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=setting.momentum, use_nesterov=True) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step) saver = tf.train.Saver(max_to_keep=None) folder_ckpt = os.path.join(root_folder, 'ckpts') if not os.path.exists(folder_ckpt): os.makedirs(folder_ckpt) folder_summary = os.path.join(root_folder, 'summary') if not os.path.exists(folder_summary): os.makedirs(folder_summary) parameter_num = np.sum([np.prod(v.shape.as_list()) for v in tf.trainable_variables()]) print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num)) config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True config.log_device_placement = False with tf.Session(config=config) as sess: summaries_op = tf.summary.merge_all('train') summaries_val_op = tf.summary.merge_all('val') summary_writer = tf.summary.FileWriter(folder_summary, sess.graph) sess.run(tf.global_variables_initializer()) # Load the model if args.load_ckpt is not None: saver.restore(sess, args.load_ckpt) print('{}-Checkpoint loaded from {}!'.format(datetime.now(), args.load_ckpt)) else: latest_ckpt = tf.train.latest_checkpoint(folder_ckpt) if latest_ckpt: print('{}-Found checkpoint {}'.format(datetime.now(), latest_ckpt)) saver.restore(sess, latest_ckpt) print('{}-Checkpoint loaded from {} (Iter {})'.format( datetime.now(), latest_ckpt, sess.run(global_step))) best_acc = 0 best_epoch = 0 for epoch in range(num_epochs): ############################### train ####################################### # Shuffle train files np.random.shuffle(seg_list) for file_idx_train in range(len(seg_list)): print('----epoch:'+str(epoch) + '--train file:' + str(file_idx_train) + '-----') filelist_train = seg_list[file_idx_train] data_train, _, data_num_train, label_train, _ = data_utils.load_seg(filelist_train) num_train = data_train.shape[0] if data_train.shape[-1] > 3: data_train = data_train[:,:,:3] data_train, data_num_train, label_train = \ data_utils.grouped_shuffle([data_train, data_num_train, label_train]) # data_train, label_train, _ = provider.shuffle_data_seg(data_train, label_train) batch_num = (num_train + batch_size - 1) // batch_size for batch_idx_train in range(batch_num): # Training start_idx = (batch_size * batch_idx_train) % num_train end_idx = min(start_idx + batch_size, num_train) batch_size_train = end_idx - start_idx points_batch = data_train[start_idx:end_idx, ...] points_num_batch = data_num_train[start_idx:end_idx, ...] labels_batch = label_train[start_idx:end_idx, ...] weights_batch = np.array(label_weights_list)[labels_batch] offset = int(random.gauss(0, sample_num * setting.sample_num_variance)) offset = max(offset, -sample_num * setting.sample_num_clip) offset = min(offset, sample_num * setting.sample_num_clip) sample_num_train = sample_num + offset xforms_np, rotations_np = pf.get_xforms(batch_size_train, rotation_range=rotation_range, scaling_range=scaling_range, order=setting.rotation_order) sess.run(reset_metrics_op) sess.run([train_op, loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: True, }) loss, t_1_acc, t_1_per_class_acc, summaries, step = sess.run([loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, summaries_op, global_step]) summary_writer.add_summary(summaries, step) log_string('{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}' .format(datetime.now(), step, loss, t_1_acc, t_1_per_class_acc)) sys.stdout.flush() ###################################################################### filename_ckpt = os.path.join(folder_ckpt, 'epoch') saver.save(sess, filename_ckpt, global_step=epoch) print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt)) sess.run(reset_metrics_op) for batch_val_idx in range(batch_num_val): start_idx = batch_size * batch_val_idx end_idx = min(start_idx + batch_size, num_val) batch_size_val = end_idx - start_idx points_batch = data_val[start_idx:end_idx, ...] points_num_batch = data_num_val[start_idx:end_idx, ...] labels_batch = label_val[start_idx:end_idx, ...] weights_batch = np.array(label_weights_list)[labels_batch] xforms_np, rotations_np = pf.get_xforms(batch_size_val, rotation_range=rotation_range_val, scaling_range=scaling_range_val, order=setting.rotation_order) sess.run([loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_val, sample_num, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter_val]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: False, }) loss_val, t_1_acc_val, t_1_per_class_acc_val, summaries_val, step = sess.run( [loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, summaries_val_op, global_step]) summary_writer.add_summary(summaries_val, step) if t_1_per_class_acc_val > best_acc: best_acc = t_1_per_class_acc_val best_epoch = epoch log_string('{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f} best epoch: {} Current epoch: {}' .format(datetime.now(), loss_val, t_1_acc_val, t_1_per_class_acc_val, best_epoch, epoch)) sys.stdout.flush() ###################################################################### print('{}-Done!'.format(datetime.now()))
def main(): parser = argparse.ArgumentParser() parser.add_argument('--filelist', '-t', help='Path to training set ground truth (.txt)', required=True) parser.add_argument('--filelist_val', '-v', help='Path to validation set ground truth (.txt)', required=True) parser.add_argument('--filelist_unseen', '-u', help='Path to validation set ground truth (.txt)', required=True) parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load') parser.add_argument( '--save_folder', '-s', help='Path to folder for saving check points and summary', required=True) parser.add_argument('--model', '-m', help='Model to use', required=True) parser.add_argument('--setting', '-x', help='Setting to use', required=True) args = parser.parse_args() time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S') root_folder = os.path.join( args.save_folder, '%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid())) if not os.path.exists(root_folder): os.makedirs(root_folder) print('PID:', os.getpid()) print(args) model = importlib.import_module(args.model) setting_path = os.path.join(os.path.dirname(__file__), args.model) sys.path.append(setting_path) setting = importlib.import_module(args.setting) num_epochs = setting.num_epochs batch_size = setting.batch_size sample_num = setting.sample_num step_val = setting.step_val label_weights_list = setting.label_weights label_weights_val = [1.0] * 1 + [1.0] * (setting.num_class - 1) rotation_range = setting.rotation_range rotation_range_val = setting.rotation_range_val scaling_range = setting.scaling_range scaling_range_val = setting.scaling_range_val jitter = setting.jitter jitter_val = setting.jitter_val # Prepare inputs print('{}-Preparing datasets...'.format(datetime.now())) is_list_of_h5_list = not data_utils.is_h5_list(args.filelist) if is_list_of_h5_list: seg_list = data_utils.load_seg_list(args.filelist) seg_list_idx = 0 filelist_train = seg_list[seg_list_idx] seg_list_idx = seg_list_idx + 1 else: filelist_train = args.filelist data_train, _, data_num_train, label_train, _ = data_utils.load_seg( filelist_train) data_val, _, data_num_val, label_val, _ = data_utils.load_seg( args.filelist_val) data_unseen, _, data_num_unseen, label_unseen, _ = data_utils.load_seg( args.filelist_unseen) # shuffle data_unseen, data_num_unseen, label_unseen = \ data_utils.grouped_shuffle([data_unseen, data_num_unseen, label_unseen]) # shuffle data_train, data_num_train, label_train = \ data_utils.grouped_shuffle([data_train, data_num_train, label_train]) num_train = data_train.shape[0] point_num = data_train.shape[1] num_val = data_val.shape[0] num_unseen = data_unseen.shape[0] batch_num_unseen = int(math.ceil(num_unseen / batch_size)) print('{}-{:d}/{:d} training/validation samples.'.format( datetime.now(), num_train, num_val)) batch_num = (num_train * num_epochs + batch_size - 1) // batch_size print('{}-{:d} training batches.'.format(datetime.now(), batch_num)) batch_num_val = int(math.ceil(num_val / batch_size)) print('{}-{:d} testing batches per test.'.format(datetime.now(), batch_num_val)) ###################################################################### # Placeholders indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices") xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms") rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations") jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range") global_step = tf.Variable(0, trainable=False, name='global_step') is_training = tf.placeholder(tf.bool, name='is_training') is_labelled_data = tf.placeholder(tf.bool, name='is_labelled_data') pts_fts = tf.placeholder(tf.float32, shape=(None, point_num, setting.data_dim), name='pts_fts') labels_seg = tf.placeholder(tf.int64, shape=(None, point_num), name='labels_seg') labels_weights = tf.placeholder(tf.float32, shape=(None, point_num), name='labels_weights') ###################################################################### pts_fts_sampled = tf.gather_nd(pts_fts, indices=indices, name='pts_fts_sampled') features_augmented = None if setting.data_dim > 3: points_sampled, features_sampled = tf.split( pts_fts_sampled, [3, setting.data_dim - 3], axis=-1, name='split_points_features') if setting.use_extra_features: if setting.with_normal_feature: if setting.data_dim < 6: print('Only 3D normals are supported!') exit() elif setting.data_dim == 6: features_augmented = pf.augment(features_sampled, rotations) else: normals, rest = tf.split(features_sampled, [3, setting.data_dim - 6]) normals_augmented = pf.augment(normals, rotations) features_augmented = tf.concat([normals_augmented, rest], axis=-1) else: features_augmented = features_sampled else: points_sampled = pts_fts_sampled points_augmented = pf.augment(points_sampled, xforms, jitter_range) labels_sampled = tf.gather_nd(labels_seg, indices=indices, name='labels_sampled') labels_weights_sampled = tf.gather_nd(labels_weights, indices=indices, name='labels_weight_sampled') net = model.Net(points_augmented, features_augmented, is_training, setting) logits = net.logits probs = tf.nn.softmax(logits, name='prob') predictions = tf.argmax(probs, axis=-1, name='predictions') with tf.variable_scope('xcrf_ker_weights'): _, point_indices = pf.knn_indices_general(points_augmented, points_augmented, 1024, True) xcrf = learningBlock(num_points=sample_num, num_classes=setting.num_class, theta_alpha=float(5), theta_beta=float(2), theta_gamma=float(1), num_iterations=5, name='xcrf', point_indices=point_indices) _logits1 = xcrf.call(net.logits, points_augmented, features_augmented, setting.data_dim) _logits2 = xcrf.call(net.logits, points_augmented, features_augmented, setting.data_dim, D=2) _logits3 = xcrf.call(net.logits, points_augmented, features_augmented, setting.data_dim, D=3) _logits4 = xcrf.call(net.logits, points_augmented, features_augmented, setting.data_dim, D=4) _logits5 = xcrf.call(net.logits, points_augmented, features_augmented, setting.data_dim, D=8) _logits6 = xcrf.call(net.logits, points_augmented, features_augmented, setting.data_dim, D=16) _logits = _logits1 + _logits2 + _logits3 + _logits4 + _logits5 + _logits6 _probs = tf.nn.softmax(_logits, name='probs_crf') _predictions = tf.argmax(_probs, axis=-1, name='predictions_crf') logits = tf.cond( is_training, lambda: tf.cond(is_labelled_data, lambda: _logits, lambda: logits), lambda: _logits) predictions = tf.cond( is_training, lambda: tf.cond(is_labelled_data, lambda: _predictions, lambda: predictions), lambda: _predictions) labels_sampled = tf.cond( is_training, lambda: tf.cond(is_labelled_data, lambda: labels_sampled, lambda: _predictions), lambda: labels_sampled) loss_op = tf.losses.sparse_softmax_cross_entropy( labels=labels_sampled, logits=logits, weights=labels_weights_sampled) with tf.name_scope('metrics'): loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op) t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy( labels_sampled, predictions, weights=labels_weights_sampled) t_1_per_class_acc_op, t_1_per_class_acc_update_op = \ tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class, weights=labels_weights_sampled) t_1_per_mean_iou_op, t_1_per_mean_iou_op_update_op = \ tf.metrics.mean_iou(labels_sampled, predictions, setting.num_class, weights=labels_weights_sampled) reset_metrics_op = tf.variables_initializer([ var for var in tf.local_variables() if var.name.split('/')[0] == 'metrics' ]) _ = tf.summary.scalar('loss/train', tensor=loss_mean_op, collections=['train']) _ = tf.summary.scalar('t_1_acc/train', tensor=t_1_acc_op, collections=['train']) _ = tf.summary.scalar('t_1_per_class_acc/train', tensor=t_1_per_class_acc_op, collections=['train']) _ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val']) _ = tf.summary.scalar('t_1_acc/val', tensor=t_1_acc_op, collections=['val']) _ = tf.summary.scalar('t_1_per_class_acc/val', tensor=t_1_per_class_acc_op, collections=['val']) _ = tf.summary.scalar('t_1_mean_iou/val', tensor=t_1_per_mean_iou_op, collections=['val']) all_variable = tf.global_variables() lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps, setting.decay_rate, staircase=True) lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min) _ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train']) reg_loss = setting.weight_decay * tf.losses.get_regularization_loss() if setting.optimizer == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op, epsilon=setting.epsilon) elif setting.optimizer == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=setting.momentum, use_nesterov=True) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) xcrf_ker_weights = [ var for var in tf.global_variables() if 'xcrf_ker_weights' in var.name ] no_xcrf_ker_weights = [ var for var in tf.global_variables() if 'xcrf_ker_weights' not in var.name ] #print(restore_values) #train_op = optimizer.minimize( loss_op+reg_loss, global_step=global_step) with tf.control_dependencies(update_ops): train_op_xcrf = optimizer.minimize(loss_op + reg_loss, var_list=no_xcrf_ker_weights, global_step=global_step) train_op_all = optimizer.minimize(loss_op + reg_loss, var_list=all_variable, global_step=global_step) train_op = tf.cond(is_labelled_data, lambda: train_op_all, lambda: train_op_xcrf) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) saver = tf.train.Saver(var_list=no_xcrf_ker_weights, max_to_keep=5) saver_best = tf.train.Saver(max_to_keep=5) # backup all code code_folder = os.path.abspath(os.path.dirname(__file__)) shutil.copytree(code_folder, os.path.join(root_folder, os.path.basename(code_folder))) folder_ckpt = os.path.join(root_folder, 'ckpts') if not os.path.exists(folder_ckpt): os.makedirs(folder_ckpt) folder_ckpt_best = os.path.join(root_folder, 'ckpt-best') if not os.path.exists(folder_ckpt_best): os.makedirs(folder_ckpt_best) folder_summary = os.path.join(root_folder, 'summary') if not os.path.exists(folder_summary): os.makedirs(folder_summary) parameter_num = np.sum( [np.prod(v.shape.as_list()) for v in tf.trainable_variables()]) print('{}-Parameter number: {:d}.'.format(datetime.now(), int(parameter_num))) _highest_val = 0.0 max_val = 10 with tf.Session() as sess: summaries_op = tf.summary.merge_all('train') summaries_val_op = tf.summary.merge_all('val') summary_values_op = tf.summary.merge_all('summary_values') summary_writer = tf.summary.FileWriter(folder_summary, sess.graph) sess.run(init_op) # Load the model if args.load_ckpt is not None: saver.restore(sess, args.load_ckpt) print('{}-Checkpoint loaded from {}!'.format( datetime.now(), args.load_ckpt)) batch_num = 50000 for batch_idx_train in range(batch_num): if (batch_idx_train % step_val == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \ or batch_idx_train == batch_num - 1: ###################################################################### # Validation filename_ckpt = os.path.join(folder_ckpt, 'iter') saver.save(sess, filename_ckpt, global_step=global_step) print('{}-Checkpoint saved to {}!'.format( datetime.now(), filename_ckpt)) sess.run(reset_metrics_op) summary_hist = None _idxVal = np.arange(num_val) np.random.shuffle(_idxVal) _pred = [] _label = [] for batch_val_idx in tqdm(range(batch_num_val)): start_idx = batch_size * batch_val_idx end_idx = min(start_idx + batch_size, num_val) batch_size_val = end_idx - start_idx points_batch = data_val[_idxVal[start_idx:end_idx], ...] points_num_batch = data_num_val[_idxVal[start_idx:end_idx], ...] labels_batch = label_val[_idxVal[start_idx:end_idx], ...] weights_batch = np.array(label_weights_val)[labels_batch] xforms_np, rotations_np = pf.get_xforms( batch_size_val, rotation_range=rotation_range_val, scaling_range=scaling_range_val, order=setting.rotation_order) _labels_sampled, _predictions, _, _, _, _ = sess.run( [ labels_sampled, predictions, loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op, t_1_per_mean_iou_op_update_op ], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_val, sample_num, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter_val]), labels_seg: labels_batch, is_labelled_data: True, labels_weights: weights_batch, is_training: False, }) _pred.append(_predictions.flatten()) _label.append(_labels_sampled.flatten()) loss_val, t_1_acc_val, t_1_per_class_acc_val, t1__mean_iou, summaries_val = sess.run( [ loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, t_1_per_mean_iou_op, summaries_val_op ]) img_d_summary = pf.plot_confusion_matrix( _label, _pred, ["Environment", "Pedestrian", "Car", "Cyclist"], tensor_name='confusion_matrix') max_val = max_val - 1 if (t_1_per_class_acc_val > _highest_val): max_val = 10 _highest_val = t_1_per_class_acc_val filename_ckpt = os.path.join(folder_ckpt_best, str(_highest_val) + "-iter-") saver_best.save(sess, filename_ckpt, global_step=global_step) if (max_val < 0): sys.exit(0) summary_writer.add_summary(summaries_val, batch_idx_train) summary_writer.add_summary(img_d_summary, batch_idx_train) #summary_writer.add_summary(summary_hist, batch_idx_train) print( '{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} Diff-Best: {:.4f} T-1 mAcc: {:.4f} T-1 mIoU: {:.4f}' .format(datetime.now(), loss_val, t_1_acc_val, _highest_val - t_1_per_class_acc_val, t_1_per_class_acc_val, t1__mean_iou)) sys.stdout.flush() ###################################################################### # Unseen Data sess.run(reset_metrics_op) summary_hist = None _idxunseen = np.arange(num_unseen) np.random.shuffle(_idxunseen) _pred = [] _label = [] unseenIndices = np.arange(batch_num_unseen) np.random.shuffle(unseenIndices) for batch_val_idx in tqdm(unseenIndices[:500]): start_idx = batch_size * batch_val_idx end_idx = min(start_idx + batch_size, num_unseen) batch_size_train = end_idx - start_idx points_batch = data_unseen[_idxunseen[start_idx:end_idx], ...] points_num_batch = data_num_unseen[ _idxunseen[start_idx:end_idx], ...] labels_batch = np.zeros(points_batch.shape[0:2], dtype=np.int32) weights_batch = np.array(label_weights_list)[labels_batch] xforms_np, rotations_np = pf.get_xforms( batch_size_train, rotation_range=rotation_range, scaling_range=scaling_range, order=setting.rotation_order) offset = int( random.gauss(0, sample_num * setting.sample_num_variance)) offset = max(offset, -sample_num * setting.sample_num_clip) offset = min(offset, sample_num * setting.sample_num_clip) sample_num_train = sample_num + offset sess.run( [ train_op, loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op, t_1_per_mean_iou_op_update_op ], feed_dict={ pts_fts: points_batch, is_labelled_data: False, indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: True, }) if batch_val_idx % 100 == 0: loss, t_1_acc, t_1_per_class_acc, t_1__mean_iou = sess.run( [ loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, t_1_per_mean_iou_op ], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch), xforms: xforms_np, is_labelled_data: False, rotations: rotations_np, jitter_range: np.array([jitter]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: True, }) print( '{}-[Train]-Unseen: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f} T-1 mIoU: {:.4f}' .format(datetime.now(), batch_val_idx, loss, t_1_acc, t_1_per_class_acc, t_1__mean_iou)) sys.stdout.flush() ###################################################################### # Training start_idx = (batch_size * batch_idx_train) % num_train end_idx = min(start_idx + batch_size, num_train) batch_size_train = end_idx - start_idx points_batch = data_train[start_idx:end_idx, ...] points_num_batch = data_num_train[start_idx:end_idx, ...] labels_batch = label_train[start_idx:end_idx, ...] weights_batch = np.array(label_weights_list)[labels_batch] if start_idx + batch_size_train == num_train: if is_list_of_h5_list: filelist_train_prev = seg_list[(seg_list_idx - 1) % len(seg_list)] filelist_train = seg_list[seg_list_idx % len(seg_list)] if filelist_train != filelist_train_prev: data_train, _, data_num_train, label_train, _ = data_utils.load_seg( filelist_train) num_train = data_train.shape[0] seg_list_idx = seg_list_idx + 1 data_train, data_num_train, label_train = \ data_utils.grouped_shuffle([data_train, data_num_train, label_train]) offset = int( random.gauss(0, sample_num * setting.sample_num_variance)) offset = max(offset, -sample_num * setting.sample_num_clip) offset = min(offset, sample_num * setting.sample_num_clip) sample_num_train = sample_num + offset xforms_np, rotations_np = pf.get_xforms( batch_size_train, rotation_range=rotation_range, scaling_range=scaling_range, order=setting.rotation_order) sess.run(reset_metrics_op) sess.run( [ train_op, loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op, t_1_per_mean_iou_op_update_op ], feed_dict={ pts_fts: points_batch, is_labelled_data: True, indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: True, }) if batch_idx_train % 100 == 0: loss, t_1_acc, t_1_per_class_acc, t_1__mean_iou, summaries = sess.run( [ loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, t_1_per_mean_iou_op, summaries_op ], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch), xforms: xforms_np, is_labelled_data: True, rotations: rotations_np, jitter_range: np.array([jitter]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: True, }) summary_writer.add_summary(summaries, batch_idx_train) print( '{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f} T-1 mIoU: {:.4f}' .format(datetime.now(), batch_idx_train, loss, t_1_acc, t_1_per_class_acc, t_1__mean_iou)) sys.stdout.flush() ###################################################################### print('{}-Done!'.format(datetime.now()))
def main(): parser = argparse.ArgumentParser() parser.add_argument('--filelist', '-t', help='Path to training set ground truth (.txt)', required=True) parser.add_argument('--filelist_val', '-v', help='Path to validation set ground truth (.txt)', required=True) parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load') parser.add_argument( '--save_folder', '-s', help='Path to folder for saving check points and summary', required=True) parser.add_argument('--model', '-m', help='Model to use', required=True) parser.add_argument('--setting', '-x', help='Setting to use', required=True) parser.add_argument('--startpoint', '-b', help='Setting to use', required=True) args = parser.parse_args() time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S') root_folder = os.path.join( args.save_folder, '%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid())) if not os.path.exists(root_folder): os.makedirs(root_folder) #sys.stdout = open(os.path.join(root_folder, 'log.txt'), 'w') print('PID:', os.getpid()) print(args) model = importlib.import_module(args.model) setting_path = os.path.join(os.path.dirname(__file__), args.model) sys.path.append(setting_path) setting = importlib.import_module(args.setting) num_epochs = setting.num_epochs batch_size = setting.batch_size sample_num = setting.sample_num step_val = setting.step_val label_weights_list = setting.label_weights label_weights_val = [1.0] * 1 + [1.0] * (setting.num_class - 1) rotation_range = setting.rotation_range rotation_range_val = setting.rotation_range_val scaling_range = setting.scaling_range scaling_range_val = setting.scaling_range_val jitter = setting.jitter jitter_val = setting.jitter_val # Prepare inputs print('{}-Preparing datasets...'.format(datetime.now())) is_list_of_h5_list = not data_utils.is_h5_list(args.filelist) if is_list_of_h5_list: seg_list = data_utils.load_seg_list(args.filelist) seg_list_idx = 0 filelist_train = seg_list[seg_list_idx] seg_list_idx = seg_list_idx + 1 else: filelist_train = args.filelist #data_train, _, data_num_train, label_train, _ = data_utils.load_seg(filelist_train) data_val, _, data_num_val, label_val, _ = data_utils.load_seg( args.filelist_val) data_train = data_val data_num_train = data_num_val label_train = label_val # shuffle data_train, data_num_train, label_train = \ data_utils.grouped_shuffle([data_train, data_num_train, label_train]) num_train = data_train.shape[0] point_num = data_train.shape[1] num_val = data_val.shape[0] print('{}-{:d}/{:d} training/validation samples.'.format( datetime.now(), num_train, num_val)) batch_num = (num_train * num_epochs + batch_size - 1) // batch_size print('{}-{:d} training batches.'.format(datetime.now(), batch_num)) batch_num_val = int(math.ceil(num_val / batch_size)) print('{}-{:d} testing batches per test.'.format(datetime.now(), batch_num_val)) ###################### folder_summary = os.path.join(root_folder, 'summary') if not os.path.exists(folder_summary): os.makedirs(folder_summary) ###################################################################### # Placeholders indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices") xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms") rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations") jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range") global_step = tf.Variable(0, trainable=False, name='global_step') is_training = tf.placeholder(tf.bool, name='is_training') pts_fts = tf.placeholder(tf.float32, shape=(None, point_num, setting.data_dim), name='pts_fts') labels_seg = tf.placeholder(tf.int64, shape=(None, point_num), name='labels_seg') labels_weights = tf.placeholder(tf.float32, shape=(None, point_num), name='labels_weights') ###################################################################### pts_fts_sampled = tf.gather_nd(pts_fts, indices=indices, name='pts_fts_sampled') features_augmented = None if setting.data_dim > 3: points_sampled, features_sampled = tf.split( pts_fts_sampled, [3, setting.data_dim - 3], axis=-1, name='split_points_features') if setting.use_extra_features: if setting.with_normal_feature: if setting.data_dim < 6: print('Only 3D normals are supported!') exit() elif setting.data_dim == 6: features_augmented = pf.augment(features_sampled, rotations) else: normals, rest = tf.split(features_sampled, [3, setting.data_dim - 6]) normals_augmented = pf.augment(normals, rotations) features_augmented = tf.concat([normals_augmented, rest], axis=-1) else: features_augmented = features_sampled else: points_sampled = pts_fts_sampled points_augmented = pf.augment(points_sampled, xforms, jitter_range) labels_sampled = tf.gather_nd(labels_seg, indices=indices, name='labels_sampled') labels_weights_sampled = tf.gather_nd(labels_weights, indices=indices, name='labels_weight_sampled') net = model.Net(points_augmented, features_augmented, is_training, setting) logits = net.logits probs = tf.nn.softmax(logits, name='probs') predictions = tf.argmax(probs, axis=-1, name='predictions') loss_op = tf.losses.sparse_softmax_cross_entropy( labels=labels_sampled, logits=logits, weights=labels_weights_sampled) with tf.name_scope('metrics'): loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op) t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy( labels_sampled, predictions, weights=labels_weights_sampled) t_1_per_class_acc_op, t_1_per_class_acc_update_op = \ tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class, weights=labels_weights_sampled) t_1_per_mean_iou_op, t_1_per_mean_iou_op_update_op = \ tf.metrics.mean_iou(labels_sampled, predictions, setting.num_class, weights=labels_weights_sampled) reset_metrics_op = tf.variables_initializer([ var for var in tf.local_variables() if var.name.split('/')[0] == 'metrics' ]) _ = tf.summary.scalar('loss/train', tensor=loss_mean_op, collections=['train']) _ = tf.summary.scalar('t_1_acc/train', tensor=t_1_acc_op, collections=['train']) _ = tf.summary.scalar('t_1_per_class_acc/train', tensor=t_1_per_class_acc_op, collections=['train']) _ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val']) _ = tf.summary.scalar('t_1_acc/val', tensor=t_1_acc_op, collections=['val']) _ = tf.summary.scalar('t_1_per_class_acc/val', tensor=t_1_per_class_acc_op, collections=['val']) _ = tf.summary.scalar('t_1_mean_iou/val', tensor=t_1_per_mean_iou_op, collections=['val']) #_ = tf.summary.histogram('summary/Add_F2', Add_F2, collections=['summary_values']) #_ = tf.summary.histogram('summary/Add_F3', Add_F3, collections=['summary_values']) #_ = tf.summary.histogram('summary/Z', Z, collections=['summary_values']) lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps, setting.decay_rate, staircase=True) lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min) _ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train']) reg_loss = setting.weight_decay * tf.losses.get_regularization_loss() if setting.optimizer == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op, epsilon=setting.epsilon) elif setting.optimizer == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=setting.momentum, use_nesterov=True) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) saver = tf.train.Saver(max_to_keep=5) saver_best = tf.train.Saver(max_to_keep=5) # backup all code code_folder = os.path.abspath(os.path.dirname(__file__)) shutil.copytree(code_folder, os.path.join(root_folder, os.path.basename(code_folder))) folder_ckpt = os.path.join(root_folder, 'ckpts') if not os.path.exists(folder_ckpt): os.makedirs(folder_ckpt) folder_ckpt_best = os.path.join(root_folder, 'ckpt-best') if not os.path.exists(folder_ckpt_best): os.makedirs(folder_ckpt_best) folder_summary = os.path.join(root_folder, 'summary') if not os.path.exists(folder_summary): os.makedirs(folder_summary) folder_cm_matrix = os.path.join(root_folder, 'cm-matrix') if not os.path.exists(folder_cm_matrix): os.makedirs(folder_cm_matrix) parameter_num = np.sum( [np.prod(v.shape.as_list()) for v in tf.trainable_variables()]) print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num)) _highest_val = 0.0 max_val = 10 with tf.Session() as sess: summaries_op = tf.summary.merge_all('train') summaries_val_op = tf.summary.merge_all('val') summary_values_op = tf.summary.merge_all('summary_values') summary_writer = tf.summary.FileWriter(folder_summary, sess.graph) #img_d_summary_writer = tf.summary.FileWriter(folder_cm_matrix, sess.graph) sess.run(init_op) # Load the model if args.load_ckpt is not None: saver.restore(sess, args.load_ckpt) print('{}-Checkpoint loaded from {}!'.format( datetime.now(), args.load_ckpt)) batch_num = 1 batch_idx_train = 1 ###################################################################### # Validation filename_ckpt = os.path.join(folder_ckpt, 'iter') saver.save(sess, filename_ckpt, global_step=global_step) print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt)) sess.run(reset_metrics_op) summary_hist = None _idxVal = np.arange(num_val) np.random.shuffle(_idxVal) _dataX = [] _dataY = [] _dataZ = [] _dataD = [] _pred = [] _label = [] for batch_val_idx in tqdm(range(batch_num_val)): start_idx = batch_size * batch_val_idx end_idx = min(start_idx + batch_size, num_val) batch_size_val = end_idx - start_idx points_batch = data_val[_idxVal[start_idx:end_idx], ...] points_num_batch = data_num_val[_idxVal[start_idx:end_idx], ...] labels_batch = label_val[_idxVal[start_idx:end_idx], ...] weights_batch = np.array(label_weights_val)[labels_batch] xforms_np, rotations_np = pf.get_xforms( batch_size_val, rotation_range=rotation_range_val, scaling_range=scaling_range_val, order=setting.rotation_order) _labels_sampled, _predictions, _, _, _, _ = sess.run( [ labels_sampled, predictions, loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op, t_1_per_mean_iou_op_update_op ], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_val, sample_num, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter_val]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: False, }) _dataX.append(points_batch[:, :, 0].flatten()) _dataY.append(points_batch[:, :, 1].flatten()) _dataZ.append(points_batch[:, :, 2].flatten()) _pred.append(_predictions.flatten()) _label.append(_labels_sampled.flatten()) loss_val, t_1_acc_val, t_1_per_class_acc_val, t1__mean_iou, summaries_val = sess.run( [ loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, t_1_per_mean_iou_op, summaries_val_op ]) img_d_summary = pf.plot_confusion_matrix( _label, _pred, ["Environment", "Pedestrian", "Car", "Cyclist"], tensor_name='confusion_matrix', normalize=False) _dataX = np.concatenate(_dataX, axis=0).flatten() _dataY = np.concatenate(_dataY, axis=0).flatten() _dataZ = np.concatenate(_dataZ, axis=0).flatten() correct_labels = np.concatenate(_label, axis=0).flatten() predict_labels = np.concatenate(_pred, axis=0).flatten() filename_pred = args.filelist_val + '_cm.h5' print('{}-Saving {}...'.format(datetime.now(), filename_pred)) file = h5py.File(filename_pred, 'w') file.create_dataset('dataX', data=_dataX) file.create_dataset('dataY', data=_dataY) file.create_dataset('dataZ', data=_dataZ) file.create_dataset('correct_labels', data=correct_labels) file.create_dataset('predict_labels', data=predict_labels) file.close() summary_writer.add_summary(img_d_summary, batch_idx_train) summary_writer.add_summary(summaries_val, batch_idx_train) y_test = correct_labels prediction = predict_labels print('Accuracy:', accuracy_score(y_test, prediction)) print('F1 score:', f1_score(y_test, prediction, average=None)) print('Recall:', recall_score(y_test, prediction, average=None)) print('Precision:', precision_score(y_test, prediction, average=None)) print('\n clasification report:\n', classification_report(y_test, prediction)) print('\n confussion matrix:\n', confusion_matrix(y_test, prediction)) print( '{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} Diff-Best: {:.4f} T-1 mAcc: {:.4f} T-1 mIoU: {:.4f}' .format(datetime.now(), loss_val, t_1_acc_val, _highest_val - t_1_per_class_acc_val, t_1_per_class_acc_val, t1__mean_iou)) sys.stdout.flush()
def main(): args = AttrDict() # Path of training set ground truth file list (.txt) args.filelist = os.path.join(SCENENN_DIR, 'train_files.txt') # Path of validation set ground truth file list (.txt) args.filelist_val = os.path.join(SCENENN_DIR, 'test_files.txt') # Path of a check point file to load args.load_ckpt = os.path.join(ROOT_DIR, '..', 'models', 'pretrained_scannet', 'ckpts', 'iter-354000') # Base directory where model checkpoint and summary files get saved in separate subdirectories args.save_folder = os.path.join(ROOT_DIR, '..', 'models') # PointCNN model to use args.model = 'pointcnn_seg' # Model setting to use args.setting = 'scenenn_x8_2048_fps' time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S') model_save_folder = os.path.join( args.save_folder, '%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid())) if not os.path.exists(model_save_folder): os.makedirs(model_save_folder) # sys.stdout = open(os.path.join(model_save_folder, 'log.txt'), 'w') print('PID:', os.getpid()) print(args) model = importlib.import_module(args.model) setting_path = os.path.join(ROOT_DIR, args.model) sys.path.append(setting_path) setting = importlib.import_module(args.setting) num_epochs = setting.num_epochs batch_size = setting.batch_size sample_num = setting.sample_num step_val = setting.step_val label_weights_list = setting.label_weights rotation_range = setting.rotation_range rotation_range_val = setting.rotation_range_val scaling_range = setting.scaling_range scaling_range_val = setting.scaling_range_val jitter = setting.jitter jitter_val = setting.jitter_val # Prepare inputs print('{}-Preparing datasets...'.format(datetime.now())) is_list_of_h5_list = not data_utils.is_h5_list(args.filelist) if is_list_of_h5_list: seg_list = data_utils.load_seg_list(args.filelist) seg_list_idx = 0 filelist_train = seg_list[seg_list_idx] seg_list_idx = seg_list_idx + 1 else: filelist_train = args.filelist data_train, _, data_num_train, label_train, _ = data_utils.load_seg( filelist_train) data_val, _, data_num_val, label_val, _ = data_utils.load_seg( args.filelist_val) # shuffle data_train, data_num_train, label_train = \ data_utils.grouped_shuffle([data_train, data_num_train, label_train]) num_train = data_train.shape[0] point_num = data_train.shape[1] num_val = data_val.shape[0] print('{}-{:d}/{:d} training/validation samples.'.format( datetime.now(), num_train, num_val)) batch_num = (num_train * num_epochs + batch_size - 1) // batch_size print('{}-{:d} training batches.'.format(datetime.now(), batch_num)) batch_num_val = math.ceil(num_val / batch_size) print('{}-{:d} testing batches per test.'.format(datetime.now(), batch_num_val)) ###################################################################### # Placeholders print('{}-Initializing TF-placeholders...'.format(datetime.now())) indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices") xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms") rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations") jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range") global_step = tf.Variable(0, trainable=False, name='global_step') is_training = tf.placeholder(tf.bool, name='is_training') pts_fts = tf.placeholder(tf.float32, shape=(None, point_num, setting.data_dim), name='pts_fts') labels_seg = tf.placeholder(tf.int64, shape=(None, point_num), name='labels_seg') labels_weights = tf.placeholder(tf.float32, shape=(None, point_num), name='labels_weights') ###################################################################### pts_fts_sampled = tf.gather_nd(pts_fts, indices=indices, name='pts_fts_sampled') features_augmented = None if setting.data_dim > 3: points_sampled, features_sampled = tf.split( pts_fts_sampled, [3, setting.data_dim - 3], axis=-1, name='split_points_features') if setting.use_extra_features: if setting.with_normal_feature: if setting.data_dim < 6: print('Only 3D normals are supported!') exit() elif setting.data_dim == 6: features_augmented = pf.augment(features_sampled, rotations) else: normals, rest = tf.split(features_sampled, [3, setting.data_dim - 6]) normals_augmented = pf.augment(normals, rotations) features_augmented = tf.concat([normals_augmented, rest], axis=-1) else: features_augmented = features_sampled else: points_sampled = pts_fts_sampled points_augmented = pf.augment(points_sampled, xforms, jitter_range) labels_sampled = tf.gather_nd(labels_seg, indices=indices, name='labels_sampled') labels_weights_sampled = tf.gather_nd(labels_weights, indices=indices, name='labels_weight_sampled') print('{}-Initializing net...'.format(datetime.now())) net = model.Net(points_augmented, features_augmented, is_training, setting) logits = net.logits probs = tf.nn.softmax(logits, name='probs') predictions = tf.argmax(probs, axis=-1, name='predictions') loss_op = tf.losses.sparse_softmax_cross_entropy( labels=labels_sampled, logits=logits, weights=labels_weights_sampled) with tf.name_scope('metrics'): loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op) t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy( labels_sampled, predictions, weights=labels_weights_sampled) t_1_per_class_acc_op, t_1_per_class_acc_update_op = \ tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class, weights=labels_weights_sampled) reset_metrics_op = tf.variables_initializer([ var for var in tf.local_variables() if var.name.split('/')[0] == 'metrics' ]) _ = tf.summary.scalar('loss/train', tensor=loss_mean_op, collections=['train']) _ = tf.summary.scalar('t_1_acc/train', tensor=t_1_acc_op, collections=['train']) _ = tf.summary.scalar('t_1_per_class_acc/train', tensor=t_1_per_class_acc_op, collections=['train']) _ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val']) _ = tf.summary.scalar('t_1_acc/val', tensor=t_1_acc_op, collections=['val']) _ = tf.summary.scalar('t_1_per_class_acc/val', tensor=t_1_per_class_acc_op, collections=['val']) lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps, setting.decay_rate, staircase=True) lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min) _ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train']) reg_loss = setting.weight_decay * tf.losses.get_regularization_loss() print('{}-Setting up optimizer...'.format(datetime.now())) if setting.optimizer == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op, epsilon=setting.epsilon) elif setting.optimizer == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=setting.momentum, use_nesterov=True) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): last_layer_train_vars = tf.get_collection( tf.GraphKeys.TRAINABLE_VARIABLES, 'logits') last_layer_train_op = optimizer.minimize( loss_op + reg_loss, global_step=global_step, var_list=last_layer_train_vars) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) saver = tf.train.Saver(max_to_keep=None) variables_to_restore = get_variables_to_restore(exclude=['logits']) restorer = tf.train.Saver(var_list=variables_to_restore, max_to_keep=None) # backup all code # code_folder = os.path.abspath(os.path.dirname(__file__)) # shutil.copytree(code_folder, os.path.join(model_save_folder, os.path.basename(code_folder)), symlinks=True) folder_ckpt = os.path.join(model_save_folder, 'ckpts') if not os.path.exists(folder_ckpt): os.makedirs(folder_ckpt) folder_summary = os.path.join(model_save_folder, 'summary') if not os.path.exists(folder_summary): os.makedirs(folder_summary) parameter_num = np.sum( [np.prod(v.shape.as_list()) for v in tf.trainable_variables()]) print('{}-Number of model parameters: {:d}.'.format( datetime.now(), parameter_num)) with tf.Session() as sess: summaries_op = tf.summary.merge_all('train') summaries_val_op = tf.summary.merge_all('val') summary_writer = tf.summary.FileWriter(folder_summary, sess.graph) print('{}-Initializing variables...'.format(datetime.now())) sess.run(init_op) # Load the model if args.load_ckpt is not None: print('{}-Loading checkpoint from {}...'.format( datetime.now(), args.load_ckpt)) restorer.restore(sess, args.load_ckpt) print('{}-Checkpoint loaded.'.format(datetime.now())) for batch_idx_train in tqdm(range(batch_num), ncols=60): if (batch_idx_train % step_val == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \ or batch_idx_train == batch_num - 1: ###################################################################### # Validation filename_ckpt = os.path.join(folder_ckpt, 'iter') saver.save(sess, filename_ckpt, global_step=global_step) tqdm.write('{}-Checkpoint saved to {}!'.format( datetime.now(), filename_ckpt)) sess.run(reset_metrics_op) for batch_val_idx in range(batch_num_val): start_idx = batch_size * batch_val_idx end_idx = min(start_idx + batch_size, num_val) batch_size_val = end_idx - start_idx points_batch = data_val[start_idx:end_idx, ...] points_num_batch = data_num_val[start_idx:end_idx, ...] labels_batch = label_val[start_idx:end_idx, ...] weights_batch = np.array(label_weights_list)[labels_batch] xforms_np, rotations_np = pf.get_xforms( batch_size_val, rotation_range=rotation_range_val, scaling_range=scaling_range_val, order=setting.rotation_order) sess.run( [ loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op ], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_val, sample_num, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter_val]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: False, }) loss_val, t_1_acc_val, t_1_per_class_acc_val, summaries_val = sess.run( [ loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, summaries_val_op ]) summary_writer.add_summary(summaries_val, batch_idx_train) tqdm.write( '{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}' .format(datetime.now(), loss_val, t_1_acc_val, t_1_per_class_acc_val)) sys.stdout.flush() ###################################################################### ###################################################################### # Training start_idx = (batch_size * batch_idx_train) % num_train end_idx = min(start_idx + batch_size, num_train) batch_size_train = end_idx - start_idx points_batch = data_train[start_idx:end_idx, ...] points_num_batch = data_num_train[start_idx:end_idx, ...] labels_batch = label_train[start_idx:end_idx, ...] weights_batch = np.array(label_weights_list)[labels_batch] if start_idx + batch_size_train == num_train: if is_list_of_h5_list: filelist_train_prev = seg_list[(seg_list_idx - 1) % len(seg_list)] filelist_train = seg_list[seg_list_idx % len(seg_list)] if filelist_train != filelist_train_prev: data_train, _, data_num_train, label_train, _ = data_utils.load_seg( filelist_train) num_train = data_train.shape[0] seg_list_idx = seg_list_idx + 1 data_train, data_num_train, label_train = \ data_utils.grouped_shuffle([data_train, data_num_train, label_train]) offset = int( random.gauss(0, sample_num * setting.sample_num_variance)) offset = max(offset, -sample_num * setting.sample_num_clip) offset = min(offset, sample_num * setting.sample_num_clip) sample_num_train = sample_num + offset xforms_np, rotations_np = pf.get_xforms( batch_size_train, rotation_range=rotation_range, scaling_range=scaling_range, order=setting.rotation_order) sess.run(reset_metrics_op) sess.run( [ last_layer_train_op, loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op ], feed_dict={ pts_fts: points_batch, indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch), xforms: xforms_np, rotations: rotations_np, jitter_range: np.array([jitter]), labels_seg: labels_batch, labels_weights: weights_batch, is_training: True, }) if batch_idx_train % 10 == 0: loss, t_1_acc, t_1_per_class_acc, summaries = sess.run([ loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, summaries_op ]) summary_writer.add_summary(summaries, batch_idx_train) # tqdm.write('{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}' # .format(datetime.now(), batch_idx_train, loss, t_1_acc, t_1_per_class_acc)) sys.stdout.flush() ###################################################################### print('{}-Done!'.format(datetime.now()))