def main(cfg): os.environ['CUDA_VISIBLE_DEVICES'] = str(cfg.gpu_id) # --------------------------------------------------------------- # Set random seed print('=> pre-porcessing') seed = 123 np.random.seed(seed) tf.set_random_seed(seed) # --------------------------------------------------------------- num_blocks = 3 num_supports = 2 placeholders = { 'features': tf.placeholder(tf.float32, shape=(None, 3), name='features'), 'img_inp': tf.placeholder(tf.float32, shape=(3, 224, 224, 3), name='img_inp'), 'labels': tf.placeholder(tf.float32, shape=(None, 6), name='labels'), 'support1': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)], 'support2': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)], 'support3': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)], 'faces': [tf.placeholder(tf.int32, shape=(None, 4)) for _ in range(num_blocks)], 'edges': [tf.placeholder(tf.int32, shape=(None, 2)) for _ in range(num_blocks)], 'lape_idx': [tf.placeholder(tf.int32, shape=(None, 10)) for _ in range(num_blocks)], # for laplace term 'pool_idx': [tf.placeholder(tf.int32, shape=(None, 2)) for _ in range(num_blocks - 1)], # for unpooling 'dropout': tf.placeholder_with_default(0., shape=()), 'num_features_nonzero': tf.placeholder(tf.int32), 'sample_coord': tf.placeholder(tf.float32, shape=(43, 3), name='sample_coord'), 'cameras': tf.placeholder(tf.float32, shape=(3, 5), name='Cameras'), 'faces_triangle': [tf.placeholder(tf.int32, shape=(None, 3)) for _ in range(num_blocks)], 'sample_adj': [tf.placeholder(tf.float32, shape=(43, 43)) for _ in range(num_supports)], } root_dir = os.path.join(cfg.save_path, cfg.name) model_dir = os.path.join(cfg.save_path, cfg.name, 'models') log_dir = os.path.join(cfg.save_path, cfg.name, 'logs') plt_dir = os.path.join(cfg.save_path, cfg.name, 'plt') if not os.path.exists(root_dir): os.mkdir(root_dir) print('==> make root dir {}'.format(root_dir)) if not os.path.exists(model_dir): os.mkdir(model_dir) print('==> make model dir {}'.format(model_dir)) if not os.path.exists(log_dir): os.mkdir(log_dir) print('==> make log dir {}'.format(log_dir)) if not os.path.exists(plt_dir): os.mkdir(plt_dir) print('==> make plt dir {}'.format(plt_dir)) summaries_dir = os.path.join(cfg.save_path, cfg.name, 'summaries') train_loss = open('{}/train_loss_record.txt'.format(log_dir), 'a') train_loss.write('Net {} | Start training | lr = {}\n'.format(cfg.name, cfg.lr)) # ------------------------------------------------------------------- print('=> build model') # Define model model = MeshNet(placeholders, logging=True, args=cfg) # --------------------------------------------------------------- print('=> load data') data = DataFetcher(file_list=cfg.train_file_path, data_root=cfg.train_data_path, image_root=cfg.train_image_path, is_val=False, mesh_root=cfg.train_mesh_root) data.setDaemon(True) data.start() # --------------------------------------------------------------- print('=> initialize session') sesscfg = tf.ConfigProto() sesscfg.gpu_options.allow_growth = True sesscfg.allow_soft_placement = True sess = tf.Session(config=sesscfg) sess.run(tf.global_variables_initializer()) train_writer = tf.summary.FileWriter(summaries_dir, sess.graph, filename_suffix='train') # --------------------------------------------------------------- if cfg.load_cnn: print('=> load pre-trained cnn') model.loadcnn(sess=sess, ckpt_path=cfg.pre_trained_cnn_path, step=cfg.cnn_step) if cfg.restore: print('=> load model') model.load(sess=sess, ckpt_path=model_dir, step=cfg.init_epoch) # --------------------------------------------------------------- # Load init ellipsoid and info about vertices and edges pkl = pickle.load(open('data/iccv_p2mpp.dat', 'rb')) # Construct Feed dict feed_dict = construct_feed_dict(pkl, placeholders) # --------------------------------------------------------------- train_number = data.number step = 0 tflearn.is_training(True, sess) print('=> start train stage 2') for epoch in range(cfg.epochs): current_epoch = epoch + 1 + cfg.init_epoch epoch_plt_dir = os.path.join(plt_dir, str(current_epoch)) if not os.path.exists(epoch_plt_dir): os.mkdir(epoch_plt_dir) mean_loss = 0 all_loss = np.zeros(train_number, dtype='float32') for iters in range(train_number): step += 1 # Fetch training data # need [img, label, pose(camera meta data), dataID] img_all_view, labels, poses, data_id, mesh = data.fetch() feed_dict.update({placeholders['features']: mesh}) feed_dict.update({placeholders['img_inp']: img_all_view}) feed_dict.update({placeholders['labels']: labels}) feed_dict.update({placeholders['cameras']: poses}) # --------------------------------------------------------------- _, dists, summaries, out1l, out2l = sess.run([model.opt_op, model.loss, model.merged_summary_op, model.output1l, model.output2l], feed_dict=feed_dict) # --------------------------------------------------------------- all_loss[iters] = dists mean_loss = np.mean(all_loss[np.where(all_loss)]) print('Epoch {}, Iteration {}, Mean loss = {}, iter loss = {}, {}, data id {}'.format(current_epoch, iters + 1, mean_loss, dists, data.queue.qsize(), data_id)) train_writer.add_summary(summaries, step) if (iters + 1) % 1000 == 0: plot_scatter(pt=out2l, data_name=data_id, plt_path=epoch_plt_dir) # --------------------------------------------------------------- # Save model model.save(sess=sess, ckpt_path=model_dir, step=current_epoch) train_loss.write('Epoch {}, loss {}\n'.format(current_epoch, mean_loss)) train_loss.flush() # --------------------------------------------------------------- data.shutdown() print('CNN-GCN Optimization Finished!')
def main(cfg): os.environ['CUDA_VISIBLE_DEVICES'] = str(cfg.gpu_id) # --------------------------------------------------------------- # Set random seed print('=> pre-porcessing') seed = 123 np.random.seed(seed) tf.set_random_seed(seed) # --------------------------------------------------------------- num_blocks = 3 num_supports = 2 placeholders = { 'features': tf.placeholder(tf.float32, shape=(None, 3), name='features'), 'img_inp': tf.placeholder(tf.float32, shape=(3, 224, 224, 3), name='img_inp'), 'labels': tf.placeholder(tf.float32, shape=(None, 6), name='labels'), 'support1': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)], 'support2': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)], 'support3': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)], 'faces': [tf.placeholder(tf.int32, shape=(None, 4)) for _ in range(num_blocks)], 'edges': [tf.placeholder(tf.int32, shape=(None, 2)) for _ in range(num_blocks)], 'lape_idx': [tf.placeholder(tf.int32, shape=(None, 10)) for _ in range(num_blocks)], # for laplace term 'pool_idx': [tf.placeholder(tf.int32, shape=(None, 2)) for _ in range(num_blocks - 1)], # for unpooling 'dropout': tf.placeholder_with_default(0., shape=()), 'num_features_nonzero': tf.placeholder(tf.int32), 'sample_coord': tf.placeholder(tf.float32, shape=(43, 3), name='sample_coord'), 'cameras': tf.placeholder(tf.float32, shape=(3, 5), name='Cameras'), 'faces_triangle': [tf.placeholder(tf.int32, shape=(None, 3)) for _ in range(num_blocks)], 'sample_adj': [tf.placeholder(tf.float32, shape=(43, 43)) for _ in range(num_supports)], } step = cfg.test_epoch root_dir = os.path.join(cfg.save_path, cfg.name) model_dir = os.path.join(cfg.save_path, cfg.name, 'models') predict_dir = os.path.join(cfg.save_path, cfg.name, 'predict', str(step)) if not os.path.exists(predict_dir): os.makedirs(predict_dir) print('==> make predict_dir {}'.format(predict_dir)) # ------------------------------------------------------------------- print('=> build model') # Define model model = MeshNetMVP2M(placeholders, logging=True, args=cfg) # --------------------------------------------------------------- print('=> load data') data = DataFetcher(file_list=cfg.test_file_path, data_root=cfg.test_data_path, image_root=cfg.test_image_path, is_val=True) data.setDaemon(True) data.start() # --------------------------------------------------------------- print('=> initialize session') sesscfg = tf.ConfigProto() sesscfg.gpu_options.allow_growth = True sesscfg.allow_soft_placement = True sess = tf.Session(config=sesscfg) sess.run(tf.global_variables_initializer()) # --------------------------------------------------------------- model.load(sess=sess, ckpt_path=model_dir, step=step) # --------------------------------------------------------------- # Load init ellipsoid and info about vertices and edges pkl = pickle.load(open('data/iccv_p2mpp.dat', 'rb')) # Construct Feed dict feed_dict = construct_feed_dict(pkl, placeholders) # --------------------------------------------------------------- test_number = data.number tflearn.is_training(False, sess) print('=> start test stage 1') for iters in range(test_number): # Fetch training data # need [img, label, pose(camera meta data), dataID] img_all_view, labels, poses, data_id, mesh = data.fetch() feed_dict.update({placeholders['img_inp']: img_all_view}) feed_dict.update({placeholders['labels']: labels}) feed_dict.update({placeholders['cameras']: poses}) # --------------------------------------------------------------- out1, out2, out3 = sess.run([model.output1, model.output2, model.output3], feed_dict=feed_dict) # --------------------------------------------------------------- # save GT label_path = os.path.join(predict_dir, data_id.replace('.dat', '_ground.xyz')) np.savetxt(label_path, labels) # save 1 # out1_path = os.path.join(predict_dir, data_id.replace('.dat', '_predict_1.xyz')) # np.savetxt(out1_path, out1) # # save 2 # out2_path = os.path.join(predict_dir, data_id.replace('.dat', '_predict_2.xyz')) # np.savetxt(out2_path, out2) # save 3 out3_path = os.path.join(predict_dir, data_id.replace('.dat', '_predict.xyz')) np.savetxt(out3_path, out3) print('Iteration {}/{}, Data id {}'.format(iters + 1, test_number, data_id)) # --------------------------------------------------------------- data.shutdown() print('CNN-GCN Optimization Finished!')