# In[11]: reset_tf_graph() ae = PointNetAutoEncoder(conf.experiment_name, conf) # Train the AE (save output to train_stats.txt) # In[1]: buf_size = 1 # Make 'training_stats' file to flush each output line regarding training. fout = open(osp.join(conf.train_dir, 'train_stats.txt'), 'a', buf_size) train_stats = ae.train(all_pc_data, conf, log_file=fout,mask_type =0) fout.close() # Get a batch of reconstuctions and their latent-codes. # In[13]: feed_pc, feed_model_names, _ = all_pc_data.next_batch(10) reconstructions = ae.reconstruct(feed_pc) latent_codes = ae.transform(feed_pc) # Use any plotting mechanism such as matplotlib to visualize the results.
enc = np.load(args["enc"])
'''
pcs = np.load("output/{}_pcs.npy".format(DATASET))
names = np.load("output/{}_names.npy".format(DATASET))
reset_tf_graph()
ae_configuration = MODEL_DIR + '/configuration'
ae_conf = Conf.load(ae_configuration)
ae_conf.encoder_args['verbose'] = False
ae_conf.decoder_args['verbose'] = False
ae = PointNetAutoEncoder(ae_conf.experiment_name, ae_conf)
ae.restore_model(MODEL_DIR, RESTORE_EPOCH, verbose=True)
recs = []
rec_losses = []
for pc in pcs:
pc = np.expand_dims(pc, axis=0)
rec, l = ae.reconstruct(pc, GT=pc, compute_loss=True)
recs.append(rec)
rec_losses.append(l)
np.save("output/{}_rec_loss".format(DATASET), np.array(rec_losses))
np.save("output/{}_recs".format(DATASET), np.array(recs))
#for id in range(0,3):
#for id in range(0,len(reconstructions)):
#print(names[id])
#points2file(reconstructions[id],"output/rec_{}".format(names[id]))
# In[11]: reset_tf_graph() ae = PointNetAutoEncoder(conf.experiment_name, conf) # Train the AE (save output to train_stats.txt) # In[1]: buf_size = 1 # Make 'training_stats' file to flush each output line regarding training. fout = open(osp.join(conf.train_dir, 'train_stats.txt'), 'a', buf_size) train_stats = ae.train(all_pc_data, conf, log_file=fout) fout.close() # Get a batch of reconstuctions and their latent-codes. # In[13]: feed_pc, feed_model_names, feed_part_pc = all_pc_data.part_next_batch(30) reconstructions = ae.reconstruct(feed_part_pc, GT=feed_pc) latent_codes = ae.transform(feed_part_pc) # Use any plotting mechanism such as matplotlib to visualize the results.
class_dir, n_threads=8, file_ending='.npy', max_num_points=2048, verbose=True, normalize=args.normalize_shape)
feed_pc, _, _ = all_pc_data.full_epoch_data()
feed_pc_tr_all = feed_pc[:, :n_pc_points]
feed_pc_te_all = feed_pc[:, -n_pc_points:]
print(feed_pc_tr_all.shape)
print(feed_pc_te_all.shape)
print("Gather samples")
all_sample = []
all_ref = []
for i in range(feed_pc_tr_all.shape[0]):
feed_pc_tr = feed_pc_tr_all[i:i+1]
feed_pc_te = feed_pc_te_all[i:i+1]
reconstructions = ae.reconstruct(feed_pc_tr)[0]
all_sample.append(reconstructions)
all_ref.append(feed_pc_te)
all_sample = np.concatenate(all_sample)
all_ref = np.concatenate(all_ref)
print(all_sample.shape, all_ref.shape)
sample_save_path = os.path.join(conf.train_dir, '%s_%s_all_sample.npy'%(class_name, syn_id))
np.save(sample_save_path, all_sample)
print("Samples save path:%s"%sample_save_path)
reference_save_path = os.path.join(conf.train_dir, '%s_%s_all_reference.npy'%(class_name, syn_id))
np.save(reference_save_path, all_ref)
print("Reference save path:%s"%reference_save_path)
# from latent_3d_points.src.evaluation_metrics_fast import MMD_COV_EMD_CD