# held_out data (if they are provided in ae.train() ).
conf.save(osp.join(train_dir, 'configuration'))
# If you ran the above lines, you can reload a saved model like this:
# In[9]:
load_pre_trained_ae = True
restore_epoch = 400
if load_pre_trained_ae:
conf = Conf.load(train_dir + '/configuration')
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
ae.restore_model(conf.train_dir, epoch=restore_epoch)
# Build AE Model.
# In[10]:
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
# Train the AE (save output to train_stats.txt)
# In[ ]:
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()
# held_out data (if they are provided in ae.train() ).
conf.save(osp.join(train_dir, 'configuration'))
# If you ran the above lines, you can reload a saved model like this:
# In[9]:
load_pre_trained_ae = False
restore_epoch = 500
if load_pre_trained_ae:
conf = Conf.load(train_dir + '/configuration')
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
ae.restore_model(conf.train_dir, epoch=restore_epoch)
# Build AE Model.
# In[10]:
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
# Train the AE (save output to train_stats.txt)
# In[ ]:
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(train_data, conf, held_out_data=val_data, log_file=fout)
fout.close()
decoder = decoder,
encoder_args = enc_args,
decoder_args = dec_args,
experiment_name = experiment_name,
val_step = 5,
test_step = 200
)
# How often to evaluate/print out loss on held_out data (if any). # epochs
conf.save(osp.join(train_dir, 'configuration'))
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
buf_size = 1 # flush each line
fout = open(osp.join(conf.train_dir, 'train_stats.txt'), 'a', buf_size)
train_stats = ae.train(train_pc, conf, log_file=fout, val_data=val_pc, test_data=test_pc)
fout.close()
print('On train hidden transform')
train_hidden, _, _ = train_pc.full_epoch_data()
train_hidden = apply_augmentations(train_hidden, conf)
train_hidden = ae.transform(train_hidden)
np.save(osp.join(train_dir, 'hidden.npy'), train_hidden)
print('On val hidden transform')
val_hidden, _, _ = val_pc.full_epoch_data()
val_hidden = apply_augmentations(val_hidden, conf)
val_hidden = ae.transform(val_hidden)
np.save(osp.join(val_dir, 'hidden.npy'), val_hidden)
# 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.
train_params['training_epochs'] = 100
encoder, decoder, enc_args, dec_args = washington_xyz_rgb(
n_pc_points, bneck_size)
pcd_dataset = PointCloudDataSet(X, copy=False)
conf = Conf(n_input=[n_pc_points, 3],
loss=ae_loss,
training_epochs=train_params['training_epochs'],
batch_size=train_params['batch_size'],
denoising=train_params['denoising'],
learning_rate=train_params['learning_rate'],
train_dir=train_dir,
loss_display_step=train_params['loss_display_step'],
saver_step=train_params['saver_step'],
z_rotate=train_params['z_rotate'],
encoder=encoder,
decoder=decoder,
encoder_args=enc_args,
decoder_args=dec_args)
conf.experiment_name = experiment_name
conf.save(os.path.join(train_dir, 'configuration'))
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
buf_size = 1 # flush each line
fout = open(os.path.join(conf.train_dir, 'train_stats.txt'), 'a', buf_size)
train_stats = ae.train(pcd_dataset, conf, log_file=fout)
fout.close()
batch_size=train_params['batch_size'],
denoising=train_params['denoising'],
learning_rate=train_params['learning_rate'],
train_dir=train_dir,
loss_display_step=train_params['loss_display_step'],
saver_step=train_params['saver_step'],
z_rotate=train_params['z_rotate'],
encoder=encoder,
decoder=decoder,
encoder_args=enc_args,
decoder_args=dec_args)
conf.experiment_name = experiment_name
conf.held_out_step = 50 # How often to evaluate/print out loss on
conf.save(osp.join(train_dir, 'configuration'))
conf = Conf.load(
osp.join(top_out_dir, experiment_name) + '/configuration')
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
if load_pre_trained_ae:
ae.restore_model(conf.train_dir, epoch=restore_epoch)
if TRAIN:
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(train_pc_data,
conf,
log_file=fout,
held_out_data=val_pc_data)
fout.close()