def load(model_path, z_rotate, num_points, point_dimension=3):
model_dir = osp.dirname(model_path)
model_epoch = int(osp.basename(model_path).split('-')[1])
experiment_name = osp.basename(osp.dirname(model_path)).split('train_')[1] #'single_class_ae_plane_chamfer_z_rotate' # Number of points per model.
bneck_size = 128 # Bottleneck-AE size
ae_loss = 'chamfer' # Loss to optimize: 'emd' or 'chamfer'
class_name = "airplane"
syn_id = snc_category_to_synth_id()[class_name]
class_dir = osp.join(top_in_dir , syn_id) # e.g. /home/yz6/code/latent_3d_points/data/shape_net_core_uniform_samples_2048/02691156
train_dir = create_dir(osp.join(top_out_dir, experiment_name))
train_params = default_train_params()
encoder, decoder, enc_args, dec_args = mlp_architecture_ala_iclr_18(num_points, bneck_size, point_dimension=point_dimension)
conf = Conf(n_input = [num_points, point_dimension],
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'],
loss_display_step = train_params['loss_display_step'],
saver_step = train_params['saver_step'],
z_rotate = z_rotate == 'True',
train_dir = train_dir,
encoder = encoder,
decoder = decoder,
encoder_args = enc_args,
decoder_args = dec_args,
experiment_name = experiment_name,
allow_gpu_growth = True
)
# pdb.set_trace()
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
ae.restore_model(model_dir, model_epoch)
return ae, conf
top_out_dir = '../data/' # Use to save Neural-Net check-points etc.
top_in_dir = '../data/shape_net_core_uniform_samples_2048/' # Top-dir of where point-clouds are stored.
experiment_name = 'test'
n_pc_points = 2048 # Number of points per model.
bneck_size = 128 # Bottleneck-AE size
ae_loss = 'chamfer' # Loss to optimize: 'emd' or 'chamfer'
# class_name = raw_input('Give me the class name (e.g. "chair"): ').lower()
class_name = 'chair'
# Load Point-Clouds
# In[5]:
syn_id = snc_category_to_synth_id()[class_name]
class_dir = osp.join(top_in_dir, syn_id)
all_pc_data = load_all_point_clouds_under_folder(class_dir,
n_threads=8,
file_ending='.ply',
verbose=True)
# Load default training parameters (some of which are listed beloq). For more details please print the configuration object.
#
# 'batch_size': 50
#
# 'denoising': False (# by default AE is not denoising)
#
# 'learning_rate': 0.0005
#
# 'z_rotate': False (# randomly rotate models of each batch)
