model.load(config_obj.data.get_string("model_save_path"))
input_ones = np.ones(
[1, dataset.input_size(),
dataset.input_size(),
dataset.input_size(), 1])
full_block_latent = model.encode_from_placeholder(
input_ones * -dataset.truncation_threshold)
empty_block_latent = model.encode_from_placeholder(
input_ones * dataset.truncation_threshold)
data_iterator = dataset.load_custom_data(
args.data_path,
fast_inference=True,
input_is_latent=True,
num_threads=1,
full_block_latent=full_block_latent,
empty_block_latent=empty_block_latent,
empty_block_detection_threshold=args.empty_block_detection_threshold)
model.set_iterators(eval_from_latent_iterator=data_iterator)
model.load(config_obj.data.get_string("model_save_path"))
batch_container = np.zeros([
dataset.number_of_blocks_per_voxelgrid(), dataset.block_size,
dataset.block_size, dataset.block_size, 1
])
start = time()
try:
while True:
section_start = time()
help="The path where to look for subdirectories with .h5py files.")
parser.add_argument('--batch_size', type=int, default=256)
parser.add_argument('--threads',
type=int,
default=4,
help="Number of threads to use in the input pipeline.")
args = parser.parse_args()
config_path = os.path.join(os.path.dirname(__file__), "config.json")
config_obj = ConfigReader(config_path)
dataset = Dataset(config_obj)
dataset.batch_size = args.batch_size
data_iterator = dataset.load_custom_data(args.path,
fast_inference=True,
num_threads=args.threads)
model = Autoencoder(config_obj, dataset)
model.set_iterators(eval_from_input_iterator=data_iterator,
eval_from_placeholder=True,
eval_uses_fast_inference=True)
model.load(config_obj.data.get_string("model_save_path"))
model.summary()
input_ones = np.ones(
[1, dataset.input_size(),
dataset.input_size(),
dataset.input_size(), 1])
