)
parser.add_argument("--store_as_npy",
help="Usually the output is saved as a .hdf5 container, "
"using this will save the output as .npy",
action="store_true")
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
model = Autoencoder(config_obj, dataset)
model.set_iterators(eval_from_placeholder=True)
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,
from src.configreader import ConfigReader
from src.dataset import Dataset
from src.autoencoder import Autoencoder
if __name__ == "__main__":
config_path = os.path.join(os.path.dirname(__file__), "config.json")
config_obj = ConfigReader(config_path)
dataset = Dataset(config_obj)
x_train = dataset.load_train_data()
x_val = dataset.load_val_data()
x_eval = dataset.load_eval_data()
model = Autoencoder(config_obj, dataset)
model.set_iterators(x_train, x_val, eval_from_input_iterator=x_eval)
for i in range(12000):
# the evaluation is quite time intensive, during it off increase the speed
do_evaluation = i % 500 == 0 and i > 0
stats = model.train(do_evaluation)
print("{}: {}".format(i, stats["loss"]))
if "val_loss" in stats:
print("Val loss: {}".format(stats["val_loss"]))
print("IO: {}, l1: {}".format(stats['iou'], stats["eval_l1"]))
if i % 1000 and i > 0:
model.save(config_obj.data.get_string("model_save_path"))
model.save(config_obj.data.get_string("model_save_path"))
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])
full_block_latent = model.encode_from_placeholder(
input_ones * -dataset.truncation_threshold)
empty_block_latent = model.encode_from_placeholder(
input_ones * dataset.truncation_threshold)
batch_container = np.zeros([