def prepare_data_for_test():
data_gen.init()
task = data_gen.find_data_task(cnf.task)
task.prepare_visualisation_data()
data_gen.collect_bins()
data_gen.print_bin_usage()
from RSE_model import RSE
import data_feeder
print("Running Residual Shuffle-Exchange network trainer.....")
if not cnf.use_two_gpus:
os.environ["CUDA_VISIBLE_DEVICES"] = cnf.gpu_instance
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] = "1"
countList = [cnf.batch_size for x in cnf.bins]
np.set_printoptions(linewidth=2000, precision=4, suppress=True)
# prepare training and test data
max_length = cnf.bins[-1]
data_gen.init()
if cnf.task in cnf.language_tasks:
task = data_gen.find_data_task(cnf.task)
task.prepare_data()
data_gen.collect_bins()
data_gen.print_bin_usage()
else:
for length in range(1, max_length + 1):
n_examples = cnf.data_size
data_gen.init_data(cnf.task, length, n_examples, cnf.n_input)
data_gen.collect_bins()
if len(data_gen.train_set[cnf.task][cnf.forward_max]) == 0:
data_gen.init_data(cnf.task, cnf.forward_max, cnf.test_data_size, cnf.n_input)
data_supplier = data_feeder.create_data_supplier()