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run.py
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run.py
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import argparse, time, os, operator
import tensorflow as tf
import source.utils as utils
import source.connector as con
import source.tf_process as tfp
import source.datamanager as dman
def main():
os.environ["CUDA_VISIBLE_DEVICES"]=FLAGS.gpu
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
print(e)
dataset = dman.DataSet()
agent = con.connect(nn=FLAGS.nn).Agent(\
dim_h=dataset.dim_h, dim_w=dataset.dim_w, dim_c=dataset.dim_c, dim_z=FLAGS.dim_z, \
ksize=FLAGS.ksize, num_flow=FLAGS.num_flow, filters=FLAGS.filters, learning_rate=FLAGS.lr, \
path_ckpt='Checkpoint')
time_tr = time.time()
tfp.training(agent=agent, dataset=dataset, \
batch_size=FLAGS.batch, epochs=FLAGS.epochs)
time_te = time.time()
tfp.test(agent=agent, dataset=dataset)
time_fin = time.time()
tr_time = time_te - time_tr
te_time = time_fin - time_te
print("Time (TR): %.5f [sec]" %(tr_time))
print("Time (TE): %.5f (%.5f [sec/sample])" %(te_time, te_time/num_model/dataset.num_te))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=str, default="0", help='')
parser.add_argument('--nn', type=int, default=0, help='')
parser.add_argument('--num_flow', type=int, default=8, help='')
parser.add_argument('--ksize', type=int, default=3, help='')
parser.add_argument('--filters', type=str, default="16,32,64", help='')
parser.add_argument('--dim_z', type=int, default=64, help='')
parser.add_argument('--lr', type=float, default=1e-4, help='')
parser.add_argument('--batch', type=int, default=32, help='')
parser.add_argument('--epochs', type=int, default=1000, help='')
FLAGS, unparsed = parser.parse_known_args()
main()