sess.graph)
logger.info('prepare coordinator')
coord = tf.train.Coordinator()
# enqueuer.set_coordinator(coord)
# enqueuer.start()
logger.info('Training Started.')
time_started = time.time()
last_gs_num = last_gs_num2 = 0
initial_gs_num = sess.run(global_step)
df.reset_state()
while True:
for dp in df.get_data():
feed = dict(zip([input_node, heatmap_node, vectmap_node], dp))
_, gs_num = sess.run([train_op, global_step], feed_dict=feed)
#print("DONEONDONEODNOEN")
if gs_num > step_per_epoch * args.max_epoch:
break
if gs_num - last_gs_num >= 10:
train_loss, train_loss_ll, train_loss_ll_paf, train_loss_ll_heat, lr_val, summary = sess.run(
[
total_loss, total_loss_ll, total_loss_ll_paf,
total_loss_ll_heat, learning_rate,
merged_summary_op
format='[lmdb_dataset] %(asctime)s %(levelname)s %(message)s')
if __name__ == '__main__':
"""
Speed Test for Getting Input batches from other nodes
"""
parser = argparse.ArgumentParser(
description='Worker for preparing input batches.')
parser.add_argument('--listen', type=str, default='tcp://0.0.0.0:1027')
parser.add_argument('--show', type=bool, default=False)
args = parser.parse_args()
df = RemoteDataZMQ(args.listen)
logging.info('tcp queue start')
df.reset_state()
t = time.time()
for i, dp in enumerate(df.get_data()):
if i == 100:
break
logging.info('Input batch %d received.' % i)
if i == 0:
for d in dp:
logging.info('%d dp shape={}'.format(d.shape))
if args.show:
CocoPose.display_image(dp[0][0], dp[1][0], dp[2][0])
logging.info('Speed Test Done for 100 Batches in %f seconds.' %
(time.time() - t))
from pose_dataset import CocoPose
logging.basicConfig(level=logging.DEBUG, format='[lmdb_dataset] %(asctime)s %(levelname)s %(message)s')
if __name__ == '__main__':
"""
Speed Test for Getting Input batches from other nodes
"""
parser = argparse.ArgumentParser(description='Worker for preparing input batches.')
parser.add_argument('--listen', type=str, default='tcp://0.0.0.0:1027')
parser.add_argument('--show', type=bool, default=False)
args = parser.parse_args()
df = RemoteDataZMQ(args.listen)
logging.info('tcp queue start')
df.reset_state()
t = time.time()
for i, dp in enumerate(df.get_data()):
if i == 100:
break
logging.info('Input batch %d received.' % i)
if i == 0:
for d in dp:
logging.info('%d dp shape={}'.format(d.shape))
if args.show:
CocoPose.display_image(dp[0][0], dp[1][0], dp[2][0])
logging.info('Speed Test Done for 100 Batches in %f seconds.' % (time.time() - t))