def get_remote_dataflow(port, nr_prefetch=1000, nr_thread=1):
ipc = 'ipc:///tmp/ipc-socket'
tcp = 'tcp://0.0.0.0:%d' % port
data_loader = RemoteDataZMQ(ipc, tcp, hwm=10000)
data_loader = BatchData(data_loader, batch_size=hp.train.batch_size)
data_loader = PrefetchData(data_loader, nr_prefetch, nr_thread)
return data_loader
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])
output_w, 19),
name='heatmap')
object_node = tf.placeholder(tf.float32,
shape=(args.batchsize, 2535, 85),
name='yolo_out')
# prepare data
if not args.remote_data:
df = get_dataflow_batch(args.datapath,
True,
args.batchsize,
img_path=args.imgpath)
print('train_df', df)
else:
# transfer inputs from ZMQ
df = RemoteDataZMQ(args.remote_data, hwm=3)
enqueuer = DataFlowToQueue(
df, [input_node, heatmap_node, vectmap_node, object_node],
queue_size=100)
q_inp, q_heat, q_vect, q_obj = enqueuer.dequeue()
print('inp/out', q_inp, q_heat, q_vect, q_obj)
df_valid = get_dataflow_batch(args.datapath,
False,
args.batchsize,
img_path=args.imgpath)
print('val_df', df_valid)
df_valid.reset_state()
validation_cache = []
val_image = get_sample_images(args.input_width, args.input_height)
shape=(args.batchsize, output_h, output_w,
38),
name='vectmap')
heatmap_node = tf.placeholder(tf.float32,
shape=(args.batchsize, output_h, output_w,
19),
name='heatmap')
# prepare data
if not args.remote_data:
df = get_dataflow_batch(args.datapath,
True,
args.batchsize,
img_path=args.imgpath)
else:
# transfer inputs from ZMQ
df = RemoteDataZMQ(args.remote_data, hwm=3)
# enqueuer = DataFlowToQueue(df, [input_node, heatmap_node, vectmap_node], queue_size=100)
q_inp, q_heat, q_vect = input_node, heatmap_node, vectmap_node # enqueuer.dequeue()
#df_valid = get_dataflow_batch(args.datapath, False, args.batchsize, img_path=args.imgpath)
# df_valid.reset_state()
validation_cache = []
# val_image = get_sample_images(args.input_width, args.input_height)
# logger.info('tensorboard val image: %d' % len(val_image))
logger.info(q_inp)
logger.info(q_heat)
logger.info(q_vect)
# define model for multi-gpu
q_inp_split, q_heat_split, q_vect_split = tf.split(
from tensorpack.dataflow.remote import RemoteDataZMQ
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])
