logger.info('define model+')
with tf.device(tf.DeviceSpec(device_type="CPU")):
input_node = tf.placeholder(tf.float32, shape=(args.batchsize, args.input_height, args.input_width, 3), name='image')
vectmap_node = tf.placeholder(tf.float32, 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
df = get_dataflow_batch(args.datapath, True, args.batchsize, img_path=args.imgpath)
enqueuer = DataFlowToQueue(df, [input_node, heatmap_node, vectmap_node], queue_size=100)
q_inp, q_heat, q_vect = 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.debug('tensorboard val image: %d' % len(val_image))
logger.debug(q_inp)
logger.debug(q_heat)
logger.debug(q_vect)
# define model for multi-gpu
q_inp_split, q_heat_split, q_vect_split = tf.split(q_inp, args.gpus), tf.split(q_heat, args.gpus), tf.split(q_vect, args.gpus)
output_vectmap = []
output_heatmap = []
losses = []
last_losses_l1 = []
last_losses_l2 = []
outputs = []
for gpu_id in range(args.gpus):
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 = 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(q_inp, args.gpus), tf.split(q_heat, args.gpus), tf.split(q_vect, args.gpus)
output_vectmap = []
output_heatmap = []
losses = []
last_losses_l1 = []
last_losses_l2 = []
outputs = []
for gpu_id in range(args.gpus):