objLoaderVID.loaderVID()
train_vid_videos = objLoaderVID.get_videos()
vid_images = 0
for vid_idx in xrange(len(train_vid_videos)):
video = train_vid_videos[vid_idx]
annos = video.annotations
vid_images += len(annos)
total_image_size = vid_images
logger.info('total training VID images size is: ' + str(vid_images))
# debug
# cur_batch = data_reader(train_vid_videos)
# network initialization
tracknet = goturn_net_coord.TRACKNET(BATCH_SIZE)
tracknet.build()
# learning policy
global_step = tf.Variable(0, trainable=False, name="global_step")
learning_rate = tf.train.piecewise_constant(
global_step, [tf.cast(v, tf.int32) for v in POLICY['step_values']],
POLICY['learning_rates'])
train_step = tf.train.AdamOptimizer(learning_rate, POLICY['momentum'],
POLICY['momentum2']).minimize(
tracknet.loss_wdecay,
global_step=global_step)
# summary
merged_summary = tf.summary.merge_all()
sess = tf.Session()
'/home/jaehyuk/code/github/vot-toolkit/tracker/examples/python/checkpoints/checkpoint.ckpt-29449',
'/home/jaehyuk/code/github/vot-toolkit/tracker/examples/python/checkpoints/checkpoint.ckpt-19633',
'/home/jaehyuk/code/github/vot-toolkit/tracker/examples/python/checkpoints/checkpoint.ckpt-9817'
]
for checkpoint in cklist:
if os.path.exists(checkpoint + '.meta'):
ckpt = checkpoint
break
imagefile = handle.frame()
if not imagefile:
sys.exit(0)
bbox_estim = bbox_estimator(False, logger)
tracknet = goturn_net_coord.TRACKNET(BATCH_SIZE, train=False)
tracknet.build()
sess = tf.Session()
init = tf.global_variables_initializer()
init_local = tf.local_variables_initializer()
sess.run(init)
sess.run(init_local)
coord = tf.train.Coordinator()
# start the threads
tf.train.start_queue_runners(sess=sess, coord=coord)
### ckpt
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
step = 1
for st in stlist:
if os.path.exists(st):
step = int(st.split('.')[1])
break
# debug
# step = 50
imagefile = handle.frame()
if not imagefile:
sys.exit(0)
bbox_estim = bbox_estimator(False, logger)
tracknet = goturn_net_coord.TRACKNET(BATCH_SIZE, train=True, online=True)
tracknet.build()
# TODO check trainiable_variables
tvars = tf.trainable_variables()
g_vars = [
var for var in tvars
if 'fc1_image' or 'fc2_image' or 'fc3_image' or 'fc4_image' in var.name
]
# g_vars = [var for var in tvars if 'fc1_image' in var.name]
train_step = tf.train.AdamOptimizer(1e-8).minimize(tracknet.loss)
sess = tf.Session()
init = tf.global_variables_initializer()
init_local = tf.local_variables_initializer()
sess.run(init)