if cfg.NETWORK == 'FCN8VGG':
path = osp.abspath(osp.join(cfg.ROOT_DIR, args.pretrained_model))
cfg.TRAIN.MODEL_PATH = path
cfg.TRAIN.TRAINABLE = False
cfg.TRAIN.VOTING_THRESHOLD = cfg.TEST.VOTING_THRESHOLD
cfg.RIG = args.rig_name
cfg.CAD = args.cad_name
cfg.POSE = args.pose_name
cfg.BACKGROUND = args.background_name
cfg.IS_TRAIN = False
from networks.factory import get_network
network = get_network(args.network_name)
print('Use network `{:s}` in training'.format(args.network_name))
# start a session
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options))
saver.restore(sess, args.model)
print(('Loading model weights from {:s}').format(args.model))
if cfg.TEST.SINGLE_FRAME:
if cfg.TEST.SEGMENTATION:
test_net_single_frame(sess, network, imdb, weights_filename, args.cad_name)
else:
test_net_detection(sess, network, imdb, weights_filename)
else:
test_net(sess, network, imdb, weights_filename, args.rig_name, args.kfusion)
roidb = get_training_roidb(imdb)
cfg.GPU_ID = args.gpu_id
device_name = '/gpu:{:d}'.format(args.gpu_id)
print device_name
cfg.TRAIN.NUM_STEPS = 1
cfg.TRAIN.GRID_SIZE = cfg.TEST.GRID_SIZE
cfg.TRAIN.TRAINABLE = False
from networks.factory import get_network
network = get_network(args.network_name)
print 'Use network `{:s}` in training'.format(args.network_name)
# start a session
saver = tf.train.Saver()
if args.kfusion:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options))
else:
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
saver.restore(sess, args.model)
print('Loading model weights from {:s}').format(args.model)
print(" ", args.network_name)
if cfg.TEST.SINGLE_FRAME:
test_net_single_frame(sess, network, imdb, weights_filename,
args.rig_name, args.kfusion)
else:
test_net(sess, network, imdb, roidb, weights_filename, args.rig_name,
args.kfusion)
cfg.CAD = args.cad_name
cfg.POSE = args.pose_name
cfg.BACKGROUND = args.background_name
cfg.IS_TRAIN = False
from networks.factory import get_network
network = get_network(args.network_name)
print 'Use network `{:s}` in training'.format(args.network_name)
# start a session
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options))
saver.restore(sess, args.model)
print('Loading model weights from {:s}').format(args.model)
if cfg.TEST.SINGLE_FRAME:
if cfg.TEST.SEGMENTATION:
test_net_single_frame(sess,
network,
imdb,
weights_filename,
args.cad_name,
start_index=args.start_index)
else:
test_net_detection(sess, network, imdb, weights_filename)
else:
test_net(sess, network, imdb, weights_filename, args.rig_name,
args.kfusion)