def __init__(self):
args = args_setter()
imdb = get_imdb(args.imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
roidb = get_training_roidb(imdb)
data_layer = GtDataLayer(roidb, imdb.num_classes)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
print('Using config:')
pprint.pprint(cfg)
if not args.randomize:
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
imdb = get_imdb(args.imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
print 'symmetry'
print imdb._symmetry
roidb = get_training_roidb(imdb)
output_dir = get_output_dir(imdb, None)
print 'Output will be saved to `{:s}`'.format(output_dir)
device_name = '/gpu:{:d}'.format(args.gpu_id)
cfg.GPU_ID = args.gpu_id
print device_name
if cfg.NETWORK == 'FCN8VGG':
path = osp.abspath(osp.join(cfg.ROOT_DIR, args.pretrained_model))
cfg.TRAIN.MODEL_PATH = path
pretrained_model = None
else:
pretrained_model = args.pretrained_model
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
print('Using config:')
pprint.pprint(cfg)
if not args.randomize:
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
imdb = get_imdb(args.imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
roidb = get_training_roidb(imdb)
output_dir = get_output_dir(imdb, None)
print 'Output will be saved to `{:s}`'.format(output_dir)
device_name = '/gpu:{:d}'.format(args.gpu_id)
cfg.GPU_ID = args.gpu_id
print device_name
network = get_network(args.network_name, args.pretrained_model)
print 'Use network `{:s}` in training'.format(args.network_name)
train_net(network, imdb, roidb, output_dir,
pretrained_model=args.pretrained_model,
max_iters=args.max_iters)