roidb = get_training_roidb(imdb)
valroidb = roidb
# output directory where the models are saved
output_dir = get_output_dir(imdb, args.tag)
print('Output will be saved to `{:s}`'.format(output_dir))
# tensorboard directory where the summaries are saved during training
tb_dir = get_output_tb_dir(imdb, args.tag)
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add the validation set, but with no flipping images
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
# _, valroidb = combined_roidb(args.imdbval_name)
print('{:d} validation roidb entries'.format(len(valroidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
if args.net == 'vgg16':
net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args.net == 'res101':
net = Resnet101(batch_size=cfg.TRAIN.IMS_PER_BATCH)
else:
raise NotImplementedError
train_net(net,
imdb,
roidb,
valroidb,
output_dir,
tb_dir,
pretrained_model=args.weight,
max_iters=args.max_iters)
tag = tag if tag else 'default'
filename = tag + '/' + filename
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if args.net == 'vgg16':
net = vgg16(batch_size=1)
elif args.net == 'res101':
net = Resnet101(batch_size=1)
else:
raise NotImplementedError
# load model
net.create_architecture(sess,
"TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES)
if args.model:
print(('Loading model check point from {:s}').format(args.model))
saver = tf.train.Saver()
saver.restore(sess, args.model)
print('Loaded.')