# 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
# load network
if args.net == 'vgg16':
net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args.net == 'res50':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args.net == 'res101':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res152':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=152)
else:
raise NotImplementedError
train_net(net, imdb, roidb, valroidb, output_dir, tb_dir,
pretrained_model=args.weight,
max_iters=args.max_iters)
cfg.TEST.HAS_RPN = True # Use RPN for proposals
args = parse_args()
# model path
demonet = args.demo_net
dataset = args.dataset
saved_model = os.path.join('output', demonet, DATASETS[dataset][0], 'default',
NETS[demonet][0] % (70000 if dataset == 'pascal_voc' else 110000))
if not os.path.isfile(saved_model):
raise IOError(('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(saved_model))
# load network
if demonet == 'vgg16':
net = vgg16()
elif demonet == 'res101':
net = resnetv1(num_layers=101)
else:
raise NotImplementedError
net.create_architecture(21, tag='default', anchor_scales=[8, 16, 32])
net.load_state_dict(torch.load(saved_model))
net.eval()
net.cuda()
print('Loaded network {:s}'.format(saved_model))
im_names = [i for i in os.listdir('data/demo/') # Pull in all jpgs
if i.lower().endswith(".jpg")]
NETS[demonet][0])
if not os.path.isfile(tfmodel + '.meta'):
raise IOError(('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(tfmodel + '.meta'))
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth=True
# init session
sess = tf.Session(config=tfconfig)
# load network
if demonet == 'vgg16':
net = vgg16(batch_size=1)
elif demonet == 'res101':
net = resnetv1(batch_size=1, num_layers=101)
else:
raise NotImplementedError
net.create_architecture(sess, "TEST", 21,
tag='default', anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
'001763.jpg', '004545.jpg']
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
tag = args.tag
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)
else:
net = Resnet101(batch_size=1)
# load model
if imdb.name.startswith('voc'):
anchors = [8, 16, 32]
else:
anchors = [4, 8, 16, 32]
net.create_architecture(sess,
"TEST",
imdb.num_classes,
caffe_weight_path=args.weight,
tag='default',
anchor_scales=anchors)
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
# load network
if args.net == 'vgg16':
net = vgg16()
elif args.net == 'res50':
net = resnetv1(num_layers=50)
elif args.net == 'res101':
net = resnetv1(num_layers=101)
elif args.net == 'res152':
net = resnetv1(num_layers=152)
elif args.net == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
train_net(net,
imdb,
roidb,
valroidb,
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
# load network
if args.net == 'vgg16':
net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args.net == 'res50':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args.net == 'res101':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res152':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=152)
# elif args.net == 'mobile':
# net = mobilenetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH)
else:
raise NotImplementedError
train_net(net,
imdb,
roidb,
valroidb,
