def main():
start_time = time.time()
paths = vcoco_config.Paths()
paths.eval_root = '/home/siyuan/projects/papers/cvpr2018/tmp/evaluation/vcoco/features'
if not os.path.exists(paths.eval_root):
os.makedirs(paths.eval_root)
collect_data(paths)
print('Time elapsed: {:.2f}s'.format(time.time() - start_time))
def main():
start_time = time.time()
paths = vcoco_config.Paths()
paths.eval_root = 'evaluation/vcoco/features'
if not os.path.exists(paths.eval_root):
os.makedirs(paths.eval_root)
collect_data(paths)
print('Time elapsed: {:.2f}s'.format(time.time() - start_time))
def parse_arguments():
paths = vcoco_config.Paths()
feature_type = 'resnet'
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--feature-type', default=feature_type, help='feature_type')
parser.add_argument('--data', metavar='DIR', default=paths.data_root, help='path to dataset')
parser.add_argument('-j', '--workers', default=1, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=100, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=32, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=5e-5, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-3, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=30, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default=os.path.join(paths.tmp_root, 'checkpoints/vcoco/finetune_{}'.format(feature_type)), type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', default=True, action='store_true',
help='use pre-trained model')
parser.add_argument('--world-size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='gloo', type=str,
help='distributed backend')
return parser.parse_args()
def main():
paths = vcoco_config.Paths()
imagesets = ['train', 'val', 'test']
for imageset in imagesets:
extract_features(paths, imageset)
def parse_arguments():
paths = vcoco_config.Paths()
parser = argparse.ArgumentParser(description='V-COCO dataset')
parser.add_argument('--data-root', default=paths.data_root, help='dataset path')
parser.add_argument('--tmp-root', default=paths.tmp_root, help='intermediate result path')
return parser.parse_args()
def main():
paths = vcoco_config.Paths()
imagesets = ['test']
for imageset in imagesets:
plot_set(paths, imageset)