Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
 Community
text
stringlengths
1
93.6k
parser.add_argument('--arch', dest='arch', default='rfcn', choices=['rcnn', 'rfcn', 'couplenet'])
parser.add_argument('--dataset', dest='dataset',
help='training dataset',
default='pascal_voc_0712_semi', type=str)
parser.add_argument('--cfg', dest='cfg_file',
help='optional config file',
default='cfgs/vgg16.yml', type=str)
parser.add_argument('--net', dest='net',
help='vgg16, res50, res101, res152',
default='res101', type=str)
parser.add_argument('--set', dest='set_cfgs',
help='set config keys', default=None,
nargs=argparse.REMAINDER)
parser.add_argument('--load_dir', dest='load_dir',
help='directory to load models', default="save",
type=str)
parser.add_argument('--cuda', dest='cuda', default=True,
help='whether use CUDA',
action='store_true')
parser.add_argument('--ls', dest='large_scale',
help='whether use large imag scale',
action='store_true')
parser.add_argument('--mGPUs', dest='mGPUs', #default=True,
help='whether use multiple GPUs',
action='store_true')
parser.add_argument('--cag', dest='class_agnostic',
help='whether perform class_agnostic bbox regression',
action='store_true')
parser.add_argument('--parallel_type', dest='parallel_type',
help='which part of model to parallel, 0: all, 1: model before roi pooling',
default=0, type=int)
parser.add_argument('--checksession', dest='checksession',
help='checksession to load model',
default=1, type=int)
parser.add_argument('--checkepoch', dest='checkepoch',
help='checkepoch to load network',
default=6, type=int)
parser.add_argument('--checkpoint', dest='checkpoint',
help='checkpoint to load network',
default=10021, type=int)
parser.add_argument('--bs', dest='batch_size', # faster_rcnn_1_1_8274
help='batch_size',
default=1, type=int)
parser.add_argument('--vis', dest='vis',
help='visualization mode',
action='store_true')
args = parser.parse_args()
return args
# faster_rcnn_1_28_2504
lr = cfg.TRAIN.LEARNING_RATE
momentum = cfg.TRAIN.MOMENTUM
weight_decay = cfg.TRAIN.WEIGHT_DECAY
if __name__ == '__main__':
args = parse_args()
if args.arch == 'rcnn':
from model.faster_rcnn.vgg16 import vgg16
from model.faster_rcnn.resnet import resnet
elif args.arch == 'rfcn':
from model.rfcn.resnet_atrous import resnet
elif args.arch == 'couplenet':
from model.couplenet.resnet_atrous import resnet
print('Called with args:')
print(args)
if torch.cuda.is_available() and not args.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
np.random.seed(cfg.RNG_SEED)
if args.dataset == "pascal_voc":
args.imdb_name = "voc_2007_trainval"
args.imdbval_name = "voc_2007_test"
args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
elif args.dataset == "pascal_voc_0712":
args.imdb_name = "voc_2007_trainval+voc_2012_trainval"
args.imdbval_name = "voc_2007_test"
args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
elif args.dataset == "pascal_voc_0712_semi":
args.imdb_name = "voc_2007_trainval"
args.imdb_name_unlabel = "voc_2012_trainval"
args.imdbval_name = "voc_2007_test"
args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '20']
elif args.dataset == "coco":
args.imdb_name = "coco_2014_train+coco_2014_valminusminival"
args.imdbval_name = "coco_2014_minival"
args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
elif args.dataset == "imagenet":
args.imdb_name = "imagenet_train"
args.imdbval_name = "imagenet_val"
args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
elif args.dataset == "vg":
args.imdb_name = "vg_150-50-50_minitrain"
args.imdbval_name = "vg_150-50-50_minival"
args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
args.cfg_file = "cfgs/{}_ls.yml".format(args.net) if args.large_scale else "cfgs/{}.yml".format(args.net)