from torchvision import transforms
from torch.autograd import Variable
from core import dataset
from core.utils import *
from core.cfg import parse_cfg
from core.cfg import cfg
from tool.darknet.darknet import Darknet
# Training settings
datacfg = sys.argv[1]
cfgfile = sys.argv[2]
weightfile = sys.argv[3]
data_options = read_data_cfg(datacfg)
net_options = parse_cfg(cfgfile)[0]
trainlist = data_options['train']
testlist = data_options['valid']
# backupdir = data_options['backup']
gpus = data_options['gpus'] # e.g. 0,1,2,3
ngpus = len(gpus.split(','))
num_workers = int(data_options['num_workers'])
batch_size = int(net_options['batch'])
max_batches = int(net_options['max_batches'])
learning_rate = float(net_options['learning_rate'])
momentum = float(net_options['momentum'])
decay = float(net_options['decay'])
steps = [float(step) for step in net_options['steps'].split(',')]
scales = [float(scale) for scale in net_options['scales'].split(',')]
cls_id = box[6 + 2 * j]
prob = det_conf * cls_conf
fps[i].write('%s %f %f %f %f %f\n' %
(imgid, prob, x1, y1, x2, y2))
for i in range(n_cls):
fps[i].close()
# import pdb; pdb.set_trace()
if __name__ == '__main__':
import sys
if len(sys.argv) in [5, 6, 7]:
datacfg = sys.argv[1]
darknet = parse_cfg(sys.argv[2])
learnet = parse_cfg(sys.argv[3])
weightfile = sys.argv[4]
if len(sys.argv) >= 6:
gpu = sys.argv[5]
else:
gpu = '0'
if len(sys.argv) == 7:
use_baserw = True
else:
use_baserw = False
data_options = read_data_cfg(datacfg)
net_options = darknet[0]
meta_options = learnet[0]
data_options['gpus'] = gpu
raise NotImplementedError("Image path note recognized!")
return labpath
if __name__ == '__main__':
from core.utils import read_data_cfg
from core.cfg import parse_cfg
datacfg = 'cfg/metayolo.data'
netcfg = 'cfg/dynamic_darknet_last.cfg'
metacfg = 'cfg/learnet_last.cfg'
data_options = read_data_cfg(datacfg)
net_options = parse_cfg(netcfg)[0]
meta_options = parse_cfg(metacfg)[0]
cfg.config_data(data_options)
cfg.config_meta(meta_options)
cfg.config_net(net_options)
cfg.num_gpus = 4
metafiles = 'data/voc_metadict1_full.txt'
trainlist = '/scratch/bykang/datasets/voc_train.txt'
metaset = MetaDataset(metafiles=metafiles, train=True)
metaloader = torch.utils.data.DataLoader(
metaset,
batch_size=metaset.batch_size,
shuffle=False,