from matplotlib import pyplot as plt
parser = OptionParser()
parser.add_option('--config',
type=str,
help="training configuration",
default="./configs/train_config.yaml")
(opts, args) = parser.parse_args()
assert isinstance(opts, object)
opt = Config(opts.config)
print(opt)
if opt.checkpoint_folder is None:
opt.checkpoint_folder = 'checkpoints'
# make dir
if not os.path.exists(opt.checkpoint_folder):
os.system('mkdir {0}'.format(opt.checkpoint_folder))
tds_ls = []
for i in range(opt.model_number):
if i == 0:
tds_ls.append(
VideoFeatDataset(root=opt.data_dir,
flist=opt.flist,
test_list=opt.test_flist,
test_number=opt.test_number,
bagging=False,
creat_test=True))
parser = OptionParser()
parser.add_option('--config',
type=str,
help="training configuration",
default="./configs/train_config.yaml")
(opts, args) = parser.parse_args()
assert isinstance(opts, object)
opt = Config(opts.config)
mylog, logfile= get_logger(fileName=opt.log_name)
print(opt)
os.popen('cat {0} >> {1}'.format(opts.config, logfile))
if opt.checkpoint_folder is None:
opt.checkpoint_folder = 'models_checkpoint'
# make dir
if not os.path.exists(opt.checkpoint_folder):
os.system('mkdir {0}'.format(opt.checkpoint_folder))
train_dataset = dset(opt.data_dir, flist=opt.flist)
mylog.info('number of train samples is: {0}'.format(len(train_dataset)))
mylog.info('finished loading data')
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
ngpu = int(opt.ngpu)
opt.manualSeed = random.randint(1, 10000) # fix seed
# opt.manualSeed = 123456
