def main():
paths = config.Paths()
start_time = time.time()
np.random.seed(0)
evaluate(paths)
# test(paths)
print('Time elapsed: {}s'.format(time.time() - start_time))
def __init__(self, dataset='VCLA_GAZE'):
self.paths_dict = {
'WNP': wnp_config.Paths(),
'VCLA_GAZE': vcla_gaze_config.Paths(),
'CAD': cad_config.Paths(),
'Breakfast': breakfast_config.Paths()
}
self.metadata_dict = {
'WNP': WNP_METADATA(),
'VCLA_GAZE': VCLA_METADATA(),
'CAD': CAD_METADATA(),
'Breakfast': BREAKFAST_METADATA()
}
self.dataset_dict = {
'WNP':
lambda path, mode, task, subsample: wnp.WNP(
path, mode, task, subsample),
'VCLA_GAZE':
lambda path, mode, task, subsample: vcla_gaze.VCLA_GAZE(
path, mode, task, subsample),
'CAD':
lambda path, mode, task, subsample: cad.CAD(
path, mode, task, subsample),
'Breakfast':
lambda path, mode, task, subsample: breakfast.Breakfast(
path, mode, task, subsample)
}
self.dataset = self.dataset_dict[dataset]
self.paths = self.paths_dict[dataset]
self.metadata = self.metadata_dict[dataset]
def main():
paths = config.Paths()
start_time = time.time()
parse_data(paths)
print('Time elapsed: {}'.format(time.time() - start_time))
def main():
paths = config.Paths()
start_time = time.time()
# induce_activity_grammar(paths)
# read_induced_grammar(paths)
test(paths)
print('Time elapsed: {}'.format(time.time() - start_time))
def main():
paths = config.Paths()
with open(os.path.join(paths.tmp_root, 'label_list.p'), 'rb') as f:
sequence_ids = pickle.load(f)
train_num = 10
keys = list(sequence_ids.keys())
shuffle(keys)
train_ids = ['1130144242$4']
train_set = CAD_FEATURE(paths, train_ids, 'affordance')
feature, label = train_set[0]
print('Finished')
def parse_args():
def restricted_float(x, inter):
x = float(x)
if x < inter[0] or x > inter[1]:
raise argparse.ArgumentTypeError("{} not in range [{}, {}]".format(
x, inter[0], inter[1]))
return x
paths = cad_config.Paths()
model_name = 'resnet'
tasks = ['affordance', 'activity']
task = tasks[0]
parser = argparse.ArgumentParser(description='VCLA feature extraction')
parser.add_argument('--task',
default=task,
type=str,
help='Default task for network training')
parser.add_argument(
'--cuda',
default=torch.cuda.is_available(),
type=bool,
help='Option flag for using cuda trining (default: True)')
parser.add_argument(
'--distributed',
default=False,
type=bool,
help='Option flag for using distributed training (default: True)')
parser.add_argument(
'--model',
default=model_name,
type=str,
help='model to use when extracting features (default: resnet)')
parser.add_argument('--workers',
default=10,
type=int,
metavar='N',
help='number of data loading workers (default: 1)')
parser.add_argument('--start_epoch',
default=0,
type=int,
metavar='N',
help='starting epoch of training (default: 0)')
parser.add_argument('--epochs',
default=10,
type=int,
metavar='N',
help='number of epochs for training (default: 100)')
parser.add_argument('--batch_size',
default=16,
type=int,
metavar='N',
help='batch size for training (default: 16)')
parser.add_argument(
'--lr',
default=1e-3,
type=float,
help='learning rate for the feature extraction process (default: 1e-3)'
)
parser.add_argument(
'--lr_decay',
type=lambda x: restricted_float(x, [0.01, 1]),
help='decay rate of learning rate (default: between 0.01 and 1)')
parser.add_argument('--log_interval',
type=int,
default=50,
metavar='N',
help='Intervals for logging (default: 10 batch)')
parser.add_argument(
'--save_interval',
type=int,
default=1,
metavar='N',
help='Intervals for saving checkpoint (default: 3 epochs)')
parser.add_argument(
'--train_ratio',
type=float,
default=0.6,
help='ratio of data for training purposes (default: 0.65)')
parser.add_argument(
'--val_ratio',
type=float,
default=0.1,
help='ratio of data for validation purposes (default: 0.1)')
parser.add_argument(
'--eval',
default=False,
type=bool,
help='indicates whether need to run evaluation on testing set')
parser.add_argument('--save',
default=False,
type=bool,
help='flag for saving likelihood')
args = parser.parse_args()
args.paths = paths
args.save_path = os.path.join(paths.inter_root, 'finetune', args.task)
args.resume = os.path.join(paths.checkpoint_root, 'finetune',
'{}'.format(model_name), '{}'.format(args.task))
return args