label, from which I got rid of during training. To simplify everything I just
won't use 0 as index for this (YTI) dataset.
-1 - background index
"""
__author__ = 'Anna Kukleva'
__date__ = 'September 2018'
import os
from ute.utils.util_functions import dir_check
from ute.utils.arg_pars import opt
import data_utils.FS_utils.update_argpars as fs_utils
actions = ['-1.', '-2.']
fs_utils.update()
dir_check(opt.gt)
label2idx = {}
idx2label = {}
videos = {}
for filename in os.listdir(opt.gt):
with open(os.path.join(opt.gt, filename), 'r') as f:
for line in f:
line = line.strip()
if label2idx.get(line, -1) == -1:
idx = len(idx2label)
label2idx[line] = idx
idx2label[idx] = line
if __name__ == '__main__':
# set root
opt.dataset_root = '/sequoia/data1/akukleva/projects/unsup_temp_embed/fs'
opt.subaction = 'rgb'
# set feature extension and dimensionality
opt.ext = 'txt'
opt.feature_dim = 64
# model name can be 'mlp' or 'nothing' for no embedding (just raw features)
opt.model_name = 'mlp'
# load an already trained model (stored in the models directory in dataset_root)
opt.load_model = False
# use background noise (e.g. for YTI)
opt.bg = False
# granularity level eval or high
opt.gr_lev = 'eval'
# update log name and absolute paths
update()
# run temporal embedding
if opt.subaction == 'all':
actions = ['rgb']
all_actions(actions)
else:
temp_embed()