import numpy as np
import scipy.io as scio
import sys
import os
import argparse
import pickle
from sklearn import metrics
import json
from utils.util import score_smoothing
from utils.paths import PATHS
sys.path.append('../')
DATA_DIR = PATHS.get_dataset_dir_path()
# normalize scores in each sub video
NORMALIZE = True
# number of history frames, since in prediction based method,
# the first 4 frames can not be predicted, so that
# the first 4frames are undecidable, we just ignore the first 4 frames
DECIDABLE_IDX = 2
def parser_args():
parser = argparse.ArgumentParser(
description='evaluating the model, computing the roc/auc.')
parser.add_argument('-f',
'--file',
type=str,
from sklearn.externals import joblib
import sys
from scripts import inference
from models.CAE import CAE_encoder
from utils import util
import os
import argparse
import numpy as np
import pickle
import time
from utils import evaluate
from utils.paths import PATHS
sys.path.append("../")
prefix = PATHS.get_dataset_dir_path()
def arg_parse():
parser = argparse.ArgumentParser()
parser.add_argument("-g",
"--gpu",
type=str,
default="0",
help="Use which gpu?")
parser.add_argument("-d",
"--dataset",
type=str,
help="Train on which dataset")
parser.add_argument("-b",
"--bn",