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",