test_model.py

# -*- coding: utf-8 -*-
"""
run file for neural walker

"""

import pickle
import time
import numpy
import theano
from theano import sandbox
import theano.tensor as tensor
import os
import scipy.io
from collections import defaultdict
from theano.tensor.shared_randomstreams import RandomStreams
import modules.utils as utils
import modules.models as models
import modules.optimizers as optimizers
import modules.trainers as trainers
import modules.data_processers as data_processers

import run_model
import datetime
import argparse
__author__ = 'Hongyuan Mei'

dtype=theano.config.floatX


#

def main():

    parser = argparse.ArgumentParser(
        description='Trainning model ... '
    )
    #
    '''
    modify here accordingly ...
    '''
    #
    id_process = os.getpid()
    time_current = datetime.datetime.now().isoformat()
    #
    #
    parser.add_argument(
        '-fd', '--FileData', required=False,
        help='Path of the dataset'
    )
    #
    parser.add_argument(
        '-fp', '--FilePretrain', required=True,
        help='File of pretrained model'
    )
    parser.add_argument(
        '-mt', '--MapTest', required=False,
        help='Test Map'
    )
    #parser.add_argument(
    #    '-sr', '--SaveResults', required=False,
    #    help='Save results ? True or False'
    #)
    parser.add_argument(
        '--saveresults', dest='saveresults',
        action='store_true'
    )
    parser.add_argument(
        '--no-saveresults', dest='saveresults',
        action='store_false'
    )
    parser.set_defaults(saveresults=False)
    #
    args = parser.parse_args()
    #
    #print ("args.saveresults : ", args.saveresults)
    #
    if args.FileData == None:
        args.FileData = None
    #
    assert(args.FilePretrain != None)
    # args.PathPretrain = os.path.abspath(args.PathPretrain)
    if args.MapTest == None:
        args.MapTest = 'l'
    else:
        args.MapTest = str(args.MapTest)
    if args.saveresults == False:
        file_save = None
    else:
        pretrain = args.FilePretrain
        tag_save = pretrain.split('track_')[1].split('/model.pkl')[0]
        file_save = './results/result_' + tag_save + '.pkl'
    #
    print ("PID is : %s" % str(id_process) )
    print ("TIME is : %s" % time_current )
    #
    print ("FileData is : %s" % args.FileData )
    print ("FilePretrain is : %s" % args.FilePretrain)
    print ("MapTest is : %s" % str(args.MapTest) )
    print ("Save Results ? : %s" % file_save )
    #
    dict_args = {
        'PID': id_process,
        'TIME': time_current,
        'FileData': args.FileData,
        'FilePretrain': args.FilePretrain,
        'MapTest': args.MapTest
    }
    #
    input_tester = {
        'path_rawdata': args.FileData,
        'path_model': args.FilePretrain,
        'args': dict_args,
        'map_test': args.MapTest,
        'file_save': file_save
    }
    #
    run_model.test_model(input_tester)
    #

if __name__ == "__main__": main()