parser.add_argument('--bag_file',
type=str,
default='train',
help='path to a bag containing base and filtered scans.')
parser.add_argument('--filtered_topic',
type=str,
default='/filtered',
help='topic to look for filtered scans')
parser.add_argument('--base_topic',
type=str,
default='/Cobot/Laser',
help='topic to look for base scans')
opt = parser.parse_args()
start_time = str(int(time.time()))
initialize_logging(start_time)
print_output(opt)
opt.manualSeed = random.randint(1, 10000) # fix seed
print_output("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
dataset = LTFDataset(opt.bag_file, opt.base_topic, opt.filtered_topic, 200)
ltf_model = SegNet(3, 2)
ltf_model.load_state_dict(torch.load(opt.model))
ltf_model.eval()
ltf_model = ltf_model.cuda()
import matplotlib.pyplot as plt
import torch.utils.data
import numpy as np
import pickle
import time
import random
from tqdm import tqdm
sys.path.append(os.path.join(os.getcwd(), '..'))
import helpers
from helpers import initialize_logging, print_output
from config import Configuration, execution_config, evaluation_config
config = Configuration(False, True).parse()
start_time = str(int(time.time()))
initialize_logging(start_time, 'evaluate_')
print_output(config)
num_workers = int(execution_config['NUM_WORKERS'])
config.manualSeed = random.randint(1, 10000) # fix seed
print_output("Random Seed: ", config.manualSeed)
random.seed(config.manualSeed)
torch.manual_seed(config.manualSeed)
scan_conv, scan_match, scan_transform = helpers.create_laser_networks(
config.model_dir, config.model_epoch)
scan_conv.eval()
scan_match.eval()
dataset = helpers.load_laser_dataset(config.bag_file, '', 0,
config.distance_cache,