import rosbag import numpy as np import torch import os import sys import evaluation_helpers sys.path.append(os.path.join(os.getcwd(), '..')) import helpers from data_processing.data_processing_helpers import LCBagDataReader, scan_to_point_cloud from config import Configuration config = Configuration(False, True) config.add_argument('--alt_bag_file', type=str) config.add_argument('--time_spacing', type=float, default=1.5) config.add_argument('--interactive', action='store_true') config = config.parse() scan_conv, scan_match, scan_transform = helpers.create_laser_networks(config.model_dir, config.model_epoch) scan_conv.eval() scan_match.eval() scan_transform.eval() convert_to_clouds = False bag = rosbag.Bag(config.bag_file) if config.alt_bag_file: alt_bag = rosbag.Bag(config.alt_bag_file) else:
import rosbag import numpy as np import torch import os import sys import evaluation_helpers sys.path.append(os.path.join(os.getcwd(), '..')) import helpers from data_processing.data_processing_helpers import LCBagDataReader, scan_to_point_cloud from config import data_generation_config, Configuration config = Configuration(False, True) config.add_argument('--alt_bag_file', type=str) config.add_argument('--time_spacing', type=float, default=1.5) config = config.parse() scan_conv, scan_match, scan_transform = helpers.create_laser_networks(config.model_dir, config.model_epoch) scan_conv.eval() scan_match.eval() scan_transform.eval() convert_to_clouds = False bag = rosbag.Bag(config.bag_file) if config.alt_bag_file: alt_bag = rosbag.Bag(config.alt_bag_file) else: alt_bag = rosbag.Bag(config.bag_file)
import rosbag import numpy as np import torch import os import sys import evaluation_helpers sys.path.append(os.path.join(os.getcwd(), '..')) import helpers from data_processing.data_processing_helpers import LCBagDataReader, scan_to_point_cloud from config import Configuration config = Configuration(False, True) config.add_argument('--map_name', type=str) config.add_argument('--time_spacing', type=float, default=5.0) config = config.parse() scan_conv, scan_uncertainty = helpers.create_lu_networks( config.model_dir, config.model_epoch) scan_conv.eval() scan_uncertainty.eval() convert_to_clouds = False bag = rosbag.Bag(config.bag_file) bag_reader = LCBagDataReader(bag, config.lidar_topic, config.localization_topic, convert_to_clouds, config.time_spacing, config.time_spacing) for idx, timestamp in enumerate(bag_reader.get_localization_timestamps()):
import torch import torch.nn.functional as F import torch.optim as optim import numpy as np from model import EmbeddingNet import time from tqdm import tqdm import helpers from helpers import print_output, initialize_logging from config import Configuration, data_config config = Configuration(True, True) config.add_argument( '--stats_file', type=str, help='path to file containing ground-truth uncertainty stats') config = config.parse() start_time = str(int(time.time())) initialize_logging(start_time) print_output(config) num_workers = 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)