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)
