def test_get_car_heading_error(self):
params_test = get_test_params()
params_test['heading'] = 0
params_test['waypoints'] = [(0, 0), (2, 0), (2, 2), (0, 2), (0, 0),
(2, 2), (4, 0)]
params_test['closest_waypoints'] = [0, 1]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_car_heading_error(), 0)
params_test['closest_waypoints'] = [1, 2]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_car_heading_error(), 90)
params_test['closest_waypoints'] = [2, 3]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_car_heading_error(), 180)
params_test['closest_waypoints'] = [3, 4]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_car_heading_error(), -90)
params_test['closest_waypoints'] = [4, 5]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_car_heading_error(), 45)
params_test['closest_waypoints'] = [5, 6]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_car_heading_error(), -45)
def test_get_way_points_distance(self):
params_test = get_test_params()
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_way_points_distance((0, 0), (2, 0)), 2)
self.assertEqual(rf.get_way_points_distance((0, 0), (2, 2)),
math.sqrt(8))
self.assertEqual(rf.get_way_points_distance((-2, 4), (-4, 2)),
math.sqrt(8))
self.assertEqual(rf.get_way_points_distance((0, 0), (1, 0)), 1)
def test_get_heading_beetween_waypoints(self):
params_test = get_test_params()
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_heading_between_waypoints((0, 0), (2, 0)), 0)
self.assertEqual(rf.get_heading_between_waypoints((0, 0), (0, 2)), 90)
self.assertEqual(rf.get_heading_between_waypoints((0, 0), (0, -2)),
-90)
self.assertEqual(rf.get_heading_between_waypoints((0, 0), (2, 2)), 45)
self.assertEqual(rf.get_heading_between_waypoints((0, 0), (-2, -2)),
-135)
def test_get_optimum_speed_ratio(self):
params_test = get_test_params()
params_test['heading'] = 0
params_test['distance_from_center'] = 0
params_test['steering_angle'] = 0
params_test['closest_waypoints'] = (0, 1)
params_test['x'] = params_test['waypoints'][
params_test['closest_waypoints'][0]][0]
params_test['y'] = params_test['waypoints'][
params_test['closest_waypoints'][0]][1]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_optimum_speed_ratio(), 1.0)
def test_get_waypoint(self):
params_test = get_test_params()
params_test['waypoints'] = [(0, 0), (1, 0), (2, 0), (3, 3)]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_way_point(0), (0, 0))
self.assertEqual(rf.get_way_point(1), (1, 0))
self.assertEqual(rf.get_way_point(2), (2, 0))
self.assertEqual(rf.get_way_point(3), (3, 3))
self.assertEqual(rf.get_way_point(4), (0, 0))
self.assertEqual(rf.get_way_point(5), (1, 0))
self.assertEqual(rf.get_way_point(-1), (3, 3))
self.assertEqual(rf.get_way_point(-2), (2, 0))
self.assertEqual(rf.get_way_point(-3), (1, 0))
def test_get_expected_turn_direction(self):
params_test = get_test_params()
params_test['heading'] = 0
params_test['waypoints'] = [(0, 0), (1, 0), (2, 0), (3, 1), (4, 1),
(5, 1), (6, -6), (-1, -6), (-1, 0)]
params_test['closest_waypoints'] = [0, 1]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_expected_turn_direction(), 'LEFT')
params_test['closest_waypoints'] = [3, 4]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_expected_turn_direction(), 'RIGHT')
params_test['closest_waypoints'] = [8, 0]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_expected_turn_direction(), 'STRAIGHT')
def test_get_turn_angle(self):
params_test = get_test_params()
params_test['heading'] = 0
params_test['waypoints'] = [(0, 0), (1, 0), (2, 0), (3, 1), (4, 1),
(5, 1), (6, -6), (-1, -6), (-1, 0)]
params_test['closest_waypoints'] = [0, 1]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_turn_angle(), 0)
params_test['closest_waypoints'] = [1, 2]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_turn_angle(), 0)
params_test['closest_waypoints'] = [2, 3]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_turn_angle(), 45)
params_test['closest_waypoints'] = [5, 6]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.get_turn_angle(), -81.86989764584403)
def test_is_in_turn(self):
params_test = get_test_params()
params_test['heading'] = 0
params_test['waypoints'] = [(0, 0), (1, 0), (2, 0), (3, 1), (4, 1),
(5, 1), (6, -6), (-1, -6), (-1, 0)]
params_test['closest_waypoints'] = [0, 1]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.is_in_turn(), False)
params_test['closest_waypoints'] = [1, 2]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.is_in_turn(), False)
params_test['closest_waypoints'] = [2, 3]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.is_in_turn(), True)
params_test['closest_waypoints'] = [5, 6]
rf = AwsDeepRacerReward(params_test)
self.assertEqual(rf.is_in_turn(), True)
import torch
import params
from lavse.data.loaders import get_loader
from lavse.model import model
from lavse.train.train import Trainer
from lavse.utils import file_utils, helper
from lavse.utils.logger import create_logger
from run import load_yaml_opts, parse_loader_name
from tqdm import tqdm
if __name__ == '__main__':
# mp.set_start_method('spawn')
# loader_name = 'precomp'
args = params.get_test_params()
opt = load_yaml_opts(args.options)
# init_distributed_mode(args)s
logger = create_logger(level='debug' if opt.engine.debug else 'info')
logger.info(f'Used args : \n{args}')
logger.info(f'Used options: \n{opt}')
if 'DATA_PATH' not in os.environ:
data_path = opt.dataset.data_path
else:
data_path = os.environ['DATA_PATH']
ngpu = torch.cuda.device_count()
import torch
import numpy as np
from pathlib import Path
from params import get_test_params
from retrieval.train import evaluation
from retrieval.data.loaders import get_loader
from retrieval.utils.logger import create_logger
from run import load_model, get_data_path, get_tokenizers
from retrieval.utils.file_utils import save_json, load_yaml_opts, parse_loader_name
if __name__ == '__main__':
args = get_test_params(ensemble=True)
opt = load_yaml_opts(args.options[0])
logger = create_logger(level='debug' if opt.engine.debug else 'info')
logger.info(f'Used args : \n{args}')
logger.info(f'Used options: \n{opt}')
train_data = opt.dataset.train_data
data_path = get_data_path(opt)
data_name, lang = parse_loader_name(opt.dataset.train.data)
loader = get_loader(
data_split=args.data_split,
data_path=data_path,
data_info=opt.dataset.train.data,
loader_name=opt.dataset.loader_name,
local_rank=args.local_rank,
