def settings_event(self):
global options
if options is None or not tk.Toplevel.winfo_exists(options):
options = tk.Toplevel()
settings.run_settings(options, self.rename, self.username)
else:
options.destroy()
options = None
else:
car = None
writer = None
log_file = None
self.start_run(agent, car, counter, filecounter, grad_buffer,
images_path, log_file, saver, sess, settings,
supervised, tmp_net, writer)
def main(setting):
f = None
if setting.timing:
f = open('times.txt', 'w')
start = time.time()
rl = RL_manual_no_tf()
if not f is None:
end = time.time()
f.write(str(end - start) + " Setting up RL\n")
rl.run(setting, time_file=f)
if __name__ == "__main__":
setup = run_settings()
np.random.seed(setup.random_seed_np)
main(setup)
rl.evaluate(setting)
# if not f is None:
# end=time.time()
# f.write(str(end-start)+ " Setting up RL\n")
# rl.run(setting, time_file=f, mem_agent=False)
if __name__ == "__main__":
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
train_supervised=False
if train_supervised:
setup=run_settings(evaluate=False)
tf.compat.v1.set_random_seed(setup.random_seed_tf)
np.random.seed(setup.random_seed_np)
main(setup, "")
else:
evaluationModels = ["STGCNN", "SGAN", "STGAT"] # our model is [""]
datasets = ["carla", "waymo"] # ["carla", "waymo", "cityscapes"]
likelihoods = [True]
pfnnOptions = [True] #[True, False]
for evaluatedModel in evaluationModels:
for dataset in datasets:
for pfnnOption in pfnnOptions:
for likelihood in likelihoods:
print(("========== RUNNING SUPERVISED MODEL: ", evaluatedModel, " on dataset: ", dataset))
data = f.readline().split(';')
data[2] = data[2].split(',')
data[0] = int(data[0])
data[1] = int(data[1])
data[2] = tuple(map(int, data[2]))
data[3] = int(data[3]) * 60 # convert minutes to seconds
return data
def check_if_logged_in():
f = open('logged_in.txt', 'r')
data = f.readline()
if data == 'True':
return True
return False
f.close()
lw.run_login()
logged_in = check_if_logged_in()
if logged_in:
s.run_settings()
config = load_config()
mc.set_call_back()
try:
mc.start_detection("dog.mp4", config[0], config[1], config[2],
config[3])
except Exception as e:
print('Finished with exit values: ' + str(e.args))