class SupervisedDriver(Driver):
def __init__(self,
trainee,
feature_transformer,
persist_path,
optimizer_params=None,
loss=None):
super(SupervisedDriver, self).__init__()
self.supervisor = MyDriver()
self.feature_transformer = feature_transformer
self.trainee = trainee
self.persist_path = persist_path
if optimizer_params is None:
optimizer_params = {"lr": 0.001, "momentum": 0.9}
if loss is None:
loss = nn.MSELoss()
# loss
self.loss = loss
# optimizer
self.optimizer = optim.SGD(self.trainee.parameters(),
**optimizer_params)
self.call_number = 0
def drive(self, state):
self.call_number += 1
command = self.supervisor.drive(state)
x = self.feature_transformer.transform(state)
y_predicted = self.trainee(x)
y_true = Variable(
torch.FloatTensor(
[command.steering, command.brake, command.accelerator]))
self.optimizer.zero_grad()
loss = self.loss(y_predicted, y_true)
loss.backward()
self.optimizer.step()
if self.call_number % 1000 == 0:
print("Step: {}".format(self.call_number))
print("Prediction", y_predicted)
print("True", y_true)
print("loss", loss)
print()
return command
def on_shutdown(self):
torch.save(self.trainee, self.persist_path)
# steeringData = SteeringTrainingData(pandas.read_csv('training-data/train_data/alpine-1.csv'))
# brakingData = BrakingTrainingData(pandas.read_csv('training-data/train_data/alpine-1.csv'))
steeringData = ExtendedSteeringData()
observations = []
driver = MyDriver()
for i in range(len(steeringData)):
state = dataToStateExtended(steeringData[i][0])
sample = Trainer.stateToSample(state)
command = driver.drive(state)
#
# observation = {
# 'target': (brakingData[i][1]).numpy()[0],
# 'actual': command.brake
# }
observation = {
'target': (steeringData[i][1]).numpy()[0],
'actual': command.steering
}
observations.append(observation)
observations.sort(key=lambda observation: observation['target'])