class SEP_TRANSLATION():
def __init__(self):
## General parameters
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
autograd.set_detect_anomaly(True)
torch.set_printoptions(precision=8)
## Set default parameters
## -> Can be changed during experiments
self.grad_bias = 1.5
############################################################################
########## PARAMETERS ####################################################
def set_weighted_gradient_bias(self, bias):
# bias > 1 => favor recent
# bias < 1 => favor earlier
self.grad_bias = bias
print(f'Reset bias for gradient weighting: {self.grad_bias}')
def set_data_parameters_(self, num_frames, num_observations,
num_input_features, num_input_dimesions):
## Define data parameters
self.num_frames = num_frames
self.num_observations = num_observations
self.num_input_features = num_input_features
self.num_input_dimensions = num_input_dimesions
self.input_per_frame = self.num_input_features * self.num_input_dimensions
self.perspective_taker = Perspective_Taker(
self.num_observations,
self.num_input_dimensions,
rotation_gradient_init=True,
translation_gradient_init=True)
self.preprocessor = Preprocessor(self.num_observations,
self.num_input_features,
self.num_input_dimensions)
self.evaluator = BAPTAT_evaluator(self.num_frames,
self.num_observations,
self.num_input_features,
self.preprocessor)
def set_tuning_parameters_(self, tuning_length, num_tuning_cycles,
loss_function, at_learning_rate_translation,
at_learning_rate_state,
at_momentum_translation):
## Define tuning parameters
self.tuning_length = tuning_length # length of tuning horizon
self.tuning_cycles = num_tuning_cycles # number of tuning cycles in each iteration
# possible loss functions
self.at_loss = loss_function
self.mse = nn.MSELoss()
self.l1Loss = nn.L1Loss()
self.smL1Loss = nn.SmoothL1Loss(reduction='sum')
self.l2Loss = lambda x, y: self.mse(x, y) * (self.num_input_dimensions
* self.num_input_features)
# define learning parameters
self.at_learning_rate = at_learning_rate_translation
self.at_learning_rate_state = at_learning_rate_state
self.c_momentum = at_momentum_translation
self.at_loss_function = self.mse
print('Parameters set.')
def init_model_(self, model_path):
## Load model
self.core_model = CORE_NET()
self.core_model.load_state_dict(torch.load(model_path))
self.core_model.eval()
self.core_model.to(self.device)
print('Model loaded.')
def init_inference_tools(self):
## Define tuning variables
# general
self.obs_count = 0
self.at_inputs = torch.tensor([]).to(self.device)
self.at_predictions = torch.tensor([]).to(self.device)
self.at_final_predictions = torch.tensor([]).to(self.device)
self.at_losses = []
# state
self.at_states = []
# translation
self.Cs = []
self.C_grads = [None] * (self.tuning_length + 1)
self.C_upd = [None] * (self.tuning_length + 1)
self.c_losses = []
def set_comparison_values(self, ideal_translation):
self.ideal_translation = ideal_translation.to(self.device)
############################################################################
########## INFERENCE #####################################################
def run_inference(self, observations, grad_calculation):
at_final_predictions = torch.tensor([]).to(self.device)
at_final_inputs = torch.tensor([]).to(self.device)
tb = self.perspective_taker.init_translation_bias_()
# print(tb)
for i in range(self.tuning_length + 1):
transba = tb.clone().to(self.device)
transba.requires_grad = True
self.Cs.append(transba)
## Core state
# define scaler
state_scaler = 0.95
# init state
at_h = torch.zeros(1, self.core_model.hidden_size).to(self.device)
at_c = torch.zeros(1, self.core_model.hidden_size).to(self.device)
at_h.requires_grad = True
at_c.requires_grad = True
init_state = (at_h, at_c)
state = (init_state[0], init_state[1])
############################################################################
########## FORWARD PASS ##################################################
for i in range(self.tuning_length):
o = observations[self.obs_count].to(self.device)
self.at_inputs = torch.cat((self.at_inputs,
o.reshape(1, self.num_input_features,
self.num_input_dimensions)),
0)
self.obs_count += 1
x_C = self.perspective_taker.translate(o, self.Cs[i])
x = self.preprocessor.convert_data_AT_to_LSTM(x_C)
state = (state[0] * state_scaler, state[1] * state_scaler)
new_prediction, state = self.core_model(x, state)
self.at_states.append(state)
self.at_predictions = torch.cat(
(self.at_predictions,
new_prediction.reshape(1, self.input_per_frame)), 0)
############################################################################
########## ACTIVE TUNING ##################################################
while self.obs_count < self.num_frames:
# TODO folgendes evtl in function auslagern
o = observations[self.obs_count].to(self.device)
self.obs_count += 1
x_C = self.perspective_taker.translate(o, self.Cs[-1])
x = self.preprocessor.convert_data_AT_to_LSTM(x_C)
## Generate current prediction
with torch.no_grad():
state = self.at_states[-1]
state = (state[0] * state_scaler, state[1] * state_scaler)
new_prediction, state = self.core_model(x, state)
## For #tuning_cycles
for cycle in range(self.tuning_cycles):
print('----------------------------------------------')
# Get prediction
p = self.at_predictions[-1]
# Calculate error
loss = self.at_loss(p, x[0])
# Propagate error back through tuning horizon
loss.backward(retain_graph=True)
self.at_losses.append(loss.clone().detach().cpu().numpy())
print(f'frame: {self.obs_count} cycle: {cycle} loss: {loss}')
# Update parameters
with torch.no_grad():
## Get gradients
for i in range(self.tuning_length + 1):
# save grads for all parameters in all time steps of tuning horizon
self.C_grads[i] = self.Cs[i].grad
# print(self.C_grads[self.tuning_length])
# Calculate overall gradients
if grad_calculation == 'lastOfTunHor':
### version 1
grad_C = self.C_grads[0]
elif grad_calculation == 'meanOfTunHor':
### version 2 / 3
grad_C = torch.mean(torch.stack(self.C_grads), dim=0)
elif grad_calculation == 'weightedInTunHor':
### version 4
weighted_grads_C = [None] * (self.tuning_length + 1)
for i in range(self.tuning_length + 1):
weighted_grads_C[i] = np.power(self.grad_bias,
i) * self.C_grads[i]
grad_C = torch.mean(torch.stack(weighted_grads_C),
dim=0)
# print(f'grad_C: {grad_C}')
# Update parameters in time step t-H with saved gradients
grad_C = grad_C.to(self.device)
upd_C = self.perspective_taker.update_translation_bias_(
self.Cs[0], grad_C, self.at_learning_rate,
self.c_momentum)
# print(upd_C)
# Compare translation bias to ideal bias
trans_loss = self.mse(self.ideal_translation, upd_C)
self.c_losses.append(trans_loss)
print(f'loss of translation bias (MSE): {trans_loss}')
# Zero out gradients for all parameters in all time steps of tuning horizon
for i in range(self.tuning_length + 1):
self.Cs[i].requires_grad = False
self.Cs[i].grad.data.zero_()
# Update all parameters for all time steps
for i in range(self.tuning_length + 1):
translation = upd_C.clone()
translation.requires_grad_()
self.Cs[i] = translation
# print(self.Cs[0])
# Initial state
g_h = at_h.grad.to(self.device)
g_c = at_c.grad.to(self.device)
upd_h = init_state[0] - self.at_learning_rate_state * g_h
upd_c = init_state[1] - self.at_learning_rate_state * g_c
at_h.data = upd_h.clone().detach().requires_grad_()
at_c.data = upd_c.clone().detach().requires_grad_()
at_h.grad.data.zero_()
at_c.grad.data.zero_()
# state_optimizer.step()
# print(f'updated init_state: {init_state}')
## REORGANIZE FOR MULTIPLE CYCLES!!!!!!!!!!!!!
# forward pass from t-H to t with new parameters
# Update init state???
init_state = (at_h, at_c)
state = (init_state[0], init_state[1])
self.at_predictions = torch.tensor([]).to(self.device)
for i in range(self.tuning_length):
x_C = self.perspective_taker.translate(
self.at_inputs[i], self.Cs[i])
x = self.preprocessor.convert_data_AT_to_LSTM(x_C)
state = (state[0] * state_scaler, state[1] * state_scaler)
upd_prediction, state = self.core_model(x, state)
self.at_predictions = torch.cat(
(self.at_predictions,
upd_prediction.reshape(1, self.input_per_frame)), 0)
# for last tuning cycle update initial state to track gradients
if cycle == (self.tuning_cycles - 1) and i == 0:
with torch.no_grad():
final_prediction = self.at_predictions[0].clone(
).detach().to(self.device)
final_input = x.clone().detach().to(self.device)
at_h = state[0].clone().detach().requires_grad_()
at_c = state[1].clone().detach().requires_grad_()
init_state = (at_h, at_c)
state = (init_state[0], init_state[1])
self.at_states[i] = state
# Update current rotation
x_C = self.perspective_taker.translate(o, self.Cs[-1])
x = self.preprocessor.convert_data_AT_to_LSTM(x_C)
# END tuning cycle
## Generate updated prediction
state = self.at_states[-1]
state = (state[0] * state_scaler, state[1] * state_scaler)
new_prediction, state = self.core_model(x, state)
## Reorganize storage variables
# observations
at_final_inputs = torch.cat(
(at_final_inputs, final_input.reshape(
1, self.input_per_frame)), 0)
self.at_inputs = torch.cat((self.at_inputs[1:],
o.reshape(1, self.num_input_features,
self.num_input_dimensions)),
0)
# predictions
at_final_predictions = torch.cat(
(at_final_predictions,
final_prediction.reshape(1, self.input_per_frame)), 0)
self.at_predictions = torch.cat(
(self.at_predictions[1:],
new_prediction.reshape(1, self.input_per_frame)), 0)
# END active tuning
# store rest of predictions in at_final_predictions
for i in range(self.tuning_length):
at_final_predictions = torch.cat(
(at_final_predictions, self.at_predictions[i].reshape(
1, self.input_per_frame)), 0)
at_final_inputs = torch.cat(
(at_final_inputs, self.at_inputs[i].reshape(
1, self.input_per_frame)), 0)
# get final translation bias
final_translation_bias = self.Cs[0].clone().detach()
print(f'final translation bias: {final_translation_bias}')
return at_final_inputs, at_final_predictions, final_translation_bias
############################################################################
########## EVALUATION #####################################################
def get_result_history(self, observations, at_final_predictions):
pred_errors = self.evaluator.prediction_errors(observations,
at_final_predictions,
self.mse)
return [pred_errors, self.at_losses, self.c_losses]