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 = []
# state_optimizer = torch.optim.Adam(init_state, at_learning_rate)
# binding
self.binder = BinderExMat(num_features=self.num_input_features,
gradient_init=True)
self.ideal_binding = torch.Tensor(np.identity(
self.num_input_features)).to(self.device)
self.Bs = []
self.B_grads = [None] * (self.tuning_length + 1)
self.B_upd = [None] * (self.tuning_length + 1)
self.bm_losses = []
self.bm_dets = []
at_loss_function = l1Loss at_learning_rate = 1 bm_momentum = 0.0 ## Define tuning variables # general obs_count = 0 at_inputs = torch.tensor([]) at_predictions = torch.tensor([]) at_final_predictions = torch.tensor([]) at_losses = [] # binding bindSM = nn.Softmax(dim=0) # columnwise binder = BinderExMat(num_features=num_input_features, gradient_init=True) ideal_binding = torch.Tensor(np.identity(num_input_features)) Bs = [] B_grads = [None] * (tuning_length+1) B_upd = [None] * (tuning_length+1) bm_losses = [] bm_dets = [] ############################################################################ ########## INITIALIZATIONS ############################################### ## Load data observations, feature_names = preprocessor.get_AT_data(data_asf_path, data_amc_path, num_frames+2)
r_momentum = 0.0
## Define tuning variables
# general
obs_count = 0
at_inputs = torch.tensor([])
at_predictions = torch.tensor([])
at_final_predictions = torch.tensor([])
at_losses = []
# state
at_states = []
# state_optimizer = torch.optim.Adam(init_state, at_learning_rate)
# binding
binder = BinderExMat(num_features=num_input_features, gradient_init=True)
ideal_binding = torch.Tensor(np.identity(num_input_features))
Bs = []
B_grads = [None] * (tuning_length + 1)
B_upd = [None] * (tuning_length + 1)
bm_losses = []
bm_dets = []
# translation
perspective_taker = Perspective_Taker(num_input_features,
num_input_dimensions,
rotation_gradient_init=True,
translation_gradient_init=True)
ideal_trans = torch.zeros(3)
class SEP_BINDING():
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)
############################################################################
########## PARAMETERS ####################################################
def set_data_parameters(self):
## Define data parameters
self.num_frames = 20
self.num_input_features = 15
self.num_input_dimensions = 3
self.preprocessor = Preprocessor(self.num_input_features,
self.num_input_dimensions)
self.evaluator = BAPTAT_evaluator(self.num_frames,
self.num_input_features,
self.preprocessor)
self.data_at_unlike_train = False ## Note: sample needs to be changed in the future
# data paths
self.data_asf_path = 'Data_Compiler/S35T07.asf'
self.data_amc_path = 'Data_Compiler/S35T07.amc'
def set_data_parameters_(self, num_frames, num_input_features,
num_input_dimesions):
## Define data parameters
self.num_frames = num_frames
self.num_input_features = num_input_features
self.num_input_dimensions = num_input_dimesions
self.preprocessor = Preprocessor(self.num_input_features,
self.num_input_dimensions)
self.evaluator = BAPTAT_evaluator(self.num_frames,
self.num_input_features,
self.preprocessor)
def set_model_parameters(self):
## Define model parameters
self.model_path = 'CoreLSTM/models/LSTM_46_cell.pt'
def set_tuning_parameters(self):
## Define tuning parameters
self.tuning_length = 10 # length of tuning horizon
self.tuning_cycles = 3 # number of tuning cycles in each iteration
# possible loss functions
self.mse = nn.MSELoss()
self.l1Loss = nn.L1Loss()
# smL1Loss = nn.SmoothL1Loss()
self.smL1Loss = nn.SmoothL1Loss(reduction='sum')
# smL1Loss = nn.SmoothL1Loss(beta=2)
# smL1Loss = nn.SmoothL1Loss(beta=0.5)
# smL1Loss = nn.SmoothL1Loss(reduction='sum', beta=0.5)
self.l2Loss = lambda x, y: self.mse(x, y) * (self.num_input_dimensions
* self.num_input_features)
self.at_loss = self.smL1Loss
# define learning parameters
self.at_learning_rate = 1
self.at_learning_rate_state = 0.0
self.bm_momentum = 0.0
self.at_loss_function = self.mse
def set_tuning_parameters_(self, tuning_length, num_tuning_cycles,
loss_function, at_learning_rate_binding,
at_learning_rate_state, at_momentum_binding):
## 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_binding
self.at_learning_rate_state = at_learning_rate_state
self.bm_momentum = at_momentum_binding
self.at_loss_function = self.mse
print('Parameters set.')
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 = []
# state_optimizer = torch.optim.Adam(init_state, at_learning_rate)
# binding
self.binder = BinderExMat(num_features=self.num_input_features,
gradient_init=True)
self.ideal_binding = torch.Tensor(np.identity(
self.num_input_features)).to(self.device)
self.Bs = []
self.B_grads = [None] * (self.tuning_length + 1)
self.B_upd = [None] * (self.tuning_length + 1)
self.bm_losses = []
self.bm_dets = []
def set_comparison_values(self, ideal_binding):
self.ideal_binding = ideal_binding
############################################################################
########## INITIALIZATIONS ###############################################
def load_data(self):
## Load data
observations, feature_names = self.preprocessor.get_AT_data(
self.data_asf_path, self.data_amc_path, self.num_frames)
return observations, feature_names
def init_model(self):
## Load model
self.core_model = CORE_NET()
self.core_model.load_state_dict(torch.load(self.model_path))
self.core_model.eval()
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()
# print('\nModel loaded:')
# print(self.core_model)
def prepare_inference(self):
self.set_data_parameters()
self.set_model_parameters()
self.set_tuning_parameters()
self.init_inference_tools()
self.init_model()
print(
'Ready to run AT inference for binding task! \nInitialized parameters with: \n'
+ f' - number of features: \t\t{self.num_input_features}\n' +
f' - number of dimensions: \t{self.num_input_dimensions}\n' +
f' - number of tuning cycles: \t{self.tuning_cycles}\n' +
f' - size of tuning horizon: \t{self.tuning_length}\n' +
f' - learning rate: \t\t{self.at_learning_rate}\n' +
f' - learning rate (state): \t{self.at_learning_rate_state}\n' +
f' - momentum: \t\t\t{self.bm_momentum}\n' +
f' - model: \t\t\t{self.model_path}\n' +
f' - number of features: \t\t{self.num_input_features}\n')
def run_inference(self, observations, order, reorder):
at_final_predictions = torch.tensor([]).to(self.device)
## Binding matrices
# Init binding entries
bm = self.binder.init_binding_matrix_det_()
# bm = binder.init_binding_matrix_rand_()
print(bm)
for i in range(self.tuning_length + 1):
matrix = bm.clone()
matrix.requires_grad_()
self.Bs.append(matrix)
print(f'BMs different in list: {self.Bs[0] is not self.Bs[1]}')
## Core state
# define scaler
state_scaler = 0.95
# init state
at_h = torch.zeros(1, self.core_model.hidden_size).requires_grad_().to(
self.device)
at_c = torch.zeros(1, self.core_model.hidden_size).requires_grad_().to(
self.device)
init_state = (at_h, at_c)
self.at_states.append(init_state)
state = (init_state[0], init_state[1])
############################################################################
########## FORWARD PASS ##################################################
for i in range(self.tuning_length):
o = observations[self.obs_count]
self.at_inputs = torch.cat((self.at_inputs,
o.reshape(1, self.num_input_features,
self.num_input_dimensions)),
0)
self.obs_count += 1
bm = self.binder.scale_binding_matrix(self.Bs[i])
x_B = self.binder.bind(o, bm)
x = self.preprocessor.convert_data_AT_to_LSTM(x_B)
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, 45)), 0)
############################################################################
########## ACTIVE TUNING ##################################################
while self.obs_count < self.num_frames:
# TODO folgendes evtl in function auslagern
o = observations[self.obs_count]
self.obs_count += 1
bm = self.binder.scale_binding_matrix(self.Bs[-1])
x_B = self.binder.bind(o, bm)
x = self.preprocessor.convert_data_AT_to_LSTM(x_B)
## Generate current prediction
with torch.no_grad():
state = (state[0] * state_scaler, state[1] * state_scaler)
new_prediction, state_new = self.core_model(
x, self.at_states[-1])
## For #tuning_cycles
for cycle in range(self.tuning_cycles):
print('----------------------------------------------')
# Get prediction
p = self.at_predictions[-1]
# Calculate error
# lam = 10
# loss = at_loss_function(p, x[0]) + l1Loss(p,x[0]) + lam / torch.norm(torch.Tensor(Bs[0].copy()))
# loss = at_loss_function(p, x[0]) + mse(p, x[0])
# loss = l1Loss(p,x[0]) + l2Loss(p,x[0])
# loss_scale = torch.square(torch.mean(torch.norm(torch.tensor(Bs[-1]), dim=1, keepdim=True)) -1.) ##COPY?????
# loss_scale = torch.square(torch.mean(torch.norm(bm.clone().detach(), dim=1, keepdim=True)) -1.) ##COPY?????
# -> länge der Vektoren
# print(f'loss scale: {loss_scale}')
# loss_scale_factor = 0.9
# l1scale = loss_scale_factor * loss_scale
# l2scale = loss_scale_factor / loss_scale
# loss = l1Loss(p,x[0]) + l2scale * l2Loss(p,x[0])
# loss = l1scale * mse(p,x[0]) + l2scale * l2Loss(p,x[0])
# loss = l2Loss(p,x[0]) + mse(p,x[0])
# loss = l2Loss(p,x[0]) + loss_scale * mse(p,x[0])
# loss = loss_scale_factor * loss_scale * l2Loss(p,x[0]) + mse(p,x[0])
# loss = loss_scale_factor * loss_scale * l2Loss(p,x[0])
# loss = loss_scale_factor * loss_scale * mse(p,x[0])
# loss = self.smL1Loss(p, x[0])
loss = self.at_loss(p, x[0])
self.at_losses.append(loss.clone().detach().numpy())
print(f'frame: {self.obs_count} cycle: {cycle} loss: {loss}')
# Propagate error back through tuning horizon
loss.backward(retain_graph=True)
# Update parameters
with torch.no_grad():
# Calculate gradients with respect to the entires
for i in range(self.tuning_length + 1):
self.B_grads[i] = self.Bs[i].grad
# print(B_grads[tuning_length])
# Calculate overall gradients
### version 1
# grad_B = B_grads[0]
### version 2 / 3
# grad_B = torch.mean(torch.stack(B_grads), 0)
### version 4
# # # # bias > 1 => favor recent
# # # # bias < 1 => favor earlier
bias = 1.5
weighted_grads_B = [None] * (self.tuning_length + 1)
for i in range(self.tuning_length + 1):
weighted_grads_B[i] = np.power(bias,
i) * self.B_grads[i]
grad_B = torch.mean(torch.stack(weighted_grads_B), dim=0)
# print(f'grad_B: {grad_B}')
# print(f'grad_B: {torch.norm(grad_B, 1)}')
# Update parameters in time step t-H with saved gradients
upd_B = self.binder.update_binding_matrix_(
self.Bs[0], grad_B, self.at_learning_rate,
self.bm_momentum)
# Compare binding matrix to ideal matrix
c_bm = self.binder.scale_binding_matrix(upd_B)
if order is not None:
c_bm = c_bm.gather(
1,
reorder.unsqueeze(0).expand(c_bm.shape))
mat_loss = self.evaluator.FBE(c_bm, self.ideal_binding)
self.bm_losses.append(mat_loss)
print(f'loss of binding matrix (FBE): {mat_loss}')
# Compute determinante of binding matrix
det = torch.det(c_bm)
self.bm_dets.append(det)
print(f'determinante of binding matrix: {det}')
# Zero out gradients for all parameters in all time steps of tuning horizon
for i in range(self.tuning_length + 1):
self.Bs[i].requires_grad = False
self.Bs[i].grad.data.zero_()
# Update all parameters for all time steps
for i in range(self.tuning_length + 1):
self.Bs[i].data = upd_B.clone().data
self.Bs[i].requires_grad = True
# print(Bs[0])
# Initial state
g_h = at_h.grad
g_c = at_c.grad
upd_h = self.at_states[0][
0] - self.at_learning_rate_state * g_h
upd_c = self.at_states[0][
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
init_state = (at_h, at_c)
state = (init_state[0], init_state[1])
for i in range(self.tuning_length):
bm = self.binder.scale_binding_matrix(self.Bs[i])
x_B = self.binder.bind(self.at_inputs[i], bm)
x = self.preprocessor.convert_data_AT_to_LSTM(x_B)
# print(f'x_B :{x_B}')
state = (state[0] * state_scaler, state[1] * state_scaler)
self.at_predictions[i], state = self.core_model(x, state)
# for last tuning cycle update initial state to track gradients
if cycle == (self.tuning_cycles - 1) and i == 0:
at_h = state[0].clone().detach().requires_grad_().to(
self.device)
at_c = state[1].clone().detach().requires_grad_().to(
self.device)
init_state = (at_h, at_c)
state = (init_state[0], init_state[1])
self.at_states[i + 1] = state
# Update current binding
bm = self.binder.scale_binding_matrix(self.Bs[-1])
x_B = self.binder.bind(o, bm)
x = self.preprocessor.convert_data_AT_to_LSTM(x_B)
# 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
# states
self.at_states.append(state)
self.at_states[0][0].requires_grad = False
self.at_states[0][1].requires_grad = False
self.at_states = self.at_states[1:]
# observations
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, self.at_predictions[0].detach().reshape(
1, 45)), 0)
self.at_predictions = torch.cat(
(self.at_predictions[1:], new_prediction.reshape(1, 45)), 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[1].reshape(1, 45)),
0)
# get final binding matrix
final_binding_matrix = self.binder.scale_binding_matrix(
self.Bs[-1].clone().detach())
print(f'final binding matrix: {final_binding_matrix}')
final_binding_entries = self.Bs[-1].clone().detach()
print(f'final binding entires: {final_binding_entries}')
return at_final_predictions, final_binding_matrix, final_binding_entries
############################################################################
########## EVALUATION #####################################################
def evaluate(self, observations, at_final_predictions, feature_names,
final_binding_matrix, final_binding_entries):
pred_errors = self.evaluator.prediction_errors(observations,
at_final_predictions,
self.at_loss_function)
self.evaluator.plot_prediction_errors(pred_errors)
self.evaluator.plot_at_losses(
self.at_losses, 'History of overall losses during active tuning')
self.evaluator.plot_at_losses(self.bm_losses,
'History of binding matrix loss (FBE)')
self.evaluator.plot_at_losses(
self.bm_dets, 'History of binding matrix determinante')
self.evaluator.plot_binding_matrix(
final_binding_matrix, feature_names,
'Binding matrix showing relative contribution of observed feature to input feature'
)
self.evaluator.plot_binding_matrix(
final_binding_entries, feature_names,
'Binding matrix entries showing contribution of observed feature to input feature'
)
# evaluator.help_visualize_devel(observations, at_final_predictions)
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.bm_losses, self.bm_dets]