def mean_position(self, mean, logvar):
batch_size = mean.shape[0]
mean = mean.view(batch_size * self._head_count,
mean.shape[1] // self._head_count)
mean_matrix = self.make_matrix(mean[:, :2], mean[:, 2:])
log_mean = SE2.log(SE2.from_matrix(mean_matrix, normalize=False))
if log_mean.dim() < 2:
log_mean = log_mean[None]
logvar = logvar.view(batch_size * self._head_count,
logvar.shape[1] // self._head_count)
inverse_sigma_matrix = self.get_inverse_sigma_matrix(logvar)
inverse_covariance_matrix = torch.bmm(
inverse_sigma_matrix.transpose(1, 2), inverse_sigma_matrix)
result_inverse_covariance_matrix = torch.sum(
inverse_covariance_matrix.reshape(-1, self._head_count, 3, 3),
dim=1)
result_covariance_matrix = torch.inverse(
result_inverse_covariance_matrix)
factors = torch.bmm(
result_covariance_matrix.repeat_interleave(self._head_count, 0),
inverse_covariance_matrix)
scaled_log_mean = torch.bmm(factors, log_mean[:, :, None])[:, :, 0]
result_log_mean = torch.sum(scaled_log_mean.reshape(
-1, self._head_count, 3),
dim=1)
mean_matrix = SE2.exp(result_log_mean).as_matrix()
if mean_matrix.dim() < 3:
mean_matrix = mean_matrix[None]
positions = torch.zeros(batch_size, 2, device=mean.device)
positions[:, 0] = mean_matrix[:, 0, 2]
positions[:, 1] = mean_matrix[:, 1, 2]
return positions
def sample(self, mean, logvar):
batch_size = mean.shape[0]
mean = mean.view(batch_size * self._head_count,
mean.shape[1] // self._head_count)
mean_matrix = self.make_matrix(mean[:, :2], mean[:, 2:])
log_mean = SE2.log(SE2.from_matrix(mean_matrix, normalize=False))
if log_mean.dim() < 2:
log_mean = log_mean[None]
logvar = logvar.view(batch_size * self._head_count,
logvar.shape[1] // self._head_count)
inverse_sigma_matrix = self.get_inverse_sigma_matrix(logvar)
inverse_covariance_matrix = torch.bmm(
inverse_sigma_matrix.transpose(1, 2), inverse_sigma_matrix)
result_inverse_covariance_matrix = torch.sum(
inverse_covariance_matrix.reshape(-1, self._head_count, 3, 3),
dim=1)
result_covariance_matrix = torch.inverse(
result_inverse_covariance_matrix)
factors = torch.bmm(
result_covariance_matrix.repeat_interleave(self._head_count, 0),
inverse_covariance_matrix)
scaled_log_mean = torch.bmm(factors, log_mean[:, :, None])[:, :, 0]
result_log_mean = torch.sum(scaled_log_mean.reshape(
-1, self._head_count, 3),
dim=1)
mean_matrix = SE2.exp(result_log_mean).as_matrix()
if mean_matrix.dim() < 3:
mean_matrix = mean_matrix[None]
try:
# inverse_sigma_matrix = torch.cholesky(result_inverse_covariance_matrix)
# sigma_matrix = torch.inverse(inverse_sigma_matrix)
sigma_matrix = torch.cholesky(result_covariance_matrix +
torch.eye(3, device=mean.device) *
1e-4)
except RuntimeError as msg:
print(inverse_covariance_matrix)
print(result_inverse_covariance_matrix)
print(result_covariance_matrix)
print("Cholesky error", msg)
sigma_matrix = (torch.eye(3, device=mean.device) * 1e4).expand(
batch_size, 3, 3)
epsilon = torch.randn(batch_size, 3, device=mean.device)
delta = torch.bmm(sigma_matrix, epsilon[:, :, None])[:, :, 0]
delta_matrix = SE2.exp(delta).as_matrix()
if delta_matrix.dim() < 3:
delta_matrix = delta_matrix[None]
position_matrix = torch.bmm(mean_matrix, delta_matrix)
positions = torch.zeros(batch_size, 3)
positions[:, 0] = position_matrix[:, 0, 2]
positions[:, 1] = position_matrix[:, 1, 2]
positions[:, 2] = torch.atan2(position_matrix[:, 1, 0],
position_matrix[:, 0, 0])
positions = positions.cpu().detach().numpy()
return positions
def log_prob(self, value, mean, logvar):
if logvar.dim() < 2:
logvar = logvar[None].expand(mean.shape[0], logvar.shape[0])
value_matrix = self.make_matrix(
value[0], torch.nn.functional.normalize(value[1]))
rotation = torch.nn.functional.normalize(mean[:, 2:4])
mean_matrix = self.make_matrix(mean[:, 0:2],
rotation).expand_as(value_matrix)
delta_matrix = torch.bmm(self.pose_matrix_inverse(mean_matrix),
value_matrix)
delta_log = SE2.log(SE2.from_matrix(delta_matrix, normalize=False))
if delta_log.dim() < 2:
delta_log = delta_log[None]
inverse_sigma_matrix = self.get_inverse_sigma_matrix(logvar).expand(
delta_log.shape[0], delta_log.shape[1], delta_log.shape[1])
delta_log = torch.bmm(inverse_sigma_matrix, delta_log[:, :,
None])[:, :, 0]
log_determinant = self.get_logvar_determinant(logvar)
log_prob = torch.sum(delta_log**2 / 2.,
dim=1) + 0.5 * log_determinant + 3 * math.log(
math.sqrt(2 * math.pi))
return log_prob
def test_exp_log_batch():
T = SE2.exp(0.1 * torch.Tensor([[1, 2, 3], [4, 5, 6]]))
assert utils.allclose(SE2.exp(SE2.log(T)).as_matrix(), T.as_matrix())
def test_exp_log():
T = SE2.exp(torch.Tensor([1, 2, 3]))
assert utils.allclose(SE2.exp(SE2.log(T)).as_matrix(), T.as_matrix())
def test_exp_log():
T = SE2.exp(torch.Tensor([1, 2, 3]))
print(T.trans)
print(T.rot.to_angle())
assert utils.allclose(SE2.exp(SE2.log(T)).as_matrix(), T.as_matrix())