dfa1d43f2e
Note that this does require rerunning the `process_data.py` scripts in the example folders so that the dill-files are updated.
64 lines
No EOL
2.7 KiB
Python
64 lines
No EOL
2.7 KiB
Python
import torch
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from trajectron.model.dynamics import Dynamic
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from trajectron.utils import block_diag
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from trajectron.model.components import GMM2D
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class SingleIntegrator(Dynamic):
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def init_constants(self):
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self.F = torch.eye(4, device=self.device, dtype=torch.float32)
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self.F[0:2, 2:] = torch.eye(2, device=self.device, dtype=torch.float32) * self.dt
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self.F_t = self.F.transpose(-2, -1)
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def integrate_samples(self, v, x=None):
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"""
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Integrates deterministic samples of velocity.
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:param v: Velocity samples
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:param x: Not used for SI.
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:return: Position samples
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"""
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p_0 = self.initial_conditions['pos'].unsqueeze(1)
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return torch.cumsum(v, dim=2) * self.dt + p_0
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def integrate_distribution(self, v_dist, x=None):
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r"""
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Integrates the GMM velocity distribution to a distribution over position.
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The Kalman Equations are used.
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.. math:: \mu_{t+1} =\textbf{F} \mu_{t}
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.. math:: \mathbf{\Sigma}_{t+1}={\textbf {F}} \mathbf{\Sigma}_{t} {\textbf {F}}^{T}
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.. math::
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\textbf{F} = \left[
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\begin{array}{cccc}
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\sigma_x^2 & \rho_p \sigma_x \sigma_y & 0 & 0 \\
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\rho_p \sigma_x \sigma_y & \sigma_y^2 & 0 & 0 \\
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0 & 0 & \sigma_{v_x}^2 & \rho_v \sigma_{v_x} \sigma_{v_y} \\
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0 & 0 & \rho_v \sigma_{v_x} \sigma_{v_y} & \sigma_{v_y}^2 \\
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\end{array}
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\right]_{t}
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:param v_dist: Joint GMM Distribution over velocity in x and y direction.
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:param x: Not used for SI.
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:return: Joint GMM Distribution over position in x and y direction.
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"""
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p_0 = self.initial_conditions['pos'].unsqueeze(1)
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ph = v_dist.mus.shape[-3]
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sample_batch_dim = list(v_dist.mus.shape[0:2])
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pos_dist_sigma_matrix_list = []
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pos_mus = p_0[:, None] + torch.cumsum(v_dist.mus, dim=2) * self.dt
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vel_dist_sigma_matrix = v_dist.get_covariance_matrix()
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pos_dist_sigma_matrix_t = torch.zeros(sample_batch_dim + [v_dist.components, 2, 2], device=self.device)
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for t in range(ph):
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vel_sigma_matrix_t = vel_dist_sigma_matrix[:, :, t]
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full_sigma_matrix_t = block_diag([pos_dist_sigma_matrix_t, vel_sigma_matrix_t])
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pos_dist_sigma_matrix_t = self.F[..., :2, :].matmul(full_sigma_matrix_t.matmul(self.F_t)[..., :2])
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pos_dist_sigma_matrix_list.append(pos_dist_sigma_matrix_t)
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pos_dist_sigma_matrix = torch.stack(pos_dist_sigma_matrix_list, dim=2)
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return GMM2D.from_log_pis_mus_cov_mats(v_dist.log_pis, pos_mus, pos_dist_sigma_matrix) |