Source code for storm_kit.mpc.cost.finite_difference_cost

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import matplotlib
matplotlib.use('tkagg')
import matplotlib.pyplot as plt

import torch
import torch.nn as nn
# import torch.nn.functional as F
from .gaussian_projection import GaussianProjection
from ..model.integration_utils import build_fd_matrix
[docs]class FiniteDifferenceCost(nn.Module): def __init__(self, tensor_args={'device':torch.device('cpu'), 'dtype':torch.float32}, weight=1.0, order=1, gaussian_params={}, **kwargs): super(FiniteDifferenceCost, self).__init__() self.order = order for _ in range(order): weight *= weight self.weight = weight self.tensor_args = tensor_args # build FD matrix self.fd_mat = None self.proj_gaussian = GaussianProjection(gaussian_params=gaussian_params) self.t_mat = None
[docs] def forward(self, ctrl_seq, dt): """ ctrl_seq: [B X H X d_act] """ dt[dt == 0.0] = 0.0 #dt[-1] dt = 1 / dt #dt = dt / torch.max(dt) dt = torch.abs(dt) #print(dt) dt[dt == float("Inf")] = 0 dt[dt > 10] = 10 #dt = dt / torch.max(dt) dt[dt != dt] = 0.0 #for _ in range(self.order-1): # dt = dt * dt #print(dt) inp_device = ctrl_seq.device ctrl_seq = ctrl_seq.to(**self.tensor_args) B, H, _ = ctrl_seq.shape H = H - self.order dt = dt[:H] # if(self.fd_mat is None or self.fd_mat.shape[0] != H): self.fd_mat = build_fd_matrix(H,device=self.tensor_args['device'], dtype=self.tensor_args['dtype'], order=self.order, PREV_STATE=True) diff = torch.matmul(self.fd_mat,ctrl_seq) res = torch.abs(diff) cost = res[:,:,-1] cost[cost < 0.0001] = 0.0 cost = self.weight * cost return cost