如何摆脱 python“Traceback (most最近一次调用最后): ...”的 Traceback 问题？

Question

我一次又一次地收到此错误。我不知道问题是什么以及如何解决。

/lem_robotics$/bin/python/home/pallabi/lem_robotics/src/control/ilqr_py/pytorch_cost_object1.py
Traceback (most recent call last):
  File "/home/pallabi/lem_robotics/src/control/ilqr_py/pytorch_cost_object1.py", line 64, in <module>
    cost = c_obj.helper_all_cost_stage(x0, u0, y0)
  File "/home/pallabi/lem_robotics/src/control/ilqr_py/pytorch_cost_object1.py", line 40, in helper_all_cost_stage
    J = self.forward(states, inputs, outputs)
  File "/home/pallabi/lem_robotics/src/control/ilqr_py/pytorch_cost_object1.py", line 20, in forward
    Jquad_x = self.helper_quadratic_cost_term(self.qx, states, self.rx)
  File "/home/pallabi/lem_robotics/src/control/ilqr_py/pytorch_cost_object1.py", line 36, in helper_quadratic_cost_term
    assert (cost.size() == torch.Size([1]))
AssertionError

这是实际的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class CostObject(nn.Module):
    def __init__(self, dynamical_system):
        super(CostObject, self).__init__()
        self.ds = dynamical_system
        self.qx = nn.Parameter(torch.zeros(self.ds.nx))
        self.qu = nn.Parameter(torch.zeros(self.ds.nu))
        self.qy = nn.Parameter(torch.zeros(self.ds.ny))
        self.rx = nn.Parameter(torch.zeros(self.ds.nx))
        self.ru = nn.Parameter(torch.zeros(self.ds.nu))
        self.ry = nn.Parameter(torch.zeros(self.ds.ny))
        self.Qx = nn.Parameter(torch.diag(self.qx))
        self.Qu = nn.Parameter(torch.diag(self.qu))
        self.Qy = nn.Parameter(torch.diag(self.qy))

    def forward(self, states, inputs, outputs):
        Jquad_x = self.helper_quadratic_cost_term(self.qx, states, self.rx)
        Jquad_u = self.helper_quadratic_cost_term(self.qu, inputs, self.ru)
        Jquad_y = self.helper_quadratic_cost_term(self.qy, outputs, self.ry)
        J = Jquad_x + Jquad_u + Jquad_y
        return J

    def helper_quadratic_cost_term(self, weights, variables, references):
        assert (weights.size() == variables.size())
        assert (weights.size() == references.size())
        error = variables - references
        D = torch.diag(weights)
        #cost = torch.matmul(error.T, torch.matmul(D, error))
        cost = (error.permute(*torch.arange(error.ndim - 1, -1, -1)) @ D @ error).squeeze()
        #cost = (error. permute(1, 0) @ D @ error).squeeze()
        #cost = (error.T @ D @ error).squeeze()

        assert (cost.size() == torch.Size([1]))
        return cost

    def helper_all_cost_stage(self, states, inputs, outputs):
        J = self.forward(states, inputs, outputs)
        Jx = torch.autograd.grad(J, states, create_graph=True)[0]
        Ju = torch.autograd.grad(J, inputs, create_graph=True)[0]
        Jxx = torch.autograd.grad(Jx, states, create_graph=True)[0]
        Jux = torch.autograd.grad(Jx, inputs, create_graph=True)[0]
        Juu = torch.autograd.grad(Ju, inputs, create_graph=True)[0]
        return J, Jx, Ju, Jxx, Jux, Juu

if __name__ == '__main__':
    from robot_model_parameters import robot_model
    import numpy as np

    robot = robot_model('ackermann')
    c_obj = CostObject(robot)
    x0 = torch.zeros(robot.nx, requires_grad=True)
    u0 = torch.zeros(robot.nu, requires_grad=True)
    y0 = torch.zeros(robot.ny, requires_grad=True)
    qx = torch.ones(robot.nx, requires_grad=True)
    qu = torch.ones(robot.nu, requires_grad=True)
    qy = torch.ones(robot.ny, requires_grad=True)
    rx = torch.ones(robot.nx, requires_grad=True)
    ru = torch.zeros(robot.nu, requires_grad=True)
    ry = rx

    cost = c_obj.helper_all_cost_stage(x0, u0, y0)
    print("cost", cost)

Answer 1

这意味着断言中的条件是错误的。也就是说，cost.size()不等于torch.Size([1])。您应该检查条件并进行调试。