我有一种情况,其中一个组件的梯度必须在另一个组件中计算。我试图做的只是让梯度成为第一个组件的输出和第二个组件的输入。我已将其设置为 pass_by_obj,以免影响其他计算。任何关于这是否是最好的方法的建议将不胜感激。不过,使用 check_partial_derivatives() 时出现错误。对于指定为 pass_by_obj 的任何输出,这似乎都是一个错误。这是一个简单的案例:
import numpy as np
from openmdao.api import Group, Problem, Component, ScipyGMRES, ExecComp, IndepVarComp
class Comp1(Component):
def __init__(self):
super(Comp1, self).__init__()
self.add_param('x', shape=1)
self.add_output('y', shape=1)
self.add_output('dz_dy', shape=1, pass_by_obj=True)
def solve_nonlinear(self, params, unknowns, resids):
x = params['x']
unknowns['y'] = 4.0*x + 1.0
unknowns['dz_dy'] = 2.0*x
def linearize(self, params, unknowns, resids):
J = {}
J['y', 'x'] = 4.0
return J
class Comp2(Component):
def __init__(self):
super(Comp2, self).__init__()
self.add_param('y', shape=1)
self.add_param('dz_dy', shape=1, pass_by_obj=True)
self.add_output('z', shape=1)
def solve_nonlinear(self, params, unknowns, resids):
y = params['y']
unknowns['z'] = y*2.0
def linearize(self, params, unknowns, resids):
J = {}
J['z', 'y'] = params['dz_dy']
return J
class TestGroup(Group):
def __init__(self):
super(TestGroup, self).__init__()
self.add('x', IndepVarComp('x', 0.0), promotes=['*'])
self.add('c1', Comp1(), promotes=['*'])
self.add('c2', Comp2(), promotes=['*'])
p = Problem()
p.root = TestGroup()
p.setup(check=False)
p['x'] = 2.0
p.run()
print p['z']
print 'gradients'
test_grad = open('partial_gradients_test.txt', 'w')
partial = p.check_partial_derivatives(out_stream=test_grad)
我收到以下错误消息:
partial = p.check_partial_derivatives(out_stream=test_grad)
File "/usr/local/lib/python2.7/site-packages/openmdao/core/problem.py", line 1699, in check_partial_derivatives
dresids._dat[u_name].val[idx] = 1.0
TypeError: '_ByObjWrapper' object does not support item assignment
我之前询问过在 check_partial_derivatives() 中检查 pass_by_obj 的参数,这可能只是检查 pass_by_obj 的未知数的问题。