extra.py 4.89 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
14
# Copyright(C) 2013-2019 Max-Planck-Society
15
#
16
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
17 18

import numpy as np
Philipp Arras's avatar
Philipp Arras committed
19

Martin Reinecke's avatar
fix  
Martin Reinecke committed
20 21 22
from .field import Field
from .linearization import Linearization
from .sugar import from_random
23

Martin Reinecke's avatar
Martin Reinecke committed
24
__all__ = ["consistency_check", "check_value_gradient_consistency",
Martin Reinecke's avatar
Martin Reinecke committed
25
           "check_value_gradient_metric_consistency"]
26

Philipp Arras's avatar
Philipp Arras committed
27

Martin Reinecke's avatar
Martin Reinecke committed
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73
def _assert_allclose(f1, f2, atol, rtol):
    if isinstance(f1, Field):
        return np.testing.assert_allclose(f1.local_data, f2.local_data,
                                          atol=atol, rtol=rtol)
    for key, val in f1.items():
        _assert_allclose(val, f2[key], atol=atol, rtol=rtol)


def _adjoint_implementation(op, domain_dtype, target_dtype, atol, rtol):
    needed_cap = op.TIMES | op.ADJOINT_TIMES
    if (op.capability & needed_cap) != needed_cap:
        return
    f1 = from_random("normal", op.domain, dtype=domain_dtype)
    f2 = from_random("normal", op.target, dtype=target_dtype)
    res1 = f1.vdot(op.adjoint_times(f2))
    res2 = op.times(f1).vdot(f2)
    np.testing.assert_allclose(res1, res2, atol=atol, rtol=rtol)


def _inverse_implementation(op, domain_dtype, target_dtype, atol, rtol):
    needed_cap = op.TIMES | op.INVERSE_TIMES
    if (op.capability & needed_cap) != needed_cap:
        return
    foo = from_random("normal", op.target, dtype=target_dtype)
    res = op(op.inverse_times(foo))
    _assert_allclose(res, foo, atol=atol, rtol=rtol)

    foo = from_random("normal", op.domain, dtype=domain_dtype)
    res = op.inverse_times(op(foo))
    _assert_allclose(res, foo, atol=atol, rtol=rtol)


def _full_implementation(op, domain_dtype, target_dtype, atol, rtol):
    _adjoint_implementation(op, domain_dtype, target_dtype, atol, rtol)
    _inverse_implementation(op, domain_dtype, target_dtype, atol, rtol)


def consistency_check(op, domain_dtype=np.float64, target_dtype=np.float64,
                      atol=0, rtol=1e-7):
    _full_implementation(op, domain_dtype, target_dtype, atol, rtol)
    _full_implementation(op.adjoint, target_dtype, domain_dtype, atol, rtol)
    _full_implementation(op.inverse, target_dtype, domain_dtype, atol, rtol)
    _full_implementation(op.adjoint.inverse, domain_dtype, target_dtype, atol,
                         rtol)


Martin Reinecke's avatar
Martin Reinecke committed
74
def _get_acceptable_location(op, loc, lin):
Martin Reinecke's avatar
Martin Reinecke committed
75
    if not np.isfinite(lin.val.sum()):
Martin Reinecke's avatar
Martin Reinecke committed
76 77 78 79
        raise ValueError('Initial value must be finite')
    dir = from_random("normal", loc.domain)
    dirder = lin.jac(dir)
    if dirder.norm() == 0:
Martin Reinecke's avatar
Martin Reinecke committed
80
        dir = dir * (lin.val.norm()*1e-5)
Martin Reinecke's avatar
Martin Reinecke committed
81
    else:
Martin Reinecke's avatar
Martin Reinecke committed
82
        dir = dir * (lin.val.norm()*1e-5/dirder.norm())
Martin Reinecke's avatar
Martin Reinecke committed
83 84 85 86
    # Find a step length that leads to a "reasonable" location
    for i in range(50):
        try:
            loc2 = loc+dir
87
            lin2 = op(Linearization.make_var(loc2, lin.want_metric))
Martin Reinecke's avatar
Martin Reinecke committed
88 89 90 91 92 93 94 95 96
            if np.isfinite(lin2.val.sum()) and abs(lin2.val.sum()) < 1e20:
                break
        except FloatingPointError:
            pass
        dir = dir*0.5
    else:
        raise ValueError("could not find a reasonable initial step")
    return loc2, lin2

Martin Reinecke's avatar
Martin Reinecke committed
97

Martin Reinecke's avatar
Martin Reinecke committed
98
def _check_consistency(op, loc, tol, ntries, do_metric):
Martin Reinecke's avatar
Martin Reinecke committed
99
    for _ in range(ntries):
100
        lin = op(Linearization.make_var(loc, do_metric))
Martin Reinecke's avatar
Martin Reinecke committed
101
        loc2, lin2 = _get_acceptable_location(op, loc, lin)
Martin Reinecke's avatar
Martin Reinecke committed
102
        dir = loc2-loc
Martin Reinecke's avatar
Martin Reinecke committed
103 104 105 106
        locnext = loc2
        dirnorm = dir.norm()
        for i in range(50):
            locmid = loc + 0.5*dir
107
            linmid = op(Linearization.make_var(locmid, do_metric))
Martin Reinecke's avatar
Martin Reinecke committed
108 109
            dirder = linmid.jac(dir)
            numgrad = (lin2.val-lin.val)
Martin Reinecke's avatar
Martin Reinecke committed
110
            xtol = tol * dirder.norm() / np.sqrt(dirder.size)
Martin Reinecke's avatar
Martin Reinecke committed
111 112
            cond = (abs(numgrad-dirder) <= xtol).all()
            if do_metric:
Martin Reinecke's avatar
Martin Reinecke committed
113 114
                dgrad = linmid.metric(dir)
                dgrad2 = (lin2.gradient-lin.gradient)
Martin Reinecke's avatar
Martin Reinecke committed
115 116
                cond = cond and (abs(dgrad-dgrad2) <= xtol).all()
            if cond:
Martin Reinecke's avatar
Martin Reinecke committed
117 118 119
                break
            dir = dir*0.5
            dirnorm *= 0.5
Martin Reinecke's avatar
Martin Reinecke committed
120
            loc2, lin2 = locmid, linmid
Martin Reinecke's avatar
Martin Reinecke committed
121 122 123
        else:
            raise ValueError("gradient and value seem inconsistent")
        loc = locnext
Martin Reinecke's avatar
Martin Reinecke committed
124 125


Martin Reinecke's avatar
Martin Reinecke committed
126 127 128 129
def check_value_gradient_consistency(op, loc, tol=1e-8, ntries=100):
    _check_consistency(op, loc, tol, ntries, False)


Martin Reinecke's avatar
Martin Reinecke committed
130
def check_value_gradient_metric_consistency(op, loc, tol=1e-8, ntries=100):
Martin Reinecke's avatar
Martin Reinecke committed
131
    _check_consistency(op, loc, tol, ntries, True)