extra.py 10.5 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
19
from numpy.testing import assert_
Philipp Arras's avatar
Philipp Arras committed
20

21
from .domain_tuple import DomainTuple
Martin Reinecke's avatar
fix    
Martin Reinecke committed
22
23
from .field import Field
from .linearization import Linearization
24
from .multi_domain import MultiDomain
25
from .multi_field import MultiField
26
from .operators.linear_operator import LinearOperator
Martin Reinecke's avatar
fix    
Martin Reinecke committed
27
from .sugar import from_random
28

Philipp Arras's avatar
Philipp Arras committed
29
30
__all__ = ["consistency_check", "check_jacobian_consistency",
           "assert_allclose"]
31

Philipp Arras's avatar
Philipp Arras committed
32

Philipp Arras's avatar
Philipp Arras committed
33
def assert_allclose(f1, f2, atol, rtol):
Martin Reinecke's avatar
Martin Reinecke committed
34
    if isinstance(f1, Field):
Martin Reinecke's avatar
stage2    
Martin Reinecke committed
35
        return np.testing.assert_allclose(f1.val, f2.val, atol=atol, rtol=rtol)
Martin Reinecke's avatar
Martin Reinecke committed
36
    for key, val in f1.items():
Philipp Arras's avatar
Philipp Arras committed
37
        assert_allclose(val, f2[key], atol=atol, rtol=rtol)
Martin Reinecke's avatar
Martin Reinecke committed
38
39


40
41
def _adjoint_implementation(op, domain_dtype, target_dtype, atol, rtol,
                            only_r_linear):
Martin Reinecke's avatar
Martin Reinecke committed
42
43
44
45
46
47
48
    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)
49
50
    if only_r_linear:
        res1, res2 = res1.real, res2.real
Martin Reinecke's avatar
Martin Reinecke committed
51
52
53
54
55
56
57
58
59
    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))
Philipp Arras's avatar
Philipp Arras committed
60
    assert_allclose(res, foo, atol=atol, rtol=rtol)
Martin Reinecke's avatar
Martin Reinecke committed
61
62
63

    foo = from_random("normal", op.domain, dtype=domain_dtype)
    res = op.inverse_times(op(foo))
Philipp Arras's avatar
Philipp Arras committed
64
    assert_allclose(res, foo, atol=atol, rtol=rtol)
Martin Reinecke's avatar
Martin Reinecke committed
65
66


67
68
69
70
def _full_implementation(op, domain_dtype, target_dtype, atol, rtol,
                         only_r_linear):
    _adjoint_implementation(op, domain_dtype, target_dtype, atol, rtol,
                            only_r_linear)
Martin Reinecke's avatar
Martin Reinecke committed
71
72
73
    _inverse_implementation(op, domain_dtype, target_dtype, atol, rtol)


74
def _check_linearity(op, domain_dtype, atol, rtol):
Martin Reinecke's avatar
Martin Reinecke committed
75
76
77
    needed_cap = op.TIMES
    if (op.capability & needed_cap) != needed_cap:
        return
78
79
    fld1 = from_random("normal", op.domain, dtype=domain_dtype)
    fld2 = from_random("normal", op.domain, dtype=domain_dtype)
Martin Reinecke's avatar
Martin Reinecke committed
80
    alpha = np.random.random()  # FIXME: this can break badly with MPI!
81
82
    val1 = op(alpha*fld1+fld2)
    val2 = alpha*op(fld1)+op(fld2)
Philipp Arras's avatar
Philipp Arras committed
83
    assert_allclose(val1, val2, atol=atol, rtol=rtol)
84
85


86
87
88
89
90
91
92
93
94
95
96
97
def _actual_domain_check(op, domain_dtype=None, inp=None):
    needed_cap = op.TIMES
    if (op.capability & needed_cap) != needed_cap:
        return
    if domain_dtype is not None:
        inp = from_random("normal", op.domain, dtype=domain_dtype)
    elif inp is None:
        raise ValueError('Need to specify either dtype or inp')
    assert_(inp.domain is op.domain)
    assert_(op(inp).domain is op.target)


Philipp Arras's avatar
Philipp Arras committed
98
def _actual_domain_check_nonlinear(op, loc):
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
    assert isinstance(loc, (Field, MultiField))
    assert_(loc.domain is op.domain)
    lin = Linearization.make_var(loc, False)
    reslin = op(lin)
    assert_(lin.domain is op.domain)
    assert_(lin.target is op.domain)
    assert_(lin.val.domain is lin.domain)

    assert_(reslin.domain is op.domain)
    assert_(reslin.target is op.target)
    assert_(reslin.val.domain is reslin.target)

    assert_(reslin.target is op.target)
    assert_(reslin.jac.domain is reslin.domain)
    assert_(reslin.jac.target is reslin.target)
    _actual_domain_check(reslin.jac, inp=loc)
Philipp Arras's avatar
Philipp Arras committed
115
    _actual_domain_check(reslin.jac.adjoint, inp=reslin.jac(loc))
116
117


118
119
120
def _domain_check(op):
    for dd in [op.domain, op.target]:
        if not isinstance(dd, (DomainTuple, MultiDomain)):
Martin Reinecke's avatar
Martin Reinecke committed
121
122
123
            raise TypeError(
                'The domain and the target of an operator need to',
                'be instances of either DomainTuple or MultiDomain.')
124
125


126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
def _performance_check(op, pos, raise_on_fail):
    class CountingOp(LinearOperator):
        def __init__(self, domain):
            from .sugar import makeDomain
            self._domain = self._target = makeDomain(domain)
            self._capability = self.TIMES | self.ADJOINT_TIMES
            self._count = 0

        def apply(self, x, mode):
            self._count += 1
            return x

        @property
        def count(self):
            return self._count
Philipp Arras's avatar
Philipp Arras committed
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
    for wm in [False, True]:
        cop = CountingOp(op.domain)
        op = op @ cop
        op(pos)
        cond = [cop.count != 1]
        lin = op(Linearization.make_var(pos, wm))
        cond.append(cop.count != 2)
        lin.jac(pos)
        cond.append(cop.count != 3)
        lin.jac.adjoint(lin.val)
        cond.append(cop.count != 4)
        if wm and op.target is DomainTuple.scalar_domain():
            lin.metric(pos)
            cond.append(cop.count != 6)
        if any(cond):
            s = 'The operator has a performance problem (want_metric={}).'.format(wm)
            from .logger import logger
            logger.error(s)
            logger.info(cond)
            if raise_on_fail:
                raise RuntimeError(s)
162
163


Martin Reinecke's avatar
Martin Reinecke committed
164
def consistency_check(op, domain_dtype=np.float64, target_dtype=np.float64,
165
                      atol=0, rtol=1e-7, only_r_linear=False):
Reimar H Leike's avatar
Reimar H Leike committed
166
167
168
169
    """
    Checks an operator for algebraic consistency of its capabilities.

    Checks whether times(), adjoint_times(), inverse_times() and
Philipp Arras's avatar
Philipp Arras committed
170
    adjoint_inverse_times() (if in capability list) is implemented
Reimar H Leike's avatar
Reimar H Leike committed
171
    consistently. Additionally, it checks whether the operator is linear.
Philipp Arras's avatar
Philipp Arras committed
172
173
174
175
176

    Parameters
    ----------
    op : LinearOperator
        Operator which shall be checked.
Reimar H Leike's avatar
Reimar H Leike committed
177
    domain_dtype : dtype
Philipp Arras's avatar
Philipp Arras committed
178
179
        The data type of the random vectors in the operator's domain. Default
        is `np.float64`.
Reimar H Leike's avatar
Reimar H Leike committed
180
    target_dtype : dtype
Philipp Arras's avatar
Philipp Arras committed
181
182
183
        The data type of the random vectors in the operator's target. Default
        is `np.float64`.
    atol : float
Martin Reinecke's avatar
Martin Reinecke committed
184
185
        Absolute tolerance for the check. If rtol is specified,
        then satisfying any tolerance will let the check pass.
Reimar H Leike's avatar
Reimar H Leike committed
186
        Default: 0.
Philipp Arras's avatar
Philipp Arras committed
187
    rtol : float
Martin Reinecke's avatar
Martin Reinecke committed
188
189
        Relative tolerance for the check. If atol is specified,
        then satisfying any tolerance will let the check pass.
Reimar H Leike's avatar
Reimar H Leike committed
190
        Default: 0.
191
192
193
    only_r_linear: bool
        set to True if the operator is only R-linear, not C-linear.
        This will relax the adjointness test accordingly.
Philipp Arras's avatar
Philipp Arras committed
194
    """
195
196
    if not isinstance(op, LinearOperator):
        raise TypeError('This test tests only linear operators.')
197
    _domain_check(op)
198
    _actual_domain_check(op, domain_dtype)
Philipp Arras's avatar
Philipp Arras committed
199
200
    _actual_domain_check(op.adjoint, target_dtype)
    _actual_domain_check(op.inverse, target_dtype)
201
    _actual_domain_check(op.adjoint.inverse, domain_dtype)
202
    _check_linearity(op, domain_dtype, atol, rtol)
Martin Reinecke's avatar
Martin Reinecke committed
203
204
205
    _check_linearity(op.adjoint, target_dtype, atol, rtol)
    _check_linearity(op.inverse, target_dtype, atol, rtol)
    _check_linearity(op.adjoint.inverse, domain_dtype, atol, rtol)
206
207
208
209
210
211
    _full_implementation(op, domain_dtype, target_dtype, atol, rtol,
                         only_r_linear)
    _full_implementation(op.adjoint, target_dtype, domain_dtype, atol, rtol,
                         only_r_linear)
    _full_implementation(op.inverse, target_dtype, domain_dtype, atol, rtol,
                         only_r_linear)
Martin Reinecke's avatar
Martin Reinecke committed
212
    _full_implementation(op.adjoint.inverse, domain_dtype, target_dtype, atol,
213
                         rtol, only_r_linear)
Martin Reinecke's avatar
Martin Reinecke committed
214
215


Martin Reinecke's avatar
Martin Reinecke committed
216
def _get_acceptable_location(op, loc, lin):
Martin Reinecke's avatar
Martin Reinecke committed
217
    if not np.isfinite(lin.val.sum()):
Martin Reinecke's avatar
Martin Reinecke committed
218
219
220
221
        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
222
        dir = dir * (lin.val.norm()*1e-5)
Martin Reinecke's avatar
Martin Reinecke committed
223
    else:
Martin Reinecke's avatar
Martin Reinecke committed
224
        dir = dir * (lin.val.norm()*1e-5/dirder.norm())
Martin Reinecke's avatar
Martin Reinecke committed
225
226
227
228
    # Find a step length that leads to a "reasonable" location
    for i in range(50):
        try:
            loc2 = loc+dir
229
            lin2 = op(Linearization.make_var(loc2, lin.want_metric))
Martin Reinecke's avatar
Martin Reinecke committed
230
231
232
233
234
235
236
237
238
            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
239

240
241
242
243
244
245
246
247
248
def _linearization_value_consistency(op, loc):
    for wm in [False, True]:
        lin = Linearization.make_var(loc, wm)
        fld0 = op(loc)
        fld1 = op(lin).val
        assert_allclose(fld0, fld1, 0, 1e-7)


def check_jacobian_consistency(op, loc, tol=1e-8, ntries=100, perf_check=True):
Martin Reinecke's avatar
Martin Reinecke committed
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
    """
    Checks the Jacobian of an operator against its finite difference
    approximation.

    Computes the Jacobian with finite differences and compares it to the
    implemented Jacobian.

    Parameters
    ----------
    op : Operator
        Operator which shall be checked.
    loc : Field or MultiField
        An Field or MultiField instance which has the same domain
        as op. The location at which the gradient is checked
    tol : float
        Tolerance for the check.
265
266
    perf_check : Boolean
        Do performance check. May be disabled for very unimportant operators.
Martin Reinecke's avatar
Martin Reinecke committed
267
    """
268
    _domain_check(op)
269
    _actual_domain_check_nonlinear(op, loc)
270
271
    _performance_check(op, loc, bool(perf_check))
    _linearization_value_consistency(op, loc)
Martin Reinecke's avatar
Martin Reinecke committed
272
    for _ in range(ntries):
273
        lin = op(Linearization.make_var(loc))
Martin Reinecke's avatar
Martin Reinecke committed
274
        loc2, lin2 = _get_acceptable_location(op, loc, lin)
Martin Reinecke's avatar
Martin Reinecke committed
275
        dir = loc2-loc
Martin Reinecke's avatar
Martin Reinecke committed
276
277
278
279
        locnext = loc2
        dirnorm = dir.norm()
        for i in range(50):
            locmid = loc + 0.5*dir
280
            linmid = op(Linearization.make_var(locmid))
Martin Reinecke's avatar
Martin Reinecke committed
281
282
            dirder = linmid.jac(dir)
            numgrad = (lin2.val-lin.val)
Martin Reinecke's avatar
Martin Reinecke committed
283
            xtol = tol * dirder.norm() / np.sqrt(dirder.size)
Martin Reinecke's avatar
Martin Reinecke committed
284
            if (abs(numgrad-dirder) <= xtol).all():
Martin Reinecke's avatar
Martin Reinecke committed
285
286
287
                break
            dir = dir*0.5
            dirnorm *= 0.5
Martin Reinecke's avatar
Martin Reinecke committed
288
            loc2, lin2 = locmid, linmid
Martin Reinecke's avatar
Martin Reinecke committed
289
290
291
        else:
            raise ValueError("gradient and value seem inconsistent")
        loc = locnext