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ift
NIFTy
Commits
516a415f
Commit
516a415f
authored
Jul 23, 2018
by
Martin Reinecke
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Merge branch 'unit-tests-for-models' into 'NIFTy_5'
Unit tests for models See merge request ift/nifty-dev!65
parents
b116c7ed
2ef525d5
Changes
2
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2 changed files
with
73 additions
and
15 deletions
+73
-15
nifty5/extra/energy_and_model_tests.py
nifty5/extra/energy_and_model_tests.py
+6
-3
test/test_models/test_model_gradients.py
test/test_models/test_model_gradients.py
+67
-12
No files found.
nifty5/extra/energy_and_model_tests.py
View file @
516a415f
...
...
@@ -33,7 +33,10 @@ def _get_acceptable_model(M):
raise
ValueError
(
'Initial Model value must be finite'
)
dir
=
from_random
(
"normal"
,
M
.
position
.
domain
)
dirder
=
M
.
jacobian
(
dir
)
dir
=
dir
*
val
*
(
1e-5
/
dirder
.
norm
())
if
dirder
.
norm
()
==
0
:
dir
=
dir
*
val
*
1e-5
else
:
dir
=
dir
*
val
*
(
1e-5
/
dirder
.
norm
())
# Find a step length that leads to a "reasonable" Model
for
i
in
range
(
50
):
try
:
...
...
@@ -88,11 +91,11 @@ def check_value_gradient_consistency(E, tol=1e-8, ntries=100):
numgrad
=
(
E2
.
value
-
val
)
/
dirnorm
if
isinstance
(
E
,
Model
):
xtol
=
tol
*
dirder
.
norm
()
/
np
.
sqrt
(
dirder
.
size
)
if
(
abs
(
numgrad
-
dirder
)
<
xtol
).
all
():
if
(
abs
(
numgrad
-
dirder
)
<
=
xtol
).
all
():
break
else
:
xtol
=
tol
*
Emid
.
gradient_norm
if
abs
(
numgrad
-
dirder
)
<
xtol
:
if
abs
(
numgrad
-
dirder
)
<
=
xtol
:
break
dir
=
dir
*
0.5
dirnorm
*=
0.5
...
...
test/test_models/test_model_gradients.py
View file @
516a415f
...
...
@@ -25,15 +25,70 @@ import numpy as np
class
Model_Tests
(
unittest
.
TestCase
):
@
expand
(
product
([
ift
.
GLSpace
(
15
),
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)],
[
4
,
78
,
23
]))
def
testMul
(
self
,
space
,
seed
):
np
.
random
.
seed
(
seed
)
S
=
ift
.
ScalingOperator
(
1.
,
space
)
s1
=
S
.
draw_sample
()
s2
=
S
.
draw_sample
()
s1_var
=
ift
.
Variable
(
ift
.
MultiField
.
from_dict
({
's1'
:
s1
}))[
's1'
]
s2_var
=
ift
.
Variable
(
ift
.
MultiField
.
from_dict
({
's2'
:
s2
}))[
's2'
]
ift
.
extra
.
check_value_gradient_consistency
(
s1_var
*
s2_var
)
def
make_model
(
self
,
type
,
**
kwargs
):
if
type
==
'Constant'
:
np
.
random
.
seed
(
kwargs
[
'seed'
])
S
=
ift
.
ScalingOperator
(
1.
,
kwargs
[
'space'
])
s
=
S
.
draw_sample
()
return
ift
.
Constant
(
ift
.
MultiField
.
from_dict
({
kwargs
[
'space_key'
]:
s
}),
ift
.
MultiField
.
from_dict
({
kwargs
[
'space_key'
]:
s
}))
elif
type
==
'Variable'
:
np
.
random
.
seed
(
kwargs
[
'seed'
])
S
=
ift
.
ScalingOperator
(
1.
,
kwargs
[
'space'
])
s
=
S
.
draw_sample
()
return
ift
.
Variable
(
ift
.
MultiField
.
from_dict
({
kwargs
[
'space_key'
]:
s
}))
elif
type
==
'LinearModel'
:
return
ift
.
LinearModel
(
inp
=
kwargs
[
'model'
],
lin_op
=
kwargs
[
'lin_op'
])
else
:
raise
ValueError
(
'unknown type passed'
)
def
make_linear_operator
(
self
,
type
,
**
kwargs
):
if
type
==
'ScalingOperator'
:
lin_op
=
ift
.
ScalingOperator
(
1.
,
kwargs
[
'space'
])
else
:
raise
ValueError
(
'unknown type passed'
)
return
lin_op
@
expand
(
product
(
[
'Variable'
,
'Constant'
],
[
ift
.
GLSpace
(
15
),
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)],
[
4
,
78
,
23
]
))
def
testBasics
(
self
,
type1
,
space
,
seed
):
model1
=
self
.
make_model
(
type1
,
space_key
=
's1'
,
space
=
space
,
seed
=
seed
)[
's1'
]
ift
.
extra
.
check_value_gradient_consistency
(
model1
)
@
expand
(
product
(
[
'Variable'
,
'Constant'
],
[
'Variable'
],
[
ift
.
GLSpace
(
15
),
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)],
[
4
,
78
,
23
]
))
def
testMul
(
self
,
type1
,
type2
,
space
,
seed
):
model1
=
self
.
make_model
(
type1
,
space_key
=
's1'
,
space
=
space
,
seed
=
seed
)[
's1'
]
model2
=
self
.
make_model
(
type2
,
space_key
=
's2'
,
space
=
space
,
seed
=
seed
+
1
)[
's2'
]
ift
.
extra
.
check_value_gradient_consistency
(
model1
*
model2
)
@
expand
(
product
(
[
'Variable'
,
'Constant'
],
[
ift
.
GLSpace
(
15
),
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)],
[
4
,
78
,
23
]
))
def
testLinModel
(
self
,
type1
,
space
,
seed
):
model1
=
self
.
make_model
(
type1
,
space_key
=
's1'
,
space
=
space
,
seed
=
seed
)[
's1'
]
lin_op
=
self
.
make_linear_operator
(
'ScalingOperator'
,
space
=
space
)
model2
=
self
.
make_model
(
'LinearModel'
,
model
=
model1
,
lin_op
=
lin_op
)
ift
.
extra
.
check_value_gradient_consistency
(
model1
*
model2
)
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