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Krishna Krishna Nikhil
Calo Opt_Nikhil
Commits
d804a782
Commit
d804a782
authored
10 months ago
by
Krishna Krishna Nikhil
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Update optimizer.py
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modules/optimizer.py
+50
-33
50 additions, 33 deletions
modules/optimizer.py
with
50 additions
and
33 deletions
modules/optimizer.py
+
50
−
33
View file @
d804a782
...
...
@@ -39,16 +39,17 @@ class Optimizer(object):
self
.
surrogate_model
.
to
(
device
)
self
.
cu_box
.
to
(
device
)
def
other_constraints
(
self
,
dataset
):
def
other_constraints
(
self
,
dataset
,
ToP
,
scale_update
):
# keep the size of the detector within 3m
raw_detector_parameters
=
self
.
detector_parameters
# this creates a loss that increases starting from 3 meters using relu
detector_length
=
torch
.
sum
(
raw_detector_parameters
[:
6
])
# changed this here so as to not include material choice in this
thickness_parameters
=
self
.
detector_parameters
[
0
:
6
]
material_parameters
=
self
.
detector_parameters
[
6
:
12
]
abs_parameters
=
self
.
detector_parameters
[
6
:
9
]
scint_parameters
=
self
.
detector_parameters
[
9
:
12
]
number_parameters
=
len
(
self
.
detector_parameters
)
divider
=
int
(((
number_parameters
//
ToP
)
*
3
)
/
2
)
thickness_parameters
=
self
.
detector_parameters
[
0
:(
number_parameters
//
ToP
)]
material_parameters
=
self
.
detector_parameters
[(
number_parameters
//
ToP
):(
number_parameters
//
ToP
)
*
2
]
abs_parameters
=
self
.
detector_parameters
[(
number_parameters
//
ToP
):
divider
]
scint_parameters
=
self
.
detector_parameters
[
divider
:
(
number_parameters
//
ToP
)
*
2
]
# cost_dic = {"G4_POLYSTYRENE": 0.001
# ,"G4_PLASTIC_SC_VINYLTOLUENE": 0.001
...
...
@@ -64,18 +65,24 @@ class Optimizer(object):
# ,"G4_Si": 8330000
# ,"G4_PbWO4": 2500 }
scale_factor
=
1
+
scale_update
*
0.02
if
self
.
constraints
is
not
None
:
zero_length
=
(
torch
.
zeros
(
number_parameters
//
2
)).
to
(
self
.
device
)
negative_length_loss
=
torch
.
mean
(
0.5
*
torch
.
nn
.
ReLU
()(
zero_length
-
thickness_parameters
)
**
2
)
if
'
length
'
in
self
.
constraints
:
detector_length
=
torch
.
sum
(
raw_detector_parameters
[:
6
])
# changed this here so as to not include material choice in this
total_length_loss
=
torch
.
mean
(
100.
*
torch
.
nn
.
ReLU
()(
detector_length
-
self
.
constraints
[
'
length
'
])
**
2
)
#constrain it to 25cm
detector_length
=
torch
.
sum
(
raw_detector_parameters
[:(
number_parameters
//
ToP
)])
# changed this here so as to not include material choice in this
total_length_loss
=
torch
.
mean
(
10.
*
torch
.
nn
.
ReLU
()(
detector_length
-
self
.
constraints
[
'
length
'
])
**
2
)
#constrain it to 25cm
if
'
lower
'
in
self
.
constraints
:
lbound
=
(
torch
.
ones
(
12
)
*
self
.
constraints
[
'
lower
'
]).
to
(
self
.
device
)
lower_loss
=
torch
.
mean
(
100.
*
torch
.
nn
.
ReLU
()(
lbound
-
raw_detector_parameters
)
**
2
)
lbound
=
(
torch
.
ones
(
number_parameters
//
2
)
*
self
.
constraints
[
'
lower
'
]).
to
(
self
.
device
)
lower_loss
=
torch
.
mean
(
0.5
*
torch
.
nn
.
ReLU
()(
lbound
-
material_parameters
)
**
2
)
if
'
upper
'
in
self
.
constraints
:
ubound
=
(
torch
.
ones
(
6
)
*
self
.
constraints
[
'
upper
'
]).
to
(
self
.
device
)
upper_loss
=
torch
.
mean
(
100.
*
torch
.
nn
.
ReLU
()(
material_parameters
-
ubound
)
**
2
)
ubound
=
(
torch
.
ones
(
number_parameters
//
2
)
*
self
.
constraints
[
'
upper
'
]).
to
(
self
.
device
)
upper_loss
=
torch
.
mean
(
0.5
*
torch
.
nn
.
ReLU
()(
material_parameters
-
ubound
)
**
2
)
if
'
diff
'
in
self
.
constraints
:
abs_maxdiff
=
max
(
abs_parameters
)
-
min
(
abs_parameters
)
scint_maxdiff
=
max
(
scint_parameters
)
-
min
(
scint_parameters
)
...
...
@@ -83,26 +90,23 @@ class Optimizer(object):
diff_loss
=
torch
.
mean
(
100.
*
torch
.
nn
.
ReLU
()(
abs_maxdiff
-
self
.
constraints
[
'
diff
'
])
**
2
)
diff_loss
+=
torch
.
mean
(
100.
*
torch
.
nn
.
ReLU
()(
scint_maxdiff
-
self
.
constraints
[
'
diff
'
])
**
2
)
if
'
cost
'
in
self
.
constraints
:
# cost = w * cost_material_a + (1-w) * cost_material_b
# then you can scale w in a way that makes it more 'strict', so e.g. with a sigmoid(10*(w-0.5)) (please plot that and check if that makes sense before using it)
def
sigmoid
(
number
):
return
1
/
(
1
+
torch
.
exp
(
-
number
))
abs_sigm
=
sigmoid
(
10
*
(
abs_parameters
-
0.5
))
abs_sigm
=
sigmoid
(
scale_factor
*
(
abs_parameters
))
cost_abs
=
abs_sigm
*
25
+
(
1
-
abs_sigm
)
*
4.166
scint_sigm
=
sigmoid
(
10
*
(
scint_parameters
-
0.5
))
cost_scint
=
scint_sigm
*
2500
+
(
1
-
scint_sigm
)
*
0.
0
01
scint_sigm
=
sigmoid
(
scale_factor
*
(
scint_parameters
))
cost_scint
=
scint_sigm
*
2500
+
(
1
-
scint_sigm
)
*
0.01
combined
=
torch
.
cat
((
cost_abs
,
cost_scint
),
dim
=
0
)
combined_cost
=
combined
.
to
(
self
.
device
)
cost
=
torch
.
sum
(
combined_cost
*
thickness_parameters
)
cost_loss
=
torch
.
mean
(
50.
*
torch
.
nn
.
ReLU
()(
cost
-
self
.
constraints
[
'
cost
'
])
**
2
)
cost_loss
=
torch
.
mean
((
2
/
self
.
constraints
[
'
cost
'
])
*
torch
.
nn
.
ReLU
()(
cost
-
self
.
constraints
[
'
cost
'
])
**
2
)
# now keep parameters such that within the box size of the generator, there are always some positive values even if the
# central parameters are negative. Both box size and raw_detector_parameters are in non-normalised space, so this is straight forward
...
...
@@ -114,7 +118,7 @@ class Optimizer(object):
# bloss = torch.mean(100.*torch.nn.ReLU()(lower_para_bound - raw_detector_parameters)**2)
# + bloss
return
total_length_loss
+
lower_loss
+
upper_loss
+
diff
_loss
+
cost
_loss
return
total_length_loss
+
lower_loss
+
upper_loss
+
cost
_loss
+
negative_length
_loss
def
clamp_parameters
(
self
):
return
...
...
@@ -134,10 +138,10 @@ class Optimizer(object):
# Adjust the original covariance matrix
self
.
generator
.
box_covariance
=
np
.
diag
(
self
.
generator
.
box_size
**
2
)
+
M_scaled
print
(
'
new box_covariance
'
,
self
.
generator
.
box_covariance
)
#
print('new box_covariance', self.generator.box_covariance)
def
optimize
(
self
,
dataset
,
batch_size
,
n_epochs
,
lr
,
add_constraints
=
False
):
def
optimize
(
self
,
dataset
,
ToP
,
scale_update
,
batch_size
,
n_epochs
,
lr
,
add_constraints
=
False
):
'''
keep both models fixed, train only the detector parameters (self.detector_start_parameters)
using the reconstruction model loss
...
...
@@ -152,12 +156,16 @@ class Optimizer(object):
data_loader
=
DataLoader
(
dataset
,
batch_size
=
batch_size
,
shuffle
=
True
)
# save the initial parameters
initial_parameters
=
self
.
detector_parameters
.
detach
().
cpu
().
numpy
()
initial_parameters
=
np
.
copy
(
self
.
detector_parameters
.
detach
().
cpu
().
numpy
()
)
# loop over the batches
mean_loss
=
0
reco_surrogate_loss
=
0
constraint_loss
=
0
for
epoch
in
range
(
n_epochs
):
mean_loss
=
0
#only use last epoch
mean_loss
=
0
reco_surrogate_loss
=
0
constraint_loss
=
0
stop_epoch
=
False
for
batch_idx
,
(
_
,
true_inputs
,
true_context
,
reco_result
)
in
enumerate
(
data_loader
):
...
...
@@ -172,10 +180,13 @@ class Optimizer(object):
true_inputs
,
true_context
)
# calculate the loss
loss
=
5
*
self
.
reconstruction_model
.
loss
(
dataset
.
unnormalise_target
(
reco_surrogate
),
dataset
.
unnormalise_target
(
true_inputs
))
loss
=
self
.
reconstruction_model
.
loss
(
dataset
.
unnormalise_target
(
reco_surrogate
),
dataset
.
unnormalise_target
(
true_inputs
))
reco_surrogate_loss
+=
loss
.
item
()
if
add_constraints
:
loss
+=
self
.
other_constraints
(
dataset
)
#
constraint_loss_value
=
self
.
other_constraints
(
dataset
,
ToP
,
scale_update
)
constraint_loss
+=
constraint_loss_value
.
item
()
loss
+=
constraint_loss_value
self
.
optimizer
.
zero_grad
()
loss
.
backward
()
#print('gradient',self.detector_parameters.grad, 'should have', self.detector_parameters.requires_grad)
...
...
@@ -198,6 +209,7 @@ class Optimizer(object):
#check if the parameters are still local otherwise stop
if
not
self
.
generator
.
is_local
(
self
.
detector_parameters
.
detach
().
cpu
().
numpy
(),
0.8
):
#a bit smaller box size to be safe
stop_epoch
=
True
print
(
"
parameter not local
"
)
break
if
batch_idx
%
20
==
0
:
...
...
@@ -211,11 +223,16 @@ class Optimizer(object):
epoch
,
loss
.
item
()))
if
stop_epoch
:
break
# if not stop_epoch:
# scale_update += 1
# print('Scale updated to : ', scale_update)
scale_update
+=
1
self
.
clamp_parameters
()
mean_loss
/=
batch_idx
+
1
reco_surrogate_loss
/=
batch_idx
+
1
constraint_loss
/=
batch_idx
+
1
self
.
adjust_generator_covariance
(
self
.
detector_parameters
.
detach
().
cpu
().
numpy
()
-
initial_parameters
)
return
self
.
detector_parameters
.
detach
().
cpu
().
numpy
(),
True
,
mean_loss
return
self
.
detector_parameters
.
detach
().
cpu
().
numpy
(),
True
,
mean_loss
,
reco_surrogate_loss
,
constraint_loss
,
scale_update
def
get_optimum
(
self
):
return
self
.
detector_parameters
.
detach
().
cpu
().
numpy
()
...
...
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