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Tamino Huxohl
mu-map
Commits
7981050c
Commit
7981050c
authored
2 years ago
by
Tamino Huxohl
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fixed for cgan training
parent
82017f08
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1 changed file
mu_map/training/cgan.py
+14
-19
14 additions, 19 deletions
mu_map/training/cgan.py
with
14 additions
and
19 deletions
mu_map/training/cgan.py
+
14
−
19
View file @
7981050c
...
...
@@ -36,16 +36,12 @@ class cGANTraining:
weight_criterion_adv
:
float
,
logger
=
None
,
):
self
.
generator
=
generator
self
.
discriminator
=
discriminator
self
.
data_loaders
=
data_loaders
self
.
epochs
=
epochs
self
.
device
=
device
self
.
snapshot_dir
=
snapshot_dir
self
.
snapshot_epoch
=
snapshot_epoch
self
.
logger
=
logger
if
logger
is
not
None
else
get_logger
()
self
.
params_g
=
params_generator
...
...
@@ -61,21 +57,21 @@ class cGANTraining:
loss_val_min
=
sys
.
maxsize
for
epoch
in
range
(
1
,
self
.
epochs
+
1
):
str_epoch
=
f
"
{
str
(
epoch
)
:
>
{
len
(
str
(
self
.
epochs
))
}}
"
logger
.
debug
(
f
"
Run epoch
{
str_epoch
}
/
{
self
.
epochs
}
...
"
)
self
.
logger
.
debug
(
f
"
Run epoch
{
str_epoch
}
/
{
self
.
epochs
}
...
"
)
self
.
_train_epoch
()
loss_train
=
self
.
_eval_epoch
(
"
train
"
)
logger
.
info
(
self
.
logger
.
info
(
f
"
Epoch
{
str_epoch
}
/
{
self
.
epochs
}
- Loss train:
{
loss_train
:
.
6
f
}
"
)
loss_val
=
self
.
_eval_epoch
(
"
validation
"
)
logger
.
info
(
self
.
logger
.
info
(
f
"
Epoch
{
str_epoch
}
/
{
self
.
epochs
}
- Loss validation:
{
loss_val
:
.
6
f
}
"
)
if
loss_val
<
loss_val_min
:
loss_val_min
=
loss_val
logger
.
info
(
self
.
logger
.
info
(
f
"
Store snapshot val_min of epoch
{
str_epoch
}
with minimal validation loss
"
)
self
.
store_snapshot
(
"
val_min
"
)
...
...
@@ -83,13 +79,12 @@ class cGANTraining:
self
.
_store_snapshot
(
epoch
)
if
self
.
params_d
.
lr_scheduler
is
not
None
:
logger
.
debug
(
"
Step LR scheduler of discriminator
"
)
self
.
logger
.
debug
(
"
Step LR scheduler of discriminator
"
)
self
.
params_d
.
lr_scheduler
.
step
()
if
self
.
params_g
.
lr_scheduler
is
not
None
:
logger
.
debug
(
"
Step LR scheduler of generator
"
)
self
.
logger
.
debug
(
"
Step LR scheduler of generator
"
)
self
.
params_g
.
lr_scheduler
.
step
()
return
loss_val
return
loss_val_min
def
_train_epoch
(
self
):
# setup training mode
...
...
@@ -154,8 +149,8 @@ class cGANTraining:
def
_eval_epoch
(
self
,
split_name
):
# setup evaluation mode
torch
.
set_grad_enabled
(
False
)
self
.
discriminator
=
self
.
discriminator
.
eval
()
self
.
generator
=
self
.
generator
.
eval
()
self
.
params_d
.
model
=
self
.
params_d
.
model
.
eval
()
self
.
params_g
.
model
=
self
.
params_g
.
model
.
eval
()
data_loader
=
self
.
data_loaders
[
split_name
]
loss
=
0.0
...
...
@@ -181,9 +176,9 @@ class cGANTraining:
def
store_snapshot
(
self
,
prefix
:
str
):
snapshot_file_d
=
os
.
path
.
join
(
self
.
snapshot_dir
,
f
"
{
prefix
}
_discriminator.pth
"
)
snapshot_file_g
=
os
.
path
.
join
(
self
.
snapshot_dir
,
f
"
{
prefix
}
_generator.pth
"
)
logger
.
debug
(
f
"
Store snapshots at
{
snapshot_file_d
}
and
{
snapshot_file_g
}
"
)
torch
.
save
(
self
.
discriminator
.
state_dict
(),
snapshot_file_d
)
torch
.
save
(
self
.
generator
.
state_dict
(),
snapshot_file_g
)
self
.
logger
.
debug
(
f
"
Store snapshots at
{
snapshot_file_d
}
and
{
snapshot_file_g
}
"
)
torch
.
save
(
self
.
params_d
.
model
.
state_dict
(),
snapshot_file_d
)
torch
.
save
(
self
.
params_g
.
model
.
state_dict
(),
snapshot_file_g
)
if
__name__
==
"
__main__
"
:
...
...
@@ -372,7 +367,7 @@ if __name__ == "__main__":
logger
=
get_logger_by_args
(
args
)
logger
.
info
(
args
)
args
.
seed
=
args
.
seed
if
args
.
seed
is
not
None
else
random
.
randint
(
0
,
2
**
32
-
1
)
args
.
seed
=
args
.
seed
if
args
.
seed
is
not
None
else
random
.
randint
(
0
,
2
**
32
-
1
)
logger
.
info
(
f
"
Seed:
{
args
.
seed
}
"
)
random
.
seed
(
args
.
seed
)
torch
.
manual_seed
(
args
.
seed
)
...
...
@@ -418,7 +413,7 @@ if __name__ == "__main__":
)
lr_scheduler
=
(
torch
.
optim
.
lr_scheduler
.
StepLR
(
optimizer
,
step_size
=
args
.
lr_decay_
factor
,
gamma
=
args
.
lr_decay_factor
optimizer
,
step_size
=
args
.
lr_decay_
epoch
,
gamma
=
args
.
lr_decay_factor
)
if
args
.
decay_lr
else
None
...
...
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