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bob
bob.learn.pytorch
Commits
72f9922d
Commit
72f9922d
authored
7 years ago
by
Guillaume HEUSCH
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[architecture, trainer] cleaned the code, added docstring
parent
1fc07678
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bob/learn/pytorch/architectures/ConditionalGAN.py
+55
-24
55 additions, 24 deletions
bob/learn/pytorch/architectures/ConditionalGAN.py
bob/learn/pytorch/trainers/ConditionalGANTrainer.py
+9
-20
9 additions, 20 deletions
bob/learn/pytorch/trainers/ConditionalGANTrainer.py
with
64 additions
and
44 deletions
bob/learn/pytorch/architectures/ConditionalGAN.py
+
55
−
24
View file @
72f9922d
...
...
@@ -6,17 +6,43 @@ import torch
import
torch.nn
as
nn
def
weights_init
(
m
):
classname
=
m
.
__class__
.
__name__
if
classname
.
find
(
'
Conv
'
)
!=
-
1
:
m
.
weight
.
data
.
normal_
(
0.0
,
0.02
)
elif
classname
.
find
(
'
BatchNorm
'
)
!=
-
1
:
m
.
weight
.
data
.
normal_
(
1.0
,
0.02
)
m
.
bias
.
data
.
fill_
(
0
)
"""
Weights initialization
**Parameters**
m:
The model
"""
classname
=
m
.
__class__
.
__name__
if
classname
.
find
(
'
Conv
'
)
!=
-
1
:
m
.
weight
.
data
.
normal_
(
0.0
,
0.02
)
elif
classname
.
find
(
'
BatchNorm
'
)
!=
-
1
:
m
.
weight
.
data
.
normal_
(
1.0
,
0.02
)
m
.
bias
.
data
.
fill_
(
0
)
class
ConditionalGAN_generator
(
nn
.
Module
):
"""
Class defining the Conditional GAN generator.
**Parameters**
noise_dim: int
The dimension of the noise.
conditional_dim: int
The dimension of the conditioning variable.
channels: int
The number of channels in the input image (default: 3).
class
ConditionalGAN_generator2
(
nn
.
Module
):
ngpu: int
The number of GPU (default: 1)
"""
def
__init__
(
self
,
noise_dim
,
conditional_dim
,
channels
=
3
,
ngpu
=
1
):
super
(
ConditionalGAN_generator2
,
self
).
__init__
()
super
(
ConditionalGAN_generator
,
self
).
__init__
()
self
.
ngpu
=
ngpu
self
.
conditional_dim
=
conditional_dim
...
...
@@ -52,14 +78,13 @@ class ConditionalGAN_generator2(nn.Module):
**Parameters**
z: pyTorch
Tensor
z: pyTorch
Variable
The minibatch of noise.
y:
int
The conditional one hot encoded vector.
y:
pyTorch Variable
The conditional one hot encoded vector
for the minibatch
.
"""
generator_input
=
torch
.
cat
((
z
,
y
),
1
)
if
isinstance
(
generator_input
.
data
,
torch
.
cuda
.
FloatTensor
)
and
self
.
ngpu
>
1
:
output
=
nn
.
parallel
.
data_parallel
(
self
.
main
,
generator_input
,
range
(
self
.
ngpu
))
else
:
...
...
@@ -67,16 +92,24 @@ class ConditionalGAN_generator2(nn.Module):
return
output
def
one_hot_vector
(
self
,
y
):
one_hot
=
torch
.
FloatTensor
(
self
.
minibatch_size
,
self
.
conditional_dim
,
1
,
1
).
zero_
()
for
k
in
range
(
self
.
minibatch_size
):
one_hot
[
k
,
y
[
k
]]
=
1
return
one_hot
class
ConditionalGAN_discriminator
(
nn
.
Module
):
"""
Class defining the Conditional GAN discriminator.
**Parameters**
conditional_dim: int
The dimension of the conditioning variable.
channels: int
The number of channels in the input image (default: 3).
class
ConditionalGAN_discriminator2
(
nn
.
Module
):
ngpu: int
The number of GPU (default: 1)
"""
def
__init__
(
self
,
conditional_dim
,
channels
=
3
,
ngpu
=
1
):
super
(
ConditionalGAN_discriminator2
,
self
).
__init__
()
super
(
ConditionalGAN_discriminator
,
self
).
__init__
()
self
.
conditional_dim
=
conditional_dim
self
.
ngpu
=
ngpu
...
...
@@ -111,17 +144,15 @@ class ConditionalGAN_discriminator2(nn.Module):
**Parameters**
image: pyTorch
Tensor
image
s
: pyTorch
Variable
The minibatch of input images.
y:
int
The conditional feature maps.
y:
pyTorch Variable
The
corresponding
conditional feature maps.
"""
input_discriminator
=
torch
.
cat
((
images
,
y
),
1
)
if
isinstance
(
input_discriminator
.
data
,
torch
.
cuda
.
FloatTensor
)
and
self
.
ngpu
>
1
:
output
=
nn
.
parallel
.
data_parallel
(
self
.
main
,
input_discriminator
,
range
(
self
.
ngpu
))
else
:
output
=
self
.
main
(
input_discriminator
)
return
output
.
view
(
-
1
,
1
).
squeeze
(
1
)
This diff is collapsed.
Click to expand it.
bob/learn/pytorch/trainers/ConditionalGANTrainer.py
+
9
−
20
View file @
72f9922d
...
...
@@ -2,6 +2,7 @@
# encoding: utf-8
import
numpy
import
time
import
torch
import
torch.nn
as
nn
...
...
@@ -9,15 +10,10 @@ import torch.optim as optim
from
torch.autograd
import
Variable
import
torchvision.utils
as
vutils
import
bob.core
logger
=
bob
.
core
.
log
.
setup
(
"
bob.learn.pytorch
"
)
import
time
from
matplotlib
import
pyplot
class
ConditionalGANTrainer2
(
object
):
class
ConditionalGANTrainer
(
object
):
"""
Class to train a Conditional GAN
...
...
@@ -64,21 +60,19 @@ class ConditionalGANTrainer2(object):
self
.
fixed_one_hot
=
torch
.
FloatTensor
(
self
.
conditional_dim
,
self
.
conditional_dim
,
1
,
1
).
zero_
()
for
k
in
range
(
self
.
conditional_dim
):
self
.
fixed_one_hot
[
k
,
k
]
=
1
# TODO: figuring out the CPU/GPU thing - Guillaume HEUSCH, 17-11-2017
self
.
fixed_noise
=
Variable
(
self
.
fixed_noise
)
self
.
fixed_one_hot
=
Variable
(
self
.
fixed_one_hot
)
# binary cross-entropy loss
self
.
criterion
=
nn
.
BCELoss
()
# move stuff to GPU if needed
if
self
.
use_gpu
:
self
.
netD
.
cuda
()
self
.
netG
.
cuda
()
self
.
criterion
.
cuda
()
#self_fixed_noise = self.fixed_noise.cuda()
#self_fixed_one_hot = self.fixed_one_hot.cuda()
self
.
fixed_noise
=
Variable
(
self
.
fixed_noise
)
self
.
fixed_one_hot
=
Variable
(
self
.
fixed_one_hot
)
def
train
(
self
,
dataloader
,
n_epochs
=
10
,
learning_rate
=
0.0002
,
beta1
=
0.5
,
output_dir
=
'
out
'
):
...
...
@@ -131,7 +125,8 @@ class ConditionalGANTrainer2(object):
for
k
in
range
(
batch_size
):
one_hot_feature_maps
[
k
,
poses
[
k
],
:,
:]
=
1
one_hot_vector
[
k
,
poses
[
k
]]
=
1
# move stuff to GPU if needed
if
self
.
use_gpu
:
real_images
=
real_images
.
cuda
()
label
=
label
.
cuda
()
...
...
@@ -139,7 +134,6 @@ class ConditionalGANTrainer2(object):
one_hot_feature_maps
=
one_hot_feature_maps
.
cuda
()
one_hot_vector
=
one_hot_vector
.
cuda
()
# =============
# DISCRIMINATOR
# =============
...
...
@@ -149,11 +143,6 @@ class ConditionalGANTrainer2(object):
label
.
resize_
(
batch_size
).
fill_
(
real_label
)
imagev
=
Variable
(
real_images
)
one_hot_fmv
=
Variable
(
one_hot_feature_maps
)
#from matplotlib import pyplot
#pyplot.title("Pose {}".format(poses[0]))
#pyplot.imshow(numpy.rollaxis(numpy.rollaxis(first_image, 2),2))
#pyplot.show()
labelv
=
Variable
(
label
)
output_real
=
self
.
netD
(
imagev
,
one_hot_fmv
)
errD_real
=
self
.
criterion
(
output_real
,
labelv
)
...
...
@@ -167,9 +156,9 @@ class ConditionalGANTrainer2(object):
output_fake
=
self
.
netD
(
fake
,
one_hot_fmv
)
errD_fake
=
self
.
criterion
(
output_fake
,
labelv
)
errD_fake
.
backward
(
retain_graph
=
True
)
errD
=
errD_real
+
errD_fake
# perform optimization (i.e. update discriminator parameters)
errD
=
errD_real
+
errD_fake
optimizerD
.
step
()
# =========
...
...
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