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c1bd1a2a
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体验新版 GitCode,发现更多精彩内容 >>
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c1bd1a2a
编写于
9月 29, 2020
作者:
L
LielinJiang
浏览文件
操作
浏览文件
下载
差异文件
fix conflict
上级
06d3c848
2354ab9d
变更
23
隐藏空白更改
内联
并排
Showing
23 changed file
with
1257 addition
and
536 deletion
+1257
-536
applications/DAIN/my_args.py
applications/DAIN/my_args.py
+4
-1
applications/DAIN/predict.py
applications/DAIN/predict.py
+55
-57
applications/DAIN/util.py
applications/DAIN/util.py
+32
-0
ppgan/datasets/builder.py
ppgan/datasets/builder.py
+2
-2
ppgan/engine/trainer.py
ppgan/engine/trainer.py
+46
-23
ppgan/metric/README.md
ppgan/metric/README.md
+9
-0
ppgan/metric/compute_fid.py
ppgan/metric/compute_fid.py
+62
-22
ppgan/metric/test_fid_score.py
ppgan/metric/test_fid_score.py
+6
-4
ppgan/models/backbones/resnet_backbone.py
ppgan/models/backbones/resnet_backbone.py
+32
-15
ppgan/models/cycle_gan_model.py
ppgan/models/cycle_gan_model.py
+5
-0
ppgan/models/discriminators/nlayers.py
ppgan/models/discriminators/nlayers.py
+30
-17
ppgan/models/generators/deoldify.py
ppgan/models/generators/deoldify.py
+1
-2
ppgan/models/generators/mobile_resnet.py
ppgan/models/generators/mobile_resnet.py
+202
-0
ppgan/models/generators/remaster.py
ppgan/models/generators/remaster.py
+215
-150
ppgan/models/generators/resnet.py
ppgan/models/generators/resnet.py
+77
-41
ppgan/models/generators/unet.py
ppgan/models/generators/unet.py
+83
-39
ppgan/models/losses.py
ppgan/models/losses.py
+13
-9
ppgan/models/pix2pix_model.py
ppgan/models/pix2pix_model.py
+6
-0
ppgan/modules/init.py
ppgan/modules/init.py
+300
-0
ppgan/modules/nn.py
ppgan/modules/nn.py
+19
-146
ppgan/modules/norm.py
ppgan/modules/norm.py
+23
-6
ppgan/utils/setup.py
ppgan/utils/setup.py
+2
-2
ppgan/utils/timer.py
ppgan/utils/timer.py
+33
-0
未找到文件。
applications/DAIN/my_args.py
浏览文件 @
c1bd1a2a
...
...
@@ -90,4 +90,7 @@ parser.add_argument('--use_cuda',
type
=
bool
,
help
=
'use cuda or not'
)
parser
.
add_argument
(
'--use_cudnn'
,
default
=
1
,
type
=
int
,
help
=
'use cudnn or not'
)
parser
.
add_argument
(
'--remove_duplicates'
,
default
=
True
,
type
=
bool
,
help
=
'remove duplicate frames or not'
)
applications/DAIN/predict.py
浏览文件 @
c1bd1a2a
...
...
@@ -80,7 +80,8 @@ class VideoFrameInterp(object):
video_path
,
use_gpu
=
True
,
key_frame_thread
=
0.
,
output_path
=
'output'
):
output_path
=
'output'
,
remove_duplicates
=
True
):
self
.
video_path
=
video_path
self
.
output_path
=
os
.
path
.
join
(
output_path
,
'DAIN'
)
if
model_path
is
None
:
...
...
@@ -138,6 +139,8 @@ class VideoFrameInterp(object):
end
=
time
.
time
()
frames
=
sorted
(
glob
.
glob
(
os
.
path
.
join
(
out_path
,
'*.png'
)))
if
remove_duplicates
:
frames
=
remove_duplicates
(
out_path
)
img
=
imread
(
frames
[
0
])
...
...
@@ -199,58 +202,51 @@ class VideoFrameInterp(object):
X0
=
img_first
.
astype
(
'float32'
).
transpose
((
2
,
0
,
1
))
/
255
X1
=
img_second
.
astype
(
'float32'
).
transpose
((
2
,
0
,
1
))
/
255
if
key_frame
:
y_
=
[
np
.
transpose
(
255.0
*
X0
.
clip
(
0
,
1.0
),
(
1
,
2
,
0
))
for
i
in
range
(
num_frames
)
]
else
:
assert
(
X0
.
shape
[
1
]
==
X1
.
shape
[
1
])
assert
(
X0
.
shape
[
2
]
==
X1
.
shape
[
2
])
X0
=
np
.
pad
(
X0
,
((
0
,
0
),
(
padding_top
,
padding_bottom
),
\
(
padding_left
,
padding_right
)),
mode
=
'edge'
)
X1
=
np
.
pad
(
X1
,
((
0
,
0
),
(
padding_top
,
padding_bottom
),
\
(
padding_left
,
padding_right
)),
mode
=
'edge'
)
X0
=
np
.
expand_dims
(
X0
,
axis
=
0
)
X1
=
np
.
expand_dims
(
X1
,
axis
=
0
)
X0
=
np
.
expand_dims
(
X0
,
axis
=
0
)
X1
=
np
.
expand_dims
(
X1
,
axis
=
0
)
X
=
np
.
concatenate
((
X0
,
X1
),
axis
=
0
)
proc_end
=
time
.
time
()
o
=
self
.
exe
.
run
(
self
.
program
,
fetch_list
=
self
.
fetch_targets
,
feed
=
{
"image"
:
X
})
y_
=
o
[
0
]
proc_timer
.
update
(
time
.
time
()
-
proc_end
)
tot_timer
.
update
(
time
.
time
()
-
end
)
end
=
time
.
time
()
y_
=
[
np
.
transpose
(
255.0
*
item
.
clip
(
0
,
1.0
)[
0
,
:,
padding_top
:
padding_top
+
int_height
,
padding_left
:
padding_left
+
int_width
],
(
1
,
2
,
0
))
for
item
in
y_
]
time_offsets
=
[
kk
*
timestep
for
kk
in
range
(
1
,
1
+
num_frames
,
1
)
]
count
=
1
for
item
,
time_offset
in
zip
(
y_
,
time_offsets
):
out_dir
=
os
.
path
.
join
(
frame_path_interpolated
,
vidname
,
"{:0>6d}_{:0>4d}.png"
.
format
(
i
,
count
))
count
=
count
+
1
imsave
(
out_dir
,
np
.
round
(
item
).
astype
(
np
.
uint8
))
assert
(
X0
.
shape
[
1
]
==
X1
.
shape
[
1
])
assert
(
X0
.
shape
[
2
]
==
X1
.
shape
[
2
])
X0
=
np
.
pad
(
X0
,
((
0
,
0
),
(
padding_top
,
padding_bottom
),
\
(
padding_left
,
padding_right
)),
mode
=
'edge'
)
X1
=
np
.
pad
(
X1
,
((
0
,
0
),
(
padding_top
,
padding_bottom
),
\
(
padding_left
,
padding_right
)),
mode
=
'edge'
)
X0
=
np
.
expand_dims
(
X0
,
axis
=
0
)
X1
=
np
.
expand_dims
(
X1
,
axis
=
0
)
X0
=
np
.
expand_dims
(
X0
,
axis
=
0
)
X1
=
np
.
expand_dims
(
X1
,
axis
=
0
)
X
=
np
.
concatenate
((
X0
,
X1
),
axis
=
0
)
proc_end
=
time
.
time
()
o
=
self
.
exe
.
run
(
self
.
program
,
fetch_list
=
self
.
fetch_targets
,
feed
=
{
"image"
:
X
})
y_
=
o
[
0
]
proc_timer
.
update
(
time
.
time
()
-
proc_end
)
tot_timer
.
update
(
time
.
time
()
-
end
)
end
=
time
.
time
()
y_
=
[
np
.
transpose
(
255.0
*
item
.
clip
(
0
,
1.0
)[
0
,
:,
padding_top
:
padding_top
+
int_height
,
padding_left
:
padding_left
+
int_width
],
(
1
,
2
,
0
))
for
item
in
y_
]
time_offsets
=
[
kk
*
timestep
for
kk
in
range
(
1
,
1
+
num_frames
,
1
)
]
count
=
1
for
item
,
time_offset
in
zip
(
y_
,
time_offsets
):
out_dir
=
os
.
path
.
join
(
frame_path_interpolated
,
vidname
,
"{:0>6d}_{:0>4d}.png"
.
format
(
i
,
count
))
count
=
count
+
1
imsave
(
out_dir
,
np
.
round
(
item
).
astype
(
np
.
uint8
))
num_frames
=
int
(
1.0
/
timestep
)
-
1
...
...
@@ -266,14 +262,16 @@ class VideoFrameInterp(object):
vidname
+
'.mp4'
)
if
os
.
path
.
exists
(
video_pattern_output
):
os
.
remove
(
video_pattern_output
)
frames2video
(
frame_pattern_combined
,
video_pattern_output
,
r2
)
frames2video
(
frame_pattern_combined
,
video_pattern_output
,
r2
)
return
frame_pattern_combined
,
video_pattern_output
if
__name__
==
'__main__'
:
args
=
parser
.
parse_args
()
predictor
=
VideoFrameInterp
(
args
.
time_step
,
args
.
saved_model
,
args
.
video_path
,
args
.
output_path
)
predictor
=
VideoFrameInterp
(
args
.
time_step
,
args
.
saved_model
,
args
.
video_path
,
args
.
output_path
,
remove_duplicates
=
args
.
remove_duplicates
)
predictor
.
run
()
applications/DAIN/util.py
浏览文件 @
c1bd1a2a
import
os
,
sys
import
glob
import
shutil
import
cv2
class
AverageMeter
(
object
):
...
...
@@ -44,3 +45,34 @@ def combine_frames(input, interpolated, combined, num_frames):
except
Exception
as
e
:
print
(
e
)
print
(
len
(
frames2
),
num_frames
,
i
,
k
,
i
*
num_frames
+
k
)
def
remove_duplicates
(
paths
):
def
dhash
(
image
,
hash_size
=
8
):
gray
=
cv2
.
cvtColor
(
image
,
cv2
.
COLOR_BGR2GRAY
)
resized
=
cv2
.
resize
(
gray
,
(
hash_size
+
1
,
hash_size
))
diff
=
resized
[:,
1
:]
>
resized
[:,
:
-
1
]
return
sum
([
2
**
i
for
(
i
,
v
)
in
enumerate
(
diff
.
flatten
())
if
v
])
hashes
=
{}
image_paths
=
sorted
(
glob
.
glob
(
os
.
path
.
join
(
paths
,
'*.png'
)))
for
image_path
in
image_paths
:
image
=
cv2
.
imread
(
image_path
)
h
=
dhash
(
image
)
p
=
hashes
.
get
(
h
,
[])
p
.
append
(
image_path
)
hashes
[
h
]
=
p
for
(
h
,
hashed_paths
)
in
hashes
.
items
():
if
len
(
hashed_paths
)
>
1
:
for
p
in
hashed_paths
[
1
:]:
os
.
remove
(
p
)
frames
=
sorted
(
glob
.
glob
(
os
.
path
.
join
(
paths
,
'*.png'
)))
for
fid
,
frame
in
enumerate
(
frames
):
new_name
=
'{:08d}'
.
format
(
fid
)
+
'.png'
new_name
=
os
.
path
.
join
(
paths
,
new_name
)
os
.
rename
(
frame
,
new_name
)
frames
=
sorted
(
glob
.
glob
(
os
.
path
.
join
(
paths
,
'*.png'
)))
return
frames
ppgan/datasets/builder.py
浏览文件 @
c1bd1a2a
...
...
@@ -56,8 +56,8 @@ class DictDataLoader():
self
.
dataset
=
DictDataset
(
dataset
)
place
=
paddle
.
fluid
.
CUDAPlace
(
ParallelEnv
().
dev_id
)
\
if
ParallelEnv
().
nranks
>
1
else
paddle
.
fluid
.
CUDAPlace
(
0
)
place
=
paddle
.
CUDAPlace
(
ParallelEnv
().
dev_id
)
\
if
ParallelEnv
().
nranks
>
1
else
paddle
.
CUDAPlace
(
0
)
sampler
=
DistributedBatchSampler
(
self
.
dataset
,
batch_size
=
batch_size
,
...
...
ppgan/engine/trainer.py
浏览文件 @
c1bd1a2a
...
...
@@ -11,8 +11,10 @@ from ..datasets.builder import build_dataloader
from
..models.builder
import
build_model
from
..utils.visual
import
tensor2img
,
save_image
from
..utils.filesystem
import
save
,
load
,
makedirs
from
..utils.timer
import
TimeAverager
from
..metric.psnr_ssim
import
calculate_psnr
,
calculate_ssim
class
Trainer
:
def
__init__
(
self
,
cfg
):
...
...
@@ -51,7 +53,6 @@ class Trainer:
self
.
time_count
=
{}
self
.
best_metric
=
{}
def
distributed_data_parallel
(
self
):
strategy
=
paddle
.
distributed
.
prepare_context
()
for
name
in
self
.
model
.
model_names
:
...
...
@@ -61,29 +62,37 @@ class Trainer:
paddle
.
DataParallel
(
net
,
strategy
))
def
train
(
self
):
reader_cost_averager
=
TimeAverager
()
batch_cost_averager
=
TimeAverager
()
for
epoch
in
range
(
self
.
start_epoch
,
self
.
epochs
):
self
.
current_epoch
=
epoch
start_time
=
step_start_time
=
time
.
time
()
for
i
,
data
in
enumerate
(
self
.
train_dataloader
):
data_time
=
time
.
time
()
reader_cost_averager
.
record
(
time
.
time
()
-
step_start_time
)
self
.
batch_id
=
i
# unpack data from dataset and apply preprocessing
# data input should be dict
self
.
model
.
set_input
(
data
)
self
.
model
.
optimize_parameters
()
self
.
data_time
=
data_time
-
step_start_time
self
.
step_time
=
time
.
time
()
-
step_start_time
batch_cost_averager
.
record
(
time
.
time
()
-
step_start_time
)
if
i
%
self
.
log_interval
==
0
:
self
.
data_time
=
reader_cost_averager
.
get_average
()
self
.
step_time
=
batch_cost_averager
.
get_average
()
self
.
print_log
()
reader_cost_averager
.
reset
()
batch_cost_averager
.
reset
()
if
i
%
self
.
visual_interval
==
0
:
self
.
visual
(
'visual_train'
)
step_start_time
=
time
.
time
()
self
.
logger
.
info
(
'train one epoch time: {}'
.
format
(
time
.
time
()
-
start_time
))
self
.
logger
.
info
(
'train one epoch time: {}'
.
format
(
time
.
time
()
-
start_time
))
if
self
.
validate_interval
>
-
1
and
epoch
%
self
.
validate_interval
:
self
.
validate
()
self
.
model
.
lr_scheduler
.
step
()
...
...
@@ -93,7 +102,8 @@ class Trainer:
def
validate
(
self
):
if
not
hasattr
(
self
,
'val_dataloader'
):
self
.
val_dataloader
=
build_dataloader
(
self
.
cfg
.
dataset
.
val
,
is_train
=
False
)
self
.
val_dataloader
=
build_dataloader
(
self
.
cfg
.
dataset
.
val
,
is_train
=
False
)
metric_result
=
{}
...
...
@@ -106,7 +116,7 @@ class Trainer:
visual_results
=
{}
current_paths
=
self
.
model
.
get_image_paths
()
current_visuals
=
self
.
model
.
get_current_visuals
()
for
j
in
range
(
len
(
current_paths
)):
short_path
=
os
.
path
.
basename
(
current_paths
[
j
])
basename
=
os
.
path
.
splitext
(
short_path
)[
0
]
...
...
@@ -115,31 +125,43 @@ class Trainer:
visual_results
.
update
({
name
:
img_tensor
[
j
]})
if
'psnr'
in
self
.
cfg
.
validate
.
metrics
:
if
'psnr'
not
in
metric_result
:
metric_result
[
'psnr'
]
=
calculate_psnr
(
tensor2img
(
current_visuals
[
'output'
][
j
],
(
0.
,
1.
)),
tensor2img
(
current_visuals
[
'gt'
][
j
],
(
0.
,
1.
)),
**
self
.
cfg
.
validate
.
metrics
.
psnr
)
metric_result
[
'psnr'
]
=
calculate_psnr
(
tensor2img
(
current_visuals
[
'output'
][
j
],
(
0.
,
1.
)),
tensor2img
(
current_visuals
[
'gt'
][
j
],
(
0.
,
1.
)),
**
self
.
cfg
.
validate
.
metrics
.
psnr
)
else
:
metric_result
[
'psnr'
]
+=
calculate_psnr
(
tensor2img
(
current_visuals
[
'output'
][
j
],
(
0.
,
1.
)),
tensor2img
(
current_visuals
[
'gt'
][
j
],
(
0.
,
1.
)),
**
self
.
cfg
.
validate
.
metrics
.
psnr
)
metric_result
[
'psnr'
]
+=
calculate_psnr
(
tensor2img
(
current_visuals
[
'output'
][
j
],
(
0.
,
1.
)),
tensor2img
(
current_visuals
[
'gt'
][
j
],
(
0.
,
1.
)),
**
self
.
cfg
.
validate
.
metrics
.
psnr
)
if
'ssim'
in
self
.
cfg
.
validate
.
metrics
:
if
'ssim'
not
in
metric_result
:
metric_result
[
'ssim'
]
=
calculate_ssim
(
tensor2img
(
current_visuals
[
'output'
][
j
],
(
0.
,
1.
)),
tensor2img
(
current_visuals
[
'gt'
][
j
],
(
0.
,
1.
)),
**
self
.
cfg
.
validate
.
metrics
.
ssim
)
metric_result
[
'ssim'
]
=
calculate_ssim
(
tensor2img
(
current_visuals
[
'output'
][
j
],
(
0.
,
1.
)),
tensor2img
(
current_visuals
[
'gt'
][
j
],
(
0.
,
1.
)),
**
self
.
cfg
.
validate
.
metrics
.
ssim
)
else
:
metric_result
[
'ssim'
]
+=
calculate_ssim
(
tensor2img
(
current_visuals
[
'output'
][
j
],
(
0.
,
1.
)),
tensor2img
(
current_visuals
[
'gt'
][
j
],
(
0.
,
1.
)),
**
self
.
cfg
.
validate
.
metrics
.
ssim
)
metric_result
[
'ssim'
]
+=
calculate_ssim
(
tensor2img
(
current_visuals
[
'output'
][
j
],
(
0.
,
1.
)),
tensor2img
(
current_visuals
[
'gt'
][
j
],
(
0.
,
1.
)),
**
self
.
cfg
.
validate
.
metrics
.
ssim
)
self
.
visual
(
'visual_val'
,
visual_results
=
visual_results
)
if
i
%
self
.
log_interval
==
0
:
self
.
logger
.
info
(
'val iter: [%d/%d]'
%
(
i
,
len
(
self
.
val_dataloader
)))
self
.
logger
.
info
(
'val iter: [%d/%d]'
%
(
i
,
len
(
self
.
val_dataloader
)))
for
metric_name
in
metric_result
.
keys
():
metric_result
[
metric_name
]
/=
len
(
self
.
val_dataloader
.
dataset
)
self
.
logger
.
info
(
'Epoch {} validate end: {}'
.
format
(
self
.
current_epoch
,
metric_result
))
self
.
logger
.
info
(
'Epoch {} validate end: {}'
.
format
(
self
.
current_epoch
,
metric_result
))
def
test
(
self
):
if
not
hasattr
(
self
,
'test_dataloader'
):
self
.
test_dataloader
=
build_dataloader
(
self
.
cfg
.
dataset
.
test
,
is_train
=
False
)
self
.
test_dataloader
=
build_dataloader
(
self
.
cfg
.
dataset
.
test
,
is_train
=
False
)
# data[0]: img, data[1]: img path index
# test batch size must be 1
...
...
@@ -163,8 +185,8 @@ class Trainer:
self
.
visual
(
'visual_test'
,
visual_results
=
visual_results
)
if
i
%
self
.
log_interval
==
0
:
self
.
logger
.
info
(
'Test iter: [%d/%d]'
%
(
i
,
len
(
self
.
test_dataloader
)))
self
.
logger
.
info
(
'Test iter: [%d/%d]'
%
(
i
,
len
(
self
.
test_dataloader
)))
def
print_log
(
self
):
losses
=
self
.
model
.
get_current_losses
()
...
...
@@ -266,6 +288,7 @@ class Trainer:
for
name
in
self
.
model
.
model_names
:
if
isinstance
(
name
,
str
):
self
.
logger
.
info
(
'laod model {} {} params!'
.
format
(
self
.
cfg
.
model
.
name
,
'net'
+
name
))
self
.
logger
.
info
(
'laod model {} {} params!'
.
format
(
self
.
cfg
.
model
.
name
,
'net'
+
name
))
net
=
getattr
(
self
.
model
,
'net'
+
name
)
net
.
set_dict
(
state_dicts
[
'net'
+
name
])
ppgan/metric/README.md
浏览文件 @
c1bd1a2a
...
...
@@ -8,3 +8,12 @@ wget https://paddlegan.bj.bcebos.com/InceptionV3.pdparams
```
python test_fid_score.py --image_data_path1 /path/to/dataset1 --image_data_path2 /path/to/dataset2 --inference_model ./InceptionV3.pdparams
```
### Inception-V3 weights converted from torchvision
Download: https://aistudio.baidu.com/aistudio/datasetdetail/51890
This model weights file is converted from official torchvision inception-v3 model. And both BigGAN and StarGAN-v2 is using it to calculate FID score.
Note that this model weights is different from above one (which is converted from tensorflow unofficial version)
ppgan/metric/compute_fid.py
浏览文件 @
c1bd1a2a
...
...
@@ -16,15 +16,18 @@ import os
import
fnmatch
import
numpy
as
np
import
cv2
from
PIL
import
Image
from
cv2
import
imread
from
scipy
import
linalg
import
paddle.fluid
as
fluid
from
inception
import
InceptionV3
from
paddle.fluid.dygraph.base
import
to_variable
def
tqdm
(
x
):
return
x
try
:
from
tqdm
import
tqdm
except
:
def
tqdm
(
x
):
return
x
""" based on https://github.com/mit-han-lab/gan-compression/blob/master/metric/fid_score.py
...
...
@@ -128,7 +131,7 @@ def calculate_fid_given_img(img_fake,
return
fid_value
def
_get_activations
(
files
,
model
,
batch_size
,
dims
,
use_gpu
,
premodel_path
):
def
_get_activations
(
files
,
model
,
batch_size
,
dims
,
use_gpu
,
premodel_path
,
style
=
None
):
if
len
(
files
)
%
batch_size
!=
0
:
print
((
'Warning: number of images is not a multiple of the '
'batch size. Some samples are going to be ignored.'
))
...
...
@@ -144,8 +147,23 @@ def _get_activations(files, model, batch_size, dims, use_gpu, premodel_path):
for
i
in
tqdm
(
range
(
n_batches
)):
start
=
i
*
batch_size
end
=
start
+
batch_size
images
=
np
.
array
(
[
imread
(
str
(
f
)).
astype
(
np
.
float32
)
for
f
in
files
[
start
:
end
]])
# same as stargan-v2 official implementation: resize to 256 first, then resize to 299
if
style
==
'stargan'
:
img_list
=
[]
for
f
in
files
[
start
:
end
]:
im
=
Image
.
open
(
str
(
f
)).
convert
(
'RGB'
)
if
im
.
size
[
0
]
!=
299
:
im
=
im
.
resize
((
256
,
256
),
2
)
im
=
im
.
resize
((
299
,
299
),
2
)
img_list
.
append
(
np
.
array
(
im
).
astype
(
'float32'
))
images
=
np
.
array
(
img_list
)
else
:
images
=
np
.
array
(
[
imread
(
str
(
f
)).
astype
(
np
.
float32
)
for
f
in
files
[
start
:
end
]])
if
len
(
images
.
shape
)
!=
4
:
images
=
imread
(
str
(
files
[
start
]))
...
...
@@ -155,33 +173,53 @@ def _get_activations(files, model, batch_size, dims, use_gpu, premodel_path):
images
=
images
.
transpose
((
0
,
3
,
1
,
2
))
images
/=
255
images
=
to_variable
(
images
)
param_dict
,
_
=
fluid
.
load_dygraph
(
premodel_path
)
model
.
set_dict
(
param_dict
)
model
.
eval
()
# imagenet normalization
if
style
==
'stargan'
:
mean
=
np
.
array
([
0.485
,
0.456
,
0.406
]).
astype
(
'float32'
)
std
=
np
.
array
([
0.229
,
0.224
,
0.225
]).
astype
(
'float32'
)
images
[:]
=
(
images
[:]
-
mean
[:,
None
,
None
])
/
std
[:,
None
,
None
]
pred
=
model
(
images
)[
0
][
0
].
numpy
()
if
style
==
'stargan'
:
pred_arr
[
start
:
end
]
=
inception_infer
(
images
,
premodel_path
)
else
:
with
fluid
.
dygraph
.
guard
():
images
=
to_variable
(
images
)
param_dict
,
_
=
fluid
.
load_dygraph
(
premodel_path
)
model
.
set_dict
(
param_dict
)
model
.
eval
()
pred_arr
[
start
:
end
]
=
pred
.
reshape
(
end
-
start
,
-
1
)
pred
=
model
(
images
)[
0
][
0
].
numpy
()
pred_arr
[
start
:
end
]
=
pred
.
reshape
(
end
-
start
,
-
1
)
return
pred_arr
def
inception_infer
(
x
,
model_path
):
exe
=
fluid
.
Executor
()
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
model_path
,
exe
)
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
x
},
fetch_list
=
fetch_targets
)
return
results
[
0
]
def
_calculate_activation_statistics
(
files
,
model
,
premodel_path
,
batch_size
=
50
,
dims
=
2048
,
use_gpu
=
False
):
use_gpu
=
False
,
style
=
None
):
act
=
_get_activations
(
files
,
model
,
batch_size
,
dims
,
use_gpu
,
premodel_path
)
premodel_path
,
style
)
mu
=
np
.
mean
(
act
,
axis
=
0
)
sigma
=
np
.
cov
(
act
,
rowvar
=
False
)
return
mu
,
sigma
def
_compute_statistics_of_path
(
path
,
model
,
batch_size
,
dims
,
use_gpu
,
premodel_path
):
premodel_path
,
style
=
None
):
if
path
.
endswith
(
'.npz'
):
f
=
np
.
load
(
path
)
m
,
s
=
f
[
'mu'
][:],
f
[
'sigma'
][:]
...
...
@@ -193,7 +231,7 @@ def _compute_statistics_of_path(path, model, batch_size, dims, use_gpu,
filenames
,
'*.jpg'
)
or
fnmatch
.
filter
(
filenames
,
'*.png'
):
files
.
append
(
os
.
path
.
join
(
root
,
filename
))
m
,
s
=
_calculate_activation_statistics
(
files
,
model
,
premodel_path
,
batch_size
,
dims
,
use_gpu
)
batch_size
,
dims
,
use_gpu
,
style
)
return
m
,
s
...
...
@@ -202,7 +240,8 @@ def calculate_fid_given_paths(paths,
batch_size
,
use_gpu
,
dims
,
model
=
None
):
model
=
None
,
style
=
None
):
assert
os
.
path
.
exists
(
premodel_path
),
'pretrain_model path {} is not exists! Please download it first'
.
format
(
...
...
@@ -211,14 +250,15 @@ def calculate_fid_given_paths(paths,
if
not
os
.
path
.
exists
(
p
):
raise
RuntimeError
(
'Invalid path: %s'
%
p
)
if
model
is
None
:
block_idx
=
InceptionV3
.
BLOCK_INDEX_BY_DIM
[
dims
]
model
=
InceptionV3
([
block_idx
],
class_dim
=
1008
)
if
model
is
None
and
style
!=
'stargan'
:
with
fluid
.
dygraph
.
guard
():
block_idx
=
InceptionV3
.
BLOCK_INDEX_BY_DIM
[
dims
]
model
=
InceptionV3
([
block_idx
],
class_dim
=
1008
)
m1
,
s1
=
_compute_statistics_of_path
(
paths
[
0
],
model
,
batch_size
,
dims
,
use_gpu
,
premodel_path
)
use_gpu
,
premodel_path
,
style
)
m2
,
s2
=
_compute_statistics_of_path
(
paths
[
1
],
model
,
batch_size
,
dims
,
use_gpu
,
premodel_path
)
use_gpu
,
premodel_path
,
style
)
fid_value
=
_calculate_frechet_distance
(
m1
,
s1
,
m2
,
s2
)
return
fid_value
ppgan/metric/test_fid_score.py
浏览文件 @
c1bd1a2a
...
...
@@ -38,6 +38,9 @@ def parse_args():
type
=
int
,
default
=
1
,
help
=
'sample number in a batch for inference.'
)
parser
.
add_argument
(
'--style'
,
type
=
str
,
help
=
'calculation style: stargan or default (gan-compression style)'
)
args
=
parser
.
parse_args
()
return
args
...
...
@@ -50,10 +53,9 @@ def main():
inference_model_path
=
args
.
inference_model
batch_size
=
args
.
batch_size
with
fluid
.
dygraph
.
guard
():
fid_value
=
calculate_fid_given_paths
(
paths
,
inference_model_path
,
batch_size
,
args
.
use_gpu
,
2048
)
print
(
'FID: '
,
fid_value
)
fid_value
=
calculate_fid_given_paths
(
paths
,
inference_model_path
,
batch_size
,
args
.
use_gpu
,
2048
,
style
=
args
.
style
)
print
(
'FID: '
,
fid_value
)
if
__name__
==
"__main__"
:
...
...
ppgan/models/backbones/resnet_backbone.py
浏览文件 @
c1bd1a2a
import
paddle
import
paddle.nn
as
nn
__all__
=
[
'ResNet'
,
'resnet18'
,
'resnet34'
,
'resnet50'
,
'resnet101'
,
'resnet152'
]
__all__
=
[
'ResNet'
,
'resnet18'
,
'resnet34'
,
'resnet50'
,
'resnet101'
,
'resnet152'
]
def
conv3x3
(
in_planes
,
out_planes
,
stride
=
1
):
"3x3 convolution with padding"
return
nn
.
Conv2d
(
in_planes
,
out_planes
,
kernel_size
=
3
,
stride
=
stride
,
padding
=
1
,
bias_attr
=
False
)
return
nn
.
Conv2d
(
in_planes
,
out_planes
,
kernel_size
=
3
,
stride
=
stride
,
padding
=
1
,
bias_attr
=
False
)
class
BasicBlock
(
paddle
.
fluid
.
Layer
):
class
BasicBlock
(
nn
.
Layer
):
expansion
=
1
def
__init__
(
self
,
inplanes
,
planes
,
stride
=
1
,
downsample
=
None
):
...
...
@@ -44,17 +48,24 @@ class BasicBlock(paddle.fluid.Layer):
return
out
class
Bottleneck
(
paddle
.
fluid
.
Layer
):
class
Bottleneck
(
nn
.
Layer
):
expansion
=
4
def
__init__
(
self
,
inplanes
,
planes
,
stride
=
1
,
downsample
=
None
):
super
(
Bottleneck
,
self
).
__init__
()
self
.
conv1
=
nn
.
Conv2d
(
inplanes
,
planes
,
kernel_size
=
1
,
bias_attr
=
False
)
self
.
bn1
=
nn
.
BatchNorm
(
planes
)
self
.
conv2
=
nn
.
Conv2d
(
planes
,
planes
,
kernel_size
=
3
,
stride
=
stride
,
padding
=
1
,
bias_attr
=
False
)
self
.
conv2
=
nn
.
Conv2d
(
planes
,
planes
,
kernel_size
=
3
,
stride
=
stride
,
padding
=
1
,
bias_attr
=
False
)
self
.
bn2
=
nn
.
BatchNorm
(
planes
)
self
.
conv3
=
nn
.
Conv2d
(
planes
,
planes
*
4
,
kernel_size
=
1
,
bias_attr
=
False
)
self
.
conv3
=
nn
.
Conv2d
(
planes
,
planes
*
4
,
kernel_size
=
1
,
bias_attr
=
False
)
self
.
bn3
=
nn
.
BatchNorm
(
planes
*
4
)
self
.
relu
=
nn
.
ReLU
()
self
.
downsample
=
downsample
...
...
@@ -82,12 +93,15 @@ class Bottleneck(paddle.fluid.Layer):
return
out
class
ResNet
(
paddle
.
fluid
.
Layer
):
class
ResNet
(
nn
.
Layer
):
def
__init__
(
self
,
block
,
layers
,
num_classes
=
1000
):
self
.
inplanes
=
64
super
(
ResNet
,
self
).
__init__
()
self
.
conv1
=
nn
.
Conv2d
(
3
,
64
,
kernel_size
=
7
,
stride
=
2
,
padding
=
3
,
self
.
conv1
=
nn
.
Conv2d
(
3
,
64
,
kernel_size
=
7
,
stride
=
2
,
padding
=
3
,
bias_attr
=
False
)
self
.
bn1
=
nn
.
BatchNorm
(
64
)
self
.
relu
=
nn
.
ReLU
()
...
...
@@ -103,8 +117,11 @@ class ResNet(paddle.fluid.Layer):
downsample
=
None
if
stride
!=
1
or
self
.
inplanes
!=
planes
*
block
.
expansion
:
downsample
=
nn
.
Sequential
(
nn
.
Conv2d
(
self
.
inplanes
,
planes
*
block
.
expansion
,
kernel_size
=
1
,
stride
=
stride
,
bias_attr
=
False
),
nn
.
Conv2d
(
self
.
inplanes
,
planes
*
block
.
expansion
,
kernel_size
=
1
,
stride
=
stride
,
bias_attr
=
False
),
nn
.
BatchNorm
(
planes
*
block
.
expansion
),
)
...
...
ppgan/models/cycle_gan_model.py
浏览文件 @
c1bd1a2a
...
...
@@ -8,6 +8,7 @@ from .discriminators.builder import build_discriminator
from
.losses
import
GANLoss
from
..solver
import
build_optimizer
from
..modules.init
import
init_weights
from
..utils.image_pool
import
ImagePool
...
...
@@ -56,10 +57,14 @@ class CycleGANModel(BaseModel):
# Code (vs. paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
self
.
netG_A
=
build_generator
(
opt
.
model
.
generator
)
self
.
netG_B
=
build_generator
(
opt
.
model
.
generator
)
init_weights
(
self
.
netG_A
)
init_weights
(
self
.
netG_B
)
if
self
.
isTrain
:
# define discriminators
self
.
netD_A
=
build_discriminator
(
opt
.
model
.
discriminator
)
self
.
netD_B
=
build_discriminator
(
opt
.
model
.
discriminator
)
init_weights
(
self
.
netD_A
)
init_weights
(
self
.
netD_B
)
if
self
.
isTrain
:
if
opt
.
lambda_identity
>
0.0
:
# only works when input and output images have the same number of channels
...
...
ppgan/models/discriminators/nlayers.py
浏览文件 @
c1bd1a2a
import
paddle
import
functools
import
numpy
as
np
import
paddle.nn
as
nn
from
...modules.nn
import
ReflectionPad2d
,
LeakyReLU
,
Dropout
,
BCEWithLogitsLoss
,
Pad2D
,
MSELoss
import
paddle
import
paddle.nn
as
nn
from
...modules.norm
import
build_norm_layer
from
.builder
import
DISCRIMINATORS
@
DISCRIMINATORS
.
register
()
class
NLayerDiscriminator
(
paddle
.
fluid
.
dygraph
.
Layer
):
class
NLayerDiscriminator
(
nn
.
Layer
):
"""Defines a PatchGAN discriminator"""
def
__init__
(
self
,
input_nc
,
ndf
=
64
,
n_layers
=
3
,
norm_type
=
'instance'
):
"""Construct a PatchGAN discriminator
...
...
@@ -24,36 +22,51 @@ class NLayerDiscriminator(paddle.fluid.dygraph.Layer):
"""
super
(
NLayerDiscriminator
,
self
).
__init__
()
norm_layer
=
build_norm_layer
(
norm_type
)
if
type
(
norm_layer
)
==
functools
.
partial
:
if
type
(
norm_layer
)
==
functools
.
partial
:
use_bias
=
norm_layer
.
func
==
nn
.
InstanceNorm
else
:
use_bias
=
norm_layer
==
nn
.
InstanceNorm
kw
=
4
padw
=
1
sequence
=
[
nn
.
Conv2d
(
input_nc
,
ndf
,
kernel_size
=
kw
,
stride
=
2
,
padding
=
padw
),
LeakyReLU
(
0.2
,
True
)]
sequence
=
[
nn
.
Conv2d
(
input_nc
,
ndf
,
kernel_size
=
kw
,
stride
=
2
,
padding
=
padw
),
nn
.
LeakyReLU
(
0.2
)
]
nf_mult
=
1
nf_mult_prev
=
1
for
n
in
range
(
1
,
n_layers
):
for
n
in
range
(
1
,
n_layers
):
nf_mult_prev
=
nf_mult
nf_mult
=
min
(
2
**
n
,
8
)
nf_mult
=
min
(
2
**
n
,
8
)
sequence
+=
[
nn
.
Conv2d
(
ndf
*
nf_mult_prev
,
ndf
*
nf_mult
,
kernel_size
=
kw
,
stride
=
2
,
padding
=
padw
,
bias_attr
=
use_bias
),
nn
.
Conv2d
(
ndf
*
nf_mult_prev
,
ndf
*
nf_mult
,
kernel_size
=
kw
,
stride
=
2
,
padding
=
padw
,
bias_attr
=
use_bias
),
norm_layer
(
ndf
*
nf_mult
),
LeakyReLU
(
0.2
,
True
)
nn
.
LeakyReLU
(
0.2
)
]
nf_mult_prev
=
nf_mult
nf_mult
=
min
(
2
**
n_layers
,
8
)
nf_mult
=
min
(
2
**
n_layers
,
8
)
sequence
+=
[
nn
.
Conv2d
(
ndf
*
nf_mult_prev
,
ndf
*
nf_mult
,
kernel_size
=
kw
,
stride
=
1
,
padding
=
padw
,
bias_attr
=
use_bias
),
nn
.
Conv2d
(
ndf
*
nf_mult_prev
,
ndf
*
nf_mult
,
kernel_size
=
kw
,
stride
=
1
,
padding
=
padw
,
bias_attr
=
use_bias
),
norm_layer
(
ndf
*
nf_mult
),
LeakyReLU
(
0.2
,
True
)
nn
.
LeakyReLU
(
0.2
)
]
sequence
+=
[
nn
.
Conv2d
(
ndf
*
nf_mult
,
1
,
kernel_size
=
kw
,
stride
=
1
,
padding
=
padw
)]
sequence
+=
[
nn
.
Conv2d
(
ndf
*
nf_mult
,
1
,
kernel_size
=
kw
,
stride
=
1
,
padding
=
padw
)
]
self
.
model
=
nn
.
Sequential
(
*
sequence
)
def
forward
(
self
,
input
):
"""Standard forward."""
return
self
.
model
(
input
)
\ No newline at end of file
return
self
.
model
(
input
)
ppgan/models/generators/deoldify.py
浏览文件 @
c1bd1a2a
...
...
@@ -432,8 +432,7 @@ class SelfAttention(nn.Layer):
self
.
key
=
conv1d
(
n_channels
,
n_channels
//
8
)
self
.
value
=
conv1d
(
n_channels
,
n_channels
)
self
.
gamma
=
self
.
create_parameter
(
shape
=
[
1
],
default_initializer
=
paddle
.
fluid
.
initializer
.
Constant
(
shape
=
[
1
],
default_initializer
=
paddle
.
nn
.
initializer
.
Constant
(
0.0
))
#nn.Parameter(tensor([0.]))
def
forward
(
self
,
x
):
...
...
ppgan/models/generators/mobile_resnet.py
0 → 100644
浏览文件 @
c1bd1a2a
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
paddle
import
paddle.nn
as
nn
import
functools
from
...modules.norm
import
build_norm_layer
from
.builder
import
GENERATORS
@
GENERATORS
.
register
()
class
MobileResnetGenerator
(
nn
.
Layer
):
def
__init__
(
self
,
input_channel
,
output_nc
,
ngf
=
64
,
norm_type
=
'instance'
,
use_dropout
=
False
,
n_blocks
=
9
,
padding_type
=
'reflect'
):
super
(
MobileResnetGenerator
,
self
).
__init__
()
norm_layer
=
build_norm_layer
(
norm_type
)
if
type
(
norm_layer
)
==
functools
.
partial
:
use_bias
=
norm_layer
.
func
==
InstanceNorm
else
:
use_bias
=
norm_layer
==
InstanceNorm
self
.
model
=
nn
.
LayerList
([
nn
.
ReflectionPad2d
([
3
,
3
,
3
,
3
]),
nn
.
Conv2d
(
input_channel
,
int
(
ngf
),
kernel_size
=
7
,
padding
=
0
,
bias_attr
=
use_bias
),
norm_layer
(
ngf
),
nn
.
ReLU
()
])
n_downsampling
=
2
for
i
in
range
(
n_downsampling
):
mult
=
2
**
i
self
.
model
.
extend
([
nn
.
Conv2d
(
ngf
*
mult
,
ngf
*
mult
*
2
,
kernel_size
=
3
,
stride
=
2
,
padding
=
1
,
bias_attr
=
use_bias
),
norm_layer
(
ngf
*
mult
*
2
),
nn
.
ReLU
()
])
mult
=
2
**
n_downsampling
for
i
in
range
(
n_blocks
):
self
.
model
.
extend
([
MobileResnetBlock
(
ngf
*
mult
,
ngf
*
mult
,
padding_type
=
padding_type
,
norm_layer
=
norm_layer
,
use_dropout
=
use_dropout
,
use_bias
=
use_bias
)
])
for
i
in
range
(
n_downsampling
):
mult
=
2
**
(
n_downsampling
-
i
)
output_size
=
(
i
+
1
)
*
128
self
.
model
.
extend
([
nn
.
ConvTranspose2d
(
ngf
*
mult
,
int
(
ngf
*
mult
/
2
),
kernel_size
=
3
,
stride
=
2
,
padding
=
1
,
output_padding
=
1
,
bias_attr
=
use_bias
),
norm_layer
(
int
(
ngf
*
mult
/
2
)),
nn
.
ReLU
()
])
self
.
model
.
extend
([
nn
.
ReflectionPad2d
([
3
,
3
,
3
,
3
])])
self
.
model
.
extend
([
nn
.
Conv2d
(
ngf
,
output_nc
,
kernel_size
=
7
,
padding
=
0
)])
self
.
model
.
extend
([
nn
.
Tanh
()])
def
forward
(
self
,
inputs
):
y
=
inputs
for
sublayer
in
self
.
model
:
y
=
sublayer
(
y
)
return
y
class
MobileResnetBlock
(
nn
.
Layer
):
def
__init__
(
self
,
in_c
,
out_c
,
padding_type
,
norm_layer
,
use_dropout
,
use_bias
):
super
(
MobileResnetBlock
,
self
).
__init__
()
self
.
padding_type
=
padding_type
self
.
use_dropout
=
use_dropout
self
.
conv_block
=
nn
.
LayerList
([])
p
=
0
if
self
.
padding_type
==
'reflect'
:
self
.
conv_block
.
extend
([
nn
.
ReflectionPad2d
([
1
,
1
,
1
,
1
])])
elif
self
.
padding_type
==
'replicate'
:
self
.
conv_block
.
extend
([
nn
.
ReplicationPad2d
([
1
,
1
,
1
,
1
])])
elif
self
.
padding_type
==
'zero'
:
p
=
1
else
:
raise
NotImplementedError
(
'padding [%s] is not implemented'
%
self
.
padding_type
)
self
.
conv_block
.
extend
([
SeparableConv2D
(
num_channels
=
in_c
,
num_filters
=
out_c
,
filter_size
=
3
,
padding
=
p
,
stride
=
1
),
norm_layer
(
out_c
),
nn
.
ReLU
()
])
self
.
conv_block
.
extend
([
nn
.
Dropout
(
0.5
)])
if
self
.
padding_type
==
'reflect'
:
self
.
conv_block
.
extend
([
nn
.
ReflectionPad2d
([
1
,
1
,
1
,
1
])])
elif
self
.
padding_type
==
'replicate'
:
self
.
conv_block
.
extend
([
nn
.
ReplicationPad2d
([
1
,
1
,
1
,
1
])])
elif
self
.
padding_type
==
'zero'
:
p
=
1
else
:
raise
NotImplementedError
(
'padding [%s] is not implemented'
%
self
.
padding_type
)
self
.
conv_block
.
extend
([
SeparableConv2D
(
num_channels
=
out_c
,
num_filters
=
in_c
,
filter_size
=
3
,
padding
=
p
,
stride
=
1
),
norm_layer
(
in_c
)
])
def
forward
(
self
,
inputs
):
y
=
inputs
for
sublayer
in
self
.
conv_block
:
y
=
sublayer
(
y
)
out
=
inputs
+
y
return
out
class
SeparableConv2D
(
nn
.
Layer
):
def
__init__
(
self
,
num_channels
,
num_filters
,
filter_size
,
stride
=
1
,
padding
=
0
,
norm_layer
=
InstanceNorm
,
use_bias
=
True
,
scale_factor
=
1
,
stddev
=
0.02
):
super
(
SeparableConv2D
,
self
).
__init__
()
self
.
conv
=
nn
.
LayerList
([
nn
.
Conv2d
(
in_channels
=
num_channels
,
out_channels
=
num_channels
*
scale_factor
,
kernel_size
=
filter_size
,
stride
=
stride
,
padding
=
padding
,
groups
=
num_channels
,
weight_attr
=
paddle
.
ParamAttr
(
initializer
=
nn
.
initializer
.
Normal
(
loc
=
0.0
,
scale
=
stddev
)),
bias_attr
=
use_bias
)
])
self
.
conv
.
extend
([
norm_layer
(
num_channels
*
scale_factor
)])
self
.
conv
.
extend
([
nn
.
Conv2d
(
in_channels
=
num_channels
*
scale_factor
,
out_channels
=
num_filters
,
kernel_size
=
1
,
stride
=
1
,
weight_attr
=
paddle
.
ParamAttr
(
initializer
=
nn
.
initializer
.
Normal
(
loc
=
0.0
,
scale
=
stddev
)),
bias_attr
=
use_bias
)
])
def
forward
(
self
,
inputs
):
for
sublayer
in
self
.
conv
:
inputs
=
sublayer
(
inputs
)
return
inputs
ppgan/models/generators/remaster.py
浏览文件 @
c1bd1a2a
...
...
@@ -2,43 +2,79 @@ import paddle
import
paddle.nn
as
nn
import
paddle.nn.functional
as
F
class
TempConv
(
nn
.
Layer
):
def
__init__
(
self
,
in_planes
,
out_planes
,
kernel_size
=
(
1
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
0
,
1
,
1
)
):
super
(
TempConv
,
self
).
__init__
()
self
.
conv3d
=
nn
.
Conv3d
(
in_planes
,
out_planes
,
kernel_size
=
kernel_size
,
stride
=
stride
,
padding
=
padding
)
self
.
bn
=
nn
.
BatchNorm
(
out_planes
)
def
forward
(
self
,
x
):
return
F
.
elu
(
self
.
bn
(
self
.
conv3d
(
x
)))
def
__init__
(
self
,
in_planes
,
out_planes
,
kernel_size
=
(
1
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
0
,
1
,
1
)):
super
(
TempConv
,
self
).
__init__
()
self
.
conv3d
=
nn
.
Conv3d
(
in_planes
,
out_planes
,
kernel_size
=
kernel_size
,
stride
=
stride
,
padding
=
padding
)
self
.
bn
=
nn
.
BatchNorm
(
out_planes
)
def
forward
(
self
,
x
):
return
F
.
elu
(
self
.
bn
(
self
.
conv3d
(
x
)))
class
Upsample
(
nn
.
Layer
):
def
__init__
(
self
,
in_planes
,
out_planes
,
scale_factor
=
(
1
,
2
,
2
)):
super
(
Upsample
,
self
).
__init__
()
self
.
scale_factor
=
scale_factor
self
.
conv3d
=
nn
.
Conv3d
(
in_planes
,
out_planes
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)
)
self
.
bn
=
nn
.
BatchNorm
(
out_planes
)
def
forward
(
self
,
x
):
out_size
=
x
.
shape
[
2
:]
for
i
in
range
(
3
):
out_size
[
i
]
=
self
.
scale_factor
[
i
]
*
out_size
[
i
]
return
F
.
elu
(
self
.
bn
(
self
.
conv3d
(
F
.
interpolate
(
x
,
size
=
out_size
,
mode
=
'trilinear'
,
align_corners
=
False
,
data_format
=
'NCDHW'
,
align_mode
=
0
))))
def
__init__
(
self
,
in_planes
,
out_planes
,
scale_factor
=
(
1
,
2
,
2
)):
super
(
Upsample
,
self
).
__init__
()
self
.
scale_factor
=
scale_factor
self
.
conv3d
=
nn
.
Conv3d
(
in_planes
,
out_planes
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
))
self
.
bn
=
nn
.
BatchNorm
(
out_planes
)
def
forward
(
self
,
x
):
out_size
=
x
.
shape
[
2
:]
for
i
in
range
(
3
):
out_size
[
i
]
=
self
.
scale_factor
[
i
]
*
out_size
[
i
]
return
F
.
elu
(
self
.
bn
(
self
.
conv3d
(
F
.
interpolate
(
x
,
size
=
out_size
,
mode
=
'trilinear'
,
align_corners
=
False
,
data_format
=
'NCDHW'
,
align_mode
=
0
))))
class
UpsampleConcat
(
nn
.
Layer
):
def
__init__
(
self
,
in_planes_up
,
in_planes_flat
,
out_planes
):
super
(
UpsampleConcat
,
self
).
__init__
()
self
.
conv3d
=
TempConv
(
in_planes_up
+
in_planes_flat
,
out_planes
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)
)
def
forward
(
self
,
x1
,
x2
):
scale_factor
=
(
1
,
2
,
2
)
def
__init__
(
self
,
in_planes_up
,
in_planes_flat
,
out_planes
):
super
(
UpsampleConcat
,
self
).
__init__
()
self
.
conv3d
=
TempConv
(
in_planes_up
+
in_planes_flat
,
out_planes
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
))
def
forward
(
self
,
x1
,
x2
):
scale_factor
=
(
1
,
2
,
2
)
out_size
=
x1
.
shape
[
2
:]
for
i
in
range
(
3
):
out_size
[
i
]
=
scale_factor
[
i
]
*
out_size
[
i
]
x1
=
F
.
interpolate
(
x1
,
size
=
out_size
,
mode
=
'trilinear'
,
align_corners
=
False
,
data_format
=
'NCDHW'
,
align_mode
=
0
)
x1
=
F
.
interpolate
(
x1
,
size
=
out_size
,
mode
=
'trilinear'
,
align_corners
=
False
,
data_format
=
'NCDHW'
,
align_mode
=
0
)
x
=
paddle
.
concat
([
x1
,
x2
],
axis
=
1
)
return
self
.
conv3d
(
x
)
class
SourceReferenceAttention
(
paddle
.
fluid
.
dygraph
.
Layer
):
class
SourceReferenceAttention
(
nn
.
Layer
):
"""
Source-Reference Attention Layer
"""
...
...
@@ -51,137 +87,166 @@ class SourceReferenceAttention(paddle.fluid.dygraph.Layer):
in_planes_r: int
Number of input reference feature vector channels.
"""
super
(
SourceReferenceAttention
,
self
).
__init__
()
super
(
SourceReferenceAttention
,
self
).
__init__
()
self
.
query_conv
=
nn
.
Conv3d
(
in_channels
=
in_planes_s
,
out_channels
=
in_planes_s
//
8
,
kernel_size
=
1
)
self
.
key_conv
=
nn
.
Conv3d
(
in_channels
=
in_planes_r
,
out_channels
=
in_planes_r
//
8
,
kernel_size
=
1
)
out_channels
=
in_planes_s
//
8
,
kernel_size
=
1
)
self
.
key_conv
=
nn
.
Conv3d
(
in_channels
=
in_planes_r
,
out_channels
=
in_planes_r
//
8
,
kernel_size
=
1
)
self
.
value_conv
=
nn
.
Conv3d
(
in_channels
=
in_planes_r
,
out_channels
=
in_planes_r
,
kernel_size
=
1
)
self
.
gamma
=
self
.
create_parameter
(
shape
=
[
1
],
dtype
=
self
.
query_conv
.
weight
.
dtype
,
default_initializer
=
paddle
.
fluid
.
initializer
.
Constant
(
0.0
))
out_channels
=
in_planes_r
,
kernel_size
=
1
)
self
.
gamma
=
self
.
create_parameter
(
shape
=
[
1
],
dtype
=
self
.
query_conv
.
weight
.
dtype
,
default_initializer
=
nn
.
initializer
.
Constant
(
0.0
))
def
forward
(
self
,
source
,
reference
):
s_batchsize
,
sC
,
sT
,
sH
,
sW
=
source
.
shape
r_batchsize
,
rC
,
rT
,
rH
,
rW
=
reference
.
shape
proj_query
=
paddle
.
reshape
(
self
.
query_conv
(
source
),
[
s_batchsize
,
-
1
,
sT
*
sH
*
sW
])
proj_query
=
paddle
.
reshape
(
self
.
query_conv
(
source
),
[
s_batchsize
,
-
1
,
sT
*
sH
*
sW
])
proj_query
=
paddle
.
transpose
(
proj_query
,
[
0
,
2
,
1
])
proj_key
=
paddle
.
reshape
(
self
.
key_conv
(
reference
),
[
r_batchsize
,
-
1
,
rT
*
rW
*
rH
])
energy
=
paddle
.
bmm
(
proj_query
,
proj_key
)
attention
=
F
.
softmax
(
energy
)
proj_value
=
paddle
.
reshape
(
self
.
value_conv
(
reference
),
[
r_batchsize
,
-
1
,
rT
*
rH
*
rW
])
out
=
paddle
.
bmm
(
proj_value
,
paddle
.
transpose
(
attention
,
[
0
,
2
,
1
]))
out
=
paddle
.
reshape
(
out
,
[
s_batchsize
,
sC
,
sT
,
sH
,
sW
])
out
=
self
.
gamma
*
out
+
source
proj_key
=
paddle
.
reshape
(
self
.
key_conv
(
reference
),
[
r_batchsize
,
-
1
,
rT
*
rW
*
rH
])
energy
=
paddle
.
bmm
(
proj_query
,
proj_key
)
attention
=
F
.
softmax
(
energy
)
proj_value
=
paddle
.
reshape
(
self
.
value_conv
(
reference
),
[
r_batchsize
,
-
1
,
rT
*
rH
*
rW
])
out
=
paddle
.
bmm
(
proj_value
,
paddle
.
transpose
(
attention
,
[
0
,
2
,
1
]))
out
=
paddle
.
reshape
(
out
,
[
s_batchsize
,
sC
,
sT
,
sH
,
sW
])
out
=
self
.
gamma
*
out
+
source
return
out
,
attention
class
NetworkR
(
nn
.
Layer
):
def
__init__
(
self
):
super
(
NetworkR
,
self
).
__init__
()
self
.
layers
=
nn
.
Sequential
(
nn
.
ReplicationPad3d
((
1
,
1
,
1
,
1
,
1
,
1
)),
TempConv
(
1
,
64
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
2
,
2
),
padding
=
(
0
,
0
,
0
)
),
TempConv
(
64
,
128
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)
),
TempConv
(
128
,
128
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)
),
TempConv
(
128
,
256
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
2
,
2
),
padding
=
(
1
,
1
,
1
)
),
TempConv
(
256
,
256
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)
),
TempConv
(
256
,
256
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)
),
TempConv
(
256
,
256
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)
),
TempConv
(
256
,
256
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)
),
Upsample
(
256
,
128
),
TempConv
(
128
,
64
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)
),
TempConv
(
64
,
64
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)
),
Upsample
(
64
,
16
),
nn
.
Conv3d
(
16
,
1
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)
)
)
def
forward
(
self
,
x
):
return
paddle
.
clip
((
x
+
paddle
.
fluid
.
layers
.
tanh
(
self
.
layers
(
((
x
*
1
).
detach
())
-
0.4462414
)
)),
0.0
,
1.0
)
class
NetworkC
(
nn
.
Layer
):
def
__init__
(
self
):
super
(
NetworkC
,
self
).
__init__
()
self
.
down1
=
nn
.
Sequential
(
nn
.
ReplicationPad3d
((
1
,
1
,
1
,
1
,
0
,
0
)),
TempConv
(
1
,
64
,
stride
=
(
1
,
2
,
2
),
padding
=
(
0
,
0
,
0
)
),
TempConv
(
64
,
128
),
TempConv
(
128
,
128
),
TempConv
(
128
,
256
,
stride
=
(
1
,
2
,
2
)
),
TempConv
(
256
,
256
),
TempConv
(
256
,
256
),
TempConv
(
256
,
512
,
stride
=
(
1
,
2
,
2
)
),
TempConv
(
512
,
512
),
TempConv
(
512
,
512
)
)
self
.
flat
=
nn
.
Sequential
(
TempConv
(
512
,
512
),
TempConv
(
512
,
512
)
)
self
.
down2
=
nn
.
Sequential
(
TempConv
(
512
,
512
,
stride
=
(
1
,
2
,
2
)
),
TempConv
(
512
,
512
),
)
self
.
stattn1
=
SourceReferenceAttention
(
512
,
512
)
# Source-Reference Attention
self
.
stattn2
=
SourceReferenceAttention
(
512
,
512
)
# Source-Reference Attention
self
.
selfattn1
=
SourceReferenceAttention
(
512
,
512
)
# Self Attention
self
.
conv1
=
TempConv
(
512
,
512
)
self
.
up1
=
UpsampleConcat
(
512
,
512
,
512
)
# 1/8
self
.
selfattn2
=
SourceReferenceAttention
(
512
,
512
)
# Self Attention
self
.
conv2
=
TempConv
(
512
,
256
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)
)
self
.
up2
=
nn
.
Sequential
(
Upsample
(
256
,
128
),
# 1/4
TempConv
(
128
,
64
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)
)
)
self
.
up3
=
nn
.
Sequential
(
Upsample
(
64
,
32
),
# 1/2
TempConv
(
32
,
16
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)
)
)
self
.
up4
=
nn
.
Sequential
(
Upsample
(
16
,
8
),
# 1/1
nn
.
Conv3d
(
8
,
2
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)
)
)
self
.
reffeatnet1
=
nn
.
Sequential
(
TempConv
(
3
,
64
,
stride
=
(
1
,
2
,
2
)
),
TempConv
(
64
,
128
),
TempConv
(
128
,
128
),
TempConv
(
128
,
256
,
stride
=
(
1
,
2
,
2
)
),
TempConv
(
256
,
256
),
TempConv
(
256
,
256
),
TempConv
(
256
,
512
,
stride
=
(
1
,
2
,
2
)
),
TempConv
(
512
,
512
),
TempConv
(
512
,
512
),
)
self
.
reffeatnet2
=
nn
.
Sequential
(
TempConv
(
512
,
512
,
stride
=
(
1
,
2
,
2
)
),
TempConv
(
512
,
512
),
TempConv
(
512
,
512
),
)
def
forward
(
self
,
x
,
x_refs
=
None
):
x1
=
self
.
down1
(
x
-
0.4462414
)
if
x_refs
is
not
None
:
x_refs
=
paddle
.
transpose
(
x_refs
,
[
0
,
2
,
1
,
3
,
4
])
# [B,T,C,H,W] --> [B,C,T,H,W]
reffeat
=
self
.
reffeatnet1
(
x_refs
-
0.48
)
x1
,
_
=
self
.
stattn1
(
x1
,
reffeat
)
x2
=
self
.
flat
(
x1
)
out
=
self
.
down2
(
x1
)
if
x_refs
is
not
None
:
reffeat2
=
self
.
reffeatnet2
(
reffeat
)
out
,
_
=
self
.
stattn2
(
out
,
reffeat2
)
out
=
self
.
conv1
(
out
)
out
,
_
=
self
.
selfattn1
(
out
,
out
)
out
=
self
.
up1
(
out
,
x2
)
out
,
_
=
self
.
selfattn2
(
out
,
out
)
out
=
self
.
conv2
(
out
)
out
=
self
.
up2
(
out
)
out
=
self
.
up3
(
out
)
out
=
self
.
up4
(
out
)
return
F
.
sigmoid
(
out
)
\ No newline at end of file
class
NetworkR
(
nn
.
Layer
):
def
__init__
(
self
):
super
(
NetworkR
,
self
).
__init__
()
self
.
layers
=
nn
.
Sequential
(
nn
.
ReplicationPad3d
((
1
,
1
,
1
,
1
,
1
,
1
)),
TempConv
(
1
,
64
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
2
,
2
),
padding
=
(
0
,
0
,
0
)),
TempConv
(
64
,
128
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)),
TempConv
(
128
,
128
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)),
TempConv
(
128
,
256
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
2
,
2
),
padding
=
(
1
,
1
,
1
)),
TempConv
(
256
,
256
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)),
TempConv
(
256
,
256
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)),
TempConv
(
256
,
256
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)),
TempConv
(
256
,
256
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)),
Upsample
(
256
,
128
),
TempConv
(
128
,
64
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)),
TempConv
(
64
,
64
,
kernel_size
=
(
3
,
3
,
3
),
padding
=
(
1
,
1
,
1
)),
Upsample
(
64
,
16
),
nn
.
Conv3d
(
16
,
1
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)))
def
forward
(
self
,
x
):
return
paddle
.
clip
(
(
x
+
F
.
tanh
(
self
.
layers
(((
x
*
1
).
detach
())
-
0.4462414
))),
0.0
,
1.0
)
class
NetworkC
(
nn
.
Layer
):
def
__init__
(
self
):
super
(
NetworkC
,
self
).
__init__
()
self
.
down1
=
nn
.
Sequential
(
nn
.
ReplicationPad3d
((
1
,
1
,
1
,
1
,
0
,
0
)),
TempConv
(
1
,
64
,
stride
=
(
1
,
2
,
2
),
padding
=
(
0
,
0
,
0
)),
TempConv
(
64
,
128
),
TempConv
(
128
,
128
),
TempConv
(
128
,
256
,
stride
=
(
1
,
2
,
2
)),
TempConv
(
256
,
256
),
TempConv
(
256
,
256
),
TempConv
(
256
,
512
,
stride
=
(
1
,
2
,
2
)),
TempConv
(
512
,
512
),
TempConv
(
512
,
512
))
self
.
flat
=
nn
.
Sequential
(
TempConv
(
512
,
512
),
TempConv
(
512
,
512
))
self
.
down2
=
nn
.
Sequential
(
TempConv
(
512
,
512
,
stride
=
(
1
,
2
,
2
)),
TempConv
(
512
,
512
),
)
self
.
stattn1
=
SourceReferenceAttention
(
512
,
512
)
# Source-Reference Attention
self
.
stattn2
=
SourceReferenceAttention
(
512
,
512
)
# Source-Reference Attention
self
.
selfattn1
=
SourceReferenceAttention
(
512
,
512
)
# Self Attention
self
.
conv1
=
TempConv
(
512
,
512
)
self
.
up1
=
UpsampleConcat
(
512
,
512
,
512
)
# 1/8
self
.
selfattn2
=
SourceReferenceAttention
(
512
,
512
)
# Self Attention
self
.
conv2
=
TempConv
(
512
,
256
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
))
self
.
up2
=
nn
.
Sequential
(
Upsample
(
256
,
128
),
# 1/4
TempConv
(
128
,
64
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)))
self
.
up3
=
nn
.
Sequential
(
Upsample
(
64
,
32
),
# 1/2
TempConv
(
32
,
16
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)))
self
.
up4
=
nn
.
Sequential
(
Upsample
(
16
,
8
),
# 1/1
nn
.
Conv3d
(
8
,
2
,
kernel_size
=
(
3
,
3
,
3
),
stride
=
(
1
,
1
,
1
),
padding
=
(
1
,
1
,
1
)))
self
.
reffeatnet1
=
nn
.
Sequential
(
TempConv
(
3
,
64
,
stride
=
(
1
,
2
,
2
)),
TempConv
(
64
,
128
),
TempConv
(
128
,
128
),
TempConv
(
128
,
256
,
stride
=
(
1
,
2
,
2
)),
TempConv
(
256
,
256
),
TempConv
(
256
,
256
),
TempConv
(
256
,
512
,
stride
=
(
1
,
2
,
2
)),
TempConv
(
512
,
512
),
TempConv
(
512
,
512
),
)
self
.
reffeatnet2
=
nn
.
Sequential
(
TempConv
(
512
,
512
,
stride
=
(
1
,
2
,
2
)),
TempConv
(
512
,
512
),
TempConv
(
512
,
512
),
)
def
forward
(
self
,
x
,
x_refs
=
None
):
x1
=
self
.
down1
(
x
-
0.4462414
)
if
x_refs
is
not
None
:
x_refs
=
paddle
.
transpose
(
x_refs
,
[
0
,
2
,
1
,
3
,
4
])
# [B,T,C,H,W] --> [B,C,T,H,W]
reffeat
=
self
.
reffeatnet1
(
x_refs
-
0.48
)
x1
,
_
=
self
.
stattn1
(
x1
,
reffeat
)
x2
=
self
.
flat
(
x1
)
out
=
self
.
down2
(
x1
)
if
x_refs
is
not
None
:
reffeat2
=
self
.
reffeatnet2
(
reffeat
)
out
,
_
=
self
.
stattn2
(
out
,
reffeat2
)
out
=
self
.
conv1
(
out
)
out
,
_
=
self
.
selfattn1
(
out
,
out
)
out
=
self
.
up1
(
out
,
x2
)
out
,
_
=
self
.
selfattn2
(
out
,
out
)
out
=
self
.
conv2
(
out
)
out
=
self
.
up2
(
out
)
out
=
self
.
up3
(
out
)
out
=
self
.
up4
(
out
)
return
F
.
sigmoid
(
out
)
ppgan/models/generators/resnet.py
浏览文件 @
c1bd1a2a
...
...
@@ -2,20 +2,25 @@ import paddle
import
paddle.nn
as
nn
import
functools
from
...modules.nn
import
ReflectionPad2d
,
LeakyReLU
,
Tanh
,
Dropout
,
BCEWithLogitsLoss
,
Pad2D
,
MSELoss
from
...modules.norm
import
build_norm_layer
from
.builder
import
GENERATORS
@
GENERATORS
.
register
()
class
ResnetGenerator
(
paddle
.
fluid
.
dygraph
.
Layer
):
class
ResnetGenerator
(
nn
.
Layer
):
"""Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
"""
def
__init__
(
self
,
input_nc
,
output_nc
,
ngf
=
64
,
norm_type
=
'instance'
,
use_dropout
=
False
,
n_blocks
=
6
,
padding_type
=
'reflect'
):
def
__init__
(
self
,
input_nc
,
output_nc
,
ngf
=
64
,
norm_type
=
'instance'
,
use_dropout
=
False
,
n_blocks
=
6
,
padding_type
=
'reflect'
):
"""Construct a Resnet-based generator
Args:
...
...
@@ -27,7 +32,7 @@ class ResnetGenerator(paddle.fluid.dygraph.Layer):
n_blocks (int) -- the number of ResNet blocks
padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero
"""
assert
(
n_blocks
>=
0
)
assert
(
n_blocks
>=
0
)
super
(
ResnetGenerator
,
self
).
__init__
()
norm_layer
=
build_norm_layer
(
norm_type
)
...
...
@@ -36,35 +41,56 @@ class ResnetGenerator(paddle.fluid.dygraph.Layer):
else
:
use_bias
=
norm_layer
==
nn
.
InstanceNorm
model
=
[
ReflectionPad2d
(
3
),
nn
.
Conv2d
(
input_nc
,
ngf
,
kernel_size
=
7
,
padding
=
0
,
bias_attr
=
use_bias
),
norm_layer
(
ngf
),
nn
.
ReLU
()]
model
=
[
nn
.
ReflectionPad2d
([
3
,
3
,
3
,
3
]),
nn
.
Conv2d
(
input_nc
,
ngf
,
kernel_size
=
7
,
padding
=
0
,
bias_attr
=
use_bias
),
norm_layer
(
ngf
),
nn
.
ReLU
()
]
n_downsampling
=
2
for
i
in
range
(
n_downsampling
):
# add downsampling layers
mult
=
2
**
i
mult
=
2
**
i
model
+=
[
nn
.
Conv2d
(
ngf
*
mult
,
ngf
*
mult
*
2
,
kernel_size
=
3
,
stride
=
2
,
padding
=
1
,
bias_attr
=
use_bias
),
norm_layer
(
ngf
*
mult
*
2
),
nn
.
ReLU
()]
mult
=
2
**
n_downsampling
for
i
in
range
(
n_blocks
):
# add ResNet blocks
nn
.
Conv2d
(
ngf
*
mult
,
ngf
*
mult
*
2
,
kernel_size
=
3
,
stride
=
2
,
padding
=
1
,
bias_attr
=
use_bias
),
norm_layer
(
ngf
*
mult
*
2
),
nn
.
ReLU
()
]
mult
=
2
**
n_downsampling
for
i
in
range
(
n_blocks
):
# add ResNet blocks
model
+=
[
ResnetBlock
(
ngf
*
mult
,
padding_type
=
padding_type
,
norm_layer
=
norm_layer
,
use_dropout
=
use_dropout
,
use_bias
=
use_bias
)]
model
+=
[
ResnetBlock
(
ngf
*
mult
,
padding_type
=
padding_type
,
norm_layer
=
norm_layer
,
use_dropout
=
use_dropout
,
use_bias
=
use_bias
)
]
for
i
in
range
(
n_downsampling
):
# add upsampling layers
mult
=
2
**
(
n_downsampling
-
i
)
mult
=
2
**
(
n_downsampling
-
i
)
model
+=
[
nn
.
ConvTranspose2d
(
ngf
*
mult
,
int
(
ngf
*
mult
/
2
),
kernel_size
=
3
,
stride
=
2
,
padding
=
1
,
output_padding
=
1
,
bias_attr
=
use_bias
),
norm_layer
(
int
(
ngf
*
mult
/
2
)),
nn
.
ReLU
()]
model
+=
[
ReflectionPad2d
(
3
)]
nn
.
ConvTranspose2d
(
ngf
*
mult
,
int
(
ngf
*
mult
/
2
),
kernel_size
=
3
,
stride
=
2
,
padding
=
1
,
output_padding
=
1
,
bias_attr
=
use_bias
),
norm_layer
(
int
(
ngf
*
mult
/
2
)),
nn
.
ReLU
()
]
model
+=
[
nn
.
ReflectionPad2d
([
3
,
3
,
3
,
3
])]
model
+=
[
nn
.
Conv2d
(
ngf
,
output_nc
,
kernel_size
=
7
,
padding
=
0
)]
model
+=
[
nn
.
Tanh
()]
...
...
@@ -75,9 +101,8 @@ class ResnetGenerator(paddle.fluid.dygraph.Layer):
return
self
.
model
(
x
)
class
ResnetBlock
(
paddle
.
fluid
.
dygraph
.
Layer
):
class
ResnetBlock
(
nn
.
Layer
):
"""Define a Resnet block"""
def
__init__
(
self
,
dim
,
padding_type
,
norm_layer
,
use_dropout
,
use_bias
):
"""Initialize the Resnet block
...
...
@@ -87,9 +112,11 @@ class ResnetBlock(paddle.fluid.dygraph.Layer):
Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
"""
super
(
ResnetBlock
,
self
).
__init__
()
self
.
conv_block
=
self
.
build_conv_block
(
dim
,
padding_type
,
norm_layer
,
use_dropout
,
use_bias
)
self
.
conv_block
=
self
.
build_conv_block
(
dim
,
padding_type
,
norm_layer
,
use_dropout
,
use_bias
)
def
build_conv_block
(
self
,
dim
,
padding_type
,
norm_layer
,
use_dropout
,
use_bias
):
def
build_conv_block
(
self
,
dim
,
padding_type
,
norm_layer
,
use_dropout
,
use_bias
):
"""Construct a convolutional block.
Parameters:
...
...
@@ -104,28 +131,37 @@ class ResnetBlock(paddle.fluid.dygraph.Layer):
conv_block
=
[]
p
=
0
if
padding_type
==
'reflect'
:
conv_block
+=
[
ReflectionPad2d
(
1
)]
conv_block
+=
[
nn
.
ReflectionPad2d
([
1
,
1
,
1
,
1
]
)]
elif
padding_type
==
'replicate'
:
conv_block
+=
[
ReplicationPad2d
(
1
)]
conv_block
+=
[
nn
.
ReplicationPad2d
([
1
,
1
,
1
,
1
]
)]
elif
padding_type
==
'zero'
:
p
=
1
else
:
raise
NotImplementedError
(
'padding [%s] is not implemented'
%
padding_type
)
conv_block
+=
[
nn
.
Conv2d
(
dim
,
dim
,
kernel_size
=
3
,
padding
=
p
,
bias_attr
=
use_bias
),
norm_layer
(
dim
),
nn
.
ReLU
()]
raise
NotImplementedError
(
'padding [%s] is not implemented'
%
padding_type
)
conv_block
+=
[
nn
.
Conv2d
(
dim
,
dim
,
kernel_size
=
3
,
padding
=
p
,
bias_attr
=
use_bias
),
norm_layer
(
dim
),
nn
.
ReLU
()
]
if
use_dropout
:
conv_block
+=
[
Dropout
(
0.5
)]
conv_block
+=
[
nn
.
Dropout
(
0.5
)]
p
=
0
if
padding_type
==
'reflect'
:
conv_block
+=
[
ReflectionPad2d
(
1
)]
conv_block
+=
[
nn
.
ReflectionPad2d
([
1
,
1
,
1
,
1
]
)]
elif
padding_type
==
'replicate'
:
conv_block
+=
[
ReplicationPad2d
(
1
)]
conv_block
+=
[
nn
.
ReplicationPad2d
([
1
,
1
,
1
,
1
]
)]
elif
padding_type
==
'zero'
:
p
=
1
else
:
raise
NotImplementedError
(
'padding [%s] is not implemented'
%
padding_type
)
conv_block
+=
[
nn
.
Conv2d
(
dim
,
dim
,
kernel_size
=
3
,
padding
=
p
,
bias_attr
=
use_bias
),
norm_layer
(
dim
)]
raise
NotImplementedError
(
'padding [%s] is not implemented'
%
padding_type
)
conv_block
+=
[
nn
.
Conv2d
(
dim
,
dim
,
kernel_size
=
3
,
padding
=
p
,
bias_attr
=
use_bias
),
norm_layer
(
dim
)
]
return
nn
.
Sequential
(
*
conv_block
)
...
...
ppgan/models/generators/unet.py
浏览文件 @
c1bd1a2a
import
functools
import
paddle
import
paddle.nn
as
nn
import
functools
from
...modules.nn
import
ReflectionPad2d
,
LeakyReLU
,
Tanh
,
Dropout
from
...modules.norm
import
build_norm_layer
from
.builder
import
GENERATORS
@
GENERATORS
.
register
()
class
UnetGenerator
(
paddle
.
fluid
.
dygraph
.
Layer
):
class
UnetGenerator
(
nn
.
Layer
):
"""Create a Unet-based generator"""
def
__init__
(
self
,
input_nc
,
output_nc
,
num_downs
,
ngf
=
64
,
norm_type
=
'batch'
,
use_dropout
=
False
):
def
__init__
(
self
,
input_nc
,
output_nc
,
num_downs
,
ngf
=
64
,
norm_type
=
'batch'
,
use_dropout
=
False
):
"""Construct a Unet generator
Args:
input_nc (int) -- the number of channels in input images
...
...
@@ -27,36 +31,64 @@ class UnetGenerator(paddle.fluid.dygraph.Layer):
super
(
UnetGenerator
,
self
).
__init__
()
norm_layer
=
build_norm_layer
(
norm_type
)
# construct unet structure
unet_block
=
UnetSkipConnectionBlock
(
ngf
*
8
,
ngf
*
8
,
input_nc
=
None
,
submodule
=
None
,
norm_layer
=
norm_layer
,
innermost
=
True
)
# add the innermost layer
for
i
in
range
(
num_downs
-
5
):
# add intermediate layers with ngf * 8 filters
unet_block
=
UnetSkipConnectionBlock
(
ngf
*
8
,
ngf
*
8
,
input_nc
=
None
,
submodule
=
unet_block
,
norm_layer
=
norm_layer
,
use_dropout
=
use_dropout
)
unet_block
=
UnetSkipConnectionBlock
(
ngf
*
8
,
ngf
*
8
,
input_nc
=
None
,
submodule
=
None
,
norm_layer
=
norm_layer
,
innermost
=
True
)
# add the innermost layer
for
i
in
range
(
num_downs
-
5
):
# add intermediate layers with ngf * 8 filters
unet_block
=
UnetSkipConnectionBlock
(
ngf
*
8
,
ngf
*
8
,
input_nc
=
None
,
submodule
=
unet_block
,
norm_layer
=
norm_layer
,
use_dropout
=
use_dropout
)
# gradually reduce the number of filters from ngf * 8 to ngf
unet_block
=
UnetSkipConnectionBlock
(
ngf
*
4
,
ngf
*
8
,
input_nc
=
None
,
submodule
=
unet_block
,
norm_layer
=
norm_layer
)
unet_block
=
UnetSkipConnectionBlock
(
ngf
*
2
,
ngf
*
4
,
input_nc
=
None
,
submodule
=
unet_block
,
norm_layer
=
norm_layer
)
unet_block
=
UnetSkipConnectionBlock
(
ngf
,
ngf
*
2
,
input_nc
=
None
,
submodule
=
unet_block
,
norm_layer
=
norm_layer
)
self
.
model
=
UnetSkipConnectionBlock
(
output_nc
,
ngf
,
input_nc
=
input_nc
,
submodule
=
unet_block
,
outermost
=
True
,
norm_layer
=
norm_layer
)
# add the outermost layer
unet_block
=
UnetSkipConnectionBlock
(
ngf
*
4
,
ngf
*
8
,
input_nc
=
None
,
submodule
=
unet_block
,
norm_layer
=
norm_layer
)
unet_block
=
UnetSkipConnectionBlock
(
ngf
*
2
,
ngf
*
4
,
input_nc
=
None
,
submodule
=
unet_block
,
norm_layer
=
norm_layer
)
unet_block
=
UnetSkipConnectionBlock
(
ngf
,
ngf
*
2
,
input_nc
=
None
,
submodule
=
unet_block
,
norm_layer
=
norm_layer
)
self
.
model
=
UnetSkipConnectionBlock
(
output_nc
,
ngf
,
input_nc
=
input_nc
,
submodule
=
unet_block
,
outermost
=
True
,
norm_layer
=
norm_layer
)
# add the outermost layer
def
forward
(
self
,
input
):
"""Standard forward"""
# tmp = self.model._sub_layers['model'][0](input)
# tmp1 = self.model._sub_layers['model'][1](tmp)
# tmp2 = self.model._sub_layers['model'][2](tmp1)
# import pickle
# pickle.dump(tmp2.numpy(), open('/workspace/notebook/align_pix2pix/tmp2-pd.pkl', 'wb'))
# tmp3 = self.model._sub_layers['model'][3](tmp2)
# pickle.dump(tmp3.numpy(), open('/workspace/notebook/align_pix2pix/tmp3-pd.pkl', 'wb'))
# tmp4 = self.model._sub_layers['model'][4](tmp3)
return
self
.
model
(
input
)
class
UnetSkipConnectionBlock
(
paddle
.
fluid
.
dygraph
.
Layer
):
class
UnetSkipConnectionBlock
(
nn
.
Layer
):
"""Defines the Unet submodule with skip connection.
X -------------------identity----------------------
|-- downsampling -- |submodule| -- upsampling --|
"""
def
__init__
(
self
,
outer_nc
,
inner_nc
,
input_nc
=
None
,
submodule
=
None
,
outermost
=
False
,
innermost
=
False
,
norm_layer
=
nn
.
BatchNorm
,
use_dropout
=
False
):
def
__init__
(
self
,
outer_nc
,
inner_nc
,
input_nc
=
None
,
submodule
=
None
,
outermost
=
False
,
innermost
=
False
,
norm_layer
=
nn
.
BatchNorm
,
use_dropout
=
False
):
"""Construct a Unet submodule with skip connections.
Parameters:
...
...
@@ -77,36 +109,48 @@ class UnetSkipConnectionBlock(paddle.fluid.dygraph.Layer):
use_bias
=
norm_layer
==
nn
.
InstanceNorm
if
input_nc
is
None
:
input_nc
=
outer_nc
downconv
=
nn
.
Conv2d
(
input_nc
,
inner_nc
,
kernel_size
=
4
,
stride
=
2
,
padding
=
1
,
bias_attr
=
use_bias
)
downrelu
=
LeakyReLU
(
0.2
,
True
)
downconv
=
nn
.
Conv2d
(
input_nc
,
inner_nc
,
kernel_size
=
4
,
stride
=
2
,
padding
=
1
,
bias_attr
=
use_bias
)
downrelu
=
nn
.
LeakyReLU
(
0.2
)
downnorm
=
norm_layer
(
inner_nc
)
uprelu
=
nn
.
ReLU
(
True
)
uprelu
=
nn
.
ReLU
()
upnorm
=
norm_layer
(
outer_nc
)
if
outermost
:
upconv
=
nn
.
ConvTranspose2d
(
inner_nc
*
2
,
outer_nc
,
kernel_size
=
4
,
stride
=
2
,
upconv
=
nn
.
ConvTranspose2d
(
inner_nc
*
2
,
outer_nc
,
kernel_size
=
4
,
stride
=
2
,
padding
=
1
)
down
=
[
downconv
]
up
=
[
uprelu
,
upconv
,
Tanh
()]
up
=
[
uprelu
,
upconv
,
nn
.
Tanh
()]
model
=
down
+
[
submodule
]
+
up
elif
innermost
:
upconv
=
nn
.
ConvTranspose2d
(
inner_nc
,
outer_nc
,
kernel_size
=
4
,
stride
=
2
,
padding
=
1
,
bias_attr
=
use_bias
)
upconv
=
nn
.
ConvTranspose2d
(
inner_nc
,
outer_nc
,
kernel_size
=
4
,
stride
=
2
,
padding
=
1
,
bias_attr
=
use_bias
)
down
=
[
downrelu
,
downconv
]
up
=
[
uprelu
,
upconv
,
upnorm
]
model
=
down
+
up
else
:
upconv
=
nn
.
ConvTranspose2d
(
inner_nc
*
2
,
outer_nc
,
kernel_size
=
4
,
stride
=
2
,
padding
=
1
,
bias_attr
=
use_bias
)
upconv
=
nn
.
ConvTranspose2d
(
inner_nc
*
2
,
outer_nc
,
kernel_size
=
4
,
stride
=
2
,
padding
=
1
,
bias_attr
=
use_bias
)
down
=
[
downrelu
,
downconv
,
downnorm
]
up
=
[
uprelu
,
upconv
,
upnorm
]
if
use_dropout
:
model
=
down
+
[
submodule
]
+
up
+
[
Dropout
(
0.5
)]
model
=
down
+
[
submodule
]
+
up
+
[
nn
.
Dropout
(
0.5
)]
else
:
model
=
down
+
[
submodule
]
+
up
...
...
@@ -115,5 +159,5 @@ class UnetSkipConnectionBlock(paddle.fluid.dygraph.Layer):
def
forward
(
self
,
x
):
if
self
.
outermost
:
return
self
.
model
(
x
)
else
:
# add skip connections
else
:
# add skip connections
return
paddle
.
concat
([
x
,
self
.
model
(
x
)],
1
)
ppgan/models/losses.py
浏览文件 @
c1bd1a2a
import
paddle
import
paddle.nn
as
nn
import
numpy
as
np
from
..modules.nn
import
BCEWithLogitsLoss
import
paddle
import
paddle.nn
as
nn
class
GANLoss
(
paddle
.
fluid
.
dygraph
.
Layer
):
class
GANLoss
(
nn
.
Layer
):
"""Define different GAN objectives.
The GANLoss class abstracts away the need to create the target label tensor
that has the same size as the input.
"""
def
__init__
(
self
,
gan_mode
,
target_real_label
=
1.0
,
target_fake_label
=
0.0
):
""" Initialize the GANLoss class.
...
...
@@ -31,7 +29,7 @@ class GANLoss(paddle.fluid.dygraph.Layer):
if
gan_mode
==
'lsgan'
:
self
.
loss
=
nn
.
MSELoss
()
elif
gan_mode
==
'vanilla'
:
self
.
loss
=
BCEWithLogitsLoss
()
self
.
loss
=
nn
.
BCEWithLogitsLoss
()
elif
gan_mode
in
[
'wgangp'
]:
self
.
loss
=
None
else
:
...
...
@@ -50,11 +48,17 @@ class GANLoss(paddle.fluid.dygraph.Layer):
if
target_is_real
:
if
not
hasattr
(
self
,
'target_real_tensor'
):
self
.
target_real_tensor
=
paddle
.
fill_constant
(
shape
=
paddle
.
shape
(
prediction
),
value
=
self
.
target_real_label
,
dtype
=
'float32'
)
self
.
target_real_tensor
=
paddle
.
fill_constant
(
shape
=
paddle
.
shape
(
prediction
),
value
=
self
.
target_real_label
,
dtype
=
'float32'
)
target_tensor
=
self
.
target_real_tensor
else
:
if
not
hasattr
(
self
,
'target_fake_tensor'
):
self
.
target_fake_tensor
=
paddle
.
fill_constant
(
shape
=
paddle
.
shape
(
prediction
),
value
=
self
.
target_fake_label
,
dtype
=
'float32'
)
self
.
target_fake_tensor
=
paddle
.
fill_constant
(
shape
=
paddle
.
shape
(
prediction
),
value
=
self
.
target_fake_label
,
dtype
=
'float32'
)
target_tensor
=
self
.
target_fake_tensor
# target_tensor.stop_gradient = True
...
...
@@ -78,4 +82,4 @@ class GANLoss(paddle.fluid.dygraph.Layer):
loss
=
-
prediction
.
mean
()
else
:
loss
=
prediction
.
mean
()
return
loss
\ No newline at end of file
return
loss
ppgan/models/pix2pix_model.py
浏览文件 @
c1bd1a2a
...
...
@@ -8,6 +8,7 @@ from .discriminators.builder import build_discriminator
from
.losses
import
GANLoss
from
..solver
import
build_optimizer
from
..modules.init
import
init_weights
from
..utils.image_pool
import
ImagePool
...
...
@@ -42,12 +43,15 @@ class Pix2PixModel(BaseModel):
# define networks (both generator and discriminator)
self
.
netG
=
build_generator
(
opt
.
model
.
generator
)
init_weights
(
self
.
netG
)
# define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
if
self
.
isTrain
:
self
.
netD
=
build_discriminator
(
opt
.
model
.
discriminator
)
init_weights
(
self
.
netD
)
if
self
.
isTrain
:
self
.
losses
=
{}
# define loss functions
self
.
criterionGAN
=
GANLoss
(
opt
.
model
.
gan_mode
)
self
.
criterionL1
=
paddle
.
nn
.
L1Loss
()
...
...
@@ -79,6 +83,7 @@ class Pix2PixModel(BaseModel):
AtoB
=
self
.
opt
.
dataset
.
train
.
direction
==
'AtoB'
self
.
real_A
=
paddle
.
to_variable
(
input
[
'A'
if
AtoB
else
'B'
])
self
.
real_B
=
paddle
.
to_variable
(
input
[
'B'
if
AtoB
else
'A'
])
self
.
image_paths
=
input
[
'A_paths'
if
AtoB
else
'B_paths'
]
def
forward
(
self
):
...
...
@@ -118,6 +123,7 @@ class Pix2PixModel(BaseModel):
# Second, G(A) = B
self
.
loss_G_L1
=
self
.
criterionL1
(
self
.
fake_B
,
self
.
real_B
)
*
self
.
opt
.
lambda_L1
# combine loss and calculate gradients
self
.
loss_G
=
self
.
loss_G_GAN
+
self
.
loss_G_L1
...
...
ppgan/modules/init.py
0 → 100644
浏览文件 @
c1bd1a2a
import
math
import
numpy
as
np
import
paddle
def
_calculate_fan_in_and_fan_out
(
tensor
):
dimensions
=
len
(
tensor
.
shape
)
if
dimensions
<
2
:
raise
ValueError
(
"Fan in and fan out can not be computed for tensor with fewer than 2 dimensions"
)
num_input_fmaps
=
tensor
.
shape
[
1
]
num_output_fmaps
=
tensor
.
shape
[
0
]
receptive_field_size
=
1
if
len
(
tensor
.
shape
)
>
2
:
receptive_field_size
=
paddle
.
numel
(
tensor
[
0
][
0
])
fan_in
=
num_input_fmaps
*
receptive_field_size
fan_out
=
num_output_fmaps
*
receptive_field_size
return
fan_in
,
fan_out
def
_calculate_correct_fan
(
tensor
,
mode
):
mode
=
mode
.
lower
()
valid_modes
=
[
'fan_in'
,
'fan_out'
]
if
mode
not
in
valid_modes
:
raise
ValueError
(
"Mode {} not supported, please use one of {}"
.
format
(
mode
,
valid_modes
))
fan_in
,
fan_out
=
_calculate_fan_in_and_fan_out
(
tensor
)
return
fan_in
if
mode
==
'fan_in'
else
fan_out
def
calculate_gain
(
nonlinearity
,
param
=
None
):
"""Return the recommended gain value for the given nonlinearity function.
The values are as follows:
================= ====================================================
nonlinearity gain
================= ====================================================
Linear / Identity :math:`1`
Conv{1,2,3}D :math:`1`
Sigmoid :math:`1`
Tanh :math:`
\f
rac{5}{3}`
ReLU :math:`\sqrt{2}`
Leaky Relu :math:`\sqrt{
\f
rac{2}{1 +
\t
ext{negative\_slope}^2}}`
================= ====================================================
Args:
nonlinearity: the non-linear function (`nn.functional` name)
param: optional parameter for the non-linear function
"""
linear_fns
=
[
'linear'
,
'conv1d'
,
'conv2d'
,
'conv3d'
,
'conv_transpose1d'
,
'conv_transpose2d'
,
'conv_transpose3d'
]
if
nonlinearity
in
linear_fns
or
nonlinearity
==
'sigmoid'
:
return
1
elif
nonlinearity
==
'tanh'
:
return
5.0
/
3
elif
nonlinearity
==
'relu'
:
return
math
.
sqrt
(
2.0
)
elif
nonlinearity
==
'leaky_relu'
:
if
param
is
None
:
negative_slope
=
0.01
elif
not
isinstance
(
param
,
bool
)
and
isinstance
(
param
,
int
)
or
isinstance
(
param
,
float
):
# True/False are instances of int, hence check above
negative_slope
=
param
else
:
raise
ValueError
(
"negative_slope {} not a valid number"
.
format
(
param
))
return
math
.
sqrt
(
2.0
/
(
1
+
negative_slope
**
2
))
else
:
raise
ValueError
(
"Unsupported nonlinearity {}"
.
format
(
nonlinearity
))
@
paddle
.
no_grad
()
def
constant_
(
x
,
value
):
temp_value
=
paddle
.
fill_constant
(
x
.
shape
,
x
.
dtype
,
value
)
x
.
set_value
(
temp_value
)
return
x
@
paddle
.
no_grad
()
def
normal_
(
x
,
mean
=
0.
,
std
=
1.
):
temp_value
=
paddle
.
normal
(
mean
,
std
,
shape
=
x
.
shape
)
x
.
set_value
(
temp_value
)
return
x
@
paddle
.
no_grad
()
def
uniform_
(
x
,
a
=-
1.
,
b
=
1.
):
temp_value
=
paddle
.
uniform
(
min
=
a
,
max
=
b
,
shape
=
x
.
shape
)
x
.
set_value
(
temp_value
)
return
x
@
paddle
.
no_grad
()
def
xavier_uniform_
(
x
,
gain
=
1.
):
"""Fills the input `Tensor` with values according to the method
described in `Understanding the difficulty of training deep feedforward
neural networks` - Glorot, X. & Bengio, Y. (2010), using a uniform
distribution. The resulting tensor will have values sampled from
:math:`\mathcal{U}(-a, a)` where
.. math::
a =
\t
ext{gain}
\t
imes \sqrt{
\f
rac{6}{
\t
ext{fan\_in} +
\t
ext{fan\_out}}}
Also known as Glorot initialization.
Args:
x: an n-dimensional `paddle.Tensor`
gain: an optional scaling factor
"""
fan_in
,
fan_out
=
_calculate_fan_in_and_fan_out
(
x
)
std
=
gain
*
math
.
sqrt
(
2.0
/
float
(
fan_in
+
fan_out
))
a
=
math
.
sqrt
(
3.0
)
*
std
# Calculate uniform bounds from standard deviation
return
uniform_
(
x
,
-
a
,
a
)
@
paddle
.
no_grad
()
def
xavier_normal_
(
x
,
gain
=
1.
):
"""Fills the input `Tensor` with values according to the method
described in `Understanding the difficulty of training deep feedforward
neural networks` - Glorot, X. & Bengio, Y. (2010), using a normal
distribution. The resulting tensor will have values sampled from
:math:`\mathcal{N}(0,
\t
ext{std}^2)` where
.. math::
\t
ext{std} =
\t
ext{gain}
\t
imes \sqrt{
\f
rac{2}{
\t
ext{fan\_in} +
\t
ext{fan\_out}}}
Also known as Glorot initialization.
Args:
tensor: an n-dimensional `paddle.Tensor`
gain: an optional scaling factor
"""
fan_in
,
fan_out
=
_calculate_fan_in_and_fan_out
(
x
)
std
=
gain
*
math
.
sqrt
(
2.0
/
float
(
fan_in
+
fan_out
))
return
normal_
(
x
,
0.
,
std
)
@
paddle
.
no_grad
()
def
kaiming_uniform_
(
x
,
a
=
0
,
mode
=
'fan_in'
,
nonlinearity
=
'leaky_relu'
):
"""Fills the input `Tensor` with values according to the method
described in `Delving deep into rectifiers: Surpassing human-level
performance on ImageNet classification` - He, K. et al. (2015), using a
uniform distribution. The resulting tensor will have values sampled from
:math:`\mathcal{U}(-
\t
ext{bound},
\t
ext{bound})` where
.. math::
\t
ext{bound} =
\t
ext{gain}
\t
imes \sqrt{
\f
rac{3}{
\t
ext{fan\_mode}}}
Also known as He initialization.
Args:
x: an n-dimensional `paddle.Tensor`
a: the negative slope of the rectifier used after this layer (only
used with ``'leaky_relu'``)
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
preserves the magnitude of the variance of the weights in the
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
backwards pass.
nonlinearity: the non-linear function (`nn.functional` name),
recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
"""
fan
=
_calculate_correct_fan
(
x
,
mode
)
gain
=
calculate_gain
(
nonlinearity
,
a
)
std
=
gain
/
math
.
sqrt
(
fan
)
bound
=
math
.
sqrt
(
3.0
)
*
std
# Calculate uniform bounds from standard deviation
temp_value
=
paddle
.
uniform
(
x
.
shape
,
min
=-
bound
,
max
=
bound
)
x
.
set_value
(
temp_value
)
return
x
@
paddle
.
no_grad
()
def
kaiming_normal_
(
x
,
a
=
0
,
mode
=
'fan_in'
,
nonlinearity
=
'leaky_relu'
):
"""Fills the input `Tensor` with values according to the method
described in `Delving deep into rectifiers: Surpassing human-level
performance on ImageNet classification` - He, K. et al. (2015), using a
normal distribution. The resulting tensor will have values sampled from
:math:`\mathcal{N}(0,
\t
ext{std}^2)` where
.. math::
\t
ext{std} =
\f
rac{
\t
ext{gain}}{\sqrt{
\t
ext{fan\_mode}}}
Also known as He initialization.
Args:
x: an n-dimensional `paddle.Tensor`
a: the negative slope of the rectifier used after this layer (only
used with ``'leaky_relu'``)
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
preserves the magnitude of the variance of the weights in the
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
backwards pass.
nonlinearity: the non-linear function (`nn.functional` name),
recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
"""
fan
=
_calculate_correct_fan
(
x
,
mode
)
gain
=
calculate_gain
(
nonlinearity
,
a
)
std
=
gain
/
math
.
sqrt
(
fan
)
temp_value
=
paddle
.
normal
(
0
,
std
,
shape
=
x
.
shape
)
x
.
set_value
(
temp_value
)
return
x
def
constant_init
(
layer
,
val
,
bias
=
0
):
if
hasattr
(
layer
,
'weight'
)
and
layer
.
weight
is
not
None
:
constant_
(
layer
.
weight
,
val
)
if
hasattr
(
layer
,
'bias'
)
and
layer
.
bias
is
not
None
:
constant_
(
layer
.
bias
,
bias
)
def
xavier_init
(
layer
,
gain
=
1
,
bias
=
0
,
distribution
=
'normal'
):
assert
distribution
in
[
'uniform'
,
'normal'
]
if
distribution
==
'uniform'
:
xavier_uniform_
(
layer
.
weight
,
gain
=
gain
)
else
:
xavier_normal_
(
layer
.
weight
,
gain
=
gain
)
if
hasattr
(
layer
,
'bias'
)
and
layer
.
bias
is
not
None
:
constant_
(
layer
.
bias
,
bias
)
def
normal_init
(
layer
,
mean
=
0
,
std
=
1
,
bias
=
0
):
normal_
(
layer
.
weight
,
mean
,
std
)
if
hasattr
(
layer
,
'bias'
)
and
layer
.
bias
is
not
None
:
constant_
(
layer
.
bias
,
bias
)
def
uniform_init
(
layer
,
a
=
0
,
b
=
1
,
bias
=
0
):
uniform_
(
layer
.
weight
,
a
,
b
)
if
hasattr
(
layer
,
'bias'
)
and
layer
.
bias
is
not
None
:
constant_
(
layer
.
bias
,
bias
)
def
kaiming_init
(
layer
,
a
=
0
,
mode
=
'fan_out'
,
nonlinearity
=
'relu'
,
bias
=
0
,
distribution
=
'normal'
):
assert
distribution
in
[
'uniform'
,
'normal'
]
if
distribution
==
'uniform'
:
kaiming_uniform_
(
layer
.
weight
,
a
=
a
,
mode
=
mode
,
nonlinearity
=
nonlinearity
)
else
:
kaiming_normal_
(
layer
.
weight
,
a
=
a
,
mode
=
mode
,
nonlinearity
=
nonlinearity
)
if
hasattr
(
layer
,
'bias'
)
and
layer
.
bias
is
not
None
:
constant_
(
layer
.
bias
,
bias
)
def
init_weights
(
net
,
init_type
=
'normal'
,
init_gain
=
0.02
):
"""Initialize network weights.
Args:
net (nn.Layer): network to be initialized
init_type (str): the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float): scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def
init_func
(
m
):
# define the initialization function
classname
=
m
.
__class__
.
__name__
if
hasattr
(
m
,
'weight'
)
and
(
classname
.
find
(
'Conv'
)
!=
-
1
or
classname
.
find
(
'Linear'
)
!=
-
1
):
if
init_type
==
'normal'
:
normal_
(
m
.
weight
,
0.0
,
init_gain
)
elif
init_type
==
'xavier'
:
xavier_normal_
(
m
.
weight
,
gain
=
init_gain
)
elif
init_type
==
'kaiming'
:
kaiming_normal_
(
m
.
weight
,
a
=
0
,
mode
=
'fan_in'
)
else
:
raise
NotImplementedError
(
'initialization method [%s] is not implemented'
%
init_type
)
if
hasattr
(
m
,
'bias'
)
and
m
.
bias
is
not
None
:
constant_
(
m
.
bias
,
0.0
)
elif
classname
.
find
(
'BatchNorm'
)
!=
-
1
:
# BatchNorm Layer's weight is not a matrix; only normal distribution applies.
normal_
(
m
.
weight
,
1.0
,
init_gain
)
constant_
(
m
.
bias
,
0.0
)
print
(
'initialize network with %s'
%
init_type
)
net
.
apply
(
init_func
)
# apply the initialization function <init_func>
ppgan/modules/nn.py
浏览文件 @
c1bd1a2a
import
paddle
import
paddle.nn
as
nn
from
paddle.fluid.dygraph
import
Layer
from
paddle
import
fluid
class
MSELoss
():
def
__init__
(
self
):
pass
def
__call__
(
self
,
prediction
,
label
):
return
fluid
.
layers
.
mse_loss
(
prediction
,
label
)
class
L1Loss
():
def
__init__
(
self
):
pass
def
__call__
(
self
,
prediction
,
label
):
return
fluid
.
layers
.
reduce_mean
(
fluid
.
layers
.
elementwise_sub
(
prediction
,
label
,
act
=
'abs'
))
class
ReflectionPad2d
(
Layer
):
def
__init__
(
self
,
size
):
super
(
ReflectionPad2d
,
self
).
__init__
()
self
.
size
=
size
def
forward
(
self
,
x
):
return
fluid
.
layers
.
pad2d
(
x
,
[
self
.
size
]
*
4
,
mode
=
"reflect"
)
class
LeakyReLU
(
Layer
):
def
__init__
(
self
,
alpha
,
inplace
=
False
):
super
(
LeakyReLU
,
self
).
__init__
()
self
.
alpha
=
alpha
def
forward
(
self
,
x
):
return
fluid
.
layers
.
leaky_relu
(
x
,
self
.
alpha
)
class
Tanh
(
Layer
):
def
__init__
(
self
):
super
(
Tanh
,
self
).
__init__
()
def
forward
(
self
,
x
):
return
fluid
.
layers
.
tanh
(
x
)
class
Dropout
(
Layer
):
def
__init__
(
self
,
prob
,
mode
=
'upscale_in_train'
):
super
(
Dropout
,
self
).
__init__
()
self
.
prob
=
prob
self
.
mode
=
mode
def
forward
(
self
,
x
):
return
fluid
.
layers
.
dropout
(
x
,
self
.
prob
,
dropout_implementation
=
self
.
mode
)
class
BCEWithLogitsLoss
():
def
__init__
(
self
,
weight
=
None
,
reduction
=
'mean'
):
self
.
weight
=
weight
self
.
reduction
=
'mean'
def
__call__
(
self
,
x
,
label
):
out
=
paddle
.
fluid
.
layers
.
sigmoid_cross_entropy_with_logits
(
x
,
label
)
if
self
.
reduction
==
'sum'
:
return
fluid
.
layers
.
reduce_sum
(
out
)
elif
self
.
reduction
==
'mean'
:
return
fluid
.
layers
.
reduce_mean
(
out
)
else
:
return
out
class
_SpectralNorm
(
paddle
.
nn
.
SpectralNorm
):
class
_SpectralNorm
(
nn
.
SpectralNorm
):
def
__init__
(
self
,
weight_shape
,
dim
=
0
,
power_iters
=
1
,
eps
=
1e-12
,
dtype
=
'float32'
):
super
(
_SpectralNorm
,
self
).
__init__
(
weight_shape
,
dim
,
power_iters
,
eps
,
dtype
)
super
(
_SpectralNorm
,
self
).
__init__
(
weight_shape
,
dim
,
power_iters
,
eps
,
dtype
)
def
forward
(
self
,
weight
):
paddle
.
fluid
.
data_feeder
.
check_variable_and_dtype
(
weight
,
"weight"
,
[
'float32'
,
'float64'
],
'SpectralNorm'
)
inputs
=
{
'Weight'
:
weight
,
'U'
:
self
.
weight_u
,
'V'
:
self
.
weight_v
}
out
=
self
.
_helper
.
create_variable_for_type_inference
(
self
.
_dtype
)
_power_iters
=
self
.
_power_iters
if
self
.
training
else
0
self
.
_helper
.
append_op
(
type
=
"spectral_norm"
,
inputs
=
inputs
,
outputs
=
{
"Out"
:
out
,
},
attrs
=
{
"dim"
:
self
.
_dim
,
"power_iters"
:
_power_iters
,
"eps"
:
self
.
_eps
,
})
self
.
_helper
.
append_op
(
type
=
"spectral_norm"
,
inputs
=
inputs
,
outputs
=
{
"Out"
:
out
,
},
attrs
=
{
"dim"
:
self
.
_dim
,
"power_iters"
:
_power_iters
,
"eps"
:
self
.
_eps
,
})
return
out
class
Spectralnorm
(
paddle
.
nn
.
Layer
):
def
__init__
(
self
,
layer
,
dim
=
0
,
power_iters
=
1
,
eps
=
1e-12
,
dtype
=
'float32'
):
def
__init__
(
self
,
layer
,
dim
=
0
,
power_iters
=
1
,
eps
=
1e-12
,
dtype
=
'float32'
):
super
(
Spectralnorm
,
self
).
__init__
()
self
.
spectral_norm
=
_SpectralNorm
(
layer
.
weight
.
shape
,
dim
,
power_iters
,
eps
,
dtype
)
self
.
spectral_norm
=
_SpectralNorm
(
layer
.
weight
.
shape
,
dim
,
power_iters
,
eps
,
dtype
)
self
.
dim
=
dim
self
.
power_iters
=
power_iters
self
.
eps
=
eps
self
.
layer
=
layer
weight
=
layer
.
_parameters
[
'weight'
]
del
layer
.
_parameters
[
'weight'
]
self
.
weight_orig
=
self
.
create_parameter
(
weight
.
shape
,
dtype
=
weight
.
dtype
)
self
.
weight_orig
=
self
.
create_parameter
(
weight
.
shape
,
dtype
=
weight
.
dtype
)
self
.
weight_orig
.
set_value
(
weight
)
def
forward
(
self
,
x
):
weight
=
self
.
spectral_norm
(
self
.
weight_orig
)
self
.
layer
.
weight
=
weight
out
=
self
.
layer
(
x
)
return
out
def
initial_type
(
input
,
op_type
,
fan_out
,
init
=
"normal"
,
use_bias
=
False
,
filter_size
=
0
,
stddev
=
0.02
,
name
=
None
):
if
init
==
"kaiming"
:
if
op_type
==
'conv'
:
fan_in
=
input
.
shape
[
1
]
*
filter_size
*
filter_size
elif
op_type
==
'deconv'
:
fan_in
=
fan_out
*
filter_size
*
filter_size
else
:
if
len
(
input
.
shape
)
>
2
:
fan_in
=
input
.
shape
[
1
]
*
input
.
shape
[
2
]
*
input
.
shape
[
3
]
else
:
fan_in
=
input
.
shape
[
1
]
bound
=
1
/
math
.
sqrt
(
fan_in
)
param_attr
=
fluid
.
ParamAttr
(
# name=name + "_w",
initializer
=
fluid
.
initializer
.
Uniform
(
low
=-
bound
,
high
=
bound
))
if
use_bias
==
True
:
bias_attr
=
fluid
.
ParamAttr
(
# name=name + '_b',
initializer
=
fluid
.
initializer
.
Uniform
(
low
=-
bound
,
high
=
bound
))
else
:
bias_attr
=
False
else
:
param_attr
=
fluid
.
ParamAttr
(
# name=name + "_w",
initializer
=
fluid
.
initializer
.
NormalInitializer
(
loc
=
0.0
,
scale
=
stddev
))
if
use_bias
==
True
:
bias_attr
=
fluid
.
ParamAttr
(
# name=name + "_b",
initializer
=
fluid
.
initializer
.
Constant
(
0.0
))
else
:
bias_attr
=
False
return
param_attr
,
bias_attr
class
Pad2D
(
fluid
.
dygraph
.
Layer
):
def
__init__
(
self
,
paddings
,
mode
,
pad_value
=
0.0
):
super
(
Pad2D
,
self
).
__init__
()
self
.
paddings
=
paddings
self
.
mode
=
mode
def
forward
(
self
,
x
):
return
fluid
.
layers
.
pad2d
(
x
,
self
.
paddings
,
self
.
mode
)
\ No newline at end of file
ppgan/modules/norm.py
浏览文件 @
c1bd1a2a
...
...
@@ -3,7 +3,7 @@ import functools
import
paddle.nn
as
nn
class
Identity
(
paddle
.
fluid
.
dygraph
.
Layer
):
class
Identity
(
nn
.
Layer
):
def
forward
(
self
,
x
):
return
x
...
...
@@ -18,11 +18,28 @@ def build_norm_layer(norm_type='instance'):
For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
"""
if
norm_type
==
'batch'
:
norm_layer
=
functools
.
partial
(
nn
.
BatchNorm
,
param_attr
=
paddle
.
ParamAttr
(
initializer
=
paddle
.
fluid
.
initializer
.
NormalInitializer
(
1.0
,
0.02
)),
bias_attr
=
paddle
.
ParamAttr
(
initializer
=
paddle
.
fluid
.
initializer
.
Constant
(
0.0
)),
trainable_statistics
=
True
)
norm_layer
=
functools
.
partial
(
nn
.
BatchNorm
,
param_attr
=
paddle
.
ParamAttr
(
initializer
=
nn
.
initializer
.
Normal
(
1.0
,
0.02
)),
bias_attr
=
paddle
.
ParamAttr
(
initializer
=
nn
.
initializer
.
Constant
(
0.0
)),
trainable_statistics
=
True
)
elif
norm_type
==
'instance'
:
norm_layer
=
functools
.
partial
(
nn
.
InstanceNorm
,
param_attr
=
paddle
.
ParamAttr
(
initializer
=
paddle
.
fluid
.
initializer
.
Constant
(
1.0
),
learning_rate
=
0.0
,
trainable
=
False
),
bias_attr
=
paddle
.
ParamAttr
(
initializer
=
paddle
.
fluid
.
initializer
.
Constant
(
0.0
),
learning_rate
=
0.0
,
trainable
=
False
))
norm_layer
=
functools
.
partial
(
nn
.
InstanceNorm
,
param_attr
=
paddle
.
ParamAttr
(
initializer
=
nn
.
initializer
.
Constant
(
1.0
),
learning_rate
=
0.0
,
trainable
=
False
),
bias_attr
=
paddle
.
ParamAttr
(
initializer
=
nn
.
initializer
.
Constant
(
0.0
),
learning_rate
=
0.0
,
trainable
=
False
))
elif
norm_type
==
'none'
:
def
norm_layer
(
x
):
return
Identity
()
def
norm_layer
(
x
):
return
Identity
()
else
:
raise
NotImplementedError
(
'normalization layer [%s] is not found'
%
norm_type
)
return
norm_layer
\ No newline at end of file
raise
NotImplementedError
(
'normalization layer [%s] is not found'
%
norm_type
)
return
norm_layer
ppgan/utils/setup.py
浏览文件 @
c1bd1a2a
...
...
@@ -19,6 +19,6 @@ def setup(args, cfg):
logger
.
info
(
'Configs: {}'
.
format
(
cfg
))
place
=
paddle
.
fluid
.
CUDAPlace
(
ParallelEnv
().
dev_id
)
\
if
ParallelEnv
().
nranks
>
1
else
paddle
.
fluid
.
CUDAPlace
(
0
)
place
=
paddle
.
CUDAPlace
(
ParallelEnv
().
dev_id
)
\
if
ParallelEnv
().
nranks
>
1
else
paddle
.
CUDAPlace
(
0
)
paddle
.
disable_static
(
place
)
ppgan/utils/timer.py
0 → 100644
浏览文件 @
c1bd1a2a
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
time
class
TimeAverager
(
object
):
def
__init__
(
self
):
self
.
reset
()
def
reset
(
self
):
self
.
_cnt
=
0
self
.
_total_time
=
0
def
record
(
self
,
usetime
):
self
.
_cnt
+=
1
self
.
_total_time
+=
usetime
def
get_average
(
self
):
if
self
.
_cnt
==
0
:
return
0
return
self
.
_total_time
/
self
.
_cnt
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