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ModelZoo
taming-transformers_pytorch
Commits
031cd626
Commit
031cd626
authored
Mar 01, 2024
by
mashun1
Browse files
taming-transformers
parent
01db7703
Changes
5
Hide whitespace changes
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Showing
5 changed files
with
2636 additions
and
1 deletion
+2636
-1
.gitignore
.gitignore
+1
-1
configs/faceshq_vqgan.yaml
configs/faceshq_vqgan.yaml
+4
-0
sample.txt
sample.txt
+2003
-0
scripts/sample_conditional.py
scripts/sample_conditional.py
+366
-0
scripts/sample_fast.py
scripts/sample_fast.py
+262
-0
No files found.
.gitignore
View file @
031cd626
...
...
@@ -6,5 +6,5 @@ nohup*
logs/
results
*.egg*
sample
*
sample
_for_test
imagenet_depth
\ No newline at end of file
configs/faceshq_vqgan.yaml
View file @
031cd626
...
...
@@ -40,3 +40,7 @@ data:
params
:
size
:
256
crop_size
:
256
lightning
:
trainer
:
max_epochs
:
2
\ No newline at end of file
sample.txt
0 → 100644
View file @
031cd626
nohup: ignoring input
logdir:logs/2024-02-19T08-49-48_faceshq_transformer
Working with z of shape (1, 256, 16, 16) = 65536 dimensions.
<class 'numpy.ndarray'> =====================
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scripts/sample_conditional.py
0 → 100644
View file @
031cd626
import
argparse
,
os
,
sys
,
glob
,
math
,
time
import
torch
import
numpy
as
np
from
omegaconf
import
OmegaConf
from
pathlib
import
Path
import
sys
parent_dir
=
Path
(
__file__
).
resolve
()
parent_dir
=
parent_dir
.
parent
.
parent
sys
.
path
.
append
(
str
(
parent_dir
))
import
streamlit
as
st
# from streamlit import caching
from
PIL
import
Image
from
main
import
instantiate_from_config
,
DataModuleFromConfig
from
torch.utils.data
import
DataLoader
from
torch.utils.data.dataloader
import
default_collate
rescale
=
lambda
x
:
(
x
+
1.
)
/
2.
def
bchw_to_st
(
x
):
return
rescale
(
x
.
detach
().
cpu
().
numpy
().
transpose
(
0
,
2
,
3
,
1
))
def
save_img
(
xstart
,
fname
):
I
=
(
xstart
.
clip
(
0
,
1
)[
0
]
*
255
).
astype
(
np
.
uint8
)
Image
.
fromarray
(
I
).
save
(
fname
)
def
get_interactive_image
(
resize
=
False
):
image
=
st
.
file_uploader
(
"Input"
,
type
=
[
"jpg"
,
"JPEG"
,
"png"
])
if
image
is
not
None
:
image
=
Image
.
open
(
image
)
if
not
image
.
mode
==
"RGB"
:
image
=
image
.
convert
(
"RGB"
)
image
=
np
.
array
(
image
).
astype
(
np
.
uint8
)
print
(
"upload image shape: {}"
.
format
(
image
.
shape
))
img
=
Image
.
fromarray
(
image
)
if
resize
:
img
=
img
.
resize
((
256
,
256
))
image
=
np
.
array
(
img
)
return
image
def
single_image_to_torch
(
x
,
permute
=
True
):
assert
x
is
not
None
,
"Please provide an image through the upload function"
x
=
np
.
array
(
x
)
x
=
torch
.
FloatTensor
(
x
/
255.
*
2.
-
1.
)[
None
,...]
if
permute
:
x
=
x
.
permute
(
0
,
3
,
1
,
2
)
return
x
def
pad_to_M
(
x
,
M
):
hp
=
math
.
ceil
(
x
.
shape
[
2
]
/
M
)
*
M
-
x
.
shape
[
2
]
wp
=
math
.
ceil
(
x
.
shape
[
3
]
/
M
)
*
M
-
x
.
shape
[
3
]
x
=
torch
.
nn
.
functional
.
pad
(
x
,
(
0
,
wp
,
0
,
hp
,
0
,
0
,
0
,
0
))
return
x
@
torch
.
no_grad
()
def
run_conditional
(
model
,
dsets
):
if
len
(
dsets
.
datasets
)
>
1
:
split
=
st
.
sidebar
.
radio
(
"Split"
,
sorted
(
dsets
.
datasets
.
keys
()))
dset
=
dsets
.
datasets
[
split
]
else
:
dset
=
next
(
iter
(
dsets
.
datasets
.
values
()))
batch_size
=
1
start_index
=
st
.
sidebar
.
number_input
(
"Example Index (Size: {})"
.
format
(
len
(
dset
)),
value
=
0
,
min_value
=
0
,
max_value
=
len
(
dset
)
-
batch_size
)
indices
=
list
(
range
(
start_index
,
start_index
+
batch_size
))
example
=
default_collate
([
dset
[
i
]
for
i
in
indices
])
x
=
model
.
get_input
(
"image"
,
example
).
to
(
model
.
device
)
cond_key
=
model
.
cond_stage_key
c
=
model
.
get_input
(
cond_key
,
example
).
to
(
model
.
device
)
scale_factor
=
st
.
sidebar
.
slider
(
"Scale Factor"
,
min_value
=
0.5
,
max_value
=
4.0
,
step
=
0.25
,
value
=
1.00
)
if
scale_factor
!=
1.0
:
x
=
torch
.
nn
.
functional
.
interpolate
(
x
,
scale_factor
=
scale_factor
,
mode
=
"bicubic"
)
c
=
torch
.
nn
.
functional
.
interpolate
(
c
,
scale_factor
=
scale_factor
,
mode
=
"bicubic"
)
quant_z
,
z_indices
=
model
.
encode_to_z
(
x
)
quant_c
,
c_indices
=
model
.
encode_to_c
(
c
)
cshape
=
quant_z
.
shape
xrec
=
model
.
first_stage_model
.
decode
(
quant_z
)
st
.
write
(
"image: {}"
.
format
(
x
.
shape
))
st
.
image
(
bchw_to_st
(
x
),
clamp
=
True
,
output_format
=
"PNG"
)
st
.
write
(
"image reconstruction: {}"
.
format
(
xrec
.
shape
))
st
.
image
(
bchw_to_st
(
xrec
),
clamp
=
True
,
output_format
=
"PNG"
)
if
cond_key
==
"segmentation"
:
# get image from segmentation mask
num_classes
=
c
.
shape
[
1
]
c
=
torch
.
argmax
(
c
,
dim
=
1
,
keepdim
=
True
)
c
=
torch
.
nn
.
functional
.
one_hot
(
c
,
num_classes
=
num_classes
)
c
=
c
.
squeeze
(
1
).
permute
(
0
,
3
,
1
,
2
).
float
()
c
=
model
.
cond_stage_model
.
to_rgb
(
c
)
st
.
write
(
f
"
{
cond_key
}
:
{
tuple
(
c
.
shape
)
}
"
)
st
.
image
(
bchw_to_st
(
c
),
clamp
=
True
,
output_format
=
"PNG"
)
idx
=
z_indices
half_sample
=
st
.
sidebar
.
checkbox
(
"Image Completion"
,
value
=
False
)
if
half_sample
:
start
=
idx
.
shape
[
1
]
//
2
else
:
start
=
0
idx
[:,
start
:]
=
0
idx
=
idx
.
reshape
(
cshape
[
0
],
cshape
[
2
],
cshape
[
3
])
start_i
=
start
//
cshape
[
3
]
start_j
=
start
%
cshape
[
3
]
if
not
half_sample
and
quant_z
.
shape
==
quant_c
.
shape
:
st
.
info
(
"Setting idx to c_indices"
)
idx
=
c_indices
.
clone
().
reshape
(
cshape
[
0
],
cshape
[
2
],
cshape
[
3
])
cidx
=
c_indices
cidx
=
cidx
.
reshape
(
quant_c
.
shape
[
0
],
quant_c
.
shape
[
2
],
quant_c
.
shape
[
3
])
xstart
=
model
.
decode_to_img
(
idx
[:,:
cshape
[
2
],:
cshape
[
3
]],
cshape
)
st
.
image
(
bchw_to_st
(
xstart
),
clamp
=
True
,
output_format
=
"PNG"
)
temperature
=
st
.
number_input
(
"Temperature"
,
value
=
1.0
)
top_k
=
st
.
number_input
(
"Top k"
,
value
=
100
)
sample
=
st
.
checkbox
(
"Sample"
,
value
=
True
)
update_every
=
st
.
number_input
(
"Update every"
,
value
=
75
)
st
.
text
(
f
"Sampling shape (
{
cshape
[
2
]
}
,
{
cshape
[
3
]
}
)"
)
animate
=
st
.
checkbox
(
"animate"
)
if
animate
:
import
imageio
outvid
=
"sampling.mp4"
writer
=
imageio
.
get_writer
(
outvid
,
fps
=
25
)
elapsed_t
=
st
.
empty
()
info
=
st
.
empty
()
st
.
text
(
"Sampled"
)
if
st
.
button
(
"Sample"
):
output
=
st
.
empty
()
start_t
=
time
.
time
()
for
i
in
range
(
start_i
,
cshape
[
2
]
-
0
):
if
i
<=
8
:
local_i
=
i
elif
cshape
[
2
]
-
i
<
8
:
local_i
=
16
-
(
cshape
[
2
]
-
i
)
else
:
local_i
=
8
for
j
in
range
(
start_j
,
cshape
[
3
]
-
0
):
if
j
<=
8
:
local_j
=
j
elif
cshape
[
3
]
-
j
<
8
:
local_j
=
16
-
(
cshape
[
3
]
-
j
)
else
:
local_j
=
8
i_start
=
i
-
local_i
i_end
=
i_start
+
16
j_start
=
j
-
local_j
j_end
=
j_start
+
16
elapsed_t
.
text
(
f
"Time:
{
time
.
time
()
-
start_t
}
seconds"
)
info
.
text
(
f
"Step: (
{
i
}
,
{
j
}
) | Local: (
{
local_i
}
,
{
local_j
}
) | Crop: (
{
i_start
}
:
{
i_end
}
,
{
j_start
}
:
{
j_end
}
)"
)
patch
=
idx
[:,
i_start
:
i_end
,
j_start
:
j_end
]
patch
=
patch
.
reshape
(
patch
.
shape
[
0
],
-
1
)
cpatch
=
cidx
[:,
i_start
:
i_end
,
j_start
:
j_end
]
cpatch
=
cpatch
.
reshape
(
cpatch
.
shape
[
0
],
-
1
)
patch
=
torch
.
cat
((
cpatch
,
patch
),
dim
=
1
)
logits
,
_
=
model
.
transformer
(
patch
[:,:
-
1
])
logits
=
logits
[:,
-
256
:,
:]
logits
=
logits
.
reshape
(
cshape
[
0
],
16
,
16
,
-
1
)
logits
=
logits
[:,
local_i
,
local_j
,:]
logits
=
logits
/
temperature
if
top_k
is
not
None
:
logits
=
model
.
top_k_logits
(
logits
,
top_k
)
# apply softmax to convert to probabilities
probs
=
torch
.
nn
.
functional
.
softmax
(
logits
,
dim
=-
1
)
# sample from the distribution or take the most likely
if
sample
:
ix
=
torch
.
multinomial
(
probs
,
num_samples
=
1
)
else
:
_
,
ix
=
torch
.
topk
(
probs
,
k
=
1
,
dim
=-
1
)
idx
[:,
i
,
j
]
=
ix
if
(
i
*
cshape
[
3
]
+
j
)
%
update_every
==
0
:
xstart
=
model
.
decode_to_img
(
idx
[:,
:
cshape
[
2
],
:
cshape
[
3
]],
cshape
,)
xstart
=
bchw_to_st
(
xstart
)
output
.
image
(
xstart
,
clamp
=
True
,
output_format
=
"PNG"
)
if
animate
:
writer
.
append_data
((
xstart
[
0
]
*
255
).
clip
(
0
,
255
).
astype
(
np
.
uint8
))
xstart
=
model
.
decode_to_img
(
idx
[:,:
cshape
[
2
],:
cshape
[
3
]],
cshape
)
xstart
=
bchw_to_st
(
xstart
)
output
.
image
(
xstart
,
clamp
=
True
,
output_format
=
"PNG"
)
#save_img(xstart, "full_res_sample.png")
if
animate
:
writer
.
close
()
st
.
video
(
outvid
)
def
get_parser
():
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"-r"
,
"--resume"
,
type
=
str
,
nargs
=
"?"
,
help
=
"load from logdir or checkpoint in logdir"
,
)
parser
.
add_argument
(
"-b"
,
"--base"
,
nargs
=
"*"
,
metavar
=
"base_config.yaml"
,
help
=
"paths to base configs. Loaded from left-to-right. "
"Parameters can be overwritten or added with command-line options of the form `--key value`."
,
default
=
list
(),
)
parser
.
add_argument
(
"-c"
,
"--config"
,
nargs
=
"?"
,
metavar
=
"single_config.yaml"
,
help
=
"path to single config. If specified, base configs will be ignored "
"(except for the last one if left unspecified)."
,
const
=
True
,
default
=
""
,
)
parser
.
add_argument
(
"--ignore_base_data"
,
action
=
"store_true"
,
help
=
"Ignore data specification from base configs. Useful if you want "
"to specify a custom datasets on the command line."
,
)
return
parser
def
load_model_from_config
(
config
,
sd
,
gpu
=
True
,
eval_mode
=
True
):
if
"ckpt_path"
in
config
.
params
:
st
.
warning
(
"Deleting the restore-ckpt path from the config..."
)
config
.
params
.
ckpt_path
=
None
if
"downsample_cond_size"
in
config
.
params
:
st
.
warning
(
"Deleting downsample-cond-size from the config and setting factor=0.5 instead..."
)
config
.
params
.
downsample_cond_size
=
-
1
config
.
params
[
"downsample_cond_factor"
]
=
0.5
try
:
if
"ckpt_path"
in
config
.
params
.
first_stage_config
.
params
:
config
.
params
.
first_stage_config
.
params
.
ckpt_path
=
None
st
.
warning
(
"Deleting the first-stage restore-ckpt path from the config..."
)
if
"ckpt_path"
in
config
.
params
.
cond_stage_config
.
params
:
config
.
params
.
cond_stage_config
.
params
.
ckpt_path
=
None
st
.
warning
(
"Deleting the cond-stage restore-ckpt path from the config..."
)
except
:
pass
model
=
instantiate_from_config
(
config
)
if
sd
is
not
None
:
missing
,
unexpected
=
model
.
load_state_dict
(
sd
,
strict
=
False
)
st
.
info
(
f
"Missing Keys in State Dict:
{
missing
}
"
)
st
.
info
(
f
"Unexpected Keys in State Dict:
{
unexpected
}
"
)
if
gpu
:
model
.
cuda
()
if
eval_mode
:
model
.
eval
()
return
{
"model"
:
model
}
def
get_data
(
config
):
# get data
data
=
instantiate_from_config
(
config
.
data
)
data
.
prepare_data
()
data
.
setup
()
return
data
@
st
.
cache
(
allow_output_mutation
=
True
,
suppress_st_warning
=
True
)
def
load_model_and_dset
(
config
,
ckpt
,
gpu
,
eval_mode
):
# get data
dsets
=
get_data
(
config
)
# calls data.config ...
# now load the specified checkpoint
if
ckpt
:
pl_sd
=
torch
.
load
(
ckpt
,
map_location
=
"cpu"
)
global_step
=
pl_sd
[
"global_step"
]
else
:
pl_sd
=
{
"state_dict"
:
None
}
global_step
=
None
model
=
load_model_from_config
(
config
.
model
,
pl_sd
[
"state_dict"
],
gpu
=
gpu
,
eval_mode
=
eval_mode
)[
"model"
]
return
dsets
,
model
,
global_step
if
__name__
==
"__main__"
:
sys
.
path
.
append
(
os
.
getcwd
())
parser
=
get_parser
()
opt
,
unknown
=
parser
.
parse_known_args
()
ckpt
=
None
if
opt
.
resume
:
if
not
os
.
path
.
exists
(
opt
.
resume
):
raise
ValueError
(
"Cannot find {}"
.
format
(
opt
.
resume
))
if
os
.
path
.
isfile
(
opt
.
resume
):
paths
=
opt
.
resume
.
split
(
"/"
)
try
:
idx
=
len
(
paths
)
-
paths
[::
-
1
].
index
(
"logs"
)
+
1
except
ValueError
:
idx
=
-
2
# take a guess: path/to/logdir/checkpoints/model.ckpt
logdir
=
"/"
.
join
(
paths
[:
idx
])
ckpt
=
opt
.
resume
else
:
assert
os
.
path
.
isdir
(
opt
.
resume
),
opt
.
resume
logdir
=
opt
.
resume
.
rstrip
(
"/"
)
ckpt
=
os
.
path
.
join
(
logdir
,
"checkpoints"
,
"last.ckpt"
)
print
(
f
"logdir:
{
logdir
}
"
)
base_configs
=
sorted
(
glob
.
glob
(
os
.
path
.
join
(
logdir
,
"configs/*-project.yaml"
)))
opt
.
base
=
base_configs
+
opt
.
base
if
opt
.
config
:
if
type
(
opt
.
config
)
==
str
:
opt
.
base
=
[
opt
.
config
]
else
:
opt
.
base
=
[
opt
.
base
[
-
1
]]
configs
=
[
OmegaConf
.
load
(
cfg
)
for
cfg
in
opt
.
base
]
cli
=
OmegaConf
.
from_dotlist
(
unknown
)
if
opt
.
ignore_base_data
:
for
config
in
configs
:
if
hasattr
(
config
,
"data"
):
del
config
[
"data"
]
config
=
OmegaConf
.
merge
(
*
configs
,
cli
)
st
.
sidebar
.
text
(
ckpt
)
gs
=
st
.
sidebar
.
empty
()
gs
.
text
(
f
"Global step: ?"
)
st
.
sidebar
.
text
(
"Options"
)
#gpu = st.sidebar.checkbox("GPU", value=True)
gpu
=
True
#eval_mode = st.sidebar.checkbox("Eval Mode", value=True)
eval_mode
=
True
#show_config = st.sidebar.checkbox("Show Config", value=False)
show_config
=
False
if
show_config
:
st
.
info
(
"Checkpoint: {}"
.
format
(
ckpt
))
st
.
json
(
OmegaConf
.
to_container
(
config
))
dsets
,
model
,
global_step
=
load_model_and_dset
(
config
,
ckpt
,
gpu
,
eval_mode
)
gs
.
text
(
f
"Global step:
{
global_step
}
"
)
run_conditional
(
model
,
dsets
)
scripts/sample_fast.py
0 → 100644
View file @
031cd626
import
argparse
,
os
,
sys
,
glob
import
torch
import
time
import
numpy
as
np
from
omegaconf
import
OmegaConf
from
PIL
import
Image
from
tqdm
import
tqdm
,
trange
from
einops
import
repeat
from
main
import
instantiate_from_config
from
taming.modules.transformer.mingpt
import
sample_with_past
rescale
=
lambda
x
:
(
x
+
1.
)
/
2.
def
chw_to_pillow
(
x
):
return
Image
.
fromarray
((
255
*
rescale
(
x
.
detach
().
cpu
().
numpy
().
transpose
(
1
,
2
,
0
))).
clip
(
0
,
255
).
astype
(
np
.
uint8
))
@
torch
.
no_grad
()
def
sample_classconditional
(
model
,
batch_size
,
class_label
,
steps
=
256
,
temperature
=
None
,
top_k
=
None
,
callback
=
None
,
dim_z
=
256
,
h
=
16
,
w
=
16
,
verbose_time
=
False
,
top_p
=
None
):
log
=
dict
()
assert
type
(
class_label
)
==
int
,
f
'expecting type int but type is
{
type
(
class_label
)
}
'
qzshape
=
[
batch_size
,
dim_z
,
h
,
w
]
assert
not
model
.
be_unconditional
,
'Expecting a class-conditional Net2NetTransformer.'
c_indices
=
repeat
(
torch
.
tensor
([
class_label
]),
'1 -> b 1'
,
b
=
batch_size
).
to
(
model
.
device
)
# class token
t1
=
time
.
time
()
index_sample
=
sample_with_past
(
c_indices
,
model
.
transformer
,
steps
=
steps
,
sample_logits
=
True
,
top_k
=
top_k
,
callback
=
callback
,
temperature
=
temperature
,
top_p
=
top_p
)
if
verbose_time
:
sampling_time
=
time
.
time
()
-
t1
print
(
f
"Full sampling takes about
{
sampling_time
:.
2
f
}
seconds."
)
x_sample
=
model
.
decode_to_img
(
index_sample
,
qzshape
)
log
[
"samples"
]
=
x_sample
log
[
"class_label"
]
=
c_indices
return
log
@
torch
.
no_grad
()
def
sample_unconditional
(
model
,
batch_size
,
steps
=
256
,
temperature
=
None
,
top_k
=
None
,
top_p
=
None
,
callback
=
None
,
dim_z
=
256
,
h
=
16
,
w
=
16
,
verbose_time
=
False
):
log
=
dict
()
qzshape
=
[
batch_size
,
dim_z
,
h
,
w
]
assert
model
.
be_unconditional
,
'Expecting an unconditional model.'
c_indices
=
repeat
(
torch
.
tensor
([
model
.
sos_token
]),
'1 -> b 1'
,
b
=
batch_size
).
to
(
model
.
device
)
# sos token
t1
=
time
.
time
()
index_sample
=
sample_with_past
(
c_indices
,
model
.
transformer
,
steps
=
steps
,
sample_logits
=
True
,
top_k
=
top_k
,
callback
=
callback
,
temperature
=
temperature
,
top_p
=
top_p
)
if
verbose_time
:
sampling_time
=
time
.
time
()
-
t1
print
(
f
"Full sampling takes about
{
sampling_time
:.
2
f
}
seconds."
)
x_sample
=
model
.
decode_to_img
(
index_sample
,
qzshape
)
log
[
"samples"
]
=
x_sample
return
log
@
torch
.
no_grad
()
def
run
(
logdir
,
model
,
batch_size
,
temperature
,
top_k
,
unconditional
=
True
,
num_samples
=
50000
,
given_classes
=
None
,
top_p
=
None
):
batches
=
[
batch_size
for
_
in
range
(
num_samples
//
batch_size
)]
+
[
num_samples
%
batch_size
]
if
not
unconditional
:
assert
given_classes
is
not
None
print
(
"Running in pure class-conditional sampling mode. I will produce "
f
"
{
num_samples
}
samples for each of the
{
len
(
given_classes
)
}
classes, "
f
"i.e.
{
num_samples
*
len
(
given_classes
)
}
in total."
)
for
class_label
in
tqdm
(
given_classes
,
desc
=
"Classes"
):
for
n
,
bs
in
tqdm
(
enumerate
(
batches
),
desc
=
"Sampling Class"
):
if
bs
==
0
:
break
logs
=
sample_classconditional
(
model
,
batch_size
=
bs
,
class_label
=
class_label
,
temperature
=
temperature
,
top_k
=
top_k
,
top_p
=
top_p
)
save_from_logs
(
logs
,
logdir
,
base_count
=
n
*
batch_size
,
cond_key
=
logs
[
"class_label"
])
else
:
print
(
f
"Running in unconditional sampling mode, producing
{
num_samples
}
samples."
)
for
n
,
bs
in
tqdm
(
enumerate
(
batches
),
desc
=
"Sampling"
):
if
bs
==
0
:
break
logs
=
sample_unconditional
(
model
,
batch_size
=
bs
,
temperature
=
temperature
,
top_k
=
top_k
,
top_p
=
top_p
)
save_from_logs
(
logs
,
logdir
,
base_count
=
n
*
batch_size
)
def
save_from_logs
(
logs
,
logdir
,
base_count
,
key
=
"samples"
,
cond_key
=
None
):
xx
=
logs
[
key
]
for
i
,
x
in
enumerate
(
xx
):
x
=
chw_to_pillow
(
x
)
count
=
base_count
+
i
if
cond_key
is
None
:
x
.
save
(
os
.
path
.
join
(
logdir
,
f
"
{
count
:
06
}
.png"
))
else
:
condlabel
=
cond_key
[
i
]
if
type
(
condlabel
)
==
torch
.
Tensor
:
condlabel
=
condlabel
.
item
()
os
.
makedirs
(
os
.
path
.
join
(
logdir
,
str
(
condlabel
)),
exist_ok
=
True
)
x
.
save
(
os
.
path
.
join
(
logdir
,
str
(
condlabel
),
f
"
{
count
:
06
}
.png"
))
def
get_parser
():
def
str2bool
(
v
):
if
isinstance
(
v
,
bool
):
return
v
if
v
.
lower
()
in
(
"yes"
,
"true"
,
"t"
,
"y"
,
"1"
):
return
True
elif
v
.
lower
()
in
(
"no"
,
"false"
,
"f"
,
"n"
,
"0"
):
return
False
else
:
raise
argparse
.
ArgumentTypeError
(
"Boolean value expected."
)
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"-r"
,
"--resume"
,
type
=
str
,
nargs
=
"?"
,
help
=
"load from logdir or checkpoint in logdir"
,
)
parser
.
add_argument
(
"-o"
,
"--outdir"
,
type
=
str
,
nargs
=
"?"
,
help
=
"path where the samples will be logged to."
,
default
=
""
)
parser
.
add_argument
(
"-b"
,
"--base"
,
nargs
=
"*"
,
metavar
=
"base_config.yaml"
,
help
=
"paths to base configs. Loaded from left-to-right. "
"Parameters can be overwritten or added with command-line options of the form `--key value`."
,
default
=
list
(),
)
parser
.
add_argument
(
"-n"
,
"--num_samples"
,
type
=
int
,
nargs
=
"?"
,
help
=
"num_samples to draw"
,
default
=
50000
)
parser
.
add_argument
(
"--batch_size"
,
type
=
int
,
nargs
=
"?"
,
help
=
"the batch size"
,
default
=
25
)
parser
.
add_argument
(
"-k"
,
"--top_k"
,
type
=
int
,
nargs
=
"?"
,
help
=
"top-k value to sample with"
,
default
=
250
,
)
parser
.
add_argument
(
"-t"
,
"--temperature"
,
type
=
float
,
nargs
=
"?"
,
help
=
"temperature value to sample with"
,
default
=
1.0
)
parser
.
add_argument
(
"-p"
,
"--top_p"
,
type
=
float
,
nargs
=
"?"
,
help
=
"top-p value to sample with"
,
default
=
1.0
)
parser
.
add_argument
(
"--classes"
,
type
=
str
,
nargs
=
"?"
,
help
=
"specify comma-separated classes to sample from. Uses 1000 classes per default."
,
default
=
"imagenet"
)
return
parser
def
load_model_from_config
(
config
,
sd
,
gpu
=
True
,
eval_mode
=
True
):
model
=
instantiate_from_config
(
config
)
if
sd
is
not
None
:
model
.
load_state_dict
(
sd
)
if
gpu
:
model
.
cuda
()
if
eval_mode
:
model
.
eval
()
return
{
"model"
:
model
}
def
load_model
(
config
,
ckpt
,
gpu
,
eval_mode
):
# load the specified checkpoint
if
ckpt
:
pl_sd
=
torch
.
load
(
ckpt
,
map_location
=
"cpu"
)
global_step
=
pl_sd
[
"global_step"
]
print
(
f
"loaded model from global step
{
global_step
}
."
)
else
:
pl_sd
=
{
"state_dict"
:
None
}
global_step
=
None
model
=
load_model_from_config
(
config
.
model
,
pl_sd
[
"state_dict"
],
gpu
=
gpu
,
eval_mode
=
eval_mode
)[
"model"
]
return
model
,
global_step
if
__name__
==
"__main__"
:
sys
.
path
.
append
(
os
.
getcwd
())
parser
=
get_parser
()
opt
,
unknown
=
parser
.
parse_known_args
()
assert
opt
.
resume
ckpt
=
None
if
not
os
.
path
.
exists
(
opt
.
resume
):
raise
ValueError
(
"Cannot find {}"
.
format
(
opt
.
resume
))
if
os
.
path
.
isfile
(
opt
.
resume
):
paths
=
opt
.
resume
.
split
(
"/"
)
try
:
idx
=
len
(
paths
)
-
paths
[::
-
1
].
index
(
"logs"
)
+
1
except
ValueError
:
idx
=
-
2
# take a guess: path/to/logdir/checkpoints/model.ckpt
logdir
=
"/"
.
join
(
paths
[:
idx
])
ckpt
=
opt
.
resume
else
:
assert
os
.
path
.
isdir
(
opt
.
resume
),
opt
.
resume
logdir
=
opt
.
resume
.
rstrip
(
"/"
)
ckpt
=
os
.
path
.
join
(
logdir
,
"checkpoints"
,
"last.ckpt"
)
base_configs
=
sorted
(
glob
.
glob
(
os
.
path
.
join
(
logdir
,
"configs/*-project.yaml"
)))
print
(
base_configs
)
opt
.
base
=
base_configs
+
opt
.
base
configs
=
[
OmegaConf
.
load
(
cfg
)
for
cfg
in
opt
.
base
]
cli
=
OmegaConf
.
from_dotlist
(
unknown
)
config
=
OmegaConf
.
merge
(
*
configs
,
cli
)
model
,
global_step
=
load_model
(
config
,
ckpt
,
gpu
=
True
,
eval_mode
=
True
)
if
opt
.
outdir
:
print
(
f
"Switching logdir from '
{
logdir
}
' to '
{
opt
.
outdir
}
'"
)
logdir
=
opt
.
outdir
if
opt
.
classes
==
"imagenet"
:
given_classes
=
[
i
for
i
in
range
(
1000
)]
else
:
cls_str
=
opt
.
classes
assert
not
cls_str
.
endswith
(
","
),
'class string should not end with a ","'
given_classes
=
[
int
(
c
)
for
c
in
cls_str
.
split
(
","
)]
logdir
=
os
.
path
.
join
(
logdir
,
"samples"
,
f
"top_k_
{
opt
.
top_k
}
_temp_
{
opt
.
temperature
:.
2
f
}
_top_p_
{
opt
.
top_p
}
"
,
f
"
{
global_step
}
"
)
print
(
f
"Logging to
{
logdir
}
"
)
os
.
makedirs
(
logdir
,
exist_ok
=
True
)
run
(
logdir
,
model
,
opt
.
batch_size
,
opt
.
temperature
,
opt
.
top_k
,
unconditional
=
model
.
be_unconditional
,
given_classes
=
given_classes
,
num_samples
=
opt
.
num_samples
,
top_p
=
opt
.
top_p
)
print
(
"done."
)
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