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View Code? Open in Web Editor NEWPULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Hi, I found this work is really interesting and I'm impressed by its effects.
However, I'm curious about the difference between PULSE and Zhu et al. Generative Visual Manipulation on the Natural Image Manifold that published in ECCV2016.
They both try to find the closet latent vector z that able to generated images lie in the natural image manifold.
I'm not very familiar with the styleGAN arch itself, i was just trying this out for fun. of all the cli arguments, the only one that didn't have a default value was tile_latent, looking the rest of the code, it's clear this is supposed to be a boolean value, and the description stated, "Whether to forcibly tile the same latent 18 times". I'm not sure what this signifies, could someone briefly explain the effect of setting this to either true or false
Hello, everyone, can you share the data downloaded from this model
conda env create -f environment.yml
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/conda/exceptions.py", line 1079, in __call__
return func(*args, **kwargs)
File "/usr/local/lib/python3.7/site-packages/conda_env/cli/main.py", line 80, in do_call
exit_code = getattr(module, func_name)(args, parser)
File "/usr/local/lib/python3.7/site-packages/conda_env/cli/main_create.py", line 80, in execute
directory=os.getcwd())
File "/usr/local/lib/python3.7/site-packages/conda_env/specs/__init__.py", line 40, in detect
if spec.can_handle():
File "/usr/local/lib/python3.7/site-packages/conda_env/specs/yaml_file.py", line 18, in can_handle
self._environment = env.from_file(self.filename)
File "/usr/local/lib/python3.7/site-packages/conda_env/env.py", line 151, in from_file
return from_yaml(yamlstr, filename=filename)
File "/usr/local/lib/python3.7/site-packages/conda_env/env.py", line 136, in from_yaml
data = yaml_load_standard(yamlstr)
File "/usr/local/lib/python3.7/site-packages/conda/common/serialize.py", line 76, in yaml_load_standard
return yaml.load(string, Loader=yaml.Loader, version="1.2")
File "/usr/local/lib/python3.7/site-packages/ruamel_yaml/main.py", line 935, in load
return loader._constructor.get_single_data()
File "/usr/local/lib/python3.7/site-packages/ruamel_yaml/constructor.py", line 109, in get_single_data
node = self.composer.get_single_node()
File "/usr/local/lib/python3.7/site-packages/ruamel_yaml/composer.py", line 78, in get_single_node
document = self.compose_document()
File "/usr/local/lib/python3.7/site-packages/ruamel_yaml/composer.py", line 104, in compose_document
self.parser.get_event()
File "/usr/local/lib/python3.7/site-packages/ruamel_yaml/parser.py", line 163, in get_event
self.current_event = self.state()
File "/usr/local/lib/python3.7/site-packages/ruamel_yaml/parser.py", line 239, in parse_document_end
token = self.scanner.peek_token()
File "/usr/local/lib/python3.7/site-packages/ruamel_yaml/scanner.py", line 182, in peek_token
self.fetch_more_tokens()
File "/usr/local/lib/python3.7/site-packages/ruamel_yaml/scanner.py", line 282, in fetch_more_tokens
return self.fetch_value()
File "/usr/local/lib/python3.7/site-packages/ruamel_yaml/scanner.py", line 655, in fetch_value
self.reader.get_mark(),
ruamel_yaml.scanner.ScannerError: mapping values are not allowed here
in "<unicode string>", line 32, column 44:
... ame="description" content="PULSE: Self-Supervised Photo Upsampli ...
^ (line: 32)
$ /usr/local/bin/conda-env create -f environment.yml
environment variables:
CIO_TEST=
CONDA_AUTO_UPDATE_CONDA=false
CONDA_ROOT=/usr/local
LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
LIBRARY_PATH=/usr/local/cuda/lib64/stubs
PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/b
in:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-
sdk/bin:/opt/bin
PYTHONPATH=/env/python
PYTHONWARNINGS=ignore:::pip._internal.cli.base_command
REQUESTS_CA_BUNDLE=
SSL_CERT_FILE=
active environment : None
user config file : /root/.condarc
populated config files : /root/.condarc
conda version : 4.8.2
conda-build version : not installed
python version : 3.7.6.final.0
virtual packages : __glibc=2.27
base environment : /usr/local (writable)
channel URLs : https://conda.anaconda.org/conda-forge/linux-64
https://conda.anaconda.org/conda-forge/noarch
https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /usr/local/pkgs
/root/.conda/pkgs
envs directories : /usr/local/envs
/root/.conda/envs
platform : linux-64
user-agent : conda/4.8.2 requests/2.22.0 CPython/3.7.6 Linux/4.19.104+ ubuntu/18.04.3 glibc/2.27
UID:GID : 0:0
netrc file : None
offline mode : False
An unexpected error has occurred. Conda has prepared the above report.
I'm running on a single image and getting the following error.
Traceback (most recent call last):
File "run.py", line 79, in <module>
for j,(HR,LR) in enumerate(model(ref_im,**kwargs)):
File "/home/ubuntu/pulse/PULSE.py", line 149, in forward
loss, loss_dict = loss_builder(latent_in, gen_im)
File "/home/ubuntu/miniconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/ubuntu/pulse/loss.py", line 54, in forward
tmp_loss = loss_fun_dict[loss_type](**var_dict)
File "/home/ubuntu/pulse/loss.py", line 23, in _loss_l2
return ((gen_im_lr - ref_im).pow(2).mean((1, 2, 3)).clamp(min=self.eps).sum())
RuntimeError: The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 1
I printed the variables gen_im_lr
& ref_im
and got the following:
torch.Size([1, 3, 32, 32]) torch.Size([1, 4, 32, 32])
Thanks for sharing the code, great job! Here is my question:
Have you tested the Gaussian mean face. It looks quite bad from my side. Is there a huge gap between this version of style GAN and the original version of tensorflow?
when i run align_face.py to downscaled the pics.when i deal 2 pics in run.py, i got the result:
Loading Synthesis Network
Optimizing
Traceback (most recent call last):
File "./run.py", line 84, in
for j,(HR,LR) in enumerate(model(ref_im,**kwargs)):
File "/usr/local/src/pulse/PULSE.py", line 149, in forward
gen_im = (self.synthesis(latent_in, noise)+1)/2
File "/root/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in call
result = self.forward(input, **kwargs)
File "/usr/local/src/pulse/stylegan.py", line 408, in forward
x = m(x, dlatents_in[:, 2i:2i+2], noise_in[2i:2*i+2])
File "/root/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in call
result = self.forward(*input, **kwargs)
File "/usr/local/src/pulse/stylegan.py", line 337, in forward
x = self.epi1(x, dlatents_in_range[:, 0], noise_in_range[0])
File "/root/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in call
result = self.forward(*input, **kwargs)
File "/usr/local/src/pulse/stylegan.py", line 277, in forward
x = self.top_epi(x)
File "/root/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in call
result = self.forward(*input, **kwargs)
File "/root/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/container.py", line 100, in forward
input = module(input)
File "/root/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in call
result = self.forward(*input, **kwargs)
File "/root/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/instancenorm.py", line 47, in forward
return F.instance_norm(
File "/root/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/functional.py", line 1943, in instance_norm
return torch.instance_norm(
RuntimeError: CUDA out of memory. Tried to allocate 16.00 MiB (GPU 0; 1024.00 MiB total capacity; 400.35 MiB already allocated; 2.86 MiB free; 462.00 MiB reserved in total by PyTorch)
my gpu memory is 1GB,i change the steps to 10, and the error is still,how to resolve it?ths
Hi,
After reading your paper, the DIP algorithm comes up in my mind. Could you help point out the similarity and difference between PULSE and DIP?
Thanks a lot for the great work.
is it possible to add license information to the repo?
Can the inference scripts and pretrained weights be shared?
Need a Chinese README.md?
If needed, I can creat a pull requests.😋
Did the experiment results are reported on the whole CelebA-HQ dataset? It consists of 30,000 images and will take a long time, I wonder whether you use a portation of the dataset in the experiment just like "Image Processing Using Multi-Code GAN Prior" (300 images for testing)
Unfortunately I don't have a GPU. Thus, I would like to suggest to bring back the web app for people to directly try pulse. Any chances?
Loading Synthesis Network
Traceback (most recent call last):
File "./run.py", line 79, in
for j,(HR,LR) in enumerate(model(ref_im,**kwargs)):
File "/usr/local/src/pulse/PULSE.py", line 126, in forward
loss_builder = LossBuilder(ref_im, loss_str, eps).cuda()
File "/usr/local/src/pulse/loss.py", line 7, in init
assert ref_im.shape[2]==ref_im.shape[3]
AssertionError
谷歌下载过多…
Will this work with a custom trained StyleGAN model using NVidia's StyleGAN repo? Will StyleGAN2 work?
Because the face is very European and American, I want to create Asian face
I have been trying to test a single photo for a couple of days now, but the quota is always exceeded.
What is the time (+timezone) the daily quota resets? This way I can try again right at that moment.
Best regards
If I try to use the pre-trained model I get this error
RuntimeError: CUDA out of memory. Tried to allocate 64.00 MiB (GPU 0; 1.95 GiB total capacity; 1.19 GiB already allocated; 57.31 MiB free; 1.25 GiB reserved in total by PyTorch)
My graphic card is a NVIDIA GeForce GTX 750 Ti with 2 GB of VRAM: is there a configuration that allows to use the model with this amount of VRAM ?
Hello Alex!
Just got this weird issue.
I indeed used align_face.py to generate the input pics from realpics folder, but when I was using run.py to upscale those pics, it keeps saying 'Could not find a face that downscales correctly within epsilon'...
Did I do something wrong? Or is there any specific requirement for original mosaic photos?
Thank you for your impressive work and for sharing the code.
I put two png images into input
folder:
size | 256x256 | 32x32 |
---|---|---|
input | ||
result |
Moreover if I downscale enhanced image back to 32x32 I got very different thumbnail from the original one:
Could you assist me what am I doing wrong and how to reproduce results from paper?
Loading Synthesis Network
Traceback (most recent call last):
File "run.py", line 58, in
model = PULSE(cache_dir=kwargs["cache_dir"])
File "D:\pulse\pulse\PULSE.py", line 24, in init
self.synthesis.load_state_dict(torch.load(f))
File "D:\ProgramData\Anaconda3\lib\site-packages\torch\serialization.py", line 593, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "D:\ProgramData\Anaconda3\lib\site-packages\torch\serialization.py", line 763, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, '\x01'.
What should I do about it?
thx ..
When running 'run.py' for the first time it gives a "Google Drive quota exceeded" message when trying to download the model. The instructions describe how to setup alternative links for these. Is there an alternative url/location I can use or any other way to get this data?
It usually works the first time, but then every time after it gives me this error:
File "run.py", line 79, in <module>
for j,(HR,LR) in enumerate(model(ref_im,**kwargs)):
File "/pulse/PULSE.py", line 149, in forward
gen_im = (self.synthesis(latent_in, noise)+1)/2
File "/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/pulse/stylegan.py", line 408, in forward
x = m(x, dlatents_in[:, 2*i:2*i+2], noise_in[2*i:2*i+2])
File "/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/pulse/stylegan.py", line 339, in forward
x = self.epi2(x, dlatents_in_range[:, 1], noise_in_range[1])
File "/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/pulse/stylegan.py", line 279, in forward
x = self.style_mod(x, dlatents_in_slice)
File "/anaconda3/envs/pulse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/pulse/stylegan.py", line 135, in forward
x = x * (style[:, 0] + 1.) + style[:, 1]
RuntimeError: CUDA out of memory. Tried to allocate 64.00 MiB (GPU 0; 2.94 GiB total capacity; 1.41 GiB already allocated; 65.19 MiB free; 1.55 GiB reserved in total by PyTorch)
even with the same picture that worked before.
nvidia-smi
doesn't show any usage from python or pytorch.
Is there any way to replace the google drive links for a simple URL ei: mydomain.com/file ?
just to avoid out of capacity issues, if someone is interested i can donate an S3 bucket to provide a fast and reliable way to host and share the pre trained model for align_face.py and PULSE.py
Cannot run align_face.
(pulse) root@e2bbc3333453:/home/pulse# python align_face.py
Downloading Shape Predictor
Downloading https://drive.google.com/uc?id=1huhv8PYpNNKbGCLOaYUjOgR1pY5pmbJx ............ failed
Traceback (most recent call last):
File "align_face.py", line 34, in <module>
f=open_url("https://drive.google.com/uc?id=1huhv8PYpNNKbGCLOaYUjOgR1pY5pmbJx", cache_dir=cache_dir, return_path=True)
File "/home/pulse/drive.py", line 66, in open_url
raise IOError("Google Drive quota exceeded")
OSError: Google Drive quota exceeded
Probably sharing a new link would fix this.
About another matter, maybe it would be best to warn people to put their input images as png. It took me a while to understand why my images weren't processed when runnin "python run.py'.
My computer runs on Ubuntu 18.04 with CUDA 10.2
I've already removed some hash tag for packages.
However, there are still a lot of package conflicts.
I wonder there is a .yml file for Ubuntu users or any other install methods?
I would like to know how could you get the pre-trianed styleGAN model? Is it convert from the tensorflow model?
I was able to download the three files required by align_face.py and PULSE.py, then I was able to downsample 3 images with no problem, but when I try to run "run.py", I get an error:
Loading Synthesis Network
Downloading https://drive.google.com/uc?id=1xKDEzjfGPa1s1iDYucH4eSxLvl1MuLE1 ... done
Traceback (most recent call last):
File "run.py", line 79, in
for j,(HR,LR) in enumerate(model(ref_im,**kwargs)):
File "/home/pyimagesearch/Downloads/pulse-master/PULSE.py", line 114, in forward
opt = SphericalOptimizer(opt_func, var_list, lr=learning_rate)
File "/home/pyimagesearch/Downloads/pulse-master/SphericalOptimizer.py", line 17, in init
self.radii = {param: (param.pow(2).sum(tuple(range(2,param.ndim)),keepdim=True)+1e-9).sqrt() for param in params}
File "/home/pyimagesearch/Downloads/pulse-master/SphericalOptimizer.py", line 17, in
self.radii = {param: (param.pow(2).sum(tuple(range(2,param.ndim)),keepdim=True)+1e-9).sqrt() for param in params}
AttributeError: 'Tensor' object has no attribute 'ndim'
I´m using a Jetson Nano, with CUDA and all the modules and dependences required by the program.
Some tip?
It turns out, as long as you have GTX 970 or higher, prerequisites installation is quite simple
Looks like there is no need to install CUDA SDK to use precompiled pytorch on Windows.
So, from clean slate:
from admin cmd
@"%SystemRoot%\System32\WindowsPowerShell\v1.0\powershell.exe" -NoProfile -InputFormat None -ExecutionPolicy Bypass -Command "[System.Net.ServicePointManager]::SecurityProtocol = 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1'))" && SET "PATH=%PATH%;%ALLUSERSPROFILE%\chocolatey\bin"
or from admin PowerShell
Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1'))
cinst -y visualstudio2019community visualstudio2017-workload-nativedesktop
to install Visual Studio with C++ compiler. Then reboot and, again, from admin cmd/Powershell
cinst -y python
cinst -y cmake --installargs 'ADD_CMAKE_TO_PATH=System'
from non-admin cmd/PowerShell
pip install numpy
pip install scipy
pip install https://download.pytorch.org/whl/cu102/torch-1.5.0-cp38-cp38-win_amd64.whl
pip install https://download.pytorch.org/whl/cu102/torchvision-0.6.0-cp38-cp38-win_amd64.whl
pip install pillow
pip install dlib
Everything's ready for use, from Data section of the Readme.
Warning, though, for some reason dlib module align_face.py
didn't work well with pngs and bmps. So jpg realpics it is.
When i do "conda env create -n pulse -f pulse.yml"
i get this very long error: Ran pip subprocess with arguments:
['D:\Program Files\Anaconda\envs\pulse\python.exe', '-m', 'pip', 'install', '-U', '-r', 'C:\Users\gijse\Downloads\PULSE\pulse-master\condaenv.93ram30n.requirements.txt']
Pip subprocess output:
Collecting dlib==19.19.0
Downloading dlib-19.19.0.tar.gz (3.2 MB)
Building wheels for collected packages: dlib
Building wheel for dlib (setup.py): started
Building wheel for dlib (setup.py): finished with status 'error'
Running setup.py clean for dlib
Failed to build dlib
Installing collected packages: dlib
Running setup.py install for dlib: started
Running setup.py install for dlib: finished with status 'error'
Pip subprocess error:
ERROR: Command errored out with exit status 1:
command: 'D:\Program Files\Anaconda\envs\pulse\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py'"'"'; file='"'"'C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\gijse\AppData\Local\Temp\pip-wheel-i0g75ms_'
cwd: C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib
Complete output (53 lines):
running bdist_wheel
running build
running build_py
package init file 'dlib_init_.py' not found (or not a regular file)
running build_ext
Traceback (most recent call last):
File "C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py", line 120, in get_cmake_version
out = subprocess.check_output(['cmake', '--version'])
File "D:\Program Files\Anaconda\envs\pulse\lib\subprocess.py", line 411, in check_output
return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,
File "D:\Program Files\Anaconda\envs\pulse\lib\subprocess.py", line 489, in run
with Popen(*popenargs, **kwargs) as process:
File "D:\Program Files\Anaconda\envs\pulse\lib\subprocess.py", line 854, in init
self._execute_child(args, executable, preexec_fn, close_fds,
File "D:\Program Files\Anaconda\envs\pulse\lib\subprocess.py", line 1307, in _execute_child
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
FileNotFoundError: [WinError 2] The system cannot find the file specified
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "", line 1, in
File "C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py", line 223, in
setup(
File "D:\Program Files\Anaconda\envs\pulse\lib\site-packages\setuptools_init_.py", line 161, in setup
return distutils.core.setup(attrs)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\core.py", line 148, in setup
dist.run_commands()
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\dist.py", line 966, in run_commands
self.run_command(cmd)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\dist.py", line 985, in run_command
cmd_obj.run()
File "D:\Program Files\Anaconda\envs\pulse\lib\site-packages\wheel\bdist_wheel.py", line 223, in run
self.run_command('build')
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\dist.py", line 985, in run_command
cmd_obj.run()
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\command\build.py", line 135, in run
self.run_command(cmd_name)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\dist.py", line 985, in run_command
cmd_obj.run()
File "C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py", line 129, in run
cmake_version = self.get_cmake_version()
File "C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py", line 122, in get_cmake_version
raise RuntimeError("\n*****************************************************************\n" +
RuntimeError:
CMake must be installed to build the following extensions: dlib
ERROR: Failed building wheel for dlib
ERROR: Command errored out with exit status 1:
command: 'D:\Program Files\Anaconda\envs\pulse\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py'"'"'; file='"'"'C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record 'C:\Users\gijse\AppData\Local\Temp\pip-record-637lz1en\install-record.txt' --single-version-externally-managed --compile --install-headers 'D:\Program Files\Anaconda\envs\pulse\Include\dlib'
cwd: C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib
Complete output (55 lines):
running install
running build
running build_py
package init file 'dlib_init_.py' not found (or not a regular file)
running build_ext
Traceback (most recent call last):
File "C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py", line 120, in get_cmake_version
out = subprocess.check_output(['cmake', '--version'])
File "D:\Program Files\Anaconda\envs\pulse\lib\subprocess.py", line 411, in check_output
return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,
File "D:\Program Files\Anaconda\envs\pulse\lib\subprocess.py", line 489, in run
with Popen(*popenargs, **kwargs) as process:
File "D:\Program Files\Anaconda\envs\pulse\lib\subprocess.py", line 854, in init
self._execute_child(args, executable, preexec_fn, close_fds,
File "D:\Program Files\Anaconda\envs\pulse\lib\subprocess.py", line 1307, in _execute_child
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
FileNotFoundError: [WinError 2] The system cannot find the file specified
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py", line 223, in <module>
setup(
File "D:\Program Files\Anaconda\envs\pulse\lib\site-packages\setuptools\__init__.py", line 161, in setup
return distutils.core.setup(**attrs)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\core.py", line 148, in setup
dist.run_commands()
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\dist.py", line 966, in run_commands
self.run_command(cmd)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\dist.py", line 985, in run_command
cmd_obj.run()
File "D:\Program Files\Anaconda\envs\pulse\lib\site-packages\setuptools\command\install.py", line 61, in run
return orig.install.run(self)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\command\install.py", line 545, in run
self.run_command('build')
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\dist.py", line 985, in run_command
cmd_obj.run()
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\command\build.py", line 135, in run
self.run_command(cmd_name)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "D:\Program Files\Anaconda\envs\pulse\lib\distutils\dist.py", line 985, in run_command
cmd_obj.run()
File "C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py", line 129, in run
cmake_version = self.get_cmake_version()
File "C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py", line 122, in get_cmake_version
raise RuntimeError("\n*******************************************************************\n" +
RuntimeError:
*******************************************************************
CMake must be installed to build the following extensions: dlib
*******************************************************************
----------------------------------------
ERROR: Command errored out with exit status 1: 'D:\Program Files\Anaconda\envs\pulse\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py'"'"'; file='"'"'C:\Users\gijse\AppData\Local\Temp\pip-install-bh9nnp7l\dlib\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record 'C:\Users\gijse\AppData\Local\Temp\pip-record-637lz1en\install-record.txt' --single-version-externally-managed --compile --install-headers 'D:\Program Files\Anaconda\envs\pulse\Include\dlib' Check the logs for full command output.
Great tks for your excellent work.
I have a question about the 'linear1cycledrop' that why you use schedule like this since it makes learning rate start at very low and get peak at the steps/2
and then linearly get down to the nearly end and linearly drop with another rate.
Is this a great trick in this task : -) ?
Hi,
Thanks for great work. Can you explain me a bit how I can train pulse on my own dataset? Which one of the files is training code? for some reason, I couldn't figure out. Thanks.
Thanks for correcting me if I miss anything.
@adamian98 @georgzoeller
Hi there,
I can’t download the pre-trained model here. How do I download it?
You said it's been tested on linux and windows but many packages only exist in osx channel
I had trained my own stylegan model, and convert the pkl file to pt format by using stylegan-pytorch project.
But when I run PULSE, it come up with some errors as follow:
Loading Synthesis Network
Traceback (most recent call last):
File "run.py", line 58, in
model = PULSE(cache_dir=kwargs["cache_dir"])
File "H:\Pulse\PULSE.py", line 25, in init
self.synthesis.load_state_dict(torch.load("./models/karras2019stylegan-ffhq-1024x1024.for_g_all.pt"))
File "D:\Anaconda3\envs\pulse\lib\site-packages\torch\nn\modules\module.py", line 846, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for G_synthesis:
Missing key(s) in state_dict: "torgb.weight", "torgb.bias", "blocks.4x4.const", "blocks.4x4.bias", "blocks.4x4.epi1.noise.weight", "blocks.4x4.epi1.style_mod.lin.weight", "blocks.4x4.epi1.style_mod.lin.bias", "blocks.4x4.conv.weight", "blocks.4x4.conv.bias", "blocks.4x4.epi2.noise.weight", "blocks.4x4.epi2.style_mod.lin.weight", "blocks.4x4.epi2.style_mod.lin.bias", "blocks.8x8.conv0_up.weight", "blocks.8x8.conv0_up.bias", "blocks.8x8.conv0_up.intermediate.kernel", "blocks.8x8.epi1.noise.weight", "blocks.8x8.epi1.style_mod.lin.weight", "blocks.8x8.epi1.style_mod.lin.bias", "blocks.8x8.conv1.weight", "blocks.8x8.conv1.bias", "blocks.8x8.epi2.noise.weight", "blocks.8x8.epi2.style_mod.lin.weight", "blocks.8x8.epi2.style_mod.lin.bias", "blocks.16x16.conv0_up.weight", "blocks.16x16.conv0_up.bias", "blocks.16x16.conv0_up.intermediate.kernel", "blocks.16x16.epi1.noise.weight", "blocks.16x16.epi1.style_mod.lin.weight", "blocks.16x16.epi1.style_mod.lin.bias", "blocks.16x16.conv1.weight", "blocks.16x16.conv1.bias", "blocks.16x16.epi2.noise.weight", "blocks.16x16.epi2.style_mod.lin.weight", "blocks.16x16.epi2.style_mod.lin.bias", "blocks.32x32.conv0_up.weight", "blocks.32x32.conv0_up.bias", "blocks.32x32.conv0_up.intermediate.kernel", "blocks.32x32.epi1.noise.weight", "blocks.32x32.epi1.style_mod.lin.weight", "blocks.32x32.epi1.style_mod.lin.bias", "blocks.32x32.conv1.weight", "blocks.32x32.conv1.bias", "blocks.32x32.epi2.noise.weight", "blocks.32x32.epi2.style_mod.lin.weight", "blocks.32x32.epi2.style_mod.lin.bias", "blocks.64x64.conv0_up.weight", "blocks.64x64.conv0_up.bias", "blocks.64x64.conv0_up.intermediate.kernel", "blocks.64x64.epi1.noise.weight", "blocks.64x64.epi1.style_mod.lin.weight", "blocks.64x64.epi1.style_mod.lin.bias", "blocks.64x64.conv1.weight", "blocks.64x64.conv1.bias", "blocks.64x64.epi2.noise.weight", "blocks.64x64.epi2.style_mod.lin.weight", "blocks.64x64.epi2.style_mod.lin.bias", "blocks.128x128.conv0_up.weight", "blocks.128x128.conv0_up.bias", "blocks.128x128.conv0_up.intermediate.kernel", "blocks.128x128.epi1.noise.weight", "blocks.128x128.epi1.style_mod.lin.weight", "blocks.128x128.epi1.style_mod.lin.bias", "blocks.128x128.conv1.weight", "blocks.128x128.conv1.bias", "blocks.128x128.epi2.noise.weight", "blocks.128x128.epi2.style_mod.lin.weight", "blocks.128x128.epi2.style_mod.lin.bias", "blocks.256x256.conv0_up.weight", "blocks.256x256.conv0_up.bias", "blocks.256x256.conv0_up.intermediate.kernel", "blocks.256x256.epi1.noise.weight", "blocks.256x256.epi1.style_mod.lin.weight", "blocks.256x256.epi1.style_mod.lin.bias", "blocks.256x256.conv1.weight", "blocks.256x256.conv1.bias", "blocks.256x256.epi2.noise.weight", "blocks.256x256.epi2.style_mod.lin.weight", "blocks.256x256.epi2.style_mod.lin.bias", "blocks.512x512.conv0_up.weight", "blocks.512x512.conv0_up.bias", "blocks.512x512.conv0_up.intermediate.kernel", "blocks.512x512.epi1.noise.weight", "blocks.512x512.epi1.style_mod.lin.weight", "blocks.512x512.epi1.style_mod.lin.bias", "blocks.512x512.conv1.weight", "blocks.512x512.conv1.bias", "blocks.512x512.epi2.noise.weight", "blocks.512x512.epi2.style_mod.lin.weight", "blocks.512x512.epi2.style_mod.lin.bias", "blocks.1024x1024.conv0_up.weight", "blocks.1024x1024.conv0_up.bias", "blocks.1024x1024.conv0_up.intermediate.kernel", "blocks.1024x1024.epi1.noise.weight", "blocks.1024x1024.epi1.style_mod.lin.weight", "blocks.1024x1024.epi1.style_mod.lin.bias", "blocks.1024x1024.conv1.weight", "blocks.1024x1024.conv1.bias", "blocks.1024x1024.epi2.noise.weight", "blocks.1024x1024.epi2.style_mod.lin.weight", "blocks.1024x1024.epi2.style_mod.lin.bias".
Unexpected key(s) in state_dict: "g_mapping.dense0.weight", "g_mapping.dense0.bias", "g_mapping.dense1.weight", "g_mapping.dense1.bias", "g_mapping.dense2.weight", "g_mapping.dense2.bias", "g_mapping.dense3.weight", "g_mapping.dense3.bias", "g_mapping.dense4.weight", "g_mapping.dense4.bias", "g_mapping.dense5.weight", "g_mapping.dense5.bias", "g_mapping.dense6.weight", "g_mapping.dense6.bias", "g_mapping.dense7.weight", "g_mapping.dense7.bias", "g_synthesis.torgb.weight", "g_synthesis.torgb.bias", "g_synthesis.blocks.4x4.const", "g_synthesis.blocks.4x4.bias", "g_synthesis.blocks.4x4.epi1.top_epi.noise.weight", "g_synthesis.blocks.4x4.epi1.style_mod.lin.weight", "g_synthesis.blocks.4x4.epi1.style_mod.lin.bias", "g_synthesis.blocks.4x4.conv.weight", "g_synthesis.blocks.4x4.conv.bias", "g_synthesis.blocks.4x4.epi2.top_epi.noise.weight", "g_synthesis.blocks.4x4.epi2.style_mod.lin.weight", "g_synthesis.blocks.4x4.epi2.style_mod.lin.bias", "g_synthesis.blocks.8x8.conv0_up.weight", "g_synthesis.blocks.8x8.conv0_up.bias", "g_synthesis.blocks.8x8.conv0_up.intermediate.kernel", "g_synthesis.blocks.8x8.epi1.top_epi.noise.weight", "g_synthesis.blocks.8x8.epi1.style_mod.lin.weight", "g_synthesis.blocks.8x8.epi1.style_mod.lin.bias", "g_synthesis.blocks.8x8.conv1.weight", "g_synthesis.blocks.8x8.conv1.bias", "g_synthesis.blocks.8x8.epi2.top_epi.noise.weight", "g_synthesis.blocks.8x8.epi2.style_mod.lin.weight", "g_synthesis.blocks.8x8.epi2.style_mod.lin.bias", "g_synthesis.blocks.16x16.conv0_up.weight", "g_synthesis.blocks.16x16.conv0_up.bias", "g_synthesis.blocks.16x16.conv0_up.intermediate.kernel", "g_synthesis.blocks.16x16.epi1.top_epi.noise.weight", "g_synthesis.blocks.16x16.epi1.style_mod.lin.weight", "g_synthesis.blocks.16x16.epi1.style_mod.lin.bias", "g_synthesis.blocks.16x16.conv1.weight", "g_synthesis.blocks.16x16.conv1.bias", "g_synthesis.blocks.16x16.epi2.top_epi.noise.weight", "g_synthesis.blocks.16x16.epi2.style_mod.lin.weight", "g_synthesis.blocks.16x16.epi2.style_mod.lin.bias", "g_synthesis.blocks.32x32.conv0_up.weight", "g_synthesis.blocks.32x32.conv0_up.bias", "g_synthesis.blocks.32x32.conv0_up.intermediate.kernel", "g_synthesis.blocks.32x32.epi1.top_epi.noise.weight", "g_synthesis.blocks.32x32.epi1.style_mod.lin.weight", "g_synthesis.blocks.32x32.epi1.style_mod.lin.bias", "g_synthesis.blocks.32x32.conv1.weight", "g_synthesis.blocks.32x32.conv1.bias", "g_synthesis.blocks.32x32.epi2.top_epi.noise.weight", "g_synthesis.blocks.32x32.epi2.style_mod.lin.weight", "g_synthesis.blocks.32x32.epi2.style_mod.lin.bias", "g_synthesis.blocks.64x64.conv0_up.weight", "g_synthesis.blocks.64x64.conv0_up.bias", "g_synthesis.blocks.64x64.conv0_up.intermediate.kernel", "g_synthesis.blocks.64x64.epi1.top_epi.noise.weight", "g_synthesis.blocks.64x64.epi1.style_mod.lin.weight", "g_synthesis.blocks.64x64.epi1.style_mod.lin.bias", "g_synthesis.blocks.64x64.conv1.weight", "g_synthesis.blocks.64x64.conv1.bias", "g_synthesis.blocks.64x64.epi2.top_epi.noise.weight", "g_synthesis.blocks.64x64.epi2.style_mod.lin.weight", "g_synthesis.blocks.64x64.epi2.style_mod.lin.bias", "g_synthesis.blocks.128x128.conv0_up.weight", "g_synthesis.blocks.128x128.conv0_up.bias", "g_synthesis.blocks.128x128.conv0_up.intermediate.kernel", "g_synthesis.blocks.128x128.epi1.top_epi.noise.weight", "g_synthesis.blocks.128x128.epi1.style_mod.lin.weight", "g_synthesis.blocks.128x128.epi1.style_mod.lin.bias", "g_synthesis.blocks.128x128.conv1.weight", "g_synthesis.blocks.128x128.conv1.bias", "g_synthesis.blocks.128x128.epi2.top_epi.noise.weight", "g_synthesis.blocks.128x128.epi2.style_mod.lin.weight", "g_synthesis.blocks.128x128.epi2.style_mod.lin.bias", "g_synthesis.blocks.256x256.conv0_up.weight", "g_synthesis.blocks.256x256.conv0_up.bias", "g_synthesis.blocks.256x256.conv0_up.intermediate.kernel", "g_synthesis.blocks.256x256.epi1.top_epi.noise.weight", "g_synthesis.blocks.256x256.epi1.style_mod.lin.weight", "g_synthesis.blocks.256x256.epi1.style_mod.lin.bias", "g_synthesis.blocks.256x256.conv1.weight", "g_synthesis.blocks.256x256.conv1.bias", "g_synthesis.blocks.256x256.epi2.top_epi.noise.weight", "g_synthesis.blocks.256x256.epi2.style_mod.lin.weight", "g_synthesis.blocks.256x256.epi2.style_mod.lin.bias", "g_synthesis.blocks.512x512.conv0_up.weight", "g_synthesis.blocks.512x512.conv0_up.bias", "g_synthesis.blocks.512x512.conv0_up.intermediate.kernel", "g_synthesis.blocks.512x512.epi1.top_epi.noise.weight", "g_synthesis.blocks.512x512.epi1.style_mod.lin.weight", "g_synthesis.blocks.512x512.epi1.style_mod.lin.bias", "g_synthesis.blocks.512x512.conv1.weight", "g_synthesis.blocks.512x512.conv1.bias", "g_synthesis.blocks.512x512.epi2.top_epi.noise.weight", "g_synthesis.blocks.512x512.epi2.style_mod.lin.weight", "g_synthesis.blocks.512x512.epi2.style_mod.lin.bias", "g_synthesis.blocks.1024x1024.conv0_up.weight", "g_synthesis.blocks.1024x1024.conv0_up.bias", "g_synthesis.blocks.1024x1024.conv0_up.intermediate.kernel", "g_synthesis.blocks.1024x1024.epi1.top_epi.noise.weight", "g_synthesis.blocks.1024x1024.epi1.style_mod.lin.weight", "g_synthesis.blocks.1024x1024.epi1.style_mod.lin.bias", "g_synthesis.blocks.1024x1024.conv1.weight", "g_synthesis.blocks.1024x1024.conv1.bias", "g_synthesis.blocks.1024x1024.epi2.top_epi.noise.weight", "g_synthesis.blocks.1024x1024.epi2.style_mod.lin.weight", "g_synthesis.blocks.1024x1024.epi2.style_mod.lin.bias".
So, is there any way that I can make it right?
FULLY RESPECT.
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