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User dedicated repo for the faceswap project
Considering the amount of users that use this app, and the multitude of available setups, it would be nice to create an MD or a set o presets for different system configurations on the long run.
I'm currently running the training stage with the test data:
(faceswap-cpu) martin@martin-H97-D3H:~/Downloads/faceswap/faceswap$ ./faceswap.py train -A test_data/data/trump/ -B test_data/data/cage/ -m test_models/ -p
Model A Directory: /home/martin/Downloads/faceswap/faceswap/test_data/data/trump
Model B Directory: /home/martin/Downloads/faceswap/faceswap/test_data/data/cage
Training data directory: /home/martin/Downloads/faceswap/faceswap/test_models
Loading data, this may take a while...
Using live preview
Loading Model from Model_Original plugin...
/home/martin/.virtualenvs/faceswap-cpu/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Failed loading existing training data.
Unable to open file (unable to open file: name = '/home/martin/Downloads/faceswap/faceswap/test_models/encoder.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
Loading Trainer from Model_Original plugin...
Starting. Press "Enter" to stop training and save model
2018-02-05 15:43:34.950140: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
saved model weights loss_A: 0.20709, loss_B: 0.20315
[15:53:02] [#00011] loss_A: 0.17902, loss_B: 0.19046
and the preview window is showing, but where I expect the conversions to be are blank:
Any ideas?
I already have installed all the things from this guide: https://www.reddit.com/r/deepfakes/comments/7nq173/v2_tutorial_intelbased_python_easy_to_follow/ this was when there didn't exist the APP.
Also i have CMAKE (cmake-gui), cuda 8, PYTHON 3.6.3 (ANACONDA3 5.0.1 64-BITS).
https://i.imgur.com/jmlqxgp.jpg
But i really want to try this repo. (i know i will need to do most of the things VIA CMD commands).
I'm not a programmer or anything, so i want to know if i foloow the installation instructions for this REPO, it will not fuck UP my other installation right?
For what i read if i do it trough Virtualenv (i suppouse it's like a virtual environment):
Windows specific:
virtualenvwrapper-win is a package that makes virtualenvs easier to manage on Windows.
pip install virtualenvwrapper-win
Everything will be downloaded/installed there without messing with other installations, i'm right?
Also if i can't do it, someone could help me VIA TEAMVIEWER to install it?
thx in advance!
My configuration
OS: ubuntu 17.10
CUDA: 8.0
Tensorflow: 1.4
Nvidia GPU model: GM108M [GeForce 920MX]
While training it results in OOM(out of memory) error.
I tried to reduce my ENCODER_DIM and Encoder conv network to 512. But that is not helping either.
Reducing batch size also doesn't helps.
My gpu has 2gb ram. After system usage it has around 1gb ram available always.
I am using 1000 images for faceA and another 1000 images for faceB
These are my changes
def Encoder():
input_ = Input(shape=IMAGE_SHAPE)
x = input_
x = conv(64)(x)
x = conv(128)(x)
x = conv(256)(x)
x = conv(512)(x)
x = Dense(ENCODER_DIM)(Flatten()(x))
x = Dense(4 * 4 * 512)(x)
x = Reshape((4, 4, 512))(x)
x = upscale(512)(x)
return Model(input_, x)
Any suggestions?
Hi!
After the latest commits I get this:
Failed to convert image: /daten/test/fs-data/Source/2fake/529.png. Reason: 'ConvertImage' object has no attribute 'check_skip'
Ubuntu 16.04
Python 3.5.2
All requirements installed and the same conversion worked 2 days ago.
python3 ./faceswap.py convert -i ../fs-data/Source/2fake/ -o ../fs-data/Output/faked/ -m ../fs-data/Models/A-S -s -sm
Mac OS High Sierra
Mac: 10.13.2
Python 3.6.3 :: Anaconda custom (64-bit)
Using CPU
Processor: 2.2 GHz Intel Core i7
Memory: 16 GB 1600 MHz DDR3
Main Issue: IndexError
This is for training the faces
I've been using this guide as a reference:
[Outdated link removed]
But got stuck on the part where it says
"python faceswap.py train -A src_faces -B jessica_faces -m model -p".
The Error:
MY NAME-MacBook-Pro-2:faceswap MY NAME$ python faceswap.py train -A vid_faces -B Celeb_faces -m model -p
Model A Directory: /Volumes/My Passport/DeepFakes/faceswap/Vid_faces
Model B Directory: /Volumes/My Passport/DeepFakes/faceswap/Celeb_faces
Training data directory: /Volumes/My Passport/DeepFakes/faceswap/model
Loading data, this may take a while...
Using live preview
Loading Model from Model_Original plugin...
/Users/MY NAME/anaconda3/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float
to np.floating
is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type
.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
loaded model weights
Loading Trainer from Model_Original plugin...
Starting. Press "Enter" to stop training and save model
Exception in thread Thread-2:
Traceback (most recent call last):
File “/Users/MY NAME/anaconda3/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/Volumes/My Passport/DeepFakes/faceswap/lib/utils.py", line 42, in run
for item in self.generator:
File "/Volumes/My Passport/DeepFakes/faceswap/lib/training_data.py", line 43, in minibatch
rtn = numpy.float32([read_image(data[j]) for j in range(i,i+size)])
File "/Volumes/My Passport/DeepFakes/faceswap/lib/training_data.py", line 43, in
rtn = numpy.float32([read_image(data[j]) for j in range(i,i+size)])
IndexError: list index out of range
After the model training, we got three models,I want to know the function of encoder.h5。can anyone tell me?Thanks
FakeApp uses GPU and extract more angles with less artefacts.
Can you make same with faceswap?
I'm trying to run the python faceswap.py command.
No matter what I do, I am returned with an error that says 'ModuleNotFoundError: No module named 'tqdm'.
I had been receiving different errors. First it said No moduel named 'lib'. I noticed in my Faceswap directory that the directory was Lib, so I changed its case (unsure if this would have any effect) to lib. That error then went away, oddly enough. But now I receive this error and am unsure how to proceed.
I'm Windows 7 x64, running Python 3.6
OpenCV Error: Assertion failed (ssize.width > 0 && ssize.height > 0) in cv::resize, file C:\projects\opencv-python\opencv\modules\imgproc\src\resize.cpp, line 4044
Failed to extract from image: input\a.jpg. Reason: C:\projects\opencv-python\opencv\modules\imgproc\src\resize.cpp:4044: error: (-215) ssize.width > 0 && ssize.height > 0 in function cv::resize
I put an image in: input\a.jpg
here is my source dir:
2017/12/29 23:10 <DIR> .
2017/12/29 23:10 <DIR> ..
2017/12/29 08:52 19 .dockerignore
2017/12/29 08:52 <DIR> .github
2017/12/29 08:52 67 .gitignore
2017/12/29 08:52 <DIR> contrib
2017/12/29 21:35 <DIR> data
2017/12/29 08:52 600 Dockerfile
2017/12/29 08:52 734 faceswap.py
2017/12/29 21:43 <DIR> input
2017/12/29 08:52 5,740 INSTALL.md
2017/12/29 21:44 <DIR> lib
2017/12/29 21:35 <DIR> models
2017/12/29 22:33 <DIR> output
2017/12/29 08:52 5,432 README.md
2017/12/29 08:52 134 requirements-gpu.txt
2017/12/29 08:52 130 requirements.txt
2017/12/29 21:43 <DIR> scripts
2017/12/29 08:52 5,885 USAGE.md
Hello.
During the installation, I made a mistake and first started:
pip install -r requirements.txt
After that I run:
pip install -r requirements-gpu.txt
When I start training, I have such messages:
...
Model A Directory: /Volumes/HDD/faceswap/source_faces
Model B Directory: /Volumes/HDD/faceswap/target_faces
Training data directory: /Volumes/HDD/faceswap/model
2018-01-31 03:37:14.842407: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.2 AVX AVX2 FMA
Starting, this may take a while...
press "q" to stop training and save model
I'm confused by this line:
2018-01-31 03:37:14.842407: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.2 AVX AVX2
Does this mean that the training was started using the CPU?
Mine's been running for about 2 hours maxing out my dual 12GB 1080 Tis - does this finish on its own? Or should i be stopping it when I think it's "done enough"?
If i have to stop it manually i might amend the project README so it's obvious
Under what license is this software being released? Apache 2 or 3 would work, so would GPL - but having no license makes this a bit difficult.
The software has no license file, is available publicly and is being improved by people.
Please license the project under something, so I can use this and expand upon it.
[del]
when I run this command:
pip3 install -r requirements.txt
I got this error:
Installing collected packages: dlib, Click, face-recognition-models, face-recognition, tqdm
Running setup.py install for dlib ... error
Complete output from command /usr/local/opt/python3/bin/python3.6 -u -c "import setuptools, tokenize;__file__='/private/tmp/pip-build-wppnuye6/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 /tmp/pip-6t5vsick-record/install-record.txt --single-version-externally-managed --compile:
running install
running build
running build_py
package init file 'dlib/__init__.py' not found (or not a regular file)
running build_ext
Invoking CMake setup: 'cmake /private/tmp/pip-build-wppnuye6/dlib/tools/python -DCMAKE_LIBRARY_OUTPUT_DIRECTORY=/private/tmp/pip-build-wppnuye6/dlib/build/lib.macosx-10.13-x86_64-3.6 -DPYTHON_EXECUTABLE=/usr/local/opt/python3/bin/python3.6 -DCMAKE_BUILD_TYPE=Release'
error: [Errno 2] No such file or directory: 'cmake': 'cmake'
----------------------------------------
Command "/usr/local/opt/python3/bin/python3.6 -u -c "import setuptools, tokenize;__file__='/private/tmp/pip-build-wppnuye6/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 /tmp/pip-6t5vsick-record/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /private/tmp/pip-build-wppnuye6/dlib/
Hi,
If you are @mentionned here, it means you have been proposed to be a new collaborator on faceswap-playground
Github/faceswap is split into 3 projects:
Being a collaborator on a repo means you can do mostly anything in it, so use with care!
Regular users and first time contributors will be welcomed in faceswap-playground so that they can freely help newcomers, and discuss/propose on faceswap usage. Of course, it is about command line usage, but also about new solutions for a user-friendlier way of releasing faceswap.
Another important part is about sharing models and content. On this part, you can freely propose ways of sharing content (FTPs, Torrents...). However, note that you must NEVER EVER share genuine/copyrighted content here!
Have fun, and don't hesitate to discuss here between collabs!
From what I can tell, the scripts don't currently support re-using an existing model. Is that true? Any tips on how to add that feature?
(I assume you have installed everything)
Copy the sample data in the same folder as the code
Create an output
folder
Run convert_trump_cage.py
and Voila!
The purpose of the project is to convert photos with a different face.
It is done by running convert_photos.py
which will take your photos from original
folder, modify it with the model in models
, and put then in modified
folder
The standard models convert faces into Nicolas Cage generated faces. If you want to create your own models, here is an overview of how to do.
Gather photos of a first person. Put them in src
folder. Run extract.py
. You will see faces in the extract
folder. Then do it for a second person.
Put those in data/trump
(person to be converted) and data/cage
(person to apply on photo). Run train.py
. It will put the new models in the models
folder.
Once done, you can convert your photos as stated above.
Note: folder structure is subject to change. Please have a look at the code if you have trouble.
Note: Installation requires a bit of knowledge. Project is a work in progress, and people able to help are welcomed to.
pip3 --no-cache-dir install -r ./requirements.txt
(pip3
for Python3 users, otherwise use pip
)docker build -t deepfakes .
docker run --rm --name deepfakes -v [path_to_your_folder]:/srv -it deepfakes bash
Note: This is for a CPU run.
If you want to use GPU, install outside Docker, and use requirements-gpu.txt
If you want to use Docker with GPU support, it is possible, but only for advanced users, so I assume you are advanced enough to find out how ;-)
Hello guys,
I'm trying to run the convert.py
script and I'm not 100% sure on how it works. I did the following steps 👍
Gatherd pictures from two person.
Extracted their faces
Trained the model using the train.py
script
Executed the following python convert.py -i ~/Téléchargements/faceswap-data/source-images/lawrence/ -o ~/Téléchargements/faceswap-data/result/
The input directory I used contained full picture (not cropped) of person A. Is it how I should use it ?
I got the following error :
Failed to extract from image: /home/edouard/Téléchargements/faceswap-data/source-images/lawrence/109.jpg. Reason: basic_filebuf::underflow error reading the file: iostream error
easy to use bat files
tested on clean Windows 7 and Windows 10
no dependencies, except NVidia Video Drivers, NVidia Cuda 8.0, and 2015,2017 redist (included in torrent)
build 155 pull request with aligments.json, so no memory error on convert
https://rutracker.org/forum/viewtopic.php?t=5519086
magnet:?xt=urn:btih:B17828F816A9598A47FFD800900514A92B48B3C0
I just get to the training using python train.py command and I got:
Traceback (most recent call last): File "train.py", line 26, in <module> images_A = load_images( images_A ) / 255.0 File "C:\Users\remi_\faceswap\utils.py", line 16, in load_images return all_images UnboundLocalError: local variable 'all_images' referenced before assignment
Adding "all_images = 0" before the loop I then got this error
Traceback (most recent call last): File "train.py", line 27, in <module> images_B = load_images( images_B ) / 255.0 File "C:\Users\ramo_\faceswap\utils.py", line 17, in load_images all_images[i] = image ValueError: could not broadcast input array from shape (795,793,3) into shape (630,626,3)
Hi @deepfakes ,
Thanks for sharing your code. But when I convert the face, the speed of conversion is very slow. My GPU is very powerful. Why is the conversion slow? The speed is close to 2s/frame. Do you know the reason? Thank you!
I just started training with GAN -not GAN-128- from @Clorr's repo. Got this issue. Will it be gone with more training or is it a bug?
See the pictures on preview windows without green dots on them.
Please provide feedback so that later coming user can find how you did and what result you obtained
I am not using floydhub. I am running it locally. Following is my output
(venv) kumaran@kumaran:~/Projects/faceswap$ python faceswap.py train -A data/trump -B data/obama -m models/obama_trump/ -p
/home/kumaran/Projects/faceswap/venv/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
/home/kumaran/Projects/faceswap/venv/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
Model A Directory: /home/kumaran/Projects/faceswap/data/trump
Model B Directory: /home/kumaran/Projects/faceswap/data/obama
Training data directory: /home/kumaran/Projects/faceswap/models/obama_trump
Not loading existing training data.
Unable to open file (unable to open file: name = '/home/kumaran/Projects/faceswap/models/obama_trump/encoder.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
Starting, this may take a while...
usage: faceswap.py [-h] {extract,train,convert} ...
positional arguments:
{extract,train,convert}
extract Extract the faces from a pictures.
train This command trains the model for the two faces A and
B.
convert Convert a source image to a new one with the face
swapped.
optional arguments:
-h, --help show this help message and exit
(venv) kumaran@kumaran:~/Projects/faceswap$
Installation guide specifies/recommends 3.6
https://github.com/deepfakes/faceswap/blob/master/INSTALL.md
But script only checks for 3.2.
Either the script or the guide should be adjusted, to make sure no confusion occurs.
It will save time and issue reports later.
I ran the extract and trained for a while, but when I go to convert the images, I end up getting this error:
Failed loading existing training data.
You are trying to load a weight file containing 6 layers into a model with 7 layers.
Model Not Found! A valid model must be provided to continue!
This is what I used to train:
faceswap.py train -A data/extracted -B data/trump -bs 64 -t LowMem -w -s 1 -p -m data/model
This is what I used for convert:
faceswap.py convert -i data/okb -o modeltest -m data/model
Am I doing something wrong? I tested this with the trump/cage data and it worked fine with no error.
[del]
[Edited for content]
Archlinux 4.15.2
Python 3.6.4
Faceswap commit 20753a6
When running any command with "python faceswap.py" it fails to run and errors out "illegal hardware instruction" and fails to launch. All deps have been installed via pip.
Found this in dmesg.
python[24460] trap invalid opcode ip:7fb4d67981ad sp:7ffd16de66c0 error:0 in dlib.cpython-36m-x86_64-linux-gnu.so[7fb4d6309000+5c5000]
Any ideas? Thanks:)
(faceswap_env) paperspace@psy21ytam:~/Desktop/SOFTWARE/faceswap-master$ python faceswap.py -hTraceback (most recent call last):
File "faceswap.py", line 10, in
from scripts.extract import ExtractTrainingData
File "/home/paperspace/Desktop/SOFTWARE/faceswap-master/scripts/extract.py", line 4, in
from lib.cli import DirectoryProcessor
File "/home/paperspace/Desktop/SOFTWARE/faceswap-master/lib/cli.py", line 7, in
from lib.FaceFilter import FaceFilter
File "/home/paperspace/Desktop/SOFTWARE/faceswap-master/lib/FaceFilter.py", line 3, in
import face_recognition
File "/home/paperspace/faceswap_env/lib/python3.4/site-packages/face_recognition/init.py", line 7, in
from .api import load_image_file, face_locations, batch_face_locations, face_landmarks, face_encodings, compare_faces, face_distance
File "/home/paperspace/faceswap_env/lib/python3.4/site-packages/face_recognition/api.py", line 4, in
import dlib
ImportError: /home/paperspace/faceswap_env/lib/python3.4/site-packages/dlib.cpython-34m.so: undefined symbol: dgeqrf_
With CNN extractor I'm getting more faces angles but the zoom and alignement are not consistent.
This hand-made image is an example of some different extractions I get (forget the white background and focus on the faces):
In a video of a frontal face I can get the first type of extraction for X frames but then suddenly I start to get zoomed in frames. Many of them are also not properly aligned and look like the images 4 and 5.
Is this good for training? Should I just use the hog extractor for now?
I know you can't use seamless with the GAN models, but is there any way to get rid of the visible square?
There is not much explanation in the documentation that I can find.
In what situations should we be using -t Original
and what situations would it be preferred to use -t GAN
? Are there things one is better than the other at?
Mac OS High Sierra
Mac: 10.13.2
Python 3.6.3 :: Anaconda custom (64-bit)
Using CPU
Processor: 2.2 GHz Intel Core i7
Memory: 16 GB 1600 MHz DDR3
Main Issue: "'NoneType' object is not iterable"
MY NAME-MBP-2:faceswap MY NAME$ python faceswap.py convert -i vid -o output -m model
Input Directory: /Volumes/My Passport/DeepFakes/faceswap/vid
Output Directory: /Volumes/My Passport/DeepFakes/faceswap/output
Starting, this may take a while...
Loading Model from Model_Original plugin...
/Users/MY NAME/anaconda3/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float
to np.floating
is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type
.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
/Users/MY NAME/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
2018-02-11 20:16:59.539510: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
loaded model weights
Loading Convert from Convert_Masked plugin...
0%| | 0/952 [00:00<?, ?it/s]Failed to convert image: /Volumes/My Passport/DeepFakes/faceswap/vid/vid 1.png. Reason: 'NoneType' object is not iterable
I have 952 video images and they all say:
"Failed to convert image: /Volumes/My Passport/DeepFakes/faceswap/vid/vid.png. Reason: 'NoneType' object is not iterable
Hope it's not too complicated and thanks in advance!
I installed everything as instructed (using requirements-gpu.txt) and I am able to successfully extract my images, but only using my CPU and not my GPU. I know this because the windows task manager shows that powershell is using 50% of my CPU but 0% of my GPU. Any idea how to get faceswap to use my GPU?
pip freeze output:
bleach==1.5.0
click==6.7
decorator==4.2.1
dlib==19.9.0
enum34==1.1.6
face-recognition==1.2.1
face-recognition-models==0.3.0
h5py==2.7.1
html5lib==0.9999999
Keras==2.1.2
Markdown==2.6.11
networkx==2.1
numpy==1.14.0
opencv-python==3.3.0.10
pathlib==1.0.1
Pillow==5.0.0
protobuf==3.5.1
PyWavelets==0.5.2
PyYAML==3.12
scandir==1.6
scikit-image==0.13.1
scipy==1.0.0
six==1.11.0
tensorflow-gpu==1.4.0
tensorflow-tensorboard==0.4.0
tqdm==4.19.5
Werkzeug==0.14.1
OpenCV Error: Assertion failed (ssize.width > 0 && ssize.height > 0) in cv::resize, file C:\projects\opencv-python\opencv\modules\imgproc\src\imgwarp.cpp, line 3483
Failed to extract from image: C:\Users\Elliot\Envs\faceswap\face-swap\data\trump\112270915.jpg. Reason: C:\projects\opencv-python\opencv\modules\imgproc\src\imgwarp.cpp:3483: error: (-215) ssize.width > 0 && ssize.height > 0 in function cv::resize
subj
I tried 64 32 16 batch sizes, but faceswap consumes same amount of GPU memory based on GPU-Z
New to deep learning and programming in general (couple months of basic programming knowledge), so my perspective is limited and I'm learning as I go. But I'm trying to understand what's going on in this case:
I have a fairly well trained model (16-20+ hours on a 980ti with 5.2 compute capability), from the preview window the results are visually satisfactory I think: https://i.imgur.com/uYoHRS6.png . Maybe the images are cropped too closely to the face? I used the extract script from this project without modifying anything.
But when I run faceswap convert, the image that is created is noticeably lower quality: https://i.imgur.com/1FcgV9V.png
Furthermore, I'm also curious about why the converted area in the image is seemingly so small, as I roughly highlighted here: https://i.imgur.com/6uhzcQ9.png
My knowledge is very limited, but I imagine this is not a simple issue if it isn't already fixed. I'm not asking for a tutorial, but maybe some pointers in the right direction, about what affects the size of the converted area on a detected face, which parameters to play with, etc.
FIRST: love the work, awesome job!
SECOND A SUGGESTION:
Currently the angle of a face often messes with the face detection of dlib
is it possible to add a condition such that if a face is not found, rotate the image by a certain degree within a bounded box ( so as not to cut out the image ) and running through the dlib face detection? It might take longer to extract and convert, but at least it will be automatic. If a face is found during the process, run it through the model ( or extraction process ) then re-rotate and output the extracted or converted face. To increase efficiency, maybe remember the last degree of rotation used since movement between frames might not change as much.
From @yappica on December 28, 2017 22:27
Hi, I tested the train in using CPU, all work fin. There was the preview window. And it saved the train date in the models.
But when I changed to train on GPU, there was no preview window. It finished the train in 2 minute. Also there was no train date in the models directory.
I don't know what happened, maybe the video card I used.
I use a GTX 660 support CUDA 3.0.
Any help for it? Thank you.
Those are screenshot when using GPU for train.
Resource exhausted: OOM when allocating tensor with shape[3, 3, 128, 256]
Copied from original issue: deepfakes/faceswap#40
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