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Good morning,
This software works with my CPU.
I'm trying to install cuda (detected by software) and libcudnn.
Libcudnn is not detected.
It's like the ldconfig (not working with regular user) doesn't detect it.
What can i do for use GPU ?
I'm on debian 10 with nvidia 1050.
I had launch script faceswap_setup_x64.sh, and setup it for Nvidia.
I made "conda activate faceswap" and run python3 faceswap.py gui
But is always my cpu that is used... :/
Code: Select all
============ System Information ============
encoding: UTF-8
git_branch: master
git_commits: ac40b0f Remove subpixel upscaling option (#1024)
gpu_cuda: No global version found. Check Conda packages for Conda Cuda
gpu_cudnn: No global version found. Check Conda packages for Conda cuDNN
gpu_devices: GPU_0: GeForce GTX 1050
gpu_devices_active: GPU_0
gpu_driver: 418.113
gpu_vram: GPU_0: 1999MB
os_machine: x86_64
os_platform: Linux-4.19.0-9-amd64-x86_64-with-debian-10.4
os_release: 4.19.0-9-amd64
py_command: faceswap.py gui
py_conda_version: conda 4.8.3
py_implementation: CPython
py_version: 3.7.7
py_virtual_env: True
sys_cores: 8
sys_processor:
sys_ram: Total: 15934MB, Available: 13287MB, Used: 2152MB, Free: 4387MB
=============== Pip Packages ===============
absl-py==0.9.0
astor==0.8.1
certifi==2020.4.5.1
cloudpickle==1.4.1
cycler==0.10.0
cytoolz==0.10.1
dask==2.16.0
decorator==4.4.2
fastcluster==1.1.26
ffmpy==0.2.2
gast==0.3.3
google-pasta==0.2.0
grpcio==1.29.0
h5py==2.10.0
imageio==2.6.1
imageio-ffmpeg @ file:///home/conda/feedstock_root/build_artifacts/imageio-ffmpeg_1589202782679/work
importlib-metadata==1.6.0
joblib==0.14.1
Keras==2.2.4
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.2
kiwisolver==1.2.0
Markdown==3.2.2
matplotlib==3.1.1
mkl-fft==1.0.15
mkl-random==1.1.0
mkl-service==2.3.0
networkx==2.4
numpy==1.17.4
nvidia-ml-py3==7.352.1
olefile==0.46
opencv-python==4.1.2.30
opt-einsum==3.1.0
pathlib==1.0.1
Pillow==6.2.1
protobuf==3.12.0
psutil==5.7.0
pyparsing==2.4.7
python-dateutil==2.8.1
PyWavelets==1.1.1
PyYAML==5.3.1
scikit-image==0.16.2
scikit-learn==0.22.1
scipy==1.4.1
six==1.14.0
tensorboard==1.14.0
tensorflow==1.14.0
tensorflow-estimator==1.14.0
termcolor==1.1.0
toolz==0.10.0
toposort==1.5
tornado==6.0.4
tqdm==4.46.0
webencodings==0.5.1
Werkzeug==1.0.1
wrapt==1.12.1
zipp==3.1.0
============== Conda Packages ==============
# packages in environment at /home/rsette/.anaconda3/envs/faceswap:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
_tflow_select 2.1.0 gpu
absl-py 0.9.0 py37_0
astor 0.8.0 py37_0
blas 1.0 mkl
bzip2 1.0.8 h516909a_2 conda-forge
c-ares 1.15.0 h7b6447c_1001
ca-certificates 2020.1.1 0
certifi 2020.4.5.1 py37_0
cloudpickle 1.4.1 py_0
cudatoolkit 10.0.130 0
cudnn 7.6.5 cuda10.0_0
cupti 10.0.130 0
cycler 0.10.0 py37_0
cytoolz 0.10.1 py37h7b6447c_0
dask-core 2.16.0 py_0
dbus 1.13.14 hb2f20db_0
decorator 4.4.2 py_0
expat 2.2.6 he6710b0_0
fastcluster 1.1.26 py37hb3f55d8_0 conda-forge
ffmpeg 4.2 h167e202_0 conda-forge
fontconfig 2.13.0 h9420a91_0
freetype 2.9.1 h8a8886c_1
gast 0.2.2 py37_0
git 2.23.0 pl526hacde149_0
glib 2.63.1 h5a9c865_0
gmp 6.2.0 he1b5a44_2 conda-forge
gnutls 3.6.5 hd3a4fd2_1002 conda-forge
google-pasta 0.2.0 py_0
grpcio 1.27.2 py37hf8bcb03_0
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
h5py 2.9.0 py37h7918eee_0
hdf5 1.10.4 hb1b8bf9_0
icu 58.2 he6710b0_3
imageio 2.8.0 py_0
imageio-ffmpeg 0.4.2 py_0 conda-forge
intel-openmp 2020.1 217
joblib 0.14.1 py_0
jpeg 9b h024ee3a_2
keras 2.2.4 0
keras-applications 1.0.8 py_0
keras-base 2.2.4 py37_0
keras-preprocessing 1.1.0 py_1
kiwisolver 1.2.0 py37hfd86e86_0
krb5 1.17.1 h173b8e3_0
lame 3.100 h14c3975_1001 conda-forge
ld_impl_linux-64 2.33.1 h53a641e_7
libcurl 7.69.1 h20c2e04_0
libedit 3.1.20181209 hc058e9b_0
libffi 3.2.1 hd88cf55_4
libgcc-ng 9.1.0 hdf63c60_0
libgfortran-ng 7.3.0 hdf63c60_0
libiconv 1.15 h516909a_1006 conda-forge
libpng 1.6.37 hbc83047_0
libprotobuf 3.11.4 hd408876_0
libssh2 1.9.0 h1ba5d50_1
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.1.0 h2733197_0
libuuid 1.0.3 h1bed415_2
libxcb 1.13 h1bed415_1
libxml2 2.9.9 hea5a465_1
markdown 3.1.1 py37_0
matplotlib 3.1.3 py37_0
matplotlib-base 3.1.3 py37hef1b27d_0
mkl 2020.1 217
mkl-service 2.3.0 py37he904b0f_0
mkl_fft 1.0.15 py37ha843d7b_0
mkl_random 1.1.0 py37hd6b4f25_0
ncurses 6.2 he6710b0_1
nettle 3.4.1 h1bed415_1002 conda-forge
networkx 2.4 py_0
numpy 1.18.1 py37h4f9e942_0
numpy-base 1.18.1 py37hde5b4d6_1
olefile 0.46 py37_0
openh264 1.8.0 hdbcaa40_1000 conda-forge
openssl 1.1.1g h7b6447c_0
opt_einsum 3.1.0 py_0
pcre 8.43 he6710b0_0
perl 5.26.2 h14c3975_0
pillow 7.1.2 py37hb39fc2d_0
pip 20.0.2 py37_3
protobuf 3.11.4 py37he6710b0_0
psutil 5.7.0 py37h7b6447c_0
pyparsing 2.4.7 py_0
pyqt 5.9.2 py37h05f1152_2
python 3.7.7 hcf32534_0_cpython
python-dateutil 2.8.1 py_0
python_abi 3.7 1_cp37m conda-forge
pywavelets 1.1.1 py37h7b6447c_0
pyyaml 5.3.1 py37h7b6447c_0
qt 5.9.7 h5867ecd_1
readline 8.0 h7b6447c_0
scikit-image 0.16.2 py37h0573a6f_0
scikit-learn 0.22.1 py37hd81dba3_0
scipy 1.4.1 py37h0b6359f_0
setuptools 46.2.0 py37_0
sip 4.19.8 py37hf484d3e_0
six 1.14.0 py37_0
sqlite 3.31.1 h62c20be_1
tensorboard 1.15.0 pyhb230dea_0
tensorflow 1.15.0 gpu_py37h0f0df58_0
tensorflow-base 1.15.0 gpu_py37h9dcbed7_0
tensorflow-estimator 1.15.1 pyh2649769_0
tensorflow-gpu 1.15.0 h0d30ee6_0
termcolor 1.1.0 py37_1
tk 8.6.8 hbc83047_0
toolz 0.10.0 py_0
toposort 1.5 py_3 conda-forge
tornado 6.0.4 py37h7b6447c_1
webencodings 0.5.1 py37_1
werkzeug 0.16.1 py_0
wheel 0.34.2 py37_0
wrapt 1.12.1 py37h7b6447c_1
x264 1!152.20180806 h14c3975_0 conda-forge
xz 5.2.5 h7b6447c_0
yaml 0.1.7 had09818_2
zlib 1.2.11 h7b6447c_3
zstd 1.3.7 h0b5b093_0
================= Configs ==================
--------- extract.ini ---------
[global]
allow_growth: False
[detect.mtcnn]
minsize: 20
threshold_1: 0.6
threshold_2: 0.7
threshold_3: 0.7
scalefactor: 0.709
batch-size: 8
[detect.s3fd]
confidence: 70
batch-size: 4
[detect.cv2_dnn]
confidence: 50
[align.fan]
batch-size: 12
[mask.unet_dfl]
batch-size: 8
[mask.vgg_obstructed]
batch-size: 2
[mask.vgg_clear]
batch-size: 6
--------- convert.ini ---------
[color.color_transfer]
clip: True
preserve_paper: True
[color.manual_balance]
colorspace: HSV
balance_1: 0.0
balance_2: 0.0
balance_3: 0.0
contrast: 0.0
brightness: 0.0
[color.match_hist]
threshold: 99.0
[mask.mask_blend]
type: normalized
kernel_size: 3
passes: 4
threshold: 4
erosion: 0.0
[mask.box_blend]
type: gaussian
distance: 11.0
radius: 5.0
passes: 1
[writer.ffmpeg]
container: mp4
codec: libx264
crf: 23
preset: medium
tune: none
profile: auto
level: auto
[writer.gif]
fps: 25
loop: 0
palettesize: 256
subrectangles: False
[writer.pillow]
format: png
draw_transparent: False
optimize: False
gif_interlace: True
jpg_quality: 75
png_compress_level: 3
tif_compression: tiff_deflate
[writer.opencv]
format: png
draw_transparent: False
jpg_quality: 75
png_compress_level: 3
[scaling.sharpen]
method: unsharp_mask
amount: 150
radius: 0.3
threshold: 5.0
--------- gui.ini ---------
[global]
fullscreen: False
tab: extract
options_panel_width: 30
console_panel_height: 20
icon_size: 14
font: default
font_size: 9
autosave_last_session: prompt
timeout: 120
auto_load_model_stats: True
--------- train.ini ---------
[global]
coverage: 68.75
mask_type: none
mask_blur_kernel: 3
mask_threshold: 4
learn_mask: False
icnr_init: False
conv_aware_init: False
reflect_padding: False
penalized_mask_loss: True
loss_function: mae
learning_rate: 5e-05
[model.unbalanced]
input_size: 128
lowmem: False
clipnorm: True
nodes: 1024
complexity_encoder: 128
complexity_decoder_a: 384
complexity_decoder_b: 512
[model.dfl_h128]
lowmem: False
[model.dfl_sae]
input_size: 128
clipnorm: True
architecture: df
autoencoder_dims: 0
encoder_dims: 42
decoder_dims: 21
multiscale_decoder: False
[model.realface]
input_size: 64
output_size: 128
dense_nodes: 1536
complexity_encoder: 128
complexity_decoder: 512
[model.original]
lowmem: False
[model.dlight]
features: best
details: good
output_size: 256
[model.villain]
lowmem: False
[trainer.original]
preview_images: 14
zoom_amount: 5
rotation_range: 10
shift_range: 5
flip_chance: 50
color_lightness: 30
color_ab: 8
color_clahe_chance: 50
color_clahe_max_size: 4
--------- .faceswap ---------
backend: nvidiaI had trying everything, make setup.py in conda, use miniconda.
But my nvidia-smi show nothing more and my cpu is used > 90%... 
Code: Select all
GPU_0: 1999MBI'm afraid you're going to struggle with that GPU. 2gb of vram is simply not enough for any serious DF work. You MIGHT be able to get away with using the GPU to accelerate extract, but training is likely beyond this card's capability.
Yes, CPU, uses system ram, GPU uses it's own ram. That GPU will struggle to do anything really useful.