私的AI研究会 > StarGAN
AI技術「GAN（敵対的生成ネットワーク）」を試してみる。「StarGAN-V2」で特徴操作・顔の合成をおこない顔を変化させる。
(py38) PS > conda info -e
(py38) PS C:\anaconda_win\workspace_py38> cd ..\work\ ← 今回のプロジェクト・フォルダ
(py38) PS C:\anaconda_win\work> conda info -e
# conda environments:
#
base C:\Users\(xxxxx)\anaconda3
:
py37 C:\Users\(xxxxx)\anaconda3\envs\py37
py38 * C:\Users\(xxxxx)\anaconda3\envs\py38 ← 現在アクティブな仮想環境
(py38) PS > conda create -n py38_gan python=3.8
(py38) PS C:\anaconda_win\work> conda create -n py38a python=3.8
Collecting package metadata (current_repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.13.0
latest version: 23.3.1
Please update conda by running
$ conda update -n base -c defaults conda
## Package Plan ##
environment location: C:\Users\izuts\anaconda3\envs\py38a
added / updated specs:
- python=3.8
The following packages will be downloaded:
package | build
---------------------------|-----------------
ca-certificates-2023.01.10 | haa95532_0 121 KB
certifi-2022.12.7 | py38haa95532_0 148 KB
libffi-3.4.2 | hd77b12b_6 109 KB
openssl-1.1.1t | h2bbff1b_0 5.5 MB
pip-23.0.1 | py38haa95532_0 2.7 MB
python-3.8.16 | h6244533_3 18.9 MB
setuptools-65.6.3 | py38haa95532_0 1.1 MB
sqlite-3.41.1 | h2bbff1b_0 897 KB
wheel-0.38.4 | py38haa95532_0 83 KB
------------------------------------------------------------
Total: 29.6 MB
The following NEW packages will be INSTALLED:
ca-certificates pkgs/main/win-64::ca-certificates-2023.01.10-haa95532_0
certifi pkgs/main/win-64::certifi-2022.12.7-py38haa95532_0
libffi pkgs/main/win-64::libffi-3.4.2-hd77b12b_6
openssl pkgs/main/win-64::openssl-1.1.1t-h2bbff1b_0
pip pkgs/main/win-64::pip-23.0.1-py38haa95532_0
python pkgs/main/win-64::python-3.8.16-h6244533_3
setuptools pkgs/main/win-64::setuptools-65.6.3-py38haa95532_0
sqlite pkgs/main/win-64::sqlite-3.41.1-h2bbff1b_0
vc pkgs/main/win-64::vc-14.2-h21ff451_1
vs2015_runtime pkgs/main/win-64::vs2015_runtime-14.27.29016-h5e58377_2
wheel pkgs/main/win-64::wheel-0.38.4-py38haa95532_0
wincertstore pkgs/main/win-64::wincertstore-0.2-py38haa95532_2
Proceed ([y]/n)?
Downloading and Extracting Packages
python-3.8.16 | 18.9 MB | #################################### | 100%
wheel-0.38.4 | 83 KB | #################################### | 100%
pip-23.0.1 | 2.7 MB | #################################### | 100%
libffi-3.4.2 | 109 KB | #################################### | 100%
sqlite-3.41.1 | 897 KB | #################################### | 100%
ca-certificates-2023 | 121 KB | #################################### | 100%
setuptools-65.6.3 | 1.1 MB | #################################### | 100%
certifi-2022.12.7 | 148 KB | #################################### | 100%
openssl-1.1.1t | 5.5 MB | #################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate py38a
#
# To deactivate an active environment, use
#
# $ conda deactivate
(py38) PS > conda activate py38_gan (py38_gan) PS > conda info -e
(py38) PS C:\anaconda_win\work> conda activate py38_gan
(py38_gan) PS C:\anaconda_win\work> conda info -e
# conda environments:
#
base C:\Users\(xxxxx)\anaconda3
:
py37 C:\Users\(xxxxx)\anaconda3\envs\py37
py38 C:\Users\(xxxxx)\anaconda3\envs\py38
py38_gan * C:\Users\(xxxxx)\anaconda3\envs\py38_gan
(py38_gan) PS > cd /anaconda_win/work/ (py38_gan) PS > pip install -r requirements.txt
# Stable Diffusion & YOLOv7_OpenVINO openvino==2022.1.0 numpy==1.19.5 opencv-python==4.5.5.64 transformers==4.16.2 diffusers==0.2.4 tqdm==4.64.0 huggingface_hub==0.9.0 scipy==1.9.0 streamlit==1.12.0 watchdog==2.1.9 ftfy==6.1.1 PyMuPDF torchvision matplotlib seaborn onnx googletrans==4.0.0-rc1
(py38_gan) PS C:\anaconda_win\work> pip install -r requirements.txt
Collecting openvino==2022.1.0 (from -r requirements.txt (line 2))
Using cached openvino-2022.1.0-7019-cp38-cp38-win_amd64.whl (22.9 MB)
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Using cached numpy-1.19.5-cp38-cp38-win_amd64.whl (13.3 MB)
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Using cached opencv_python-4.5.5.64-cp36-abi3-win_amd64.whl (35.4 MB)
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Installing collected packages: wcwidth, tokenizers, rfc3986, pytz, mpmath, hyperframe, hpack, h11, chardet, zipp, watchdog, validators, urllib3, tzdata, typing-extensions, tqdm, tornado, toolz, toml, sympy, sniffio, smmap, semver, rpds-py, regex, pyyaml, python-dateutil, pyparsing, PyMuPDFb, pympler, pygments, protobuf, pkgutil-resolve-name, Pillow, packaging, numpy, networkx, mdurl, MarkupSafe, kiwisolver, joblib, idna, hstspreload, h2, ftfy, fsspec, fonttools, filelock, cycler, click, charset-normalizer, certifi, cachetools, blinker, backports.zoneinfo, attrs, tzlocal, scipy, sacremoses, requests, referencing, PyMuPDF, pyarrow, pandas, openvino, opencv-python, onnx, markdown-it-py, jinja2, importlib-resources, importlib-metadata, httpcore, gitdb, contourpy, torch, rich, pydeck, matplotlib, jsonschema-specifications, huggingface_hub, httpx, gitpython, transformers, torchvision, seaborn, jsonschema, googletrans, diffusers, altair, streamlit
Successfully installed MarkupSafe-2.1.3 Pillow-10.0.1 PyMuPDF-1.23.4 PyMuPDFb-1.23.3 altair-5.1.2 attrs-23.1.0 backports.zoneinfo-0.2.1 blinker-1.6.3 cachetools-5.3.1 certifi-2023.7.22 chardet-3.0.4 charset-normalizer-3.3.0 click-8.1.7 contourpy-1.1.1 cycler-0.12.1 diffusers-0.2.4 filelock-3.12.4 fonttools-4.43.1 fsspec-2023.9.2 ftfy-6.1.1 gitdb-4.0.10 gitpython-3.1.37 googletrans-4.0.0rc1 h11-0.9.0 h2-3.2.0 hpack-3.0.0 hstspreload-2023.1.1 httpcore-0.9.1 httpx-0.13.3 huggingface_hub-0.9.0 hyperframe-5.2.0 idna-2.10 importlib-metadata-6.8.0 importlib-resources-6.1.0 jinja2-3.1.2 joblib-1.3.2 jsonschema-4.19.1 jsonschema-specifications-2023.7.1 kiwisolver-1.4.5 markdown-it-py-3.0.0 matplotlib-3.6.3 mdurl-0.1.2 mpmath-1.3.0 networkx-3.1 numpy-1.19.5 onnx-1.14.1 opencv-python-4.5.5.64 openvino-2022.1.0 packaging-23.2 pandas-1.4.4 pkgutil-resolve-name-1.3.10 protobuf-3.20.3 pyarrow-13.0.0 pydeck-0.8.1b0 pygments-2.16.1 pympler-1.0.1 pyparsing-3.1.1 python-dateutil-2.8.2 pytz-2023.3.post1 pyyaml-6.0.1 referencing-0.30.2 regex-2023.10.3 requests-2.31.0 rfc3986-1.5.0 rich-13.6.0 rpds-py-0.10.4 sacremoses-0.0.53 scipy-1.9.0 seaborn-0.12.2 semver-3.0.1 smmap-5.0.1 sniffio-1.3.0 streamlit-1.12.0 sympy-1.12 tokenizers-0.13.3 toml-0.10.2 toolz-0.12.0 torch-2.1.0 torchvision-0.16.0 tornado-6.3.3 tqdm-4.64.0 transformers-4.16.2 typing-extensions-4.8.0 tzdata-2023.3 tzlocal-5.1 urllib3-2.0.6 validators-0.22.0 watchdog-2.1.9 wcwidth-0.2.8 zipp-3.17.0
(py38_gan) PS > cd /anaconda_win/work/ (py38_gan) PS > git clone https://github.com/clovaai/stargan-v2.git
(py38_gan) PS C:\anaconda_win\work> git clone https://github.com/clovaai/stargan-v2.git Cloning into 'stargan-v2'... remote: Enumerating objects: 269, done. remote: Total 269 (delta 0), reused 0 (delta 0), pack-reused 269 Receiving objects: 100% (269/269), 38.86 MiB | 19.21 MiB/s, done. Resolving deltas: 100% (60/60), done.
(py38_gan) PS > pip install munch scikit-image ffmpeg
(py38_gan) PS C:\anaconda_win\work> pip install munch scikit-image ffmpeg
Collecting munch
Obtaining dependency information for munch from https://files.pythonhosted.org/packages/56/b3/7c69b37f03260a061883bec0e7b05be7117c1b1c85f5212c72c8c2bc3c8c/munch-4.0.0-py2.py3-none-any.whl.metadata
Using cached munch-4.0.0-py2.py3-none-any.whl.metadata (5.9 kB)
Collecting scikit-image
Obtaining dependency information for scikit-image from https://files.pythonhosted.org/packages/d7/d1/a4c715ad640c9eb0daaa77c4ce561b06e086bec44cbc79083e3548b00b76/scikit_image-0.21.0-cp38-cp38-win_amd64.whl.metadata
Using cached scikit_image-0.21.0-cp38-cp38-win_amd64.whl.metadata (14 kB)
Collecting ffmpeg
Using cached ffmpeg-1.4-py3-none-any.whl
Collecting numpy>=1.21.1 (from scikit-image)
Obtaining dependency information for numpy>=1.21.1 from https://files.pythonhosted.org/packages/69/65/0d47953afa0ad569d12de5f65d964321c208492064c38fe3b0b9744f8d44/numpy-1.24.4-cp38-cp38-win_amd64.whl.metadata
Using cached numpy-1.24.4-cp38-cp38-win_amd64.whl.metadata (5.6 kB)
Requirement already satisfied: scipy>=1.8 in c:\users\izuts\anaconda3\envs\py38_gan\lib\site-packages (from scikit-image) (1.9.0)
Requirement already satisfied: networkx>=2.8 in c:\users\izuts\anaconda3\envs\py38_gan\lib\site-packages (from scikit-image) (3.1)
Requirement already satisfied: pillow>=9.0.1 in c:\users\izuts\anaconda3\envs\py38_gan\lib\site-packages (from scikit-image) (10.0.1)
Collecting imageio>=2.27 (from scikit-image)
Obtaining dependency information for imageio>=2.27 from https://files.pythonhosted.org/packages/f6/37/e21e6f38b93878ba80302e95b8ccd4718d80f0c53055ccae343e606b1e2d/imageio-2.31.5-py3-none-any.whl.metadata
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Obtaining dependency information for tifffile>=2022.8.12 from https://files.pythonhosted.org/packages/06/a3/68d17088a4f09565bc7341fd20490da8191ec4cddde479daaabbe07bb603/tifffile-2023.7.10-py3-none-any.whl.metadata
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Obtaining dependency information for lazy_loader>=0.2 from https://files.pythonhosted.org/packages/a1/c3/65b3814e155836acacf720e5be3b5757130346670ac454fee29d3eda1381/lazy_loader-0.3-py3-none-any.whl.metadata
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Installing collected packages: ffmpeg, numpy, munch, lazy_loader, tifffile, PyWavelets, imageio, scikit-image
Attempting uninstall: numpy
Found existing installation: numpy 1.19.5
Uninstalling numpy-1.19.5:
Successfully uninstalled numpy-1.19.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
openvino 2022.1.0 requires numpy<1.20,>=1.16.6, but you have numpy 1.24.4 which is incompatible.
Successfully installed PyWavelets-1.4.1 ffmpeg-1.4 imageio-2.31.5 lazy_loader-0.3 munch-4.0.0 numpy-1.24.4 scikit-image-0.21.0 tifffile-2023.7.10
conda activate py38_gan
(py38_gan) PS > cd /anaconda_win/work/stargan-v2/
(py38_gan) PS > python main.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_0 --ref_dir assets/representative/celeba_hq/ref_0
(py38_gan) PS C:\anaconda_win\work\stargan-v2> python main.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_0 --ref_dir assets/representative/celeba_hq/ref_0 Namespace(batch_size=8, beta1=0.0, beta2=0.99, checkpoint_dir='expr/checkpoints/celeba_hq', ds_iter=100000, eval_dir='expr/eval', eval_every=50000, f_lr=1e-06, hidden_dim=512, img_size=256, inp_dir='assets/representative/custom/female', lambda_cyc=1, lambda_ds=1, lambda_reg=1, lambda_sty=1, latent_dim=16, lm_path='expr/checkpoints/celeba_lm_mean.npz', lr=0.0001, mode='sample', num_domains=2, num_outs_per_domain=10, num_workers=4, out_dir='assets/representative/celeba_hq/src/female', print_every=10, randcrop_prob=0.5, ref_dir='assets/representative/celeba_hq/ref_0', result_dir='expr/results/celeba_hq', resume_iter=100000, sample_dir='expr/samples', sample_every=5000, save_every=10000, seed=777, src_dir='assets/representative/celeba_hq/src_0', style_dim=64, total_iters=100000, train_img_dir='data/celeba_hq/train', val_batch_size=32, val_img_dir='data/celeba_hq/val', w_hpf=1.0, weight_decay=0.0001, wing_path='expr/checkpoints/wing.ckpt') Number of parameters of generator: 43467395 Number of parameters of mapping_network: 2438272 Number of parameters of style_encoder: 20916928 Number of parameters of discriminator: 20852290 Number of parameters of fan: 6333603 Initializing generator... Initializing mapping_network... Initializing style_encoder... Initializing discriminator... Preparing DataLoader for the generation phase... Preparing DataLoader for the generation phase... Loading checkpoint from expr/checkpoints/celeba_hq\100000_nets_ema.ckpt... Working on expr/results/celeba_hq\reference.jpg...
(py38_gan) PS > python main2.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_0 --ref_dir assets/representative/celeba_hq/ref_0
# -*- coding: utf-8 -*-
##------------------------------------------
## StarGAN v2 Ver 0.01
## Generating interpolation image
##
## 2023.10.02 Masahiro Izutsu
##------------------------------------------
## Official StarGAN v2 https://github.com/clovaai/stargan-v2
##
## main2.py (original: main.py)
"""
StarGAN v2
Copyright (c) 2020-present NAVER Corp.
This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""
import os
import argparse
from munch import Munch
from torch.backends import cudnn
import torch
from core.data_loader import get_train_loader
from core.data_loader import get_test_loader
from core.solver import Solver
def str2bool(v):
return v.lower() in ('true')
def subdirs(dname):
return [d for d in os.listdir(dname)
if os.path.isdir(os.path.join(dname, d))]
def frame_resize(image, maxsize):
import cv2
if maxsize > 300:
img_h, img_w = image.shape[:2]
if (img_w > img_h):
if (img_w > maxsize):
height = round(img_h * (maxsize / img_w))
image = cv2.resize(image, dsize = (maxsize, height))
else:
if (img_h > maxsize):
width = round(img_w * (maxsize / img_h))
image = cv2.resize(image, dsize = (width, maxsize))
return image
def viewFile(filename):
import cv2
window_name = 'Output Image'
img = cv2.imread(filename)
if img is None:
print(' Output image not found !!')
else:
cv2.namedWindow(window_name, flags=cv2.WINDOW_AUTOSIZE | cv2.WINDOW_GUI_NORMAL)
img = frame_resize(img, 1000)
cv2.imshow(window_name, img)
while(True):
key = cv2.waitKey(1)
if key == 27 or key == 113: # 'esc' or 'q'
break
cv2.destroyAllWindows()
return
def main(args):
print(args)
cudnn.benchmark = True
torch.manual_seed(args.seed)
solver = Solver(args)
if args.mode == 'train':
assert len(subdirs(args.train_img_dir)) == args.num_domains
assert len(subdirs(args.val_img_dir)) == args.num_domains
loaders = Munch(src=get_train_loader(root=args.train_img_dir,
which='source',
img_size=args.img_size,
batch_size=args.batch_size,
prob=args.randcrop_prob,
num_workers=args.num_workers),
ref=get_train_loader(root=args.train_img_dir,
which='reference',
img_size=args.img_size,
batch_size=args.batch_size,
prob=args.randcrop_prob,
num_workers=args.num_workers),
val=get_test_loader(root=args.val_img_dir,
img_size=args.img_size,
batch_size=args.val_batch_size,
shuffle=True,
num_workers=args.num_workers))
solver.train(loaders)
elif args.mode == 'sample':
assert len(subdirs(args.src_dir)) == args.num_domains
assert len(subdirs(args.ref_dir)) == args.num_domains
loaders = Munch(src=get_test_loader(root=args.src_dir,
img_size=args.img_size,
batch_size=args.val_batch_size,
shuffle=False,
num_workers=args.num_workers),
ref=get_test_loader(root=args.ref_dir,
img_size=args.img_size,
batch_size=args.val_batch_size,
shuffle=False,
num_workers=args.num_workers))
solver.sample(loaders)
viewFile(args.result_dir + '/reference.jpg')
elif args.mode == 'eval':
solver.evaluate()
elif args.mode == 'align':
from core.wing import align_faces
align_faces(args, args.inp_dir, args.out_dir)
else:
raise NotImplementedError
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# model arguments
parser.add_argument('--img_size', type=int, default=256,
help='Image resolution')
parser.add_argument('--num_domains', type=int, default=2,
help='Number of domains')
parser.add_argument('--latent_dim', type=int, default=16,
help='Latent vector dimension')
parser.add_argument('--hidden_dim', type=int, default=512,
help='Hidden dimension of mapping network')
parser.add_argument('--style_dim', type=int, default=64,
help='Style code dimension')
# weight for objective functions
parser.add_argument('--lambda_reg', type=float, default=1,
help='Weight for R1 regularization')
parser.add_argument('--lambda_cyc', type=float, default=1,
help='Weight for cyclic consistency loss')
parser.add_argument('--lambda_sty', type=float, default=1,
help='Weight for style reconstruction loss')
parser.add_argument('--lambda_ds', type=float, default=1,
help='Weight for diversity sensitive loss')
parser.add_argument('--ds_iter', type=int, default=100000,
help='Number of iterations to optimize diversity sensitive loss')
parser.add_argument('--w_hpf', type=float, default=1,
help='weight for high-pass filtering')
# training arguments
parser.add_argument('--randcrop_prob', type=float, default=0.5,
help='Probabilty of using random-resized cropping')
parser.add_argument('--total_iters', type=int, default=100000,
help='Number of total iterations')
parser.add_argument('--resume_iter', type=int, default=0,
help='Iterations to resume training/testing')
parser.add_argument('--batch_size', type=int, default=8,
help='Batch size for training')
parser.add_argument('--val_batch_size', type=int, default=32,
help='Batch size for validation')
parser.add_argument('--lr', type=float, default=1e-4,
help='Learning rate for D, E and G')
parser.add_argument('--f_lr', type=float, default=1e-6,
help='Learning rate for F')
parser.add_argument('--beta1', type=float, default=0.0,
help='Decay rate for 1st moment of Adam')
parser.add_argument('--beta2', type=float, default=0.99,
help='Decay rate for 2nd moment of Adam')
parser.add_argument('--weight_decay', type=float, default=1e-4,
help='Weight decay for optimizer')
parser.add_argument('--num_outs_per_domain', type=int, default=10,
help='Number of generated images per domain during sampling')
# misc
parser.add_argument('--mode', type=str, required=True,
choices=['train', 'sample', 'eval', 'align'],
help='This argument is used in solver')
parser.add_argument('--num_workers', type=int, default=4,
help='Number of workers used in DataLoader')
parser.add_argument('--seed', type=int, default=777,
help='Seed for random number generator')
# directory for training
parser.add_argument('--train_img_dir', type=str, default='data/celeba_hq/train',
help='Directory containing training images')
parser.add_argument('--val_img_dir', type=str, default='data/celeba_hq/val',
help='Directory containing validation images')
parser.add_argument('--sample_dir', type=str, default='expr/samples',
help='Directory for saving generated images')
parser.add_argument('--checkpoint_dir', type=str, default='expr/checkpoints',
help='Directory for saving network checkpoints')
# directory for calculating metrics
parser.add_argument('--eval_dir', type=str, default='expr/eval',
help='Directory for saving metrics, i.e., FID and LPIPS')
# directory for testing
parser.add_argument('--result_dir', type=str, default='expr/results',
help='Directory for saving generated images and videos')
parser.add_argument('--src_dir', type=str, default='assets/representative/celeba_hq/src',
help='Directory containing input source images')
parser.add_argument('--ref_dir', type=str, default='assets/representative/celeba_hq/ref',
help='Directory containing input reference images')
parser.add_argument('--inp_dir', type=str, default='assets/representative/custom/female',
help='input directory when aligning faces')
parser.add_argument('--out_dir', type=str, default='assets/representative/celeba_hq/src/female',
help='output directory when aligning faces')
# face alignment
parser.add_argument('--wing_path', type=str, default='expr/checkpoints/wing.ckpt')
parser.add_argument('--lm_path', type=str, default='expr/checkpoints/celeba_lm_mean.npz')
# step size
parser.add_argument('--print_every', type=int, default=10)
parser.add_argument('--sample_every', type=int, default=5000)
parser.add_argument('--save_every', type=int, default=10000)
parser.add_argument('--eval_every', type=int, default=50000)
args = parser.parse_args()
main(args)
(py38_gan) PS > python main.py --mode align --inp_dir assets/representative/custom_1/male --out_dir assets/representative/celeba_hq/ref_test/male
(py38_gan) PS C:\anaconda_win\work\stargan-v2> python main.py --mode align --inp_dir assets/representative/custom_1/male --out_dir assets/representative/celeba_hq/ref_test/male Namespace(batch_size=8, beta1=0.0, beta2=0.99, checkpoint_dir='expr/checkpoints', ds_iter=100000, eval_dir='expr/eval', eval_every=50000, f_lr=1e-06, hidden_dim=512, img_size=256, inp_dir='assets/representative/custom_1/male', lambda_cyc=1, lambda_ds=1, lambda_reg=1, lambda_sty=1, latent_dim=16, lm_path='expr/checkpoints/celeba_lm_mean.npz', lr=0.0001, mode='align', num_domains=2, num_outs_per_domain=10, num_workers=4, out_dir='assets/representative/celeba_hq/ref_test/male', print_every=10, randcrop_prob=0.5, ref_dir='assets/representative/celeba_hq/ref', result_dir='expr/results', resume_iter=0, sample_dir='expr/samples', sample_every=5000, save_every=10000, seed=777, src_dir='assets/representative/celeba_hq/src', style_dim=64, total_iters=100000, train_img_dir='data/celeba_hq/train', val_batch_size=32, val_img_dir='data/celeba_hq/val', w_hpf=1, weight_decay=0.0001, wing_path='expr/checkpoints/wing.ckpt') Number of parameters of generator: 43467395 Number of parameters of mapping_network: 2438272 Number of parameters of style_encoder: 20916928 Number of parameters of discriminator: 20852290 Number of parameters of fan: 6333603 Initializing generator... Initializing mapping_network... Initializing style_encoder... Initializing discriminator... Saved the aligned image to izutsu_m1.jpg... Saved the aligned image to izutsu_m2.jpg... Saved the aligned image to nitta_m1.jpg... Saved the aligned image to nitta_m2.jpg... Saved the aligned image to okegawa_m1.jpg... Saved the aligned image to okegawa_m2.jpg... Saved the aligned image to okegawa_m3.JPG... Saved the aligned image to yaoi_m1.jpg... Saved the aligned image to yaoi_m2.jpg...
(py38_gan) PS > python main2.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src --ref_dir assets/representative/celeba_hq/ref_1例２：東洋人のソース画像
(py38_gan) PS > python main2.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_3 --ref_dir assets/representative/celeba_hq/ref_1
(py38_gan) PS > python main2.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src --ref_dir assets/representative/celeba_hq/ref_0
(py38_gan) PS > python main2.py --mode sample --num_domains 3 --resume_iter 100000 --w_hpf 0 --checkpoint_dir expr/checkpoints/afhq --result_dir expr/results/afhq --src_dir assets/representative/afhq/src_0 --ref_dir assets/representative/afhq/ref_0
git : 用語 'git' は、コマンドレット、関数、スクリプト ファイル、または操作可能なプログラムの名前として認識されません。名前が正しく記述されていることを確認し、パスが含まれている場合はそのパスが正しいことを確認してから、再試行してください。
発生場所 行:1 文字:1
+ git clone https://github.com/clovaai/stargan-v2.git
+ ~~~
+ CategoryInfo : ObjectNotFound: (git:String) [], CommandNotFound Exception
+ FullyQualifiedErrorId : CommandNotFoundException
・Linuxコマンド 'git' が見つかりません。次の方法でインストールできます: sudo apt install git
sudo apt install git
fatal: destination path 'stargan-v2' already exists and is not an empty directory
(py38_gan) PS C:\anaconda_win\work\stargan-v2> python main.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_0 --ref_dir assets/representative/celeba_hq/ref_0
Traceback (most recent call last):
File "main.py", line 14, in <module>
from munch import Munch
ModuleNotFoundError: No module named 'munch'
pip install munch
(py38_gan) PS C:\anaconda_win\work\stargan-v2> pip install munch Collecting munch Obtaining dependency information for munch from https://files.pythonhosted.org/packages/56/b3/7c69b37f03260a061883bec0e7b05be7117c1b1c85f5212c72c8c2bc3c8c/munch-4.0.0-py2.py3-none-any.whl.metadata Downloading munch-4.0.0-py2.py3-none-any.whl.metadata (5.9 kB) Using cached munch-4.0.0-py2.py3-none-any.whl (9.9 kB) Installing collected packages: munch Successfully installed munch-4.0.0
(py38_gan) PS C:\anaconda_win\work\stargan-v2> python main.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_0 --ref_dir assets/representative/celeba_hq/ref_0
Traceback (most recent call last):
File "main.py", line 20, in <module>
from core.solver import Solver
File "N:\anaconda_win\work\stargan-v2\core\solver.py", line 21, in <module>
from core.model import build_model
File "N:\anaconda_win\work\stargan-v2\core\model.py", line 20, in <module>
from core.wing import FAN
File "N:\anaconda_win\work\stargan-v2\core\wing.py", line 21, in <module>
from skimage.filters import gaussian
ModuleNotFoundError: No module named 'skimage'
pip install scikit-image
(py38_gan) PS C:\anaconda_win\work\stargan-v2> pip install scikit-image
Collecting scikit-image
Obtaining dependency information for scikit-image from https://files.pythonhosted.org/packages/d7/d1/a4c715ad640c9eb0daaa77c4ce561b06e086bec44cbc79083e3548b00b76/scikit_image-0.21.0-cp38-cp38-win_amd64.whl.metadata
Downloading scikit_image-0.21.0-cp38-cp38-win_amd64.whl.metadata (14 kB)
Collecting numpy>=1.21.1 (from scikit-image)
Obtaining dependency information for numpy>=1.21.1 from https://files.pythonhosted.org/packages/69/65/0d47953afa0ad569d12de5f65d964321c208492064c38fe3b0b9744f8d44/numpy-1.24.4-cp38-cp38-win_amd64.whl.metadata
Downloading numpy-1.24.4-cp38-cp38-win_amd64.whl.metadata (5.6 kB)
Requirement already satisfied: scipy>=1.8 in c:\users\izuts\anaconda3\envs\py38_gan\lib\site-packages (from scikit-image) (1.9.0)
Requirement already satisfied: networkx>=2.8 in c:\users\izuts\anaconda3\envs\py38_gan\lib\site-packages (from scikit-image) (3.1)
Requirement already satisfied: pillow>=9.0.1 in c:\users\izuts\anaconda3\envs\py38_gan\lib\site-packages (from scikit-image) (10.0.1)
Collecting imageio>=2.27 (from scikit-image)
Obtaining dependency information for imageio>=2.27 from https://files.pythonhosted.org/packages/f6/37/e21e6f38b93878ba80302e95b8ccd4718d80f0c53055ccae343e606b1e2d/imageio-2.31.5-py3-none-any.whl.metadata
Downloading imageio-2.31.5-py3-none-any.whl.metadata (4.6 kB)
Collecting tifffile>=2022.8.12 (from scikit-image)
Obtaining dependency information for tifffile>=2022.8.12 from https://files.pythonhosted.org/packages/06/a3/68d17088a4f09565bc7341fd20490da8191ec4cddde479daaabbe07bb603/tifffile-2023.7.10-py3-none-any.whl.metadata
Downloading tifffile-2023.7.10-py3-none-any.whl.metadata (31 kB)
Collecting PyWavelets>=1.1.1 (from scikit-image)
Using cached PyWavelets-1.4.1-cp38-cp38-win_amd64.whl (4.2 MB)
Requirement already satisfied: packaging>=21 in c:\users\izuts\anaconda3\envs\py38_gan\lib\site-packages (from scikit-image) (23.2)
Collecting lazy_loader>=0.2 (from scikit-image)
Obtaining dependency information for lazy_loader>=0.2 from https://files.pythonhosted.org/packages/a1/c3/65b3814e155836acacf720e5be3b5757130346670ac454fee29d3eda1381/lazy_loader-0.3-py3-none-any.whl.metadata
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 313.2/313.2 kB 3.2 MB/s eta 0:00:00
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Using cached tifffile-2023.7.10-py3-none-any.whl (220 kB)
Installing collected packages: numpy, lazy_loader, tifffile, PyWavelets, imageio, scikit-image
Attempting uninstall: numpy
Found existing installation: numpy 1.19.5
Uninstalling numpy-1.19.5:
Successfully uninstalled numpy-1.19.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
openvino 2022.1.0 requires numpy<1.20,>=1.16.6, but you have numpy 1.24.4 which is incompatible.
Successfully installed PyWavelets-1.4.1 imageio-2.31.5 lazy_loader-0.3 numpy-1.24.4 scikit-image-0.21.0 tifffile-2023.7.10
(py38_gan) PS C:\anaconda_win\work\stargan-v2> python main.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_0 --ref_dir assets/representative/celeba_hq/ref_0
Traceback (most recent call last):
File "main.py", line 20, in <module>
from core.solver import Solver
File "N:\anaconda_win\work\stargan-v2\core\solver.py", line 24, in <module>
import core.utils as utils
File "N:\anaconda_win\work\stargan-v2\core\utils.py", line 18, in <module>
import ffmpeg
ModuleNotFoundError: No module named 'ffmpeg'
pip install ffmpeg
(py38_gan) PS C:\anaconda_win\work\stargan-v2> pip install ffmpeg Collecting ffmpeg Using cached ffmpeg-1.4-py3-none-any.whl Installing collected packages: ffmpeg Successfully installed ffmpeg-1.4
(py38_gan) PS C:\anaconda_win\work\stargan-v2> python main.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_0 --ref_dir assets/representative/celeba_hq/ref_0
Namespace(batch_size=8, beta1=0.0, beta2=0.99, checkpoint_dir='expr/checkpoints/celeba_hq', ds_iter=100000, eval_dir='expr/eval', eval_every=50000, f_lr=1e-06, hidden_dim=512, img_size=256, inp_dir='assets/representative/custom/female', lambda_cyc=1, lambda_ds=1, lambda_reg=1, lambda_sty=1, latent_dim=16, lm_path='expr/checkpoints/celeba_lm_mean.npz', lr=0.0001, mode='sample', num_domains=2, num_outs_per_domain=10, num_workers=4, out_dir='assets/representative/celeba_hq/src/female', print_every=10, randcrop_prob=0.5, ref_dir='assets/representative/celeba_hq/ref_0', result_dir='expr/results/celeba_hq', resume_iter=100000, sample_dir='expr/samples', sample_every=5000, save_every=10000, seed=777, src_dir='assets/representative/celeba_hq/src_0', style_dim=64, total_iters=100000, train_img_dir='data/celeba_hq/train', val_batch_size=32, val_img_dir='data/celeba_hq/val', w_hpf=1.0, weight_decay=0.0001, wing_path='expr/checkpoints/wing.ckpt')
Number of parameters of generator: 43467395
Number of parameters of mapping_network: 2438272
Number of parameters of style_encoder: 20916928
Number of parameters of discriminator: 20852290
Number of parameters of fan: 6333603
Traceback (most recent call last):
File "main.py", line 182, in <module>
main(args)
File "main.py", line 37, in main
solver = Solver(args)
File "N:\anaconda_win\work\stargan-v2\core\solver.py", line 58, in __init__
self.ckptios = [CheckpointIO(ospj(args.checkpoint_dir, '{:06d}_nets_ema.ckpt'), data_parallel=True, **self.nets_ema)]
File "N:\anaconda_win\work\stargan-v2\core\checkpoint.py", line 17, in __init__
os.makedirs(os.path.dirname(fname_template), exist_ok=True)
File "C:\Users\izuts\anaconda3\envs\py38_gan\lib\os.py", line 223, in makedirs
mkdir(name, mode)
FileNotFoundError: [WinError 3] 指定されたパスが見つかりません。: '{:'
else:
# self.ckptios = [CheckpointIO(ospj(args.checkpoint_dir, '{:06d}_nets_ema.ckpt'), data_parallel=True, **self.nets_ema)]
self.ckptios = [CheckpointIO(ospj(args.checkpoint_dir, '100000_nets_ema.ckpt'), data_parallel=True, **self.nets_ema)](py38_gan) PS C:\anaconda_win\work\stargan-v2> python main.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_0 --ref_dir assets/representative/celeba_hq/ref_0
Namespace(batch_size=8, beta1=0.0, beta2=0.99, checkpoint_dir='expr/checkpoints/celeba_hq', ds_iter=100000, eval_dir='expr/eval', eval_every=50000, f_lr=1e-06, hidden_dim=512, img_size=256, inp_dir='assets/representative/custom/female', lambda_cyc=1, lambda_ds=1, lambda_reg=1, lambda_sty=1, latent_dim=16, lm_path='expr/checkpoints/celeba_lm_mean.npz', lr=0.0001, mode='sample', num_domains=2, num_outs_per_domain=10, num_workers=4, out_dir='assets/representative/celeba_hq/src/female', print_every=10, randcrop_prob=0.5, ref_dir='assets/representative/celeba_hq/ref_0', result_dir='expr/results/celeba_hq', resume_iter=100000, sample_dir='expr/samples', sample_every=5000, save_every=10000, seed=777, src_dir='assets/representative/celeba_hq/src_0', style_dim=64, total_iters=100000, train_img_dir='data/celeba_hq/train', val_batch_size=32, val_img_dir='data/celeba_hq/val', w_hpf=1.0, weight_decay=0.0001, wing_path='expr/checkpoints/wing.ckpt')
Number of parameters of generator: 43467395
Number of parameters of mapping_network: 2438272
Number of parameters of style_encoder: 20916928
Number of parameters of discriminator: 20852290
Number of parameters of fan: 6333603
Initializing generator...
Initializing mapping_network...
Initializing style_encoder...
Initializing discriminator...
Preparing DataLoader for the generation phase...
Preparing DataLoader for the generation phase...
Loading checkpoint from expr/checkpoints/celeba_hq\100000_nets_ema.ckpt...
Traceback (most recent call last):
File "main.py", line 182, in <module>
main(args)
File "main.py", line 73, in main
solver.sample(loaders)
File "C:\Users\izuts\anaconda3\envs\py38_gan\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "N:\anaconda_win\work\stargan-v2\core\solver.py", line 179, in sample
self._load_checkpoint(args.resume_iter)
File "N:\anaconda_win\work\stargan-v2\core\solver.py", line 74, in _load_checkpoint
ckptio.load(step)
File "N:\anaconda_win\work\stargan-v2\core\checkpoint.py", line 48, in load
module.module.load_state_dict(module_dict[name])
File "C:\Users\izuts\anaconda3\envs\py38_gan\lib\site-packages\torch\nn\modules\module.py", line 2152, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Generator:
Missing key(s) in state_dict: "hpf.filter".
if self.data_parallel:
# module.module.load_state_dict(module_dict[name])
module.module.load_state_dict(module_dict[name], False)(py38_gan) PS C:\anaconda_win\work\stargan-v2> python main.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 --checkpoint_dir expr/checkpoints/celeba_hq --result_dir expr/results/celeba_hq --src_dir assets/representative/celeba_hq/src_0 --ref_dir assets/representative/celeba_hq/ref_0
Namespace(batch_size=8, beta1=0.0, beta2=0.99, checkpoint_dir='expr/checkpoints/celeba_hq', ds_iter=100000, eval_dir='expr/eval', eval_every=50000, f_lr=1e-06, hidden_dim=512, img_size=256, inp_dir='assets/representative/custom/female', lambda_cyc=1, lambda_ds=1, lambda_reg=1, lambda_sty=1, latent_dim=16, lm_path='expr/checkpoints/celeba_lm_mean.npz', lr=0.0001, mode='sample', num_domains=2, num_outs_per_domain=10, num_workers=4, out_dir='assets/representative/celeba_hq/src/female', print_every=10, randcrop_prob=0.5, ref_dir='assets/representative/celeba_hq/ref_0', result_dir='expr/results/celeba_hq', resume_iter=100000, sample_dir='expr/samples', sample_every=5000, save_every=10000, seed=777, src_dir='assets/representative/celeba_hq/src_0', style_dim=64, total_iters=100000, train_img_dir='data/celeba_hq/train', val_batch_size=32, val_img_dir='data/celeba_hq/val', w_hpf=1.0, weight_decay=0.0001, wing_path='expr/checkpoints/wing.ckpt')
Number of parameters of generator: 43467395
Number of parameters of mapping_network: 2438272
Number of parameters of style_encoder: 20916928
Number of parameters of discriminator: 20852290
Number of parameters of fan: 6333603
Initializing generator...
Initializing mapping_network...
Initializing style_encoder...
Initializing discriminator...
Preparing DataLoader for the generation phase...
Preparing DataLoader for the generation phase...
Loading checkpoint from expr/checkpoints/celeba_hq\100000_nets_ema.ckpt...
Working on expr/results/celeba_hq\reference.jpg...
Working on expr/results/celeba_hq\video_ref.mp4...
video_ref: 100%|█████████████████████████████████████████| 2/2 [00:00<?, ?it/s]
Traceback (most recent call last):
File "main.py", line 182, in <module>
main(args)
File "main.py", line 73, in main
solver.sample(loaders)
File "C:\Users\izuts\anaconda3\envs\py38_gan\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "N:\anaconda_win\work\stargan-v2\core\solver.py", line 190, in sample
utils.video_ref(nets_ema, args, src.x, ref.x, ref.y, fname)
File "C:\Users\izuts\anaconda3\envs\py38_gan\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "N:\anaconda_win\work\stargan-v2\core\utils.py", line 222, in video_ref
video.append(frames[-1:])
UnboundLocalError: local variable 'frames' referenced before assignment
# fname = ospj(args.result_dir, 'video_ref.mp4')
# print('Working on {}...'.format(fname))
# utils.video_ref(nets_ema, args, src.x, ref.x, ref.y, fname)(py38_gan) PS C:\anaconda_win\work\stargan-v2> python main.py --mode align --inp_dir assets/representative/custom_1/male --out_dir assets/representative/celeba_hq/ref_test/male
Namespace(batch_size=8, beta1=0.0, beta2=0.99, checkpoint_dir='expr/checkpoints', ds_iter=100000, eval_dir='expr/eval', eval_every=50000, f_lr=1e-06, hidden_dim=512, img_size=256, inp_dir='assets/representative/custom_1/male', lambda_cyc=1, lambda_ds=1, lambda_reg=1, lambda_sty=1, latent_dim=16, lm_path='expr/checkpoints/celeba_lm_mean.npz', lr=0.0001, mode='align', num_domains=2, num_outs_per_domain=10, num_workers=4, out_dir='assets/representative/celeba_hq/ref_test/male', print_every=10, randcrop_prob=0.5, ref_dir='assets/representative/celeba_hq/ref', result_dir='expr/results', resume_iter=0, sample_dir='expr/samples', sample_every=5000, save_every=10000, seed=777, src_dir='assets/representative/celeba_hq/src', style_dim=64, total_iters=100000, train_img_dir='data/celeba_hq/train', val_batch_size=32, val_img_dir='data/celeba_hq/val', w_hpf=1, weight_decay=0.0001, wing_path='expr/checkpoints/wing.ckpt')
Number of parameters of generator: 43467395
Number of parameters of mapping_network: 2438272
Number of parameters of style_encoder: 20916928
Number of parameters of discriminator: 20852290
Number of parameters of fan: 6333603
Initializing generator...
Initializing mapping_network...
Initializing style_encoder...
Initializing discriminator...
Traceback (most recent call last):
File "main.py", line 182, in <module>
main(args)
File "main.py", line 78, in main
align_faces(args, args.inp_dir, args.out_dir)
File "N:\anaconda_win\work\stargan-v2\core\wing.py", line 427, in align_faces
x_aligned = aligner.align(x)
File "N:\anaconda_win\work\stargan-v2\core\wing.py", line 305, in align
img_np, landmarks = pad_mirror(img_np, landmarks)
File "N:\anaconda_win\work\stargan-v2\core\wing.py", line 392, in pad_mirror
small_blurred = gaussian(cv2.resize(img, (W, H)), H//100, multichannel=True)
TypeError: gaussian() got an unexpected keyword argument 'multichannel'
# small_blurred = gaussian(cv2.resize(img, (W, H)), H//100, multichannel=True)
small_blurred = gaussian(cv2.resize(img, (W, H)), H//100, channel_axis=1)
conda create -n stargan-v2 python=3.6.7 conda activate stargan-v2 conda install -y pytorch=1.4.0 torchvision=0.5.0 cudatoolkit=10.0 -c pytorch conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge pip install opencv-python==4.1.2.30 ffmpeg-python==0.2.0 scikit-image==0.16.2 pip install pillow==7.0.0 scipy==1.2.1 tqdm==4.43.0 munch==2.5.0※ 現在ネット上に存在しないパッケージがありインストールできない
bash download.sh celeba-hq-dataset・「AFHQ.」（動物の顔）
bash download.sh afhq-dataset
| ダウンロード・フォルダ名 | 画像枚数 | |||
| data | celeba_hq | train | female | 17,943 |
| male | 10.057 | |||
| val | female | 1,000 | ||
| male | 1,000 | |||
| afhq | train | cat | 5,153 | |
| dog | 4,739 | |||
| wild | 4,738 | |||
| val | cat | 500 | ||
| dog | 500 | |||
| wild | 500 | |||
bash download.sh pretrained-network-celeba-hq bash download.sh wing・「AFHQ.」（動物の顔）
bash download.sh pretrained-network-afhq※ windows では「bash」コマンドが使えないので Linux で実行してできたファイルを配置して使用
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