StarGAN のバックアップ(No.1)

(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

仮想環境「py38_gan」を構築

(py38) PS > conda create -n py38_gan python=3.8

▼「conda create -n py38_gan python=3.8」ログ

(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

できた環境にアクティベートして環境確認

(py38) PS > conda activate py38_gan
(py38_gan) PS > conda info -e

▼「conda activate py38_gan」ログ

(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

↑

新規の仮想環境「py38_gan」にこれまでのプロジェクトに必要なパッケージをインストール †

パッケージのインストール

(py38_gan) PS > cd /anaconda_win/work/
(py38_gan) PS > pip install -r requirements.txt

▼「anaconda_win/work/requirements.txt」設定ファイル

# 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

▼「pip install -r requirements.txt」ログ

(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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Collecting transformers==4.16.2 (from -r requirements.txt (line 5))
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Collecting diffusers==0.2.4 (from -r requirements.txt (line 6))
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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

↑

「StarGAN-V2」の導入 †

オフィシャルサイトから stargan-v2一式をダウンロード

(py38_gan) PS > cd /anaconda_win/work/
(py38_gan) PS > git clone https://github.com/clovaai/stargan-v2.git

▼「git clone https://github.com/clovaai/stargan-v2.git」ログ

(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.

学習済みモデルのダウンロード
・上記の『事前準備』でダウンロードしたファイルを解凍し「work/」フォルダ以降を上書き配置する

「StarGAN-V2」実行に必要な不足しているパッケージをインストール

(py38_gan) PS > pip install munch scikit-image ffmpeg

▼「pip install munch scikit-image ffmpeg」ログ

(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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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
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  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
  Using cached lazy_loader-0.3-py3-none-any.whl.metadata (4.3 kB)
Using cached munch-4.0.0-py2.py3-none-any.whl (9.9 kB)
Using cached scikit_image-0.21.0-cp38-cp38-win_amd64.whl (22.7 MB)
Using cached imageio-2.31.5-py3-none-any.whl (313 kB)
Using cached lazy_loader-0.3-py3-none-any.whl (9.1 kB)
Using cached numpy-1.24.4-cp38-cp38-win_amd64.whl (14.9 MB)
Using cached tifffile-2023.7.10-py3-none-any.whl (220 kB)
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

↑

StarGAN-V2 の実行 †

「py38_gan」仮想環境下で実行する（異なる環境の場合、下記コマンドでアクティベートする）
```
conda activate py38_gan
```

カレントディレクトリを移動する

(py38_gan) PS > cd /anaconda_win/work/stargan-v2/

↑

付属サンプル画像を使って実行する †

コマンド

(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

▼「python main.py --mode sample ... 」ログ

(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...

実行結果

・オフィシャルサイトのコマンドパラメータを一部変更（実行時間を短縮するため）
　「src」→「src_0」「ref」→「ref_0」
　「src」「ref」フォルダ内（female/,male/）から1画像づつ抽出して「src_0」「ref_0」としている

・エラーが発生した場合は『発生したエラーと対処法法』に従って対処

・エラーなく終了すれば「stargan-v2/expr/results/celeba_hq/」に画像ファイル「reference.jpg」が作成される

↑

プログラムを改良する †

「main.py」をコピーして「main2.py」を作成
プログラム終了後にできた画像ファイルを画面に収まるように縮小表示する
プログラムの終了は画像表示ウインドウにフォーカスがある状態で 'Esc' キーを押す

コマンド

(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

▼「main2.py」ソースコード

# -*- 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)

↑

カスタム画像を変換する †

任意の顔写真を用意して全体に占める顔の割合がサンプル画像と同じ割合になるようトリミングする

次のコマンドを実行して、さらに細かい回転とトリミングをおこなう
「--inp_dir」パラメータで指定したフォルダ内の画像を「--out_dir」パラメータで指定したフォルダ内に作成

(py38_gan) PS > python main.py --mode align --inp_dir assets/representative/custom_1/male --out_dir assets/representative/celeba_hq/ref_test/male

▼「python main.py --mode align ... 」ログ

(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...

実行結果
・上記の例では「assets/representative/custom_1/」フォルダ内の画像を「assets/representative/celeba_hq/ref_test/」フォルダ内に作成

・エラーが発生した場合は『発生したエラーと対処法法』に従って対処

↑

カスタム画像を使って実行する †

用意した画像（プロジェクトのサンプルを含む）

コマンド
例１：欧米人のソース画像

(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 --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

実行結果

　※ 顔画像入手先 → FREEPIK

↑

各種実行例 †

人の顔

(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

動物の顔

(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

動物の顔プロジェクトのサンプル画像

↑

発生したエラーと対処法法 †

↑

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 †

エラー内容

▼　gitコマンドエラー: ログ

・Windows

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

対処法：パッケージのインストール
・Windows
　オフィシャルサイトからダウンロードする
　デフォールト設定でインストールする　参考：→ WindowsにGitをインストールする手順
・Linux
```
sudo apt install git
```

↑

fatal: destination path 'stargan-v2' already exists and is not an empty directory †

エラー内容

▼　'stargan-v2' フォルダが既に存在する: ログ

▲　'stargan-v2' フォルダが既に存在する: ログ
```
fatal: destination path 'stargan-v2' already exists and is not an empty directory
```

対処法：「stargan-v2」ディレクトリを削除する
Update ファイルは git コマンド実行後に上書き配置する

↑

ModuleNotFoundError: No module named 'munch' †

エラー内容

▼　ModuleNotFoundError: ログ

(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'

対処法：パッケージのインストール

pip install munch

▼「pip install munch」ログ

(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

↑

ModuleNotFoundError: No module named 'skimage' †

エラー内容

▼　ModuleNotFoundError: ログ

(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'

対処法：パッケージのインストール

pip install scikit-image

▼「pip install scikit-image」ログ

(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
  Downloading lazy_loader-0.3-py3-none-any.whl.metadata (4.3 kB)
Using cached scikit_image-0.21.0-cp38-cp38-win_amd64.whl (22.7 MB)
Downloading imageio-2.31.5-py3-none-any.whl (313 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 313.2/313.2 kB 3.2 MB/s eta 0:00:00
Using cached lazy_loader-0.3-py3-none-any.whl (9.1 kB)
Using cached numpy-1.24.4-cp38-cp38-win_amd64.whl (14.9 MB)
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

↑

ModuleNotFoundError: No module named 'ffmpeg' †

エラー内容

▼　ModuleNotFoundError: ログ

(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'

対処法：パッケージのインストール

pip install ffmpeg

▼「pip install ffmpeg」ログ

(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

↑

FileNotFoundError: [WinError 3] 指定されたパスが見つかりません。: '{:' †

エラー内容

▼　FileNotFoundError: ログ

(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。: '{:'

参考サイト
Path error upon running the example you provided to transform a custom image #3

対処法：「stargan-v2/core/solver.py」 #58 変更

        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)]

↑

RuntimeError: Error(s) in loading state_dict for Generator: Missing key(s) in state_dict: "hpf.filter" †

エラー内容

▼　RuntimeError: ログ

(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".

参考サイト
Error when loading state_dict for Generator #103

対処法：「stargan-v2/core/checkpoint.py」 #48 変更

            if self.data_parallel:
#                module.module.load_state_dict(module_dict[name])
                module.module.load_state_dict(module_dict[name], False)

↑

UnboundLocalError: local variable 'frames' referenced before assignment †

エラー内容

▼　UnboundLocalError: ログ

(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

対処法：「stargan-v2/core/solver.py」 #187 変更
生成画像を保存した後、保管ビデオの作成時にエラーとなる（詳細不明）、保管ビデオの作成を止める
```
#        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)
```

↑

TypeError: gaussian() got an unexpected keyword argument 'multichannel' †

エラー内容

▼　TypeError: ログ

(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'

参考サイト
TypeError: rescale() got an unexpected keyword argument 'multichannel' #6
scikit-image API reference

対処法：「stargan-v2/core/wing.py」 #392 変更

#    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)

↑

StarGAN-V2 ここまでのまとめ †

オフィシャルサイト StarGAN v2 - Official PyTorch Implementation より

↑

Software installation（環境構築） †

オフィシャルサイトの手順

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

いろいろ試して、anaconda python3.8 の仮想環境でパッケージバージョンを指定せず構築した

↑

Datasets and pre-trained networks（データセットと学習済みモデル） †

データセット
・「CelebA-HQ.」（人の顔）
```
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

※ 今回使用しない（学習を実行しないので）

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

学習済みモデルのダウンロード
・「CelebA-HQ.」（人の顔）
```
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

↑

Generating interpolation videos（補間ビデオの生成） †

『発生したエラーと対処法法』の対処で出力画像は得ることができた
同時に生成できるはずの補間ビデオはエラー発生のため今のところ生成できていない

↑

Training networks（学習の実行） †

現状の生成画像は日本人の顔が欧米人の顔のように見えるが、日本人の顔で学習し直せばより完成度が上がるかもしれない
StarGAN v2 を最初からトレーニングするには上記のデータセットを使用し、Tesla V100 GPU でおよそ３日の時間を要するらしい！
参考 → NVIDIA GPUの性能比較

↑

Google Colaboratory で動かす †

「Google Colaboratory」で動かす場合は、『StarGAN-V2』の導入から手順を実行する

↑

更新履歴 †

2023/10/10 初版

↑

参考資料 †

StarGAN

GAN
- NVIDIA StyleGAN2-ADA — Official PyTorch implementation
- StyleGAN-Humanで、全身画像を生成する

データセット

StarGAN のバックアップ(No.1)

顔の合成：StarGAN-v2 †

動作環境の準備と設定 †

事前準備 †

新しく Python3.8 仮想環境「py38_gan」を構築 †

新規の仮想環境「py38_gan」にこれまでのプロジェクトに必要なパッケージをインストール †

「StarGAN-V2」の導入 †

StarGAN-V2 の実行 †

付属サンプル画像を使って実行する †

プログラムを改良する †

カスタム画像を変換する †

カスタム画像を使って実行する †

各種実行例 †

発生したエラーと対処法法 †

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 †

fatal: destination path 'stargan-v2' already exists and is not an empty directory †

ModuleNotFoundError: No module named 'munch' †

ModuleNotFoundError: No module named 'skimage' †

ModuleNotFoundError: No module named 'ffmpeg' †

FileNotFoundError: [WinError 3] 指定されたパスが見つかりません。: '{:' †

RuntimeError: Error(s) in loading state_dict for Generator: Missing key(s) in state_dict: "hpf.filter" †

UnboundLocalError: local variable 'frames' referenced before assignment †

TypeError: gaussian() got an unexpected keyword argument 'multichannel' †

StarGAN-V2 ここまでのまとめ †

Software installation（環境構築） †

Datasets and pre-trained networks（データセットと学習済みモデル） †

Generating interpolation videos（補間ビデオの生成） †

Training networks（学習の実行） †

Google Colaboratory で動かす †

更新履歴 †

参考資料 †

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 †