Major revision: Support Pypi (#37)

* reorganize

* update inference

* update inference

* format

.github/workflows/publish-pip.yml

+name: PyPI Publish
+
+on: push
+
+jobs:
+  build-n-publish:
+    runs-on: ubuntu-latest
+    if: startsWith(github.event.ref, 'refs/tags')
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.8
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Install PyTorch (cpu)
+        run: pip install torch==1.7.0+cpu torchvision==0.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install dependencies
+        run: pip install -r requirements.txt
+      - name: Build and install
+        run: rm -rf .eggs && pip install -e .
+      - name: Build for distribution
+        # remove bdist_wheel for pip installation with compiling cuda extensions
+        run: python setup.py sdist
+      - name: Publish distribution to PyPI
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          password: ${{ secrets.PYPI_API_TOKEN }}

.github/workflows/pylint.yml

@@ -25,5 +25,5 @@ jobs:
     - name: Lint
       run: |
         flake8 .
-        isort --check-only --diff data/ archs/ models/ train.py inference_gfpgan_full.py
-        yapf -r -d data/ archs/ models/ train.py inference_gfpgan_full.py
+        isort --check-only --diff gfpgan/ scripts/ inference_gfpgan.py setup.py
+        yapf -r -d gfpgan/ scripts/ inference_gfpgan.py setup.py

.gitignore

-.vscode
+# ignored folders
 datasets/*
 experiments/*
+results/*
 tb_logger/*
+wandb/*
+tmp/*
 
-# ignored files
 version.py
-
-# ignored files with suffix
-*.html
-*.png
-*.jpeg
-*.jpg
-*.gif
-*.pth
-*.zip
-
-# template
+.vscode
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -39,6 +31,8 @@ parts/
 sdist/
 var/
 wheels/
+pip-wheel-metadata/
+share/python-wheels/
 *.egg-info/
 .installed.cfg
 *.egg
@@ -57,12 +51,14 @@ pip-delete-this-directory.txt
 # Unit test / coverage reports
 htmlcov/
 .tox/
+.nox/
 .coverage
 .coverage.*
 .cache
 nosetests.xml
 coverage.xml
 *.cover
+*.py,cover
 .hypothesis/
 .pytest_cache/
 
@@ -74,6 +70,7 @@ coverage.xml
 *.log
 local_settings.py
 db.sqlite3
+db.sqlite3-journal
 
 # Flask stuff:
 instance/
@@ -91,11 +88,26 @@ target/
 # Jupyter Notebook
 .ipynb_checkpoints
 
+# IPython
+profile_default/
+ipython_config.py
+
 # pyenv
 .python-version
 
-# celery beat schedule file
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
 celerybeat-schedule
+celerybeat.pid
 
 # SageMath parsed files
 *.sage.py
@@ -121,3 +133,8 @@ venv.bak/
 
 # mypy
 .mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

MANIFEST.in

+include assets/*
+include inputs/*
+include scripts/*.py
+include inference_gfpgan.py
+include VERSION
+include LICENSE
+include requirements.txt
+include gfpgan/weights/README.md

PaperModel.md

@@ -27,6 +27,7 @@ If you want want to use the original model in our paper, please follow the instr
     pip install facexlib
 
     pip install -r requirements.txt
+    python setup.py develop
 
     # remember to set BASICSR_JIT=True before your running commands
     ```
@@ -45,6 +46,7 @@ If you want want to use the original model in our paper, please follow the instr
     pip install facexlib
 
     pip install -r requirements.txt
+    python setup.py develop
     ```
 
 ## :zap: Quick Inference
@@ -58,17 +60,17 @@ wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth
 - Option 1: Load extensions just-in-time(JIT)
 
     ```bash
-    BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
+    BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
 
     # for aligned images
-    BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
+    BASICSR_JIT=True python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
     ```
 
 - Option 2: Have successfully compiled extensions during installation
 
     ```bash
-    python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
+    python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
 
     # for aligned images
-    python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
+    python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1 --aligned
     ```

README.md

 # GFPGAN (CVPR 2021)
 
 [![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
+[![PyPI](https://img.shields.io/pypi/v/gfpgan)](https://pypi.org/project/gfpgan/)
 [![Open issue](https://isitmaintained.com/badge/open/TencentARC/GFPGAN.svg)](https://github.com/TencentARC/GFPGAN/issues)
 [![LICENSE](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE)
 [![python lint](https://github.com/TencentARC/GFPGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml)
+[![Publish-pip](https://github.com/TencentARC/GFPGAN/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/publish-pip.yml)
 
 1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>; (Another [Colab Demo](https://colab.research.google.com/drive/1Oa1WwKB4M4l1GmR7CtswDVgOCOeSLChA?usp=sharing) for the original paper model)
 1. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**.
@@ -59,6 +61,7 @@ If you want want to use the original model in our paper, please see [PaperModel.
     pip install facexlib
 
     pip install -r requirements.txt
+    python setup.py develop
     ```
 
 ## :zap: Quick Inference
@@ -72,7 +75,7 @@ wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1
 **Inference!**
 
 ```bash
-python inference_gfpgan_full.py --upscale_factor 2 --test_path inputs/whole_imgs --save_root results
+python inference_gfpgan.py --upscale_factor 2 --test_path inputs/whole_imgs --save_root results
 ```
 
 ## :european_castle: Model Zoo
@@ -90,10 +93,9 @@ You could improve it according to your own needs.
 1. More high quality faces can improve the restoration quality.
 2. You may need to perform some pre-processing, such as beauty makeup.
 
-
 **Procedures**
 
-(You can try a simple version ( `train_gfpgan_v1_simple.yml`) that does not require face component landmarks.)
+(You can try a simple version ( `options/train_gfpgan_v1_simple.yml`) that does not require face component landmarks.)
 
 1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
 
@@ -102,11 +104,11 @@ You could improve it according to your own needs.
     1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
     1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)
 
-1. Modify the configuration file `train_gfpgan_v1.yml` accordingly.
+1. Modify the configuration file `options/train_gfpgan_v1.yml` accordingly.
 
 1. Training
 
-> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
+> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 gfpgan/train.py -opt options/train_gfpgan_v1.yml --launcher pytorch
 
 ## :scroll: License and Acknowledgement
 

VERSION

+0.2.1

gfpgan/__init__.py

+# flake8: noqa
+from .archs import *
+from .data import *
+from .models import *
+from .utils import *
+from .version import __gitsha__, __version__

archs/__init__.py → gfpgan/archs/__init__.py

 import importlib
-from os import path as osp
-
 from basicsr.utils import scandir
+from os import path as osp
 
 # automatically scan and import arch modules for registry
-# scan all the files under the 'archs' folder and collect files ending with
-# '_arch.py'
+# scan all the files that end with '_arch.py' under the archs folder
 arch_folder = osp.dirname(osp.abspath(__file__))
 arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
 # import all the arch modules
-_arch_modules = [importlib.import_module(f'archs.{file_name}') for file_name in arch_filenames]
+_arch_modules = [importlib.import_module(f'gfpgan.archs.{file_name}') for file_name in arch_filenames]

archs/arcface_arch.py → gfpgan/archs/arcface_arch.py

 import torch.nn as nn
-
 from basicsr.utils.registry import ARCH_REGISTRY
 
 

archs/gfpganv1_arch.py → gfpgan/archs/gfpganv1_arch.py

 import math
 import random
 import torch
-from torch import nn
-from torch.nn import functional as F
-
 from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU,
                                           StyleGAN2Generator)
 from basicsr.ops.fused_act import FusedLeakyReLU
 from basicsr.utils.registry import ARCH_REGISTRY
+from torch import nn
+from torch.nn import functional as F
 
 
 class StyleGAN2GeneratorSFT(StyleGAN2Generator):

archs/stylegan2_clean_arch.py → gfpgan/archs/stylegan2_clean_arch.py

 import math
 import random
 import torch
-from torch import nn
-from torch.nn import functional as F
-
 from basicsr.archs.arch_util import default_init_weights
 from basicsr.utils.registry import ARCH_REGISTRY
+from torch import nn
+from torch.nn import functional as F
 
 
 class NormStyleCode(nn.Module):

data/__init__.py → gfpgan/data/__init__.py

 import importlib
-from os import path as osp
-
 from basicsr.utils import scandir
+from os import path as osp
 
 # automatically scan and import dataset modules for registry
-# scan all the files under the data folder with '_dataset' in file names
+# scan all the files that end with '_dataset.py' under the data folder
 data_folder = osp.dirname(osp.abspath(__file__))
 dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
 # import all the dataset modules
-_dataset_modules = [importlib.import_module(f'data.{file_name}') for file_name in dataset_filenames]
+_dataset_modules = [importlib.import_module(f'gfpgan.data.{file_name}') for file_name in dataset_filenames]

data/ffhq_degradation_dataset.py → gfpgan/data/ffhq_degradation_dataset.py

@@ -4,14 +4,13 @@ import numpy as np
 import os.path as osp
 import torch
 import torch.utils.data as data
-from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation,
-                                               normalize)
-
 from basicsr.data import degradations as degradations
 from basicsr.data.data_util import paths_from_folder
 from basicsr.data.transforms import augment
 from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
 from basicsr.utils.registry import DATASET_REGISTRY
+from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation,
+                                               normalize)
 
 
 @DATASET_REGISTRY.register()

models/__init__.py → gfpgan/models/__init__.py

 import importlib
-from os import path as osp
-
 from basicsr.utils import scandir
+from os import path as osp
 
 # automatically scan and import model modules for registry
-# scan all the files under the 'models' folder and collect files ending with
-# '_model.py'
+# scan all the files that end with '_model.py' under the model folder
 model_folder = osp.dirname(osp.abspath(__file__))
 model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
 # import all the model modules
-_model_modules = [importlib.import_module(f'models.{file_name}') for file_name in model_filenames]
+_model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames]

models/gfpgan_model.py → gfpgan/models/gfpgan_model.py

 import math
 import os.path as osp
 import torch
-from collections import OrderedDict
-from torch.nn import functional as F
-from torchvision.ops import roi_align
-from tqdm import tqdm
-
 from basicsr.archs import build_network
 from basicsr.losses import build_loss
 from basicsr.losses.losses import r1_penalty
@@ -13,6 +8,10 @@ from basicsr.metrics import calculate_metric
 from basicsr.models.base_model import BaseModel
 from basicsr.utils import get_root_logger, imwrite, tensor2img
 from basicsr.utils.registry import MODEL_REGISTRY
+from collections import OrderedDict
+from torch.nn import functional as F
+from torchvision.ops import roi_align
+from tqdm import tqdm
 
 
 @MODEL_REGISTRY.register()

train.py → gfpgan/train.py

+# flake8: noqa
 import os.path as osp
-
-import archs  # noqa: F401
-import data  # noqa: F401
-import models  # noqa: F401
 from basicsr.train import train_pipeline
 
+import gfpgan.archs
+import gfpgan.data
+import gfpgan.models
+
 if __name__ == '__main__':
-    root_path = osp.abspath(osp.join(__file__, osp.pardir))
+    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
     train_pipeline(root_path)

gfpgan/utils.py

+import cv2
+import os
+import torch
+from basicsr.utils import img2tensor, tensor2img
+from facexlib.utils.face_restoration_helper import FaceRestoreHelper
+from torch.hub import download_url_to_file, get_dir
+from torchvision.transforms.functional import normalize
+from urllib.parse import urlparse
+
+from gfpgan.archs.gfpganv1_arch import GFPGANv1
+from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+class GFPGANer():
+
+    def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None):
+        self.upscale = upscale
+        self.bg_upsampler = bg_upsampler
+
+        # initialize model
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        # initialize the GFP-GAN
+        if arch == 'clean':
+            self.gfpgan = GFPGANv1Clean(
+                out_size=512,
+                num_style_feat=512,
+                channel_multiplier=channel_multiplier,
+                decoder_load_path=None,
+                fix_decoder=False,
+                num_mlp=8,
+                input_is_latent=True,
+                different_w=True,
+                narrow=1,
+                sft_half=True)
+        else:
+            self.gfpgan = GFPGANv1(
+                out_size=512,
+                num_style_feat=512,
+                channel_multiplier=channel_multiplier,
+                decoder_load_path=None,
+                fix_decoder=True,
+                num_mlp=8,
+                input_is_latent=True,
+                different_w=True,
+                narrow=1,
+                sft_half=True)
+        # initialize face helper
+        self.face_helper = FaceRestoreHelper(
+            upscale,
+            face_size=512,
+            crop_ratio=(1, 1),
+            det_model='retinaface_resnet50',
+            save_ext='png',
+            device=self.device)
+
+        if model_path.startswith('https://'):
+            model_path = load_file_from_url(url=model_path, model_dir='gfpgan/weights', progress=True, file_name=None)
+        loadnet = torch.load(model_path)
+        if 'params_ema' in loadnet:
+            keyname = 'params_ema'
+        else:
+            keyname = 'params'
+        self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
+        self.gfpgan.eval()
+        self.gfpgan = self.gfpgan.to(self.device)
+
+    @torch.no_grad()
+    def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True):
+        self.face_helper.clean_all()
+
+        if has_aligned:
+            img = cv2.resize(img, (512, 512))
+            self.face_helper.cropped_faces = [img]
+        else:
+            self.face_helper.read_image(img)
+            # get face landmarks for each face
+            self.face_helper.get_face_landmarks_5(only_center_face=only_center_face)
+            # align and warp each face
+            self.face_helper.align_warp_face()
+
+        # face restoration
+        for cropped_face in self.face_helper.cropped_faces:
+            # prepare data
+            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+            cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
+
+            try:
+                output = self.gfpgan(cropped_face_t, return_rgb=False)[0]
+                # convert to image
+                restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
+            except RuntimeError as error:
+                print(f'\tFailed inference for GFPGAN: {error}.')
+                restored_face = cropped_face
+
+            restored_face = restored_face.astype('uint8')
+            self.face_helper.add_restored_face(restored_face)
+
+        if not has_aligned and paste_back:
+
+            if self.bg_upsampler is not None:
+                # Now only support RealESRGAN
+                bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
+            else:
+                bg_img = None
+
+            self.face_helper.get_inverse_affine(None)
+            # paste each restored face to the input image
+            restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img)
+            return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img
+        else:
+            return self.face_helper.cropped_faces, self.face_helper.restored_faces, None
+
+
+def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
+    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+    """
+    if model_dir is None:
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, 'checkpoints')
+
+    os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
+
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    if file_name is not None:
+        filename = file_name
+    cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
+    return cached_file

gfpgan/weights/README.md

+# Weights
+
+Put the downloaded weights to this folder.

inference_gfpgan.py

+import argparse
+import cv2
+import glob
+import numpy as np
+import os
+import torch
+from basicsr.utils import imwrite
+
+from gfpgan import GFPGANer
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--upscale', type=int, default=2)
+    parser.add_argument('--arch', type=str, default='clean')
+    parser.add_argument('--channel', type=int, default=2)
+    parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth')
+    parser.add_argument('--bg_upsampler', type=str, default='realesrgan')
+    parser.add_argument('--bg_tile', type=int, default=0)
+    parser.add_argument('--test_path', type=str, default='inputs/whole_imgs')
+    parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
+    parser.add_argument('--only_center_face', action='store_true')
+    parser.add_argument('--aligned', action='store_true')
+    parser.add_argument('--paste_back', action='store_false')
+    parser.add_argument('--save_root', type=str, default='results')
+
+    args = parser.parse_args()
+    if args.test_path.endswith('/'):
+        args.test_path = args.test_path[:-1]
+    os.makedirs(args.save_root, exist_ok=True)
+
+    # background upsampler
+    if args.bg_upsampler == 'realesrgan':
+        if not torch.cuda.is_available():  # CPU
+            import warnings
+            warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
+                          'If you really want to use it, please modify the corresponding codes.')
+            bg_upsampler = None
+        else:
+            from realesrgan import RealESRGANer
+            bg_upsampler = RealESRGANer(
+                scale=2,
+                model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
+                tile=args.bg_tile,
+                tile_pad=10,
+                pre_pad=0,
+                half=True)  # need to set False in CPU mode
+    else:
+        bg_upsampler = None
+    # set up GFPGAN restorer
+    restorer = GFPGANer(
+        model_path=args.model_path,
+        upscale=args.upscale,
+        arch=args.arch,
+        channel_multiplier=args.channel,
+        bg_upsampler=bg_upsampler)
+
+    img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
+    for img_path in img_list:
+        # read image
+        img_name = os.path.basename(img_path)
+        print(f'Processing {img_name} ...')
+        basename, ext = os.path.splitext(img_name)
+        input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
+
+        cropped_faces, restored_faces, restored_img = restorer.enhance(
+            input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back)
+
+        # save faces
+        for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)):
+            # save cropped face
+            save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png')
+            imwrite(restored_face, save_crop_path)
+            # save restored face
+            if args.suffix is not None:
+                save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png'
+            else:
+                save_face_name = f'{basename}_{idx:02d}.png'
+            save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name)
+            imwrite(restored_face, save_restore_path)
+            # save cmp image
+            cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
+            imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png'))
+        # save restored img
+        if args.suffix is not None:
+            save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}_{args.suffix}{ext}')
+        else:
+            save_restore_path = os.path.join(args.save_root, 'restored_imgs', img_name)
+        imwrite(restored_img, save_restore_path)
+
+    print(f'Results are in the [{args.save_root}] folder.')
+
+
+if __name__ == '__main__':
+    main()

inference_gfpgan_full.py

-import argparse
-import cv2
-import glob
-import numpy as np
-import os
-import torch
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-from torchvision.transforms.functional import normalize
-
-from archs.gfpganv1_arch import GFPGANv1
-from archs.gfpganv1_clean_arch import GFPGANv1Clean
-from basicsr.utils import img2tensor, imwrite, tensor2img
-
-
-def restoration(gfpgan,
-                face_helper,
-                img_path,
-                save_root,
-                has_aligned=False,
-                only_center_face=True,
-                suffix=None,
-                paste_back=False,
-                device='cuda'):
-    # read image
-    img_name = os.path.basename(img_path)
-    print(f'Processing {img_name} ...')
-    basename, _ = os.path.splitext(img_name)
-    input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
-    face_helper.clean_all()
-
-    if has_aligned:
-        input_img = cv2.resize(input_img, (512, 512))
-        face_helper.cropped_faces = [input_img]
-    else:
-        face_helper.read_image(input_img)
-        # get face landmarks for each face
-        face_helper.get_face_landmarks_5(only_center_face=only_center_face)
-        # align and warp each face
-        save_crop_path = os.path.join(save_root, 'cropped_faces', img_name)
-        face_helper.align_warp_face(save_crop_path)
-
-    # face restoration
-    for idx, cropped_face in enumerate(face_helper.cropped_faces):
-        # prepare data
-        cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-        normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-        cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
-
-        try:
-            with torch.no_grad():
-                output = gfpgan(cropped_face_t, return_rgb=False)[0]
-                # convert to image
-                restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
-        except RuntimeError as error:
-            print(f'\tFailed inference for GFPGAN: {error}.')
-            restored_face = cropped_face
-
-        restored_face = restored_face.astype('uint8')
-        face_helper.add_restored_face(restored_face)
-
-        if suffix is not None:
-            save_face_name = f'{basename}_{idx:02d}_{suffix}.png'
-        else:
-            save_face_name = f'{basename}_{idx:02d}.png'
-        save_restore_path = os.path.join(save_root, 'restored_faces', save_face_name)
-        imwrite(restored_face, save_restore_path)
-
-        # save cmp image
-        cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
-        imwrite(cmp_img, os.path.join(save_root, 'cmp', f'{basename}_{idx:02d}.png'))
-
-    if not has_aligned and paste_back:
-        face_helper.get_inverse_affine(None)
-        save_restore_path = os.path.join(save_root, 'restored_imgs', img_name)
-        # paste each restored face to the input image
-        face_helper.paste_faces_to_input_image(save_restore_path)
-
-
-if __name__ == '__main__':
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--upscale_factor', type=int, default=2)
-    parser.add_argument('--arch', type=str, default='clean')
-    parser.add_argument('--channel', type=int, default=2)
-    parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth')
-    parser.add_argument('--test_path', type=str, default='inputs/whole_imgs')
-    parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
-    parser.add_argument('--only_center_face', action='store_true')
-    parser.add_argument('--aligned', action='store_true')
-    parser.add_argument('--paste_back', action='store_false')
-    parser.add_argument('--save_root', type=str, default='results')
-
-    args = parser.parse_args()
-    if args.test_path.endswith('/'):
-        args.test_path = args.test_path[:-1]
-    os.makedirs(args.save_root, exist_ok=True)
-
-    # initialize the GFP-GAN
-    if args.arch == 'clean':
-        gfpgan = GFPGANv1Clean(
-            out_size=512,
-            num_style_feat=512,
-            channel_multiplier=args.channel,
-            decoder_load_path=None,
-            fix_decoder=False,
-            # for stylegan decoder
-            num_mlp=8,
-            input_is_latent=True,
-            different_w=True,
-            narrow=1,
-            sft_half=True)
-    else:
-        gfpgan = GFPGANv1(
-            out_size=512,
-            num_style_feat=512,
-            channel_multiplier=args.channel,
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            input_is_latent=True,
-            different_w=True,
-            narrow=1,
-            sft_half=True)
-
-    gfpgan.load_state_dict(torch.load(args.model_path, map_location=lambda storage, loc: storage)['params_ema'])
-    gfpgan.to(device).eval()
-
-    # initialize face helper
-    face_helper = FaceRestoreHelper(
-        args.upscale_factor,
-        face_size=512,
-        crop_ratio=(1, 1),
-        det_model='retinaface_resnet50',
-        save_ext='png',
-        device=device)
-
-    img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
-    for img_path in img_list:
-        restoration(
-            gfpgan,
-            face_helper,
-            img_path,
-            args.save_root,
-            has_aligned=args.aligned,
-            only_center_face=args.only_center_face,
-            suffix=args.suffix,
-            paste_back=args.paste_back,
-            device=device)
-
-    print(f'Results are in the [{args.save_root}] folder.')

scripts/parse_landmark.py

@@ -2,9 +2,8 @@ import cv2
 import json
 import numpy as np
 import torch
-from collections import OrderedDict
-
 from basicsr.utils import FileClient, imfrombytes
+from collections import OrderedDict
 
 print('Load JSON metadata...')
 # use the json file in FFHQ dataset

setup.cfg

@@ -16,7 +16,7 @@ split_before_expression_after_opening_paren = true
 line_length = 120
 multi_line_output = 0
 known_standard_library = pkg_resources,setuptools
-known_first_party = basicsr
-known_third_party = cv2,facexlib,numpy,torch,torchvision,tqdm
+known_first_party = gfpgan
+known_third_party = basicsr,cv2,facexlib,numpy,torch,torchvision,tqdm
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

setup.py

+#!/usr/bin/env python
+
+from setuptools import find_packages, setup
+
+import os
+import subprocess
+import time
+
+version_file = 'gfpgan/version.py'
+
+
+def readme():
+    with open('README.md', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+
+def get_git_hash():
+
+    def _minimal_ext_cmd(cmd):
+        # construct minimal environment
+        env = {}
+        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
+            v = os.environ.get(k)
+            if v is not None:
+                env[k] = v
+        # LANGUAGE is used on win32
+        env['LANGUAGE'] = 'C'
+        env['LANG'] = 'C'
+        env['LC_ALL'] = 'C'
+        out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
+        return out
+
+    try:
+        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
+        sha = out.strip().decode('ascii')
+    except OSError:
+        sha = 'unknown'
+
+    return sha
+
+
+def get_hash():
+    if os.path.exists('.git'):
+        sha = get_git_hash()[:7]
+    elif os.path.exists(version_file):
+        try:
+            from facexlib.version import __version__
+            sha = __version__.split('+')[-1]
+        except ImportError:
+            raise ImportError('Unable to get git version')
+    else:
+        sha = 'unknown'
+
+    return sha
+
+
+def write_version_py():
+    content = """# GENERATED VERSION FILE
+# TIME: {}
+__version__ = '{}'
+__gitsha__ = '{}'
+version_info = ({})
+"""
+    sha = get_hash()
+    with open('VERSION', 'r') as f:
+        SHORT_VERSION = f.read().strip()
+    VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
+
+    version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
+    with open(version_file, 'w') as f:
+        f.write(version_file_str)
+
+
+def get_version():
+    with open(version_file, 'r') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+def get_requirements(filename='requirements.txt'):
+    here = os.path.dirname(os.path.realpath(__file__))
+    with open(os.path.join(here, filename), 'r') as f:
+        requires = [line.replace('\n', '') for line in f.readlines()]
+    return requires
+
+
+if __name__ == '__main__':
+    write_version_py()
+    setup(
+        name='gfpgan',
+        version=get_version(),
+        description='GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration',
+        long_description=readme(),
+        long_description_content_type='text/markdown',
+        author='Xintao Wang',
+        author_email='xintao.wang@outlook.com',
+        keywords='computer vision, pytorch, image restoration, super-resolution, face restoration, gan, gfpgan',
+        url='https://github.com/TencentARC/GFPGAN',
+        include_package_data=True,
+        packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
+        classifiers=[
+            'Development Status :: 4 - Beta',
+            'License :: OSI Approved :: Apache Software License',
+            'Operating System :: OS Independent',
+            'Programming Language :: Python :: 3',
+            'Programming Language :: Python :: 3.7',
+            'Programming Language :: Python :: 3.8',
+        ],
+        license='Apache License Version 2.0',
+        setup_requires=['cython', 'numpy'],
+        install_requires=get_requirements(),
+        zip_safe=False)