Major revision: Support Pypi (TencentARC#37)

* reorganize

* update inference

* update inference

* format

.github/workflows/publish-pip.yml

@@ -0,0 +1,30 @@
+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

@@ -1,21 +1,13 @@
-.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

@@ -0,0 +1,8 @@
+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

@@ -1,9 +1,11 @@
 # 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

@@ -0,0 +1 @@
+0.2.1

gfpgan/__init__.py

@@ -0,0 +1,6 @@
+# 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

@@ -1,12 +1,10 @@
 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

@@ -1,5 +1,4 @@
 import torch.nn as nn
-
 from basicsr.utils.registry import ARCH_REGISTRY
 
 

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

@@ -1,13 +1,12 @@
 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

@@ -1,11 +1,10 @@
 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

@@ -1,11 +1,10 @@
 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 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

@@ -1,12 +1,10 @@
 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

@@ -1,18 +1,17 @@
 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
 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()

gfpgan/train.py

@@ -0,0 +1,11 @@
+# flake8: noqa
+import os.path as osp
+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, osp.pardir))
+    train_pipeline(root_path)