From 7272e458874cb9a3b1b68c38a7136b2b8dd71f69 Mon Sep 17 00:00:00 2001 From: Xintao Date: Sun, 4 Sep 2022 20:12:31 +0800 Subject: [PATCH] update replicate (#248) * update util * update predict * update predict * update predict * update predict * update predict * update predict * update predict * update predict * merge replicate update --- gfpgan/utils.py | 3 +- predict.py | 81 +++++++++++++++++++++++++++++++++++++------------ 2 files changed, 63 insertions(+), 21 deletions(-) diff --git a/gfpgan/utils.py b/gfpgan/utils.py index 35658c5..f371729 100644 --- a/gfpgan/utils.py +++ b/gfpgan/utils.py @@ -80,7 +80,8 @@ class GFPGANer(): det_model='retinaface_resnet50', save_ext='png', use_parse=True, - device=self.device) + device=self.device, + model_rootpath='gfpgan/weights') if model_path.startswith('https://'): model_path = load_file_from_url( diff --git a/predict.py b/predict.py index a602b7d..43193de 100644 --- a/predict.py +++ b/predict.py @@ -1,5 +1,9 @@ # flake8: noqa # This file is used for deploying replicate models +# running: cog predict -i img=@inputs/whole_imgs/10045.png -i version='v1.4' -i scale=2 +# push: cog push r8.im/tencentarc/gfpgan +# push (backup): cog push r8.im/xinntao/gfpgan + import os os.system('python setup.py develop') @@ -10,6 +14,7 @@ import shutil import tempfile import torch from basicsr.archs.srvgg_arch import SRVGGNetCompact +from pathlib import Path from gfpgan import GFPGANer @@ -24,33 +29,46 @@ class Predictor(BasePredictor): def setup(self): # download weights - if not os.path.exists('realesr-general-x4v3.pth'): + if not os.path.exists('gfpgan/weights/realesr-general-x4v3.pth'): os.system( - 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .') - if not os.path.exists('GFPGANv1.2.pth'): - os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .') - if not os.path.exists('GFPGANv1.3.pth'): - os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .') + 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./gfpgan/weights' + ) + if not os.path.exists('gfpgan/weights/GFPGANv1.2.pth'): + os.system( + 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P ./gfpgan/weights') + if not os.path.exists('gfpgan/weights/GFPGANv1.3.pth'): + os.system( + 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P ./gfpgan/weights') + if not os.path.exists('gfpgan/weights/GFPGANv1.4.pth'): + os.system( + 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./gfpgan/weights') # background enhancer with RealESRGAN model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') - model_path = 'realesr-general-x4v3.pth' + model_path = 'gfpgan/weights/realesr-general-x4v3.pth' half = True if torch.cuda.is_available() else False - upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half) + self.upsampler = RealESRGANer( + scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half) # Use GFPGAN for face enhancement - self.face_enhancer_v3 = GFPGANer( - model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) - self.face_enhancer_v2 = GFPGANer( - model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) - os.makedirs('output', exist_ok=True) + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/GFPGANv1.4.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=self.upsampler) + self.current_version = 'v1.4' def predict( self, img: Path = Input(description='Input'), - version: str = Input(description='GFPGAN version', choices=['v1.2', 'v1.3'], default='v1.3'), + version: str = Input( + description='GFPGAN version. v1.3: better quality. v1.4: more details and better identity.', + choices=['v1.2', 'v1.3', 'v1.4'], + default='v1.4'), scale: float = Input(description='Rescaling factor', default=2) ) -> Path: + print(img, version, scale) try: img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED) if len(img.shape) == 3 and img.shape[2] == 4: @@ -62,12 +80,35 @@ class Predictor(BasePredictor): if h < 300: img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) - if version == 'v1.2': - face_enhancer = self.face_enhancer_v2 - else: - face_enhancer = self.face_enhancer_v3 + if self.current_version != version: + if version == 'v1.2': + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/GFPGANv1.2.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=self.upsampler) + self.current_version = 'v1.2' + elif version == 'v1.3': + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/GFPGANv1.3.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=self.upsampler) + self.current_version = 'v1.3' + elif version == 'v1.4': + self.face_enhancer = GFPGANer( + model_path='gfpgan/weights/GFPGANv1.4.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=self.upsampler) + self.current_version = 'v1.4' + try: - _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) + _, _, output = self.face_enhancer.enhance( + img, has_aligned=False, only_center_face=False, paste_back=True) except RuntimeError as error: print('Error', error) else: @@ -86,7 +127,7 @@ class Predictor(BasePredictor): extension = 'jpg' save_path = f'output/out.{extension}' cv2.imwrite(save_path, output) - out_path = os.path.join(tempfile.mkdtemp(), 'output.png') + out_path = Path(tempfile.mkdtemp()) / 'output.png' cv2.imwrite(str(out_path), output) except Exception as error: print('global exception', error)