2021SC@SDUSC
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)
初始化模型,根据参数arch选择性初始化GFP-GAN,初始化face helper。
self.face_helper = FaceRestoreHelper(
upscale,
face_size=512,
crop_ratio=(1, 1),
det_model='retinaface_resnet50',
save_ext='png',
device=self.device)
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)
包torch包含了多维疑是的数据结构及基于其上的多种数学操作。
torch.load() 使用 Python 的 解压工具(unpickling)来反序列化 pickled object 到对应存储设备上。首先在 CPU 上对压缩对象进行反序列化并且移动到它们保存的存储设备上,如果失败了(如:由于系统中没有相应的存储设备),就会抛出一个异常。用户可以通过 register_package 进行扩展,使用自己定义的标记和反序列化方法。
从指定url中读取model下载文件,如果路径是网址,则调用这个函数下载相应的model
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
"""
hub_dir = get_dir()
model_dir = os.path.join(hub_dir, 'checkpoints')
print('hub_dir',hub_dir)
print('model_dir',model_dir)
if model_dir is None:
hub_dir = get_dir()
model_dir = os.path.join(hub_dir, 'checkpoints')
#做路径的拼接,并递归创建目录
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