Re-implement universal model loading
d8ahazard
3 years ago
parent
bfb7f15d46
commit
740070ea9c
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# GFPGAN likes to download stuff "wherever", and we're trying to fix that, so this is a copy of the original...
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import cv2
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import os
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import torch
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from basicsr.utils import img2tensor, tensor2img
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from basicsr.utils.download_util import load_file_from_url
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from facexlib.utils.face_restoration_helper import FaceRestoreHelper
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from torchvision.transforms.functional import normalize
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from gfpgan.archs.gfpgan_bilinear_arch import GFPGANBilinear
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from gfpgan.archs.gfpganv1_arch import GFPGANv1
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from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
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ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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class GFPGANerr():
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"""Helper for restoration with GFPGAN.
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It will detect and crop faces, and then resize the faces to 512x512.
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GFPGAN is used to restored the resized faces.
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The background is upsampled with the bg_upsampler.
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Finally, the faces will be pasted back to the upsample background image.
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Args:
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model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically).
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upscale (float): The upscale of the final output. Default: 2.
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arch (str): The GFPGAN architecture. Option: clean | original. Default: clean.
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channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
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bg_upsampler (nn.Module): The upsampler for the background. Default: None.
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"""
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def __init__(self, model_path, model_dir, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None, device=None):
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self.upscale = upscale
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self.bg_upsampler = bg_upsampler
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# initialize model
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
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# initialize the GFP-GAN
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if arch == 'clean':
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self.gfpgan = GFPGANv1Clean(
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out_size=512,
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num_style_feat=512,
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channel_multiplier=channel_multiplier,
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decoder_load_path=None,
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fix_decoder=False,
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num_mlp=8,
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input_is_latent=True,
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different_w=True,
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narrow=1,
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sft_half=True)
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elif arch == 'bilinear':
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self.gfpgan = GFPGANBilinear(
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out_size=512,
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num_style_feat=512,
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channel_multiplier=channel_multiplier,
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decoder_load_path=None,
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fix_decoder=False,
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num_mlp=8,
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input_is_latent=True,
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different_w=True,
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narrow=1,
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sft_half=True)
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elif arch == 'original':
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self.gfpgan = GFPGANv1(
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out_size=512,
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num_style_feat=512,
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channel_multiplier=channel_multiplier,
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decoder_load_path=None,
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fix_decoder=True,
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num_mlp=8,
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input_is_latent=True,
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different_w=True,
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narrow=1,
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sft_half=True)
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elif arch == 'RestoreFormer':
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from gfpgan.archs.restoreformer_arch import RestoreFormer
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self.gfpgan = RestoreFormer()
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# initialize face helper
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self.face_helper = FaceRestoreHelper(
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upscale,
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face_size=512,
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crop_ratio=(1, 1),
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det_model='retinaface_resnet50',
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save_ext='png',
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use_parse=True,
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device=self.device,
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model_rootpath=model_dir)
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if model_path.startswith('https://'):
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model_path = load_file_from_url(
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url=model_path, model_dir=model_dir, progress=True, file_name=None)
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loadnet = torch.load(model_path)
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if 'params_ema' in loadnet:
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keyname = 'params_ema'
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else:
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keyname = 'params'
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self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
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self.gfpgan.eval()
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self.gfpgan = self.gfpgan.to(self.device)
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@torch.no_grad()
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def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True, weight=0.5):
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self.face_helper.clean_all()
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if has_aligned: # the inputs are already aligned
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img = cv2.resize(img, (512, 512))
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self.face_helper.cropped_faces = [img]
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else:
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self.face_helper.read_image(img)
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# get face landmarks for each face
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self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5)
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# eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels
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# TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations.
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# align and warp each face
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self.face_helper.align_warp_face()
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# face restoration
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for cropped_face in self.face_helper.cropped_faces:
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# prepare data
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cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
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normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
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cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
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try:
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output = self.gfpgan(cropped_face_t, return_rgb=False, weight=weight)[0]
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# convert to image
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restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
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except RuntimeError as error:
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print(f'\tFailed inference for GFPGAN: {error}.')
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restored_face = cropped_face
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restored_face = restored_face.astype('uint8')
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self.face_helper.add_restored_face(restored_face)
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if not has_aligned and paste_back:
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# upsample the background
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if self.bg_upsampler is not None:
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# Now only support RealESRGAN for upsampling background
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bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
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else:
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bg_img = None
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self.face_helper.get_inverse_affine(None)
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# paste each restored face to the input image
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restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img)
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return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img
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else:
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return self.face_helper.cropped_faces, self.face_helper.restored_faces, None
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@ -0,0 +1,65 @@
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import os
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from urllib.parse import urlparse
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from basicsr.utils.download_util import load_file_from_url
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def load_models(model_path: str, model_url: str = None, command_path: str = None, dl_name: str = None, existing=None,
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ext_filter=None) -> list:
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"""
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A one-and done loader to try finding the desired models in specified directories.
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@param dl_name: The file name to use for downloading a model. If not specified, it will be used from the URL.
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@param model_url: If specified, attempt to download model from the given URL.
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@param model_path: The location to store/find models in.
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@param command_path: A command-line argument to search for models in first.
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@param existing: An array of existing model paths.
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@param ext_filter: An optional list of filename extensions to filter by
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@return: A list of paths containing the desired model(s)
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"""
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if ext_filter is None:
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ext_filter = []
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if existing is None:
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existing = []
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try:
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places = []
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if command_path is not None and command_path != model_path:
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pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
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if os.path.exists(pretrained_path):
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places.append(pretrained_path)
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elif os.path.exists(command_path):
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places.append(command_path)
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places.append(model_path)
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for place in places:
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if os.path.exists(place):
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for file in os.listdir(place):
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if os.path.isdir(file):
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continue
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if len(ext_filter) != 0:
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model_name, extension = os.path.splitext(file)
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if extension not in ext_filter:
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continue
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if file not in existing:
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path = os.path.join(place, file)
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existing.append(path)
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if model_url is not None:
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if dl_name is not None:
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model_file = load_file_from_url(url=model_url, model_dir=model_path, file_name=dl_name, progress=True)
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else:
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model_file = load_file_from_url(url=model_url, model_dir=model_path, progress=True)
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if os.path.exists(model_file) and os.path.isfile(model_file) and model_file not in existing:
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existing.append(model_file)
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except:
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pass
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return existing
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def friendly_name(file: str):
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if "http" in file:
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file = urlparse(file).path
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file = os.path.basename(file)
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model_name, extension = os.path.splitext(file)
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model_name = model_name.replace("_", " ").title()
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return model_name
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