from torchvision.transforms import Normalize import torchvision.transforms as T import torch.nn as nn from PIL import Image import numpy as np import torch import timm from tqdm import tqdm # https://github.com/Whiax/NSFW-Classifier/raw/main/nsfwmodel_281.pth normalize_t = Normalize((0.4814, 0.4578, 0.4082), (0.2686, 0.2613, 0.2757)) #nsfw classifier class NSFWClassifier(nn.Module): def __init__(self): super().__init__() nsfw_model=self nsfw_model.root_model = timm.create_model('convnext_base_in22ft1k', pretrained=True) nsfw_model.linear_probe = nn.Linear(1024, 1, bias=False) def forward(self, x): nsfw_model = self x = normalize_t(x) x = nsfw_model.root_model.stem(x) x = nsfw_model.root_model.stages(x) x = nsfw_model.root_model.head.global_pool(x) x = nsfw_model.root_model.head.norm(x) x = nsfw_model.root_model.head.flatten(x) x = nsfw_model.linear_probe(x) return x def is_nsfw(self, img_paths, threshold = 0.98): skip_step = 1 total_len = len(img_paths) if total_len < 100: skip_step = 1 if total_len > 100 and total_len < 500: skip_step = 10 if total_len > 500 and total_len < 1000: skip_step = 20 if total_len > 1000 and total_len < 10000: skip_step = 50 if total_len > 10000: skip_step = 100 for idx in tqdm(range(0, total_len, skip_step), total=int(total_len // skip_step), desc="Checking for NSFW contents"): _img = Image.open(img_paths[idx]).convert('RGB') img = _img.resize((224, 224)) img = np.array(img)/255 img = T.ToTensor()(img).unsqueeze(0).float() if next(self.parameters()).is_cuda: img = img.cuda() with torch.no_grad(): score = self.forward(img).sigmoid()[0].item() if score > threshold: print(f"Detected nsfw score:{score}") _img.save("nsfw.jpg") return True return False def get_nsfw_detector(model_path='nsfwmodel_281.pth', device="cpu"): #load base model nsfw_model = NSFWClassifier() nsfw_model = nsfw_model.eval() #load linear weights linear_pth = model_path linear_state_dict = torch.load(linear_pth, map_location='cpu') nsfw_model.linear_probe.load_state_dict(linear_state_dict) nsfw_model = nsfw_model.to(device) return nsfw_model