| 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 |
|
|
| |
| normalize_t = Normalize((0.4814, 0.4578, 0.4082), (0.2686, 0.2613, 0.2757)) |
|
|
| |
| 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"): |
| |
| nsfw_model = NSFWClassifier() |
| nsfw_model = nsfw_model.eval() |
| |
| 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 |
|
|
