Spaces:
Running
Running
| 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)) | |
| #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 = 0.93): | |
| 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=total_len, 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: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 | |