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| import torch | |
| from diffusers import StableDiffusionPipeline | |
| from .artifact_extractor import VAEReconEncoder | |
| from torchvision import transforms | |
| from utils import data_augment | |
| # Artifact Detector (Extract artifact features using VAE) | |
| class ArtifactDetector(torch.nn.Module): | |
| def __init__(self, dim_artifact=512, num_classes=1): | |
| super(ArtifactDetector, self).__init__() | |
| # Load the pre-trained VAE | |
| model_id = "CompVis/stable-diffusion-v1-4" | |
| vae = StableDiffusionPipeline.from_pretrained(model_id).vae | |
| # Freeze the VAE visual encoder | |
| vae.requires_grad_(False) | |
| self.artifact_encoder = VAEReconEncoder(vae) | |
| # Classifier | |
| self.fc = torch.nn.Linear(dim_artifact, num_classes) | |
| # Normalization | |
| self.mean = [0.0, 0.0, 0.0] | |
| self.std = [1.0, 1.0, 1.0] | |
| # Resolution | |
| self.loadSize = 256 | |
| self.cropSize = 224 | |
| # Data augmentation | |
| self.blur_prob = 0.0 | |
| self.blur_sig = [0.0, 3.0] | |
| self.jpg_prob = 0.5 | |
| self.jpg_method = ['cv2', 'pil'] | |
| self.jpg_qual = list(range(70, 96)) | |
| # Define the augmentation configuration | |
| self.aug_config = { | |
| "blur_prob": self.blur_prob, | |
| "blur_sig": self.blur_sig, | |
| "jpg_prob": self.jpg_prob, | |
| "jpg_method": self.jpg_method, | |
| "jpg_qual": self.jpg_qual, | |
| } | |
| # Pre-processing | |
| crop_func = transforms.RandomCrop(self.cropSize) | |
| flip_func = transforms.RandomHorizontalFlip() | |
| rz_func = transforms.Resize(self.loadSize) | |
| aug_func = transforms.Lambda(lambda x: data_augment(x, self.aug_config)) | |
| self.train_transform = transforms.Compose([ | |
| aug_func, | |
| rz_func, | |
| crop_func, | |
| flip_func, | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=self.mean, std=self.std), | |
| ]) | |
| self.test_transform = transforms.Compose([ | |
| rz_func, | |
| crop_func, | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=self.mean, std=self.std), | |
| ]) | |
| def forward(self, x, return_feat=False): | |
| feat = self.artifact_encoder(x) | |
| out = self.fc(feat) | |
| if return_feat: | |
| return feat, out | |
| return out | |
| def save_weights(self, weights_path): | |
| save_params = {k: v.cpu() for k, v in self.state_dict().items()} | |
| torch.save(save_params, weights_path) | |
| def load_weights(self, weights_path): | |
| weights = torch.load(weights_path) | |
| self.load_state_dict(weights) | |