| from dataclasses import dataclass | |
| from typing import Tuple | |
| # ============== Training Configuration ============== | |
| class TrainCfg: | |
| method: str = "DouNet" | |
| model_name: str = "ViT-L/14" # Only used if you are using the open_clip version | |
| pretrained: str = "openai" | |
| classnames: Tuple[str, str] = ("real", "generated") | |
| csc: bool = True # Class-specific context | |
| n_ctx: int = 16 | |
| ctx_init: str = None # None = random initialization, or e.g. "a photo of a" | |
| class_token_position: str = "end" # end|middle|front | |
| img_size: int = 224 | |
| learn_logit_scale: bool = False | |
| # Optimization / scheduling | |
| lr: float = 1e-3 # Prompt context | |
| lr_alpha: float = 1e-3 | |
| lr_tau: float = 1e-4 # Temperature | |
| weight_decay: float = 1e-2 | |
| epochs: int = 30 | |
| batch_size: int = 256 | |
| workers: int = 0 | |
| grad_accum_steps: int = 1 | |
| amp: bool = True | |
| warmup_steps: int = 500 | |
| # Early stopping / checkpoint saving | |
| early_stop_patience: int = 5 | |
| outdir: str = "runs/dou" | |
| save_best_metric: str = "acc" # auroc|acc | |
| # Data augmentation | |
| blur_prob: float = 0 | |
| blur_sig = (0.0, 3.0) | |
| jpg_prob: float = 0 | |
| jpg_method = ('cv2', 'pil') | |
| jpg_qual = (70, 100) | |
| # Miscellaneous | |
| seed: int = 42 | |
| log_interval: int = 10 | |
| alpha: float = 0.5 # Contrastive loss weight | |
| lambda_CIB: float = 1e-3 # Information bottleneck weight | |
| lambda_AFCL: float = 0.5 # HSIC loss weight | |
| lambda_reg: float = 0.5 # Alpha regularization weight | |
| train_alpha: bool = True | |
| train_proj: bool = True | |