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# author: Zhiyuan Yan
# email: zhiyuanyan@link.cuhk.edu.cn
# date: 2023-0706
# description: Abstract Class for the Deepfake Detector
import abc
import torch
import torch.nn as nn
from typing import Union
class AbstractDetector(nn.Module, metaclass=abc.ABCMeta):
"""
All deepfake detectors should subclass this class.
"""
def __init__(self, config=None, load_param: Union[bool, str] = False):
"""
config: (dict)
configurations for the model
load_param: (False | True | Path(str))
False Do not read; True Read the default path; Path Read the required path
"""
super().__init__()
@abc.abstractmethod
def features(self, data_dict: dict) -> torch.tensor:
"""
Returns the features from the backbone given the input data.
"""
pass
@abc.abstractmethod
def forward(self, data_dict: dict, inference=False) -> dict:
"""
Forward pass through the model, returning the prediction dictionary.
"""
pass
@abc.abstractmethod
def classifier(self, features: torch.tensor) -> torch.tensor:
"""
Classifies the features into classes.
"""
pass
@abc.abstractmethod
def build_backbone(self, config):
"""
Builds the backbone of the model.
"""
pass
@abc.abstractmethod
def build_loss(self, config):
"""
Builds the loss function for the model.
"""
pass
@abc.abstractmethod
def get_losses(self, data_dict: dict, pred_dict: dict) -> dict:
"""
Returns the losses for the model.
"""
pass
@abc.abstractmethod
def get_train_metrics(self, data_dict: dict, pred_dict: dict) -> dict:
"""
Returns the training metrics for the model.
"""
pass |