model/model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import clip

class CLIP_ViT_B_32(nn.Module):
    def __init__(self, pretrained=True):
        super().__init__()
        if pretrained:
            self.model, _ = clip.load("ViT-B/32", device="cpu")
        else:
            self.model, _ = clip.load("ViT-B/32", device="cpu", jit=False)
        
        # Remove the final classification layer
        self.feature_dim = 512  # CLIP ViT-B/32 feature dimension
    
    def freeze_weights(self):
        """Freeze all CLIP weights"""
        for param in self.model.parameters():
            param.requires_grad = False
    
    def forward(self, x):
        """Extract features from CLIP"""
        with torch.no_grad() if not self.training else torch.enable_grad():
            features = self.model.encode_image(x)
        return features.float()

class CosineClassifier(nn.Module):
    def __init__(self, dim, num_classes, scale_factor=30):
        super().__init__()
        self.num_classes = num_classes
        self.scale_factor = scale_factor
        self.weights = nn.Parameter(torch.randn(num_classes, dim))
        nn.init.xavier_uniform_(self.weights)

    def forward(self, x):
        # Normalize input features
        x = F.normalize(x, dim=-1)
        # Normalize weights
        w = F.normalize(self.weights, dim=-1)
        # Compute cosine similarity and scale
        logits = torch.matmul(x, w.T) * self.scale_factor
        return logits

class FaceFairnessModel(nn.Module):
    def __init__(self, num_classes, scale_factor=30):
        super().__init__()
        # CLIP feature extractor
        self.clip_model = CLIP_ViT_B_32(pretrained=True)
        self.clip_model.freeze_weights()
        
        # Cosine classifier
        self.classifier = CosineClassifier(
            dim=self.clip_model.feature_dim, 
            num_classes=num_classes,
            scale_factor=scale_factor
        )
    
    def forward(self, x):
        # Extract features with CLIP
        features = self.clip_model(x)
        # Classify with cosine classifier
        logits = self.classifier(features)
        return logits