model.py

import torch
import torch.nn as nn
import timm


class DeepfakeEffNetTransformer(nn.Module):

    def __init__(self):
        super().__init__()

        # CNN BACKBONE
        self.cnn = timm.create_model(
            "tf_efficientnetv2_b1",
            pretrained=False,
            num_classes=0,
            global_pool=""
        )

        self.pool = nn.AdaptiveAvgPool2d(1)
        feat_dim = self.cnn.num_features

        # PROJECTION
        self.proj = nn.Linear(feat_dim, 512)

        # POSITIONAL EMBEDDING
        self.pos_embed = nn.Parameter(
            torch.randn(1, 32, 512)
        )

        # TRANSFORMER
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=512,
            nhead=8,
            dim_feedforward=1024,
            batch_first=True
        )

        self.transformer = nn.TransformerEncoder(
            encoder_layer,
            num_layers=2
        )

        # CLASSIFIER
        self.classifier = nn.Sequential(
            nn.Linear(512, 128),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(128, 2)
        )

    def forward(self, x):
        B, T, C, H, W = x.shape
        x = x.view(B * T, C, H, W)

        # CNN FEATURES
        feats = self.cnn(x)
        feats = self.pool(feats)
        feats = feats.view(B, T, -1)

        # PROJECTION
        feats = self.proj(feats)
        feats = feats + self.pos_embed

        # TEMPORAL TRANSFORMER
        out = self.transformer(feats)
        out = out.mean(dim=1)
        return self.classifier(out)