def forward(self, features, targets=None):
        """
        Partial FC forward, which will sample positive weights and part of negative weights,
        then compute logits and get the grad of features.
        """
        if not self.pfc_enabled:
            return super().forward(features, targets)
        else:
            pool_feat = self.pool_layer(features)
            neck_feat = self.bottleneck(pool_feat)
            neck_feat = neck_feat[..., 0, 0]
            return neck_feat

        Partial FC forward, which will sample positive weights and part of negative weights,
        then compute logits and get the grad of features.
        
projects/FastFace/fastface/modeling/face_head.py