def get_msp_scores()

in ood/metrics.py [0:0]
62 lines of code
14 McCabe index (conditional complexity)

def get_msp_scores(logits, ood_logits=None, method='MCM', ret_near_ood=False):
    assert method in ['MCM', 'Energy', 'MaxScore',
                      'MCM_Full', 'MCM_Full_Hard', 'MCM_Full_Soft', 'MCM_Full_Neg', 'MCM_Pair_Hard', 'MCM_Pair_Soft',
                      'MCM_Pair_Scale', 'MCM_Full_Scale'], \
        'OOD inference method %s has not been implemented.' % method
    
    probs = F.softmax(logits, dim=1)
    msp = probs.max(dim=1).values
    inconsistent = None
    
    if method == 'Energy':
        tau = 100.
        scores = -torch.logsumexp(logits * tau, dim=1)
    
    elif method == 'MCM':
        # assert ood_logits is None

        scores = - msp  # higher score means OOD

    else:
        assert ood_logits is not None

        pred = logits.argmax(dim=1)
        xrange = torch.arange(logits.shape[0])

        if 'MCM_Full' in method:
            full_logits = torch.cat((logits, ood_logits), dim=1)
            full_probs = F.softmax(full_logits, dim=1)
            full_pred = full_logits.argmax(dim=1)
            inconsistent = pred != full_pred  # probs < full_probs/ood_probs
            
            if 'Neg' in method:
                cls_num = logits.shape[1]
                scores = full_probs[:, cls_num:].sum(dim=1)   # higher score means OOD
            else:
                scores = - full_probs[xrange, pred]
                if 'Hard' in method:
                    scores[inconsistent] = 0.   # higher score means OOD
                elif 'Soft' in method:
                    # negative score adding a positive delta brings a higer score
                    scores += full_probs[xrange, full_pred] - full_probs[xrange, pred]  # higher score means OOD
                elif 'Scale' in method:
                    max_id_sim, max_ood_sim = logits.max(dim=1)[0], ood_logits.max(dim=1)[0]
                    pair_logits = torch.stack((max_id_sim, max_ood_sim), dim=1)
                    scale = F.softmax(pair_logits, dim=1)[:, :1].clamp(min=0.5)
                    # scale = F.softmax(pair_logits * 8., dim=1)[:, :1].clamp(min=0.5) / 8.
                    full_probs = F.softmax(full_logits * scale, dim=1)
                    scores = - full_probs[xrange, pred]
                else:
                    assert method == 'MCM_Full'

        elif 'MCM_Pair' in method:
            scores = - msp  # higher score means OOD

            pair_logits = torch.stack((logits[xrange, pred], ood_logits[xrange, pred]), dim=1)  # shape(nb,2)
            inconsistent = pair_logits[:, 0] < pair_logits[:, 1]  # id_sim < ood_sim
            pair_probs = F.softmax(pair_logits, dim=1)

            if 'Hard' in method:
                scores[inconsistent] = 0. # higher score means OOD
            elif 'Soft' in method:
                # negative score multiplying a smaller value brings a higher score
                scores *= pair_probs[:, 0].clamp(min=0.5)   # higher score means OOD
            elif 'Scale' in method:
                # print(F.softmax(pair_logits, dim=1)[:, :1].detach())
                scale = F.softmax(pair_logits, dim=1)[:, :1].clamp(min=0.5)  # 498
                # scale = F.softmax(pair_logits * 8., dim=1)[:, :1].clamp(min=0.) / 8.
                probs = F.softmax(logits * scale, dim=1)
                scores = - probs[xrange, pred]
            else:
                raise NotImplementedError

        elif method == 'MaxScore':
            pair_logits = torch.stack((logits[xrange, pred], ood_logits[xrange, pred]), dim=1)  # shape(nb,2)
            pair_probs = F.softmax(pair_logits*10., dim=1)

            scores = -pair_probs[:, 0] * logits[xrange, pred]
    
        
    if ret_near_ood:
        return scores, inconsistent
    else:
        return scores