def evaluate_predictions()

in archs/models.py [0:0]

• 26 lines of code
• 6 McCabe index (conditional complexity)

    def evaluate_predictions(self, predictions, attr_truth, obj_truth, topk=1):

        # put everything on cpu
        attr_truth, obj_truth = attr_truth.cpu(), obj_truth.cpu()
        pairs = list(
            zip(list(attr_truth.cpu().numpy()), list(obj_truth.cpu().numpy())))
        seen_ind = torch.LongTensor([
            i for i in range(len(attr_truth)) if pairs[i] in self.train_pairs
        ])
        unseen_ind = torch.LongTensor([
            i for i in range(len(attr_truth))
            if pairs[i] not in self.train_pairs
        ])

        # top 1 pair accuracy
        # open world: attribute, object and pair
        attr_match = (attr_truth.unsqueeze(1).repeat(
            1, topk) == predictions['open'][0][:, :topk])
        obj_match = (obj_truth.unsqueeze(1).repeat(
            1, topk) == predictions['open'][1][:, :topk])
        open_match = (attr_match * obj_match).any(1).float()
        attr_match = attr_match.any(1).float()
        obj_match = obj_match.any(1).float()
        open_seen_match = open_match[seen_ind]
        open_unseen_match = open_match[unseen_ind]

        # closed world, obj_oracle: pair
        closed_match = (attr_truth == predictions['closed'][0][:, 0]).float(
        ) * (obj_truth == predictions['closed'][1][:, 0]).float()

        obj_oracle_match = (
            attr_truth == predictions['object_oracle'][0][:, 0]).float() * (
                obj_truth == predictions['object_oracle'][1][:, 0]).float()

        return attr_match, obj_match, closed_match, open_match, obj_oracle_match, open_seen_match, open_unseen_match