def __init__()

in archs/models.py [0:0]

• 33 lines of code
• 16 McCabe index (conditional complexity)

    def __init__(self, dset, model):

        self.dset = dset

        # convert text pairs to idx tensors: [('sliced', 'apple'), ('ripe', 'apple'), ...] --> torch.LongTensor([[0,1],[1,1], ...])
        pairs = [(dset.attr2idx[attr], dset.obj2idx[obj])
                 for attr, obj in dset.pairs]
        self.train_pairs = [(dset.attr2idx[attr], dset.obj2idx[obj])
                            for attr, obj in dset.train_pairs]
        self.pairs = torch.LongTensor(pairs)

        # mask over pairs that occur in closed world
        if dset.phase == 'train':
            print('Evaluating with train pairs')
            test_pair_set = set(dset.train_pairs)
        elif dset.phase == 'val':
            print('Evaluating with val pairs')
            test_pair_set = set(dset.val_pairs + dset.train_pairs)
        else:
            print('Evaluating with test pairs')
            test_pair_set = set(dset.test_pairs + dset.train_pairs)
        self.test_pairs = [(dset.attr2idx[attr], dset.obj2idx[obj])
                           for attr, obj in list(test_pair_set)]
        mask = [1 if pair in test_pair_set else 0 for pair in dset.pairs]
        self.closed_mask = torch.ByteTensor(mask)

        seen_pair_set = set(dset.train_pairs)
        mask = [1 if pair in seen_pair_set else 0 for pair in dset.pairs]
        self.seen_mask = torch.ByteTensor(mask)

        # object specific mask over which pairs occur in the object oracle setting
        oracle_obj_mask = []
        for _obj in dset.objs:
            mask = [1 if _obj == obj else 0 for attr, obj in dset.pairs]
            oracle_obj_mask.append(torch.ByteTensor(mask))
        self.oracle_obj_mask = torch.stack(oracle_obj_mask, 0)

        # decide if the model being evaluated is a manifold model or not
        mname = model.__class__.__name__
        if 'VisualProduct' in mname:
            self.score_model = self.score_clf_model
        else:
            self.score_model = self.score_manifold_model