model.py 文件源码

def _algo_2_vert_comp(self, sent1_block_a, sent2_block_a, sent1_block_b, sent2_block_b):
        comparison_feats = []
        ws_no_inf = [w for w in self.filter_widths if not np.isinf(w)]
        for pool in ('max', 'min', 'mean'):
            for ws1 in self.filter_widths:
                x1 = sent1_block_a[ws1][pool]
                batch_size = x1.size()[0]
                for ws2 in self.filter_widths:
                    x2 = sent2_block_a[ws2][pool]
                    if (not np.isinf(ws1) and not np.isinf(ws2)) or (np.isinf(ws1) and np.isinf(ws2)):
                        comparison_feats.append(F.cosine_similarity(x1, x2).contiguous().view(batch_size, 1))
                        comparison_feats.append(F.pairwise_distance(x1, x2))
                        comparison_feats.append(torch.abs(x1 - x2))

        for pool in ('max', 'min'):
            for ws in ws_no_inf:
                oG_1B = sent1_block_b[ws][pool]
                oG_2B = sent2_block_b[ws][pool]
                for i in range(0, self.n_per_dim_filters):
                    x1 = oG_1B[:, :, i]
                    x2 = oG_2B[:, :, i]
                    batch_size = x1.size()[0]
                    comparison_feats.append(F.cosine_similarity(x1, x2).contiguous().view(batch_size, 1))
                    comparison_feats.append(F.pairwise_distance(x1, x2))
                    comparison_feats.append(torch.abs(x1 - x2))

        return torch.cat(comparison_feats, dim=1)