def get_nn_avg_dist()

in src/utils.py [0:0]

• 24 lines of code
• 4 McCabe index (conditional complexity)

def get_nn_avg_dist(emb, query, knn):
    """
    Compute the average distance of the `knn` nearest neighbors
    for a given set of embeddings and queries.
    Use Faiss if available.
    """
    if FAISS_AVAILABLE:
        emb = emb.cpu().numpy()
        query = query.cpu().numpy()
        if hasattr(faiss, 'StandardGpuResources'):
            # gpu mode
            res = faiss.StandardGpuResources()
            config = faiss.GpuIndexFlatConfig()
            config.device = 0
            index = faiss.GpuIndexFlatIP(res, emb.shape[1], config)
        else:
            # cpu mode
            index = faiss.IndexFlatIP(emb.shape[1])
        index.add(emb)
        distances, _ = index.search(query, knn)
        return distances.mean(1)
    else:
        bs = 1024
        all_distances = []
        emb = emb.transpose(0, 1).contiguous()
        for i in range(0, query.shape[0], bs):
            distances = query[i:i + bs].mm(emb)
            best_distances, _ = distances.topk(knn, dim=1, largest=True, sorted=True)
            all_distances.append(best_distances.mean(1).cpu())
        all_distances = torch.cat(all_distances)
        return all_distances.numpy()