Python faiss.GpuIndexFlatConfig() Examples

def run_kmeans(x, nmb_clusters, verbose=False):
    """Runs kmeans on 1 GPU.
    Args:
        x: data
        nmb_clusters (int): number of clusters
    Returns:
        list: ids of data in each cluster
    """
    n_data, d = x.shape

    # faiss implementation of k-means
    clus = faiss.Clustering(d, nmb_clusters)

    # Change faiss seed at each k-means so that the randomly picked
    # initialization centroids do not correspond to the same feature ids
    # from an epoch to another.
    clus.seed = np.random.randint(1234)

    clus.niter = 20
    clus.max_points_per_centroid = 10000000
    res = faiss.StandardGpuResources()
    flat_config = faiss.GpuIndexFlatConfig()
    flat_config.useFloat16 = False
    flat_config.device = 0
    index = faiss.GpuIndexFlatL2(res, d, flat_config)

    # perform the training
    clus.train(x, index)
    _, I = index.search(x, 1)
    losses = faiss.vector_to_array(clus.obj)
    if verbose:
        print('k-means loss evolution: {0}'.format(losses))

    return [int(n[0]) for n in I], losses[-1] 

def test_knn_search(size=10000, gpu_id=None):
    x = np.random.rand(size, 512)
    x = x.reshape(x.shape[0], -1).astype('float32')
    d = x.shape[1]

    tic = time.time()
    if gpu_id is None:
        index = faiss.IndexFlatL2(d)
    else:
        cfg = faiss.GpuIndexFlatConfig()
        cfg.useFloat16 = False
        cfg.device = gpu_id

        flat_config = [cfg]
        resources = [faiss.StandardGpuResources()]
        index = faiss.GpuIndexFlatL2(resources[0], d, flat_config[0])
    index.add(x)
    print('Index built in {} sec'.format(time.time() - tic))
    distances, I = index.search(x, 21)
    print('Searched in {} sec'.format(time.time() - tic))
    print(distances.shape)
    print(I.shape)
    print(distances[:5])
    print(I[:5]) 

def test_nmi_faiss(embeddings, labels):
    res = faiss.StandardGpuResources()
    flat_config = faiss.GpuIndexFlatConfig()
    flat_config.device = 0

    unique_labels = np.unique(labels)
    d = embeddings.shape[1]
    kmeans = faiss.Clustering(d, unique_labels.size)
    kmeans.verbose = True
    kmeans.niter = 300
    kmeans.nredo = 10
    kmeans.seed = 0

    index = faiss.GpuIndexFlatL2(res, d, flat_config)

    kmeans.train(embeddings, index)

    dists, pred_labels = index.search(embeddings, 1)

    pred_labels = pred_labels.squeeze()

    nmi = normalized_mutual_info_score(labels, pred_labels)

    print("NMI: {}".format(nmi))
    return nmi 

def reserve_faiss_gpu_memory(gpu_id=0):
    """
    Reserves around 2.4 Gb memory on Titan Xp.
    `r = reserve_faiss_gpu_memory()`
    To release the memory run `del r`

    Something like 200 Mb will still be hold afterwards.
    """
    res = faiss.StandardGpuResources()
    cfg = faiss.GpuIndexFlatConfig()
    cfg.useFloat16 = False
    cfg.device = gpu_id
    index = faiss.GpuIndexFlatL2(res, 2048, cfg)
    return index, res 

def knn_faiss(feats, k):
    import torch
    import faiss
    import pdb
    N, dim = feats.shape
    res = faiss.StandardGpuResources()
    feats /= np.linalg.norm(feats).reshape(-1, 1)
    flat_config = faiss.GpuIndexFlatConfig()
    flat_config.device = int(torch.cuda.device_count()) - 1
    index = faiss.GpuIndexFlatL2(res, dim, flat_config)
    index.add(feats)
    D, I = index.search(feats, k + 1)
    pdb.set_trace() 

def __init__(self, opt):
        super(ChamferLoss, self).__init__()
        self.opt = opt
        self.dimension = 3
        self.k = 1

        # we need only a StandardGpuResources per GPU
        self.res = faiss.StandardGpuResources()
        self.res.setTempMemoryFraction(0.1)
        self.flat_config = faiss.GpuIndexFlatConfig()
        self.flat_config.device = opt.gpu_id

        # place holder
        self.forward_loss = torch.FloatTensor([0])
        self.backward_loss = torch.FloatTensor([0]) 

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() 

def BuildKNNGraphByFAISS_GPU(db,k):
    dbsize, dim = db.shape
    flat_config = faiss.GpuIndexFlatConfig()
    flat_config.device = 0
    res = faiss.StandardGpuResources()
    nn = faiss.GpuIndexFlatL2(res, dim, flat_config)
    nn.add(db)
    dists,idx = nn.search(db, k+1)
    return idx[:,1:],dists[:,1:] 

def BuildKNNGraphByFAISS_GPU(db,k):
    dbsize, dim = db.shape
    flat_config = faiss.GpuIndexFlatConfig()
    flat_config.device = 0
    res = faiss.StandardGpuResources()
    nn = faiss.GpuIndexFlatL2(res, dim, flat_config)
    nn.add(db)
    dists,idx = nn.search(db, k+1)
    return idx[:,1:],dists[:,1:] 

def __init__(self, opt):
        super(ChamferLoss, self).__init__()
        self.opt = opt
        self.dimension = 3
        self.k = 1

        # we need only a StandardGpuResources per GPU
        self.res = faiss.StandardGpuResources()
        self.res.setTempMemoryFraction(0.1)
        self.flat_config = faiss.GpuIndexFlatConfig()
        self.flat_config.device = opt.gpu_id

        # place holder
        self.forward_loss = torch.FloatTensor([0])
        self.backward_loss = torch.FloatTensor([0]) 

def __init__(self, opt):
        super(ChamferLoss, self).__init__()
        self.opt = opt
        self.dimension = 3
        self.k = 1

        # we need only a StandardGpuResources per GPU
        self.res = faiss.StandardGpuResources()
        self.res.setTempMemoryFraction(0.1)
        self.flat_config = faiss.GpuIndexFlatConfig()
        self.flat_config.device = opt.gpu_id

        # place holder
        self.forward_loss = torch.FloatTensor([0])
        self.backward_loss = torch.FloatTensor([0]) 

def _retrieve_knn_faiss_gpu_inner_product(query_embeddings, db_embeddings, k, gpu_id=0):
    """
        Retrieve k nearest neighbor based on inner product

        Args:
            query_embeddings:           numpy array of size [NUM_QUERY_IMAGES x EMBED_SIZE]
            db_embeddings:              numpy array of size [NUM_DB_IMAGES x EMBED_SIZE]
            k:                          number of nn results to retrieve excluding query
            gpu_id:                     gpu device id to use for nearest neighbor (if possible for `metric` chosen)

        Returns:
            dists:                      numpy array of size [NUM_QUERY_IMAGES x k], distances of k nearest neighbors
                                        for each query
            retrieved_db_indices:       numpy array of size [NUM_QUERY_IMAGES x k], indices of k nearest neighbors
                                        for each query
    """
    import faiss

    res = faiss.StandardGpuResources()
    flat_config = faiss.GpuIndexFlatConfig()
    flat_config.device = gpu_id

    # Evaluate with inner product
    index = faiss.GpuIndexFlatIP(res, db_embeddings.shape[1], flat_config)
    index.add(db_embeddings)
    # retrieved k+1 results in case that query images are also in the db
    dists, retrieved_result_indices = index.search(query_embeddings, k + 1)

    return dists, retrieved_result_indices 

def _retrieve_knn_faiss_gpu_euclidean(query_embeddings, db_embeddings, k, gpu_id=0):
    """
        Retrieve k nearest neighbor based on inner product

        Args:
            query_embeddings:           numpy array of size [NUM_QUERY_IMAGES x EMBED_SIZE]
            db_embeddings:              numpy array of size [NUM_DB_IMAGES x EMBED_SIZE]
            k:                          number of nn results to retrieve excluding query
            gpu_id:                     gpu device id to use for nearest neighbor (if possible for `metric` chosen)

        Returns:
            dists:                      numpy array of size [NUM_QUERY_IMAGES x k], distances of k nearest neighbors
                                        for each query
            retrieved_db_indices:       numpy array of size [NUM_QUERY_IMAGES x k], indices of k nearest neighbors
                                        for each query
    """
    import faiss

    res = faiss.StandardGpuResources()
    flat_config = faiss.GpuIndexFlatConfig()
    flat_config.device = gpu_id

    # Evaluate with inner product
    index = faiss.GpuIndexFlatL2(res, db_embeddings.shape[1], flat_config)
    index.add(db_embeddings)
    # retrieved k+1 results in case that query images are also in the db
    dists, retrieved_result_indices = index.search(query_embeddings, k + 1)

    return dists, retrieved_result_indices 

def train_kmeans(x, num_clusters=1000, gpu_ids=None, niter=100, nredo=1, verbose=0):
    """
    Runs k-means clustering on one or several GPUs
    """
    assert np.all(~np.isnan(x)), 'x contains NaN'
    assert np.all(np.isfinite(x)), 'x contains Inf'
    if isinstance(gpu_ids, int):
        gpu_ids = [gpu_ids]
    assert gpu_ids is None or len(gpu_ids)

    d = x.shape[1]
    kmeans = faiss.Clustering(d, num_clusters)
    kmeans.verbose = bool(verbose)
    kmeans.niter = niter
    kmeans.nredo = nredo

    # otherwise the kmeans implementation sub-samples the training set
    kmeans.max_points_per_centroid = 10000000

    if gpu_ids is not None:
        res = [faiss.StandardGpuResources() for i in gpu_ids]

        flat_config = []
        for i in gpu_ids:
            cfg = faiss.GpuIndexFlatConfig()
            cfg.useFloat16 = False
            cfg.device = i
            flat_config.append(cfg)

        if len(gpu_ids) == 1:
            index = faiss.GpuIndexFlatL2(res[0], d, flat_config[0])
        else:
            indexes = [faiss.GpuIndexFlatL2(res[i], d, flat_config[i])
                       for i in range(len(gpu_ids))]
            index = faiss.IndexProxy()
            for sub_index in indexes:
                index.addIndex(sub_index)
    else:
        index = faiss.IndexFlatL2(d)

    # perform the training
    kmeans.train(x, index)
    centroids = faiss.vector_float_to_array(kmeans.centroids)

    objective = faiss.vector_float_to_array(kmeans.obj)
    #logging.debug("Final objective: %.4g" % objective[-1])

    return centroids.reshape(num_clusters, d) 

def find_nearest_neighbors(x, queries=None, k=5, gpu_id=None):
    """
    Find k nearest neighbors for each of the n examples.
    Distances are computed using Squared Euclidean distance metric.

    Arguments:
    ----------
    queries
    x (ndarray): N examples to search within. [N x d].
    gpu_id (int): use CPU if None else use GPU with the specified id.
    queries (ndarray): find nearest neigbor for each query example. [M x d] matrix
        If None than find k nearest neighbors for each row of x
        (excluding self exampels).
    k (int): number of nearest neighbors to find.

    Return
    I (ndarray): Indices of the nearest neighnpors. [M x k]
    distances (ndarray): Distances to the nearest neighbors. [M x k]

    """
    if gpu_id is not None and not isinstance(gpu_id, int):
        raise ValueError('gpu_id must be None or int')
    x = np.asarray(x.reshape(x.shape[0], -1), dtype=np.float32)
    remove_self = False # will we have queries in the search results?
    if queries is None:
        remove_self = True
        queries = x
        k += 1

    d = x.shape[1]

    tic = time.time()
    if gpu_id is None:
        logging.debug('FAISS: cpu::find {} nearest neighbors'\
                     .format(k - int(remove_self)))
        index = faiss.IndexFlatL2(d)
    else:
        logging.debug('FAISS: gpu[{}]::find {} nearest neighbors'\
                     .format(gpu_id, k - int(remove_self)))
        cfg = faiss.GpuIndexFlatConfig()
        cfg.useFloat16 = False
        cfg.device = gpu_id

        flat_config = [cfg]
        resources = [faiss.StandardGpuResources()]
        index = faiss.GpuIndexFlatL2(resources[0], d, flat_config[0])
    index.add(x)
    distances, nns = index.search(queries, k)
    if remove_self:
        for i in range(len(nns)):
            indices = np.nonzero(nns[i, :] != i)[0]
            indices.sort()
            if len(indices) > k - 1:
                indices = indices[:-1]
            nns[i, :-1] = nns[i, indices]
            distances[i, :-1] = distances[i, indices]
        nns = nns[:, :-1]
        distances = distances[:, :-1]
    logging.debug('FAISS: Neighbors search total elapsed time: {:.2f} sec'.format(time.time() - tic))
    return nns, distances 

def get_nn_avg_dist_mog(emb, query, knn):
    """
    Compute the average distance of the `knn` nearest neighbors
    for a given set of embeddings and queries.
    Use Faiss if available.

    emb has divided sqrt(2) * var
    """
    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.GpuIndexFlatL2(res, emb.shape[1], config)
        else:
            # cpu mode
            index = faiss.IndexFlatL2(emb.shape[1])
        index.add(emb)
        # Ad-hoc implementation
        topK = 1000
        temp = 2.
        topK = 10
        distances, idxes = index.search(query, topK)
        return distances.mean(1)
        #query_idx = np.tile(np.arange(query.shape[0]) + 1, (topK, 1)).transpose()
        #rank_diff = abs(np.log(idxes + 1) - np.log(query_idx)) / temp
        #mog_distances_sorted = np.sort(distances + rank_diff)[:, :knn]
        # return: qN, knn
        #return mog_distances_sorted.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()