Function test

animation/helper/eval/verification.py:227–274

(data_set, backbone, batch_size, nfolds=10)

@torch.no_grad()
def test(data_set, backbone, batch_size, nfolds=10):
    print('testing verification..')
    data_list = data_set[0]
    issame_list = data_set[1]
    embeddings_list = []
    time_consumed = 0.0
    for i in range(len(data_list)):
        data = data_list[i]
        embeddings = None
        ba = 0
        while ba < data.shape[0]:
            bb = min(ba + batch_size, data.shape[0])
            count = bb - ba
            _data = data[bb - batch_size: bb]
            time0 = datetime.datetime.now()
            img = ((_data / 255) - 0.5) / 0.5
            net_out: torch.Tensor = backbone(img)
            _embeddings = net_out.detach().cpu().numpy()
            time_now = datetime.datetime.now()
            diff = time_now - time0
            time_consumed += diff.total_seconds()
            if embeddings is None:
                embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
            embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :]
            ba = bb
        embeddings_list.append(embeddings)

    _xnorm = 0.0
    _xnorm_cnt = 0
    for embed in embeddings_list:
        for i in range(embed.shape[0]):
            _em = embed[i]
            _norm = np.linalg.norm(_em)
            _xnorm += _norm
            _xnorm_cnt += 1
    _xnorm /= _xnorm_cnt

    embeddings = embeddings_list[0].copy()
    embeddings = sklearn.preprocessing.normalize(embeddings)
    acc1 = 0.0
    std1 = 0.0
    embeddings = embeddings_list[0] + embeddings_list[1]
    embeddings = sklearn.preprocessing.normalize(embeddings)
    print(embeddings.shape)
    print('infer time', time_consumed)
    _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds)
    acc2, std2 = np.mean(accuracy), np.std(accuracy)
    return acc1, std1, acc2, std2, _xnorm, embeddings_list


def dumpR(data_set,