metric_ops_test.py 文件源码

def test_3d_nan(self):
    predictions = [[[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9],
                    [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]],
                   [[0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6],
                    [0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9]]]
    sparse_labels = _binary_3d_label_to_sparse_value(
        [[[0, 0, 1, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 1, 0, 0, 0, 0]],
         [[0, 1, 1, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 1, 1, 0]]])
    dense_labels = np.array(
        [[[2, 7, 8], [1, 2, 5]], [
            [1, 2, 5],
            [2, 7, 8],
        ]], dtype=np.int64)

    for labels in (sparse_labels, dense_labels):
      # Classes 0,3,4,6,9 have 0 labels, class 10 is out of range.
      for class_id in (0, 3, 4, 6, 9, 10):
        self._test_streaming_sparse_recall_at_k(
            predictions, labels, k=5, expected=NAN, class_id=class_id)