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]]]
    top_k_predictions = [[
        [9, 4, 6, 2, 0],
        [5, 7, 2, 9, 6],
    ], [
        [5, 7, 2, 9, 6],
        [9, 4, 6, 2, 0],
    ]]
    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, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 1, 0]]])

    # Classes 1,3,8 have 0 predictions, classes -1 and 10 are out of range.
    for class_id in (-1, 1, 3, 8, 10):
      self._test_streaming_sparse_precision_at_k(
          predictions, labels, k=5, expected=NAN, class_id=class_id)
      self._test_streaming_sparse_precision_at_top_k(
          top_k_predictions, labels, expected=NAN, class_id=class_id)