example_proto_coder_test.py 文件源码

def test_example_proto_coder_default_value(self):
    input_schema = dataset_schema.from_feature_spec({
        'scalar_feature_3':
            tf.FixedLenFeature(shape=[], dtype=tf.float32, default_value=1.0),
        'scalar_feature_4':
            tf.FixedLenFeature(shape=[], dtype=tf.float32, default_value=0.0),
        '1d_vector_feature':
            tf.FixedLenFeature(
                shape=[1], dtype=tf.float32, default_value=[2.0]),
        '2d_vector_feature':
            tf.FixedLenFeature(
                shape=[2, 2],
                dtype=tf.float32,
                default_value=[[1.0, 2.0], [3.0, 4.0]]),
    })
    coder = example_proto_coder.ExampleProtoCoder(input_schema)

    # Python types.
    example_proto_text = """
    features {
    }
    """
    example = tf.train.Example()
    text_format.Merge(example_proto_text, example)
    data = example.SerializeToString()

    # Assert the data is decoded into the expected format.
    expected_decoded = {
        'scalar_feature_3': 1.0,
        'scalar_feature_4': 0.0,
        '1d_vector_feature': [2.0],
        '2d_vector_feature': [[1.0, 2.0], [3.0, 4.0]],
    }
    decoded = coder.decode(data)
    np.testing.assert_equal(expected_decoded, decoded)