1b11b4eeb6bd4b163ce351af680c5a14d823443d,keras/backend/tensorflow_backend.py,,resize_images,#Any#Any#Any#Any#,494

Before Change

        new_shape *= tf.constant(np.array([height_factor, width_factor]).astype("int32"))
        X = permute_dimensions(X, [0, 2, 3, 1])
        X = tf.image.resize_nearest_neighbor(X, new_shape)
        return permute_dimensions(X, [0, 3, 1, 2])
    elif dim_ordering == "tf":
        new_shape = tf.shape(X)[1:3]
        new_shape *= tf.constant(np.array([height_factor, width_factor]).astype("int32"))
        return tf.image.resize_nearest_neighbor(X, new_shape)

After Change

        X.set_shape((None, None, original_shape[2] * height_factor, original_shape[3] * width_factor))
        return X
    elif dim_ordering == "tf":
        original_shape = int_shape(X)
        new_shape = tf.shape(X)[1:3]
        new_shape *= tf.constant(np.array([height_factor, width_factor]).astype("int32"))
        X = tf.image.resize_nearest_neighbor(X, new_shape)
        X.set_shape((None, original_shape[1] * height_factor, original_shape[2] * width_factor, None))
        return X
    else:
        raise Exception("Invalid dim_ordering: " + dim_ordering)

Instances