_____ Test_compute_shape_after_pooling.test_random_shapes_and_kernel_sizes _____

self = <test_pooling.Test_compute_shape_after_pooling testMethod=test_random_shapes_and_kernel_sizes>

    def test_random_shapes_and_kernel_sizes(self):
        shapes = [
            (6, 5),
            (5, 6),
            (6, 6),
            (11, 1),
            (1, 11),
            (0, 1),
            (1, 0),
            (0, 0)
        ]
        kernel_sizes = [1, 2, 3, 5]
        nb_channels_lst = [None, 1, 3, 4]
    
        # 8*(4*4)*4 = 512 subtests
        gen = itertools.product(shapes, nb_channels_lst)
        for shape_nochan, nb_channels in gen:
            shape = shape_nochan
            if nb_channels is not None:
                shape = tuple(list(shape) + [nb_channels])
            image = np.zeros(shape, dtype=np.uint8)
    
            for ksize_h, ksize_w in itertools.product(kernel_sizes,
                                                      kernel_sizes):
                with self.subTest(shape=shape, ksize_h=ksize_h,
                                  ksize_w=ksize_w):
>                   image_pooled = ia.avg_pool(image, (ksize_h, ksize_w))

test/augmenters/test_pooling.py:55: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/nix/store/z2kzli3m2snpm3x8j7g06vvwmkj7wy68-python3.8-imgaug-0.4.0/lib/python3.8/site-packages/imgaug/imgaug.py:1791: in avg_pool
    return pool(arr, block_size, np.average, pad_mode=pad_mode,
/nix/store/z2kzli3m2snpm3x8j7g06vvwmkj7wy68-python3.8-imgaug-0.4.0/lib/python3.8/site-packages/imgaug/imgaug.py:1740: in pool
    arr_reduced = skimage.measure.block_reduce(arr, tuple(block_size), func,
/nix/store/km6km2a0mjm916qwr49b0qy1psxkmh21-python3.8-scikit-image-0.18.1/lib/python3.8/site-packages/skimage/measure/block.py:81: in block_reduce
    image = np.pad(image, pad_width=pad_width, mode='constant',
<__array_function__ internals>:5: in pad
    ???
/nix/store/ssf3b310n2f64gc5hc84ha24c993ih4l-python3.8-numpy-1.20.1/lib/python3.8/site-packages/numpy/lib/arraypad.py:803: in pad
    _set_pad_area(roi, axis, width_pair, value_pair)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

padded = array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]], dtype=uint8)
axis = 0, width_pair = [0, 0], value_pair = (None, None)

    def _set_pad_area(padded, axis, width_pair, value_pair):
        """
        Set empty-padded area in given dimension.
    
        Parameters
        ----------
        padded : ndarray
            Array with the pad area which is modified inplace.
        axis : int
            Dimension with the pad area to set.
        width_pair : (int, int)
            Pair of widths that mark the pad area on both sides in the given
            dimension.
        value_pair : tuple of scalars or ndarrays
            Values inserted into the pad area on each side. It must match or be
            broadcastable to the shape of `arr`.
        """
        left_slice = _slice_at_axis(slice(None, width_pair[0]), axis)
>       padded[left_slice] = value_pair[0]
E       TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'

/nix/store/ssf3b310n2f64gc5hc84ha24c993ih4l-python3.8-numpy-1.20.1/lib/python3.8/site-packages/numpy/lib/arraypad.py:147: TypeError