Some question about data_gen

[cpystan]

You have done great job! But i am confused about some code.

`def data_loader(path, batch_size, imSize,
mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5],
ignore_val=44, pos_val=255, neg_val=155, pos_class=[0,1], neg_class=[2]):
# pos_class and neg_class in the folder name for keras ImageDataGenerator input
# 0,1,2 are low, high, normal

def imerge(img_gen, mask_gen):
    for (imgs, img_labels), (mask, mask_labels) in itertools.zip_longest(img_gen, mask_gen):
        # compute weight to ignore particular pixels
        # mask = np.expand_dims(mask[:,:,:,0], axis=3)
        mask = mask[:,:,:,0]
        weight = np.ones(mask.shape, np.float32)
        weight[mask==ignore_val] = 0.5 # this is set by experience

        # In mask, ignored pixel has value ignore_val.
        # The weight of these pixel is set to zero, so they do not contribute to loss
        # The returned mask is still binary.
        # compute per sample
        for c, mask_label in enumerate(mask_labels):
            assert(mask_labels[c] == img_labels[c])
            mask_pointer = mask[c]
            if mask_label in pos_class:
                assert(np.where(mask_pointer == neg_val)[0].size == 0)
                mask_pointer[mask_pointer==pos_val] = 1
            elif mask_label in neg_class:
                assert(np.where(mask_pointer == pos_val)[0].size == 0)
                mask_pointer[mask_pointer==neg_val] = 0
            else:
                print ('WARNING: mask beyond the expected class range')
                mask_pointer /= 255.0

            mask_pointer[mask_pointer==ignore_val] = 0

        assert set(np.unique(mask)).issubset([0, 1])
        # assert set(np.unique(weight)).issubset([0, 1])

        # img, mask = preprocess(imgs, mean, std, mask)

        yield imgs, mask, weight, img_labels

`

code above in segmentation/data_gen.py. what is the aim to calculate 'mask_pointer'? it seems that it is not returned finally?
And 'assert set(np.unique(mask)).issubset([0, 1])' is apparently cannot be satisfied since the pixels in mask only belong to [44 255 155].