Question about class_weights in loss functions

Hello,

I’m a bit confused by class_weights parameter in loss functions. For instance, in JaccardLoss description it says: class_weights: Array (``np.array``) of class weights (``len(weights) = num_classes``) So, it’s a numpy array. What dimension should it be? I assume it should be (image width x image height x number of classes). But how can I assign specific weights to specific classes? I beleive the process should be different gor binary segmentation and for multiclass segmentation problems.

[EtagiBI]

Thanks for your reply!

I was confused by a PyCharm warning. It claims that list is an unexpected data type for class_weights parameter of loss functions.

As for difference between binary segmentation and multiclass segmentation, I thought that one-hot encoding isn’t widely used for binary problems since it’s possible to determine both classes using one mask with a single channel.

[JordanMakesMaps]

It doesn’t need to be an numpy array, and it’s definitely not a 2D array. It can just be a python list where each index contains the weight for the class whose channel is the same when one-hot-encoded.

class_weights = [.5, .1, .95] 

The number of indexes/classes is just to tell a helper function which channel to pull from both the ground-truth and the prediction:

# base/functional.py, line 88
gt, pr = gather_channels(gt, pr, indexes=class_indexes, **kwargs)

The determination of what the class weight values should be, is the question to ask. Usually people will use sklearn.utils.class_weight.compute_class_weight, but you could implement your own version by calculating the numbers of samples per class and dividing the total number of samples over each one. See here for discussion. How you calculate the number of samples in a multi-channel image? I’m not sure how others do it, but I count the number of pixels for each class in the whole dataset, along with the total number of pixels in the dataset and compute the weights from that.

In this case, it’s not different for binary and multi-class classification because if you think about it, binary classification in semantic segmentation is still two classes. If you’re looking at pictures of cats, the two classes are ‘cat’, and ‘not cat’.

...

# score calculation
intersection = backend.sum(gt * pr, axis=axes)
union = backend.sum(gt + pr, axis=axes) - intersection

score = (intersection + smooth) / (union + smooth)
score = average(score, per_image, class_weights, **kwargs) 

return score

...

def average(x, per_image=False, class_weights=None, **kwargs):
    backend = kwargs['backend']
    if per_image:
        x = backend.mean(x, axis=0)
    if class_weights is not None:
        x = x * class_weights
    return backend.mean(x)