Weighted Pixel Wise Categorical Cross Entropy for Semantic Segmentation

Question

I have recently started learning about Semantic Segmentation. I am trying to train a UNet for the same. My input is RGB 128x128x3 images. My masks are made up of 4 classes 0, 1, 2, 3 and are One-Hot Encoded with dimension 128x128x4.

def weighted_cce(y_true, y_pred):
        weights = []
        t_inf = tf.convert_to_tensor(1e9, dtype = 'float32')
        t_zero = tf.convert_to_tensor(0, dtype = 'int64')
        for i in range(0, 4):
            l = tf.argmax(y_true, axis = -1) == i
            n = tf.cast(tf.math.count_nonzero(l), 'float32') + K.epsilon()
            weights.append(n)

        weights = [batch_size/j for j in weights]

        y_pred /= K.sum(y_pred, axis=-1, keepdims=True)
        # clip to prevent NaN's and Inf's
        y_pred = K.clip(y_pred, K.epsilon(), 1 - K.epsilon())
        # calc
        loss = y_true * K.log(y_pred) * weights
        loss = -K.sum(loss, -1)
        return loss

This is the loss function that I am using but it classifies every pixel as 2. What am I doing wrong?

Answer 1

You should have weights based on you entire data (unless your batch size is reasonably big so you have sort of stable weights).

If some class is underrepresented, with a small batch size, it will have near infinity weights.

If your target data is numpy array:

shp = y_train.shape
totalPixels = shp[0] * shp[1] * shp[2]

weights = np.sum(y_train, axis=(0, 1, 2)) #final shape (4,)
weights = totalPixels/weights           

If your data is in a Sequence generator:

totalPixels = 0
counts = np.zeros((4,))

for i in range(len(generator)):
    x, y = generator[i]

    shp = y.shape
    totalPixels += shp[0] * shp[1] * shp[2]

    counts = counts + np.sum(y, axis=(0,1,2))

weights = totalPixels / counts

If your data is in a yield generator (you must know how many batches you have in an epoch):

for i in range(batches_per_epoch):
    x, y = next(generator)
    #the rest is equal to the Sequence example above

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Attempt 1

I don't know if newer versions of Keras are able to handle this, but you can try the simplest approach first: simply call fit or fit_generator with the class_weight argument:

model.fit(...., class_weight = {0: weights[0], 1: weights[1], 2: weights[2], 3: weights[3]})

Attempt 2

Make a healthier loss function:

weights = weights.reshape((1,1,1,4))
kWeights = K.constant(weights)

def weighted_cce(y_true, y_pred):
    yWeights = kWeights * y_pred         #shape (batch, 128, 128, 4)
    yWeights = K.sum(yWeights, axis=-1)  #shape (batch, 128, 128)  

    loss = K.categorical_crossentropy(y_true, y_pred) #shape (batch, 128, 128)
    wLoss = yWeights * loss

    return K.sum(wLoss, axis=(1,2))