Preprocessing layers with seed not producing the same data augmentation for images and masks-CodePudding

I'm trying to create a simple preprocessing augmentation layer, following this Tensorflow

Alright, now I used the example from the

I would expect the image and its mask to be flip the same way since I set the seed in the Augment class as suggested in the Tensorflow tutorial.

CodePudding user response：

Augmentation can be done on the concatenated image and mask along the channel axis to form a single array and then recover the image and label back, which is shown below:

class Augment(tf.keras.layers.Layer):
    def __init__(self):
        super().__init__()
        # both use the same seed, so they'll make the same random changes.
        self.augment_inputs = tf.keras.layers.RandomRotation(0.3)


    def call(self, inputs, labels):
        
        output = self.augment_inputs(tf.concat([inputs, labels], -1) )
        
        inputs = output[:,:,0:4]
        labels = output[:,:,4:]

        return inputs, labels