The prob of applying augmentation?

[ghost]

Hi and thanks for this awesome repo.

I just checked the original TensorFlow implementation and found a part different from them. In the original implementation. There is a probability of applying and not applying the augmentation. But I did not find it in this repo.

The link for TensorFlow version: https://github.com/tensorflow/tpu/blob/5144289ba9c9e5b1e55cc118b69fe62dd868657c/models/official/efficientnet/autoaugment.py#L532

Original:
with tf.name_scope('randaug_layer_{}'.format(layer_num)):
for (i, op_name) in enumerate(available_ops):
prob = tf.random_uniform([], minval=0.2, maxval=0.8, dtype=tf.float32)
func, _, args = _parse_policy_info(op_name, prob, random_magnitude,
replace_value, augmentation_hparams)

this repo:
ops = random.choices(self.augment_list, k=self.n)
# print (ops)
for op, minval, maxval in ops:
val = (float(self.m) / 30) * float(maxval - minval) + minval
img = op(img, val)

May I ask is there any reason for this? Or is there any part I missing?

Thanks in advance

[JiyueWang]

In addition, the Identity operation is not included