Some questions about the paper and code

[RenShuhuai-Andy]

Hi~ I have some questions about the paper and code:

Questions about the code

1. What does "tta" mean in the function eval_tta() (in search.py)？
2. Why for _ in range(1): are used in some places like search.py and class Augmentation in data.py?

Questions about the algorithm

3. It seems that CIFAR-10 dataset does not have an official valid set, so cross-validation is often used. If a dataset already has its valid set, can we just use training set as $D_M$ (defined in paper) and use valid set as $D_A$ directly? If so, can we search policies without the process of cross-validation?
4. Section 3.2.1 of the paper says:

our goal is to improve the generalization ability by searching the augmentation policies that match the density of $D_{train}$ with density of augmented $D_{valid}$

However, the algorithm of ffa just seems to fit the trained model. This algorithm may only pick
augmented data that can make model get high score easily, and these 'easy' augmented data may not match training data? Is there any theoretical guarantee that this algorithm can work?

Inconsistency (maybe) between code and paper

5. Section 3.1 of the paper says:

$\mathcal{T}$ indicates a set of augmented images of dataset D transformed by every sub-policies $\tau \in \mathcal{T}$

However, in class Augmentation in data.py, policy = random.choice(self.policies) is used, so only one of five policies will be used during searching test time augmentation policies. policy in the code is the same as sub-policy right? But this method is actually used in AutoAugment, not faa?

6. It seems that you choose best top N policies from every fold according figure 2 and code:
   
   So I think line 7 and 8 should be in the first loop, not the second?
   

Thanks very much if you can offer some help!