using search.py to find out autoaugment

[huangd1999]

[2020-08-03 21:23:54,603] [Fast AutoAugment] [INFO] processed in 76.2692 secs [2020-08-03 21:23:54,603] [Fast AutoAugment] [INFO] ----- Search Test-Time Augmentation Policies ----- search_cifar10_wresnet40_2_fold0_ratio0.1 Traceback (most recent call last): File "search.py", line 230, in <module> algo = HyperOptSearch(space, max_concurrent=4*20, reward_attr=reward_attr) TypeError: __init__() got an unexpected keyword argument 'reward_attr' [*test 0000/0010]: 100%|██████████| 79/79 [00:01<00:00, 50.82it/s, loss=0.459, top1=0.848, top5=0.994, loss_ema=0.423]
when I use python search.py -c confs/wresnet40x2_cifar.yaml --aug default there are some errors. and i want to know where i can see the autoaugment policy i searched.

[michklim]

@Hdong179 this looks like an error with HyperOptSearch. Try using ray==0.6.5 in your virtual env.

[sgondala]

I get an error "ERROR: No matching distribution found for ray==0.6.5"
How to resolve this? Or can't we use new ray?

[michklim]

@sgondala and @Hdong179, below you can find what has worked for me:

dependencies:
  - python=3.6.9
  - pytorch=1.2.0
  - torchvision=0.4.0
  - cudatoolkit=10
  - pip
  - pip:
      - git+https://github.com/wbaek/theconf@de32022f8c0651a043dc812d17194cdfd62066e8
      - git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git@08f7d5e
      - git+https://github.com/ildoonet/pystopwatch2.git
      - git+https://github.com/hyperopt/hyperopt.git
      - pretrainedmodels
      - gorilla
      - tabulate
      - pandas
      - tqdm
      - tensorboardx
      - sklearn
      - ray==0.6.5
      - psutil
      - setproctitle
      - requests
      - tensorflow==1.15