# NAS-BENCH-102: Extending the Scope of Reproducible Neural Architecture Search

We propose an algorithm-agnostic NAS benchmark (NAS-Bench-102) with a fixed search space, which provides a unified benchmark for almost any up-to-date NAS algorithms.
The design of our search space is inspired from that used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph.
Each edge here is associated with an operation selected from a predefined operation set.
For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-102 includes 4 nodes and 5 associated operation options, which generates 15,625 neural cell candidates in total.

In this Markdown file, we provide:
- Detailed instruction to reproduce NAS-Bench-102.
- 10 NAS algorithms evaluated in our paper.

Note: please use `PyTorch >= 1.2.0` and `Python >= 3.6.0`.

## How to Use NAS-Bench-102

1. Creating an API instance from a file:
```
from nas_102_api import NASBench102API
api = NASBench102API('$path_to_meta_nas_bench_file')
api = NASBench102API('NAS-Bench-102-v1_0.pth')
```

2. Show the number of architectures `len(api)` and each architecture `api[i]`:
```
num = len(api)
for i, arch_str in enumerate(api):
  print ('{:5d}/{:5d} : {:}'.format(i, len(api), arch_str))
```

3. Show the results of all trials for a single architecture:
```
# show all information for a specific architecture
api.show(1)
api.show(2)

# show the mean loss and accuracy of an architecture
info = api.query_meta_info_by_index(1)
loss, accuracy = info.get_metrics('cifar10', 'train')
flops, params, latency = info.get_comput_costs('cifar100')

# get the detailed information
results = api.query_by_index(1, 'cifar100')
print ('There are {:} trials for this architecture [{:}] on cifar100'.format(len(results), api[1]))
print ('Latency : {:}'.format(results[0].get_latency()))
print ('Train Info : {:}'.format(results[0].get_train()))
print ('Valid Info : {:}'.format(results[0].get_eval('x-valid')))
print ('Test  Info : {:}'.format(results[0].get_eval('x-test')))
# for the metric after a specific epoch
print ('Train Info [10-th epoch] : {:}'.format(results[0].get_train(10)))
```

4. Query the index of an architecture by string
```
index = api.query_index_by_arch('|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|')
api.show(index)
```

5. For other usages, please see `lib/aa_nas_api/api.py`

### Detailed Instruction

In `nas_102_api`, we define three classes: `NASBench102API`, `ArchResults`, `ResultsCount`.

`ResultsCount` maintains all information of a specific trial. One can instantiate ResultsCount and get the info via the following codes (`000157-FULL.pth` saves all information of all trials of 157-th architecture):
```
from nas_102_api import ResultsCount
xdata  = torch.load('000157-FULL.pth')
odata  = xdata['full']['all_results'][('cifar10-valid', 777)]
result = ResultsCount.create_from_state_dict( odata )
print(result) # print it
print(result.get_train())   # print the final training loss/accuracy/[optional:time-cost-of-a-training-epoch]
print(result.get_train(11)) # print the training info of the 11-th epoch
print(result.get_eval('x-valid'))     # print the final evaluation info on the validation set
print(result.get_eval('x-valid', 11)) # print the info on the validation set of the 11-th epoch
print(result.get_latency())           # print the evaluation latency [in batch]
result.get_net_param()                # the trained parameters of this trial
arch_config = result.get_config(CellStructure.str2structure) # create the network with params
net_config  = dict2config(arch_config, None)
network    = get_cell_based_tiny_net(net_config)
network.load_state_dict(result.get_net_param())
```

`ArchResults` maintains all information of all trials of an architecture. Please see the following usages:
```
from nas_102_api import ArchResults
xdata   = torch.load('000157-FULL.pth')
archRes = ArchResults.create_from_state_dict(xdata['less']) # load trials trained with  12 epochs
archRes = ArchResults.create_from_state_dict(xdata['full']) # load trials trained with 200 epochs

print(archRes.arch_idx_str())      # print the index of this architecture 
print(archRes.get_dataset_names()) # print the supported training data
print(archRes.get_comput_costs('cifar10-valid')) # print all computational info when training on cifar10-valid 
print(archRes.get_metrics('cifar10-valid', 'x-valid', None, False)) # print the average loss/accuracy/time on all trials
print(archRes.get_metrics('cifar10-valid', 'x-valid', None,  True)) # print loss/accuracy/time of a randomly selected trial
```

`NASBench102API` is the topest level api. Please see the following usages:
```
from nas_102_api import NASBench102API as API
api = API('NAS-Bench-102-v1_0.pth')
```

## Instruction to Re-Generate NAS-Bench-102

1. generate the meta file for NAS-Bench-102 using the following script, where `NAS-BENCH-102` indicates the name and `4` indicates the maximum number of nodes in a cell.
```
bash scripts-search/NAS-Bench-102/meta-gen.sh NAS-BENCH-102 4
```

2. train earch architecture on a single GPU (see commands in `output/NAS-BENCH-102-4/BENCH-102-N4.opt-full.script`, which is automatically generated by step-1).
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NAS-Bench-102/train-models.sh 0     0   389 -1 '777 888 999'
```
This command will train 390 architectures (id from 0 to 389) using the following four kinds of splits with three random seeds (777, 888, 999).

|     Dataset     |     Train     |      Eval    |
|:---------------:|:-------------:|:------------:|
| CIFAR-10        | train         | valid / test |
| CIFAR-10        | train + valid | test         |
| CIFAR-100       | train         | valid / test |
| ImageNet-16-120 | train         | valid / test |

3. calculate the latency, merge the results of all architectures, and simplify the results.
(see commands in `output/NAS-BENCH-102-4/meta-node-4.cal-script.txt` which is automatically generated by step-1).
```
OMP_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0 python exps/NAS-Bench-102/statistics.py --mode cal --target_dir 000000-000389-C16-N5
```

4. merge all results into a single file for NAS-Bench-102-API.
```
OMP_NUM_THREADS=4 python exps/NAS-Bench-102/statistics.py --mode merge
```
This command will generate a single file `output/NAS-BENCH-102-4/simplifies/C16-N5-final-infos.pth` contains all the data for NAS-Bench-102.
This generated file will serve as the input for our NAS-Bench-102 API.

[option] train a single architecture on a single GPU.
```
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NAS-Bench-102/train-a-net.sh resnet 16 5
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NAS-Bench-102/train-a-net.sh '|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|skip_connect~1|skip_connect~2|' 16 5
```

## To Reproduce 10 Baseline NAS Algorithms in NAS-Bench-102

We have tried our best to implement each method. However, still, some algorithms might obtain non-optimal results since their hyper-parameters might not fit our NAS-Bench-102.
If researchers can provide better results with different hyper-parameters, we are happy to update results according to the new experimental results. We also welcome more NAS algorithms to test on our dataset and would include them accordingly.

- [1] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 -1`
- [2] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 -1`
- [3] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh     cifar10 -1`
- [4] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh     cifar10 -1`
- [5] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/ENAS.sh     cifar10 -1`
- [6] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/RANDOM-NAS.sh cifar10 -1`
- [7] `bash ./scripts-search/algos/R-EA.sh -1`
- [8] `bash ./scripts-search/algos/Random.sh -1`
- [9] `bash ./scripts-search/algos/REINFORCE.sh -1`
- [10] `bash ./scripts-search/algos/BOHB.sh -1`