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TransNAS-Bench-101

This repository contains the publishable code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search.

In this Markdown file, we show an example how to use TransNAS-Bench-101. The complete network training and evaluation information file can be found through VEGA or this Google Drive Folder.

How to use TransNAS-Bench-101

1. Import the API object in ./code/api/api.py and create an API instance from the .pth file in ./api_home/: ​

from api import TransNASBenchAPI as API
path2nas_bench_file = "./api_home/transnas-bench_v10141024.pth"
api = API(path2nas_bench_file)

2. Check the task information, number of architectures evaluated, and search spaces:

# show number of architectures and number of tasks
length = len(api)
task_list = api.task_list # list of tasks
print(f"This API contains {length} architectures in total across {len(task_list)} tasks.")
# This API contains 7352 architectures in total across 7 tasks.

# Check all model encoding
search_spaces = api.search_spaces # list of search space names
all_arch_dict = api.all_arch_dict # {search_space : list_of_architecture_names}
for ss in search_spaces:
   print(f"Search space '{ss}' contains {len(all_arch_dict[ss])} architectures.")
print(f"Names of 7 tasks: {task_list}")
# Search space 'macro' contains 3256 architectures.
# Search space 'micro' contains 4096 architectures.
# Names of 7 tasks: ['class_scene', 'class_object', 'room_layout', 'jigsaw', 'segmentsemantic', 'normal', 'autoencoder']

3. Since different tasks may require different evaluation metrics, hence metric_dict showing the used metrics can be retrieved from api.metrics_dict. TransNAS-Bench API also recorded the model inference time, backbone/model parameters, backbone/model FLOPs in api.infor_names.

metrics_dict = api.metrics_dict # {task_name : list_of_metrics}
info_names = api.info_names # list of model info names

# check the training information of the example task
task = "class_object"
print(f"Task {task} recorded the following metrics: {metrics_dict[task]}")
print(f"The following model information are also recorded: {info_names}")
# Task class_object recorded the following metrics: ['train_top1', 'train_top5', 'train_loss', 'valid_top1', 'valid_top5', 'valid_loss', 'test_top1', 'test_top5', 'test_loss', 'time_elapsed']
# The following model information are also recorded: ['inference_time', 'encoder_params', 'model_params', 'model_FLOPs', 'encoder_FLOPs']

4. Query the results of an architecture by arch string ​

# Given arch string
xarch = api.index2arch(1) # '64-2311-basic'
for xtask in api.task_list:
    print(f'----- {xtask} -----')
    print(f'--- info ---')
    for xinfo in api.info_names:
        print(f"{xinfo} : {api.get_model_info(xarch, xtask, xinfo)}")
    print(f'--- metrics ---')
    for xmetric in api.metrics_dict[xtask]:
        print(f"{xmetric} : {api.get_single_metric(xarch, xtask, xmetric, mode='best')}")
        print(f"best epoch : {api.get_best_epoch_status(xarch, xtask, metric=xmetric)}")
        print(f"final epoch : {api.get_epoch_status(xarch, xtask, epoch=-1)}")
        if ('valid' in xmetric and 'loss' not in xmetric) or ('valid' in xmetric and 'neg_loss' in xmetric):
            print(f"\nbest_arch -- {xmetric}: {api.get_best_archs(xtask, xmetric, 'micro')[0]}")

A complete example is given in code/api/example.py

• cd code/api
• python example.py

Example network encoding in both search spaces

Macro example network: 64-1234-basic
- Base channel: 64
- Macro skeleton: 1234 (4 stacked modules)
  - [m1(normal)-m2(channelx2)-m3(resolution/2)-m4(channelx2 & resolution/2)]
- Cell structure: basic (ResNet Basic Block)

Micro example network: 64-41414-1_02_333
- Base channel: 64
- Macro skeleton: 41414 (5 stacked modules)
  - [m1(channelx2 & resolution/2)-m2(normal)-m3(channelx2 & resolution/2)-m4(normal)-m5(channelx2 & resolution/2)]
- Cell structure: 1_02_333 (4 nodes, 6 edges)
  - node0: input tensor
  - node1: Skip-Connect( node0 ) # 1
  - node2: None( node0 ) + Conv1x1( node1 ) # 02
  - node3: Conv3x3( node0 ) + Conv3x3( node1 ) + Conv3x3( node2 ) # 333

Citation

If you find that TransNAS-Bench-101 helps your research, please consider citing it:

@inproceedings{duan2021transnas,
  title = {TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search},
  author = {Duan, Yawen and Chen, Xin and Xu, Hang and Chen, Zewei and Liang, Xiaodan and Zhang, Tong and Li, Zhenguo},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages = {5251--5260},
  year = {2021}
}

License

MIT license.