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MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning

Official PyTorch Implementation of MTL-NAS

Please refer to our paper for more technical details:

Yuan Gao*, Haoping Bai*, Zequn Jie, Jiayi Ma, Kui Jia, Wei Liu. MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [arXiv]

overview

If this code is helpful to your research, please consider citing our paper by:

@inproceedings{mtlnas2020,
    title={MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning},
    author={Yuan Gao and Haoping Bai and Zequn Jie and Jiayi Ma and Kui Jia and Wei Liu},
    year={2020},
    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}
}

Setup

Install the necessary dependencies:

$ pip install -r requirements.txt

Dataset

Follow the instruction here to prepare the dataset. Alternatively, download the preprocessed dataset here. Download the converted PyTorch models from here, then create a weights directory and unzip the models inside.

When you are all set, you should have the following file structure:

datasets/nyu_v2/list
datasets/nyu_v2/nyu_v2_mean.npy
datasets/nyu_v2/nyu_train_val
weights/vgg_deeplab_lfov/tf_deeplab.pth
weights/nyu_v2/tf_finetune_seg.pth
weights/nyu_v2/tf_finetune_normal.pth

Training

All the arguments to train/eval MTLNAS are shown in configs/defaults.py. The configuration files for different experiments are also provided in the configs directory. To run the NDDR-CNN baseline with VGG-16 architecture, simply call:

$ CUDA_VISIBLE_DEVICES=0 python tools/train.py --config-file configs/vgg/vgg_nyuv2_nddr.yaml

To run MTLNAS training with default configuration, call:

$ CUDA_VISIBLE_DEVICES=0 python tools/train_nas.py --config-file configs/ablation/vgg_nyuv2_default.yaml

Evaluation

To evaluate the final checkpoint for the NDDR-CNN baseline experiment, call:

$ CUDA_VISIBLE_DEVICES=0 python tools/eval.py --config-file configs/vgg/vgg_nyuv2_nddr.yaml

To evaluate the final checkpoint for default MTLNAS, call:

$ CUDA_VISIBLE_DEVICES=0 python tools/eval_nas.py --config-file configs/ablation/vgg_nyuv2_default.yaml

You can download and extract the final checkpoint for default MTLNAS to ckpts directory and evaluate it by running the command above.


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