AutoDynamicDeepLab

This repository is for the IROS 2021 paper ADD: A Fine-grained Dynamic Inference Architecture for Semantic Image Segmentation.

Dynamic-Auto-DeepLab performs three-stage training by firstly searching for the architecture. Second, train the model with the searched network architecture. Third, train earlier-exit-decision.

Modifiy path to your Cityscapes in mypath.py

Neural Architecture Search

We search for the architecture on Cityscapes

cd scripts
bash search_cityscapes.sh

The searched architecture and searching progress can be seen by:

tensorboard --logdir path-to-your-exp

Train model:

One can choose network to train by modified .sh file. Note that we the batch size is #GPU/16 since we use torch.distributed

bash train_dist.sh

Train earlier-decision-maker (EDM) with the feature processed by the model we just trained:

bash train_edm.sh

Evaluation on Cityscapes:

bash eval.sh

Requirements

• Pytorch version 1.0+

• Python 3

• tensorboardX

• pycocotools

• tqdm

• apex

Citation

Acknowledgement

Auto-DeepLab

pytorch-deeplab-xception

DeepLabv3.pytorch

Synchronized-BatchNorm-PyTorch