UCD

The offical Pytorch code for "Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation"

Guanglei Yang,Enrico Fini,Dan Xu,Paolo Rota,Mingli Ding,Moin Nabi,Xavier Alameda-Pineda,Elisa Ricci

TPAMI 2022

Overview:

Requirements

This repository uses the following libraries:

• Python (3.6)
• Pytorch (1.2)
• torchvision (0.4.0)
• tensorboardX (1.8)
• apex (0.1)
• matplotlib (3.3.1)
• numpy (1.17.2)
• inplace-abn (1.0.7)

We also assume to have installed pytorch.distributed package.

To facilitate your work in installing all dependencies, we provide you the requirement (requirements.txt) file.

We also support docker image. You can build your own docker image via Dockerfile.

How to download data

In this project we use two dataset, ADE20K and Pascal-VOC 2012. We provide the scripts to download them in 'data/download_<dataset_name>.sh'. The script takes no inputs but use it in the target directory (where you want to download data).

How to perform training

The most important file is run.py, that is in charge to start the training or test procedure. To run it, simpy use the following command:

python -m torch.distributed.launch --nproc_per_node=<num_GPUs> run.py --data_root <data_folder> --name <exp_name> .. other args ..

The default is to use a pretraining for the backbone used, that is searched in the pretrained folder of the project. We used the pretrained model released by the authors of In-place ABN (as said in the paper), that can be found here: link. Since the pretrained are made on multiple-gpus, they contain a prefix "module." in each key of the network. Please, be sure to remove them to be compatible with this code (simply rename them using key = key[7:]). If you don't want to use pretrained, please use --no-pretrained.

There are many options (you can see them all by using --help option), but we arranged the code to being straightforward to test the reported methods. Leaving all the default parameters, you can replicate the experiments by setting the following options.

• please specify the data folder using: --data_root <data_root>
• dataset: --dataset voc (Pascal-VOC 2012) | ade (ADE20K) | city (Cityscapes)
• task: --task <task>, where tasks are
  • 15-5, 15-5s, 19-1, 10-10, 10-10s (VOC), 100-50, 100-10, 50(ADE, b indicates the order), 17-2 , 13-6, 13-6s (city)
• step (each step is run separately): --step <N>, where N is the step number, starting from 0
• (only for Pascal-VOC) disjoint is default setup, to enable overlapped: --overlapped
• learning rate: --lr 0.01 (for step 0) | 0.001 (for step > 0)
• batch size: --batch_size <24/num_GPUs>
• epochs: --epochs 30 (Pascal-VOC 2012) | 60 (ADE20K) | 60 (Cityscapes)
• method: --method <method name>, where names are
  • FT, LWF, LWF-MC, ILT, EWC, RW, PI, UCD

For all details please follow the information provided using the help option.

Example commands

UCD on the 50 setting of ADE20K, step 2:

python -m torch.distributed.launch --nproc_per_node=2 run.py --data_root ./dataset --batch_size 12 --dataset ade --name UCD --task 100-50 --step 2 --lr 0.001 --epochs 60 --method UCD

UCD on 15-1 overlapped setting of VOC, step 1:

python -m torch.distributed.launch --nproc_per_node=2 run.py --data_root ./dataset --batch_size 12 --dataset voc --name UCD --task 15-5s --overlapped --step 1 --lr 0.001 --epochs 30 --method UCD

Once you trained the model, you can see the result on tensorboard (we perform the test after the whole training) or you can test it by using the same script and parameters but using the command

python -m torch.distributed.launch --nproc_per_node=1 test.py --data_root ./dataset --batch_size 80 --dataset voc --name UCD --task 19-1 --step 1 --lr 0.01 --epochs 30 --method UCD --step_ckpt ./path/to/checkpoint;

Result:

Reference

MiB

PLOP