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Research Code for Swapping SHOT Backbones

For the original paper and source code please see: SHOT

This repository contains the research code used to test the SHOT domain adaptation technique with using different backbone networks. Specifically the backbone was swapped from a ResNet backbone to a SWIN and an HRNet-V2 model.

Setup

Clone Repository

  1. Clone repository 
      git clone git@github.com:ddp5730/SHOT.git
  2. Install submodules 
    git submodule update --init --recursive

Requirements

See Dockerfile for full list of install dependencies and packages

  • CUDA == 11.3.1
  • torch == 1.10.2+cu113
  • torchvision=0.11.3+cu113
  • apex

Build Docker Container (Optional)

You may use the provided Dockerfile to build a container with all of the necessary requirements required to run the provided code. However, you must have some version of CUDA, Docker and the NVIDIA container toolkit installed (see link).

  1. Update .dockerignore with any added directories as necessary
  2. Build the Docker container 
    $ docker build -t <desired-tag> -f Dockerfile .
  3. Run Docker Container. <tag> must be the same tag used in step 4. 
    docker run -it --gpus all --shm-size=25G -e HOME=$HOME -e USER=$USER -v $HOME:$HOME -w $HOME --user developer <tag>
  4. Navigate to code (Home directories will be linked) and run

Download Datasets

Datasets are loaded using the DatasetFolder class. Therefore all datasets should be downloaded in directories as such:

directory/
├── train
    ├── class_x
    │   ├── xxx.ext
    │   ├── xxy.ext
    │   └── ...
    │       └── xxz.ext
    └── class_y
        ├── 123.ext
        ├── nsdf3.ext
        └── ...
        └── asd932_.ext
├── test
    ├── class_x
    │   ├── xxx.ext
    │   ├── xxy.ext
    │   └── ...
    │       └── xxz.ext
    └── class_y
        ├── 123.ext
        ├── nsdf3.ext
        └── ...
        └── asd932_.ext

The file utils/partition_dota_xview.py provides a helpful script for partitioning a dataset into training/validation splits.

Download Pretrained models

To recreate the results download the following pretrained Swin and HRNet models.

HRNet-W48-C

swin_base_patch4_window12_384_22k

Run the Code

Sample config files and scripts are contained in sample_configs and sample_scripts respectively.

Fine Tune Model on Target Dataset

The file image_source.py is used to fine tune a model onto a source dataset. To fine tune a Swin-B model pretrained on ImageNet-22k to DOTA you could run the following command:

PYTHONPATH=.:./swin:$PYTHONPATH python3 -m torch.distributed.launch \
--nproc_per_node 1 \
--master_port 12345 \
object/image_source.py \
--trte val \
--da uda \
--output output \
--gpu_id 0 \
--cfg sample_configs/swin_base_patch4_window12_384_22ktodota_transfer.yaml \
--pretrained data/swin_base_patch4_window12_384_22k.pth \
--dset dota \
--data-path /home/poppfd/data/dota-xview/DOTA_ImageFolder \
--t-dset xview \
--t-data-path /home/poppfd/data/dota-xview/XVIEW_ImageFolder \
--evals-per-epoch 1 \
--batch_size=20 \
--net=swin-b \
--transfer-dataset \
--source 1 \
--target 0 \
--name=swin-dota-source-1

For this code, the TOP_N performing models on the target domain will be saved in output/<name>/T/ for further analysis. The TOP_N value is a global variable in the image_source.py script.

Evaluate Model Generalization

The file image_eval.py is used to evaluate the performance of a model on both a source and target dataset. This script does not perform any training and is for evaluation only.

To evaluate the performance of the Swin-B model fine-tuned on the DOTA dataset you could run the following command:

PYTHONPATH=.:./swin:$PYTHONPATH python3 -m torch.distributed.launch \
--nproc_per_node 1 \
--master_port 12345 \
object/image_eval.py \
--output output \
--gpu_id 0 \
--cfg /home/poppfd/College/Research/SHOT/configs/swin_base_patch4_window12_384_22ktodota_transfer.yaml \
--pretrained /home/poppfd/College/Research/SHOT/output/swin-dota-to-xview-target-3/X/ckpt_epoch_6_eval_10.pth \
--dset dota \
--data-path /home/poppfd/data/dota-xview/DOTA_ImageFolder \
--t-dset xview \
--t-data-path /home/poppfd/data/dota-xview/XVIEW_ImageFolder \
--batch_size=128 \
--net=swin-b \
--transfer-dataset \
--source 1 \
--target 0 \
--name=swin-dota-source-1

Any script that loads netF, netB, and netC only needs to be pointed to the saved netF path using the --pretrained argument

Note that the saved output of this evaluation is placed in output/eval/<name>.

Also if the --t-dset and --t-data-path arguments are omitted this script can simply evaluate the model on a given dataset.

Adapt model using SHOT to Target Domain

The script image_target.py is used to adapt a given model onto a target domain using the unsupervised domain adaptation technique SHOT.

To perform this adaptation on a Swin-B model fine-tuned on DOTA and adapt it to the XVIEW dataset, the following command could be used:

PYTHONPATH=.:./swin:$PYTHONPATH python3 -m torch.distributed.launch \
--nproc_per_node 1 \
--master_port 12345 \
object/image_target.py \
--cls_par 0.3 \
--da uda \
--output output \
--gpu_id 0 \
--cfg sample_configs/swin_base_patch4_window12_384_dota_to_xview_adaptatiopn.yaml \
--pretrained output/swin-dota-source-1/V/ckpt_epoch_9_eval_8.pth \
--dset xview \
--data-path /home/poppfd/data/dota-xview/ \
--batch_size=20 \
--evals-per-epoch=2 \
--net=swin-b \
--transfer-dataset \
--source -1 \
--target 0 \
--name=swin-dota-to-xview-1

VERY IMPORTANT: Due to a bug when generating pseudo-labels, the --batch-size argument must perfectly divide the target dataset

i.e. dataset_size % batch_size == 0

Tensorboard

Important training metrics for this project are logged using Tensorboard. When training these metrics can be seen by:

  1. $ tensorboard --logdir='logs/<name>
  2. Open a web-browser and navigate to localhost:6006

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