Learning to Downsample for Segmentation of Ultra-High Resolution Images in PyTorch

This is a PyTorch implementation of Learning to Downsample for Segmentation of Ultra-High Resolution Images which published at ICLR 2022.

Updates

• ICLR 2022 talk available HERE
• For more details/examples/video demos visit our project page HERE

Table of Contents

1. Environment-Setup
2. Data-preparation
3. Reproduce
4. Citation

Environment-Setup

Special dependencies for interp2d

pip install Cython
cd spatial
python setup.py build_ext --inplace

Install dependencies

Install dependencies with one of the following options:

• Method 1: Pip installation:

python -m pip install -r requirements.txt

• Method 2: Conda installation with miniconda3 PATH /home/miniconda3/:

conda env create -f deform_seg_env.yml
conda activate deform_seg_env

This code was tested with python 3.7, pytorch 1.2 and CUDA 11.0

Data preparation

1. Download the Cityscapes, DeepGlobe and PCa-histo datasets.

2. Your directory tree should be look like this:

$SEG_ROOT/data
├── cityscapes
│   ├── annotations
│   │   ├── testing
│   │   ├── training
│   │   └── validation
│   └── images
│       ├── testing
│       ├── training
│       └── validation
├── histomri
│   ├── train
│   │   ├── images
│   │   ├── labels
│   └── val
│   │   ├── images
│   │   ├── labels
├── deepglob
│   ├── land-train
│   └── land_train_gt_processed

note Histo_MRI is the PCa-histo dataset

3. Data list .odgt files are provided in ./data prepare correspondingly for local datasets

Reproduce

full configuration bash provided to reproduced paper results, suitable for large scale experiment in multiple GPU Environment, Syncronized Batch Normalization are deployed.

Training

Train a model by selecting the GPUs ($GPUS) and configuration file ($CFG) to use. During training, last checkpoints by default are saved in folder ckpt.

python3 train_fove.py --gpus $GPUS --cfg $CFG

• To choose which gpus to use, you can either do --gpus 0-7, or --gpus 0,2,4,6.

• Bashes and configurations are provided to reproduce our results:

• note you will need to specify your root path 'SEG_ROOT' for DATASET.root_dataset option in those scripts.

bash quick_start_bash/cityscape_64_128_ours.sh
bash quick_start_bash/cityscape_64_128_uniform.sh
bash quick_start_bash/deepglob_300_300_ours.sh
bash quick_start_bash/deepglob_300_300_uniform.sh
bash quick_start_bash/pcahisto_80_800_ours.sh
bash quick_start_bash/pcahisto_80_800_uniform.sh

• You can also override options in commandline, for example python3 train_deform.py TRAIN.num_epoch 10 .

Evaluation

1. Evaluate a trained model on the validation set, simply override following options TRAIN.start_epoch 125 TRAIN.num_epoch 126 TRAIN.eval_per_epoch 1 TRAIN.skip_train_for_eval True

• Alternatively, you can quick start with provided bash script:

bash quick_start_bash/eval/cityscape_64_128_ours.sh
bash quick_start_bash/eval/cityscape_64_128_uniform.sh
bash quick_start_bash/eval/deepglob_300_300_ours.sh
bash quick_start_bash/eval/deepglob_300_300_uniform.sh
bash quick_start_bash/eval/pcahisto_80_800_ours.sh
bash quick_start_bash/eval/pcahisto_80_800_uniform.sh

Citation

If you use this code for your research, please cite our paper:

@article{jin2021learning,
  title={Learning to Downsample for Segmentation of Ultra-High Resolution Images},
  author={Jin, Chen and Tanno, Ryutaro and Mertzanidou, Thomy and Panagiotaki, Eleftheria and Alexander, Daniel C},
  journal={arXiv preprint arXiv:2109.11071},
  year={2021}
}