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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

Apology for the long delayed code cleaning, which is now done! Please let me know if you would like further clarification of any part :)
ICLR 2022 talk available HERE
For more details/examples/video demos visit our project page HERE

Environment-Setup

Install dependencies

Install dependencies with one of the following options: Conda installation with miniconda3 PATH /home/miniconda3/:

conda env create -f deform_seg_env.yml
conda activate deform_seg_env

Above environment is built with conda version: 4.7.11

Data preparation

Download the Cityscapes, DeepGlobe and PCa-histo datasets.
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

Data list .odgt files are provided in ./data prepare correspondingly for local datasets. (Note: for cityscapes please check its ./data/Cityscape/*.odgt, in my example I removed the city subfolders and put all images under one folder, if your data tree is different please modify accordingly e.g. change "images/training/tubingen_000025_000019_leftImg8bit.png" to "images/training/tubingen/000025_000019_leftImg8bit.png"

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_deform.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

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}

@inproceedings{
jin2022learning,
title={Learning to Downsample for Segmentation of Ultra-High Resolution Images},
author={Chen Jin and Ryutaro Tanno and Thomy Mertzanidou and Eleftheria Panagiotaki and Daniel C. Alexander},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=HndgQudNb91}
}

	if segSize is None and ((self.cfg.TRAIN.opt_deform_LabelEdge or self.cfg.TRAIN.deform_joint_loss) and epoch <= self.cfg.TRAIN.deform_pretrain):
	min_saliency_len = min(self.input_size)
	s = random.randint(min_saliency_len//3, min_saliency_len)
	x_low = nn.AdaptiveAvgPool2d((s,s))(x_low)
	x_low = nn.Upsample(size=self.input_size,mode='bilinear')(x_low)

	# EXPLAIN: pretraining trick following A. Recasens et,al. (2018)
	N_pretraining = self.cfg.TRAIN.deform_pretrain
	epoch = self.cfg.TRAIN.global_epoch
	if self.cfg.TRAIN.deform_pretrain_bol or (epoch>=N_pretraining and (epoch<self.cfg.TRAIN.smooth_deform_2nd_start or epoch>self.cfg.TRAIN.smooth_deform_2nd_end)):
	p=1 # non-pretain stage: no random size pooling to x_sampled
	else:
	p=0 # pretrain stage: random size pooling to x_sampled

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deformation-segmentation's Introduction

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

Updates

Table of Contents

Environment-Setup

Install dependencies

Data preparation

Reproduce

Training

Evaluation

Citation

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