Get start

conda create -n rsseg python=3.9
conda activate rsseg
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt

Folder Structure

Prepare the following folders to organize this repo:

rssegmentation
├── rssegmentation (code)
├── work_dirs (save the model weights and training logs)
├── data
│   ├── LoveDA
│   │   ├── Train
│   │   │   ├── Urban
│   │   │   │   ├── images_png (original images)
│   │   │   │   ├── masks_png (original labels)
│   │   │   ├── Rural
│   │   │   │   ├── images_png (original images)
│   │   │   │   ├── masks_png (original labels)
│   │   ├── Val (the same with Train)
│   │   ├── Test
│   ├── vaihingen
│   │   ├── ISPRS_semantic_labeling_Vaihingen 
│   │   │   ├── top (original images)
│   │   ├── ISPRS_semantic_labeling_Vaihingen_ground_truth_COMPLETE (original labels)
│   │   ├── ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE (original noBoundary lables)
│   │   ├── train (processed)
│   │   ├── test (processed)
│   ├── potsdam (the same with vaihingen)
│   │   ├── 2_Ortho_RGB (original images)
│   │   ├── 5_Labels_all (original labels)
│   │   ├── 5_Labels_all_noBoundary (original noBoundary lables)
│   │   ├── train (processed)
│   │   ├── test (processed)

Data Processing

Vaihingen

train

python tools/dataset_patch_split.py \
--dataset-type "vaihingen" \
--img-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen/top" \
--mask-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen_ground_truth_COMPLETE" \
--output-img-dir "data/vaihingen/train/images_1024" \
--output-mask-dir "data/vaihingen/train/masks_1024" \
--split-size 1024 \
--stride 512 \
--mode "train"

test and val

python tools/dataset_patch_split.py \
--dataset-type "vaihingen" \
--img-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen/top" \
--mask-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen_ground_truth_COMPLETE" \
--output-img-dir "data/vaihingen/test/images_1024" \
--output-mask-dir "data/vaihingen/test/masks_1024_RGB" \
--split-size 1024 \
--stride 1024 \
--mode "test"

python tools/dataset_patch_split.py \
--dataset-type "vaihingen" \
--img-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen/top" \
--mask-dir "/home/xwma/data/Vaihingen/ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE" \
--output-img-dir "data/vaihingen/test/images_1024" \
--output-mask-dir "data/vaihingen/test/masks_1024" \
--split-size 1024 \
--stride 1024 \
--mode "test"

potsdam

train

python tools/dataset_patch_split.py \
--dataset-type "potsdam" \
--img-dir "/home/xwma/data/Potsdam/2_Ortho_RGB" \
--mask-dir "/home/xwma/data/Potsdam/5_Labels_all" \
--output-img-dir "data/potsdam/train/images_1024" \
--output-mask-dir "data/potsdam/train/masks_1024" \
--split-size 1024 \
--stride 512 \
--mode "train"

test and val

python tools/dataset_patch_split.py \
--dataset-type "potsdam" \
--img-dir "/home/xwma/data/Potsdam/2_Ortho_RGB" \
--mask-dir "/home/xwma/data/Potsdam/5_Labels_all_noBoundary" \
--output-img-dir "data/potsdam/test/images_1024" \
--output-mask-dir "data/potsdam/test/masks_1024" \
--split-size 1024 \
--stride 1024 \
--mode "test"

python tools/dataset_patch_split.py \
--dataset-type "potsdam" \
--img-dir "/home/xwma/data/Potsdam/2_Ortho_RGB" \
--mask-dir "/home/xwma/data/Potsdam/5_Labels_all" \
--output-img-dir "data/potsdam/test/images_1024" \
--output-mask-dir "data/potsdam/test/masks_1024_RGB" \
--split-size 1024 \
--stride 1024 \
--mode "test"

Training

python train.py -c "configs/logcan.py"

Testing

Vaihingen and Potsdam

python test.py \
-c "configs/logcan.py" \
--ckpt "work_dirs/LoGCAN_ResNet50_Loveda/epoch=45.ckpt" \

LoveDA

python test.py \
-c "configs/logcan.py" \
--ckpt "work_dirs/LoGCAN_ResNet50_Loveda/epoch=45.ckpt" \

Useful tools

python tools/flops_params_count.py \
-c "configs/logcan.py" \

python tools/latency_count.py \
-c "configs/logcan.py" \
--ckpt "work_dirs/LoGCAN_ResNet50_Loveda/epoch=45.ckpt" \