Awesome
Implementing
This is a fold online for the "Transferable Attack for Semantic Segmentation" implementation.
1. Prediction
python predict.py --input datasets/data/cityscapes/leftImg8bit/train/bremen --dataset cityscapes --model deeplabv3_resnet50 --ckpt checkpoints/best_deeplabv3_resnet50_cityscapes_os16.pth --save_val_results_to test_results
Pascal VOC
1. Requirements
pip install -r requirements.txt
2. Prepare Datasets
2.1 Standard Pascal VOC
You can run train.py with "--download" option to download pascal voc dataset.
The defaut path is './datasets/data':
/datasets
/data
/VOCdevkit
/VOC2012
/SegmentationClass
/JPEGImages
...
...
/VOCtrainval_11-May-2012.tar
...
2.2 Pascal VOC trainaug
The original dataset contains 1464 (train), 1449 (val), and 1456 (test) pixel-level annotated images. Pascal VOC 2012 aug have 10582 (trainaug) training images.
Download their labels from Dropbox .
Extract SegmentationClassAug to the VOC2012.
/datasets
/data
/VOCdevkit
/VOC2012
/SegmentationClass
/SegmentationClassAug # <= the trainaug labels
/JPEGImages
...
...
/VOCtrainval_11-May-2012.tar
...
3. Training on Pascal VOC2012 Aug
#### 3.1 Training
Run main.py with *"--year 2012_aug"* to train the model on Pascal VOC2012 Aug.
Parallel training on 2 GPUs with '--gpu_id 0,1'
```bash
python main.py --model deeplabv3_resnet50 --gpu_id 0 --year 2012_aug --crop_val --lr 0.01 --crop_size 513 --batch_size 16 --output_stride 16
python main.py ... --ckpt YOUR_CKPT --continue_training
3.2. Testing
Results will be saved at ./results.
python main.py --model deeplabv3_resnet50 --gpu_id 0 --year 2012_aug --crop_val --lr 0.01 --crop_size 513 --batch_size 16 --output_stride 16 --ckpt checkpoints/best_deeplabv3_resnet50_voc_os16.pth --test_only --save_val_results
Cityscapes
1. Download cityscapes and extract it to 'datasets/data/cityscapes'
/datasets
/data
/cityscapes
/gtFine
/leftImg8bit
2. Train your model on Cityscapes
python main.py --model deeplabv3_resnet50 --dataset cityscapes --gpu_id 0 --lr 0.1 --crop_size 768 --batch_size 16 --output_stride 16 --data_root ./datasets/data/cityscapes
Partial code are from
[1]https://github.com/VainF/DeepLabV3Plus-Pytorch