├── .gitattributes
├── .idea
    └── workspace.xml
├── ADVENT
    ├── .gitignore
    ├── Dockerfile
    ├── LICENSE
    ├── README.md
    ├── advent
    │   ├── __init__.py
    │   ├── dataset
    │   │   ├── Potsdam.py
    │   │   ├── PotsdamIRRG
    │   │   │   ├── all.txt
    │   │   │   ├── test.txt
    │   │   │   ├── train.txt
    │   │   │   └── val.txt
    │   │   ├── PotsdamRGB
    │   │   │   ├── all.txt
    │   │   │   ├── info.json
    │   │   │   ├── test.txt
    │   │   │   ├── train.txt
    │   │   │   └── val.txt
    │   │   ├── Vaihingen.py
    │   │   ├── Vaihingen
    │   │   │   ├── Vaihingen_all.txt
    │   │   │   ├── Vaihingen_test.txt
    │   │   │   ├── Vaihingen_validation.txt
    │   │   │   ├── info.json
    │   │   │   ├── test.txt
    │   │   │   ├── train.txt
    │   │   │   └── val.txt
    │   │   ├── __init__.py
    │   │   ├── base_dataset.py
    │   │   ├── cityscapes.py
    │   │   └── gta5.py
    │   ├── model
    │   │   ├── __init__.py
    │   │   ├── deeplabv3.py
    │   │   └── discriminator.py
    │   ├── scripts
    │   │   ├── configs
    │   │   │   └── advent.yml
    │   │   ├── test.py
    │   │   └── train.py
    │   ├── train_setup
    │   │   ├── __init__.py
    │   │   ├── config.py
    │   │   ├── eval_single.py
    │   │   └── train_single.py
    │   ├── unet
    │   │   ├── __init__.py
    │   │   ├── unet_model.py
    │   │   └── unet_parts.py
    │   └── utils
    │   │   ├── __init__.py
    │   │   ├── func.py
    │   │   ├── loss.py
    │   │   ├── serialization.py
    │   │   └── viz_segmask.py
    ├── setup.py
    ├── teaser.jpg
    └── tox.ini
└── README.md


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/ADVENT/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | *.egg-info/
 24 | .installed.cfg
 25 | *.egg
 26 | MANIFEST
 27 | 
 28 | # PyInstaller
 29 | #  Usually these files are written by a python script from a template
 30 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 31 | *.manifest
 32 | *.spec
 33 | 
 34 | # Installer logs
 35 | pip-log.txt
 36 | pip-delete-this-directory.txt
 37 | 
 38 | # Unit test / coverage reports
 39 | htmlcov/
 40 | .tox/
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 42 | .coverage.*
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 45 | coverage.xml
 46 | *.cover
 47 | .hypothesis/
 48 | .pytest_cache/
 49 | 
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100 | # mkdocs documentation
101 | /site
102 | 
103 | # mypy
104 | .mypy_cache/
105 | .idea/
106 | 


--------------------------------------------------------------------------------
/ADVENT/Dockerfile:
--------------------------------------------------------------------------------
 1 | FROM nvidia/cuda:9.0-base as base_image
 2 | 
 3 | RUN apt-get update && apt-get install -y wget bzip2
 4 | RUN wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
 5 | RUN bash miniconda.sh -b -p /opt/conda && \
 6 |     rm miniconda.sh
 7 | ENV PATH="/opt/conda/bin:${PATH}"
 8 | RUN conda config --set always_yes yes
 9 | RUN conda install python=3.6
10 | 
11 | RUN conda install pytorch=0.4.1 cuda90 torchvision -c pytorch
12 | RUN conda install -c menpo opencv
13 | RUN pip install tensorboardX scikit-image tqdm pyyaml easydict future
14 | 
15 | COPY ./ ./ADVENT
16 | RUN pip install -e ./ADVENT
17 | 
18 | WORKDIR ./ADVENT
19 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/ADVENT/README.md:
--------------------------------------------------------------------------------
  1 | # ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
  2 | 
  3 | ## Updates
  4 | - *02/2020*: Using CycleGAN translated images, The AdvEnt model achieves (**46.3%**) on GTA5-2-Cityscapes
  5 | - *09/2019*: check out our new paper [DADA: Depth-aware Domain Adaptation in Semantic Segmentation](https://arxiv.org/abs/1904.01886) (accepted to ICCV 2019). With a depth-aware UDA framework, we leverage depth as the privileged information at train time to boost target performance. [Pytorch](https://github.com/valeoai/DADA) code and pre-trained models are coming soon.
  6 | 
  7 | ## Paper
  8 | ![](./teaser.jpg)
  9 | 
 10 | [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://arxiv.org/abs/1811.12833)  
 11 |  [Tuan-Hung Vu](https://tuanhungvu.github.io/),  [Himalaya Jain](https://himalayajain.github.io/), [Maxime Bucher](https://maximebucher.github.io/), [Matthieu Cord](http://webia.lip6.fr/~cord/), [Patrick Pérez](https://ptrckprz.github.io/)  
 12 |  valeo.ai, France  
 13 |  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (**Oral**)
 14 | 
 15 | If you find this code useful for your research, please cite our [paper](https://arxiv.org/abs/1811.12833):
 16 | 
 17 | ```
 18 | @inproceedings{vu2018advent,
 19 |   title={ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation},
 20 |   author={Vu, Tuan-Hung and Jain, Himalaya and Bucher, Maxime and Cord, Mathieu and P{\'e}rez, Patrick},
 21 |   booktitle={CVPR},
 22 |   year={2019}
 23 | }
 24 | ```
 25 | 
 26 | ## Abstract
 27 | Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major challenge. In numerous real world applications, there is indeed a large gap between data distributions in train and test domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we propose two novel, complementary methods using (i) an entropy loss and (ii) an adversarial loss respectively. We demonstrate state-of-the-art performance in semantic segmentation on two challenging *synthetic-2-real* set-ups and show that the approach can also be used for detection.
 28 | 
 29 | ## Demo
 30 | [![](http://img.youtube.com/vi/Ihmz0yEqrq0/0.jpg)](http://www.youtube.com/watch?v=Ihmz0yEqrq0 "")
 31 | 
 32 | ## Preparation
 33 | 
 34 | ### Pre-requisites
 35 | * Python 3.7
 36 | * Pytorch >= 0.4.1
 37 | * CUDA 9.0 or higher
 38 | 
 39 | ### Installation
 40 | 0. Clone the repo:
 41 | ```bash
 42 | $ git clone https://github.com/valeoai/ADVENT
 43 | $ cd ADVENT
 44 | ```
 45 | 
 46 | 1. Install OpenCV if you don't already have it:
 47 | 
 48 | ```bash
 49 | $ conda install -c menpo opencv
 50 | ```
 51 | 
 52 | 2. Install this repository and the dependencies using pip:
 53 | ```bash
 54 | $ pip install -e <root_dir>
 55 | ```
 56 | 
 57 | With this, you can edit the ADVENT code on the fly and import function 
 58 | and classes of ADVENT in other project as well.
 59 | 
 60 | 3. Optional. To uninstall this package, run:
 61 | ```bash
 62 | $ pip uninstall ADVENT
 63 | ```
 64 | 
 65 | You can take a look at the [Dockerfile](./Dockerfile) if you are uncertain about steps to install this project.
 66 | 
 67 | ### Datasets
 68 | By default, the datasets are put in ```<root_dir>/data```. We use symlinks to hook the ADVENT codebase to the datasets. An alternative option is to explicitlly specify the parameters ```DATA_DIRECTORY_SOURCE``` and ```DATA_DIRECTORY_TARGET``` in YML configuration files.
 69 | 
 70 | * **GTA5**: Please follow the instructions [here](https://download.visinf.tu-darmstadt.de/data/from_games/) to download images and semantic segmentation annotations. The GTA5 dataset directory should have this basic structure:
 71 | ```bash
 72 | <root_dir>/data/GTA5/                               % GTA dataset root
 73 | <root_dir>/data/GTA5/images/                        % GTA images
 74 | <root_dir>/data/GTA5/labels/                        % Semantic segmentation labels
 75 | ...
 76 | ```
 77 | 
 78 | * **Cityscapes**: Please follow the instructions in [Cityscape](https://www.cityscapes-dataset.com/) to download the images and validation ground-truths. The Cityscapes dataset directory should have this basic structure:
 79 | ```bash
 80 | <root_dir>/data/Cityscapes/                         % Cityscapes dataset root
 81 | <root_dir>/data/Cityscapes/leftImg8bit              % Cityscapes images
 82 | <root_dir>/data/Cityscapes/leftImg8bit/val
 83 | <root_dir>/data/Cityscapes/gtFine                   % Semantic segmentation labels
 84 | <root_dir>/data/Cityscapes/gtFine/val
 85 | ...
 86 | ```
 87 | 
 88 | ### Pre-trained models
 89 | Pre-trained models can be downloaded [here](https://github.com/valeoai/ADVENT/releases) and put in ```<root_dir>/pretrained_models```
 90 | 
 91 | ## Running the code
 92 | For evaluation, execute:
 93 | ```bash
 94 | $ cd <root_dir>/advent/scripts
 95 | $ python test.py --cfg ./configs/advent_pretrained.yml
 96 | $ python test.py --cfg ./configs/advent_cyclegan_pretrained.yml 	% trained on cycleGAN translated images
 97 | $ python test.py --cfg ./configs/minent_pretrained.yml
 98 | $ python test.py --cfg ./configs/advent+minent.yml
 99 | ```
100 | 
101 | ### Training
102 | For the experiments done in the paper, we used pytorch 0.4.1 and CUDA 9.0. To ensure reproduction, the random seed has been fixed in the code. Still, you may need to train a few times to reach the comparable performance.
103 | 
104 | By default, logs and snapshots are stored in ```<root_dir>/experiments``` with this structure:
105 | ```bash
106 | <root_dir>/experiments/logs
107 | <root_dir>/experiments/snapshots
108 | ```
109 | 
110 | To train AdvEnt:
111 | ```bash
112 | $ cd <root_dir>/advent/scripts
113 | $ python train.py --cfg ./configs/advent.yml
114 | $ python train.py --cfg ./configs/advent.yml --tensorboard         % using tensorboard
115 | ```
116 | To train MinEnt:
117 | ```bash
118 | $ python train.py --cfg ./configs/minent.yml
119 | $ python train.py --cfg ./configs/minent.yml --tensorboard         % using tensorboard
120 | ```
121 | 
122 | ### Testing
123 | To test AdvEnt:
124 | ```bash
125 | $ cd <root_dir>/advent/scripts
126 | $ python test.py --cfg ./configs/advent.yml
127 | ```
128 | To test MinEnt:
129 | ```bash
130 | $ python test.py --cfg ./configs/minent.yml
131 | ```
132 | 
133 | ## Acknowledgements
134 | This codebase is heavily borrowed from [AdaptSegNet](https://github.com/wasidennis/AdaptSegNet) and [Pytorch-Deeplab](https://github.com/speedinghzl/Pytorch-Deeplab).
135 | 
136 | ## License
137 | ADVENT is released under the [Apache 2.0 license](./LICENSE).
138 | 


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/ADVENT/advent/__init__.py:
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https://raw.githubusercontent.com/BOBrown/SegNet_Source/4dc9c93c77a64b0338038cc9e192006b11a5efe2/ADVENT/advent/__init__.py


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/ADVENT/advent/dataset/Potsdam.py:
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 1 | import numpy as np
 2 | 
 3 | from advent.dataset.base_dataset import BaseDataset
 4 | from advent.utils.serialization import json_load
 5 | from advent.utils import project_root
 6 | 
 7 | DEFAULT_INFO_PATH = project_root / 'advent/dataset/Vaihingen/info.json'
 8 | 
 9 | class Potsdam(BaseDataset):
10 |     def __init__(self, root, list_path, set='all',
11 |                  max_iters=None, crop_size=(512, 512), mean=(128, 128, 128),
12 |                  info_path = DEFAULT_INFO_PATH):
13 |         super().__init__(root, list_path, set, max_iters, crop_size, None, mean)
14 | 
15 |         self.info = json_load(info_path)
16 |         self.class_names = np.array(self.info['label'], dtype=np.str)
17 | 
18 |     def get_metadata(self, name):
19 |         img_file = self.root / 'images' / name
20 |         label_file = self.root / 'labels' / name
21 |         return img_file, label_file
22 | 
23 |     def __getitem__(self, index):
24 |         img_file, label_file, name = self.files[index]
25 |         image = self.get_image(img_file)
26 |         label = self.get_labels(label_file)
27 |         image = self.preprocess(image)
28 |         return image.copy(), label-1, np.array(image.shape), name
29 | 


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673 | top_potsdam_4_10_IRRG_99.png
674 | top_potsdam_4_11_IRRG_01.png
675 | top_potsdam_4_11_IRRG_02.png
676 | top_potsdam_4_11_IRRG_03.png
677 | top_potsdam_4_11_IRRG_04.png
678 | top_potsdam_4_11_IRRG_05.png
679 | top_potsdam_4_11_IRRG_06.png
680 | top_potsdam_4_11_IRRG_07.png
681 | top_potsdam_4_11_IRRG_08.png
682 | top_potsdam_4_11_IRRG_09.png
683 | top_potsdam_4_11_IRRG_10.png
684 | top_potsdam_4_11_IRRG_100.png
685 | top_potsdam_4_11_IRRG_101.png
686 | top_potsdam_4_11_IRRG_102.png
687 | top_potsdam_4_11_IRRG_103.png
688 | top_potsdam_4_11_IRRG_104.png
689 | top_potsdam_4_11_IRRG_105.png
690 | top_potsdam_4_11_IRRG_106.png
691 | top_potsdam_4_11_IRRG_107.png
692 | top_potsdam_4_11_IRRG_108.png
693 | top_potsdam_4_11_IRRG_109.png
694 | top_potsdam_4_11_IRRG_11.png
695 | top_potsdam_4_11_IRRG_110.png
696 | top_potsdam_3_10_IRRG_56.png
697 | top_potsdam_3_10_IRRG_57.png
698 | top_potsdam_3_10_IRRG_58.png
699 | top_potsdam_3_10_IRRG_59.png
700 | top_potsdam_3_10_IRRG_60.png
701 | top_potsdam_3_10_IRRG_61.png
702 | top_potsdam_3_10_IRRG_62.png
703 | top_potsdam_3_10_IRRG_63.png
704 | top_potsdam_3_10_IRRG_64.png
705 | top_potsdam_3_10_IRRG_65.png
706 | top_potsdam_3_10_IRRG_66.png
707 | top_potsdam_3_10_IRRG_67.png
708 | top_potsdam_3_10_IRRG_68.png
709 | top_potsdam_3_10_IRRG_69.png
710 | top_potsdam_3_10_IRRG_70.png
711 | top_potsdam_3_10_IRRG_71.png
712 | top_potsdam_3_10_IRRG_72.png
713 | top_potsdam_3_10_IRRG_73.png
714 | top_potsdam_3_10_IRRG_74.png
715 | top_potsdam_3_10_IRRG_75.png
716 | top_potsdam_3_10_IRRG_76.png
717 | top_potsdam_3_10_IRRG_77.png
718 | top_potsdam_3_10_IRRG_78.png
719 | top_potsdam_3_10_IRRG_79.png
720 | top_potsdam_3_10_IRRG_80.png
721 | top_potsdam_3_10_IRRG_81.png
722 | top_potsdam_3_10_IRRG_82.png
723 | top_potsdam_4_10_IRRG_01.png
724 | top_potsdam_4_10_IRRG_02.png
725 | top_potsdam_4_10_IRRG_03.png
726 | top_potsdam_4_10_IRRG_04.png
727 | top_potsdam_4_10_IRRG_05.png
728 | top_potsdam_4_10_IRRG_06.png
729 | top_potsdam_4_10_IRRG_07.png
730 | top_potsdam_4_10_IRRG_08.png
731 | top_potsdam_4_10_IRRG_09.png
732 | top_potsdam_4_10_IRRG_10.png
733 | top_potsdam_4_10_IRRG_100.png
734 | top_potsdam_4_10_IRRG_101.png
735 | top_potsdam_4_10_IRRG_102.png
736 | top_potsdam_4_10_IRRG_103.png
737 | top_potsdam_4_10_IRRG_104.png
738 | top_potsdam_4_10_IRRG_105.png
739 | top_potsdam_4_10_IRRG_106.png
740 | top_potsdam_4_10_IRRG_107.png
741 | top_potsdam_4_10_IRRG_108.png
742 | top_potsdam_4_10_IRRG_109.png
743 | top_potsdam_4_10_IRRG_11.png
744 | top_potsdam_4_10_IRRG_110.png
745 | top_potsdam_4_10_IRRG_111.png
746 | top_potsdam_4_10_IRRG_112.png
747 | top_potsdam_4_10_IRRG_113.png
748 | top_potsdam_4_10_IRRG_114.png
749 | top_potsdam_4_10_IRRG_115.png
750 | top_potsdam_4_10_IRRG_116.png
751 | top_potsdam_4_10_IRRG_117.png
752 | top_potsdam_4_10_IRRG_118.png
753 | top_potsdam_4_10_IRRG_119.png
754 | top_potsdam_4_10_IRRG_12.png
755 | top_potsdam_4_10_IRRG_120.png
756 | top_potsdam_4_10_IRRG_121.png
757 | top_potsdam_4_10_IRRG_13.png
758 | top_potsdam_4_10_IRRG_14.png
759 | top_potsdam_4_10_IRRG_15.png
760 | top_potsdam_4_10_IRRG_16.png
761 | top_potsdam_4_10_IRRG_17.png
762 | top_potsdam_4_10_IRRG_18.png
763 | top_potsdam_4_10_IRRG_19.png
764 | top_potsdam_4_10_IRRG_20.png
765 | top_potsdam_4_10_IRRG_21.png
766 | top_potsdam_4_10_IRRG_22.png
767 | top_potsdam_4_10_IRRG_23.png
768 | top_potsdam_4_10_IRRG_24.png
769 | top_potsdam_4_10_IRRG_25.png
770 | top_potsdam_4_10_IRRG_26.png
771 | top_potsdam_4_10_IRRG_27.png
772 | top_potsdam_4_10_IRRG_28.png
773 | top_potsdam_4_10_IRRG_29.png
774 | top_potsdam_4_10_IRRG_30.png
775 | top_potsdam_4_10_IRRG_31.png
776 | top_potsdam_4_10_IRRG_32.png
777 | top_potsdam_4_10_IRRG_33.png
778 | top_potsdam_4_10_IRRG_34.png
779 | top_potsdam_4_10_IRRG_35.png
780 | top_potsdam_4_10_IRRG_36.png
781 | top_potsdam_4_10_IRRG_37.png
782 | top_potsdam_4_10_IRRG_38.png
783 | top_potsdam_4_10_IRRG_39.png
784 | top_potsdam_4_10_IRRG_40.png
785 | top_potsdam_4_10_IRRG_41.png
786 | top_potsdam_4_10_IRRG_42.png
787 | top_potsdam_5_13_IRRG_63.png
788 | top_potsdam_5_13_IRRG_64.png
789 | top_potsdam_5_13_IRRG_65.png
790 | top_potsdam_5_13_IRRG_66.png
791 | top_potsdam_5_13_IRRG_67.png
792 | top_potsdam_5_13_IRRG_68.png
793 | top_potsdam_5_13_IRRG_69.png
794 | top_potsdam_5_13_IRRG_70.png
795 | top_potsdam_5_13_IRRG_71.png
796 | top_potsdam_5_13_IRRG_72.png
797 | top_potsdam_5_13_IRRG_73.png
798 | top_potsdam_5_13_IRRG_74.png
799 | top_potsdam_5_13_IRRG_75.png
800 | top_potsdam_5_13_IRRG_76.png
801 | top_potsdam_5_13_IRRG_77.png
802 | top_potsdam_5_13_IRRG_78.png
803 | top_potsdam_5_13_IRRG_79.png
804 | top_potsdam_5_13_IRRG_80.png
805 | top_potsdam_5_13_IRRG_81.png
806 | top_potsdam_5_13_IRRG_82.png
807 | top_potsdam_5_13_IRRG_83.png
808 | top_potsdam_5_13_IRRG_84.png
809 | top_potsdam_5_13_IRRG_85.png
810 | top_potsdam_5_13_IRRG_86.png
811 | top_potsdam_5_13_IRRG_87.png
812 | top_potsdam_5_13_IRRG_88.png
813 | top_potsdam_5_13_IRRG_89.png
814 | top_potsdam_5_13_IRRG_90.png
815 | top_potsdam_5_13_IRRG_91.png
816 | top_potsdam_5_13_IRRG_92.png
817 | top_potsdam_5_13_IRRG_93.png
818 | top_potsdam_5_13_IRRG_94.png
819 | top_potsdam_5_13_IRRG_95.png
820 | top_potsdam_5_13_IRRG_96.png
821 | top_potsdam_5_13_IRRG_97.png
822 | top_potsdam_5_13_IRRG_98.png
823 | top_potsdam_5_13_IRRG_99.png
824 | top_potsdam_5_14_IRRG_01.png
825 | top_potsdam_5_14_IRRG_02.png
826 | top_potsdam_5_14_IRRG_03.png


--------------------------------------------------------------------------------
/ADVENT/advent/dataset/PotsdamRGB/info.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "classes":19,
 3 |   "label2train":[
 4 |     [0, 255],
 5 |     [1, 255],
 6 |     [2, 255],
 7 |     [3, 255],
 8 |     [4, 255],
 9 |     [5, 255],
10 |     [6, 255],
11 |     [7, 0],
12 |     [8, 1],
13 |     [9, 255],
14 |     [10, 255],
15 |     [11, 2],
16 |     [12, 3],
17 |     [13, 4],
18 |     [14, 255],
19 |     [15, 255],
20 |     [16, 255],
21 |     [17, 5],
22 |     [18, 255],
23 |     [19, 6],
24 |     [20, 7],
25 |     [21, 8],
26 |     [22, 9],
27 |     [23, 10],
28 |     [24, 11],
29 |     [25, 12],
30 |     [26, 13],
31 |     [27, 14],
32 |     [28, 15],
33 |     [29, 255],
34 |     [30, 255],
35 |     [31, 16],
36 |     [32, 17],
37 |     [33, 18],
38 |     [-1, 255]],
39 |   "label":[
40 |     "road",
41 |     "sidewalk",
42 |     "building",
43 |     "wall",
44 |     "fence",
45 |     "pole",
46 |     "light",
47 |     "sign",
48 |     "vegetation",
49 |     "terrain",
50 |     "sky",
51 |     "person",
52 |     "rider",
53 |     "car",
54 |     "truck",
55 |     "bus",
56 |     "train",
57 |     "motocycle",
58 |     "bicycle"],
59 |   "palette":[
60 |     [128,64,128],
61 |     [244,35,232],
62 |     [70,70,70],
63 |     [102,102,156],
64 |     [190,153,153],
65 |     [153,153,153],
66 |     [250,170,30],
67 |     [220,220,0],
68 |     [107,142,35],
69 |     [152,251,152],
70 |     [70,130,180],
71 |     [220,20,60],
72 |     [255,0,0],
73 |     [0,0,142],
74 |     [0,0,70],
75 |     [0,60,100],
76 |     [0,80,100],
77 |     [0,0,230],
78 |     [119,11,32],
79 |     [0,0,0]],
80 |   "mean":[
81 |     73.158359210711552,
82 |     82.908917542625858,
83 |     72.392398761941593],
84 |   "std":[
85 |     47.675755341814678,
86 |     48.494214368814916,
87 |     47.736546325441594]
88 | }
89 | 


--------------------------------------------------------------------------------
/ADVENT/advent/dataset/PotsdamRGB/val.txt:
--------------------------------------------------------------------------------
  1 | top_potsdam_2_10_RGB_01.png
  2 | top_potsdam_2_10_RGB_02.png
  3 | top_potsdam_2_10_RGB_03.png
  4 | top_potsdam_2_10_RGB_04.png
  5 | top_potsdam_2_10_RGB_05.png
  6 | top_potsdam_2_10_RGB_06.png
  7 | top_potsdam_2_10_RGB_07.png
  8 | top_potsdam_2_10_RGB_08.png
  9 | top_potsdam_2_10_RGB_09.png
 10 | top_potsdam_2_10_RGB_10.png
 11 | top_potsdam_2_10_RGB_100.png
 12 | top_potsdam_2_10_RGB_101.png
 13 | top_potsdam_2_10_RGB_102.png
 14 | top_potsdam_2_10_RGB_103.png
 15 | top_potsdam_2_10_RGB_104.png
 16 | top_potsdam_2_10_RGB_105.png
 17 | top_potsdam_2_10_RGB_106.png
 18 | top_potsdam_2_10_RGB_107.png
 19 | top_potsdam_2_10_RGB_108.png
 20 | top_potsdam_2_10_RGB_109.png
 21 | top_potsdam_2_10_RGB_11.png
 22 | top_potsdam_2_10_RGB_110.png
 23 | top_potsdam_2_10_RGB_111.png
 24 | top_potsdam_2_10_RGB_112.png
 25 | top_potsdam_2_10_RGB_113.png
 26 | top_potsdam_2_10_RGB_114.png
 27 | top_potsdam_2_10_RGB_115.png
 28 | top_potsdam_2_10_RGB_116.png
 29 | top_potsdam_2_10_RGB_117.png
 30 | top_potsdam_2_10_RGB_118.png
 31 | top_potsdam_2_10_RGB_119.png
 32 | top_potsdam_2_10_RGB_12.png
 33 | top_potsdam_7_8_RGB_106.png
 34 | top_potsdam_7_8_RGB_107.png
 35 | top_potsdam_7_8_RGB_108.png
 36 | top_potsdam_7_8_RGB_109.png
 37 | top_potsdam_7_8_RGB_11.png
 38 | top_potsdam_7_8_RGB_110.png
 39 | top_potsdam_7_8_RGB_111.png
 40 | top_potsdam_7_8_RGB_112.png
 41 | top_potsdam_7_8_RGB_113.png
 42 | top_potsdam_7_8_RGB_114.png
 43 | top_potsdam_7_8_RGB_115.png
 44 | top_potsdam_7_8_RGB_116.png
 45 | top_potsdam_7_8_RGB_117.png
 46 | top_potsdam_7_8_RGB_118.png
 47 | top_potsdam_7_8_RGB_119.png
 48 | top_potsdam_7_8_RGB_12.png
 49 | top_potsdam_7_8_RGB_120.png
 50 | top_potsdam_7_8_RGB_121.png
 51 | top_potsdam_7_8_RGB_13.png
 52 | top_potsdam_7_8_RGB_14.png
 53 | top_potsdam_7_8_RGB_15.png
 54 | top_potsdam_7_8_RGB_16.png
 55 | top_potsdam_7_8_RGB_17.png
 56 | top_potsdam_7_8_RGB_18.png
 57 | top_potsdam_7_8_RGB_19.png
 58 | top_potsdam_7_8_RGB_20.png
 59 | top_potsdam_7_8_RGB_21.png
 60 | top_potsdam_7_8_RGB_22.png
 61 | top_potsdam_7_8_RGB_23.png
 62 | top_potsdam_7_8_RGB_24.png
 63 | top_potsdam_7_8_RGB_25.png
 64 | top_potsdam_7_8_RGB_26.png
 65 | top_potsdam_2_10_RGB_27.png
 66 | top_potsdam_2_10_RGB_28.png
 67 | top_potsdam_2_10_RGB_29.png
 68 | top_potsdam_2_10_RGB_30.png
 69 | top_potsdam_2_10_RGB_31.png
 70 | top_potsdam_2_10_RGB_32.png
 71 | top_potsdam_2_10_RGB_33.png
 72 | top_potsdam_2_10_RGB_34.png
 73 | top_potsdam_2_10_RGB_35.png
 74 | top_potsdam_2_10_RGB_36.png
 75 | top_potsdam_2_10_RGB_37.png
 76 | top_potsdam_2_10_RGB_38.png
 77 | top_potsdam_2_10_RGB_39.png
 78 | top_potsdam_2_10_RGB_40.png
 79 | top_potsdam_2_10_RGB_41.png
 80 | top_potsdam_2_10_RGB_42.png
 81 | top_potsdam_2_10_RGB_43.png
 82 | top_potsdam_2_10_RGB_44.png
 83 | top_potsdam_2_10_RGB_45.png
 84 | top_potsdam_2_10_RGB_46.png
 85 | top_potsdam_2_10_RGB_47.png
 86 | top_potsdam_2_10_RGB_48.png
 87 | top_potsdam_2_10_RGB_49.png
 88 | top_potsdam_2_10_RGB_50.png
 89 | top_potsdam_2_10_RGB_51.png
 90 | top_potsdam_2_10_RGB_52.png
 91 | top_potsdam_2_10_RGB_53.png
 92 | top_potsdam_2_10_RGB_54.png
 93 | top_potsdam_2_10_RGB_55.png
 94 | top_potsdam_2_10_RGB_56.png
 95 | top_potsdam_2_10_RGB_57.png
 96 | top_potsdam_2_10_RGB_58.png
 97 | top_potsdam_2_10_RGB_59.png
 98 | top_potsdam_2_10_RGB_60.png
 99 | top_potsdam_2_10_RGB_61.png
100 | top_potsdam_2_10_RGB_62.png
101 | top_potsdam_2_10_RGB_63.png
102 | top_potsdam_2_10_RGB_64.png
103 | top_potsdam_2_10_RGB_65.png
104 | top_potsdam_2_10_RGB_66.png
105 | top_potsdam_2_10_RGB_67.png
106 | top_potsdam_2_10_RGB_68.png
107 | top_potsdam_2_10_RGB_69.png
108 | top_potsdam_2_10_RGB_70.png
109 | top_potsdam_2_10_RGB_71.png
110 | top_potsdam_2_10_RGB_72.png
111 | top_potsdam_2_10_RGB_73.png
112 | top_potsdam_2_10_RGB_74.png
113 | top_potsdam_2_10_RGB_75.png
114 | top_potsdam_2_10_RGB_76.png
115 | top_potsdam_2_10_RGB_77.png
116 | top_potsdam_2_10_RGB_78.png
117 | top_potsdam_2_10_RGB_79.png
118 | top_potsdam_2_10_RGB_80.png
119 | top_potsdam_2_10_RGB_81.png
120 | top_potsdam_2_10_RGB_82.png
121 | top_potsdam_2_10_RGB_83.png
122 | top_potsdam_2_10_RGB_84.png
123 | top_potsdam_2_10_RGB_85.png
124 | top_potsdam_2_10_RGB_86.png
125 | top_potsdam_2_10_RGB_87.png
126 | top_potsdam_2_10_RGB_88.png
127 | top_potsdam_2_10_RGB_89.png
128 | top_potsdam_2_10_RGB_90.png
129 | top_potsdam_2_10_RGB_91.png
130 | top_potsdam_2_10_RGB_92.png
131 | top_potsdam_2_10_RGB_93.png
132 | top_potsdam_2_10_RGB_94.png
133 | top_potsdam_2_10_RGB_95.png
134 | top_potsdam_2_10_RGB_96.png
135 | top_potsdam_2_10_RGB_97.png
136 | top_potsdam_2_10_RGB_98.png
137 | top_potsdam_2_10_RGB_99.png
138 | top_potsdam_2_11_RGB_01.png
139 | top_potsdam_2_11_RGB_02.png
140 | top_potsdam_2_11_RGB_03.png
141 | top_potsdam_2_11_RGB_04.png
142 | top_potsdam_2_11_RGB_05.png
143 | top_potsdam_2_11_RGB_06.png
144 | top_potsdam_2_11_RGB_07.png
145 | top_potsdam_2_11_RGB_08.png
146 | top_potsdam_2_11_RGB_09.png
147 | top_potsdam_2_11_RGB_10.png
148 | top_potsdam_2_11_RGB_100.png
149 | top_potsdam_2_11_RGB_101.png
150 | top_potsdam_2_11_RGB_102.png
151 | top_potsdam_2_11_RGB_103.png
152 | top_potsdam_2_11_RGB_104.png
153 | top_potsdam_2_11_RGB_105.png
154 | top_potsdam_2_11_RGB_106.png
155 | top_potsdam_2_11_RGB_107.png
156 | top_potsdam_2_11_RGB_108.png
157 | top_potsdam_2_11_RGB_109.png
158 | top_potsdam_2_11_RGB_11.png
159 | top_potsdam_2_11_RGB_110.png
160 | top_potsdam_2_11_RGB_111.png
161 | top_potsdam_2_11_RGB_112.png
162 | top_potsdam_2_11_RGB_113.png
163 | top_potsdam_2_11_RGB_114.png
164 | top_potsdam_2_11_RGB_115.png
165 | top_potsdam_2_11_RGB_116.png
166 | top_potsdam_2_11_RGB_117.png
167 | top_potsdam_2_11_RGB_118.png
168 | top_potsdam_2_11_RGB_119.png
169 | top_potsdam_2_11_RGB_12.png
170 | top_potsdam_2_11_RGB_120.png
171 | top_potsdam_2_11_RGB_121.png
172 | top_potsdam_2_11_RGB_13.png
173 | top_potsdam_2_11_RGB_14.png
174 | top_potsdam_2_11_RGB_15.png
175 | top_potsdam_2_11_RGB_16.png
176 | top_potsdam_2_11_RGB_17.png
177 | top_potsdam_2_11_RGB_18.png
178 | top_potsdam_2_11_RGB_19.png
179 | top_potsdam_2_11_RGB_20.png
180 | top_potsdam_2_11_RGB_21.png
181 | top_potsdam_2_11_RGB_22.png
182 | top_potsdam_2_11_RGB_23.png
183 | top_potsdam_2_11_RGB_24.png
184 | top_potsdam_2_11_RGB_25.png
185 | top_potsdam_2_11_RGB_26.png
186 | top_potsdam_2_11_RGB_27.png
187 | top_potsdam_2_11_RGB_28.png
188 | top_potsdam_2_11_RGB_29.png
189 | top_potsdam_2_11_RGB_30.png
190 | top_potsdam_2_11_RGB_31.png
191 | top_potsdam_2_11_RGB_32.png
192 | top_potsdam_2_11_RGB_33.png
193 | top_potsdam_2_11_RGB_34.png
194 | top_potsdam_2_11_RGB_35.png
195 | top_potsdam_2_11_RGB_36.png
196 | top_potsdam_2_11_RGB_37.png
197 | top_potsdam_2_11_RGB_38.png
198 | top_potsdam_2_11_RGB_39.png
199 | top_potsdam_2_11_RGB_40.png
200 | top_potsdam_2_11_RGB_41.png
201 | top_potsdam_2_11_RGB_42.png
202 | top_potsdam_2_11_RGB_43.png
203 | top_potsdam_2_11_RGB_44.png
204 | top_potsdam_2_11_RGB_45.png
205 | top_potsdam_2_11_RGB_46.png
206 | top_potsdam_2_11_RGB_47.png
207 | top_potsdam_2_11_RGB_48.png
208 | top_potsdam_2_11_RGB_49.png
209 | top_potsdam_2_11_RGB_50.png
210 | top_potsdam_2_11_RGB_51.png
211 | top_potsdam_2_11_RGB_52.png
212 | top_potsdam_2_11_RGB_53.png
213 | top_potsdam_2_11_RGB_54.png
214 | top_potsdam_2_11_RGB_55.png
215 | top_potsdam_2_11_RGB_56.png
216 | top_potsdam_2_11_RGB_57.png
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643 | top_potsdam_4_10_RGB_69.png
644 | top_potsdam_4_10_RGB_70.png
645 | top_potsdam_4_10_RGB_71.png
646 | top_potsdam_4_10_RGB_72.png
647 | top_potsdam_4_10_RGB_73.png
648 | top_potsdam_4_10_RGB_74.png
649 | top_potsdam_4_10_RGB_75.png
650 | top_potsdam_4_10_RGB_76.png
651 | top_potsdam_4_10_RGB_77.png
652 | top_potsdam_4_10_RGB_78.png
653 | top_potsdam_4_10_RGB_79.png
654 | top_potsdam_4_10_RGB_80.png
655 | top_potsdam_4_10_RGB_81.png
656 | top_potsdam_4_10_RGB_82.png
657 | top_potsdam_4_10_RGB_83.png
658 | top_potsdam_4_10_RGB_84.png
659 | top_potsdam_4_10_RGB_85.png
660 | top_potsdam_4_10_RGB_86.png
661 | top_potsdam_4_10_RGB_87.png
662 | top_potsdam_4_10_RGB_88.png
663 | top_potsdam_4_10_RGB_89.png
664 | top_potsdam_4_10_RGB_90.png
665 | top_potsdam_4_10_RGB_91.png
666 | top_potsdam_4_10_RGB_92.png
667 | top_potsdam_4_10_RGB_93.png
668 | top_potsdam_4_10_RGB_94.png
669 | top_potsdam_4_10_RGB_95.png
670 | top_potsdam_4_10_RGB_96.png
671 | top_potsdam_4_10_RGB_97.png
672 | top_potsdam_4_10_RGB_98.png
673 | top_potsdam_4_10_RGB_99.png
674 | top_potsdam_4_11_RGB_01.png
675 | top_potsdam_4_11_RGB_02.png
676 | top_potsdam_4_11_RGB_03.png
677 | top_potsdam_4_11_RGB_04.png
678 | top_potsdam_4_11_RGB_05.png
679 | top_potsdam_4_11_RGB_06.png
680 | top_potsdam_4_11_RGB_07.png
681 | top_potsdam_4_11_RGB_08.png
682 | top_potsdam_4_11_RGB_09.png
683 | top_potsdam_4_11_RGB_10.png
684 | top_potsdam_4_11_RGB_100.png
685 | top_potsdam_4_11_RGB_101.png
686 | top_potsdam_4_11_RGB_102.png
687 | top_potsdam_4_11_RGB_103.png
688 | top_potsdam_4_11_RGB_104.png
689 | top_potsdam_4_11_RGB_105.png
690 | top_potsdam_4_11_RGB_106.png
691 | top_potsdam_4_11_RGB_107.png
692 | top_potsdam_4_11_RGB_108.png
693 | top_potsdam_4_11_RGB_109.png
694 | top_potsdam_4_11_RGB_11.png
695 | top_potsdam_4_11_RGB_110.png
696 | top_potsdam_3_10_RGB_56.png
697 | top_potsdam_3_10_RGB_57.png
698 | top_potsdam_3_10_RGB_58.png
699 | top_potsdam_3_10_RGB_59.png
700 | top_potsdam_3_10_RGB_60.png
701 | top_potsdam_3_10_RGB_61.png
702 | top_potsdam_3_10_RGB_62.png
703 | top_potsdam_3_10_RGB_63.png
704 | top_potsdam_3_10_RGB_64.png
705 | top_potsdam_3_10_RGB_65.png
706 | top_potsdam_3_10_RGB_66.png
707 | top_potsdam_3_10_RGB_67.png
708 | top_potsdam_3_10_RGB_68.png
709 | top_potsdam_3_10_RGB_69.png
710 | top_potsdam_3_10_RGB_70.png
711 | top_potsdam_3_10_RGB_71.png
712 | top_potsdam_3_10_RGB_72.png
713 | top_potsdam_3_10_RGB_73.png
714 | top_potsdam_3_10_RGB_74.png
715 | top_potsdam_3_10_RGB_75.png
716 | top_potsdam_3_10_RGB_76.png
717 | top_potsdam_3_10_RGB_77.png
718 | top_potsdam_3_10_RGB_78.png
719 | top_potsdam_3_10_RGB_79.png
720 | top_potsdam_3_10_RGB_80.png
721 | top_potsdam_3_10_RGB_81.png
722 | top_potsdam_3_10_RGB_82.png
723 | top_potsdam_4_10_RGB_01.png
724 | top_potsdam_4_10_RGB_02.png
725 | top_potsdam_4_10_RGB_03.png
726 | top_potsdam_4_10_RGB_04.png
727 | top_potsdam_4_10_RGB_05.png
728 | top_potsdam_4_10_RGB_06.png
729 | top_potsdam_4_10_RGB_07.png
730 | top_potsdam_4_10_RGB_08.png
731 | top_potsdam_4_10_RGB_09.png
732 | top_potsdam_4_10_RGB_10.png
733 | top_potsdam_4_10_RGB_100.png
734 | top_potsdam_4_10_RGB_101.png
735 | top_potsdam_4_10_RGB_102.png
736 | top_potsdam_4_10_RGB_103.png
737 | top_potsdam_4_10_RGB_104.png
738 | top_potsdam_4_10_RGB_105.png
739 | top_potsdam_4_10_RGB_106.png
740 | top_potsdam_4_10_RGB_107.png
741 | top_potsdam_4_10_RGB_108.png
742 | top_potsdam_4_10_RGB_109.png
743 | top_potsdam_4_10_RGB_11.png
744 | top_potsdam_4_10_RGB_110.png
745 | top_potsdam_4_10_RGB_111.png
746 | top_potsdam_4_10_RGB_112.png
747 | top_potsdam_4_10_RGB_113.png
748 | top_potsdam_4_10_RGB_114.png
749 | top_potsdam_4_10_RGB_115.png
750 | top_potsdam_4_10_RGB_116.png
751 | top_potsdam_4_10_RGB_117.png
752 | top_potsdam_4_10_RGB_118.png
753 | top_potsdam_4_10_RGB_119.png
754 | top_potsdam_4_10_RGB_12.png
755 | top_potsdam_4_10_RGB_120.png
756 | top_potsdam_4_10_RGB_121.png
757 | top_potsdam_4_10_RGB_13.png
758 | top_potsdam_4_10_RGB_14.png
759 | top_potsdam_4_10_RGB_15.png
760 | top_potsdam_4_10_RGB_16.png
761 | top_potsdam_4_10_RGB_17.png
762 | top_potsdam_4_10_RGB_18.png
763 | top_potsdam_4_10_RGB_19.png
764 | top_potsdam_4_10_RGB_20.png
765 | top_potsdam_4_10_RGB_21.png
766 | top_potsdam_4_10_RGB_22.png
767 | top_potsdam_4_10_RGB_23.png
768 | top_potsdam_4_10_RGB_24.png
769 | top_potsdam_4_10_RGB_25.png
770 | top_potsdam_4_10_RGB_26.png
771 | top_potsdam_4_10_RGB_27.png
772 | top_potsdam_4_10_RGB_28.png
773 | top_potsdam_4_10_RGB_29.png
774 | top_potsdam_4_10_RGB_30.png
775 | top_potsdam_4_10_RGB_31.png
776 | top_potsdam_4_10_RGB_32.png
777 | top_potsdam_4_10_RGB_33.png
778 | top_potsdam_4_10_RGB_34.png
779 | top_potsdam_4_10_RGB_35.png
780 | top_potsdam_4_10_RGB_36.png
781 | top_potsdam_4_10_RGB_37.png
782 | top_potsdam_4_10_RGB_38.png
783 | top_potsdam_4_10_RGB_39.png
784 | top_potsdam_4_10_RGB_40.png
785 | top_potsdam_4_10_RGB_41.png
786 | top_potsdam_4_10_RGB_42.png
787 | top_potsdam_5_13_RGB_63.png
788 | top_potsdam_5_13_RGB_64.png
789 | top_potsdam_5_13_RGB_65.png
790 | top_potsdam_5_13_RGB_66.png
791 | top_potsdam_5_13_RGB_67.png
792 | top_potsdam_5_13_RGB_68.png
793 | top_potsdam_5_13_RGB_69.png
794 | top_potsdam_5_13_RGB_70.png
795 | top_potsdam_5_13_RGB_71.png
796 | top_potsdam_5_13_RGB_72.png
797 | top_potsdam_5_13_RGB_73.png
798 | top_potsdam_5_13_RGB_74.png
799 | top_potsdam_5_13_RGB_75.png
800 | top_potsdam_5_13_RGB_76.png
801 | top_potsdam_5_13_RGB_77.png
802 | top_potsdam_5_13_RGB_78.png
803 | top_potsdam_5_13_RGB_79.png
804 | top_potsdam_5_13_RGB_80.png
805 | top_potsdam_5_13_RGB_81.png
806 | top_potsdam_5_13_RGB_82.png
807 | top_potsdam_5_13_RGB_83.png
808 | top_potsdam_5_13_RGB_84.png
809 | top_potsdam_5_13_RGB_85.png
810 | top_potsdam_5_13_RGB_86.png
811 | top_potsdam_5_13_RGB_87.png
812 | top_potsdam_5_13_RGB_88.png
813 | top_potsdam_5_13_RGB_89.png
814 | top_potsdam_5_13_RGB_90.png
815 | top_potsdam_5_13_RGB_91.png
816 | top_potsdam_5_13_RGB_92.png
817 | top_potsdam_5_13_RGB_93.png
818 | top_potsdam_5_13_RGB_94.png
819 | top_potsdam_5_13_RGB_95.png
820 | top_potsdam_5_13_RGB_96.png
821 | top_potsdam_5_13_RGB_97.png
822 | top_potsdam_5_13_RGB_98.png
823 | top_potsdam_5_13_RGB_99.png
824 | top_potsdam_5_14_RGB_01.png
825 | top_potsdam_5_14_RGB_02.png
826 | top_potsdam_5_14_RGB_03.png


--------------------------------------------------------------------------------
/ADVENT/advent/dataset/Vaihingen.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | 
 3 | from advent.utils import project_root
 4 | from advent.utils.serialization import json_load
 5 | from advent.dataset.base_dataset import BaseDataset
 6 | 
 7 | DEFAULT_INFO_PATH = project_root / 'advent/dataset/Vaihingen/info.json'
 8 | 
 9 | 
10 | class Vaihingen(BaseDataset):
11 |     def __init__(self, root, list_path, set='val',
12 |                  max_iters=None,
13 |                  crop_size=(512, 512), mean=(128, 128, 128),
14 |                  load_labels=True,
15 |                  info_path=DEFAULT_INFO_PATH, labels_size=None):
16 |         super().__init__(root, list_path, set, max_iters, crop_size, labels_size, mean)
17 | 
18 |         self.load_labels = load_labels
19 |         self.info = json_load(info_path)
20 |         self.class_names = np.array(self.info['label'], dtype=np.str)
21 | 
22 |     def get_metadata(self, name):
23 |         img_file = self.root / 'images' / name
24 |         label_file = self.root / 'labels' / name
25 |         return img_file, label_file
26 | 
27 |     def __getitem__(self, index):
28 |         img_file, label_file, name = self.files[index]
29 |         label = self.get_labels(label_file)
30 |         image = self.get_image(img_file)
31 |         image = self.preprocess(image)
32 |         return image.copy(), label-1, np.array(image.shape), name


--------------------------------------------------------------------------------
/ADVENT/advent/dataset/Vaihingen/Vaihingen_test.txt:
--------------------------------------------------------------------------------
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   5 | top_mosaic_09cm_area10_13.png
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  60 | top_mosaic_09cm_area11_2.png
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  68 | top_mosaic_09cm_area11_27.png
  69 | top_mosaic_09cm_area11_28.png
  70 | top_mosaic_09cm_area11_29.png
  71 | top_mosaic_09cm_area11_3.png
  72 | top_mosaic_09cm_area11_30.png
  73 | top_mosaic_09cm_area11_31.png
  74 | top_mosaic_09cm_area11_32.png
  75 | top_mosaic_09cm_area11_33.png
  76 | top_mosaic_09cm_area11_34.png
  77 | top_mosaic_09cm_area11_35.png
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  79 | top_mosaic_09cm_area11_37.png
  80 | top_mosaic_09cm_area11_38.png
  81 | top_mosaic_09cm_area11_39.png
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 100 | top_mosaic_09cm_area11_7.png
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 103 | top_mosaic_09cm_area12_1.png
 104 | top_mosaic_09cm_area12_10.png
 105 | top_mosaic_09cm_area12_11.png
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 120 | top_mosaic_09cm_area12_25.png
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 122 | top_mosaic_09cm_area12_27.png
 123 | top_mosaic_09cm_area12_28.png
 124 | top_mosaic_09cm_area12_29.png
 125 | top_mosaic_09cm_area12_3.png
 126 | top_mosaic_09cm_area12_30.png
 127 | top_mosaic_09cm_area12_31.png
 128 | top_mosaic_09cm_area12_32.png
 129 | top_mosaic_09cm_area12_33.png
 130 | top_mosaic_09cm_area12_34.png
 131 | top_mosaic_09cm_area12_35.png
 132 | top_mosaic_09cm_area12_36.png
 133 | top_mosaic_09cm_area12_37.png
 134 | top_mosaic_09cm_area12_38.png
 135 | top_mosaic_09cm_area12_39.png
 136 | top_mosaic_09cm_area12_4.png
 137 | top_mosaic_09cm_area12_40.png
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 140 | top_mosaic_09cm_area12_43.png
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1104 | top_mosaic_09cm_area4_18.png
1105 | top_mosaic_09cm_area4_19.png
1106 | top_mosaic_09cm_area4_2.png
1107 | top_mosaic_09cm_area4_20.png
1108 | top_mosaic_09cm_area4_21.png
1109 | top_mosaic_09cm_area4_22.png
1110 | top_mosaic_09cm_area4_23.png
1111 | top_mosaic_09cm_area4_24.png
1112 | top_mosaic_09cm_area4_25.png
1113 | top_mosaic_09cm_area4_26.png
1114 | top_mosaic_09cm_area4_27.png
1115 | top_mosaic_09cm_area4_28.png
1116 | top_mosaic_09cm_area4_29.png
1117 | top_mosaic_09cm_area4_3.png
1118 | top_mosaic_09cm_area4_30.png
1119 | top_mosaic_09cm_area4_31.png
1120 | top_mosaic_09cm_area4_32.png
1121 | top_mosaic_09cm_area4_33.png
1122 | top_mosaic_09cm_area4_34.png
1123 | top_mosaic_09cm_area4_35.png
1124 | top_mosaic_09cm_area4_36.png
1125 | top_mosaic_09cm_area4_37.png
1126 | top_mosaic_09cm_area4_38.png
1127 | top_mosaic_09cm_area4_39.png
1128 | top_mosaic_09cm_area4_4.png
1129 | top_mosaic_09cm_area4_40.png
1130 | top_mosaic_09cm_area4_41.png
1131 | top_mosaic_09cm_area4_42.png
1132 | top_mosaic_09cm_area4_43.png
1133 | top_mosaic_09cm_area4_44.png
1134 | top_mosaic_09cm_area4_45.png
1135 | top_mosaic_09cm_area4_46.png
1136 | top_mosaic_09cm_area4_47.png
1137 | top_mosaic_09cm_area4_48.png
1138 | top_mosaic_09cm_area4_5.png
1139 | top_mosaic_09cm_area4_6.png
1140 | top_mosaic_09cm_area4_7.png
1141 | top_mosaic_09cm_area4_8.png
1142 | top_mosaic_09cm_area4_9.png
1143 | top_mosaic_09cm_area6_1.png
1144 | top_mosaic_09cm_area6_10.png
1145 | top_mosaic_09cm_area6_11.png
1146 | top_mosaic_09cm_area6_12.png
1147 | top_mosaic_09cm_area6_13.png
1148 | top_mosaic_09cm_area6_14.png
1149 | top_mosaic_09cm_area6_15.png
1150 | top_mosaic_09cm_area6_16.png
1151 | top_mosaic_09cm_area6_17.png
1152 | top_mosaic_09cm_area6_18.png
1153 | top_mosaic_09cm_area6_19.png
1154 | top_mosaic_09cm_area6_2.png
1155 | top_mosaic_09cm_area6_20.png
1156 | top_mosaic_09cm_area6_21.png
1157 | top_mosaic_09cm_area6_22.png
1158 | top_mosaic_09cm_area6_23.png
1159 | top_mosaic_09cm_area6_24.png
1160 | top_mosaic_09cm_area6_25.png
1161 | top_mosaic_09cm_area6_26.png
1162 | top_mosaic_09cm_area6_27.png
1163 | top_mosaic_09cm_area6_28.png
1164 | top_mosaic_09cm_area6_29.png
1165 | top_mosaic_09cm_area6_3.png
1166 | top_mosaic_09cm_area6_30.png
1167 | top_mosaic_09cm_area6_31.png
1168 | top_mosaic_09cm_area6_32.png
1169 | top_mosaic_09cm_area6_33.png
1170 | top_mosaic_09cm_area6_34.png
1171 | top_mosaic_09cm_area6_35.png
1172 | top_mosaic_09cm_area6_36.png
1173 | top_mosaic_09cm_area6_37.png
1174 | top_mosaic_09cm_area6_38.png
1175 | top_mosaic_09cm_area6_39.png
1176 | top_mosaic_09cm_area6_4.png
1177 | top_mosaic_09cm_area6_40.png
1178 | top_mosaic_09cm_area6_41.png
1179 | top_mosaic_09cm_area6_42.png
1180 | top_mosaic_09cm_area6_43.png
1181 | top_mosaic_09cm_area6_44.png
1182 | top_mosaic_09cm_area6_45.png
1183 | top_mosaic_09cm_area6_46.png
1184 | top_mosaic_09cm_area6_47.png
1185 | top_mosaic_09cm_area6_48.png
1186 | top_mosaic_09cm_area6_5.png
1187 | top_mosaic_09cm_area6_6.png
1188 | top_mosaic_09cm_area6_7.png
1189 | top_mosaic_09cm_area6_8.png
1190 | top_mosaic_09cm_area6_9.png
1191 | 


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/ADVENT/advent/dataset/Vaihingen/Vaihingen_validation.txt:
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  1 | top_mosaic_09cm_area13_1.png
  2 | top_mosaic_09cm_area13_10.png
  3 | top_mosaic_09cm_area13_11.png
  4 | top_mosaic_09cm_area13_12.png
  5 | top_mosaic_09cm_area13_13.png
  6 | top_mosaic_09cm_area13_14.png
  7 | top_mosaic_09cm_area13_15.png
  8 | top_mosaic_09cm_area13_16.png
  9 | top_mosaic_09cm_area13_17.png
 10 | top_mosaic_09cm_area13_18.png
 11 | top_mosaic_09cm_area13_19.png
 12 | top_mosaic_09cm_area13_2.png
 13 | top_mosaic_09cm_area13_20.png
 14 | top_mosaic_09cm_area13_21.png
 15 | top_mosaic_09cm_area13_22.png
 16 | top_mosaic_09cm_area13_23.png
 17 | top_mosaic_09cm_area13_24.png
 18 | top_mosaic_09cm_area13_25.png
 19 | top_mosaic_09cm_area13_26.png
 20 | top_mosaic_09cm_area13_27.png
 21 | top_mosaic_09cm_area13_28.png
 22 | top_mosaic_09cm_area13_29.png
 23 | top_mosaic_09cm_area13_3.png
 24 | top_mosaic_09cm_area13_30.png
 25 | top_mosaic_09cm_area13_31.png
 26 | top_mosaic_09cm_area13_32.png
 27 | top_mosaic_09cm_area13_33.png
 28 | top_mosaic_09cm_area13_34.png
 29 | top_mosaic_09cm_area13_35.png
 30 | top_mosaic_09cm_area13_36.png
 31 | top_mosaic_09cm_area13_37.png
 32 | top_mosaic_09cm_area13_38.png
 33 | top_mosaic_09cm_area13_39.png
 34 | top_mosaic_09cm_area13_4.png
 35 | top_mosaic_09cm_area13_40.png
 36 | top_mosaic_09cm_area13_41.png
 37 | top_mosaic_09cm_area13_42.png
 38 | top_mosaic_09cm_area13_43.png
 39 | top_mosaic_09cm_area13_44.png
 40 | top_mosaic_09cm_area13_45.png
 41 | top_mosaic_09cm_area13_46.png
 42 | top_mosaic_09cm_area13_47.png
 43 | top_mosaic_09cm_area13_48.png
 44 | top_mosaic_09cm_area13_49.png
 45 | top_mosaic_09cm_area13_5.png
 46 | top_mosaic_09cm_area13_50.png
 47 | top_mosaic_09cm_area13_51.png
 48 | top_mosaic_09cm_area13_52.png
 49 | top_mosaic_09cm_area13_53.png
 50 | top_mosaic_09cm_area13_54.png
 51 | top_mosaic_09cm_area13_55.png
 52 | top_mosaic_09cm_area13_56.png
 53 | top_mosaic_09cm_area13_57.png
 54 | top_mosaic_09cm_area13_58.png
 55 | top_mosaic_09cm_area13_59.png
 56 | top_mosaic_09cm_area13_6.png
 57 | top_mosaic_09cm_area13_60.png
 58 | top_mosaic_09cm_area13_61.png
 59 | top_mosaic_09cm_area13_62.png
 60 | top_mosaic_09cm_area13_63.png
 61 | top_mosaic_09cm_area13_64.png
 62 | top_mosaic_09cm_area13_65.png
 63 | top_mosaic_09cm_area13_66.png
 64 | top_mosaic_09cm_area13_67.png
 65 | top_mosaic_09cm_area13_68.png
 66 | top_mosaic_09cm_area13_69.png
 67 | top_mosaic_09cm_area13_7.png
 68 | top_mosaic_09cm_area13_70.png
 69 | top_mosaic_09cm_area13_71.png
 70 | top_mosaic_09cm_area13_72.png
 71 | top_mosaic_09cm_area13_73.png
 72 | top_mosaic_09cm_area13_74.png
 73 | top_mosaic_09cm_area13_75.png
 74 | top_mosaic_09cm_area13_76.png
 75 | top_mosaic_09cm_area13_77.png
 76 | top_mosaic_09cm_area13_78.png
 77 | top_mosaic_09cm_area13_79.png
 78 | top_mosaic_09cm_area13_8.png
 79 | top_mosaic_09cm_area13_80.png
 80 | top_mosaic_09cm_area13_9.png
 81 | top_mosaic_09cm_area20_1.png
 82 | top_mosaic_09cm_area20_10.png
 83 | top_mosaic_09cm_area20_11.png
 84 | top_mosaic_09cm_area20_12.png
 85 | top_mosaic_09cm_area20_13.png
 86 | top_mosaic_09cm_area20_14.png
 87 | top_mosaic_09cm_area20_15.png
 88 | top_mosaic_09cm_area20_16.png
 89 | top_mosaic_09cm_area20_17.png
 90 | top_mosaic_09cm_area20_18.png
 91 | top_mosaic_09cm_area20_19.png
 92 | top_mosaic_09cm_area20_2.png
 93 | top_mosaic_09cm_area20_20.png
 94 | top_mosaic_09cm_area20_21.png
 95 | top_mosaic_09cm_area20_22.png
 96 | top_mosaic_09cm_area20_23.png
 97 | top_mosaic_09cm_area20_24.png
 98 | top_mosaic_09cm_area20_25.png
 99 | top_mosaic_09cm_area20_26.png
100 | top_mosaic_09cm_area20_27.png
101 | top_mosaic_09cm_area20_28.png
102 | top_mosaic_09cm_area20_29.png
103 | top_mosaic_09cm_area20_3.png
104 | top_mosaic_09cm_area20_30.png
105 | top_mosaic_09cm_area20_31.png
106 | top_mosaic_09cm_area20_32.png
107 | top_mosaic_09cm_area20_33.png
108 | top_mosaic_09cm_area20_34.png
109 | top_mosaic_09cm_area20_35.png
110 | top_mosaic_09cm_area20_36.png
111 | top_mosaic_09cm_area20_37.png
112 | top_mosaic_09cm_area20_38.png
113 | top_mosaic_09cm_area20_39.png
114 | top_mosaic_09cm_area20_4.png
115 | top_mosaic_09cm_area20_40.png
116 | top_mosaic_09cm_area20_41.png
117 | top_mosaic_09cm_area20_42.png
118 | top_mosaic_09cm_area20_43.png
119 | top_mosaic_09cm_area20_44.png
120 | top_mosaic_09cm_area20_45.png
121 | top_mosaic_09cm_area20_46.png
122 | top_mosaic_09cm_area20_47.png
123 | top_mosaic_09cm_area20_48.png
124 | top_mosaic_09cm_area20_5.png
125 | top_mosaic_09cm_area20_6.png
126 | top_mosaic_09cm_area20_7.png
127 | top_mosaic_09cm_area20_8.png
128 | top_mosaic_09cm_area20_9.png
129 | top_mosaic_09cm_area22_1.png
130 | top_mosaic_09cm_area22_10.png
131 | top_mosaic_09cm_area22_11.png
132 | top_mosaic_09cm_area22_12.png
133 | top_mosaic_09cm_area22_13.png
134 | top_mosaic_09cm_area22_14.png
135 | top_mosaic_09cm_area22_15.png
136 | top_mosaic_09cm_area22_16.png
137 | top_mosaic_09cm_area22_17.png
138 | top_mosaic_09cm_area22_18.png
139 | top_mosaic_09cm_area22_19.png
140 | top_mosaic_09cm_area22_2.png
141 | top_mosaic_09cm_area22_20.png
142 | top_mosaic_09cm_area22_21.png
143 | top_mosaic_09cm_area22_22.png
144 | top_mosaic_09cm_area22_23.png
145 | top_mosaic_09cm_area22_24.png
146 | top_mosaic_09cm_area22_25.png
147 | top_mosaic_09cm_area22_26.png
148 | top_mosaic_09cm_area22_27.png
149 | top_mosaic_09cm_area22_28.png
150 | top_mosaic_09cm_area22_29.png
151 | top_mosaic_09cm_area22_3.png
152 | top_mosaic_09cm_area22_30.png
153 | top_mosaic_09cm_area22_31.png
154 | top_mosaic_09cm_area22_32.png
155 | top_mosaic_09cm_area22_33.png
156 | top_mosaic_09cm_area22_34.png
157 | top_mosaic_09cm_area22_35.png
158 | top_mosaic_09cm_area22_36.png
159 | top_mosaic_09cm_area22_37.png
160 | top_mosaic_09cm_area22_38.png
161 | top_mosaic_09cm_area22_39.png
162 | top_mosaic_09cm_area22_4.png
163 | top_mosaic_09cm_area22_40.png
164 | top_mosaic_09cm_area22_41.png
165 | top_mosaic_09cm_area22_42.png
166 | top_mosaic_09cm_area22_43.png
167 | top_mosaic_09cm_area22_44.png
168 | top_mosaic_09cm_area22_45.png
169 | top_mosaic_09cm_area22_46.png
170 | top_mosaic_09cm_area22_47.png
171 | top_mosaic_09cm_area22_48.png
172 | top_mosaic_09cm_area22_5.png
173 | top_mosaic_09cm_area22_6.png
174 | top_mosaic_09cm_area22_7.png
175 | top_mosaic_09cm_area22_8.png
176 | top_mosaic_09cm_area22_9.png
177 | top_mosaic_09cm_area24_1.png
178 | top_mosaic_09cm_area24_10.png
179 | top_mosaic_09cm_area24_11.png
180 | top_mosaic_09cm_area24_12.png
181 | top_mosaic_09cm_area24_13.png
182 | top_mosaic_09cm_area24_14.png
183 | top_mosaic_09cm_area24_15.png
184 | top_mosaic_09cm_area24_16.png
185 | top_mosaic_09cm_area24_17.png
186 | top_mosaic_09cm_area24_18.png
187 | top_mosaic_09cm_area24_19.png
188 | top_mosaic_09cm_area24_2.png
189 | top_mosaic_09cm_area24_20.png
190 | top_mosaic_09cm_area24_21.png
191 | top_mosaic_09cm_area24_22.png
192 | top_mosaic_09cm_area24_23.png
193 | top_mosaic_09cm_area24_24.png
194 | top_mosaic_09cm_area24_25.png
195 | top_mosaic_09cm_area24_26.png
196 | top_mosaic_09cm_area24_27.png
197 | top_mosaic_09cm_area24_28.png
198 | top_mosaic_09cm_area24_29.png
199 | top_mosaic_09cm_area24_3.png
200 | top_mosaic_09cm_area24_30.png
201 | top_mosaic_09cm_area24_31.png
202 | top_mosaic_09cm_area24_32.png
203 | top_mosaic_09cm_area24_33.png
204 | top_mosaic_09cm_area24_34.png
205 | top_mosaic_09cm_area24_35.png
206 | top_mosaic_09cm_area24_36.png
207 | top_mosaic_09cm_area24_37.png
208 | top_mosaic_09cm_area24_38.png
209 | top_mosaic_09cm_area24_39.png
210 | top_mosaic_09cm_area24_4.png
211 | top_mosaic_09cm_area24_40.png
212 | top_mosaic_09cm_area24_41.png
213 | top_mosaic_09cm_area24_42.png
214 | top_mosaic_09cm_area24_43.png
215 | top_mosaic_09cm_area24_44.png
216 | top_mosaic_09cm_area24_45.png
217 | top_mosaic_09cm_area24_46.png
218 | top_mosaic_09cm_area24_47.png
219 | top_mosaic_09cm_area24_48.png
220 | top_mosaic_09cm_area24_5.png
221 | top_mosaic_09cm_area24_6.png
222 | top_mosaic_09cm_area24_7.png
223 | top_mosaic_09cm_area24_8.png
224 | top_mosaic_09cm_area24_9.png
225 | top_mosaic_09cm_area27_1.png
226 | top_mosaic_09cm_area27_10.png
227 | top_mosaic_09cm_area27_11.png
228 | top_mosaic_09cm_area27_12.png
229 | top_mosaic_09cm_area27_13.png
230 | top_mosaic_09cm_area27_14.png
231 | top_mosaic_09cm_area27_15.png
232 | top_mosaic_09cm_area27_16.png
233 | top_mosaic_09cm_area27_17.png
234 | top_mosaic_09cm_area27_18.png
235 | top_mosaic_09cm_area27_19.png
236 | top_mosaic_09cm_area27_2.png
237 | top_mosaic_09cm_area27_20.png
238 | top_mosaic_09cm_area27_21.png
239 | top_mosaic_09cm_area27_22.png
240 | top_mosaic_09cm_area27_23.png
241 | top_mosaic_09cm_area27_24.png
242 | top_mosaic_09cm_area27_25.png
243 | top_mosaic_09cm_area27_26.png
244 | top_mosaic_09cm_area27_27.png
245 | top_mosaic_09cm_area27_28.png
246 | top_mosaic_09cm_area27_29.png
247 | top_mosaic_09cm_area27_3.png
248 | top_mosaic_09cm_area27_30.png
249 | top_mosaic_09cm_area27_31.png
250 | top_mosaic_09cm_area27_32.png
251 | top_mosaic_09cm_area27_33.png
252 | top_mosaic_09cm_area27_34.png
253 | top_mosaic_09cm_area27_35.png
254 | top_mosaic_09cm_area27_36.png
255 | top_mosaic_09cm_area27_37.png
256 | top_mosaic_09cm_area27_38.png
257 | top_mosaic_09cm_area27_39.png
258 | top_mosaic_09cm_area27_4.png
259 | top_mosaic_09cm_area27_40.png
260 | top_mosaic_09cm_area27_41.png
261 | top_mosaic_09cm_area27_42.png
262 | top_mosaic_09cm_area27_43.png
263 | top_mosaic_09cm_area27_44.png
264 | top_mosaic_09cm_area27_45.png
265 | top_mosaic_09cm_area27_46.png
266 | top_mosaic_09cm_area27_47.png
267 | top_mosaic_09cm_area27_48.png
268 | top_mosaic_09cm_area27_49.png
269 | top_mosaic_09cm_area27_5.png
270 | top_mosaic_09cm_area27_50.png
271 | top_mosaic_09cm_area27_51.png
272 | top_mosaic_09cm_area27_52.png
273 | top_mosaic_09cm_area27_53.png
274 | top_mosaic_09cm_area27_54.png
275 | top_mosaic_09cm_area27_55.png
276 | top_mosaic_09cm_area27_56.png
277 | top_mosaic_09cm_area27_57.png
278 | top_mosaic_09cm_area27_58.png
279 | top_mosaic_09cm_area27_59.png
280 | top_mosaic_09cm_area27_6.png
281 | top_mosaic_09cm_area27_60.png
282 | top_mosaic_09cm_area27_61.png
283 | top_mosaic_09cm_area27_62.png
284 | top_mosaic_09cm_area27_63.png
285 | top_mosaic_09cm_area27_64.png
286 | top_mosaic_09cm_area27_65.png
287 | top_mosaic_09cm_area27_66.png
288 | top_mosaic_09cm_area27_7.png
289 | top_mosaic_09cm_area27_8.png
290 | top_mosaic_09cm_area27_9.png
291 | top_mosaic_09cm_area2_1.png
292 | top_mosaic_09cm_area2_10.png
293 | top_mosaic_09cm_area2_11.png
294 | top_mosaic_09cm_area2_12.png
295 | top_mosaic_09cm_area2_13.png
296 | top_mosaic_09cm_area2_14.png
297 | top_mosaic_09cm_area2_15.png
298 | top_mosaic_09cm_area2_16.png
299 | top_mosaic_09cm_area2_17.png
300 | top_mosaic_09cm_area2_18.png
301 | top_mosaic_09cm_area2_19.png
302 | top_mosaic_09cm_area2_2.png
303 | top_mosaic_09cm_area2_20.png
304 | top_mosaic_09cm_area2_21.png
305 | top_mosaic_09cm_area2_22.png
306 | top_mosaic_09cm_area2_23.png
307 | top_mosaic_09cm_area2_24.png
308 | top_mosaic_09cm_area2_25.png
309 | top_mosaic_09cm_area2_26.png
310 | top_mosaic_09cm_area2_27.png
311 | top_mosaic_09cm_area2_28.png
312 | top_mosaic_09cm_area2_29.png
313 | top_mosaic_09cm_area2_3.png
314 | top_mosaic_09cm_area2_30.png
315 | top_mosaic_09cm_area2_31.png
316 | top_mosaic_09cm_area2_32.png
317 | top_mosaic_09cm_area2_33.png
318 | top_mosaic_09cm_area2_34.png
319 | top_mosaic_09cm_area2_35.png
320 | top_mosaic_09cm_area2_36.png
321 | top_mosaic_09cm_area2_37.png
322 | top_mosaic_09cm_area2_38.png
323 | top_mosaic_09cm_area2_39.png
324 | top_mosaic_09cm_area2_4.png
325 | top_mosaic_09cm_area2_40.png
326 | top_mosaic_09cm_area2_41.png
327 | top_mosaic_09cm_area2_42.png
328 | top_mosaic_09cm_area2_43.png
329 | top_mosaic_09cm_area2_44.png
330 | top_mosaic_09cm_area2_45.png
331 | top_mosaic_09cm_area2_46.png
332 | top_mosaic_09cm_area2_47.png
333 | top_mosaic_09cm_area2_48.png
334 | top_mosaic_09cm_area2_49.png
335 | top_mosaic_09cm_area2_5.png
336 | top_mosaic_09cm_area2_50.png
337 | top_mosaic_09cm_area2_51.png
338 | top_mosaic_09cm_area2_52.png
339 | top_mosaic_09cm_area2_53.png
340 | top_mosaic_09cm_area2_54.png
341 | top_mosaic_09cm_area2_55.png
342 | top_mosaic_09cm_area2_56.png
343 | top_mosaic_09cm_area2_57.png
344 | top_mosaic_09cm_area2_58.png
345 | top_mosaic_09cm_area2_59.png
346 | top_mosaic_09cm_area2_6.png
347 | top_mosaic_09cm_area2_60.png
348 | top_mosaic_09cm_area2_61.png
349 | top_mosaic_09cm_area2_62.png
350 | top_mosaic_09cm_area2_63.png
351 | top_mosaic_09cm_area2_64.png
352 | top_mosaic_09cm_area2_65.png
353 | top_mosaic_09cm_area2_66.png
354 | top_mosaic_09cm_area2_67.png
355 | top_mosaic_09cm_area2_68.png
356 | top_mosaic_09cm_area2_69.png
357 | top_mosaic_09cm_area2_7.png
358 | top_mosaic_09cm_area2_70.png
359 | top_mosaic_09cm_area2_71.png
360 | top_mosaic_09cm_area2_72.png
361 | top_mosaic_09cm_area2_8.png
362 | top_mosaic_09cm_area2_9.png
363 | top_mosaic_09cm_area5_1.png
364 | top_mosaic_09cm_area5_10.png
365 | top_mosaic_09cm_area5_11.png
366 | top_mosaic_09cm_area5_12.png
367 | top_mosaic_09cm_area5_13.png
368 | top_mosaic_09cm_area5_14.png
369 | top_mosaic_09cm_area5_15.png
370 | top_mosaic_09cm_area5_16.png
371 | top_mosaic_09cm_area5_17.png
372 | top_mosaic_09cm_area5_18.png
373 | top_mosaic_09cm_area5_19.png
374 | top_mosaic_09cm_area5_2.png
375 | top_mosaic_09cm_area5_20.png
376 | top_mosaic_09cm_area5_21.png
377 | top_mosaic_09cm_area5_22.png
378 | top_mosaic_09cm_area5_23.png
379 | top_mosaic_09cm_area5_24.png
380 | top_mosaic_09cm_area5_25.png
381 | top_mosaic_09cm_area5_26.png
382 | top_mosaic_09cm_area5_27.png
383 | top_mosaic_09cm_area5_28.png
384 | top_mosaic_09cm_area5_29.png
385 | top_mosaic_09cm_area5_3.png
386 | top_mosaic_09cm_area5_30.png
387 | top_mosaic_09cm_area5_31.png
388 | top_mosaic_09cm_area5_32.png
389 | top_mosaic_09cm_area5_33.png
390 | top_mosaic_09cm_area5_34.png
391 | top_mosaic_09cm_area5_35.png
392 | top_mosaic_09cm_area5_36.png
393 | top_mosaic_09cm_area5_37.png
394 | top_mosaic_09cm_area5_38.png
395 | top_mosaic_09cm_area5_39.png
396 | top_mosaic_09cm_area5_4.png
397 | top_mosaic_09cm_area5_40.png
398 | top_mosaic_09cm_area5_41.png
399 | top_mosaic_09cm_area5_42.png
400 | top_mosaic_09cm_area5_43.png
401 | top_mosaic_09cm_area5_44.png
402 | top_mosaic_09cm_area5_45.png
403 | top_mosaic_09cm_area5_46.png
404 | top_mosaic_09cm_area5_47.png
405 | top_mosaic_09cm_area5_48.png
406 | top_mosaic_09cm_area5_5.png
407 | top_mosaic_09cm_area5_6.png
408 | top_mosaic_09cm_area5_7.png
409 | top_mosaic_09cm_area5_8.png
410 | top_mosaic_09cm_area5_9.png
411 | top_mosaic_09cm_area7_1.png
412 | top_mosaic_09cm_area7_10.png
413 | top_mosaic_09cm_area7_11.png
414 | top_mosaic_09cm_area7_12.png
415 | top_mosaic_09cm_area7_13.png
416 | top_mosaic_09cm_area7_14.png
417 | top_mosaic_09cm_area7_15.png
418 | top_mosaic_09cm_area7_16.png
419 | top_mosaic_09cm_area7_17.png
420 | top_mosaic_09cm_area7_18.png
421 | top_mosaic_09cm_area7_19.png
422 | top_mosaic_09cm_area7_2.png
423 | top_mosaic_09cm_area7_20.png
424 | top_mosaic_09cm_area7_21.png
425 | top_mosaic_09cm_area7_22.png
426 | top_mosaic_09cm_area7_23.png
427 | top_mosaic_09cm_area7_24.png
428 | top_mosaic_09cm_area7_25.png
429 | top_mosaic_09cm_area7_26.png
430 | top_mosaic_09cm_area7_27.png
431 | top_mosaic_09cm_area7_28.png
432 | top_mosaic_09cm_area7_29.png
433 | top_mosaic_09cm_area7_3.png
434 | top_mosaic_09cm_area7_30.png
435 | top_mosaic_09cm_area7_31.png
436 | top_mosaic_09cm_area7_32.png
437 | top_mosaic_09cm_area7_33.png
438 | top_mosaic_09cm_area7_34.png
439 | top_mosaic_09cm_area7_35.png
440 | top_mosaic_09cm_area7_36.png
441 | top_mosaic_09cm_area7_37.png
442 | top_mosaic_09cm_area7_38.png
443 | top_mosaic_09cm_area7_39.png
444 | top_mosaic_09cm_area7_4.png
445 | top_mosaic_09cm_area7_40.png
446 | top_mosaic_09cm_area7_41.png
447 | top_mosaic_09cm_area7_42.png
448 | top_mosaic_09cm_area7_43.png
449 | top_mosaic_09cm_area7_44.png
450 | top_mosaic_09cm_area7_45.png
451 | top_mosaic_09cm_area7_46.png
452 | top_mosaic_09cm_area7_47.png
453 | top_mosaic_09cm_area7_48.png
454 | top_mosaic_09cm_area7_5.png
455 | top_mosaic_09cm_area7_6.png
456 | top_mosaic_09cm_area7_7.png
457 | top_mosaic_09cm_area7_8.png
458 | top_mosaic_09cm_area7_9.png
459 | top_mosaic_09cm_area8_1.png
460 | top_mosaic_09cm_area8_10.png
461 | top_mosaic_09cm_area8_11.png
462 | top_mosaic_09cm_area8_12.png
463 | top_mosaic_09cm_area8_13.png
464 | top_mosaic_09cm_area8_14.png
465 | top_mosaic_09cm_area8_15.png
466 | top_mosaic_09cm_area8_16.png
467 | top_mosaic_09cm_area8_17.png
468 | top_mosaic_09cm_area8_18.png
469 | top_mosaic_09cm_area8_19.png
470 | top_mosaic_09cm_area8_2.png
471 | top_mosaic_09cm_area8_20.png
472 | top_mosaic_09cm_area8_21.png
473 | top_mosaic_09cm_area8_22.png
474 | top_mosaic_09cm_area8_23.png
475 | top_mosaic_09cm_area8_24.png
476 | top_mosaic_09cm_area8_25.png
477 | top_mosaic_09cm_area8_26.png
478 | top_mosaic_09cm_area8_27.png
479 | top_mosaic_09cm_area8_28.png
480 | top_mosaic_09cm_area8_29.png
481 | top_mosaic_09cm_area8_3.png
482 | top_mosaic_09cm_area8_30.png
483 | top_mosaic_09cm_area8_31.png
484 | top_mosaic_09cm_area8_32.png
485 | top_mosaic_09cm_area8_33.png
486 | top_mosaic_09cm_area8_34.png
487 | top_mosaic_09cm_area8_35.png
488 | top_mosaic_09cm_area8_36.png
489 | top_mosaic_09cm_area8_37.png
490 | top_mosaic_09cm_area8_38.png
491 | top_mosaic_09cm_area8_39.png
492 | top_mosaic_09cm_area8_4.png
493 | top_mosaic_09cm_area8_40.png
494 | top_mosaic_09cm_area8_41.png
495 | top_mosaic_09cm_area8_42.png
496 | top_mosaic_09cm_area8_43.png
497 | top_mosaic_09cm_area8_44.png
498 | top_mosaic_09cm_area8_45.png
499 | top_mosaic_09cm_area8_46.png
500 | top_mosaic_09cm_area8_47.png
501 | top_mosaic_09cm_area8_48.png
502 | top_mosaic_09cm_area8_5.png
503 | top_mosaic_09cm_area8_6.png
504 | top_mosaic_09cm_area8_7.png
505 | top_mosaic_09cm_area8_8.png
506 | top_mosaic_09cm_area8_9.png
507 | 


--------------------------------------------------------------------------------
/ADVENT/advent/dataset/Vaihingen/info.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "classes":6,
 3 |   "label2train":[
 4 |     [0, 255],
 5 |     [1, 255],
 6 |     [2, 255],
 7 |     [3, 255],
 8 |     [4, 255],
 9 |     [5, 255],
10 |     [6, 255],
11 |     [7, 0],
12 |     [8, 1],
13 |     [9, 255],
14 |     [10, 255],
15 |     [11, 2],
16 |     [12, 3],
17 |     [13, 4],
18 |     [14, 255],
19 |     [15, 255],
20 |     [16, 255],
21 |     [17, 5],
22 |     [18, 255],
23 |     [19, 6],
24 |     [20, 7],
25 |     [21, 8],
26 |     [22, 9],
27 |     [23, 10],
28 |     [24, 11],
29 |     [25, 12],
30 |     [26, 13],
31 |     [27, 14],
32 |     [28, 15],
33 |     [29, 255],
34 |     [30, 255],
35 |     [31, 16],
36 |     [32, 17],
37 |     [33, 18],
38 |     [-1, 255]],
39 |   "label":[
40 |     "Clutter background",
41 |     "Impervious surfaces",
42 |     "Car",
43 |     "Tree",
44 |     "Low vegetation",
45 |     "Building"],
46 |   "palette":[
47 |     [128,64,128],
48 |     [244,35,232],
49 |     [70,70,70],
50 |     [102,102,156],
51 |     [190,153,153],
52 |     [153,153,153],
53 |     [250,170,30],
54 |     [220,220,0],
55 |     [107,142,35],
56 |     [152,251,152],
57 |     [70,130,180],
58 |     [220,20,60],
59 |     [255,0,0],
60 |     [0,0,142],
61 |     [0,0,70],
62 |     [0,60,100],
63 |     [0,80,100],
64 |     [0,0,230],
65 |     [119,11,32],
66 |     [0,0,0]],
67 |   "mean":[
68 |     73.158359210711552,
69 |     82.908917542625858,
70 |     72.392398761941593],
71 |   "std":[
72 |     47.675755341814678,
73 |     48.494214368814916,
74 |     47.736546325441594]
75 | }
76 | 


--------------------------------------------------------------------------------
/ADVENT/advent/dataset/Vaihingen/val.txt:
--------------------------------------------------------------------------------
  1 | top_mosaic_09cm_area13_1.png
  2 | top_mosaic_09cm_area13_10.png
  3 | top_mosaic_09cm_area13_11.png
  4 | top_mosaic_09cm_area13_12.png
  5 | top_mosaic_09cm_area13_13.png
  6 | top_mosaic_09cm_area13_14.png
  7 | top_mosaic_09cm_area13_15.png
  8 | top_mosaic_09cm_area13_16.png
  9 | top_mosaic_09cm_area13_17.png
 10 | top_mosaic_09cm_area13_18.png
 11 | top_mosaic_09cm_area13_19.png
 12 | top_mosaic_09cm_area13_2.png
 13 | top_mosaic_09cm_area13_20.png
 14 | top_mosaic_09cm_area13_21.png
 15 | top_mosaic_09cm_area13_22.png
 16 | top_mosaic_09cm_area13_23.png
 17 | top_mosaic_09cm_area13_24.png
 18 | top_mosaic_09cm_area13_25.png
 19 | top_mosaic_09cm_area13_26.png
 20 | top_mosaic_09cm_area13_27.png
 21 | top_mosaic_09cm_area13_28.png
 22 | top_mosaic_09cm_area13_29.png
 23 | top_mosaic_09cm_area13_3.png
 24 | top_mosaic_09cm_area13_30.png
 25 | top_mosaic_09cm_area13_31.png
 26 | top_mosaic_09cm_area13_32.png
 27 | top_mosaic_09cm_area13_33.png
 28 | top_mosaic_09cm_area13_34.png
 29 | top_mosaic_09cm_area13_35.png
 30 | top_mosaic_09cm_area13_36.png
 31 | top_mosaic_09cm_area13_37.png
 32 | top_mosaic_09cm_area13_38.png
 33 | top_mosaic_09cm_area13_39.png
 34 | top_mosaic_09cm_area13_4.png
 35 | top_mosaic_09cm_area13_40.png
 36 | top_mosaic_09cm_area13_41.png
 37 | top_mosaic_09cm_area13_42.png
 38 | top_mosaic_09cm_area13_43.png
 39 | top_mosaic_09cm_area13_44.png
 40 | top_mosaic_09cm_area13_45.png
 41 | top_mosaic_09cm_area13_46.png
 42 | top_mosaic_09cm_area13_47.png
 43 | top_mosaic_09cm_area13_48.png
 44 | top_mosaic_09cm_area13_49.png
 45 | top_mosaic_09cm_area13_5.png
 46 | top_mosaic_09cm_area13_50.png
 47 | top_mosaic_09cm_area13_51.png
 48 | top_mosaic_09cm_area13_52.png
 49 | top_mosaic_09cm_area13_53.png
 50 | top_mosaic_09cm_area13_54.png
 51 | top_mosaic_09cm_area13_55.png
 52 | top_mosaic_09cm_area13_56.png
 53 | top_mosaic_09cm_area13_57.png
 54 | top_mosaic_09cm_area13_58.png
 55 | top_mosaic_09cm_area13_59.png
 56 | top_mosaic_09cm_area13_6.png
 57 | top_mosaic_09cm_area13_60.png
 58 | top_mosaic_09cm_area13_61.png
 59 | top_mosaic_09cm_area13_62.png
 60 | top_mosaic_09cm_area13_63.png
 61 | top_mosaic_09cm_area13_64.png
 62 | top_mosaic_09cm_area13_65.png
 63 | top_mosaic_09cm_area13_66.png
 64 | top_mosaic_09cm_area13_67.png
 65 | top_mosaic_09cm_area13_68.png
 66 | top_mosaic_09cm_area13_69.png
 67 | top_mosaic_09cm_area13_7.png
 68 | top_mosaic_09cm_area13_70.png
 69 | top_mosaic_09cm_area13_71.png
 70 | top_mosaic_09cm_area13_72.png
 71 | top_mosaic_09cm_area13_73.png
 72 | top_mosaic_09cm_area13_74.png
 73 | top_mosaic_09cm_area13_75.png
 74 | top_mosaic_09cm_area13_76.png
 75 | top_mosaic_09cm_area13_77.png
 76 | top_mosaic_09cm_area13_78.png
 77 | top_mosaic_09cm_area13_79.png
 78 | top_mosaic_09cm_area13_8.png
 79 | top_mosaic_09cm_area13_80.png
 80 | top_mosaic_09cm_area13_9.png
 81 | top_mosaic_09cm_area20_1.png
 82 | top_mosaic_09cm_area20_10.png
 83 | top_mosaic_09cm_area20_11.png
 84 | top_mosaic_09cm_area20_12.png
 85 | top_mosaic_09cm_area20_13.png
 86 | top_mosaic_09cm_area20_14.png
 87 | top_mosaic_09cm_area20_15.png
 88 | top_mosaic_09cm_area20_16.png
 89 | top_mosaic_09cm_area20_17.png
 90 | top_mosaic_09cm_area20_18.png
 91 | top_mosaic_09cm_area20_19.png
 92 | top_mosaic_09cm_area20_2.png
 93 | top_mosaic_09cm_area20_20.png
 94 | top_mosaic_09cm_area20_21.png
 95 | top_mosaic_09cm_area20_22.png
 96 | top_mosaic_09cm_area20_23.png
 97 | top_mosaic_09cm_area20_24.png
 98 | top_mosaic_09cm_area20_25.png
 99 | top_mosaic_09cm_area20_26.png
100 | top_mosaic_09cm_area20_27.png
101 | top_mosaic_09cm_area20_28.png
102 | top_mosaic_09cm_area20_29.png
103 | top_mosaic_09cm_area20_3.png
104 | top_mosaic_09cm_area20_30.png
105 | top_mosaic_09cm_area20_31.png
106 | top_mosaic_09cm_area20_32.png
107 | top_mosaic_09cm_area20_33.png
108 | top_mosaic_09cm_area20_34.png
109 | top_mosaic_09cm_area20_35.png
110 | top_mosaic_09cm_area20_36.png
111 | top_mosaic_09cm_area20_37.png
112 | top_mosaic_09cm_area20_38.png
113 | top_mosaic_09cm_area20_39.png
114 | top_mosaic_09cm_area20_4.png
115 | top_mosaic_09cm_area20_40.png
116 | top_mosaic_09cm_area20_41.png
117 | top_mosaic_09cm_area20_42.png
118 | top_mosaic_09cm_area20_43.png
119 | top_mosaic_09cm_area20_44.png
120 | top_mosaic_09cm_area20_45.png
121 | top_mosaic_09cm_area20_46.png
122 | top_mosaic_09cm_area20_47.png
123 | top_mosaic_09cm_area20_48.png
124 | top_mosaic_09cm_area20_5.png
125 | top_mosaic_09cm_area20_6.png
126 | top_mosaic_09cm_area20_7.png
127 | top_mosaic_09cm_area20_8.png
128 | top_mosaic_09cm_area20_9.png
129 | top_mosaic_09cm_area22_1.png
130 | top_mosaic_09cm_area22_10.png
131 | top_mosaic_09cm_area22_11.png
132 | top_mosaic_09cm_area22_12.png
133 | top_mosaic_09cm_area22_13.png
134 | top_mosaic_09cm_area22_14.png
135 | top_mosaic_09cm_area22_15.png
136 | top_mosaic_09cm_area22_16.png
137 | top_mosaic_09cm_area22_17.png
138 | top_mosaic_09cm_area22_18.png
139 | top_mosaic_09cm_area22_19.png
140 | top_mosaic_09cm_area22_2.png
141 | top_mosaic_09cm_area22_20.png
142 | top_mosaic_09cm_area22_21.png
143 | top_mosaic_09cm_area22_22.png
144 | top_mosaic_09cm_area22_23.png
145 | top_mosaic_09cm_area22_24.png
146 | top_mosaic_09cm_area22_25.png
147 | top_mosaic_09cm_area22_26.png
148 | top_mosaic_09cm_area22_27.png
149 | top_mosaic_09cm_area22_28.png
150 | top_mosaic_09cm_area22_29.png
151 | top_mosaic_09cm_area22_3.png
152 | top_mosaic_09cm_area22_30.png
153 | top_mosaic_09cm_area22_31.png
154 | top_mosaic_09cm_area22_32.png
155 | top_mosaic_09cm_area22_33.png
156 | top_mosaic_09cm_area22_34.png
157 | top_mosaic_09cm_area22_35.png
158 | top_mosaic_09cm_area22_36.png
159 | top_mosaic_09cm_area22_37.png
160 | top_mosaic_09cm_area22_38.png
161 | top_mosaic_09cm_area22_39.png
162 | top_mosaic_09cm_area22_4.png
163 | top_mosaic_09cm_area22_40.png
164 | top_mosaic_09cm_area22_41.png
165 | top_mosaic_09cm_area22_42.png
166 | top_mosaic_09cm_area22_43.png
167 | top_mosaic_09cm_area22_44.png
168 | top_mosaic_09cm_area22_45.png
169 | top_mosaic_09cm_area22_46.png
170 | top_mosaic_09cm_area22_47.png
171 | top_mosaic_09cm_area22_48.png
172 | top_mosaic_09cm_area22_5.png
173 | top_mosaic_09cm_area22_6.png
174 | top_mosaic_09cm_area22_7.png
175 | top_mosaic_09cm_area22_8.png
176 | top_mosaic_09cm_area22_9.png
177 | top_mosaic_09cm_area24_1.png
178 | top_mosaic_09cm_area24_10.png
179 | top_mosaic_09cm_area24_11.png
180 | top_mosaic_09cm_area24_12.png
181 | top_mosaic_09cm_area24_13.png
182 | top_mosaic_09cm_area24_14.png
183 | top_mosaic_09cm_area24_15.png
184 | top_mosaic_09cm_area24_16.png
185 | top_mosaic_09cm_area24_17.png
186 | top_mosaic_09cm_area24_18.png
187 | top_mosaic_09cm_area24_19.png
188 | top_mosaic_09cm_area24_2.png
189 | top_mosaic_09cm_area24_20.png
190 | top_mosaic_09cm_area24_21.png
191 | top_mosaic_09cm_area24_22.png
192 | top_mosaic_09cm_area24_23.png
193 | top_mosaic_09cm_area24_24.png
194 | top_mosaic_09cm_area24_25.png
195 | top_mosaic_09cm_area24_26.png
196 | top_mosaic_09cm_area24_27.png
197 | top_mosaic_09cm_area24_28.png
198 | top_mosaic_09cm_area24_29.png
199 | top_mosaic_09cm_area24_3.png
200 | top_mosaic_09cm_area24_30.png
201 | top_mosaic_09cm_area24_31.png
202 | top_mosaic_09cm_area24_32.png
203 | top_mosaic_09cm_area24_33.png
204 | top_mosaic_09cm_area24_34.png
205 | top_mosaic_09cm_area24_35.png
206 | top_mosaic_09cm_area24_36.png
207 | top_mosaic_09cm_area24_37.png
208 | top_mosaic_09cm_area24_38.png
209 | top_mosaic_09cm_area24_39.png
210 | top_mosaic_09cm_area24_4.png
211 | top_mosaic_09cm_area24_40.png
212 | top_mosaic_09cm_area24_41.png
213 | top_mosaic_09cm_area24_42.png
214 | top_mosaic_09cm_area24_43.png
215 | top_mosaic_09cm_area24_44.png
216 | top_mosaic_09cm_area24_45.png
217 | top_mosaic_09cm_area24_46.png
218 | top_mosaic_09cm_area24_47.png
219 | top_mosaic_09cm_area24_48.png
220 | top_mosaic_09cm_area24_5.png
221 | top_mosaic_09cm_area24_6.png
222 | top_mosaic_09cm_area24_7.png
223 | top_mosaic_09cm_area24_8.png
224 | top_mosaic_09cm_area24_9.png
225 | top_mosaic_09cm_area27_1.png
226 | top_mosaic_09cm_area27_10.png
227 | top_mosaic_09cm_area27_11.png
228 | top_mosaic_09cm_area27_12.png
229 | top_mosaic_09cm_area27_13.png
230 | top_mosaic_09cm_area27_14.png
231 | top_mosaic_09cm_area27_15.png
232 | top_mosaic_09cm_area27_16.png
233 | top_mosaic_09cm_area27_17.png
234 | top_mosaic_09cm_area27_18.png
235 | top_mosaic_09cm_area27_19.png
236 | top_mosaic_09cm_area27_2.png
237 | top_mosaic_09cm_area27_20.png
238 | top_mosaic_09cm_area27_21.png
239 | top_mosaic_09cm_area27_22.png
240 | top_mosaic_09cm_area27_23.png
241 | top_mosaic_09cm_area27_24.png
242 | top_mosaic_09cm_area27_25.png
243 | top_mosaic_09cm_area27_26.png
244 | top_mosaic_09cm_area27_27.png
245 | top_mosaic_09cm_area27_28.png
246 | top_mosaic_09cm_area27_29.png
247 | top_mosaic_09cm_area27_3.png
248 | top_mosaic_09cm_area27_30.png
249 | top_mosaic_09cm_area27_31.png
250 | top_mosaic_09cm_area27_32.png
251 | top_mosaic_09cm_area27_33.png
252 | top_mosaic_09cm_area27_34.png
253 | top_mosaic_09cm_area27_35.png
254 | top_mosaic_09cm_area27_36.png
255 | top_mosaic_09cm_area27_37.png
256 | top_mosaic_09cm_area27_38.png
257 | top_mosaic_09cm_area27_39.png
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455 | top_mosaic_09cm_area7_6.png
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458 | top_mosaic_09cm_area7_9.png
459 | top_mosaic_09cm_area8_1.png
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506 | top_mosaic_09cm_area8_9.png


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/ADVENT/advent/dataset/__init__.py:
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https://raw.githubusercontent.com/BOBrown/SegNet_Source/4dc9c93c77a64b0338038cc9e192006b11a5efe2/ADVENT/advent/dataset/__init__.py


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/ADVENT/advent/dataset/base_dataset.py:
--------------------------------------------------------------------------------
 1 | from pathlib import Path
 2 | 
 3 | import numpy as np
 4 | from PIL import Image
 5 | from torch.utils import data
 6 | 
 7 | 
 8 | class BaseDataset(data.Dataset):
 9 |     def __init__(self, root, list_path, set_,
10 |                  max_iters, image_size, labels_size, mean):
11 |         self.root = Path(root)
12 |         self.set = set_
13 |         self.list_path = list_path.format(self.set)
14 |         self.image_size = image_size
15 |         if labels_size is None:
16 |             self.labels_size = self.image_size
17 |         else:
18 |             self.labels_size = labels_size
19 |         self.mean = mean
20 |         with open(self.list_path) as f:
21 |             self.img_ids = [i_id.strip() for i_id in f]
22 |         if max_iters is not None:
23 |             self.img_ids = self.img_ids * int(np.ceil(float(max_iters) / len(self.img_ids)))
24 |         self.files = []
25 |         for name in self.img_ids:
26 |             img_file, label_file = self.get_metadata(name)
27 |             self.files.append((img_file, label_file, name))
28 | 
29 |     def get_metadata(self, name):
30 |         raise NotImplementedError
31 | 
32 |     def __len__(self):
33 |         return len(self.files)
34 | 
35 |     def preprocess(self, image):
36 |         image = image[:, :, ::-1]  # change to BGR
37 |         image -= self.mean
38 |         return image.transpose((2, 0, 1))
39 | 
40 |     def get_image(self, file):
41 |         return _load_img(file, self.image_size, Image.BICUBIC, rgb=True)
42 | 
43 |     def get_labels(self, file):
44 |         return _load_img(file, self.labels_size, Image.NEAREST, rgb=False)
45 | 
46 | 
47 | def _load_img(file, size, interpolation, rgb):
48 |     img = Image.open(file)
49 |     if rgb:
50 |         img = img.convert('RGB')
51 |     img = img.resize(size, interpolation)
52 |     return np.asarray(img, np.float32)
53 | 


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/ADVENT/advent/dataset/cityscapes.py:
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 1 | import numpy as np
 2 | 
 3 | from advent.utils import project_root
 4 | from advent.utils.serialization import json_load
 5 | from advent.dataset.base_dataset import BaseDataset
 6 | 
 7 | DEFAULT_INFO_PATH = project_root / 'advent/dataset/cityscapes_list/info.json'
 8 | 
 9 | 
10 | class CityscapesDataSet(BaseDataset):
11 |     def __init__(self, root, list_path, set='val',
12 |                  max_iters=None,
13 |                  crop_size=(321, 321), mean=(128, 128, 128),
14 |                  load_labels=True,
15 |                  info_path=DEFAULT_INFO_PATH, labels_size=None):
16 |         super().__init__(root, list_path, set, max_iters, crop_size, labels_size, mean)
17 | 
18 |         self.load_labels = load_labels
19 |         self.info = json_load(info_path)
20 |         self.class_names = np.array(self.info['label'], dtype=np.str)
21 |         self.mapping = np.array(self.info['label2train'], dtype=np.int)
22 |         self.map_vector = np.zeros((self.mapping.shape[0],), dtype=np.int64)
23 |         for source_label, target_label in self.mapping:
24 |             self.map_vector[source_label] = target_label
25 | 
26 |     def get_metadata(self, name):
27 |         img_file = self.root / 'leftImg8bit' / self.set / name
28 |         label_name = name.replace("leftImg8bit", "gtFine_labelIds")
29 |         label_file = self.root / 'gtFine' / self.set / label_name
30 |         return img_file, label_file
31 | 
32 |     def map_labels(self, input_):
33 |         return self.map_vector[input_.astype(np.int64, copy=False)]
34 | 
35 |     def __getitem__(self, index):
36 |         img_file, label_file, name = self.files[index]
37 |         label = self.get_labels(label_file)
38 |         label = self.map_labels(label).copy()
39 |         image = self.get_image(img_file)
40 |         image = self.preprocess(image)
41 |         return image.copy(), label, np.array(image.shape), name
42 | 


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/ADVENT/advent/dataset/gta5.py:
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 1 | import numpy as np
 2 | 
 3 | from advent.dataset.base_dataset import BaseDataset
 4 | 
 5 | 
 6 | class GTA5DataSet(BaseDataset):
 7 |     def __init__(self, root, list_path, set='all',
 8 |                  max_iters=None, crop_size=(321, 321), mean=(128, 128, 128)):
 9 |         super().__init__(root, list_path, set, max_iters, crop_size, None, mean)
10 | 
11 |         # map to cityscape's ids
12 |         self.id_to_trainid = {7: 0, 8: 1, 11: 2, 12: 3, 13: 4, 17: 5,
13 |                               19: 6, 20: 7, 21: 8, 22: 9, 23: 10, 24: 11, 25: 12,
14 |                               26: 13, 27: 14, 28: 15, 31: 16, 32: 17, 33: 18}
15 | 
16 |     def get_metadata(self, name):
17 |         img_file = self.root / 'images' / name
18 |         label_file = self.root / 'labels' / name
19 |         return img_file, label_file
20 | 
21 |     def __getitem__(self, index):
22 |         img_file, label_file, name = self.files[index]
23 |         image = self.get_image(img_file)
24 |         label = self.get_labels(label_file)
25 |         # re-assign labels to match the format of Cityscapes
26 |         label_copy = 255 * np.ones(label.shape, dtype=np.float32)
27 |         for k, v in self.id_to_trainid.items():
28 |             label_copy[label == k] = v
29 |         image = self.preprocess(image)
30 |         return image.copy(), label_copy.copy(), np.array(image.shape), name
31 | 


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/ADVENT/advent/model/__init__.py:
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https://raw.githubusercontent.com/BOBrown/SegNet_Source/4dc9c93c77a64b0338038cc9e192006b11a5efe2/ADVENT/advent/model/__init__.py


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/ADVENT/advent/model/deeplabv3.py:
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  1 | import torch.nn as nn
  2 | import torch.nn.functional as F
  3 | 
  4 | affine_par = True
  5 | 
  6 | 
  7 | class Bottleneck(nn.Module):
  8 |     expansion = 4
  9 | 
 10 |     def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
 11 |         super(Bottleneck, self).__init__()
 12 |         # change
 13 |         self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)
 14 |         self.bn1 = nn.BatchNorm2d(planes, affine=affine_par)
 15 |         for i in self.bn1.parameters():
 16 |             i.requires_grad = False
 17 |         padding = dilation
 18 |         # change
 19 |         self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
 20 |                                padding=padding, bias=False, dilation=dilation)
 21 |         self.bn2 = nn.BatchNorm2d(planes, affine=affine_par)
 22 |         for i in self.bn2.parameters():
 23 |             i.requires_grad = False
 24 |         self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
 25 |         self.bn3 = nn.BatchNorm2d(planes * 4, affine=affine_par)
 26 |         for i in self.bn3.parameters():
 27 |             i.requires_grad = False
 28 |         self.relu = nn.ReLU(inplace=True)
 29 |         self.downsample = downsample
 30 |         self.stride = stride
 31 | 
 32 |     def forward(self, x):
 33 |         residual = x
 34 |         out = self.conv1(x)
 35 |         out = self.bn1(out)
 36 |         out = self.relu(out)
 37 |         out = self.conv2(out)
 38 |         out = self.bn2(out)
 39 |         out = self.relu(out)
 40 |         out = self.conv3(out)
 41 |         out = self.bn3(out)
 42 |         if self.downsample is not None:
 43 |             residual = self.downsample(x)
 44 |         out += residual
 45 |         out = self.relu(out)
 46 | 
 47 |         return out
 48 | 
 49 | 
 50 | class ClassifierModule(nn.Module):
 51 |     def __init__(self, inplanes, dilation_series, padding_series, num_classes):
 52 |         super(ClassifierModule, self).__init__()
 53 |         self.conv2d_list = nn.ModuleList()
 54 |         for dilation, padding in zip(dilation_series, padding_series):
 55 |             self.conv2d_list.append(
 56 |                 nn.Conv2d(inplanes, num_classes, kernel_size=3, stride=1, padding=padding,
 57 |                           dilation=dilation, bias=True))
 58 | 
 59 |         for m in self.conv2d_list:
 60 |             m.weight.data.normal_(0, 0.01)
 61 | 
 62 |     def forward(self, x):
 63 |         out = self.conv2d_list[0](x)
 64 |         for i in range(len(self.conv2d_list) - 1):
 65 |             out += self.conv2d_list[i + 1](x)
 66 |         return out
 67 | 
 68 | class ClassifierModule_GAP(nn.Module):
 69 |     def __init__(self, inplanes, dilation_series, padding_series, num_classes):
 70 |         super(ClassifierModule_GAP, self).__init__()
 71 |         self.conv2d_list = nn.ModuleList()
 72 |         for dilation, padding in zip(dilation_series, padding_series):
 73 |             self.conv2d_list.append(
 74 |                 nn.Conv2d(inplanes, num_classes, kernel_size=3, stride=1, padding=padding,
 75 |                           dilation=dilation, bias=True))
 76 | 
 77 |         for m in self.conv2d_list:
 78 |             m.weight.data.normal_(0, 0.01)
 79 | 
 80 |         self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
 81 |                                              nn.Conv2d(inplanes, num_classes, 1, stride=1, bias=False),
 82 |                                              nn.BatchNorm2d(num_classes),
 83 |                                              nn.ReLU())
 84 | 
 85 |     def forward(self, x):
 86 |         out = self.conv2d_list[0](x)
 87 |         out_gap = self.global_avg_pool(x)
 88 |         out_gap = F.upsample(out_gap, size=out.size()[2:], mode='bilinear', align_corners=True)
 89 |         for i in range(len(self.conv2d_list) - 1):
 90 |             out += self.conv2d_list[i + 1](x)
 91 |         out = out + out_gap
 92 |         return out
 93 | 
 94 | 
 95 | class ResNetMulti(nn.Module):
 96 |     def __init__(self, block, layers, num_classes, multi_level):
 97 |         self.multi_level = multi_level
 98 |         self.inplanes = 64
 99 |         super(ResNetMulti, self).__init__()
100 |         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
101 |                                bias=False)
102 |         self.bn1 = nn.BatchNorm2d(64, affine=affine_par)
103 |         for i in self.bn1.parameters():
104 |             i.requires_grad = False
105 |         self.relu = nn.ReLU(inplace=True)
106 |         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True)  # change
107 |         self.layer1 = self._make_layer(block, 64, layers[0])
108 |         self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
109 |         if self.multi_level:
110 |             self.low_layers = ClassifierModule(512, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)
111 |         self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
112 |         self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4)
113 |         if self.multi_level:
114 |             self.layer5 = ClassifierModule(1024, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)
115 |         #self.layer6 = ClassifierModule(2048, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)
116 |         self.layer6 = ClassifierModule_GAP(2048, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)
117 | 
118 |         for m in self.modules():
119 |             if isinstance(m, nn.Conv2d):
120 |                 m.weight.data.normal_(0, 0.01)
121 |             elif isinstance(m, nn.BatchNorm2d):
122 |                 m.weight.data.fill_(1)
123 |                 m.bias.data.zero_()
124 | 
125 |     def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
126 |         downsample = None
127 |         if (stride != 1
128 |                 or self.inplanes != planes * block.expansion
129 |                 or dilation == 2
130 |                 or dilation == 4):
131 |             downsample = nn.Sequential(
132 |                 nn.Conv2d(self.inplanes, planes * block.expansion,
133 |                           kernel_size=1, stride=stride, bias=False),
134 |                 nn.BatchNorm2d(planes * block.expansion, affine=affine_par))
135 |         for i in downsample._modules['1'].parameters():
136 |             i.requires_grad = False
137 |         layers = []
138 |         layers.append(
139 |             block(self.inplanes, planes, stride, dilation=dilation, downsample=downsample))
140 |         self.inplanes = planes * block.expansion
141 |         for i in range(1, blocks):
142 |             layers.append(block(self.inplanes, planes, dilation=dilation))
143 | 
144 |         return nn.Sequential(*layers)
145 | 
146 |     def forward(self, x):
147 |         x = self.conv1(x)
148 |         x = self.bn1(x)
149 |         x = self.relu(x)
150 |         x = self.maxpool(x)
151 |         x = self.layer1(x)
152 |         x = self.layer2(x)
153 |         x = self.layer3(x)
154 |         if self.multi_level:
155 |             x1 = self.layer5(x)  # produce segmap 2
156 |         else:
157 |             x1 = None
158 |         x2 = self.layer4(x)
159 |         x2 = self.layer6(x2)  # produce segmap 3
160 |         return x1, x2
161 | 
162 |     def get_1x_lr_params_no_scale(self):
163 |         """
164 |         This generator returns all the parameters of the net except for
165 |         the last classification layer. Note that for each batchnorm layer,
166 |         requires_grad is set to False in deeplab_resnet.py, therefore this function does not return
167 |         any batchnorm parameter
168 |         """
169 |         b = []
170 | 
171 |         b.append(self.conv1)
172 |         b.append(self.bn1)
173 |         b.append(self.layer1)
174 |         b.append(self.layer2)
175 |         b.append(self.layer3)
176 |         b.append(self.layer4)
177 | 
178 |         for i in range(len(b)):
179 |             for j in b[i].modules():
180 |                 jj = 0
181 |                 for k in j.parameters():
182 |                     jj += 1
183 |                     if k.requires_grad:
184 |                         yield k
185 | 
186 |     def get_10x_lr_params(self):
187 |         """
188 |         This generator returns all the parameters for the last layer of the net,
189 |         which does the classification of pixel into classes
190 |         """
191 |         b = []
192 |         if self.multi_level:
193 |             b.append(self.layer5.parameters())
194 |         b.append(self.layer6.parameters())
195 | 
196 |         for j in range(len(b)):
197 |             for i in b[j]:
198 |                 yield i
199 | 
200 |     def optim_parameters(self, lr):
201 |         return [{'params': self.get_1x_lr_params_no_scale(), 'lr': lr},
202 |                 {'params': self.get_10x_lr_params(), 'lr': 10 * lr}]
203 | 
204 | class ResNetSingle(nn.Module):
205 |     def __init__(self, block, layers, num_classes, multi_level):
206 |         self.multi_level = multi_level
207 |         self.inplanes = 64
208 |         super(ResNetSingle, self).__init__()
209 |         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
210 |                                bias=False)
211 |         self.bn1 = nn.BatchNorm2d(64, affine=affine_par)
212 |         for i in self.bn1.parameters():
213 |             i.requires_grad = False
214 |         self.relu = nn.ReLU(inplace=True)
215 |         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True)  # change
216 |         self.layer1 = self._make_layer(block, 64, layers[0])
217 |         self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
218 |         self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
219 |         self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4)
220 |         if self.multi_level:
221 |             self.layer5 = ClassifierModule(1024, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)
222 |         #self.layer6 = ClassifierModule(2048, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)
223 |         self.layer6 = ClassifierModule_GAP(2048, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)
224 |         for m in self.modules():
225 |             if isinstance(m, nn.Conv2d):
226 |                 m.weight.data.normal_(0, 0.01)
227 |             elif isinstance(m, nn.BatchNorm2d):
228 |                 m.weight.data.fill_(1)
229 |                 m.bias.data.zero_()
230 | 
231 |     def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
232 |         downsample = None
233 |         if (stride != 1
234 |                 or self.inplanes != planes * block.expansion
235 |                 or dilation == 2
236 |                 or dilation == 4):
237 |             downsample = nn.Sequential(
238 |                 nn.Conv2d(self.inplanes, planes * block.expansion,
239 |                           kernel_size=1, stride=stride, bias=False),
240 |                 nn.BatchNorm2d(planes * block.expansion, affine=affine_par))
241 |         for i in downsample._modules['1'].parameters():
242 |             i.requires_grad = False
243 |         layers = []
244 |         layers.append(
245 |             block(self.inplanes, planes, stride, dilation=dilation, downsample=downsample))
246 |         self.inplanes = planes * block.expansion
247 |         for i in range(1, blocks):
248 |             layers.append(block(self.inplanes, planes, dilation=dilation))
249 | 
250 |         return nn.Sequential(*layers)
251 | 
252 |     def forward(self, x):
253 |         x = self.conv1(x)
254 |         x = self.bn1(x)
255 |         x = self.relu(x)
256 |         x = self.maxpool(x)
257 |         x = self.layer1(x)
258 |         x = self.layer2(x)
259 |         x = self.layer3(x)
260 |         if self.multi_level:
261 |             x1 = self.layer5(x)  # produce segmap 1
262 |         else:
263 |             x1 = None
264 |         x2 = self.layer4(x)
265 |         x2 = self.layer6(x2)  # produce segmap 2
266 |         return x1, x2
267 | 
268 |     def get_1x_lr_params_no_scale(self):
269 |         """
270 |         This generator returns all the parameters of the net except for
271 |         the last classification layer. Note that for each batchnorm layer,
272 |         requires_grad is set to False in deeplab_resnet.py, therefore this function does not return
273 |         any batchnorm parameter
274 |         """
275 |         b = []
276 | 
277 |         b.append(self.conv1)
278 |         b.append(self.bn1)
279 |         b.append(self.layer1)
280 |         b.append(self.layer2)
281 |         b.append(self.layer3)
282 |         b.append(self.layer4)
283 | 
284 |         for i in range(len(b)):
285 |             for j in b[i].modules():
286 |                 jj = 0
287 |                 for k in j.parameters():
288 |                     jj += 1
289 |                     if k.requires_grad:
290 |                         yield k
291 | 
292 |     def get_10x_lr_params(self):
293 |         """
294 |         This generator returns all the parameters for the last layer of the net,
295 |         which does the classification of pixel into classes
296 |         """
297 |         b = []
298 |         if self.multi_level:
299 |             b.append(self.layer5.parameters())
300 |         b.append(self.layer6.parameters())
301 | 
302 |         for j in range(len(b)):
303 |             for i in b[j]:
304 |                 yield i
305 | 
306 |     def optim_parameters(self, lr):
307 |         return [{'params': self.get_1x_lr_params_no_scale(), 'lr': lr},
308 |                 {'params': self.get_10x_lr_params(), 'lr': 10 * lr}]
309 | 
310 | 
311 | def get_deeplab_v3(num_classes=19, multi_level=True):
312 |     model = ResNetMulti(Bottleneck, [3, 4, 23, 3], num_classes, multi_level)
313 |     return model
314 | 
315 | def get_deeplab_v3_single(num_classes=19, multi_level=True):
316 |     model = ResNetSingle(Bottleneck, [3, 4, 23, 3], num_classes, multi_level)
317 |     return model
318 | 


--------------------------------------------------------------------------------
/ADVENT/advent/model/discriminator.py:
--------------------------------------------------------------------------------
 1 | from torch import nn
 2 | 
 3 | 
 4 | def get_fc_discriminator(num_classes, ndf=128):
 5 |     return nn.Sequential(
 6 |         nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2, padding=1),
 7 |         nn.LeakyReLU(negative_slope=0.2, inplace=True),
 8 |         nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1),
 9 |         nn.LeakyReLU(negative_slope=0.2, inplace=True),
10 |         nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2, padding=1),
11 |         nn.LeakyReLU(negative_slope=0.2, inplace=True),
12 |         nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1),
13 |         nn.LeakyReLU(negative_slope=0.2, inplace=True),
14 |         nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2, padding=1),
15 |     )
16 | 
17 | # def get_fc_discriminator(num_classes, ndf=32):
18 | #     return nn.Sequential(
19 | #         nn.Conv2d(num_classes, ndf, kernel_size=3, stride=1, padding=1),
20 | #         nn.LeakyReLU(negative_slope=0.2, inplace=True),
21 | #         nn.Conv2d(ndf, ndf * 2, kernel_size=3, stride=1, padding=1),
22 | #         nn.LeakyReLU(negative_slope=0.2, inplace=True),
23 | #         nn.Conv2d(ndf * 2, ndf * 4, kernel_size=3, stride=1, padding=1),
24 | #         nn.LeakyReLU(negative_slope=0.2, inplace=True),
25 | #         nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1),
26 | #         nn.LeakyReLU(negative_slope=0.2, inplace=True),
27 | #         nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2, padding=1),
28 | #     )


--------------------------------------------------------------------------------
/ADVENT/advent/scripts/configs/advent.yml:
--------------------------------------------------------------------------------
 1 | SOURCE: PotsdamIRRG #Vaihingen  PotsdamIRRG
 2 | TARGET: Vaihingen
 3 | NUM_WORKERS: 8
 4 | GPU_ID: 0
 5 | EXP_NAME: 'deeplab_v3_PotsdamIRRG_source'
 6 | 
 7 | 
 8 | TRAIN:
 9 |   MODEL: DeepLabv3
10 |   RESTORE_FROM: ../../pretrained_models/DeepLab_resnet_pretrained_imagenet.pth
11 |   #../../pretrained_models/DeepLab_resnet_pretrained_imagenet.pth
12 |   MULTI_LEVEL: False
13 |   LAMBDA_SEG_MAIN: 1.0
14 |   LAMBDA_SEG_AUX: 0.1
15 |   LAMBDA_SEG_LOW: 0.1
16 | 
17 | TEST:
18 |   MODE: best


--------------------------------------------------------------------------------
/ADVENT/advent/scripts/test.py:
--------------------------------------------------------------------------------
  1 | import sys
  2 | sys.path.append('./ADVENT')
  3 | import pdb
  4 | 
  5 | import argparse
  6 | import os
  7 | import os.path as osp
  8 | import pprint
  9 | import warnings
 10 | 
 11 | from torch.utils import data
 12 | 
 13 | from advent.model.deeplabv3 import get_deeplab_v3_single as get_deeplab_v3
 14 | 
 15 | from advent.dataset.Vaihingen import Vaihingen
 16 | from advent.dataset.Potsdam import Potsdam
 17 | from advent.train_setup.config import cfg, cfg_from_file
 18 | from advent.train_setup.eval_single import evaluate_single_model
 19 | 
 20 | warnings.filterwarnings("ignore", message="numpy.dtype size changed")
 21 | warnings.filterwarnings("ignore")
 22 | 
 23 | 
 24 | def get_arguments():
 25 |     """
 26 |     Parse input arguments
 27 |     """
 28 |     parser = argparse.ArgumentParser(description="Code for evaluation")
 29 |     parser.add_argument('--cfg', type=str, default=None,
 30 |                         help='optional config file', )
 31 |     parser.add_argument("--exp-suffix", type=str, default=None,
 32 |                         help="optional experiment suffix")
 33 |     return parser.parse_args()
 34 | 
 35 | 
 36 | def main(config_file, exp_suffix):
 37 |     # LOAD ARGS
 38 |     assert config_file is not None, 'Missing cfg file'
 39 |     cfg_from_file(config_file)
 40 |     # auto-generate exp name if not specified
 41 |     if cfg.EXP_NAME == '':
 42 |         cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}'
 43 |     if exp_suffix:
 44 |         cfg.EXP_NAME += f'_{exp_suffix}'
 45 |     # auto-generate snapshot path if not specified
 46 |     if cfg.TEST.SNAPSHOT_DIR[0] == '':
 47 |         cfg.TEST.SNAPSHOT_DIR[0] = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
 48 |         os.makedirs(cfg.TEST.SNAPSHOT_DIR[0], exist_ok=True)
 49 | 
 50 |     print('Using config:')
 51 |     pprint.pprint(cfg)
 52 |     # load models
 53 |     models = []
 54 |     n_models = len(cfg.TEST.MODEL)
 55 |     if cfg.TEST.MODE == 'best':
 56 |         assert n_models == 1, 'Not yet supported'
 57 |     for i in range(n_models):
 58 |         if cfg.TEST.MODEL[i] == 'DeepLabv3':
 59 |             model = get_deeplab_v3(num_classes=cfg.NUM_CLASSES,
 60 |                                    multi_level=cfg.TEST.MULTI_LEVEL[i])
 61 |         elif cfg.TEST.MODEL[i] == 'UNET':
 62 |             model = UNet(n_channels=3, n_classes=cfg.NUM_CLASSES, bilinear=True)
 63 |         else:
 64 |             raise NotImplementedError(f"Not yet supported {cfg.TEST.MODEL[i]}")
 65 |         models.append(model)
 66 | 
 67 |     if os.environ.get('ADVENT_DRY_RUN', '0') == '1':
 68 |         return
 69 | 
 70 |     # dataloaders
 71 |     #pdb.set_trace()
 72 |     # test_dataset = Vaihingen(root=cfg.DATA_DIRECTORY_TARGET,
 73 |     #                          list_path='/root/code/CCDA_LGFA/ADVENT/advent/dataset/Vaihingen/{}.txt',
 74 |     #                          set=cfg.TEST.SET_TARGET,
 75 |     #                          info_path=cfg.TEST.INFO_TARGET,
 76 |     #                          crop_size=cfg.TEST.INPUT_SIZE_TARGET,
 77 |     #                          mean=cfg.TEST.IMG_MEAN,
 78 |     #                          labels_size=cfg.TEST.OUTPUT_SIZE_TARGET)
 79 |     # test_loader = data.DataLoader(test_dataset,
 80 |     #                               batch_size=cfg.TEST.BATCH_SIZE_TARGET,
 81 |     #                               num_workers=cfg.NUM_WORKERS,
 82 |     #                               shuffle=False,
 83 |     #                               pin_memory=True)
 84 |     # -----reverse-----
 85 |     test_dataset = Potsdam(root=cfg.DATA_DIRECTORY_TARGET,
 86 |                              list_path='/root/code/CCDA_LGFA/ADVENT/advent/dataset/PotsdamIRRG/{}.txt',
 87 |                              set=cfg.TEST.SET_TARGET,
 88 |                              info_path=cfg.TEST.INFO_TARGET,
 89 |                              crop_size=cfg.TEST.INPUT_SIZE_TARGET,
 90 |                              mean=cfg.TEST.IMG_MEAN)
 91 |     test_loader = data.DataLoader(test_dataset,
 92 |                                   batch_size=cfg.TEST.BATCH_SIZE_TARGET,
 93 |                                   num_workers=cfg.NUM_WORKERS,
 94 |                                   shuffle=False,
 95 |                                   pin_memory=True)
 96 | 
 97 |     # eval
 98 |     evaluate_single_model(models, test_loader, cfg)
 99 | 
100 | 
101 | if __name__ == '__main__':
102 |     args = get_arguments()
103 |     print('Called with args:')
104 |     print(args)
105 |     main(args.cfg, args.exp_suffix)
106 | 


--------------------------------------------------------------------------------
/ADVENT/advent/scripts/train.py:
--------------------------------------------------------------------------------
  1 | import argparse
  2 | import os
  3 | import os.path as osp
  4 | import pprint
  5 | import random
  6 | import warnings
  7 | 
  8 | import numpy as np
  9 | import yaml
 10 | import torch
 11 | from torch.utils import data
 12 | 
 13 | from advent.model.deeplabv3 import get_deeplab_v3_single as get_deeplab_v3
 14 | 
 15 | 
 16 | from advent.dataset.Potsdam import Potsdam
 17 | from advent.dataset.Vaihingen import Vaihingen
 18 | 
 19 | from advent.train_setup.config import cfg, cfg_from_file
 20 | from advent.train_setup.train_single import train_single_model
 21 | 
 22 | warnings.filterwarnings("ignore", message="numpy.dtype size changed")
 23 | warnings.filterwarnings("ignore")
 24 | 
 25 | 
 26 | def get_arguments():
 27 |     """
 28 |     Parse input arguments
 29 |     """
 30 |     parser = argparse.ArgumentParser(description="Code for domain adaptation (DA) training")
 31 |     parser.add_argument('--cfg', type=str, default=None,
 32 |                         help='optional config file', )
 33 |     parser.add_argument("--random-train", action="store_true",
 34 |                         help="not fixing random seed.")
 35 |     parser.add_argument("--tensorboard", action="store_true",
 36 |                         help="visualize training loss with tensorboardX.")
 37 |     parser.add_argument("--viz-every-iter", type=int, default=None,
 38 |                         help="visualize results.")
 39 |     parser.add_argument("--exp-suffix", type=str, default=None,
 40 |                         help="optional experiment suffix")
 41 |     return parser.parse_args()
 42 | 
 43 | 
 44 | def main():
 45 |     # LOAD ARGS
 46 |     args = get_arguments()
 47 |     print('Called with args:')
 48 |     print(args)
 49 | 
 50 |     assert args.cfg is not None, 'Missing cfg file'
 51 |     cfg_from_file(args.cfg)
 52 |     # auto-generate exp name if not specified
 53 |     if cfg.EXP_NAME == '':
 54 |         cfg.EXP_NAME = f'{cfg.SOURCE}2{cfg.TARGET}_{cfg.TRAIN.MODEL}_{cfg.TRAIN.DA_METHOD}'
 55 | 
 56 |     if args.exp_suffix:
 57 |         cfg.EXP_NAME += f'_{args.exp_suffix}'
 58 |     # auto-generate snapshot path if not specified
 59 |     if cfg.TRAIN.SNAPSHOT_DIR == '':
 60 |         cfg.TRAIN.SNAPSHOT_DIR = osp.join(cfg.EXP_ROOT_SNAPSHOT, cfg.EXP_NAME)
 61 |         os.makedirs(cfg.TRAIN.SNAPSHOT_DIR, exist_ok=True)
 62 |     # tensorboard
 63 |     if args.tensorboard:
 64 |         if cfg.TRAIN.TENSORBOARD_LOGDIR == '':
 65 |             cfg.TRAIN.TENSORBOARD_LOGDIR = osp.join(cfg.EXP_ROOT_LOGS, 'tensorboard', cfg.EXP_NAME)
 66 |         os.makedirs(cfg.TRAIN.TENSORBOARD_LOGDIR, exist_ok=True)
 67 |         if args.viz_every_iter is not None:
 68 |             cfg.TRAIN.TENSORBOARD_VIZRATE = args.viz_every_iter
 69 |     else:
 70 |         cfg.TRAIN.TENSORBOARD_LOGDIR = ''
 71 |     print('Using config:')
 72 |     pprint.pprint(cfg)
 73 | 
 74 |     # INIT
 75 |     _init_fn = None
 76 |     if not args.random_train:
 77 |         torch.manual_seed(cfg.TRAIN.RANDOM_SEED)
 78 |         torch.cuda.manual_seed(cfg.TRAIN.RANDOM_SEED)
 79 |         np.random.seed(cfg.TRAIN.RANDOM_SEED)
 80 |         random.seed(cfg.TRAIN.RANDOM_SEED)
 81 | 
 82 |         def _init_fn(worker_id):
 83 |             np.random.seed(cfg.TRAIN.RANDOM_SEED + worker_id)
 84 | 
 85 |     if os.environ.get('ADVENT_DRY_RUN', '0') == '1':
 86 |         return
 87 | 
 88 |     # LOAD SEGMENTATION NET
 89 |     assert osp.exists(cfg.TRAIN.RESTORE_FROM), f'Missing init model {cfg.TRAIN.RESTORE_FROM}'
 90 |     if cfg.TRAIN.MODEL == 'DeepLabv3':
 91 |         model = get_deeplab_v3(num_classes=cfg.NUM_CLASSES, multi_level=cfg.TRAIN.MULTI_LEVEL)
 92 |         saved_state_dict = torch.load(cfg.TRAIN.RESTORE_FROM)
 93 |         if 'DeepLab_resnet_pretrained_imagenet' in cfg.TRAIN.RESTORE_FROM:
 94 |             new_params = model.state_dict().copy()
 95 |             for i in saved_state_dict:
 96 |                 i_parts = i.split('.')
 97 |                 if not i_parts[1] == 'layer5':
 98 |                     new_params['.'.join(i_parts[1:])] = saved_state_dict[i]
 99 |             model.load_state_dict(new_params)
100 |         else:
101 |             model.load_state_dict(saved_state_dict)
102 |     elif cfg.TRAIN.MODEL == 'UNET':
103 |         model = UNet(n_channels=3, n_classes=cfg.NUM_CLASSES, bilinear=True)
104 |     else:
105 |         raise NotImplementedError(f"Not yet supported {cfg.TRAIN.MODEL}")
106 |     print('Model loaded')
107 | 
108 |     # DATALOADERS
109 |     # source_dataset = Potsdam(root=cfg.DATA_DIRECTORY_SOURCE,
110 |     #                             list_path=cfg.DATA_LIST_SOURCE,
111 |     #                             set=cfg.TRAIN.SET_SOURCE,
112 |     #                             max_iters=cfg.TRAIN.MAX_ITERS * cfg.TRAIN.BATCH_SIZE_SOURCE,
113 |     #                             crop_size=cfg.TRAIN.INPUT_SIZE_SOURCE,
114 |     #                             mean=cfg.TRAIN.IMG_MEAN)
115 |     #
116 |     # source_loader = data.DataLoader(source_dataset,
117 |     #                                 batch_size=cfg.TRAIN.BATCH_SIZE_SOURCE,
118 |     #                                 num_workers=cfg.NUM_WORKERS,
119 |     #                                 shuffle=True,
120 |     #                                 pin_memory=True,
121 |     #                                 worker_init_fn=_init_fn)
122 | 
123 |     # ---==reverse-----
124 |     source_dataset = Vaihingen(root=cfg.DATA_DIRECTORY_SOURCE,
125 |                              list_path=cfg.DATA_LIST_SOURCE,
126 |                              set=cfg.TRAIN.SET_SOURCE,
127 |                              max_iters=cfg.TRAIN.MAX_ITERS * cfg.TRAIN.BATCH_SIZE_SOURCE,
128 |                              crop_size=cfg.TRAIN.INPUT_SIZE_SOURCE,
129 |                              mean=cfg.TRAIN.IMG_MEAN)
130 |     source_loader = data.DataLoader(source_dataset,
131 |                                     batch_size=cfg.TRAIN.BATCH_SIZE_SOURCE,
132 |                                     num_workers=cfg.NUM_WORKERS,
133 |                                     shuffle=True,
134 |                                     pin_memory=True,
135 |                                     worker_init_fn=_init_fn)
136 | 
137 |     with open(osp.join(cfg.TRAIN.SNAPSHOT_DIR, 'train_cfg.yml'), 'w') as yaml_file:
138 |         yaml.dump(cfg, yaml_file, default_flow_style=False)
139 | 
140 |     # UDA TRAINING
141 |     train_single_model(model, source_loader, cfg)
142 | 
143 | 
144 | if __name__ == '__main__':
145 |     main()
146 | 


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/ADVENT/advent/train_setup/__init__.py:
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https://raw.githubusercontent.com/BOBrown/SegNet_Source/4dc9c93c77a64b0338038cc9e192006b11a5efe2/ADVENT/advent/train_setup/__init__.py


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/ADVENT/advent/train_setup/config.py:
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  1 | import os.path as osp
  2 | 
  3 | import numpy as np
  4 | from easydict import EasyDict
  5 | 
  6 | from advent.utils import project_root
  7 | from advent.utils.serialization import yaml_load
  8 | 
  9 | 
 10 | cfg = EasyDict()
 11 | 
 12 | # COMMON CONFIGS
 13 | # source domain
 14 | cfg.SOURCE = 'PotsdamIRRG'
 15 | #cfg.SOURCE = 'Vaihingen' #Vaihingen
 16 | # target domain
 17 | cfg.TARGET = 'Vaihingen'
 18 | # Number of workers for dataloading
 19 | cfg.NUM_WORKERS = 4
 20 | # List of training images
 21 | #cfg.DATA_LIST_SOURCE = str(project_root / 'advent/dataset/PotsdamRGB/{}.txt')
 22 | cfg.DATA_LIST_SOURCE = str(project_root / 'advent/dataset/PotsdamIRRG/{}.txt')
 23 | cfg.DATA_LIST_TARGET = str(project_root / 'advent/dataset/Vaihingen/{}.txt')
 24 | #------reverse-------
 25 | #cfg.DATA_LIST_SOURCE = str(project_root / 'advent/dataset/Vaihingen/{}.txt')
 26 | #cfg.DATA_LIST_TARGET = str(project_root / 'advent/dataset/PotsdamIRRG/{}.txt')
 27 | 
 28 | # Directories
 29 | #cfg.DATA_DIRECTORY_SOURCE = str(project_root / 'data/PotsdamRGB') #str(project_root / 'data/GTA5')
 30 | cfg.DATA_DIRECTORY_SOURCE = str(project_root / 'data/PotsdamIRRG')
 31 | cfg.DATA_DIRECTORY_TARGET = str(project_root / 'data/Vaihingen')
 32 | #------reverse-------
 33 | #cfg.DATA_DIRECTORY_SOURCE = str(project_root / 'data/Vaihingen')
 34 | #cfg.DATA_DIRECTORY_TARGET = str(project_root / 'data/PotsdamIRRG')
 35 | 
 36 | # Number of object classes
 37 | cfg.NUM_CLASSES = 6
 38 | # Exp dirs
 39 | cfg.EXP_NAME = 'PotsIRRG_to_Vaih_4_3'
 40 | 
 41 | cfg.EXP_ROOT = project_root / 'experiments'
 42 | cfg.EXP_ROOT_SNAPSHOT = osp.join(cfg.EXP_ROOT, 'snapshots')
 43 | cfg.EXP_ROOT_LOGS = osp.join(cfg.EXP_ROOT, 'logs')
 44 | # CUDA
 45 | cfg.GPU_ID = 0
 46 | 
 47 | # TRAIN CONFIGS
 48 | cfg.TRAIN = EasyDict()
 49 | cfg.TRAIN.SET_SOURCE = 'train'
 50 | cfg.TRAIN.SET_TARGET = 'train'
 51 | cfg.TRAIN.BATCH_SIZE_SOURCE = 4 # NEED TO CHANGE; 4 for deeplab; 8 for UNET
 52 | cfg.TRAIN.BATCH_SIZE_TARGET = 4
 53 | cfg.TRAIN.IGNORE_LABEL = 255
 54 | cfg.TRAIN.INPUT_SIZE_SOURCE = (512, 512)
 55 | cfg.TRAIN.INPUT_SIZE_TARGET = (512, 512)
 56 | # Class info
 57 | cfg.TRAIN.INFO_SOURCE = ''
 58 | cfg.TRAIN.INFO_TARGET = str(project_root / 'advent/dataset/Vaihingen/info.json')
 59 | # Segmentation network params
 60 | cfg.TRAIN.MODEL = 'DeepLabv3'
 61 | cfg.TRAIN.MULTI_LEVEL = False
 62 | cfg.TRAIN.RESTORE_FROM = ''
 63 | #----Vaihingen------#
 64 | #R_mean is 119.997608, G_mean is 81.249737, B_mean is 80.672294
 65 | #R_var is 54.817944, G_var is 38.977894, B_var is 37.568813
 66 | 
 67 | #----PotsdamRGB------#
 68 | #R_mean is 86.761490, G_mean is 92.735321, B_mean is 86.099505
 69 | #R_var is 35.850524, G_var is 35.415636, B_var is 36.807795
 70 | 
 71 | #------PotsdamIRRG----#
 72 | #R_mean is 97.835882, G_mean is 92.735321, B_mean is 86.099505
 73 | #R_var is 36.242923, G_var is 35.415636, B_var is 36.807795
 74 | 
 75 | #ori
 76 | #B:104.00698793, G:116.66876762, R:122.67891434
 77 | cfg.TRAIN.IMG_MEAN = np.array((104.00698793, 116.66876762, 122.67891434), dtype=np.float32)
 78 | cfg.TRAIN.IMG_MEAN_V = np.array((80.672294, 81.249737, 119.997608), dtype=np.float32)
 79 | cfg.TRAIN.IMG_MEAN_PR = np.array((86.099505, 92.735321, 86.761490), dtype=np.float32)
 80 | cfg.TRAIN.IMG_MEAN_PIR = np.array((86.099505, 92.735321, 97.835882), dtype=np.float32)
 81 | cfg.TRAIN.LEARNING_RATE = 2.5e-4 # NEED TO CHANGE; 2.5e-4 for deeplab; 0.001 for UNET
 82 | cfg.TRAIN.MOMENTUM = 0.9
 83 | cfg.TRAIN.WEIGHT_DECAY = 0.0005
 84 | cfg.TRAIN.POWER = 0.9 # learning rate decay
 85 | cfg.TRAIN.LAMBDA_SEG_MAIN = 1.0
 86 | cfg.TRAIN.LAMBDA_SEG_AUX = 0.1  # weight of conv4 prediction. Used in multi-level setting.
 87 | cfg.TRAIN.LAMBDA_SEG_LOW = 0.1
 88 | 
 89 | 
 90 | # Adversarial training params
 91 | cfg.TRAIN.GANLOSS = 'LS' # Option: BCE and LS
 92 | cfg.TRAIN.LEARNING_RATE_D = 1e-4
 93 | 
 94 | # Other params
 95 | cfg.TRAIN.MAX_ITERS = 60000 #250000
 96 | cfg.TRAIN.EARLY_STOP = 30000 #120000
 97 | cfg.TRAIN.SAVE_PRED_EVERY = 1000
 98 | cfg.TRAIN.SNAPSHOT_DIR = ''
 99 | cfg.TRAIN.RANDOM_SEED = 1234
100 | cfg.TRAIN.TENSORBOARD_LOGDIR = ''
101 | cfg.TRAIN.TENSORBOARD_VIZRATE = 50 #100
102 | 
103 | # TEST CONFIGS
104 | cfg.TEST = EasyDict()
105 | cfg.TEST.MODE = 'best'  # {'single', 'best'}
106 | # model
107 | cfg.TEST.MODEL = ('DeepLabv3',) # DeepLabv3 UNET
108 | cfg.TEST.MODEL_WEIGHT = (1.0,)
109 | cfg.TEST.MULTI_LEVEL = (False,)
110 | cfg.TEST.IMG_MEAN = np.array((104.00698793, 116.66876762, 122.67891434), dtype=np.float32)
111 | cfg.TEST.IMG_MEAN_V = np.array((80.672294, 81.249737, 119.997608), dtype=np.float32)
112 | cfg.TEST.IMG_MEAN_PR = np.array((86.099505, 92.735321, 86.761490), dtype=np.float32)
113 | cfg.TEST.IMG_MEAN_PIR = np.array((86.099505, 92.735321, 97.835882), dtype=np.float32)
114 | cfg.TEST.RESTORE_FROM = ('',)
115 | cfg.TEST.SNAPSHOT_DIR = ('',)  # used in 'best' mode
116 | cfg.TEST.SNAPSHOT_STEP = 1000  # used in 'best' mode
117 | cfg.TEST.SNAPSHOT_MAXITER = 60000  # used in 'best' mode
118 | # Test sets
119 | cfg.TEST.SET_TARGET = 'test'
120 | cfg.TEST.BATCH_SIZE_TARGET = 1
121 | cfg.TEST.INPUT_SIZE_TARGET = (512, 512)
122 | cfg.TEST.OUTPUT_SIZE_TARGET = (512, 512)
123 | cfg.TEST.INFO_TARGET = str(project_root / 'advent/dataset/Vaihingen/info.json')
124 | cfg.TEST.WAIT_MODEL = True
125 | 
126 | 
127 | def _merge_a_into_b(a, b):
128 |     """Merge config dictionary a into config dictionary b, clobbering the
129 |     options in b whenever they are also specified in a.
130 |     """
131 |     if type(a) is not EasyDict:
132 |         return
133 | 
134 |     for k, v in a.items():
135 |         # a must specify keys that are in b
136 |         # if not b.has_key(k):
137 |         if k not in b:
138 |             raise KeyError(f'{k} is not a valid config key')
139 | 
140 |         # the types must match, too
141 |         old_type = type(b[k])
142 |         if old_type is not type(v):
143 |             if isinstance(b[k], np.ndarray):
144 |                 v = np.array(v, dtype=b[k].dtype)
145 |             else:
146 |                 raise ValueError(f'Type mismatch ({type(b[k])} vs. {type(v)}) '
147 |                                  f'for config key: {k}')
148 | 
149 |         # recursively merge dicts
150 |         if type(v) is EasyDict:
151 |             try:
152 |                 _merge_a_into_b(a[k], b[k])
153 |             except Exception:
154 |                 print(f'Error under config key: {k}')
155 |                 raise
156 |         else:
157 |             b[k] = v
158 | 
159 | 
160 | def cfg_from_file(filename):
161 |     """Load a config file and merge it into the default options.
162 |     """
163 |     yaml_cfg = EasyDict(yaml_load(filename))
164 |     _merge_a_into_b(yaml_cfg, cfg)
165 | 


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/ADVENT/advent/train_setup/eval_single.py:
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  1 | import os.path as osp
  2 | import time
  3 | 
  4 | import numpy as np
  5 | import torch
  6 | from torch import nn
  7 | from tqdm import tqdm
  8 | 
  9 | from advent.utils.func import per_class_iu, fast_hist
 10 | from advent.utils.serialization import pickle_dump, pickle_load
 11 | 
 12 | 
 13 | def evaluate_single_model( models, test_loader, cfg,
 14 |                                 fixed_test_size=True,
 15 |                                 verbose=True):
 16 |     device = cfg.GPU_ID
 17 |     interp = None
 18 |     if fixed_test_size:
 19 |         interp = nn.Upsample(size=(cfg.TEST.OUTPUT_SIZE_TARGET[1], cfg.TEST.OUTPUT_SIZE_TARGET[0]), mode='bilinear', align_corners=True)
 20 |     # eval
 21 |     if cfg.TEST.MODE == 'single':
 22 |         eval_single(cfg, models,
 23 |                     device, test_loader, interp, fixed_test_size,
 24 |                     verbose)
 25 |     elif cfg.TEST.MODE == 'best':
 26 |         eval_best(cfg, models,
 27 |                   device, test_loader, interp, fixed_test_size,
 28 |                   verbose)
 29 |     else:
 30 |         raise NotImplementedError(f"Not yet supported test mode {cfg.TEST.MODE}")
 31 | 
 32 | 
 33 | def eval_single(cfg, models,
 34 |                 device, test_loader, interp,
 35 |                 fixed_test_size, verbose):
 36 |     assert len(cfg.TEST.RESTORE_FROM) == len(models), 'Number of models are not matched'
 37 |     for checkpoint, model in zip(cfg.TEST.RESTORE_FROM, models):
 38 |         load_checkpoint_for_evaluation(model, checkpoint, device)
 39 |     # eval
 40 |     hist = np.zeros((cfg.NUM_CLASSES, cfg.NUM_CLASSES))
 41 |     for index, batch in tqdm(enumerate(test_loader)):
 42 |         image, label, _, name = batch
 43 |         if not fixed_test_size:
 44 |             interp = nn.Upsample(size=(label.shape[1], label.shape[2]), mode='bilinear', align_corners=True)
 45 |         with torch.no_grad():
 46 |             output = None
 47 |             for model, model_weight in zip(models, cfg.TEST.MODEL_WEIGHT):
 48 |                 pred_main = model(image.cuda(device))[1]
 49 |                 output_ = interp(pred_main).cpu().data[0].numpy()
 50 |                 if output is None:
 51 |                     output = model_weight * output_
 52 |                 else:
 53 |                     output += model_weight * output_
 54 |             assert output is not None, 'Output is None'
 55 |             output = output.transpose(1, 2, 0)
 56 |             output = np.argmax(output, axis=2)
 57 |         label = label.numpy()[0]
 58 |         hist += fast_hist(label.flatten(), output.flatten(), cfg.NUM_CLASSES)
 59 |     inters_over_union_classes = per_class_iu(hist)
 60 |     print(f'mIoU = \t{round(np.nanmean(inters_over_union_classes) * 100, 2)}')
 61 |     if verbose:
 62 |         display_stats(cfg, test_loader.dataset.class_names, inters_over_union_classes)
 63 | 
 64 | 
 65 | def eval_best(cfg, models,
 66 |               device, test_loader, interp,
 67 |               fixed_test_size, verbose):
 68 |     assert len(models) == 1, 'Not yet supported multi models in this mode'
 69 |     assert osp.exists(cfg.TEST.SNAPSHOT_DIR[0]), 'SNAPSHOT_DIR is not found'
 70 |     start_iter = cfg.TEST.SNAPSHOT_STEP
 71 |     step = cfg.TEST.SNAPSHOT_STEP
 72 |     max_iter = cfg.TEST.SNAPSHOT_MAXITER
 73 |     cache_path = osp.join(cfg.TEST.SNAPSHOT_DIR[0], 'all_res.pkl')
 74 |     if osp.exists(cache_path):
 75 |         all_res = pickle_load(cache_path)
 76 |     else:
 77 |         all_res = {}
 78 |     cur_best_miou = -1
 79 |     cur_best_model = ''
 80 |     for i_iter in range(start_iter, max_iter + 1, step):
 81 |         restore_from = osp.join(cfg.TEST.SNAPSHOT_DIR[0], f'model_{i_iter}.pth')
 82 |         if not osp.exists(restore_from):
 83 |             # continue
 84 |             if cfg.TEST.WAIT_MODEL:
 85 |                 print('Waiting for model..!')
 86 |                 while not osp.exists(restore_from):
 87 |                     time.sleep(5)
 88 |         print("Evaluating model", restore_from)
 89 |         if i_iter not in all_res.keys():
 90 |             load_checkpoint_for_evaluation(models[0], restore_from, device)
 91 |             # eval
 92 |             hist = np.zeros((cfg.NUM_CLASSES, cfg.NUM_CLASSES))
 93 |             # for index, batch in enumerate(test_loader):
 94 |             #     image, _, _, name = batch
 95 |             test_iter = iter(test_loader)
 96 |             for index in tqdm(range(len(test_loader))):
 97 |                 image, label, _, name = next(test_iter)
 98 |                 if not fixed_test_size:
 99 |                     interp = nn.Upsample(size=(label.shape[1], label.shape[2]), mode='bilinear', align_corners=True)
100 |                 with torch.no_grad():
101 |                     pred_main = models[0](image.cuda(device))[1]
102 |                     output = interp(pred_main).cpu().data[0].numpy()
103 |                     output = output.transpose(1, 2, 0)
104 |                     output = np.argmax(output, axis=2)
105 |                 label = label.numpy()[0]
106 |                 hist += fast_hist(label.flatten(), output.flatten(), cfg.NUM_CLASSES)
107 |                 if verbose and index > 0 and index % 100 == 0:
108 |                     print('{:d} / {:d}: {:0.2f}'.format(
109 |                         index, len(test_loader), 100 * np.nanmean(per_class_iu(hist))))
110 |             inters_over_union_classes = per_class_iu(hist)
111 |             all_res[i_iter] = inters_over_union_classes
112 |             pickle_dump(all_res, cache_path)
113 |         else:
114 |             inters_over_union_classes = all_res[i_iter]
115 |         computed_miou = round(np.nanmean(inters_over_union_classes) * 100, 2)
116 |         if cur_best_miou < computed_miou:
117 |             cur_best_miou = computed_miou
118 |             cur_best_model = restore_from
119 |         print('\tCurrent mIoU:', computed_miou)
120 |         print('\tCurrent best model:', cur_best_model)
121 |         print('\tCurrent best mIoU:', cur_best_miou)
122 |         if verbose:
123 |             display_stats(cfg, test_loader.dataset.class_names, inters_over_union_classes)
124 | 
125 | 
126 | def load_checkpoint_for_evaluation(model, checkpoint, device):
127 |     saved_state_dict = torch.load(checkpoint)
128 |     model.load_state_dict(saved_state_dict)
129 |     model.eval()
130 |     model.cuda(device)
131 | 
132 | 
133 | def display_stats(cfg, name_classes, inters_over_union_classes):
134 |     for ind_class in range(cfg.NUM_CLASSES):
135 |         print(name_classes[ind_class]
136 |               + '\t' + str(round(inters_over_union_classes[ind_class] * 100, 2)))
137 | 


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/ADVENT/advent/train_setup/train_single.py:
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  1 | # --------------------------------------------------------
  2 | # Domain adpatation training
  3 | # Copyright (c) 2019 valeo.ai
  4 | #
  5 | # Written by Tuan-Hung Vu
  6 | # --------------------------------------------------------
  7 | import os
  8 | import sys
  9 | from pathlib import Path
 10 | 
 11 | import os.path as osp
 12 | import numpy as np
 13 | import torch
 14 | import torch.backends.cudnn as cudnn
 15 | import torch.nn.functional as F
 16 | import torch.optim as optim
 17 | from tensorboardX import SummaryWriter
 18 | from torch import nn
 19 | from torchvision.utils import make_grid
 20 | from tqdm import tqdm
 21 | 
 22 | from advent.model.discriminator import get_fc_discriminator
 23 | from advent.utils.func import adjust_learning_rate, adjust_learning_rate_discriminator
 24 | from advent.utils.func import loss_calc, bce_loss, ls_loss
 25 | from advent.utils.loss import entropy_loss
 26 | from advent.utils.func import prob_2_entropy
 27 | from advent.utils.viz_segmask import colorize_mask
 28 | 
 29 | 
 30 | def train_deeplab(model, trainloader, cfg):
 31 | 
 32 |     # Create the model and start the training.
 33 |     input_size_source = cfg.TRAIN.INPUT_SIZE_SOURCE
 34 |     device = cfg.GPU_ID
 35 |     num_classes = cfg.NUM_CLASSES
 36 |     viz_tensorboard = os.path.exists(cfg.TRAIN.TENSORBOARD_LOGDIR)
 37 |     if viz_tensorboard:
 38 |         writer = SummaryWriter(log_dir=cfg.TRAIN.TENSORBOARD_LOGDIR)
 39 | 
 40 |     # SEGMNETATION NETWORK
 41 |     model.train()
 42 |     model.to(device)
 43 |     cudnn.benchmark = True
 44 |     cudnn.enabled = True
 45 | 
 46 |     # OPTIMIZERS
 47 |     # segnet's optimizer
 48 |     optimizer = optim.SGD(model.optim_parameters(cfg.TRAIN.LEARNING_RATE),
 49 |                           lr=cfg.TRAIN.LEARNING_RATE,
 50 |                           momentum=cfg.TRAIN.MOMENTUM,
 51 |                           weight_decay=cfg.TRAIN.WEIGHT_DECAY)
 52 | 
 53 |     # interpolate output segmaps
 54 |     interp = nn.Upsample(size=(input_size_source[1], input_size_source[0]), mode='bilinear',
 55 |                          align_corners=True)
 56 | 
 57 |     trainloader_iter = enumerate(trainloader)
 58 |     for i_iter in tqdm(range(cfg.TRAIN.EARLY_STOP + 1)):
 59 | 
 60 |         # reset optimizers
 61 |         optimizer.zero_grad()
 62 |         # adapt LR if needed
 63 |         adjust_learning_rate(optimizer, i_iter, cfg)
 64 | 
 65 |         # train on source
 66 |         _, batch = trainloader_iter.__next__()
 67 |         images_source, labels, _, _ = batch
 68 |         pred_src_aux, pred_src_main = model(images_source.cuda(device))
 69 |         if cfg.TRAIN.MULTI_LEVEL:
 70 |             pred_src_aux = interp(pred_src_aux)
 71 |             loss_seg_src_aux = loss_calc(pred_src_aux, labels, device)
 72 |         else:
 73 |             loss_seg_src_aux = 0
 74 |         pred_src_main = interp(pred_src_main)
 75 |         loss_seg_src_main = loss_calc(pred_src_main, labels, device)
 76 |         loss = (cfg.TRAIN.LAMBDA_SEG_MAIN * loss_seg_src_main
 77 |                 + cfg.TRAIN.LAMBDA_SEG_AUX * loss_seg_src_aux)
 78 |         loss.backward()
 79 | 
 80 |         optimizer.step()
 81 |         current_losses = {'loss_seg_src_aux': loss_seg_src_aux,
 82 |                           'loss_seg_src_main': loss_seg_src_main}
 83 |         print_losses(current_losses, i_iter)
 84 | 
 85 |         if i_iter % cfg.TRAIN.SAVE_PRED_EVERY == 0 and i_iter != 0:
 86 |             print('taking snapshot ...')
 87 |             print('exp =', cfg.TRAIN.SNAPSHOT_DIR)
 88 |             snapshot_dir = Path(cfg.TRAIN.SNAPSHOT_DIR)
 89 |             torch.save(model.state_dict(), snapshot_dir / f'model_{i_iter}.pth')
 90 |             if i_iter >= cfg.TRAIN.EARLY_STOP - 1:
 91 |                 break
 92 |         sys.stdout.flush()
 93 | 
 94 |         # Visualize with tensorboard
 95 |         if viz_tensorboard:
 96 |             log_losses_tensorboard(writer, current_losses, i_iter)
 97 | 
 98 |             if i_iter % cfg.TRAIN.TENSORBOARD_VIZRATE == cfg.TRAIN.TENSORBOARD_VIZRATE - 1:
 99 |                 draw_in_tensorboard(writer, images_source, i_iter, pred_src_main, num_classes, 'S')
100 | 
101 | 
102 | def draw_in_tensorboard(writer, images, i_iter, pred_main, num_classes, type_):
103 |     grid_image = make_grid(images[:3].clone().cpu().data, 3, normalize=True)
104 |     writer.add_image(f'Image - {type_}', grid_image, i_iter)
105 | 
106 |     grid_image = make_grid(torch.from_numpy(np.array(colorize_mask(np.asarray(
107 |         np.argmax(F.softmax(pred_main).cpu().data[0].numpy().transpose(1, 2, 0),
108 |                   axis=2), dtype=np.uint8)).convert('RGB')).transpose(2, 0, 1)), 3,
109 |                            normalize=False, range=(0, 255))
110 |     writer.add_image(f'Prediction - {type_}', grid_image, i_iter)
111 | 
112 |     output_sm = F.softmax(pred_main).cpu().data[0].numpy().transpose(1, 2, 0)
113 |     output_ent = np.sum(-np.multiply(output_sm, np.log2(output_sm)), axis=2,
114 |                         keepdims=False)
115 |     grid_image = make_grid(torch.from_numpy(output_ent), 3, normalize=True,
116 |                            range=(0, np.log2(num_classes)))
117 |     writer.add_image(f'Entropy - {type_}', grid_image, i_iter)
118 | 
119 | def print_losses(current_losses, i_iter):
120 |     list_strings = []
121 |     for loss_name, loss_value in current_losses.items():
122 |         list_strings.append(f'{loss_name} = {to_numpy(loss_value):.3f} ')
123 |     full_string = ' '.join(list_strings)
124 |     tqdm.write(f'iter = {i_iter} {full_string}')
125 | 
126 | 
127 | def log_losses_tensorboard(writer, current_losses, i_iter):
128 |     for loss_name, loss_value in current_losses.items():
129 |         writer.add_scalar(f'data/{loss_name}', to_numpy(loss_value), i_iter)
130 | 
131 | 
132 | def to_numpy(tensor):
133 |     if isinstance(tensor, (int, float)):
134 |         return tensor
135 |     else:
136 |         return tensor.data.cpu().numpy()
137 | 
138 | 
139 | def train_single_model(model, trainloader, cfg):
140 |     train_deeplab(model, trainloader, cfg)


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/ADVENT/advent/unet/__init__.py:
--------------------------------------------------------------------------------
1 | from .unet_model import UNet
2 | 


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/ADVENT/advent/unet/unet_model.py:
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 1 | """ Full assembly of the parts to form the complete network """
 2 | 
 3 | import torch.nn.functional as F
 4 | 
 5 | from .unet_parts import *
 6 | 
 7 | 
 8 | class UNet(nn.Module):
 9 |     def __init__(self, n_channels, n_classes, bilinear=True):
10 |         super(UNet, self).__init__()
11 |         self.n_channels = n_channels
12 |         self.n_classes = n_classes
13 |         self.bilinear = bilinear
14 | 
15 |         self.inc = DoubleConv(n_channels, 64)
16 |         self.down1 = Down(64, 128)
17 |         self.down2 = Down(128, 256)
18 |         self.down3 = Down(256, 512)
19 |         factor = 2 if bilinear else 1
20 |         self.down4 = Down(512, 1024 // factor)
21 |         self.up1 = Up(1024, 512 // factor, bilinear)
22 |         self.up2 = Up(512, 256 // factor, bilinear)
23 |         self.up3 = Up(256, 128 // factor, bilinear)
24 |         self.up4 = Up(128, 64, bilinear)
25 |         self.outc = OutConv(64, n_classes)
26 | 
27 |     def forward(self, x):
28 |         x1 = self.inc(x)
29 |         x2 = self.down1(x1)
30 |         x3 = self.down2(x2)
31 |         x4 = self.down3(x3)
32 |         x5 = self.down4(x4)
33 |         x = self.up1(x5, x4)
34 |         x = self.up2(x, x3)
35 |         x = self.up3(x, x2)
36 |         x = self.up4(x, x1)
37 |         logits = self.outc(x)
38 |         return logits
39 | 


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/ADVENT/advent/unet/unet_parts.py:
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 1 | """ Parts of the U-Net model """
 2 | 
 3 | import torch
 4 | import torch.nn as nn
 5 | import torch.nn.functional as F
 6 | 
 7 | 
 8 | class DoubleConv(nn.Module):
 9 |     """(convolution => [BN] => ReLU) * 2"""
10 | 
11 |     def __init__(self, in_channels, out_channels, mid_channels=None):
12 |         super().__init__()
13 |         if not mid_channels:
14 |             mid_channels = out_channels
15 |         self.double_conv = nn.Sequential(
16 |             nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
17 |             nn.BatchNorm2d(mid_channels),
18 |             nn.ReLU(inplace=True),
19 |             nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
20 |             nn.BatchNorm2d(out_channels),
21 |             nn.ReLU(inplace=True)
22 |         )
23 | 
24 |     def forward(self, x):
25 |         return self.double_conv(x)
26 | 
27 | 
28 | class Down(nn.Module):
29 |     """Downscaling with maxpool then double conv"""
30 | 
31 |     def __init__(self, in_channels, out_channels):
32 |         super().__init__()
33 |         self.maxpool_conv = nn.Sequential(
34 |             nn.MaxPool2d(2),
35 |             DoubleConv(in_channels, out_channels)
36 |         )
37 | 
38 |     def forward(self, x):
39 |         return self.maxpool_conv(x)
40 | 
41 | 
42 | class Up(nn.Module):
43 |     """Upscaling then double conv"""
44 | 
45 |     def __init__(self, in_channels, out_channels, bilinear=True):
46 |         super().__init__()
47 | 
48 |         # if bilinear, use the normal convolutions to reduce the number of channels
49 |         if bilinear:
50 |             self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
51 |             self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
52 |         else:
53 |             self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
54 |             self.conv = DoubleConv(in_channels, out_channels)
55 | 
56 | 
57 |     def forward(self, x1, x2):
58 |         x1 = self.up(x1)
59 |         # input is CHW
60 |         diffY = x2.size()[2] - x1.size()[2]
61 |         diffX = x2.size()[3] - x1.size()[3]
62 | 
63 |         x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
64 |                         diffY // 2, diffY - diffY // 2])
65 |         # if you have padding issues, see
66 |         # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
67 |         # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
68 |         x = torch.cat([x2, x1], dim=1)
69 |         return self.conv(x)
70 | 
71 | 
72 | class OutConv(nn.Module):
73 |     def __init__(self, in_channels, out_channels):
74 |         super(OutConv, self).__init__()
75 |         self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
76 | 
77 |     def forward(self, x):
78 |         return self.conv(x)
79 | 


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/ADVENT/advent/utils/__init__.py:
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1 | import pathlib
2 | 
3 | project_root = pathlib.Path(__file__).resolve().parents[2]
4 | 
5 | __all__ = ['project_root']
6 | 


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/ADVENT/advent/utils/func.py:
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 1 | import numpy as np
 2 | import torch
 3 | import torch.nn as nn
 4 | 
 5 | from advent.utils.loss import cross_entropy_2d
 6 | 
 7 | 
 8 | def bce_loss(y_pred, y_label):
 9 |     y_truth_tensor = torch.FloatTensor(y_pred.size())
10 |     y_truth_tensor.fill_(y_label)
11 |     y_truth_tensor = y_truth_tensor.to(y_pred.get_device())
12 |     return nn.BCEWithLogitsLoss()(y_pred, y_truth_tensor)
13 | 
14 | def ls_loss(y_pred, y_label):
15 |     y_truth_tensor = torch.FloatTensor(y_pred.size())
16 |     y_truth_tensor.fill_(y_label)
17 |     y_truth_tensor = y_truth_tensor.to(y_pred.get_device())
18 |     return nn.MSELoss()(y_pred, y_truth_tensor)
19 | 
20 | def loss_calc(pred, label, device):
21 |     """
22 |     This function returns cross entropy loss for semantic segmentation
23 |     """
24 |     # out shape batch_size x channels x h x w -> batch_size x channels x h x w
25 |     # label shape h x w x 1 x batch_size  -> batch_size x 1 x h x w
26 |     label = label.long().to(device)
27 |     return cross_entropy_2d(pred, label)
28 | 
29 | 
30 | def lr_poly(base_lr, iter, max_iter, power):
31 |     """ Poly_LR scheduler
32 |     """
33 |     return base_lr * ((1 - float(iter) / max_iter) ** power)
34 | 
35 | 
36 | def _adjust_learning_rate(optimizer, i_iter, cfg, learning_rate):
37 |     lr = lr_poly(learning_rate, i_iter, cfg.TRAIN.MAX_ITERS, cfg.TRAIN.POWER)
38 |     optimizer.param_groups[0]['lr'] = lr
39 |     if len(optimizer.param_groups) > 1:
40 |         optimizer.param_groups[1]['lr'] = lr * 10
41 | 
42 | 
43 | def adjust_learning_rate(optimizer, i_iter, cfg):
44 |     """ adject learning rate for main segnet
45 |     """
46 |     _adjust_learning_rate(optimizer, i_iter, cfg, cfg.TRAIN.LEARNING_RATE)
47 | 
48 | 
49 | def adjust_learning_rate_discriminator(optimizer, i_iter, cfg):
50 |     _adjust_learning_rate(optimizer, i_iter, cfg, cfg.TRAIN.LEARNING_RATE_D)
51 | 
52 | 
53 | def prob_2_entropy(prob):
54 |     """ convert probabilistic prediction maps to weighted self-information maps
55 |     """
56 |     n, c, h, w = prob.size()
57 |     return -torch.mul(prob, torch.log2(prob + 1e-30)) / np.log2(c)
58 | 
59 | 
60 | def fast_hist(a, b, n):
61 |     k = (a >= 0) & (a < n)
62 |     return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)
63 | 
64 | 
65 | def per_class_iu(hist):
66 |     return np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
67 | 


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/ADVENT/advent/utils/loss.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import torch
 3 | import torch.nn.functional as F
 4 | from torch.autograd import Variable
 5 | 
 6 | 
 7 | def cross_entropy_2d(predict, target):
 8 |     """
 9 |     Args:
10 |         predict:(n, c, h, w)
11 |         target:(n, h, w)
12 |     """
13 |     assert not target.requires_grad
14 |     assert predict.dim() == 4
15 |     assert target.dim() == 3
16 |     assert predict.size(0) == target.size(0), f"{predict.size(0)} vs {target.size(0)}"
17 |     assert predict.size(2) == target.size(1), f"{predict.size(2)} vs {target.size(1)}"
18 |     assert predict.size(3) == target.size(2), f"{predict.size(3)} vs {target.size(3)}"
19 |     n, c, h, w = predict.size()
20 |     target_mask = (target >= 0) * (target != 255)
21 |     target = target[target_mask]
22 |     if not target.data.dim():
23 |         return Variable(torch.zeros(1))
24 |     predict = predict.transpose(1, 2).transpose(2, 3).contiguous()
25 |     predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)
26 |     loss = F.cross_entropy(predict, target, size_average=True)
27 |     return loss
28 | 
29 | 
30 | def entropy_loss(v):
31 |     """
32 |         Entropy loss for probabilistic prediction vectors
33 |         input: batch_size x channels x h x w
34 |         output: batch_size x 1 x h x w
35 |     """
36 |     assert v.dim() == 4
37 |     n, c, h, w = v.size()
38 |     return -torch.sum(torch.mul(v, torch.log2(v + 1e-30))) / (n * h * w * np.log2(c))
39 | 


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/ADVENT/advent/utils/serialization.py:
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 1 | import pickle
 2 | import json
 3 | import yaml
 4 | from pathlib import Path
 5 | import os
 6 | 
 7 | 
 8 | def make_parent(file_path):
 9 |     file_path = Path(file_path)
10 |     os.makedirs(file_path.parent, exist_ok=True)
11 | 
12 | 
13 | def pickle_dump(python_object, file_path):
14 |     make_parent(file_path)
15 |     with open(file_path, 'wb') as f:
16 |         pickle.dump(python_object, f)
17 | 
18 | 
19 | def pickle_load(file_path):
20 |     with open(file_path, 'rb') as f:
21 |         return pickle.load(f)
22 | 
23 | 
24 | def json_load(file_path):
25 |     with open(file_path, 'r') as fp:
26 |         return json.load(fp)
27 | 
28 | 
29 | def yaml_dump(python_object, file_path):
30 |     make_parent(file_path)
31 |     with open(file_path, 'w') as f:
32 |         yaml.dump(python_object, f, default_flow_style=False)
33 | 
34 | 
35 | def yaml_load(file_path):
36 |     with open(file_path, 'r') as f:
37 |         return yaml.load(f)
38 | 


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/ADVENT/advent/utils/viz_segmask.py:
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 1 | import numpy as np
 2 | from PIL import Image
 3 | 
 4 | palette = [128, 64, 128, 244, 35, 232, 70, 70, 70, 102, 102, 156,
 5 |            190, 153, 153, 153, 153, 153, 250,
 6 |            170, 30,
 7 |            220, 220, 0, 107, 142, 35, 152, 251, 152,
 8 |            70, 130, 180, 220, 20, 60, 255, 0, 0, 0, 0,
 9 |            142, 0, 0, 70,
10 |            0, 60, 100, 0, 80, 100, 0, 0, 230, 119, 11, 32]
11 | zero_pad = 256 * 3 - len(palette)
12 | for i in range(zero_pad):
13 |     palette.append(0)
14 | 
15 | 
16 | def colorize_mask(mask):
17 |     # mask: numpy array of the mask
18 |     new_mask = Image.fromarray(mask.astype(np.uint8)).convert('P')
19 |     new_mask.putpalette(palette)
20 |     return new_mask
21 | 


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/ADVENT/setup.py:
--------------------------------------------------------------------------------
 1 | from setuptools import find_packages
 2 | from setuptools import setup
 3 | 
 4 | setup(name='ADVENT',
 5 |       install_requires=['pyyaml', 'tensorboardX',
 6 |                         'easydict', 'matplotlib',
 7 |                         'scipy', 'scikit-image',
 8 |                         'future', 'setuptools',
 9 |                         'tqdm', 'cffi'],
10 |       packages=find_packages())
11 | 


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/ADVENT/teaser.jpg:
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https://raw.githubusercontent.com/BOBrown/SegNet_Source/4dc9c93c77a64b0338038cc9e192006b11a5efe2/ADVENT/teaser.jpg


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/ADVENT/tox.ini:
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1 | [flake8]
2 | exclude = .git,__pycache__,build,dist
3 | max-line-length = 99
4 | ignore = E121,E123,E126,E226,E24,E704,W503,W504,N812
5 | 


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/README.md:
--------------------------------------------------------------------------------
 1 | <div align="center">
 2 |   <h1>Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation<br></h1>
 3 | </div>
 4 | 
 5 | <!-- <div align="center">
 6 |   <h3><a href=></a>, <a href=></a>, <a href=></a>, <a href=></a></h3>
 7 | </div> -->
 8 | 
 9 | <div align="center">
10 |   <h4> <a href=https://ieeexplore.ieee.org/document/9576523>[paper] IEEE Link</a></h4>
11 | </div>
12 | 
13 | <div align="center">
14 |   <h4> <a href=https://ieeexplore.ieee.org/document/9576523>[paper] ArXiv Link</a></h4>
15 | </div>
16 | 
17 | <div align="center">
18 |   <img src="./figs_tabs/2.png" width=800>
19 | </div>
20 | 
21 | 
22 |  <br><br/>
23 |  
24 | If you find our code or paper useful to your research work, please consider citing our work using the following bibtex:
25 | ```
26 | @article{zhang2021curriculum,
27 |   title={Curriculum-Style Local-to-Global Adaptation for Cross-Domain Remote Sensing Image Segmentation},
28 |   author={Zhang, Bo and Chen, Tao and Wang, Bin},
29 |   journal={IEEE Transactions on Geoscience and Remote Sensing},
30 |   volume={60},
31 |   pages={1--12},
32 |   year={2021},
33 |   publisher={IEEE}
34 | }
35 | ```
36 | 
37 | ## Preprocessing data
38 | Following [DualGAN](https://www.sciencedirect.com/science/article/pii/S0924271621000423), we crop the whole images in Potsdam IR-R-G dataset into the size of 512 × 512 with both horizontal and vertical strides of 512 pixels, and generate 4598 patches. For Vaihingen dataset, we crop the whole images into a size of 512 × 512 with both horizontal and vertical strides of 256 pixels and obtain 1696 patches
39 | 
40 | The following processed datasets are used in our paper: 
41 | - PotsdamIRRG \[[Dataset Page](https://drive.google.com/file/d/1EuTBY25cq65KBYfCcCkcMqB0pMOQHGNw/view?usp=sharing)\]
42 | - PotsdamRGB \[[Dataset Page](https://drive.google.com/file/d/1EuTBY25cq65KBYfCcCkcMqB0pMOQHGNw/view?usp=sharing)\]
43 | - Vaihingen \[[Dataset Page](https://drive.google.com/file/d/1EuTBY25cq65KBYfCcCkcMqB0pMOQHGNw/view?usp=sharing)\]
44 | 
45 | After dowloading datasets, copy the data.zip to /ADVENT/. and extract it:
46 | ```
47 | unzip data.zip
48 | ```
49 | 
50 | ## Dowloading the ImageNet pretrained model
51 | - ImageNet pretrained weights \[[Dataset Page](https://drive.google.com/file/d/1CZIJ5IJMPrsFB5URU76GJ5LjWqpRKjSM/view?usp=sharing)\]
52 | 
53 | ## Train the source-only model from ImageNet pretrained model
54 | ```
55 | cd /SegNet_Source/ADVENT/.
56 | pip install -e .
57 | cd /SegNet_Source/ADVENT/scripts/.
58 | python train.py --cfg /root/code/SegNet_Source/ADVENT/advent/scripts/configs/advent.yml
59 | ```
60 | 
61 | ## Test the source-only model
62 | ```
63 | python test.py --cfg /root/code/SegNet_Source/ADVENT/advent/scripts/configs/advent.yml
64 | ```
65 | 
66 | ## Contact
67 | We have tried our best to verify the correctness of our released data, code and trained model weights. 
68 | However, there are a large number of experiment settings, all of which have been extracted and reorganized from our original codebase. 
69 | There may be some undetected bugs or errors in the current release. 
70 | If you encounter any issues or have questions about using this code, please feel free to contact us via bo.zhangzx@gmail.com
71 | 
72 | 


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