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jinwonkim93
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update prepare.md
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‎configs/_base_/datasets/occlude_face.py

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@@ -42,20 +42,28 @@
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dataset_train_A = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='CelebAMask-HQ-original/image',
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ann_dir='CelebAMask-HQ-original/mask_edited',
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split='CelebAMask-HQ-original/split/train.txt',
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img_dir='NatOcc_hand_sot/img',
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ann_dir='NatOcc_hand_sot/mask',
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split='train.txt',
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pipeline=train_pipeline)
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dataset_train_B = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='NatOcc-SOT/image',
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ann_dir='NatOcc-SOT/mask',
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split='NatOcc-SOT/split/train.txt',
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img_dir='NatOcc_object/img',
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ann_dir='NatOcc_object/mask',
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split='train.txt',
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pipeline=train_pipeline)
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dataset_train_C = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='RandOcc/img',
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ann_dir='RandOcc/mask',
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split='train.txt',
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pipeline=train_pipeline)
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dataset_valid = dict(
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type=dataset_type,
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data_root=data_root,
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split='RealOcc/split/val.txt',
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pipeline=test_pipeline)
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dataset_test = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='RealOcc/image',
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ann_dir='RealOcc/mask',
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split='RealOcc/test.txt',
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pipeline=test_pipeline)
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=[
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dataset_train_A,dataset_train_B
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dataset_train_A, dataset_train_B, dataset_train_C
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],
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val= dataset_valid,
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test=dataset_test)
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val= dataset_valid)

‎configs/deeplabv3plus/deeplabv3plus_r101_512x512_C-CM+C-WO-NatOcc-SOT.py

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evaluation = dict(
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interval=400, metric=['mIoU', 'mDice', 'mFscore'], pre_eval=True)
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work_dir = './work_dirs/deeplabv3plus_r101_512x512_C-CM+C-WO-NatOcc-SOT'
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gpu_ids = range(0, 2)
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auto_resume = False

‎docs/en/dataset_prepare.md

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@@ -392,11 +392,58 @@ The extracted and upsampled COCO objects images and masks can be found in this [
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Please extract CelebAMask-HQ and 11k Hands images based on the splits found in [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing).
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download file to ./data_materials
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```none
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CelebAMask-HQ.zip
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CelebAMask-HQ-masks_corrected.7z
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RealOcc.7z
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RealOcc-Wild.7z
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11k-hands_mask.7z
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11k-hands_image.7z
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coco_object.7z
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dtd-r1.0.1.tar.gz
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```
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---
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```bash
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apt-get install p7zip-full
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cd data_materials
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#extract celebAMask-HQ and split by train-set
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unzip CelebAMask-HQ.zip
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7za x CelebAMask-HQ-masks_corrected.7z -o./CelebAMask-HQ
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#suggest better code if you have
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rsync -a ./CelebAMask-HQ/CelebA-HQ-img/ --files-from=./CelebAMask-HQ-WO-train.txt ./CelebAMask-HQ-WO-Train_img
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basename -s .jpg ./CelebAMask-HQ-train/* > train.txt
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xargs -n 1 -i echo {}.png < train.txt > mask_train.txt
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rsync -a ./CelebAMask-HQ/CelebAMask-HQ-masks_corrected/ --files-from=./mask_train.txt ./CelebAMask-HQ-WO-Train_mask
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mv train.txt ../data/occlusion-aware-face-dataset
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#extact DTD
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tar -zxvf dtd-r1.0.1.tar.gz
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mv dtd DTD
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#extract hands dataset and split by 200 samples
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7za x 11k-hands_masks.7z -o.
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unzip Hands.zip
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rsync -a ./Hands/ --files-from=./11k_hands_sample.txt ./11k-hands_img
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#extract upscaled coco object
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7za x coco_object.7z -o.
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mv coco_object/* .
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#extract validation set
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7za x RealOcc.7z -o../data/occlusion-aware-face-dataset
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```
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**Dataset Organization:**
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```none
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├── dataset
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├── data_materials
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│ ├── CelebAMask-HQ-WO-Train_img
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│ │ ├── {image}.jpg
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│ ├── CelebAMask-HQ-WO-Train_mask
@@ -413,20 +460,75 @@ Please extract CelebAMask-HQ and 11k Hands images based on the splits found in [
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│ │ ├── {mask}.png
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│ ├── object_image_sr
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│ │ ├── {image}.jpg
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│ ├── object_image_x4
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│ ├── object_mask_x4
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│ │ ├── {mask}.png
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├── data
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│ ├── occlusion-aware-face-dataset
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│ │ ├── train.txt
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│ │ ├── NatOcc_hand_sot
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│ │ │ ├── img
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│ │ │ │ ├── {image}.jpg
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│ │ │ ├── mask
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│ │ │ │ ├── {mask}.png
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│ │ ├── NatOcc_object
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│ │ │ ├── img
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│ │ │ │ ├── {image}.jpg
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│ │ │ ├── mask
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│ │ │ │ ├── {mask}.png
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│ │ ├── RandOcc
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│ │ │ ├── img
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│ │ │ │ ├── {image}.jpg
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│ │ │ ├── mask
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│ │ │ │ ├── {mask}.png
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│ │ ├── RandOcc
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│ │ │ ├── img
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│ │ │ │ ├── {image}.jpg
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│ │ │ ├── mask
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│ │ │ │ ├── {mask}.png
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│ │ │ ├── split
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│ │ │ │ ├── val.txt
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```
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## Data Generation
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Example script to generate NatOcc dataset
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bash NatOcc.sh
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```bash
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git clone https://github.com/jinwonkim93/face-occlusion-generation.git
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cd face_occlusion-generation
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```
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Example script to generate NatOcc hand dataset
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```bash
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CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
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--config ./configs/natocc_hand.yaml \
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--opts OUTPUT_PATH "path/to/mmsegmentation/data/occlusion-aware-face-dataset/NatOcc_hand_sot"\
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AUGMENTATION.SOT True \
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SOURCE_DATASET.IMG_DIR "path/to/data_materials/CelebAMask-HQ-WO-Train_img" \
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SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
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OCCLUDER_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/11k-hands_img" \
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OCCLUDER_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/11k-hands_masks"
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```
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Example script to generate NatOcc object dataset
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```bash
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CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
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--config ./configs/natocc_objects.yaml \
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--opts OUTPUT_PATH "path/to/mmsegmentation/data/occlusion-aware-face-dataset/NatOcc_object" \
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SOURCE_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_img" \
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SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
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OCCLUDER_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/object_image_sr" \
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OCCLUDER_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/object_mask_x4"
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```
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Example script to generate RandOcc dataset
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bash RandOcc.sh
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```bash
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CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
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--config ./configs/randocc.yaml \
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--opts OUTPUT_PATH "path/to/mmsegmentation/data/occlusion-aware-face-dataset/RandOcc" \
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SOURCE_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_img/" \
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SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
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OCCLUDER_DATASET.IMG_DIR "path/to/jw93/mmsegmentation/data_materials/DTD/images"
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```
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<!-- #endregion -->
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```python

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