formatting

Author: jinwonkim93

configs/_base_/datasets/occlude_face.py

@@ -55,7 +55,6 @@
     split='train.txt',
     pipeline=train_pipeline)
 
-
 dataset_train_C = dict(
     type=dataset_type,
     data_root=data_root,
@@ -65,17 +64,15 @@
     pipeline=train_pipeline)
 
 dataset_valid = dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='RealOcc/image',
-        ann_dir='RealOcc/mask',
-        split='RealOcc/split/val.txt',
-        pipeline=test_pipeline)
+    type=dataset_type,
+    data_root=data_root,
+    img_dir='RealOcc/image',
+    ann_dir='RealOcc/mask',
+    split='RealOcc/split/val.txt',
+    pipeline=test_pipeline)
 
 data = dict(
     samples_per_gpu=2,
     workers_per_gpu=2,
-    train=[
-            dataset_train_A, dataset_train_B, dataset_train_C
-    ],
-    val= dataset_valid)
+    train=[dataset_train_A, dataset_train_B, dataset_train_C],
+    val=dataset_valid)

docs/en/dataset_prepare.md

@@ -1,4 +1,5 @@
 <!-- #region -->
+
 ## Prepare datasets
 
 It is recommended to symlink the dataset root to `$MMSEGMENTATION/data`.
@@ -139,6 +140,21 @@ mmsegmentation
 │   │   ├── ann_dir
 │   │   │   ├── train
 │   │   │   ├── val
+│   ├── occlusion-aware-face-dataset
+│   │   ├── train.txt
+│   │   ├── NatOcc_hand_sot
+│   │   │   ├── img
+│   │   │   ├── mask
+│   │   ├── NatOcc_object
+│   │   │   ├── img
+│   │   │   ├── mask
+│   │   ├── RandOcc
+│   │   │   ├── img
+│   │   │   ├── mask
+│   │   ├── RealOcc
+│   │   │   ├── img
+│   │   │   ├── mask
+│   │   │   ├── split
 ```
 
 ### Cityscapes
@@ -378,21 +394,20 @@ python tools/convert_datasets/isaid.py /path/to/iSAID
 
 In our default setting (`patch_width`=896, `patch_height`=896,　`overlap_area`=384), it will generate 33978 images for training and 11644 images for validation.
 
-
 ### Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets
 
 The dataset is generated by two techniques, Naturalistic occlusion generation, Random occlusion generation. you must install face-occlusion-generation and dataset. see more guide in https://github.com/kennyvoo/face-occlusion-generation.git
 
-  
 ## Dataset Preparation
 
-Please download the masks from this [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing) and the images from [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ), [11k Hands](https://sites.google.com/view/11khands) and [DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/). 
+Please download the masks from this [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing) and the images from [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ), [11k Hands](https://sites.google.com/view/11khands) and [DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/).
 
 The extracted and upsampled COCO objects images and masks can be found in this [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing).
 
-Please extract CelebAMask-HQ and 11k Hands images based on the splits found in [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing). 
+Please extract CelebAMask-HQ and 11k Hands images based on the splits found in [drive](https://drive.google.com/drive/folders/15nZETWlGMdcKY6aHbchRsWkUI42KTNs5?usp=sharing).
 
 download file to ./data_materials
+
 ```none
 CelebAMask-HQ.zip
 CelebAMask-HQ-masks_corrected.7z
@@ -403,7 +418,8 @@ RealOcc-Wild.7z
 coco_object.7z
 dtd-r1.0.1.tar.gz
 ```
----
+
+______________________________________________________________________
 
 ```bash
 apt-get install p7zip-full
@@ -420,7 +436,7 @@ xargs -n 1 -i echo {}.png < train.txt > mask_train.txt
 rsync -a ./CelebAMask-HQ/CelebAMask-HQ-masks_corrected/ --files-from=./mask_train.txt ./CelebAMask-HQ-WO-Train_mask
 mv train.txt ../data/occlusion-aware-face-dataset
 
-#extact DTD
+#extract DTD
 tar -zxvf dtd-r1.0.1.tar.gz
 mv dtd DTD
 
@@ -438,7 +454,6 @@ mv coco_object/* .
 
 ```
 
-
 **Dataset Organization:**
 
 ```none
@@ -481,7 +496,7 @@ mv coco_object/* .
 │   │   │   │   ├── {image}.jpg
 │   │   │   ├── mask
 │   │   │   │   ├── {mask}.png
-│   │   ├── RandOcc
+│   │   ├── RealOcc
 │   │   │   ├── img
 │   │   │   │   ├── {image}.jpg
 │   │   │   ├── mask
@@ -496,7 +511,8 @@ mv coco_object/* .
 git clone https://github.com/jinwonkim93/face-occlusion-generation.git
 cd face_occlusion-generation
 ```
-Example script to generate NatOcc hand dataset 
+
+Example script to generate NatOcc hand dataset
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
@@ -508,7 +524,8 @@ SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-
 OCCLUDER_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/11k-hands_img" \
 OCCLUDER_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/11k-hands_masks"
 ```
-Example script to generate NatOcc object dataset 
+
+Example script to generate NatOcc object dataset
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 NUM_WORKERS=4 python main.py \
@@ -519,6 +536,7 @@ SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-
 OCCLUDER_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/object_image_sr" \
 OCCLUDER_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/object_mask_x4"
 ```
+
 Example script to generate RandOcc dataset
 
 ```bash
@@ -529,8 +547,5 @@ SOURCE_DATASET.IMG_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-T
 SOURCE_DATASET.MASK_DIR "path/to/mmsegmentation/data_materials/CelebAMask-HQ-WO-Train_mask" \
 OCCLUDER_DATASET.IMG_DIR "path/to/jw93/mmsegmentation/data_materials/DTD/images"
 ```
-<!-- #endregion -->
 
-```python
-
-```
+<!-- #endregion -->