[Feature] Support ACDC dataset

README.md

@@ -264,6 +264,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 - [x] [LEVIR-CD](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#levir-cd)
 - [x] [BDD100K](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#bdd100K)
 - [x] [NYU](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#nyu)
+- [x] [ACDC](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#acdc)
 
 </details>
 

README_zh-CN.md

@@ -253,6 +253,7 @@ MMSegmentation v1.x 在 0.x 版本的基础上有了显著的提升，提供了
 - [x] [LEVIR-CD](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/zh_cn/user_guides/2_dataset_prepare.md#levir-cd)
 - [x] [BDD100K](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/zh_cn/user_guides/2_dataset_prepare.md#bdd100K)
 - [x] [NYU](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#nyu)
+- [x] [ACDC](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/zh_cn/user_guides/2_dataset_prepare.md#acdc)
 
 </details>
 

configs/_base_/datasets/acdc.py

@@ -0,0 +1,65 @@
+# dataset settings
+dataset_type = 'ACDCDataset'
+data_root = 'data/acdc/'
+crop_size = (512, 1024)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(
+        type='RandomResize',
+        scale=(2048, 1024),
+        ratio_range=(0.5, 2.0),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
+    # add loading annotation after ``Resize`` because ground truth
+    # does not need to do resize data transform
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs')
+]
+img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
+tta_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=None),
+    dict(
+        type='TestTimeAug',
+        transforms=[
+            [
+                dict(type='Resize', scale_factor=r, keep_ratio=True)
+                for r in img_ratios
+            ],
+            [
+                dict(type='RandomFlip', prob=0., direction='horizontal'),
+                dict(type='RandomFlip', prob=1., direction='horizontal')
+            ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
+        ])
+]
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(img_path='rgb_anno/train', seg_map_path='gt/train'),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(img_path='rgb_anno/test', seg_map_path='gt/test'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator

configs/pspnet/pspnet_r50-d8_4xb2-40k_acdc-512x1024.py

@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/acdc.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (512, 1024)
+data_preprocessor = dict(size=crop_size)
+model = dict(data_preprocessor=data_preprocessor)

docs/en/user_guides/2_dataset_prepare.md

@@ -205,6 +205,13 @@ mmsegmentation
 │   │   ├── annotations
 │   │   │   ├── train
 │   │   │   ├── test
+│   ├── acdc
+│   │   ├── gt
+│   │   │   ├── test
+│   │   │   ├── train
+│   │   ├── rgb_anno
+│   │   │   ├── test
+│   │   │   ├── train
 ```
 
 ## Download dataset via MIM
@@ -752,3 +759,30 @@ mmsegmentation
   ```bash
   python tools/dataset_converters/nyu.py nyu.zip
   ```
+
+## ACDC Dataset
+
+The data could be found [here](https://acdc.vision.ee.ethz.ch/download).
+
+Download and unzip the data into `$ACDC`, it should have the following structure:
+
+```
+$ACDC
+├── gt
+│   ├── fog
+│   ├── night
+│   ├── rain
+│   ├── snow
+├── rgb_anon
+│   ├── fog
+│   ├── night
+│   ├── rain
+│   ├── snow
+```
+
+We provided a script to process the ACDC dataset into mmsegmentation accepted format.
+
+```shell
+# --split means the weather split, available options [fog, night, rain, snow, all]
+python tools/dataset_converters/acdc.py $ACDC --split $split
+```

docs/zh_cn/user_guides/2_dataset_prepare.md

@@ -748,3 +748,30 @@ mmsegmentation
   ```bash
   python tools/dataset_converters/nyu.py nyu.zip
   ```
+
+## ACDC
+
+您可以从 [这个链接](https://acdc.vision.ee.ethz.ch/download) 下载 ACDC 数据集。
+
+下载完成后请将数据集解压缩到 `$ACDC`, 得到以下结构:
+
+```
+$ACDC
+├── gt
+│   ├── fog
+│   ├── night
+│   ├── rain
+│   ├── snow
+├── rgb_anon
+│   ├── fog
+│   ├── night
+│   ├── rain
+│   ├── snow
+```
+
+请运行以下命令转换 ACDC 数据集：
+
+```shell
+# --split 是天气选项, 可以从 [fog, night, rain, snow, all] 中选择
+python tools/dataset_converters/acdc.py $ACDC --split $split
+```

mmseg/datasets/__init__.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # yapf: disable
+from .acdc import ACDCDataset
 from .ade import ADE20KDataset
 from .basesegdataset import BaseCDDataset, BaseSegDataset
 from .bdd100k import BDD100KDataset
@@ -60,5 +61,5 @@
     'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset',
     'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile',
     'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset',
-    'NYUDataset'
+    'NYUDataset', 'ACDCDataset'
 ]

mmseg/datasets/acdc.py

@@ -0,0 +1,15 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmseg.registry import DATASETS
+from .cityscapes import CityscapesDataset
+
+
+@DATASETS.register_module()
+class ACDCDataset(CityscapesDataset):
+    """ACDCDataset dataset."""
+
+    def __init__(self,
+                 img_suffix='_rgb_anon.png',
+                 seg_map_suffix='_gt_labelTrainIds.png',
+                 **kwargs) -> None:
+        super().__init__(
+            img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs)

tools/dataset_converters/acdc.py

@@ -0,0 +1,72 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import shutil
+
+from mmengine.utils import mkdir_or_exist
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert ACDC dataset to mmsegmentation format')
+    parser.add_argument('raw_data', help='the path of raw data')
+    parser.add_argument(
+        '-o', '--out_dir', help='output path', default='./data/acdc')
+    parser.add_argument(
+        '--split',
+        choices=['fog', 'night', 'rain', 'snow', 'all'],
+        default='night')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    print('Making directories...')
+    mkdir_or_exist(args.out_dir)
+    for subdir in ['gt/test', 'rgb_anno/test', 'gt/train', 'rgb_anno/train']:
+        mkdir_or_exist(osp.join(args.out_dir, subdir))
+
+    print('Moving images and annotations...')
+
+    if args.split == 'all':
+        anno_str_test = '/val/'
+        gt_str_test = '/val/'
+        anno_str_train = '/train/'
+        gt_str_train = '/train/'
+    else:
+        anno_str_test = f'rgb_anon/{args.split}/val/'
+        gt_str_test = f'gt/{args.split}/val/'
+        anno_str_train = f'rgb_anon/{args.split}/train/'
+        gt_str_train = f'gt/{args.split}/train/'
+
+    for dirpath, _, filenames in os.walk(args.raw_data):
+        for filename in filenames:
+            full_path = os.path.join(dirpath, filename)
+
+            if anno_str_test in full_path:
+                if full_path.endswith('_rgb_anon.png'):
+                    new_path = os.path.join(args.out_dir, 'rgb_anno/test',
+                                            filename)
+                    shutil.copy(full_path, new_path)
+            elif gt_str_test in full_path:
+                if full_path.endswith('_gt_labelTrainIds.png'):
+                    new_path = os.path.join(args.out_dir, 'gt/test', filename)
+                    shutil.copy(full_path, new_path)
+            if anno_str_train in full_path:
+                if full_path.endswith('_rgb_anon.png'):
+                    new_path = os.path.join(args.out_dir, 'rgb_anno/train',
+                                            filename)
+                    shutil.copy(full_path, new_path)
+            elif gt_str_train in full_path:
+                if full_path.endswith('_gt_labelTrainIds.png'):
+                    new_path = os.path.join(args.out_dir, 'gt/train', filename)
+                    shutil.copy(full_path, new_path)
+
+    print('Done!')
+
+
+if __name__ == '__main__':
+    main()