update LoveDA dataset api

configs/_base_/datasets/loveda.py

@@ -0,0 +1,49 @@
+# dataset settings
+dataset_type = 'LoveDADataset'
+data_root = 'data/loveda'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 512)
+train_pipeline = [
+    # dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', reduce_zero_label=True),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    # dict(type='LoadImageFromFile'),
+    dict(
+        img_scale=(1024, 1024),
+        transforms=[
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        reduce_zero_label=True,
+        img_dir='img_dir/train',
+        ann_dir='ann_dir/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        reduce_zero_label=True,
+        img_dir='img_dir/val',
+        ann_dir='ann_dir/val',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        reduce_zero_label=True,
+        img_dir='img_dir/test',
+        ann_dir=None,
+        pipeline=test_pipeline))

docs/dataset_prepare.md

@@ -253,3 +253,31 @@ Since we only support test models on this dataset, you may only download [the va
 ### Nighttime Driving
 
 Since we only support test models on this dataset, you may only download [the test set](http://data.vision.ee.ethz.ch/daid/NighttimeDriving/NighttimeDrivingTest.zip).
+
+
+### LoveDA
+The data could be downloaded [here](https://drive.google.com/drive/folders/1ibYV0qwn4yuuh068Rnc-w4tPi0U0c-ti?usp=sharing)
+
+For LoveDA dataset, please run the following command to download and re-organize the dataset.
+
+```shell
+# download
+mkdir loveda && cd loveda
+wget https://drive.google.com/drive/folders/1ibYV0qwn4yuuh068Rnc-w4tPi0U0c-ti?usp=sharing
+
+# unzip
+unzip \*.zip
+
+# Convert into segmentation splits
+mkdir -p img_dir/train img_dir/val img_dir/test ann_dir/train ann_dir/val
+mv Train\Rural\images_png\* img_dir\train
+mv Train\Urban\images_png\* img_dir\train
+mv Val\Rural\images_png\* img_dir\val
+mv Val\Uban\images_png\* img_dir\val
+mv Test\Rural\images_png\* img_dir\val
+mv Test\Uban\images_png\* img_dir\val
+mv Train\Rural\masks_png\* ann_dir\train
+mv Train\Uban\masks_png\* ann_dir\train
+mv Val\Rural\masks_png\* ann_dir\val
+mv Val\Uban\masks_png\* ann_dir\val
+```

docs_zh-CN/dataset_prepare.md

@@ -195,3 +195,31 @@ python tools/convert_datasets/stare.py /path/to/stare-images.tar /path/to/labels
 ### Nighttime Driving
 
 因为我们只支持在此数据集上测试模型，所以您只需下载[测试集](http://data.vision.ee.ethz.ch/daid/NighttimeDriving/NighttimeDrivingTest.zip)。
+
+
+### LoveDA
+
+下载[LoveDA数据集](https://drive.google.com/drive/folders/1ibYV0qwn4yuuh068Rnc-w4tPi0U0c-ti?usp=sharing)。
+
+对于 LoveDA 数据集，请运行以下命令下载并组织数据集
+```shell
+# download
+mkdir loveda && cd loveda
+wget https://drive.google.com/drive/folders/1ibYV0qwn4yuuh068Rnc-w4tPi0U0c-ti?usp=sharing
+
+# unzip
+unzip \*.zip
+
+# Convert into segmentation splits
+mkdir -p img_dir/train img_dir/val img_dir/test ann_dir/train ann_dir/val
+mv Train/Rural/images_png/* img_dir/train
+mv Train/Urban/images_png/* img_dir/train
+mv Val/Rural/images_png/* img_dir/val
+mv Val/Urban/images_png/* img_dir/val
+mv Test/Rural/images_png/* img_dir/val
+mv Test/Urban/images_png/* img_dir/val
+mv Train/Rural/masks_png/* ann_dir/train
+mv Train/Urban/masks_png/* ann_dir/train
+mv Val/Rural/masks_png/* ann_dir/val
+mv Val/Urban/masks_png/* ann_dir/val
+```

mmseg/datasets/__init__.py

@@ -13,6 +13,8 @@ from .night_driving import NightDrivingDataset
 from .pascal_context import PascalContextDataset, PascalContextDataset59
 from .stare import STAREDataset
 from .voc import PascalVOCDataset
+from .loveda import LoveDADataset
+
 
 __all__ = [
     'CustomDataset', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
@@ -20,5 +22,5 @@ __all__ = [
     'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset',
     'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
     'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset',
-    'COCOStuffDataset'
+    'COCOStuffDataset', 'LoveDADataset',
 ]

mmseg/datasets/loveda.py

@@ -0,0 +1,21 @@
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class LoveDADataset(CustomDataset):
+    """LoveDA dataset.
+
+    In segmentation map annotation for LoveDA, 0 is the ignore index.
+    ``reduce_zero_label`` should be set to True.
+    The ``img_suffix`` and ``seg_map_suffix`` are both fixed to '.png'.
+    """
+    CLASSES = ('background', 'building', 'road', 'water', 'barren', 'forest', 'agricultural')
+
+    PALETTE = [[255, 255, 255], [255, 0, 0], [255, 255, 0], [0, 0, 255],
+               [159, 129, 183], [0, 255, 0], [255, 195, 128]]
+
+    def __init__(self, **kwargs):
+        super(LoveDADataset, self).__init__(
+            img_suffix='.png', seg_map_suffix='.png', reduce_zero_label=True, **kwargs)
+

tests/test_data/test_dataset.py

@@ -13,7 +13,7 @@ from PIL import Image
 
 from mmseg.core.evaluation import get_classes, get_palette
 from mmseg.datasets import (DATASETS, ADE20KDataset, CityscapesDataset,
-                            ConcatDataset, CustomDataset, PascalVOCDataset,
+                            ConcatDataset, CustomDataset, PascalVOCDataset, LoveDADataset,
                             RepeatDataset, build_dataset)