[Feature] Add GDAL backend and Support LEVIR-CD Dataset (open-mmlab#2903

) ## Motivation For support with reading multiple remote sensing image formats, please refer to https://gdal.org/drivers/raster/index.html. Byte, UInt16, Int16, UInt32, Int32, Float32, Float64, CInt16, CInt32, CFloat32 and CFloat64 are supported for reading and writing. Support input of two images for change detection tasks, and support the LEVIR-CD dataset. ## Modification Add LoadSingleRSImageFromFile in 'mmseg/datasets/transforms/loading.py'. Load a single remote sensing image for object segmentation tasks. Add LoadMultipleRSImageFromFile in 'mmseg/datasets/transforms/loading.py'. Load two remote sensing images for change detection tasks. Add ConcatCDInput in 'mmseg/datasets/transforms/transforms.py'. Combine images that have been separately augmented for data enhancement. Add BaseCDDataset in 'mmseg/datasets/basesegdataset.py' Base class for datasets used in change detection tasks. --------- Co-authored-by: xiexinch <xiexinch@outlook.com>
SheffieldCao · May 8, 2023 · 77591b9 · 77591b9
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diff --git a/configs/_base_/datasets/levir_256x256.py b/configs/_base_/datasets/levir_256x256.py
@@ -0,0 +1,59 @@
+# dataset settings
+dataset_type = 'LEVIRCDDataset'
+data_root = r'data/LEVIRCD'
+
+albu_train_transforms = [
+    dict(type='RandomBrightnessContrast', p=0.2),
+    dict(type='HorizontalFlip', p=0.5),
+    dict(type='VerticalFlip', p=0.5)
+]
+
+train_pipeline = [
+    dict(type='LoadMultipleRSImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Albu', transforms=albu_train_transforms),
+    dict(type='ConcatCDInput'),
+    dict(type='PackSegInputs')
+]
+test_pipeline = [
+    dict(type='LoadMultipleRSImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='ConcatCDInput'),
+    dict(type='PackSegInputs')
+]
+
+tta_pipeline = [
+    dict(type='LoadMultipleRSImageFromFile'),
+    dict(
+        type='TestTimeAug',
+        transforms=[[dict(type='LoadAnnotations')],
+                    [dict(type='ConcatCDInput')],
+                    [dict(type='PackSegInputs')]])
+]
+train_dataloader = dict(
+    batch_size=4,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='train/A',
+            img_path2='train/B',
+            seg_map_path='train/label'),
+        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='test/A', img_path2='test/B', seg_map_path='test/label'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator
diff --git a/configs/swin/swin-tiny-patch4-window7_upernet_1xb8-20k_levir-256x256.py b/configs/swin/swin-tiny-patch4-window7_upernet_1xb8-20k_levir-256x256.py
@@ -0,0 +1,56 @@
+_base_ = [
+    '../_base_/models/upernet_swin.py', '../_base_/datasets/levir_256x256.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py'
+]
+crop_size = (256, 256)
+norm_cfg = dict(type='BN', requires_grad=True)
+data_preprocessor = dict(
+    size=crop_size,
+    type='SegDataPreProcessor',
+    mean=[123.675, 116.28, 103.53, 123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375, 58.395, 57.12, 57.375])
+
+model = dict(
+    data_preprocessor=data_preprocessor,
+    backbone=dict(
+        in_channels=6,
+        embed_dims=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        use_abs_pos_embed=False,
+        drop_path_rate=0.3,
+        patch_norm=True),
+    decode_head=dict(in_channels=[96, 192, 384, 768], num_classes=2),
+    auxiliary_head=dict(in_channels=384, num_classes=2))
+
+# AdamW optimizer, no weight decay for position embedding & layer norm
+# in backbone
+optim_wrapper = dict(
+    _delete_=True,
+    type='OptimWrapper',
+    optimizer=dict(
+        type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01),
+    paramwise_cfg=dict(
+        custom_keys={
+            'absolute_pos_embed': dict(decay_mult=0.),
+            'relative_position_bias_table': dict(decay_mult=0.),
+            'norm': dict(decay_mult=0.)
+        }))
+
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
+    dict(
+        type='PolyLR',
+        eta_min=0.0,
+        power=1.0,
+        begin=1500,
+        end=20000,
+        by_epoch=False,
+    )
+]
+
+train_dataloader = dict(batch_size=4)
+val_dataloader = dict(batch_size=1)
+test_dataloader = val_dataloader
diff --git a/docs/en/get_started.md b/docs/en/get_started.md
@@ -195,6 +195,16 @@ Run it with
 docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmsegmentation/data mmsegmentation
 ```
 
+### Optional Dependencies
+
+#### Install GDAL
+
+[GDAL](https://gdal.org/) is a translator library for raster and vector geospatial data formats. Install GDAL to read complex formats and extremely large remote sensing images.
+
+```shell
+conda install GDAL
+```
+
 ## Trouble shooting
 
 If you have some issues during the installation, please first view the [FAQ](notes/faq.md) page.

diff --git a/docs/en/user_guides/2_dataset_prepare.md b/docs/en/user_guides/2_dataset_prepare.md
@@ -620,3 +620,33 @@ It includes 400 images for training, 400 images for validation and 400 images fo
 
 - You could set Datasets version with `MapillaryDataset_v1` and `MapillaryDataset_v2` in your configs.
   View the Mapillary Vistas Datasets config file here [V1.2](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/_base_/datasets/mapillary_v1.py) and [V2.0](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/_base_/datasets/mapillary_v2.py)
+
+## LEVIR-CD
+
+[LEVIR-CD](https://justchenhao.github.io/LEVIR/) Large-scale Remote Sensing Change Detection Dataset for Building.
+
+Download the dataset from [here](https://justchenhao.github.io/LEVIR/).
+
+The supplement version of the dataset can be requested on the [homepage](https://github.com/S2Looking/Dataset)
+
+Please download the supplement version of the dataset, then unzip `LEVIR-CD+.zip`, the contents of original datasets include:
+
+```none
+│   ├── LEVIR-CD+
+│   │   ├── train
+│   │   │   ├── A
+│   │   │   ├── B
+│   │   │   ├── label
+│   │   ├── test
+│   │   │   ├── A
+│   │   │   ├── B
+│   │   │   ├── label
+```
+
+For LEVIR-CD dataset, please run the following command to crop images without overlap:
+
+```shell
+python tools/dataset_converters/levircd.py --dataset-path /path/to/LEVIR-CD+ --out_dir /path/to/LEVIR-CD
+```
+
+The size of cropped image is 256x256, which is consistent with the original paper.
diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md
@@ -194,6 +194,16 @@ docker build -t mmsegmentation docker/
 docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmsegmentation/data mmsegmentation
 ```
 
+### 可选依赖
+
+#### 安装 GDAL
+
+[GDAL](https://gdal.org/) 是一个用于栅格和矢量地理空间数据格式的转换库。安装 GDAL 可以读取复杂格式和极大的遥感图像。
+
+```shell
+conda install GDAL
+```
+
 ## 问题解答
 
 如果您在安装过程中遇到了其他问题，请第一时间查阅 [FAQ](notes/faq.md) 文件。如果没有找到答案，您也可以在 GitHub 上提出 [issue](https://github.com/open-mmlab/mmsegmentation/issues/new/choose)
diff --git a/docs/zh_cn/user_guides/2_dataset_prepare.md b/docs/zh_cn/user_guides/2_dataset_prepare.md
@@ -616,3 +616,33 @@ python tools/convert_datasets/refuge.py --raw_data_root=/path/to/refuge/REFUGE2/
 
 - 您可以在配置中使用 `MapillaryDataset_v1` 和 `Mapillary Dataset_v2` 设置数据集版本。
   在此处 [V1.2](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/_base_/datasets/mapillary_v1.py) 和 [V2.0](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/_base_/datasets/mapillary_v2.py) 查看 Mapillary Vistas 数据集配置文件
+
+## LEVIR-CD
+
+[LEVIR-CD](https://justchenhao.github.io/LEVIR/) 大规模遥感建筑变化检测数据集。
+
+数据集可以在[主页](https://justchenhao.github.io/LEVIR/)上请求获得。
+
+数据集的补充版本可以在[主页](https://github.com/S2Looking/Dataset)上请求获得。
+
+请下载数据集的补充版本，然后解压 `LEVIR-CD+.zip`，数据集的内容包括：
+
+```none
+│   ├── LEVIR-CD+
+│   │   ├── train
+│   │   │   ├── A
+│   │   │   ├── B
+│   │   │   ├── label
+│   │   ├── test
+│   │   │   ├── A
+│   │   │   ├── B
+│   │   │   ├── label
+```
+
+对于 LEVIR-CD 数据集，请运行以下命令无重叠裁剪影像：
+
+```shell
+python tools/dataset_converters/levircd.py --dataset-path /path/to/LEVIR-CD+ --out_dir /path/to/LEVIR-CD
+```
+
+裁剪后的影像大小为256x256，与原论文保持一致。
diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py
@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # yapf: disable
 from .ade import ADE20KDataset
-from .basesegdataset import BaseSegDataset
+from .basesegdataset import BaseCDDataset, BaseSegDataset
 from .chase_db1 import ChaseDB1Dataset
 from .cityscapes import CityscapesDataset
 from .coco_stuff import COCOStuffDataset
@@ -12,6 +12,7 @@
 from .hrf import HRFDataset
 from .isaid import iSAIDDataset
 from .isprs import ISPRSDataset
+from .levir import LEVIRCDDataset
 from .lip import LIPDataset
 from .loveda import LoveDADataset
 from .mapillary import MapillaryDataset_v1, MapillaryDataset_v2
@@ -25,13 +26,15 @@
 from .transforms import (CLAHE, AdjustGamma, Albu, BioMedical3DPad,
                          BioMedical3DRandomCrop, BioMedical3DRandomFlip,
                          BioMedicalGaussianBlur, BioMedicalGaussianNoise,
-                         BioMedicalRandomGamma, GenerateEdge, LoadAnnotations,
-                         LoadBiomedicalAnnotation, LoadBiomedicalData,
-                         LoadBiomedicalImageFromFile, LoadImageFromNDArray,
-                         PackSegInputs, PhotoMetricDistortion, RandomCrop,
-                         RandomCutOut, RandomMosaic, RandomRotate,
-                         RandomRotFlip, Rerange, ResizeShortestEdge,
-                         ResizeToMultiple, RGB2Gray, SegRescale)
+                         BioMedicalRandomGamma, ConcatCDInput, GenerateEdge,
+                         LoadAnnotations, LoadBiomedicalAnnotation,
+                         LoadBiomedicalData, LoadBiomedicalImageFromFile,
+                         LoadImageFromNDArray, LoadMultipleRSImageFromFile,
+                         LoadSingleRSImageFromFile, PackSegInputs,
+                         PhotoMetricDistortion, RandomCrop, RandomCutOut,
+                         RandomMosaic, RandomRotate, RandomRotFlip, Rerange,
+                         ResizeShortestEdge, ResizeToMultiple, RGB2Gray,
+                         SegRescale)
 from .voc import PascalVOCDataset
 
 # yapf: enable
@@ -51,5 +54,7 @@
     'BioMedicalGaussianNoise', 'BioMedicalGaussianBlur',
     'BioMedicalRandomGamma', 'BioMedical3DPad', 'RandomRotFlip',
     'SynapseDataset', 'REFUGEDataset', 'MapillaryDataset_v1',
-    'MapillaryDataset_v2', 'Albu'
+    'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset',
+    'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile',
+    'ConcatCDInput', 'BaseCDDataset'
 ]