NOTE
要复现论文的github：RSRefSeg2

下载并安装 Miniconda#

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sudo apt update

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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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# 非代理服务器用下面这个
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wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-latest-Linux-x86_64.sh

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bash Miniconda3-latest-Linux-x86_64.sh

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Do you wish the installer to initialize Miniconda3? yes

创建干净环境（Python 3.10）#

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conda create -n rsrefseg2 python=3.11

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conda activate rsrefseg2

安装 A800 的 PyTorch#

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pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 -f https://mirrors.aliyun.com/pytorch-wheels/cu121/

安装 MMCV2.2.x #

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pip install -U openmim

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mim install mmcv==2.2.0

安装其他依赖项#

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pip install deepspeed==0.17.2

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pip install transformers==4.53.1 datasets==2.16.1

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pip install -U ipdb braceexpand mat4py pycocotools shapely ftfy scipy terminaltables wandb prettytable torchmetrics importlib_metadata einops peft

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pip install hydra-core iopath

拉取 RSRefSeg 2 工程#

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cd ~

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git clone https://github.com/KyanChen/RSRefSeg2.git

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cd RSRefSeg2

下载 RefSegRS 数据集#

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export HF_ENDPOINT=https://hf-mirror.com

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pip install hf_transfer

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export HF_HUB_ENABLE_HF_TRANSFER=1

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huggingface-cli download JessicaYuan/RefSegRS \
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  --repo-type dataset \
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  --local-dir /home/ubuntu/RSRefSeg2/datasets/refsegrs

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sudo apt install unzip

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unzip RefSegRS.zip -d /home/ubuntu/RSRefSeg2/datasets/refsegrs

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RSRefSeg2/datasets/refsegrs
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├── images/
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├── masks/
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├── output_phrase_test.txt
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├── output_phrase_train.txt
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├── output_phrase_val.txt

下载 RRSIS-D 数据集#

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huggingface-cli download \
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  --repo-type dataset VoyageWang/rrsis-d \
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  --local-dir /home/ubuntu/RSRefSeg2/datasets/rrsis_d

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unzip JPEGImages.zip -d /home/ubuntu/RSRefSeg2/datasets/rrsis_d/images

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ann_split 自行下载

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unzip ann_split.zip -d /home/ubuntu/RSRefSeg2/datasets/rrsis_d/images

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RSRefSeg2/datasets/rrsis_d
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├── rrsisd
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│   ├── refs(unc).p
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│   └── instances.json
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├── images
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    └── rrsisd
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        ├── JPEGImages/
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        └── ann_split/

下载官方 checkpoint#

自己创建文件夹 /home/ubuntu/RSRefSeg2/checkpoints

Refsegrs#

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huggingface-cli download KyanChen/RSRefSeg2 \
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  --local-dir /home/ubuntu/RSRefSeg2/checkpoints \
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  --local-dir-use-symlinks False \
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  --include "refsegrs.pth"

RRSIS-D#

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huggingface-cli download KyanChen/RSRefSeg2 \
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  --local-dir /home/ubuntu/RSRefSeg2/checkpoints \
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  --local-dir-use-symlinks False \
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  --include "rrsisd.pth"

验证实验#

Refsegrs#

修改 /home/ubuntu/RSRefSeg2/configs_RSRefSeg2/refsegrs_infer.py 中的数据集与保存路径

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work_dir = '/home/ubuntu/RSRefSeg2/work_dirs/refsegrs'
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data_root = '/home/ubuntu/RSRefSeg2/datasets/refsegrs/images'
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cache_dir = '/home/ubuntu/.cache/hf_models'

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python tools_mmseg/test.py configs_RSRefSeg2/refsegrs_infer.py checkpoints/refsegrs.pth

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02/14 05:06:32 - mmengine - INFO - per class results:
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02/14 05:06:32 - mmengine - INFO -
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+------------+-------+-------+---------+---------+---------+---------+---------+
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|   class    |  cIoU |  gIoU | seg_0.5 | seg_0.6 | seg_0.7 | seg_0.8 | seg_0.9 |
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+------------+-------+-------+---------+---------+---------+---------+---------+
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| background | 98.80 | 98.67 |  100.0  |  99.94  |  99.89  |  99.61  |  98.29  |
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|    mask    | 81.21 | 77.36 |  88.11  |  82.94  |  73.97  |  60.81  |  34.40  |
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+------------+-------+-------+---------+---------+---------+---------+---------+

RRSIS-D#

修改 /home/ubuntu/RSRefSeg2/configs_RSRefSeg2/rrsisd_infer.py 中的数据集与保存路径

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work_dir = '/home/ubuntu/RSRefSeg2/work_dirs/rrsis_d'
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data_root = '/home/ubuntu/RSRefSeg2/datasets/rrsis_d/images/JPEGImages'
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cache_dir = '/home/ubuntu/.cache/hf_models'

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python tools_mmseg/test.py configs_RSRefSeg2/rrsisd_infer.py checkpoints/rrsisd.pth

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02/14 05:27:16 - mmengine - INFO - per class results:
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02/14 05:27:16 - mmengine - INFO -
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+------------+-------+-------+---------+---------+---------+---------+---------+
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|   class    |  cIoU |  gIoU | seg_0.5 | seg_0.6 | seg_0.7 | seg_0.8 | seg_0.9 |
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+------------+-------+-------+---------+---------+---------+---------+---------+
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| background | 98.90 | 98.85 |  100.0  |  99.94  |  99.80  |  99.37  |  97.80  |
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|    mask    | 79.66 | 69.06 |  80.03  |  75.64  |  65.18  |  50.59  |  31.08  |
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+------------+-------+-------+---------+---------+---------+---------+---------+
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``

下载并安装 Miniconda#

创建干净环境（Python 3.10）#

安装 A800 的 PyTorch#

安装 MMCV2.2.x#

安装其他依赖项#

拉取 RSRefSeg 2 工程#

下载 RefSegRS 数据集#

下载 RRSIS-D 数据集#

下载官方 checkpoint#

Refsegrs#

RRSIS-D#

验证实验#

Refsegrs#

RRSIS-D#

安装 MMCV2.2.x #