RainMamba
[ACM MM'24 Oral] RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining
파일 탐색기
최종 버전 다운로드 (.zip)- __init__.py
- causal_conv1d_interface.py
- causal_conv1d.cpp
- causal_conv1d.h
- causal_conv1d_bwd.cu
- causal_conv1d_common.h
- causal_conv1d_fwd.cu
- causal_conv1d_update.cu
- static_switch.h
- test_causal_conv1d.py
- AUTHORS
- LICENSE
- README.md
- setup.py
- Waterdrop.py
- hilbert_curve_large_scale.pt
- hilbert_curve_small_scale.pt
- cal.py
- utils_image.py
- __init__.py
- restoration_inference.py
- restoration_video_inference.py
- test.py
- train.py
- __init__.py
- eval_hooks.py
- metric_utils.py
- metrics.py
- niqe_pris_params.npz
- __init__.py
- wrappers.py
- __init__.py
- ema.py
- visualization.py
- __init__.py
- builder.py
- __init__.py
- lr_updater.py
- __init__.py
- dist_utils.py
- __init__.py
- distributed_wrapper.py
- mask.py
- misc.py
- __init__.py
- augmentation.py
- blur_kernels.py
- compose.py
- crop.py
- formating.py
- generate_assistant.py
- loading.py
- matlab_like_resize.py
- matting_aug.py
- normalization.py
- random_degradations.py
- random_down_sampling.py
- utils.py
- __init__.py
- distributed_sampler.py
- __init__.py
- base_dataset.py
- base_sr_dataset.py
- builder.py
- dataset_wrappers.py
- registry.py
- sr_folder_gt_dataset.py
- sr_folder_multiple_gt_dataset.py
- __init__.py
- RainMamba.py
- __init__.py
- __init__.py
- derainer.py
- DrainNet.py
- __init__.py
- pixelwise_loss.py
- utils.py
- __init__.py
- base.py
- builder.py
- registry.py
- __init__.py
- cli.py
- collect_env.py
- logger.py
- misc.py
- setup_env.py
- __init__.py
- version.py
- convnext.py
- head.py
- Hilbert3d.py
- mambablock.py
- runtime.txt
- tests.txt
- mmedit2torchserve.py
- mmedit_handler.py
- test_torchserver.py
- deploy_test.py
- dist_test.sh
- dist_train.sh
- onnx2tensorrt.py
- pytorch2onnx.py
- slurm_test.sh
- slurm_train.sh
- test.py
- train.py
- LICENSE
- MANIFEST.in
- requirements.txt
- setup.cfg
- setup.py
- RainSynAll100.py
- hilbert_curve_large_scale.pt
- hilbert_curve_small_scale.pt
- __init__.py
- restoration_inference.py
- restoration_video_inference.py
- test.py
- train.py
- __init__.py
- eval_hooks.py
- metric_utils.py
- metrics.py
- niqe_pris_params.npz
- __init__.py
- wrappers.py
- __init__.py
- ema.py
- visualization.py
- __init__.py
- builder.py
- __init__.py
- lr_updater.py
- __init__.py
- dist_utils.py
- __init__.py
- distributed_wrapper.py
- mask.py
- misc.py
- __init__.py
- augmentation.py
- augmentationfortestdata.py
- blur_kernels.py
- compose.py
- crop.py
- formating.py
- generate_assistant.py
- loading.py
- matlab_like_resize.py
- matting_aug.py
- normalization.py
- random_degradations.py
- random_down_sampling.py
- utils.py
- __init__.py
- distributed_sampler.py
- __init__.py
- base_dataset.py
- base_sr_dataset.py
- builder.py
- dataset_wrappers.py
- registry.py
- sr_folder_gt_dataset.py
- sr_folder_multiple_gt_dataset.py
- __init__.py
- RainMamba.py
- __init__.py
- __init__.py
- derainer.py
- DrainNet.py
- __init__.py
- pixelwise_loss.py
- utils.py
- __init__.py
- base.py
- builder.py
- registry.py
- __init__.py
- cli.py
- collect_env.py
- logger.py
- misc.py
- setup_env.py
- __init__.py
- version.py
- convnext.py
- head.py
- Hilbert3d.py
- mambablock.py
- runtime.txt
- tests.txt
- mmedit2torchserve.py
- mmedit_handler.py
- test_torchserver.py
- deploy_test.py
- dist_test.sh
- dist_train.sh
- onnx2tensorrt.py
- pytorch2onnx.py
- slurm_test.sh
- slurm_train.sh
- test.py
- train.py
- LICENSE
- MANIFEST.in
- requirements.txt
- setup.cfg
- setup.py
- RainVIDSS.py
- hilbert_curve_large_scale.pt
- hilbert_curve_small_scale.pt
- Cam_Vid+.py
- ImageNet_VID+.py
- utils_image.py
- __init__.py
- restoration_inference.py
- restoration_video_inference.py
- test.py
- train.py
- __init__.py
- eval_hooks.py
- metric_utils.py
- metrics.py
- niqe_pris_params.npz
- __init__.py
- wrappers.py
- __init__.py
- ema.py
- visualization.py
- __init__.py
- builder.py
- __init__.py
- lr_updater.py
- __init__.py
- dist_utils.py
- __init__.py
- distributed_wrapper.py
- mask.py
- misc.py
- __init__.py
- augmentation.py
- blur_kernels.py
- compose.py
- crop.py
- formating.py
- generate_assistant.py
- loading.py
- matlab_like_resize.py
- matting_aug.py
- normalization.py
- random_degradations.py
- random_down_sampling.py
- utils.py
- __init__.py
- distributed_sampler.py
- __init__.py
- base_dataset.py
- base_sr_dataset.py
- builder.py
- dataset_wrappers.py
- registry.py
- sr_folder_gt_dataset.py
- sr_folder_multiple_gt_dataset.py
- __init__.py
- RainMamba.py
- __init__.py
- __init__.py
- derainer.py
- DrainNet.py
- __init__.py
- pixelwise_loss.py
- utils.py
- __init__.py
- base.py
- builder.py
- registry.py
- __init__.py
- cli.py
- collect_env.py
- logger.py
- misc.py
- setup_env.py
- __init__.py
- version.py
- convnext.py
- head.py
- Hilbert3d.py
- mambablock.py
- runtime.txt
- tests.txt
- mmedit2torchserve.py
- mmedit_handler.py
- test_torchserver.py
- deploy_test.py
- dist_test.sh
- dist_train.sh
- onnx2tensorrt.py
- pytorch2onnx.py
- slurm_test.sh
- slurm_train.sh
- test.py
- train.py
- LICENSE
- MANIFEST.in
- requirements.txt
- setup.cfg
- setup.py
- VRDS.py
- restoration_video_demo.py
- hilbert_curve_large_scale.pt
- hilbert_curve_small_scale.pt
- cal-iqa.py
- utils_image.py
- __init__.py
- matting_inference.py
- restoration_inference.py
- restoration_video_inference.py
- test.py
- train.py
- __init__.py
- eval_hooks.py
- metric_utils.py
- metrics.py
- niqe_pris_params.npz
- __init__.py
- wrappers.py
- __init__.py
- ema.py
- visualization.py
- __init__.py
- builder.py
- __init__.py
- lr_updater.py
- __init__.py
- dist_utils.py
- __init__.py
- distributed_wrapper.py
- mask.py
- misc.py
- __init__.py
- augmentation.py
- blur_kernels.py
- compose.py
- crop.py
- formating.py
- generate_assistant.py
- loading.py
- matlab_like_resize.py
- matting_aug.py
- normalization.py
- random_degradations.py
- random_down_sampling.py
- utils.py
- __init__.py
- distributed_sampler.py
- __init__.py
- base_dataset.py
- base_sr_dataset.py
- builder.py
- dataset_wrappers.py
- registry.py
- sr_folder_gt_dataset.py
- sr_folder_multiple_gt_dataset.py
- __init__.py
- RainMamba.py
- __init__.py
- __init__.py
- derainer.py
- DrainNet.py
- __init__.py
- pixelwise_loss.py
- utils.py
- __init__.py
- base.py
- builder.py
- registry.py
- __init__.py
- cli.py
- collect_env.py
- logger.py
- misc.py
- setup_env.py
- __init__.py
- version.py
- convnext.py
- head.py
- Hilbert3d.py
- mambablock.py
- runtime.txt
- tests.txt
- mmedit2torchserve.py
- mmedit_handler.py
- test_torchserver.py
- deploy_test.py
- dist_test.sh
- dist_train.sh
- onnx2tensorrt.py
- pytorch2onnx.py
- slurm_test.sh
- slurm_train.sh
- test.py
- train.py
- LICENSE
- MANIFEST.in
- requirements.txt
- setup.cfg
- setup.py
- efficiency.png
- pipeline.png
- Profile.jpg
- scans.png
- twoscans.gif
- valse2025-poster.png
- VRDSResults.png
- selection.png
- benchmark_generation_mamba_simple.py
- reverse_scan.cuh
- selective_scan.cpp
- selective_scan.h
- selective_scan_bwd_bf16_complex.cu
- selective_scan_bwd_bf16_real.cu
- selective_scan_bwd_fp16_complex.cu
- selective_scan_bwd_fp16_real.cu
- selective_scan_bwd_fp32_complex.cu
- selective_scan_bwd_fp32_real.cu
- selective_scan_bwd_kernel.cuh
- selective_scan_common.h
- selective_scan_fwd_bf16.cu
- selective_scan_fwd_fp16.cu
- selective_scan_fwd_fp32.cu
- selective_scan_fwd_kernel.cuh
- static_switch.h
- uninitialized_copy.cuh
- lm_harness_eval.py
- __init__.py
- mixer_seq_simple.py
- __init__.py
- mamba_simple.py
- __init__.py
- layernorm.py
- selective_state_update.py
- __init__.py
- selective_scan_interface.py
- __init__.py
- generation.py
- hf.py
- __init__.py
- dependency_links.txt
- PKG-INFO
- requires.txt
- SOURCES.txt
- top_level.txt
- test_selective_state_update.py
- test_selective_scan.py
- AUTHORS
- LICENSE
- README.md
- setup.py
- test_mamba_module.py
- LICENSE
- README.md
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/TonyHongtaoWu/RainMamba
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd RainMamba
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. 공식 설치 스크립트
쉬움 추천사전 준비물
- Python 3 pip 명령어를 쓰려면 Python이 필요합니다.
pip3 install openmim timm
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
pip install ninja==1.11.1.1
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.
이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.
3. Python
쉬움사전 준비물
pip3 install openmim timm
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
pip3 install -e .
requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.
pip install ninja==1.11.1.1
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.
이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.
// repository documentation
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