Mambavision installation

Ref: https://github.com/NVlabs/MambaVision/issues/86

- Docker image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel

- pip install causal-conv1d==1.5.0.post5

- wget https://github.com/state-spaces/mamba/releases/download/v2.2.3.post2/mamba_ssm-2.2.3.post2+cu12torch2.5cxx11abiFALSE-cp311-cp311-linux_x86_64.whl

- pip install mamba_ssm-2.2.3.post2+cu12torch2.5cxx11abiFALSE-cp311-cp311-linux_x86_64.whl

- git clone https://github.com/NVlabs/MambaVision.git

- cd MambaVision

- Replace setup.py #40 line: mamba-ssm==2.2.4" with "mamba-ssm"

- pip install -e .

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sklearn tsne + matplotlib scatter

import os import torch import clip import matplotlib . pyplot as plt from PIL import Image from sklearn . manifold import TSNE from tqdm import tqdm from collections import defaultdict from random import shuffle   X = torch . cat ( X , dim = 0 ) X_emb = TSNE ( init = "pca" , perplexity = 30.0 ). fit_transform ( X . cpu ()) labels = [ "source" , "z_star/foggy-1" , "z_star/foggy-2" , "z_star/foggy-3" , "z_bar/foggy-1" , "z_bar/foggy-2" , "z_bar/foggy-3" ] fig , ax = plt . subplots ( 1 ) group_len = len ( zs ) for i in range ( 7 ): ax . scatter ( X_emb [ i * group_len :( i + 1 )* group_len , 0 ], X_emb [ i * group_len :( i + 1 )* group_len , 1 ], label = labels [ i ], s = 4 ) ax . legend () fig .savefig( "temp.png" )
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  rsync -ah --progress source-file destination-file **주의: dest를 폴더명으로 지정하지 않도록 하기 ref:  https://askubuntu.com/questions/17275/how-to-show-the-transfer-progress-and-speed-when-copying-files-with-cp verfication: diff -rq --no-dereference /path/to/old/drive/ /path/to/new/drive/ ref: https://unix.stackexchange.com/questions/313089/verifying-a-large-directory-after-copy-from-one-hard-drive-to-another To remote server: rsync -avz -e ssh SOURCE USERNAME@IPADDRESS:TARGET
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