Use Python to:
- send a plain text email
- send an email with attachment
- receive and filter emails according to some criteria
shelhamer
/ load_caffemodel.py
Last active
November 9, 2020 00:46
Load binary proto .caffemodel to inspect layer names.
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| import sys | |
| sys.path.insert(0, 'python/') | |
| import caffe | |
| from caffe.proto import caffe_pb2 | |
| net_param = caffe_pb2.NetParameter() | |
| net_str = open('lenet_iter_5000.caffemodel', 'r').read() | |
| net_param.ParseFromString(net_str) | |
| print net_param.layer[0].name # first layer |
TimothyRHuertas
/ frames.sh
Created
June 20, 2017 23:24
Use ffmpeg to center crop and scale an image to 1:1
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| #Given a video of any aspect ratio this script will extract center cropped thumbnails at 299x299. | |
| #Useful for gathering image training data. | |
| #Assumes ffmpeg 3.3.2 | |
| #If you get an error about exact that means you are using an older version of ffmpeg | |
| #Simply remove the :exact=1. | |
| #This will make it work, but may cause the output size to be off by a pixel | |
| #Script assumes you are downsampling. | |
| #!/bin/bash |
dinos80152
/ mega-links-decrypter.html
Last active
October 20, 2022 20:20
Decode/decypher MEGA mega://enc? links http://dinolai.com/toys/mega-link-decrypter.html
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| <!-- | |
| Inspired by | |
| https://gist.github.com/mndza/74736feb073fc6b65334#file-gistfile1-html | |
| https://github.com/denysvitali/megadecrypter | |
| https://gist.github.com/artjomb/7ef1ee574a411ba0dd1933c1ef4690d1 | |
| About Mega Links | |
| http://megadownloaderapp.blogspot.com/2013/03/explaining-mega-links.html | |
| --> | |
| <!DOCTYPE html> | |
| <html> |
- A simple note for how to start multi-node-training on slurm scheduler with PyTorch.
- Useful especially when scheduler is too busy that you cannot get multiple GPUs allocated, or you need more than 4 GPUs for a single job.
- Requirement: Have to use PyTorch DistributedDataParallel(DDP) for this purpose.
- Warning: might need to re-factor your own code.
- Warning: might be secretly condemned by your colleagues because using too many GPUs.
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