drscotthawley

drscotthawley

drscotthawley

import torch
from torch import nn
import matplotlib.pyplot as plt
import torch.nn.functional as F
import wandb 


class ResidualBlock(nn.Module):
    def __init__(self, channels, use_skip=True, use_bn=True, act=nn.GELU):
        super().__init__()

drscotthawley

# Usage: python fix_notebook.py <.ipynb file> 
# WARNING: this overwrites the file, so save a backup first 

# To execute on a whole folder of files, run this bash command: 
# find . -name "*.ipynb" -exec python fix_notebook.py {} \;

import json
import sys

def fix_notebook(filename):

drscotthawley

# syncs python files in dir and subdirs so you don't have to run resource-hogging VSCode Remote 
# usage: pysync /path/to/local/directory/ user@remote:/path/to/remote/directory/
#  UPDATED version: more code than before, but now it follows symbolic links on the remote server instead of just overwriting them
pysync() {
    if [ "$#" -ne 2 ]; then
        echo "Usage: pysync <source> <destination>"
        return 1
    fi
    
    fswatch -o "$1" | while read f; do

drscotthawley

#!/usr/bin/env python3
import json
import subprocess
import argparse
import sys
import os

def parse_override_args(args):
    """Parse CLI arguments that start with '--' into a dict."""
    overrides = {}

drscotthawley

#!/usr/bin/env python3
# audio_data2url.py 
# Author: Scott H. Hawley
# License: MIT
# Date: Sep 2, 2024

# Description: This script will convert base64 audio src data in a Jupyter notebook to URLs of the same audio
# which is saved in a separate branch of the same GitHub repository. The script will save the audio files in a
# directory named 'audio_files' and commit them to the 'audio-storage' branch. The script will then replace the
# base64 data with the raw URL of the audio file in the notebook. The script can be run on a single notebook file

drscotthawley

#! /usr/bin/env python 

# OS-Agnostic PS5 DualSense Controller Listener
# Author: Scott H. Hawley

# Instructions: 
# 1. Pair the DualSense controller with your computer
# 2. Install hidapi system binary, e.g. on Mac: brew install hidapi 
# 3. Install Python packages: pip install hidapi pygame numpy
# 4. Run this script! 

drscotthawley

# tails output of any SLURM job that is listed in the queue. 
# if job is pending, tailjob will wait until the output file exists
# usage: tailjob <job_id>
tailjob() {
    local job_id=$1
    if [[ -n "$job_id" ]]; then
        local stdout_file=$(scontrol show job "$job_id" | awk -F= '/StdOut=/{print $2}')
        echo "Running tail -F $stdout_file"
        tail -F "$stdout_file"
    else

drscotthawley

from einops import rearrange as _rearrange

class RearrangeWrapper():
    "wrapper to endow einops.rearrange with an 'inverse' operation"
    def __init__(self):
        self.shape, self.s = None, None     # just in case someone tries to call inverse first
      
    def __call__(self, x, s:str, **kwargs): # this 'forward' call is lightweight to preserve original usage
        self.shape, self.s = x.shape, s
        return _rearrange(x, s, **kwargs)