mattdesl

/**
 * General-purpose NodeJS CLI/API wrapping the Stable-Diffusion python scripts.
 *
 * Note that this uses an older fork of stable-diffusion
 * with the 'txt2img.py' script, and that script was modified to
 * support the --outfile command.
 */

var { spawn, exec } = require("child_process");
var path = require("path");

alxcnwy

# answer to this reddit post: 
# https://www.reddit.com/r/learnmachinelearning/comments/o6br1e/calculate_bounding_box_coordinates_from_contour/

import numpy as np
from numpy import sin, cos, sqrt, pi
import math
import matplotlib.pyplot as plt 

center = (332, 209)
width = 56

ttchengab

"""
get_info() function reads the image using openCV and performs thresholding, dilation, noise removal, and
contouring to finally retrieve bounding boxes from the contour.


Below are some of the additional available functions from openCV for preprocessing:

Median filter: median filter blurs out noises by taking the medium from a set of pixels
cv2.medianBlur()

tamuhey

How to calculate the alignment between BERT and spaCy tokens effectively and robustly

site: https://tamuhey.github.io/tokenizations/

Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still require language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm that simplifies calculating correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm. Here are the library and the demo site links:

• repo: https://github.com/tamuhey/tokenizations

IanColdwater

Mute these words in your settings here: https://twitter.com/settings/muted_keywords

ActivityTweet
generic_activity_highlights
generic_activity_momentsbreaking
RankedOrganicTweet
suggest_activity
suggest_activity_feed
suggest_activity_highlights
suggest_activity_tweet

dojoteef

'''
Memory profiling utilities
'''
import gc
import inspect
import linecache
import os.path
import sys
import time
import threading

Lyken17

# tested on AWS p2.xlarge August 29, 2018

# install CUDA
sudo apt-get update && sudo apt-get install wget -y --no-install-recommends
CUDA_URL="https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64"
wget -c ${CUDA_URL} -O cuda.deb
sudo dpkg --install cuda.deb
sudo apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
sudo apt-get update
sudo apt-get install -y cuda

yuzeh

# MIT License
#
# Copyright (c) 2018 Yuze Huang ([email protected])
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

nasimrahaman

import torch
import torch.nn as nn


def log_sum_exp(x):
    # See implementation detail in
    # http://timvieira.github.io/blog/post/2014/02/11/exp-normalize-trick/
    # b is a shift factor. see link.
    # x.size() = [N, C]:
    b, _ = torch.max(x, 1)

thomasdullien

"""
  Simple, hacked-up image similarity search using Tensorflow + the inception
  CNN as feature extractor and ANNoy for nearest neighbor search.

  Requires Tensorflow and ANNoy.

  Based on gist code under
    https://gist.github.com/david90/e98e1c41a0ebc580e5a9ce25ff6a972d
"""
from annoy import AnnoyIndex