walkoncross

_tm_complete() {
	local rx
	local token=${COMP_WORDS[$COMP_CWORD]}
	local IFS=$'\t'
	local words
	if [ $COMP_CWORD -eq 2 ]; then
		words=$(tmux list-windows -t ${COMP_WORDS[1]} 2> /dev/null | awk '{print $2}' | tr -d '*-' | tr "\n" "\t")
	elif [ $COMP_CWORD -eq 1 ]; then
		words=$(tmux -q list-sessions 2> /dev/null | cut -f 1 -d ':' | tr "\n" "	")
	fi

walkoncross

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

walkoncross

1. Download tmux-autocompletion.sh from this gist
2. In your ~/.bashrc, Add a line:
source /path/to/tmux-autocompletion.sh
3. Source ~/.bashrc

Bravo!!!

walkoncross

# unpack the library  
gzip -d cudnn-6.5-linux-x64-v2.tar.gz  
tar xf cudnn-6.5-linux-x64-v2.tar  
  
# copy the library files into CUDA's include and lib folders  
sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda-7.0/include  
sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda-7.0/lib64  

walkoncross

__author__ = 'k0emt'

class Greeter:
    def __init__(self):
        self.message = 'Hello world'
        # print self.message

walkoncross

	.file	"A.cpp"
	.section .rdata,"dr"
	.align 8
.LC0:
	.ascii "bits1: %5lu, bits2: %5lu, builtin: %5lu\12\0"
	.text
	.p2align 4,,15
	.def	_Z6printfPKcz.constprop.0;	.scl	3;	.type	32;	.endef
	.seh_proc	_Z6printfPKcz.constprop.0
_Z6printfPKcz.constprop.0:

walkoncross

How to write shared libraries

link: https://www.akkadia.org/drepper/dsohowto.pdf

walkoncross

import json
import os
import time
import requests
from PIL import Image
from StringIO import StringIO
from requests.exceptions import ConnectionError

def go(query, path):
  """Download full size images from Google image search.

walkoncross

(from https://www.scivision.co/numpy-image-bgr-to-rgb/)

Numpy / OpenCV image BGR to RGB

Conversion between any/all of BGR, RGB, and GBR may be necessary when working with Matplotlib expects M x N x 3 image, where last dimension is RGB.

OpenCV expects M x N x 3 image, where last dimension is BGR.

Scientific Cameras, some of which output an M X N x 3 image, where last dimension is GBR

walkoncross

name: "CenterFace_Resnet"

input: "data"
input_dim: 1
input_dim: 3
input_dim: 112
input_dim: 96
layer {
  name: "conv1a"
  type: "Convolution"