jeasinema / gist:3ee0b45e12c40f1878335336c0bb68d8

Created February 25, 2021 08:18

CUDA utils

	export CUDA_HOME=$HOME/tools/cuda-9.0 # change to your path
	export CUDA_TOOLKIT_ROOT_DIR=$CUDA_HOME
	export LD_LIBRARY_PATH="$CUDA_HOME/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
	export LIBRARY_PATH=$CUDA_HOME/lib64:$LIBRARY_PATH
	export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
	export CFLAGS="-I$CUDA_HOME/include $CFLAGS"
	export PATH="$CUDA_HOME/bin:$PATH"

jeasinema / NoteDup_patched

Last active October 12, 2020 21:45

Remove the yinxiang->evernote warning of NoteDup (https://github.com/ppyyf/notedup-bin)

java -jar notedup_patched.jar

jeasinema / gradcam.py

Created September 9, 2019 11:16

	import torch
	import numpy as np
	import argparse
	from torchvision import models
	import matplotlib.pyplot as plt

	import cv2
	import os

	resnet = models.resnet50(pretrained=True)

jeasinema / imagenet1000_clsidx_to_labels.txt

Created June 27, 2019 05:46 — forked from yrevar/imagenet1000_clsidx_to_labels.txt

text: imagenet 1000 class idx to human readable labels (Fox, E., & Guestrin, C. (n.d.). Coursera Machine Learning Specialization.)

	{0: 'tench, Tinca tinca',
	1: 'goldfish, Carassius auratus',
	2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
	3: 'tiger shark, Galeocerdo cuvieri',
	4: 'hammerhead, hammerhead shark',
	5: 'electric ray, crampfish, numbfish, torpedo',
	6: 'stingray',
	7: 'cock',
	8: 'hen',
	9: 'ostrich, Struthio camelus',

jeasinema / jeasinema.sty

Last active May 12, 2019 07:41

Some useful macros for writing technical papers

	% packages
	\usepackage{amsmath,amsfonts,amssymb,amsthm}
	\usepackage{hyperref}
	\usepackage{xspace}
	\usepackage{algorithm}
	\usepackage{algorithmic}
	\usepackage{color}
	\usepackage{graphicx}
	\usepackage{mathtools}
	\usepackage{footmisc}

jeasinema / create_legend.py

Last active September 2, 2019 06:53

Create customized legend

	import matplotlib.pyplot as plt
	from matplotlib.lines import Line2D
	import seaborn as sns
	import numpy as np
	fig = plt.figure()
	fig.set_size_inches(18, 3)
	ax = fig.subplots(1)
	label = ['Expert', 'Demo', 'Pre-train', 'POfD', 'Penalty', 'Penalty + Ann.', 'Ours']
	flatui = ["#9b59b6", "#999900", "#3498db", "#95a5a6", "#009900", "#fc9272", "#e74c3c"]
	clrs = sns.color_palette(flatui)

jeasinema / transmission-daemon-quickstart.md

Created February 10, 2019 09:31 — forked from eriknomitch/transmission-daemon-quickstart.md

Transmission Daemon (transmission-daemon) Quickstart

This should work for Debian or Debian-deriviants (e.g., Ubuntu)

Quickstart

Install transmission-daemon

sudo apt-get install transmission-daemon

jeasinema / dm_control_to_gym.py

Last active October 16, 2018 04:00

Wrap DeepMind dm_control into OpenAI gym

	#!/usr/bin/env python
	# -- coding:UTF-8 --
	# File Name : dm_control_wrapper.py
	# Creation Date : 09-10-2018
	# Created By : Jeasine Ma [jeasinema[at]gmail[dot]com]

	import glfw
	try:
	glfw.init()
	except:

jeasinema / nm_l2tp_ipsec_vpn.md

Created October 3, 2018 00:13 — forked from pastleo/nm_l2tp_ipsec_vpn.md

setup L2TP IPSEC VPN in archlinux using NetworkManager

setup L2TP IPsec VPN in archlinux using NetworkManager

install networkmanager-l2tp first:

yaourt -S networkmanager-l2tp

then reboot or systemctl restart NetworkManager

jeasinema / smooth_plot.py

Last active February 26, 2019 08:15

Plot smooth curve like other people do

	import numpy as np
	import matplotlib.pyplot as plt
	import seaborn as sns


	def smooth(y, box_pts):
	box_pts = max(box_pts, 1) if len(y) > box_pts else 1
	box = np.ones(box_pts)/box_pts
	y_smooth = np.convolve(y, box, mode='valid')
	return y_smooth