travishsu

import os
import json
import subprocess
import numpy as np
import pandas as pd
from skimage.measure import find_contours


class CocoDatasetHandler:
    def __init__(self, jsonpath, imgpath):

amirhfarzaneh

import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch
import torchvision
import matplotlib.pyplot as plt
import numpy as np

from custom_transforms import NRandomCrop

nischalbasuti

set-window-option -g mode-keys vi

# set -g default-terminal "screen"

# enable mouse
set -g mouse on

# vi like navigation
bind h select-pane -L
bind j select-pane -D

sonots

Usually, located at /usr/local/cuda/bin

Non-Visual Profiler

$ nvprof python train_mnist.py

I prefer to use --print-gpu-trace.

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

drmalex07

#!/bin/bash

#
# Example using getopt (vs builtin getopts) that can also handle long options.
# Another clean example can be found at:
# http://www.bahmanm.com/blogs/command-line-options-how-to-parse-in-bash-using-getopt
#

aflag=n
bflag=n

zhengjia

=Navigating=
    visit('/projects')
    visit(post_comments_path(post))

=Clicking links and buttons=
    click_link('id-of-link')
    click_link('Link Text')
    click_button('Save')
    click('Link Text') # Click either a link or a button
    click('Button Value')