danielhavir

danielhavir

danielhavir

import bson # pip install pymongo
import numpy as np
import pandas as pd
import os
from tqdm import tqdm_notebook

out_folder = 'images'

# Create output folder
if not os.path.exists(out_folder):

danielhavir

Keybase proof

I hereby claim:

• I am danielhavir on github.
• I am danielhavir (https://keybase.io/danielhavir) on keybase.
• I have a public key ASDEKLlI3mzQBn3N1IThJD64Xb7T-vHA8XxTKcX4-u5rOgo

To claim this, I am signing this object:

danielhavir

if [ $# -ne 2 ];then
  echo "Usage: `basename $0` idVendor idProduct"
  exit 1
fi


for X in /sys/bus/usb/devices/*; do 
    if [ "$1" == "$(cat "$X/idVendor" 2>/dev/null)" -a "$2" == "$(cat "$X/idProduct" 2>/dev/null)" ]
    then
        echo "$X"

danielhavir

### Removes Node and NPM from mac OS ###
# Uninstall using NPM
npm uninstall npm -g || echo "NPM uninstall didn't work"
# Try if Node's installed with Brew
(brew uninstall node&&brew prune)||echo "Node was not installed with Brew"
# Remove all files and folders including Node or NPM
rm -rf /usr/local/{lib/node{,/.npm,_modules},bin,share/man}/{npm*,node*,man1/node*}
rm -rf /usr/local/bin/npm /usr/local/share/man/man1/node* /usr/local/lib/dtrace/node.d ~/.npm ~/.node-gyp /opt/local/bin/node opt/local/include/node /opt/local/lib/node_modules
rm -rf /usr/local/{lib/node{,/.npm,_modules},bin,share/man}/npm*
rm -rf /usr/local/include/node*

danielhavir

tput setaf 6; echo '#### Downloading and installing Anaconda ####'; tput sgr0;
wget https://repo.anaconda.com/archive/Anaconda3-5.3.0-Linux-x86_64.sh
bash ./Anaconda3-5.3.0-Linux-x86_64.sh
tput setaf 2; echo '#### Anaconda has been successfully installed ####'; tput sgr0;

# Stop Xorg
sudo systemctl stop lightdm.service

# Remove .deb packages and Anaconda installer
rm -f Anaconda* nvidia* cuda*

danielhavir

def gram_matrix(feature_map):
    C, H, W = feature_map.size()
    reshaped_map = feature_map.view(C, H*W)
    G = reshaped_map.mm(reshaped_map.t()) / (C * H * W)
    return G

danielhavir

def content_loss(content_features, generated_features, j):
    loss_content = F.mse_loss(generated_features[j], content_features[j])
    return loss_content

danielhavir

def style_loss(style_features, generated_features, J):
    loss_style = 0.
    for j in J:
        gram_style = gram_matrix(style_features[j])
        gram_gen = gram_matrix(generated_features[j])
        loss_style += F.mse_loss(gram_gen, gram_style)
    return loss_style