jeetsukumaran

#! /bin/bash

GCC_VERSION="5.2.0"
WORKDIR="$HOME/src/"
INSTALLDIR="/platform"

## NOTE: XCode must be installed (through App Store) and the following run to install command-line tools.
## THIS IS IMPORTANT! Among other things, it creates '/usr/include' and installs the system header files.
# xcode-select --install

wllhf

""" 
Python implementation of the color map function for the PASCAL VOC data set. 
Official Matlab version can be found in the PASCAL VOC devkit 
http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit
"""
import numpy as np
from skimage.io import imshow
import matplotlib.pyplot as plt

def color_map(N=256, normalized=False):

SunnyRaj

#!/usr/bin/env bash

sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
sudo apt update

sudo update-alternatives --remove-all gcc
sudo update-alternatives --remove-all g++
sudo apt-get install -y gcc-4.8 g++-4.8 gcc-4.9 g++-4.9 gcc-5 g++-5 gcc-6 g++-6 gcc-7 g++-7

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 10

adarsh0806

def delete_first(self):
    deleted = self.head
    if self.head:
        self.head = self.head.next
        deleted.next = None
    return deleted

apaszke

import torch                                                                                          
                                                                                                      
def jacobian(y, x, create_graph=False):                                                               
    jac = []                                                                                          
    flat_y = y.reshape(-1)                                                                            
    grad_y = torch.zeros_like(flat_y)                                                                 
    for i in range(len(flat_y)):                                                                      
        grad_y[i] = 1.                                                                                
        grad_x, = torch.autograd.grad(flat_y, x, grad_y, retain_graph=True, create_graph=create_graph)
        jac.append(grad_x.reshape(x.shape))