AruniRC

import os
import sys
import pickle
import json
import numpy as np
import sys
import matplotlib 
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import os.path as osp

AruniRC

force_color_prompt=yes

if [ -n "$force_color_prompt" ]; then
    if [ -x /usr/bin/tput ] && tput setaf 1 >&/dev/null; then
        # We have color support; assume it's compliant with Ecma-48
        # (ISO/IEC-6429). (Lack of such support is extremely rare, and such
        # a case would tend to support setf rather than setaf.)
        color_prompt=yes
    else
        color_prompt=yes=no

AruniRC

export PS1="\[\033[36m\]\u\[\033[m\]@\[\033[32m\]\h:\[\033[33;1m\]\w\[\033[m\]\n\$ "
export CLICOLOR=1
export LSCOLORS=ExFxBxDxCxegedabagacad

# User defined aliases
alias ls='ls -GFh'

# Mounting remote drives (create folder manually first under ~/Mount/remote-name)
alias mount-fisher='sshfs arunirc@fisher.cs.umass.edu:/ ~/Mount/fisher -o volname=fisher'

AruniRC

# saliency
sal = cluster_saliency[cluster_label] # [ (grad-norm, grad-max)
grad_max = sal[1] / max(sal[1])


feat_vertices = features[cluster_ids, :]
adj_mat = get_adjmat(feat_vertices, is_norm_adj=False)
adj_mat_normed = get_adjmat(feat_vertices, is_norm_adj=True)

# create networkx graph from adjacency matrix

AruniRC

import numpy as np
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable
from torch.autograd import Function
from scipy.spatial.distance import pdist

AruniRC

    def forward(self, x):
        x = self.features(x)
        [bs, ch, h, w] = x.shape
        x = x.view(bs, ch, -1).transpose(2, 1)
        # x.register_hook(self.save_grad('x'))

        # Gram Matrix NxN for the N input features "x"
        K = x.bmm(x.transpose(2, 1))
        K = x * x;   # < --- IS THIS CORRECT for 1st order features????

AruniRC

Intro Vim

• Intro and setup for IDE users: https://blog.stevenocchipinti.com/2014/03/17/introduction-to-vim-for-ide-users/
• Quick list: https://github.com/hackjutsu/vim-cheatsheet
• Quick reference card for print-out: http://tnerual.eriogerg.free.fr/vimqrc.pdf

Vim for Python

• Tabs and Spaces: https://linuxhint.com/vim-python-development/

AruniRC

	  ---			-----------------			----
	  Map			Quick Explanation			Link
	  ---			-----------------			----
<	 <F1>	Causes Netrw to issue help
	 <cr>	Netrw will enter the directory or read the file      |netrw-cr|
	 <del>	Netrw will attempt to remove the file/directory      |netrw-del|
	 <c-h>	Edit file hiding list                                |netrw-ctrl-h|
	 <c-l>	Causes Netrw to refresh the directory listing        |netrw-ctrl-l|
	 <c-r>	Browse using a gvim server                           |netrw-ctrl-r|
	 <c-tab> Shrink/expand a netrw/explore window                |netrw-c-tab|

AruniRC

This walkthrough describes setting up Detectron (3rd party pytorch implementation) and Graph Conv Net (GCN) repos on the UMass cluster Gypsum. Most commands are specific to that setting.

Gypsum environment

$ module list
Currently Loaded Modulefiles:
  1) slurm/16.05.8                         3) hdf5/1.6.10                           5) gcc5/5.4.0                            7) cudnn/5.1
 2) openmpi/gcc/64/1.10.1 4) fftw2/openmpi/open64/64/float/2.1.5 6) cuda80/toolkit/8.0.61 8) hdf5_18/1.8.17

AruniRC

if self.distill:
  soft_target = Variable(data[2].cuda())
  distill_loss = torch.mean(torch.sum(- nn.Softmax(dim=1)(soft_target/self.T) * nn.LogSoftmax(dim=1)(out_data/self.T), 1))  
  loss += self.lbda*distill_loss
  self.writer.add_scalar('train/distill_loss', distill_loss, i_acc+i+1)