| 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 |
| 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 |
| 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 [email protected]:/ ~/Mount/fisher -o volname=fisher' |
| # 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 |
| 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 |
| 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???? |
| --- ----------------- ---- | |
| 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| |
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.
$ 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
| 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) |