Tushar-N

import torch
import torch.nn as nn
import torch.nn.functional as F

import torchvision.models as tmodels
from functools import partial
import collections

# dummy data: 10 batches of images with batch size 16
dataset = [torch.rand(16,3,224,224).cuda() for _ in range(10)]

Tushar-N

import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

seqs = ['gigantic_string','tiny_str','medium_str']

# make <pad> idx 0
vocab = ['<pad>'] + sorted(set(''.join(seqs)))

# make model

Tushar-N

function getDriveFolderFromPath (path) {
  return (path || "/").split("/").reduce ( function(prev,current) {
    if (prev && current) {
      var fldrs = prev.getFoldersByName(current);
      return fldrs.hasNext() ? fldrs.next() : null;
    }
    else {
      return current ? null : prev;
    }
  },DriveApp.getRootFolder());

Tushar-N

Video player with live chat playback

Tushar-N

import cv2
import numpy as np
import argparse

'''
usage: python click_heatmap.py <image file>
    left-click: add point to heatmap
    s: save image (000.png, 001.png, ...) 
    q: quit
    r: reset

Tushar-N

cmd=$@ 
cv_dir=`expr "$cmd" : '.*--cv_dir \([^ ]*\).*'`

mkdir -p $cv_dir

# temporarily create two files: submit.sh and run.sh
cat > $cv_dir/submit.sh << EOF
universe = vanilla 
Executable = /lusr/bin/bash
Arguments = $cv_dir/run.sh

Tushar-N

import torch
import io
from PIL import Image
import numpy as np

# Dataset class for extracting binary data from images to store
class ImageDataset:
    def __init__(self):
        super(ImageDataset, self).__init__()
        self.images = [] # some list of PIL images