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

Tushar-N

import cv2
import numpy as np

import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--img', default='image.jpg')
args = parser.parse_args()

def collect_clicks(event, x, y, flags, param):
    global points

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

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 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