Dmitry Ulyanov DmitryUlyanov

## wget_drive.py
import requests

def download_file_from_google_drive(id, destination):
    def get_confirm_token(response):
        for key, value in response.cookies.items():
            if key.startswith('download_warning'):
                return value

        return None

## sample_entropy.py
import torch
import torch.nn
from torch.autograd import Variable

def pairwise_euclidean(samples):

    B = samples.size(0)

    samples_norm = samples.mul(samples).sum(1)
    samples_norm = samples_norm.expand(B, B)

## Springleaf.html
<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>Feats_work-clean</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<style type="text/css">
    /*!
*

## html_server.py
from flask import Flask, request, render_template
app = Flask(__name__, static_url_path='', static_folder='')


@app.route('/')
def root():
    return app.send_static_file('sr_set5.html')


@app.route('/readme')

## imscatter.py
def imscatter(images, positions):

    positions = np.array(positions)

    bottoms = positions[:,1] - np.array([im.shape[1]/2.0 for im in images])
    tops = bottoms + np.array([im.shape[1] for im in images])

    lefts = positions[:,0] - np.array([im.shape[0]/2.0 for im in images])
    rigths = lefts + np.array([im.shape[0] for im in images])


## trilinear_interpolation.py
import numba
def FindLowAndHighIndices(x, m):
    xf = np.floor(x)


    l = np.clip(xf, 0, m-1).astype(int)
    h = np.clip(xf + 1, 0, m-1).astype(int)

    return l, h

## get_sample_grid.py
import random
from PIL import Image
import glob
import numpy as np

def get_sample_grid_(paths, grid_w=4, grid_h=4, imsize_w=256, imsize_h=256):
    imgs = []
    for p in paths:
        imgs.append(np.array(Image.open(p).convert('RGB').resize([imsize_w,imsize_h])))

## run_batch.py
from joblib import Parallel, delayed
import Queue
import os

# Define number of GPUs available
N_GPU = 4

# Put indices in queue
q = Queue.Queue(maxsize=N_GPU)
for i in range(N_GPU):

## run_batch2.py
from __future__ import print_function
import threading
from joblib import Parallel, delayed
import Queue
import os

# Fix print
_print = print
_rlock = threading.RLock()
def print(*args, **kwargs):

## test_backend.py
from __future__ import print_function
import threading
from joblib import Parallel, delayed
import Queue

# Fix print
_print = print
_rlock = threading.RLock()
def print(*args, **kwargs):
    with _rlock:
	import requests

	def download_file_from_google_drive(id, destination):
	def get_confirm_token(response):
	for key, value in response.cookies.items():
	if key.startswith('download_warning'):
	return value

	return None
	import torch
	import torch.nn
	from torch.autograd import Variable

	def pairwise_euclidean(samples):

	B = samples.size(0)

	samples_norm = samples.mul(samples).sum(1)
	samples_norm = samples_norm.expand(B, B)
	<!DOCTYPE html>
	<html>
	<head><meta charset="utf-8" />
	<title>Feats_work-clean</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

	<style type="text/css">
	/*!
	*
	from flask import Flask, request, render_template
	app = Flask(__name__, static_url_path='', static_folder='')


	@app.route('/')
	def root():
	return app.send_static_file('sr_set5.html')


	@app.route('/readme')
	def imscatter(images, positions):

	positions = np.array(positions)

	bottoms = positions[:,1] - np.array([im.shape[1]/2.0 for im in images])
	tops = bottoms + np.array([im.shape[1] for im in images])

	lefts = positions[:,0] - np.array([im.shape[0]/2.0 for im in images])
	rigths = lefts + np.array([im.shape[0] for im in images])
	import numba
	def FindLowAndHighIndices(x, m):
	xf = np.floor(x)


	l = np.clip(xf, 0, m-1).astype(int)
	h = np.clip(xf + 1, 0, m-1).astype(int)

	return l, h
	import random
	from PIL import Image
	import glob
	import numpy as np

	def get_sample_grid_(paths, grid_w=4, grid_h=4, imsize_w=256, imsize_h=256):
	imgs = []
	for p in paths:
	imgs.append(np.array(Image.open(p).convert('RGB').resize([imsize_w,imsize_h])))
	from joblib import Parallel, delayed
	import Queue
	import os

	# Define number of GPUs available
	N_GPU = 4

	# Put indices in queue
	q = Queue.Queue(maxsize=N_GPU)
	for i in range(N_GPU):
	from __future__ import print_function
	import threading
	from joblib import Parallel, delayed
	import Queue
	import os

	# Fix print
	_print = print
	_rlock = threading.RLock()
	def print(args, *kwargs):