Dmitry Ulyanov DmitryUlyanov

## trylib_examples.ipynb

      
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              September 24, 2015 19:16
            
          
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## SpatialConv_cudnn_vs_nn.lua
require 'nn'
require 'image'
require 'cutorch'
require 'cunn'
require 'cudnn'

-- adds first dummy dimension
function torch.Tensor:add_dummy()
  local sz = self:size()
  local new_sz = torch.Tensor(sz:size()+1)

## 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:

## 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):

## 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):

## 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])))

## 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

## 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])


## 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')

## 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">
    /*!
*
* Twitter Bootstrap
	require 'nn'
	require 'image'
	require 'cutorch'
	require 'cunn'
	require 'cudnn'

	-- adds first dummy dimension
	function torch.Tensor:add_dummy()
	local sz = self:size()
	local new_sz = torch.Tensor(sz:size()+1)
	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:
	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):
	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])))
	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
	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])
	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')
	<!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">
	/*!
	*
	* Twitter Bootstrap