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%matplotlib inline
import importlib
import os, sys; sys.path.insert(1, os.path.join('../utils'))
from utils2 import *


    

    




Using TensorFlow backend.
/home/wnixalo/miniconda3/envs/FAI3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)

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import torch, torch.nn as nn, torch.nn.functional as F, torch.optim as optim
from torch.autograd import Variable
from torch.utils.serialization import load_lua
from torch.utils.data import DataLoader
from torchvision import transforms, models, datasets


    

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path = '../data/nst/'
fnames = pickle.load(open(path+'fnames.pkl','rb'))


    

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img = Image.open(path + fnames[0]); img


    

    
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rn_mean = np.array([123.68, 116.779, 103.939], dtype=np.float32).reshape((1,1,1,3))
preproc = lambda x: (x - rn_mean)[:,:,:,::-1]

img_arr = preproc(np.expand_dims(np.array(img),0))
shp = img_arr.shape

deproc = lambda x: x[:,:,:,::-1] + rn_mena


    

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def download_convert_vgg16_model():
    model_url = 'http://cs.stanford.edu/people/jcjohns/fast-neural-style/models/vgg16.t7'
    file = get_file(model_url, cache_subdir='models')
    vgglua = load_lua(file).parameters()
    vgg = models.VGGFeature()
    for (src, dst) in zip(vgglua[0], vgg.parameters()): dst[:] = src[:]
    torch.save(vgg.state_dict(), path + 'vgg16_feature.pth')


    

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url = 'https://s3-us-west-2.amazonaws.com/jcjohns-models/'
fname = 'vgg16-00b39a1b.pth'
file = get_file(fname, url+fname, cache_subdir='models')


    

    




Downloading data from https://s3-us-west-2.amazonaws.com/jcjohns-models/vgg16-00b39a1b.pth

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vgg = models.vgg.vgg16()
vgg.load_state_dict(torch.load(file))
optimizer = optim.Adam(vgg.parameters())


    

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vgg.cuda();


    

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arr_lr = bcolz.open(path + 'trn_resized_72.bc')[:]
arr_hr = bcolz.open(path + 'trn_resized_288.bc')[:]

arr = bcolz.open(dpath + 'trn_resized.bc')[:]


    

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x = Variable(arr[0])
y = model(x)


    

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url = 'http://www.files.fast.ai/models/'
fname = 'imagenet_class_index.json'
fpath = get_file(fname, url + fname, cache_subdir='models')


    

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class ResidualBlock(nn.Module):
    def __init__(self, num):
        super(ResideualBlock, self).__init__()
        self.c1 = nn.Conv2d(num, num, kernel_size=3, stride=1, padding=1)
        self.c2 = nn.Conv2d(num, num, kernel_size=3, stride=1, padding=1)
        self.b1 = nn.BatchNorm2d(num)
        self.b2 = nn.BatchNorm2d(num)
    
    def forward(self, x):
        h = F.relu(self.b1(self.c1(x)))
        h = self.b2(self.c2(h))
        return h + x

class FastStyleNet(nn.Module):
    def __init__(self):
        super(FastStyleNet, self).__init__()
        self.cs = [nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=4),
                   nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1),
                   nn.Conv2d(64, 128, kernel_size=4, stride=2, padding1)]
        self.b1s = [nn.BatchNorm2d(i) for i in [32, 64, 128]]
        self.rs = [ResidualBlock(128) for i in range(5)]
        self.ds = [nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),
                   nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1)]
        self.b2s = [nn.BatchNorm2d(i) for i in [64, 32]]
        self.d3 = nn.Conv2d(32, 3, kernel_size=9, stride=1, padding=4)
    
    def forward(self, h):
        for i in range(3): h = F.relu(self.b1s[i](self.cs[i](x)))
        for r in self.rs: h = r(h)
        for i in range(2): h = F.relu(self.b2s[i](self.ds[i](x)))
        return self.d3(h)


    

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