#!/usr/bin/env python3
""" _ _
_ __ ___ _ _ _ __ __ _| | ___ _ __ | |__ __ _ _ __ ___ ___
| '_ \ / _ \ | | | '__/ _` | | / _ \ '_ \| '_ \ / _` | '_ \ / __/ _ \
| | | | __/ |_| | | | (_| | | | __/ | | | | | | (_| | | | | (_| __/
|_| |_|\___|\__,_|_| \__,_|_| \___|_| |_|_| |_|\__,_|_| |_|\___\___|
"""
#
# Copyright (c) 2016, Alex J. Champandard.
#
# Neural Enhance is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General
# Public License version 3. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#
__version__ = '0.3'
import io
import os
import sys
import bz2
import glob
import math
import time
import pickle
import random
import argparse
import itertools
import threading
import collections
# Configure all options first so we can later custom-load other libraries (Theano) based on device specified by user.
parser = argparse.ArgumentParser(description='Generate a new image by applying style onto a content image.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
add_arg = parser.add_argument
add_arg('files', nargs='*', default=[])
add_arg('--zoom', default=2, type=int, help='Resolution increase factor for inference.')
add_arg('--rendering-tile', default=80, type=int, help='Size of tiles used for rendering images.')
add_arg('--rendering-overlap', default=24, type=int, help='Number of pixels padding around each tile.')
add_arg('--rendering-histogram',default=False, action='store_true', help='Match color histogram of output to input.')
add_arg('--type', default='photo', type=str, help='Name of the neural network to load/save.')
add_arg('--model', default='default', type=str, help='Specific trained version of the model.')
add_arg('--train', default=False, type=str, help='File pattern to load for training.')
add_arg('--train-scales', default=0, type=int, help='Randomly resize images this many times.')
add_arg('--train-blur', default=None, type=int, help='Sigma value for gaussian blur preprocess.')
add_arg('--train-noise', default=None, type=float, help='Radius for preprocessing gaussian blur.')
add_arg('--train-jpeg', default=[], nargs='+', type=int, help='JPEG compression level & range in preproc.')
add_arg('--epochs', default=10, type=int, help='Total number of iterations in training.')
add_arg('--epoch-size', default=72, type=int, help='Number of batches trained in an epoch.')
add_arg('--save-every', default=10, type=int, help='Save generator after every training epoch.')
add_arg('--batch-shape', default=192, type=int, help='Resolution of images in training batch.')
add_arg('--batch-size', default=15, type=int, help='Number of images per training batch.')
add_arg('--buffer-size', default=1500, type=int, help='Total image fragments kept in cache.')
add_arg('--buffer-fraction', default=5, type=int, help='Fragments cached for each image loaded.')
add_arg('--learning-rate', default=1E-4, type=float, help='Parameter for the ADAM optimizer.')
add_arg('--learning-period', default=75, type=int, help='How often to decay the learning rate.')
add_arg('--learning-decay', default=0.5, type=float, help='How much to decay the learning rate.')
add_arg('--generator-upscale', default=2, type=int, help='Steps of 2x up-sampling as post-process.')
add_arg('--generator-downscale',default=0, type=int, help='Steps of 2x down-sampling as preprocess.')
add_arg('--generator-filters', default=[64], nargs='+', type=int, help='Number of convolution units in network.')
add_arg('--generator-blocks', default=4, type=int, help='Number of residual blocks per iteration.')
add_arg('--generator-residual', default=2, type=int, help='Number of layers in a residual block.')
add_arg('--perceptual-layer', default='conv2_2', type=str, help='Which VGG layer to use as loss component.')
add_arg('--perceptual-weight', default=1e0, type=float, help='Weight for VGG-layer perceptual loss.')
add_arg('--discriminator-size', default=32, type=int, help='Multiplier for number of filters in D.')
add_arg('--smoothness-weight', default=2e5, type=float, help='Weight of the total-variation loss.')
add_arg('--adversary-weight', default=5e2, type=float, help='Weight of adversarial loss compoment.')
add_arg('--generator-start', default=0, type=int, help='Epoch count to start training generator.')
add_arg('--discriminator-start',default=1, type=int, help='Epoch count to update the discriminator.')
add_arg('--adversarial-start', default=2, type=int, help='Epoch for generator to use discriminator.')
add_arg('--device', default='cpu', type=str, help='Name of the CPU/GPU to use, for Theano.')
args = parser.parse_args()
#----------------------------------------------------------------------------------------------------------------------
# Color coded output helps visualize the information a little better, plus it looks cool!
class ansi:
WHITE = '\033[0;97m'
WHITE_B = '\033[1;97m'
YELLOW = '\033[0;33m'
YELLOW_B = '\033[1;33m'
RED = '\033[0;31m'
RED_B = '\033[1;31m'
BLUE = '\033[0;94m'
BLUE_B = '\033[1;94m'
CYAN = '\033[0;36m'
CYAN_B = '\033[1;36m'
ENDC = '\033[0m'
def error(message, *lines):
string = "\n{}ERROR: " + message + "{}\n" + "\n".join(lines) + ("{}\n" if lines else "{}")
print(string.format(ansi.RED_B, ansi.RED, ansi.ENDC))
sys.exit(-1)
def warn(message, *lines):
string = "\n{}WARNING: " + message + "{}\n" + "\n".join(lines) + "{}\n"
print(string.format(ansi.YELLOW_B, ansi.YELLOW, ansi.ENDC))
def extend(lst): return itertools.chain(lst, itertools.repeat(lst[-1]))
print("""{} {}Super Resolution for images and videos powered by Deep Learning!{}
- Code licensed as AGPLv3, models under CC BY-NC-SA.{}""".format(ansi.CYAN_B, __doc__, ansi.CYAN, ansi.ENDC))
# Load the underlying deep learning libraries based on the device specified. If you specify THEANO_FLAGS manually,
# the code assumes you know what you are doing and they are not overriden!
os.environ.setdefault('THEANO_FLAGS', 'floatX=float32,device={},force_device=True,allow_gc=True,'\
'print_active_device=False'.format(args.device))
# Scientific & Imaging Libraries
import numpy as np
import scipy.ndimage, scipy.misc, PIL.Image
# Numeric Computing (GPU)
import theano, theano.tensor as T
T.nnet.softminus = lambda x: x - T.nnet.softplus(x)
# Support ansi colors in Windows too.
if sys.platform == 'win32':
import colorama
# Deep Learning Framework
import lasagne
from lasagne.layers import Conv2DLayer as ConvLayer, Deconv2DLayer as DeconvLayer, Pool2DLayer as PoolLayer
from lasagne.layers import InputLayer, ConcatLayer, ElemwiseSumLayer, batch_norm
print('{} - Using the device `{}` for neural computation.{}\n'.format(ansi.CYAN, theano.config.device, ansi.ENDC))
#======================================================================================================================
# Image Processing
#======================================================================================================================
class DataLoader(threading.Thread):
def __init__(self):
super(DataLoader, self).__init__(daemon=True)
self.data_ready = threading.Event()
self.data_copied = threading.Event()
self.orig_shape, self.seed_shape = args.batch_shape, args.batch_shape // args.zoom
self.orig_buffer = np.zeros((args.buffer_size, 3, self.orig_shape, self.orig_shape), dtype=np.float32)
self.seed_buffer = np.zeros((args.buffer_size, 3, self.seed_shape, self.seed_shape), dtype=np.float32)
self.files = glob.glob(args.train)
if len(self.files) == 0:
error("There were no files found to train from searching for `{}`".format(args.train),
" - Try putting all your images in one folder and using `--train=data/*.jpg`")
self.available = set(range(args.buffer_size))
self.ready = set()
self.cwd = os.getcwd()
self.start()
def run(self):
while True:
random.shuffle(self.files)
for f in self.files:
self.add_to_buffer(f)
def add_to_buffer(self, f):
filename = os.path.join(self.cwd, f)
try:
orig = PIL.Image.open(filename).convert('RGB')
scale = 2 ** random.randint(0, args.train_scales)
if scale > 1 and all(s//scale >= args.batch_shape for s in orig.size):
orig = orig.resize((orig.size[0]//scale, orig.size[1]//scale), resample=PIL.Image.LANCZOS)
if any(s < args.batch_shape for s in orig.size):
raise ValueError('Image is too small for training with size {}'.format(orig.size))
except Exception as e:
warn('Could not load `{}` as image.'.format(filename),
' - Try fixing or removing the file before next run.')
self.files.remove(f)
return
seed = orig
if args.train_blur is not None:
seed = seed.filter(PIL.ImageFilter.GaussianBlur(radius=random.randint(0, args.train_blur*2)))
if args.zoom > 1:
seed = seed.resize((orig.size[0]//args.zoom, orig.size[1]//args.zoom), resample=PIL.Image.LANCZOS)
if len(args.train_jpeg) > 0:
buffer, rng = io.BytesIO(), args.train_jpeg[-1] if len(args.train_jpeg) > 1 else 15
seed.save(buffer, format='jpeg', quality=args.train_jpeg[0]+random.randrange(-rng, +rng))
seed = PIL.Image.open(buffer)
orig = scipy.misc.fromimage(orig).astype(np.float32)
seed = scipy.misc.fromimage(seed).astype(np.float32)
if args.train_noise is not None:
seed += scipy.random.normal(scale=args.train_noise, size=(seed.shape[0], seed.shape[1], 1))
for _ in range(seed.shape[0] * seed.shape[1] // (args.buffer_fraction * self.seed_shape ** 2)):
h = random.randint(0, seed.shape[0] - self.seed_shape)
w = random.randint(0, seed.shape[1] - self.seed_shape)
seed_chunk = seed[h:h+self.seed_shape, w:w+self.seed_shape]
h, w = h * args.zoom, w * args.zoom
orig_chunk = orig[h:h+self.orig_shape, w:w+self.orig_shape]
while len(self.available) == 0:
self.data_copied.wait()
self.data_copied.clear()
i = self.available.pop()
self.orig_buffer[i] = np.transpose(orig_chunk.astype(np.float32) / 255.0 - 0.5, (2, 0, 1))
self.seed_buffer[i] = np.transpose(seed_chunk.astype(np.float32) / 255.0 - 0.5, (2, 0, 1))
self.ready.add(i)
if len(self.ready) >= args.batch_size:
self.data_ready.set()
def copy(self, origs_out, seeds_out):
self.data_ready.wait()
self.data_ready.clear()
for i, j in enumerate(random.sample(self.ready, args.batch_size)):
origs_out[i] = self.orig_buffer[j]
seeds_out[i] = self.seed_buffer[j]
self.available.add(j)
self.data_copied.set()
#======================================================================================================================
# Convolution Networks
#======================================================================================================================
class SubpixelReshuffleLayer(lasagne.layers.Layer):
"""Based on the code by ajbrock: https://github.com/ajbrock/Neural-Photo-Editor/
"""
def __init__(self, incoming, channels, upscale, **kwargs):
super(SubpixelReshuffleLayer, self).__init__(incoming, **kwargs)
self.upscale = upscale
self.channels = channels
def get_output_shape_for(self, input_shape):
def up(d): return self.upscale * d if d else d
return (input_shape[0], self.channels, up(input_shape[2]), up(input_shape[3]))
def get_output_for(self, input, deterministic=False, **kwargs):
out, r = T.zeros(self.get_output_shape_for(input.shape)), self.upscale
for y, x in itertools.product(range(r), repeat=2):
out=T.inc_subtensor(out[:,:,y::r,x::r], input[:,r*y+x::r*r,:,:])
return out
class Model(object):
def __init__(self):
self.network = collections.OrderedDict()
self.network['img'] = InputLayer((None, 3, None, None))
self.network['seed'] = InputLayer((None, 3, None, None))
config, params = self.load_model()
self.setup_generator(self.last_layer(), config)
if args.train:
concatenated = lasagne.layers.ConcatLayer([self.network['img'], self.network['out']], axis=0)
self.setup_perceptual(concatenated)
self.load_perceptual()
self.setup_discriminator()
self.load_generator(params)
self.compile()
#------------------------------------------------------------------------------------------------------------------
# Network Configuration
#------------------------------------------------------------------------------------------------------------------
def last_layer(self):
return list(self.network.values())[-1]
def make_layer(self, name, input, units, filter_size=(3,3), stride=(1,1), pad=(1,1), alpha=0.25):
conv = ConvLayer(input, units, filter_size, stride=stride, pad=pad, nonlinearity=None)
prelu = lasagne.layers.ParametricRectifierLayer(conv, alpha=lasagne.init.Constant(alpha))
self.network[name+'x'] = conv
self.network[name+'>'] = prelu
return prelu
def make_block(self, name, input, units):
self.make_layer(name+'-A', input, units, alpha=0.1)
# self.make_layer(name+'-B', self.last_layer(), units, alpha=1.0)
return ElemwiseSumLayer([input, self.last_layer()]) if args.generator_residual else self.last_layer()
def setup_generator(self, input, config):
for k, v in config.items(): setattr(args, k, v)
args.zoom = 2**(args.generator_upscale - args.generator_downscale)
units_iter = extend(args.generator_filters)
units = next(units_iter)
self.make_layer('iter.0', input, units, filter_size=(7,7), pad=(3,3))
for i in range(0, args.generator_downscale):
self.make_layer('downscale%i'%i, self.last_layer(), next(units_iter), filter_size=(4,4), stride=(2,2))
units = next(units_iter)
for i in range(0, args.generator_blocks):
self.make_block('iter.%i'%(i+1), self.last_layer(), units)
for i in range(0, args.generator_upscale):
u = next(units_iter)
self.make_layer('upscale%i.2'%i, self.last_layer(), u*4)
self.network['upscale%i.1'%i] = SubpixelReshuffleLayer(self.last_layer(), u, 2)
self.network['out'] = ConvLayer(self.last_layer(), 3, filter_size=(7,7), pad=(3,3), nonlinearity=None)
def setup_perceptual(self, input):
"""Use lasagne to create a network of convolution layers using pre-trained VGG19 weights.
"""
offset = np.array([103.939, 116.779, 123.680], dtype=np.float32).reshape((1,3,1,1))
self.network['percept'] = lasagne.layers.NonlinearityLayer(input, lambda x: ((x+0.5)*255.0) - offset)
self.network['mse'] = self.network['percept']
self.network['conv1_1'] = ConvLayer(self.network['percept'], 64, 3, pad=1)
self.network['conv1_2'] = ConvLayer(self.network['conv1_1'], 64, 3, pad=1)
self.network['pool1'] = PoolLayer(self.network['conv1_2'], 2, mode='max')
self.network['conv2_1'] = ConvLayer(self.network['pool1'], 128, 3, pad=1)
self.network['conv2_2'] = ConvLayer(self.network['conv2_1'], 128, 3, pad=1)
self.network['pool2'] = PoolLayer(self.network['conv2_2'], 2, mode='max')
self.network['conv3_1'] = ConvLayer(self.network['pool2'], 256, 3, pad=1)
self.network['conv3_2'] = ConvLayer(self.network['conv3_1'], 256, 3, pad=1)
self.network['conv3_3'] = ConvLayer(self.network['conv3_2'], 256, 3, pad=1)
self.network['conv3_4'] = ConvLayer(self.network['conv3_3'], 256, 3, pad=1)
self.network['pool3'] = PoolLayer(self.network['conv3_4'], 2, mode='max')
self.network['conv4_1'] = ConvLayer(self.network['pool3'], 512, 3, pad=1)
self.network['conv4_2'] = ConvLayer(self.network['conv4_1'], 512, 3, pad=1)
self.network['conv4_3'] = ConvLayer(self.network['conv4_2'], 512, 3, pad=1)
self.network['conv4_4'] = ConvLayer(self.network['conv4_3'], 512, 3, pad=1)
self.network['pool4'] = PoolLayer(self.network['conv4_4'], 2, mode='max')
self.network['conv5_1'] = ConvLayer(self.network['pool4'], 512, 3, pad=1)
self.network['conv5_2'] = ConvLayer(self.network['conv5_1'], 512, 3, pad=1)
self.network['conv5_3'] = ConvLayer(self.network['conv5_2'], 512, 3, pad=1)
self.network['conv5_4'] = ConvLayer(self.network['conv5_3'], 512, 3, pad=1)
def setup_discriminator(self):
c = args.discriminator_size
self.make_layer('disc1.1', batch_norm(self.network['conv1_2']), 1*c, filter_size=(5,5), stride=(2,2), pad=(2,2))
self.make_layer('disc1.2', self.last_layer(), 1*c, filter_size=(5,5), stride=(2,2), pad=(2,2))
self.make_layer('disc2', batch_norm(self.network['conv2_2']), 2*c, filter_size=(5,5), stride=(2,2), pad=(2,2))
self.make_layer('disc3', batch_norm(self.network['conv3_2']), 3*c, filter_size=(3,3), stride=(1,1), pad=(1,1))
hypercolumn = ConcatLayer([self.network['disc1.2>'], self.network['disc2>'], self.network['disc3>']])
self.make_layer('disc4', hypercolumn, 4*c, filter_size=(1,1), stride=(1,1), pad=(0,0))
self.make_layer('disc5', self.last_layer(), 3*c, filter_size=(3,3), stride=(2,2))
self.make_layer('disc6', self.last_layer(), 2*c, filter_size=(1,1), stride=(1,1), pad=(0,0))
self.network['disc'] = batch_norm(ConvLayer(self.last_layer(), 1, filter_size=(1,1),
nonlinearity=lasagne.nonlinearities.linear))
#------------------------------------------------------------------------------------------------------------------
# Input / Output
#------------------------------------------------------------------------------------------------------------------
def load_perceptual(self):
"""Open the serialized parameters from a pre-trained network, and load them into the model created.
"""
vgg19_file = os.path.join(os.path.dirname(__file__), 'vgg19_conv.pkl.bz2')
if not os.path.exists(vgg19_file):
error("Model file with pre-trained convolution layers not found. Download here...",
"https://github.com/alexjc/neural-doodle/releases/download/v0.0/vgg19_conv.pkl.bz2")
data = pickle.load(bz2.open(vgg19_file, 'rb'))
layers = lasagne.layers.get_all_layers(self.last_layer(), treat_as_input=[self.network['percept']])
for p, d in zip(itertools.chain(*[l.get_params() for l in layers]), data): p.set_value(d)
def list_generator_layers(self):
for l in lasagne.layers.get_all_layers(self.network['out'], treat_as_input=[self.network['img']]):
if not l.get_params(): continue
name = list(self.network.keys())[list(self.network.values()).index(l)]
yield (name, l)
def get_filename(self, absolute=False):
filename = 'ne%ix-%s-%s-%s.pkl.bz2' % (args.zoom, args.type, args.model, __version__)
return os.path.join(os.path.dirname(__file__), filename) if absolute else filename
def save_generator(self):
def cast(p): return p.get_value().astype(np.float16)
params = {k: [cast(p) for p in l.get_params()] for (k, l) in self.list_generator_layers()}
config = {k: getattr(args, k) for k in ['generator_blocks', 'generator_residual', 'generator_filters'] + \
['generator_upscale', 'generator_downscale']}
pickle.dump((config, params), bz2.open(self.get_filename(absolute=True), 'wb'))
print(' - Saved model as `{}` after training.'.format(self.get_filename()))
def load_model(self):
if not os.path.exists(self.get_filename(absolute=True)):
if args.train: return {}, {}
error("Model file with pre-trained convolution layers not found. Download it here...",
"https://github.com/alexjc/neural-enhance/releases/download/v%s/%s"%(__version__, self.get_filename()))
print(' - Loaded file `{}` with trained model.'.format(self.get_filename()))
return pickle.load(bz2.open(self.get_filename(absolute=True), 'rb'))
def load_generator(self, params):
if len(params) == 0: return
for k, l in self.list_generator_layers():
assert k in params, "Couldn't find layer `%s` in loaded model.'" % k
assert len(l.get_params()) == len(params[k]), "Mismatch in types of layers."
for p, v in zip(l.get_params(), params[k]):
assert v.shape == p.get_value().shape, "Mismatch in number of parameters for layer {}.".format(k)
p.set_value(v.astype(np.float32))
#------------------------------------------------------------------------------------------------------------------
# Training & Loss Functions
#------------------------------------------------------------------------------------------------------------------
def loss_perceptual(self, p):
return lasagne.objectives.squared_error(p[:args.batch_size], p[args.batch_size:]).mean()
def loss_total_variation(self, x):
return T.mean(((x[:,:,:-1,:-1] - x[:,:,1:,:-1])**2 + (x[:,:,:-1,:-1] - x[:,:,:-1,1:])**2)**1.25)
def loss_adversarial(self, d):
return T.mean(1.0 - T.nnet.softminus(d[args.batch_size:]))
def loss_discriminator(self, d):
return T.mean(T.nnet.softminus(d[args.batch_size:]) - T.nnet.softplus(d[:args.batch_size]))
def compile(self):
# Helper function for rendering test images during training, or standalone inference mode.
input_tensor, seed_tensor = T.tensor4(), T.tensor4()
input_layers = {self.network['img']: input_tensor, self.network['seed']: seed_tensor}
output = lasagne.layers.get_output([self.network[k] for k in ['seed','out']], input_layers, deterministic=True)
self.predict = theano.function([seed_tensor], output)
if not args.train: return
output_layers = [self.network['out'], self.network[args.perceptual_layer], self.network['disc']]
gen_out, percept_out, disc_out = lasagne.layers.get_output(output_layers, input_layers, deterministic=False)
# Generator loss function, parameters and updates.
self.gen_lr = theano.shared(np.array(0.0, dtype=theano.config.floatX))
self.adversary_weight = theano.shared(np.array(0.0, dtype=theano.config.floatX))
gen_losses = [self.loss_perceptual(percept_out) * args.perceptual_weight,
self.loss_total_variation(gen_out) * args.smoothness_weight,
self.loss_adversarial(disc_out) * self.adversary_weight]
gen_params = lasagne.layers.get_all_params(self.network['out'], trainable=True)
print(' - {} tensors learned for generator.'.format(len(gen_params)))
gen_updates = lasagne.updates.adam(sum(gen_losses, 0.0), gen_params, learning_rate=self.gen_lr)
# Discriminator loss function, parameters and updates.
self.disc_lr = theano.shared(np.array(0.0, dtype=theano.config.floatX))
disc_losses = [self.loss_discriminator(disc_out)]
disc_params = list(itertools.chain(*[l.get_params() for k, l in self.network.items() if 'disc' in k]))
print(' - {} tensors learned for discriminator.'.format(len(disc_params)))
grads = [g.clip(-5.0, +5.0) for g in T.grad(sum(disc_losses, 0.0), disc_params)]
disc_updates = lasagne.updates.adam(grads, disc_params, learning_rate=self.disc_lr)
# Combined Theano function for updating both generator and discriminator at the same time.
updates = collections.OrderedDict(list(gen_updates.items()) + list(disc_updates.items()))
self.fit = theano.function([input_tensor, seed_tensor], gen_losses + [disc_out.mean(axis=(1,2,3))], updates=updates)
class NeuralEnhancer(object):
def __init__(self, loader):
if args.train:
print('{}Training {} epochs on random image sections with batch size {}.{}'\
.format(ansi.BLUE_B, args.epochs, args.batch_size, ansi.BLUE))
else:
if len(args.files) == 0: error("Specify the image(s) to enhance on the command-line.")
print('{}Enhancing {} image(s) specified on the command-line.{}'\
.format(ansi.BLUE_B, len(args.files), ansi.BLUE))
self.thread = DataLoader() if loader else None
self.model = Model()
print('{}'.format(ansi.ENDC))
def imsave(self, fn, img):
scipy.misc.toimage(np.transpose(img + 0.5, (1, 2, 0)).clip(0.0, 1.0) * 255.0, cmin=0, cmax=255).save(fn)
def show_progress(self, orign, scald, repro):
os.makedirs('valid', exist_ok=True)
for i in range(args.batch_size):
self.imsave('valid/%s_%03i_origin.png' % (args.model, i), orign[i])
self.imsave('valid/%s_%03i_pixels.png' % (args.model, i), scald[i])
self.imsave('valid/%s_%03i_reprod.png' % (args.model, i), repro[i])
def decay_learning_rate(self):
l_r, t_cur = args.learning_rate, 0
while True:
yield l_r
t_cur += 1
if t_cur % args.learning_period == 0: l_r *= args.learning_decay
def train(self):
seed_size = args.batch_shape // args.zoom
images = np.zeros((args.batch_size, 3, args.batch_shape, args.batch_shape), dtype=np.float32)
seeds = np.zeros((args.batch_size, 3, seed_size, seed_size), dtype=np.float32)
learning_rate = self.decay_learning_rate()
try:
average, start = None, time.time()
for epoch in range(args.epochs):
total, stats = None, None
l_r = next(learning_rate)
if epoch >= args.generator_start: self.model.gen_lr.set_value(l_r)
if epoch >= args.discriminator_start: self.model.disc_lr.set_value(l_r)
for _ in range(args.epoch_size):
self.thread.copy(images, seeds)
output = self.model.fit(images, seeds)
losses = np.array(output[:3], dtype=np.float32)
stats = (stats + output[3]) if stats is not None else output[3]
total = total + losses if total is not None else losses
l = np.sum(losses)
assert not np.isnan(losses).any()
average = l if average is None else average * 0.95 + 0.05 * l
print('↑' if l > average else '↓', end='', flush=True)
scald, repro = self.model.predict(seeds)
self.show_progress(images, scald, repro)
total /= args.epoch_size
stats /= args.epoch_size
totals, labels = [sum(total)] + list(total), ['total', 'prcpt', 'smthn', 'advrs']
gen_info = ['{}{}{}={:4.2e}'.format(ansi.WHITE_B, k, ansi.ENDC, v) for k, v in zip(labels, totals)]
print('\rEpoch #{} at {:4.1f}s, lr={:4.2e}{}'.format(epoch+1, time.time()-start, l_r, ' '*(args.epoch_size-30)))
print(' - generator {}'.format(' '.join(gen_info)))
real, fake = stats[:args.batch_size], stats[args.batch_size:]
print(' - discriminator', real.mean(), len(np.where(real > 0.5)[0]),
fake.mean(), len(np.where(fake < -0.5)[0]))
if epoch == args.adversarial_start-1:
print(' - generator now optimizing against discriminator.')
self.model.adversary_weight.set_value(args.adversary_weight)
running = None
if (epoch+1) % args.save_every == 0:
print(' - saving current generator layers to disk...')
self.model.save_generator()
except KeyboardInterrupt:
pass
print('\n{}Trained {}x super-resolution for {} epochs.{}'\
.format(ansi.CYAN_B, args.zoom, epoch+1, ansi.CYAN))
self.model.save_generator()
print(ansi.ENDC)
def match_histograms(self, A, B, rng=(0.0, 255.0), bins=64):
(Ha, Xa), (Hb, Xb) = [np.histogram(i, bins=bins, range=rng, density=True) for i in [A, B]]
X = np.linspace(rng[0], rng[1], bins, endpoint=True)
Hpa, Hpb = [np.cumsum(i) * (rng[1] - rng[0]) ** 2 / float(bins) for i in [Ha, Hb]]
inv_Ha = scipy.interpolate.interp1d(X, Hpa, bounds_error=False, fill_value='extrapolate')
map_Hb = scipy.interpolate.interp1d(Hpb, X, bounds_error=False, fill_value='extrapolate')
return map_Hb(inv_Ha(A).clip(0.0, 255.0))
def process(self, original):
# Snap the image to a shape that's compatible with the generator (2x, 4x)
s = 2 ** max(args.generator_upscale, args.generator_downscale)
by, bx = original.shape[0] % s, original.shape[1] % s
original = original[by-by//2:original.shape[0]-by//2,bx-bx//2:original.shape[1]-bx//2,:]
# Prepare paded input image as well as output buffer of zoomed size.
s, p, z = args.rendering_tile, args.rendering_overlap, args.zoom
image = np.pad(original, ((p, p), (p, p), (0, 0)), mode='reflect')
output = np.zeros((original.shape[0] * z, original.shape[1] * z, 3), dtype=np.float32)
# Iterate through the tile coordinates and pass them through the network.
for y, x in itertools.product(range(0, original.shape[0], s), range(0, original.shape[1], s)):
img = np.transpose(image[y:y+p*2+s,x:x+p*2+s,:] / 255.0 - 0.5, (2, 0, 1))[np.newaxis].astype(np.float32)
*_, repro = self.model.predict(img)
output[y*z:(y+s)*z,x*z:(x+s)*z,:] = np.transpose(repro[0] + 0.5, (1, 2, 0))[p*z:-p*z,p*z:-p*z,:]
print('.', end='', flush=True)
output = output.clip(0.0, 1.0) * 255.0
# Match color histograms if the user specified this option.
if args.rendering_histogram:
for i in range(3):
output[:,:,i] = self.match_histograms(output[:,:,i], original[:,:,i])
return scipy.misc.toimage(output, cmin=0, cmax=255)
if __name__ == "__main__":
if args.train:
args.zoom = 2**(args.generator_upscale - args.generator_downscale)
enhancer = NeuralEnhancer(loader=True)
enhancer.train()
else:
enhancer = NeuralEnhancer(loader=False)
for filename in args.files:
print(filename, end=' ')
img = scipy.ndimage.imread(filename, mode='RGB')
out = enhancer.process(img)
out.save(os.path.splitext(filename)[0]+'_ne%ix.png' % args.zoom)
print(flush=True)
print(ansi.ENDC)
