Alex J. Champandard
9 years ago
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#!/usr/bin/env python3
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""" _ _
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_ __ ___ _ _ _ __ __ _| | ___ _ __ | |__ __ _ _ __ ___ ___
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| '_ \ / _ \ | | | '__/ _` | | / _ \ '_ \| '_ \ / _` | '_ \ / __/ _ \
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| | | | __/ |_| | | | (_| | | | __/ | | | | | | (_| | | | | (_| __/
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|_| |_|\___|\__,_|_| \__,_|_| \___|_| |_|_| |_|\__,_|_| |_|\___\___|
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"""
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#
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# Copyright (c) 2016, Alex J. Champandard.
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#
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# Neural Enhance is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General
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# Public License version 3. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
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# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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#
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import os
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import sys
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import bz2
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import glob
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import math
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import pickle
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import random
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import argparse
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import itertools
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import threading
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import collections
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# Configure all options first so we can later custom-load other libraries (Theano) based on device specified by user.
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parser = argparse.ArgumentParser(description='Generate a new image by applying style onto a content image.',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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add_arg = parser.add_argument
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add_arg('--batch-size', default=15, type=int)
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add_arg('--batch-resolution', default=128, type=int)
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add_arg('--epoch-size', default=72, type=int)
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add_arg('--epochs', default=100, type=int)
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add_arg('--network-filters', default=64, type=int)
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add_arg('--perceptual-layer', default='mse', type=str)
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add_arg('--perceptual-weight', default=1e0, type=float)
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add_arg('--smoothness-weight', default=0.0, type=float)
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add_arg('--adversary-weight', default=1e4, type=float)
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add_arg('--scales', default=1, type=int, help='')
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add_arg('--device', default='gpu0', type=str, help='Name of the CPU/GPU number to use, for Theano.')
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args = parser.parse_args()
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#----------------------------------------------------------------------------------------------------------------------
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# Color coded output helps visualize the information a little better, plus it looks cool!
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class ansi:
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BOLD = '\033[1;97m'
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WHITE = '\033[0;97m'
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YELLOW = '\033[0;33m'
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YELLOW_B = '\033[1;33m'
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RED = '\033[0;31m'
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RED_B = '\033[1;31m'
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BLUE = '\033[0;94m'
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BLUE_B = '\033[1;94m'
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CYAN = '\033[0;36m'
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CYAN_B = '\033[1;36m'
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ENDC = '\033[0m'
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def error(message, *lines):
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string = "\n{}ERROR: " + message + "{}\n" + "\n".join(lines) + "{}\n"
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print(string.format(ansi.RED_B, ansi.RED, ansi.ENDC))
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sys.exit(-1)
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def warn(message, *lines):
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string = "\n{}WARNING: " + message + "{}\n" + "\n".join(lines) + "{}\n"
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print(string.format(ansi.YELLOW_B, ansi.YELLOW, ansi.ENDC))
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print("""{} {}Super Resolution for images and videos powered by Deep Learning!{}
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- Code licensed as AGPLv3, models under CC BY-NC-SA.{}""".format(ansi.CYAN_B, __doc__, ansi.CYAN, ansi.ENDC))
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# Load the underlying deep learning libraries based on the device specified. If you specify THEANO_FLAGS manually,
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# the code assumes you know what you are doing and they are not overriden!
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os.environ.setdefault('THEANO_FLAGS', 'floatX=float32,device={},force_device=True,allow_gc=True,'\
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'print_active_device=False'.format(args.device))
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# Scientific & Imaging Libraries
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import numpy as np
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import scipy.optimize, scipy.ndimage, scipy.misc
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# Numeric Computing (GPU)
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import theano
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import theano.tensor as T
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import theano.tensor.nnet.neighbours
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# Support ansi colors in Windows too.
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if sys.platform == 'win32':
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import colorama
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# Deep Learning Framework
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import lasagne
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from lasagne.layers import Conv2DLayer as ConvLayer, Deconv2DLayer as DeconvLayer, Pool2DLayer as PoolLayer
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from lasagne.layers import InputLayer, ConcatLayer, batch_norm
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print('{} - Using the device `{}` for neural computation.{}\n'.format(ansi.CYAN, theano.config.device, ansi.ENDC))
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#----------------------------------------------------------------------------------------------------------------------
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# Image Processing
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#----------------------------------------------------------------------------------------------------------------------
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class DataLoader(threading.Thread):
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def __init__(self):
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super(DataLoader, self).__init__(daemon=True)
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self.data_ready = threading.Event()
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self.data_copied = threading.Event()
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self.resolution = args.batch_resolution
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self.images = np.zeros((args.batch_size, 3, self.resolution, self.resolution), dtype=np.float32)
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self.cwd = os.getcwd()
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self.start()
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def run(self):
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files, cache = glob.glob('train/*.jpg'), {}
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while True:
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random.shuffle(files)
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for i, f in enumerate(files[:args.batch_size]):
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filename = os.path.join(self.cwd, f)
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try:
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if f in cache:
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img = cache[f]
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else:
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img = scipy.ndimage.imread(filename, mode='RGB')
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cache[f] = img
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except Exception as e:
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warn('Could not load `{}` as image.'.format(filename),
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' - Try fixing or removing the file before next run.')
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files.remove(f)
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continue
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if random.choice([True, False]):
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img[:,:] = img[:,::-1]
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h = random.randint(0, img.shape[0] - self.resolution)
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w = random.randint(0, img.shape[1] - self.resolution)
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img = img[h:h+self.resolution, w:w+self.resolution]
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self.images[i] = np.transpose(img / 255.0 - 0.5, (2, 0, 1))
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self.data_ready.set()
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self.data_copied.wait()
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self.data_copied.clear()
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def copy(self, output):
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self.data_ready.wait()
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self.data_ready.clear()
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output[:] = self.images
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self.data_copied.set()
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#----------------------------------------------------------------------------------------------------------------------
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# Convolution Networks
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#----------------------------------------------------------------------------------------------------------------------
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class Model(object):
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def __init__(self):
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self.network = collections.OrderedDict()
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self.network['img'] = InputLayer((None, 3, None, None))
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self.network['img.scaled'] = PoolLayer(self.network['img'], pool_size=2**args.scales)
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self.setup_generator(self.network['img.scaled'])
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concatenated = lasagne.layers.ConcatLayer([self.network['img'], self.network['out']], axis=0)
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self.setup_perceptual(concatenated)
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self.load_perceptual()
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self.compile()
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def last_layer(self):
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return list(self.network.values())[-1]
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def setup_perceptual(self, input):
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"""Use lasagne to create a network of convolution layers using pre-trained VGG19 weights.
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"""
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offset = np.array([103.939, 116.779, 123.680], dtype=np.float32).reshape((1,3,1,1))
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self.network['percept'] = lasagne.layers.NonlinearityLayer(input, lambda x: ((x+0.5).clip(0.0, 1.0)*255.0) - offset)
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self.network['mse'] = self.network['percept']
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self.network['conv1_1'] = ConvLayer(self.network['percept'], 64, 3, pad=1)
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self.network['conv1_2'] = ConvLayer(self.network['conv1_1'], 64, 3, pad=1)
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self.network['pool1'] = PoolLayer(self.network['conv1_2'], 2, mode='max')
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self.network['conv2_1'] = ConvLayer(self.network['pool1'], 128, 3, pad=1)
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self.network['conv2_2'] = ConvLayer(self.network['conv2_1'], 128, 3, pad=1)
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self.network['pool2'] = PoolLayer(self.network['conv2_2'], 2, mode='max')
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self.network['conv3_1'] = ConvLayer(self.network['pool2'], 256, 3, pad=1)
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self.network['conv3_2'] = ConvLayer(self.network['conv3_1'], 256, 3, pad=1)
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self.network['conv3_3'] = ConvLayer(self.network['conv3_2'], 256, 3, pad=1)
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self.network['conv3_4'] = ConvLayer(self.network['conv3_3'], 256, 3, pad=1)
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self.network['pool3'] = PoolLayer(self.network['conv3_4'], 2, mode='max')
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self.network['conv4_1'] = ConvLayer(self.network['pool3'], 512, 3, pad=1)
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self.network['conv4_2'] = ConvLayer(self.network['conv4_1'], 512, 3, pad=1)
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self.network['conv4_3'] = ConvLayer(self.network['conv4_2'], 512, 3, pad=1)
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self.network['conv4_4'] = ConvLayer(self.network['conv4_3'], 512, 3, pad=1)
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self.network['pool4'] = PoolLayer(self.network['conv4_4'], 2, mode='max')
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self.network['conv5_1'] = ConvLayer(self.network['pool4'], 512, 3, pad=1)
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self.network['conv5_2'] = ConvLayer(self.network['conv5_1'], 512, 3, pad=1)
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self.network['conv5_3'] = ConvLayer(self.network['conv5_2'], 512, 3, pad=1)
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self.network['conv5_4'] = ConvLayer(self.network['conv5_3'], 512, 3, pad=1)
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def load_perceptual(self):
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"""Open the serialized parameters from a pre-trained network, and load them into the model created.
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"""
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vgg19_file = os.path.join(os.path.dirname(__file__), 'vgg19_conv.pkl.bz2')
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if not os.path.exists(vgg19_file):
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error("Model file with pre-trained convolution layers not found. Download here...",
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"https://github.com/alexjc/neural-doodle/releases/download/v0.0/vgg19_conv.pkl.bz2")
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data = pickle.load(bz2.open(vgg19_file, 'rb'))
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layers = lasagne.layers.get_all_layers(self.last_layer(), treat_as_input=[self.network['percept']])
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for p, d in zip(itertools.chain(*[l.get_params() for l in layers]), data): p.set_value(d)
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def setup_generator(self, input):
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f = args.network_filters
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self.network['iter.0'] = ConvLayer(input, f, filter_size=(1,1), stride=(1,1), pad=0,)
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for i in range(args.scales, 0, -1):
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self.network['scale%i.2'%i] = DeconvLayer(self.last_layer(), f, filter_size=(4,4), stride=(2,2), crop=1)
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self.network['scale%i.1'%i] = ConvLayer(self.network['scale%i.2'%i], f, filter_size=(3,3), pad=1)
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self.network['out'] = ConvLayer(self.last_layer(), 3, filter_size=(1,1), stride=(1,1), pad=0, b=None,
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nonlinearity=lasagne.nonlinearities.tanh)
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def compile(self):
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self.learning_rate = theano.shared(np.array(1e-4, dtype=theano.config.floatX))
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input_tensor = T.tensor4()
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output_layers = [self.network['out'], self.network[args.perceptual_layer]]
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input_layers = {self.network['img']: input_tensor}
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gen_out, percept_out = lasagne.layers.get_output(output_layers, input_layers, deterministic=False)
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losses = [self.loss_perceptual(percept_out) * args.perceptual_weight,
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self.loss_total_variation(gen_out) * args.smoothness_weight]
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params = lasagne.layers.get_all_params(self.network['out'], trainable=True)
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updates = lasagne.updates.adam(sum(losses, 0.0), params, learning_rate=self.learning_rate)
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self.fit = theano.function([input_tensor], losses, updates=updates)
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gen_out, gen_inp = lasagne.layers.get_output([self.network['out'], self.network['img']],
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input_layers, deterministic=True)
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self.predict = theano.function([input_tensor], [gen_out, gen_inp])
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def loss_perceptual(self, p):
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return lasagne.objectives.squared_error(p[:args.batch_size], p[args.batch_size:]).mean()
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def loss_total_variation(self, x):
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return (((x[:,:,:-1,:-1] - x[:,:,1:,:-1])**2 + (x[:,:,:-1,:-1] - x[:,:,:-1,1:])**2)**1.25).mean()
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class NeuralEnhancer(object):
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def __init__(self):
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self.thread = DataLoader()
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self.model = Model()
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def imsave(self, fn, img):
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img = np.transpose(img + 0.5, (1, 2, 0)).clip(0.0, 1.0)
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image = scipy.misc.toimage(img * 255.0, cmin=0, cmax=255)
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image.save(fn)
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def show_progress(self, repro, orign):
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for i in range(args.batch_size):
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self.imsave('test/%03i_orign.png' % i, orign[i])
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self.imsave('test/%03i_repro.png' % i, repro[i])
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def train(self):
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print('\n{}Training {} epochs with batch size {}.{}'\
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.format(ansi.BLUE_B, args.epochs, args.batch_size, ansi.ENDC))
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images = np.zeros((args.batch_size, 3, args.batch_resolution, args.batch_resolution), dtype=np.float32)
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l_min, l_max, l_mult = 1E-7, 1E-3, 0.2
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t_cur, t_i, t_mult = 0, 150, 1
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i, last, running = 0, float('inf'), None
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for _ in range(args.epochs):
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total = 0.0
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for _ in range(args.epoch_size):
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i += 1
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l_r = l_min + 0.5 * (l_max - l_min) * (1.0 + math.cos(t_cur / t_i * math.pi))
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t_cur += 1
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self.model.learning_rate.set_value(l_r)
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if t_cur >= t_i:
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t_cur = 0
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t_i = int(t_i * t_mult)
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l_max = max(l_max * l_mult, 1e-10)
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l_min = max(l_min * l_mult, 1e-6)
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self.thread.copy(images)
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losses = self.model.fit(images)
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l = sum(losses)
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total += l
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running = l if running is None else running * 0.9 + 0.1 * l
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print('↑' if l >= running else '↓', end='', flush=True)
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self.show_progress(*self.model.predict(images))
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last = total / args.epoch_size
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print('\nLosses total:', last)
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if __name__ == "__main__":
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enhancer = NeuralEnhancer()
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try:
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enhancer.train()
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except KeyboardInterrupt:
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pass
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