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models.py
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models.py
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from keras import backend as K
from keras.models import Model
from keras.layers import Input, merge, BatchNormalization, LeakyReLU, Flatten, Dense
from keras.layers.convolutional import Convolution2D, MaxPooling2D, UpSampling2D
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical
from keras.utils.data_utils import get_file
from keras_ops import fit as bypass_fit, smooth_gan_labels
from layers import Normalize, Denormalize, SubPixelUpscaling
from loss import AdversarialLossRegularizer, ContentVGGRegularizer, TVRegularizer, psnr, dummy_loss
import os
import time
import h5py
import numpy as np
import json
from scipy.misc import imresize, imsave
from scipy.ndimage.filters import gaussian_filter
THEANO_WEIGHTS_PATH_NO_TOP = r'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels_notop.h5'
TF_WEIGHTS_PATH_NO_TOP = r"https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5"
if not os.path.exists("weights/"):
os.makedirs("weights/")
if not os.path.exists("val_images/"):
os.makedirs("val_images/")
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
class VGGNetwork:
'''
Helper class to load VGG and its weights to the FastNet model
'''
def __init__(self, img_width=384, img_height=384, vgg_weight=1.0):
self.img_height = img_height
self.img_width = img_width
self.vgg_weight = vgg_weight
self.vgg_layers = None
def append_vgg_network(self, x_in, true_X_input, pre_train=False):
# Append the initial inputs to the outputs of the SRResNet
x = merge([x_in, true_X_input], mode='concat', concat_axis=0)
# Normalize the inputs via custom VGG Normalization layer
x = Normalize(name="normalize_vgg")(x)
# Begin adding the VGG layers
x = Convolution2D(64, 3, 3, activation='relu', name='vgg_conv1_1', border_mode='same')(x)
x = Convolution2D(64, 3, 3, activation='relu', name='vgg_conv1_2', border_mode='same')(x)
x = MaxPooling2D(name='vgg_maxpool1')(x)
x = Convolution2D(128, 3, 3, activation='relu', name='vgg_conv2_1', border_mode='same')(x)
if pre_train:
vgg_regularizer2 = ContentVGGRegularizer(weight=self.vgg_weight)
x = Convolution2D(128, 3, 3, activation='relu', name='vgg_conv2_2', border_mode='same',
activity_regularizer=vgg_regularizer2)(x)
else:
x = Convolution2D(128, 3, 3, activation='relu', name='vgg_conv2_2', border_mode='same')(x)
x = MaxPooling2D(name='vgg_maxpool2')(x)
x = Convolution2D(256, 3, 3, activation='relu', name='vgg_conv3_1', border_mode='same')(x)
x = Convolution2D(256, 3, 3, activation='relu', name='vgg_conv3_2', border_mode='same')(x)
x = Convolution2D(256, 3, 3, activation='relu', name='vgg_conv3_3', border_mode='same')(x)
x = MaxPooling2D(name='vgg_maxpool3')(x)
x = Convolution2D(512, 3, 3, activation='relu', name='vgg_conv4_1', border_mode='same')(x)
x = Convolution2D(512, 3, 3, activation='relu', name='vgg_conv4_2', border_mode='same')(x)
x = Convolution2D(512, 3, 3, activation='relu', name='vgg_conv4_3', border_mode='same')(x)
x = MaxPooling2D(name='vgg_maxpool4')(x)
x = Convolution2D(512, 3, 3, activation='relu', name='vgg_conv5_1', border_mode='same')(x)
x = Convolution2D(512, 3, 3, activation='relu', name='vgg_conv5_2', border_mode='same')(x)
if not pre_train:
vgg_regularizer5 = ContentVGGRegularizer(weight=self.vgg_weight)
x = Convolution2D(512, 3, 3, activation='relu', name='vgg_conv5_3', border_mode='same',
activity_regularizer=vgg_regularizer5)(x)
else:
x = Convolution2D(512, 3, 3, activation='relu', name='vgg_conv5_3', border_mode='same')(x)
x = MaxPooling2D(name='vgg_maxpool5')(x)
return x
def load_vgg_weight(self, model):
# Loading VGG 16 weights
if K.image_dim_ordering() == "th":
weights = get_file('vgg16_weights_th_dim_ordering_th_kernels_notop.h5', THEANO_WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
else:
weights = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', TF_WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
f = h5py.File(weights)
layer_names = [name for name in f.attrs['layer_names']]
if self.vgg_layers is None:
self.vgg_layers = [layer for layer in model.layers
if 'vgg_' in layer.name]
for i, layer in enumerate(self.vgg_layers):
g = f[layer_names[i]]
weights = [g[name] for name in g.attrs['weight_names']]
layer.set_weights(weights)
# Freeze all VGG layers
for layer in self.vgg_layers:
layer.trainable = False
return model
class DiscriminatorNetwork:
def __init__(self, img_width=384, img_height=384, adversarial_loss_weight=1, small_model=False):
self.img_width = img_width
self.img_height = img_height
self.adversarial_loss_weight = adversarial_loss_weight
self.small_model = small_model
self.k = 3
self.mode = 2
self.weights_path = "weights/Discriminator weights.h5"
self.gan_layers = None
def append_gan_network(self, true_X_input):
# Normalize the inputs via custom VGG Normalization layer
x = Normalize(type="gan", value=127.5, name="gan_normalize")(true_X_input)
x = Convolution2D(64, self.k, self.k, border_mode='same', name='gan_conv1_1')(x)
x = LeakyReLU(0.3, name="gan_lrelu1_1")(x)
x = Convolution2D(64, self.k, self.k, border_mode='same', name='gan_conv1_2', subsample=(2, 2))(x)
x = LeakyReLU(0.3, name='gan_lrelu1_2')(x)
x = BatchNormalization(mode=self.mode, axis=channel_axis, name='gan_batchnorm1_1')(x)
filters = [128, 256] if self.small_model else [128, 256, 512]
for i, nb_filters in enumerate(filters):
for j in range(2):
subsample = (2, 2) if j == 1 else (1, 1)
x = Convolution2D(nb_filters, self.k, self.k, border_mode='same', subsample=subsample,
name='gan_conv%d_%d' % (i + 2, j + 1))(x)
x = LeakyReLU(0.3, name='gan_lrelu_%d_%d' % (i + 2, j + 1))(x)
x = BatchNormalization(mode=self.mode, axis=channel_axis, name='gan_batchnorm%d_%d' % (i + 2, j + 1))(x)
x = Flatten(name='gan_flatten')(x)
output_dim = 128 if self.small_model else 1024
x = Dense(output_dim, name='gan_dense1')(x)
x = LeakyReLU(0.3, name='gan_lrelu5')(x)
gan_regulrizer = AdversarialLossRegularizer(weight=self.adversarial_loss_weight)
x = Dense(2, activation="softmax", activity_regularizer=gan_regulrizer, name='gan_output')(x)
return x
def set_trainable(self, model, value=True):
if self.gan_layers is None:
disc_model = [layer for layer in model.layers
if 'model' in layer.name][0] # Only disc model is an inner model
self.gan_layers = [layer for layer in disc_model.layers
if 'gan_' in layer.name]
for layer in self.gan_layers:
layer.trainable = value
def load_gan_weights(self, model):
f = h5py.File(self.weights_path)
layer_names = [name for name in f.attrs['layer_names']]
layer_names = layer_names[1:] # First is an input layer. Not needed.
if self.gan_layers is None:
self.gan_layers = [layer for layer in model.layers
if 'gan_' in layer.name]
for i, layer in enumerate(self.gan_layers):
g = f[layer_names[i]]
weights = [g[name] for name in g.attrs['weight_names']]
layer.set_weights(weights)
print("GAN Model weights loaded.")
return model
def save_gan_weights(self, model):
print('GAN Weights are being saved.')
model.save_weights(self.weights_path, overwrite=True)
print('GAN Weights saved.')
class GenerativeNetwork:
def __init__(self, img_width=96, img_height=96, batch_size=16, nb_upscales=2, small_model=False,
content_weight=1, tv_weight=2e5, gen_channels=64):
self.img_width = img_width
self.img_height = img_height
self.batch_size = batch_size
self.small_model = small_model
self.nb_scales = nb_upscales
self.content_weight = content_weight
self.tv_weight = tv_weight
self.filters = gen_channels
self.mode = 2
self.init = 'glorot_uniform'
self.sr_res_layers = None
self.sr_weights_path = "weights/SRGAN.h5"
self.output_func = None
def create_sr_model(self, ip):
x = Convolution2D(self.filters, 5, 5, activation='linear', border_mode='same', name='sr_res_conv1',
init=self.init)(ip)
x = BatchNormalization(axis=channel_axis, mode=self.mode, name='sr_res_bn_1')(x)
x = LeakyReLU(alpha=0.25, name='sr_res_lr1')(x)
# x = Convolution2D(self.filters, 5, 5, activation='linear', border_mode='same', name='sr_res_conv2')(x)
# x = BatchNormalization(axis=channel_axis, mode=self.mode, name='sr_res_bn_2')(x)
# x = LeakyReLU(alpha=0.25, name='sr_res_lr2')(x)
nb_residual = 5 if self.small_model else 15
for i in range(nb_residual):
x = self._residual_block(x, i + 1)
for scale in range(self.nb_scales):
x = self._upscale_block(x, scale + 1)
scale = 2 ** self.nb_scales
tv_regularizer = TVRegularizer(img_width=self.img_width * scale, img_height=self.img_height * scale,
weight=self.tv_weight) #self.tv_weight)
x = Convolution2D(3, 5, 5, activation='tanh', border_mode='same', activity_regularizer=tv_regularizer,
init=self.init, name='sr_res_conv_final')(x)
x = Denormalize(name='sr_res_conv_denorm')(x)
return x
def _residual_block(self, ip, id):
init = ip
x = Convolution2D(self.filters, 3, 3, activation='linear', border_mode='same', name='sr_res_conv_' + str(id) + '_1',
init=self.init)(ip)
x = BatchNormalization(axis=channel_axis, mode=self.mode, name='sr_res_bn_' + str(id) + '_1')(x)
x = LeakyReLU(alpha=0.25, name="sr_res_activation_" + str(id) + "_1")(x)
x = Convolution2D(self.filters, 3, 3, activation='linear', border_mode='same', name='sr_res_conv_' + str(id) + '_2',
init=self.init)(x)
x = BatchNormalization(axis=channel_axis, mode=self.mode, name='sr_res_bn_' + str(id) + '_2')(x)
x = LeakyReLU(alpha=0.3, name="sr_res_activation_" + str(id) + "_2")(x)
m = merge([x, init], mode='sum', name="sr_res_merge_" + str(id))
return m
def _upscale_block(self, ip, id):
'''
As per suggestion from http://distill.pub/2016/deconv-checkerboard/, I am swapping out
SubPixelConvolution to simple Nearest Neighbour Upsampling
'''
init = ip
x = Convolution2D(128, 3, 3, activation="linear", border_mode='same', name='sr_res_upconv1_%d' % id,
init=self.init)(init)
x = LeakyReLU(alpha=0.25, name='sr_res_up_lr_%d_1_1' % id)(x)
x = UpSampling2D(name='sr_res_upscale_%d' % id)(x)
#x = SubPixelUpscaling(r=2, channels=32)(x)
x = Convolution2D(128, 3, 3, activation="linear", border_mode='same', name='sr_res_filter1_%d' % id,
init=self.init)(x)
x = LeakyReLU(alpha=0.3, name='sr_res_up_lr_%d_1_2' % id)(x)
return x
def set_trainable(self, model, value=True):
if self.sr_res_layers is None:
self.sr_res_layers = [layer for layer in model.layers
if 'sr_res_' in layer.name]
for layer in self.sr_res_layers:
layer.trainable = value
def get_generator_output(self, input_img, srgan_model):
if self.output_func is None:
gen_output_layer = [layer for layer in srgan_model.layers
if layer.name == "sr_res_conv_denorm"][0]
self.output_func = K.function([srgan_model.layers[0].input],
[gen_output_layer.output])
return self.output_func([input_img])
class SRGANNetwork:
def __init__(self, img_width=96, img_height=96, batch_size=16, nb_scales=2):
self.img_width = img_width
self.img_height = img_height
self.batch_size = batch_size
self.nb_scales = nb_scales
self.discriminative_network = None # type: DiscriminatorNetwork
self.generative_network = None # type: GenerativeNetwork
self.vgg_network = None # type: VGGNetwork
self.srgan_model_ = None # type: Model
self.generative_model_ = None # type: Model
self.discriminative_model_ = None #type: Model
def build_srgan_pretrain_model(self, use_small_srgan=False):
large_width = self.img_width * 4
large_height = self.img_height * 4
self.generative_network = GenerativeNetwork(self.img_width, self.img_height, self.batch_size, self.nb_scales,
use_small_srgan)
self.vgg_network = VGGNetwork(large_width, large_height)
ip = Input(shape=(3, self.img_width, self.img_height), name='x_generator')
ip_vgg = Input(shape=(3, large_width, large_height), name='x_vgg') # Actual X images
sr_output = self.generative_network.create_sr_model(ip)
self.generative_model_ = Model(ip, sr_output)
vgg_output = self.vgg_network.append_vgg_network(sr_output, ip_vgg, pre_train=True)
self.srgan_model_ = Model(input=[ip, ip_vgg],
output=vgg_output)
self.vgg_network.load_vgg_weight(self.srgan_model_)
srgan_optimizer = Adam(lr=1e-4)
generator_optimizer = Adam(lr=1e-4)
self.generative_model_.compile(generator_optimizer, dummy_loss)
self.srgan_model_.compile(srgan_optimizer, dummy_loss)
return self.srgan_model_
def build_discriminator_pretrain_model(self, use_small_srgan=False, use_small_discriminator=False):
large_width = self.img_width * 4
large_height = self.img_height * 4
self.generative_network = GenerativeNetwork(self.img_width, self.img_height, self.batch_size, self.nb_scales,
use_small_srgan)
self.discriminative_network = DiscriminatorNetwork(large_width, large_height,
small_model=use_small_discriminator)
ip = Input(shape=(3, self.img_width, self.img_height), name='x_generator')
ip_gan = Input(shape=(3, large_width, large_height), name='x_discriminator') # Actual X images
sr_output = self.generative_network.create_sr_model(ip)
self.generative_model_ = Model(ip, sr_output)
#self.generative_network.set_trainable(self.generative_model_, value=False)
gan_output = self.discriminative_network.append_gan_network(ip_gan)
self.discriminative_model_ = Model(ip_gan, gan_output)
generator_out = self.generative_model_(ip)
gan_output = self.discriminative_model_(generator_out)
self.srgan_model_ = Model(input=ip, output=gan_output)
srgan_optimizer = Adam(lr=1e-4)
generator_optimizer = Adam(lr=1e-4)
discriminator_optimizer = Adam(lr=1e-4)
self.generative_model_.compile(generator_optimizer, loss='mse')
self.discriminative_model_.compile(discriminator_optimizer, loss='categorical_crossentropy', metrics=['acc'])
self.srgan_model_.compile(srgan_optimizer, loss='categorical_crossentropy', metrics=['acc'])
return self.discriminative_model_
def build_srgan_model(self, use_small_srgan=False, use_small_discriminator=False):
large_width = self.img_width * 4
large_height = self.img_height * 4
self.generative_network = GenerativeNetwork(self.img_width, self.img_height, self.batch_size, nb_upscales=self.nb_scales,
small_model=use_small_srgan)
self.discriminative_network = DiscriminatorNetwork(large_width, large_height,
small_model=use_small_discriminator)
self.vgg_network = VGGNetwork(large_width, large_height)
ip = Input(shape=(3, self.img_width, self.img_height), name='x_generator')
ip_gan = Input(shape=(3, large_width, large_height), name='x_discriminator') # Actual X images
ip_vgg = Input(shape=(3, large_width, large_height), name='x_vgg') # Actual X images
sr_output = self.generative_network.create_sr_model(ip)
self.generative_model_ = Model(ip, sr_output)
gan_output = self.discriminative_network.append_gan_network(ip_gan)
self.discriminative_model_ = Model(ip_gan, gan_output)
gan_output = self.discriminative_model_(self.generative_model_.output)
vgg_output = self.vgg_network.append_vgg_network(self.generative_model_.output, ip_vgg)
self.srgan_model_ = Model(input=[ip, ip_gan, ip_vgg], output=[gan_output, vgg_output])
self.vgg_network.load_vgg_weight(self.srgan_model_)
srgan_optimizer = Adam(lr=1e-4)
generator_optimizer = Adam(lr=1e-4)
discriminator_optimizer = Adam(lr=1e-4)
self.generative_model_.compile(generator_optimizer, dummy_loss)
self.discriminative_model_.compile(discriminator_optimizer, loss='categorical_crossentropy', metrics=['acc'])
self.srgan_model_.compile(srgan_optimizer, dummy_loss)
return self.srgan_model_
def pre_train_srgan(self, image_dir, nb_images=50000, nb_epochs=1, use_small_srgan=False):
self.build_srgan_pretrain_model(use_small_srgan=use_small_srgan)
self._train_model(image_dir, nb_images=nb_images, nb_epochs=nb_epochs, pre_train_srgan=True,
load_generative_weights=True)
def pre_train_discriminator(self, image_dir, nb_images=50000, nb_epochs=1, batch_size=128,
use_small_discriminator=False):
self.batch_size = batch_size
self.build_discriminator_pretrain_model(use_small_discriminator)
self._train_model(image_dir, nb_images, nb_epochs, pre_train_discriminator=True,
load_generative_weights=True)
def train_full_model(self, image_dir, nb_images=50000, nb_epochs=10, use_small_srgan=False,
use_small_discriminator=False):
self.build_srgan_model(use_small_srgan, use_small_discriminator)
self._train_model(image_dir, nb_images, nb_epochs, load_generative_weights=True, load_discriminator_weights=True)
def _train_model(self, image_dir, nb_images=80000, nb_epochs=10, pre_train_srgan=False,
pre_train_discriminator=False, load_generative_weights=False, load_discriminator_weights=False,
save_loss=True, disc_train_flip=0.1):
assert self.img_width >= 16, "Minimum image width must be at least 16"
assert self.img_height >= 16, "Minimum image height must be at least 16"
if load_generative_weights:
try:
self.generative_model_.load_weights(self.generative_network.sr_weights_path)
print("Generator weights loaded.")
except:
print("Could not load generator weights.")
if load_discriminator_weights:
try:
self.discriminative_network.load_gan_weights(self.srgan_model_)
print("Discriminator weights loaded.")
except:
print("Could not load discriminator weights.")
datagen = ImageDataGenerator(rescale=1. / 255)
img_width = self.img_width * 4
img_height = self.img_height * 4
early_stop = False
iteration = 0
prev_improvement = -1
if save_loss:
if pre_train_srgan:
loss_history = {'generator_loss' : [],
'val_psnr' : [], }
elif pre_train_discriminator:
loss_history = {'discriminator_loss' : [],
'discriminator_acc' : [], }
else:
loss_history = {'discriminator_loss' : [],
'discriminator_acc' : [],
'generator_loss' : [],
'val_psnr': [], }
y_vgg_dummy = np.zeros((self.batch_size * 2, 3, img_width // 32, img_height // 32)) # 5 Max Pools = 2 ** 5 = 32
print("Training SRGAN network")
for i in range(nb_epochs):
print()
print("Epoch : %d" % (i + 1))
for x in datagen.flow_from_directory(image_dir, class_mode=None, batch_size=self.batch_size,
target_size=(img_width, img_height)):
try:
t1 = time.time()
if not pre_train_srgan and not pre_train_discriminator:
x_vgg = x.copy() * 255 # VGG input [0 - 255 scale]
# resize images
x_temp = x.copy()
x_temp = x_temp.transpose((0, 2, 3, 1))
x_generator = np.empty((self.batch_size, self.img_width, self.img_height, 3))
for j in range(self.batch_size):
img = gaussian_filter(x_temp[j], sigma=0.1)
img = imresize(img, (self.img_width, self.img_height), interp='bicubic')
x_generator[j, :, :, :] = img
x_generator = x_generator.transpose((0, 3, 1, 2))
if iteration % 50 == 0 and iteration != 0 and not pre_train_discriminator:
print("Validation image..")
output_image_batch = self.generative_network.get_generator_output(x_generator,
self.srgan_model_)
if type(output_image_batch) == list:
output_image_batch = output_image_batch[0]
mean_axis = (0, 2, 3) if K.image_dim_ordering() == 'th' else (0, 1, 2)
average_psnr = 0.0
print('gen img mean :', np.mean(output_image_batch / 255., axis=mean_axis))
print('val img mean :', np.mean(x, axis=mean_axis))
for x_i in range(self.batch_size):
average_psnr += psnr(x[x_i], np.clip(output_image_batch[x_i], 0, 255) / 255.)
average_psnr /= self.batch_size
if save_loss:
loss_history['val_psnr'].append(average_psnr)
iteration += self.batch_size
t2 = time.time()
print("Time required : %0.2f. Average validation PSNR over %d samples = %0.2f" %
(t2 - t1, self.batch_size, average_psnr))
for x_i in range(self.batch_size):
real_path = "val_images/epoch_%d_iteration_%d_num_%d_real_.png" % (i + 1, iteration, x_i + 1)
generated_path = "val_images/epoch_%d_iteration_%d_num_%d_generated.png" % (i + 1,
iteration,
x_i + 1)
val_x = x[x_i].copy() * 255.
val_x = val_x.transpose((1, 2, 0))
val_x = np.clip(val_x, 0, 255).astype('uint8')
output_image = output_image_batch[x_i]
output_image = output_image.transpose((1, 2, 0))
output_image = np.clip(output_image, 0, 255).astype('uint8')
imsave(real_path, val_x)
imsave(generated_path, output_image)
'''
Don't train of validation images for now.
Note that if nb_epochs > 1, there is a chance that
validation images may be used for training purposes as well.
In that case, this isn't strictly a validation measure, instead of
just a check to see what the network has learned.
'''
continue
if pre_train_srgan:
# Train only generator + vgg network
# Use custom bypass_fit to bypass the check for same input and output batch size
hist = bypass_fit(self.srgan_model_, [x_generator, x * 255], y_vgg_dummy,
batch_size=self.batch_size, nb_epoch=1, verbose=0)
sr_loss = hist.history['loss'][0]
if save_loss:
loss_history['generator_loss'].extend(hist.history['loss'])
if prev_improvement == -1:
prev_improvement = sr_loss
improvement = (prev_improvement - sr_loss) / prev_improvement * 100
prev_improvement = sr_loss
iteration += self.batch_size
t2 = time.time()
print("Iter : %d / %d | Improvement : %0.2f percent | Time required : %0.2f seconds | "
"Generative Loss : %0.2f" % (iteration, nb_images, improvement, t2 - t1, sr_loss))
elif pre_train_discriminator:
# Train only discriminator
X_pred = self.generative_model_.predict(x_generator, self.batch_size)
X = np.concatenate((X_pred, x * 255))
# Using soft and noisy labels
if np.random.uniform() > disc_train_flip:
# give correct classifications
y_gan = [0] * self.batch_size + [1] * self.batch_size
else:
# give wrong classifications (noisy labels)
y_gan = [1] * self.batch_size + [0] * self.batch_size
y_gan = np.asarray(y_gan, dtype=np.int).reshape(-1, 1)
y_gan = to_categorical(y_gan, nb_classes=2)
y_gan = smooth_gan_labels(y_gan)
hist = self.discriminative_model_.fit(X, y_gan, batch_size=self.batch_size,
nb_epoch=1, verbose=0)
discriminator_loss = hist.history['loss'][-1]
discriminator_acc = hist.history['acc'][-1]
if save_loss:
loss_history['discriminator_loss'].extend(hist.history['loss'])
loss_history['discriminator_acc'].extend(hist.history['acc'])
if prev_improvement == -1:
prev_improvement = discriminator_loss
improvement = (prev_improvement - discriminator_loss) / prev_improvement * 100
prev_improvement = discriminator_loss
iteration += self.batch_size
t2 = time.time()
print("Iter : %d / %d | Improvement : %0.2f percent | Time required : %0.2f seconds | "
"Discriminator Loss / Acc : %0.4f / %0.2f" % (iteration, nb_images,
improvement, t2 - t1,
discriminator_loss, discriminator_acc))
else:
# Train only discriminator, disable training of srgan
self.discriminative_network.set_trainable(self.srgan_model_, value=True)
self.generative_network.set_trainable(self.srgan_model_, value=False)
# Use custom bypass_fit to bypass the check for same input and output batch size
# hist = bypass_fit(self.srgan_model_, [x_generator, x * 255, x_vgg],
# [y_gan, y_vgg_dummy],
# batch_size=self.batch_size, nb_epoch=1, verbose=0)
X_pred = self.generative_model_.predict(x_generator, self.batch_size)
X = np.concatenate((X_pred, x * 255))
# Using soft and noisy labels
if np.random.uniform() > disc_train_flip:
# give correct classifications
y_gan = [0] * self.batch_size + [1] * self.batch_size
else:
# give wrong classifications (noisy labels)
y_gan = [1] * self.batch_size + [0] * self.batch_size
y_gan = np.asarray(y_gan, dtype=np.int).reshape(-1, 1)
y_gan = to_categorical(y_gan, nb_classes=2)
y_gan = smooth_gan_labels(y_gan)
hist1 = self.discriminative_model_.fit(X, y_gan, verbose=0, batch_size=self.batch_size,
nb_epoch=1)
discriminator_loss = hist1.history['loss'][-1]
# Train only generator, disable training of discriminator
self.discriminative_network.set_trainable(self.srgan_model_, value=False)
self.generative_network.set_trainable(self.srgan_model_, value=True)
# Using soft labels
y_model = [1] * self.batch_size
y_model = np.asarray(y_model, dtype=np.int).reshape(-1, 1)
y_model = to_categorical(y_model, nb_classes=2)
y_model = smooth_gan_labels(y_model)
# Use custom bypass_fit to bypass the check for same input and output batch size
hist2 = bypass_fit(self.srgan_model_, [x_generator, x, x_vgg], [y_model, y_vgg_dummy],
batch_size=self.batch_size, nb_epoch=1, verbose=0)
generative_loss = hist2.history['loss'][0]
if save_loss:
loss_history['discriminator_loss'].extend(hist1.history['loss'])
loss_history['discriminator_acc'].extend(hist1.history['acc'])
loss_history['generator_loss'].extend(hist2.history['loss'])
if prev_improvement == -1:
prev_improvement = discriminator_loss
improvement = (prev_improvement - discriminator_loss) / prev_improvement * 100
prev_improvement = discriminator_loss
iteration += self.batch_size
t2 = time.time()
print("Iter : %d / %d | Improvement : %0.2f percent | Time required : %0.2f seconds | "
"Discriminator Loss : %0.3f | Generative Loss : %0.3f" %
(iteration, nb_images, improvement, t2 - t1, discriminator_loss, generative_loss))
if iteration % 1000 == 0 and iteration != 0:
print("Saving model weights.")
# Save predictive (SR network) weights
self._save_model_weights(pre_train_srgan, pre_train_discriminator)
self._save_loss_history(loss_history, pre_train_srgan, pre_train_discriminator, save_loss)
if iteration >= nb_images:
break
except KeyboardInterrupt:
print("Keyboard interrupt detected. Stopping early.")
early_stop = True
break
iteration = 0
if early_stop:
break
print("Finished training SRGAN network. Saving model weights.")
# Save predictive (SR network) weights
self._save_model_weights(pre_train_srgan, pre_train_discriminator)
self._save_loss_history(loss_history, pre_train_srgan, pre_train_discriminator, save_loss)
def _save_model_weights(self, pre_train_srgan, pre_train_discriminator):
if not pre_train_discriminator:
self.generative_model_.save_weights(self.generative_network.sr_weights_path, overwrite=True)
if not pre_train_srgan:
# Save GAN (discriminative network) weights
self.discriminative_network.save_gan_weights(self.discriminative_model_)
def _save_loss_history(self, loss_history, pre_train_srgan, pre_train_discriminator, save_loss):
if save_loss:
print("Saving loss history")
if pre_train_srgan:
with open('pretrain losses - srgan.json', 'w') as f:
json.dump(loss_history, f)
elif pre_train_discriminator:
with open('pretrain losses - discriminator.json', 'w') as f:
json.dump(loss_history, f)
else:
with open('fulltrain losses.json', 'w') as f:
json.dump(loss_history, f)
print("Saved loss history")
if __name__ == "__main__":
from keras.utils.visualize_util import plot
# Path to MS COCO dataset
coco_path = r"D:\Yue\Documents\Dataset\coco2014\train2014"
'''
Base Network manager for the SRGAN model
Width / Height = 32 to reduce the memory requirement for the discriminator.
Batch size = 1 is slower, but uses the least amount of gpu memory, and also acts as
Instance Normalization (batch norm with 1 input image) which speeds up training slightly.
'''
srgan_network = SRGANNetwork(img_width=32, img_height=32, batch_size=1)
srgan_network.build_srgan_model()
#plot(srgan_network.srgan_model_, 'SRGAN.png', show_shapes=True)
# Pretrain the SRGAN network
#srgan_network.pre_train_srgan(coco_path, nb_images=80000, nb_epochs=1)
# Pretrain the discriminator network
#srgan_network.pre_train_discriminator(coco_path, nb_images=40000, nb_epochs=1, batch_size=16)
# Fully train the SRGAN with VGG loss and Discriminator loss
srgan_network.train_full_model(coco_path, nb_images=80000, nb_epochs=5)