Compare DCGAN and pix2pix with keras
Video
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
torch7 pix2pix
torch7 is the original http://qiita.com/masataka46/items/3d5a2b34d3d7fd29a6e3
DCGAN
DCGAN architecture
https://www.slideshare.net/xavigiro/deep-learning-for-computer-vision-generative-models-and-adversarial-training-upc-2016
https://blog.openai.com/generative-models/
Generate an image with hostile generation. Input noise and generate a fake image with the generator. The discriminator determines the real image.
Output the probability distribution of the image with the generator. The discriminator determines whether it is genuine or not.
An easy-to-understand explanation of the loss function
eshare.net/hamadakoichi/laplacian-pyramid-of-generative-adversarial-networks-lapgan-nips2015-reading-nipsyomi
Implementation of DCGAN with keras Part 1
Source https://github.com/jacobgil/keras-dcgan
Let's look at the definition from above. Generator for generation
python
def generator_model():
model = Sequential()
model.add(Dense(input_dim=100, output_dim=1024))
model.add(Activation('tanh'))
model.add(Dense(128*7*7))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Reshape((128, 7, 7), input_shape=(128*7*7,)))
model.add(UpSampling2D(size=(2, 2)))
model.add(Convolution2D(64, 5, 5, border_mode='same'))
model.add(Activation('tanh'))
model.add(UpSampling2D(size=(2, 2)))
model.add(Convolution2D(1, 5, 5, border_mode='same'))
model.add(Activation('tanh'))
return model
Discriminator for judgment
python
def discriminator_model():
model = Sequential()
model.add(Convolution2D(
64, 5, 5,
border_mode='same',
input_shape=(1, 28, 28)))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(128, 5, 5))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('tanh'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
return model
A model that connects a generator and a discriminator Used when propagating errors.
python
def generator_containing_discriminator(generator, discriminator):
model = Sequential()
model.add(generator)
discriminator.trainable = False
model.add(discriminator)
return model
A function that saves the output results in one image.
python
def combine_images(generated_images):
num = generated_images.shape[0]
width = int(math.sqrt(num))
height = int(math.ceil(float(num)/width))
shape = generated_images.shape[2:]
image = np.zeros((height*shape[0], width*shape[1]),
dtype=generated_images.dtype)
for index, img in enumerate(generated_images):
i = int(index/width)
j = index % width
image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \
img[0, :, :]
return image
Definition of learning. Get mnist data. Normalize the image and put it back in X_train. Define a model that combines the generator and discriminator. SGD defines an optimization function for a model that combines a generator and a discriminator. Create noise for batch size.
Input noise to the generator. generated_images = generator.predict(noise, verbose=0) Combine the original image and the output image to make X. X = np.concatenate((image_batch, generated_images)) Input X and y to the discriminator to learn and generate an error. d_loss = discriminator.train_on_batch(X, y) The model that combines the two models is trained and an error is generated. g_loss = discriminator_on_generator.train_on_batch(noise, [1] * BATCH_SIZE)
python
def train(BATCH_SIZE):
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = (X_train.astype(np.float32) - 127.5)/127.5
X_train = X_train.reshape((X_train.shape[0], 1) + X_train.shape[1:])
discriminator = discriminator_model()
generator = generator_model()
discriminator_on_generator = \
generator_containing_discriminator(generator, discriminator)
d_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)
g_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)
generator.compile(loss='binary_crossentropy', optimizer="SGD")
discriminator_on_generator.compile(
loss='binary_crossentropy', optimizer=g_optim)
discriminator.trainable = True
discriminator.compile(loss='binary_crossentropy', optimizer=d_optim)
noise = np.zeros((BATCH_SIZE, 100))
for epoch in range(100):
print("Epoch is", epoch)
print("Number of batches", int(X_train.shape[0]/BATCH_SIZE))
for index in range(int(X_train.shape[0]/BATCH_SIZE)):
for i in range(BATCH_SIZE):
noise[i, :] = np.random.uniform(-1, 1, 100)
image_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]
generated_images = generator.predict(noise, verbose=0)
if index % 20 == 0:
image = combine_images(generated_images)
image = image*127.5+127.5
Image.fromarray(image.astype(np.uint8)).save(
str(epoch)+"_"+str(index)+".png ")
X = np.concatenate((image_batch, generated_images))
y = [1] * BATCH_SIZE + [0] * BATCH_SIZE
d_loss = discriminator.train_on_batch(X, y)
print("batch %d d_loss : %f" % (index, d_loss))
for i in range(BATCH_SIZE):
noise[i, :] = np.random.uniform(-1, 1, 100)
discriminator.trainable = False
g_loss = discriminator_on_generator.train_on_batch(
noise, [1] * BATCH_SIZE)
discriminator.trainable = True
print("batch %d g_loss : %f" % (index, g_loss))
if index % 10 == 9:
generator.save_weights('generator', True)
discriminator.save_weights('discriminator', True)
Definition of the generated part. Save_weights is done at the time of learning, so load_weights. nice is False when executed by default. If nice is specified, images with good estimates will be sorted and saved together.
python
def generate(BATCH_SIZE, nice=False):
generator = generator_model()
generator.compile(loss='binary_crossentropy', optimizer="SGD")
generator.load_weights('generator')
if nice:
discriminator = discriminator_model()
discriminator.compile(loss='binary_crossentropy', optimizer="SGD")
discriminator.load_weights('discriminator')
noise = np.zeros((BATCH_SIZE*20, 100))
for i in range(BATCH_SIZE*20):
noise[i, :] = np.random.uniform(-1, 1, 100)
generated_images = generator.predict(noise, verbose=1)
d_pret = discriminator.predict(generated_images, verbose=1)
index = np.arange(0, BATCH_SIZE*20)
index.resize((BATCH_SIZE*20, 1))
pre_with_index = list(np.append(d_pret, index, axis=1))
pre_with_index.sort(key=lambda x: x[0], reverse=True)
nice_images = np.zeros((BATCH_SIZE, 1) +
(generated_images.shape[2:]), dtype=np.float32)
for i in range(int(BATCH_SIZE)):
idx = int(pre_with_index[i][1])
nice_images[i, 0, :, :] = generated_images[idx, 0, :, :]
image = combine_images(nice_images)
else:
noise = np.zeros((BATCH_SIZE, 100))
for i in range(BATCH_SIZE):
noise[i, :] = np.random.uniform(-1, 1, 100)
generated_images = generator.predict(noise, verbose=1)
image = combine_images(generated_images)
image = image*127.5+127.5
Image.fromarray(image.astype(np.uint8)).save(
"generated_image.png ")
Argument definition.
python
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--mode", type=str)
parser.add_argument("--batch_size", type=int, default=128)
parser.add_argument("--nice", dest="nice", action="store_true")
parser.set_defaults(nice=False)
args = parser.parse_args()
return args
Execute. Train when learning. Generate for estimation.
python
if __name__ == "__main__":
args = get_args()
if args.mode == "train":
train(BATCH_SIZE=args.batch_size)
elif args.mode == "generate":
generate(BATCH_SIZE=args.batch_size, nice=args.nice)
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Implementation of DCGAN with keras Part 2
It's just a little different. Source https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/GAN
In this code, the coding is such that the image data is once converted to HDF5 and then learned.
//conversion
python make_dataset.py --img_size 64
//Learning
python main.py --img_dim 64
Only train in train_GAN.py is called.
main.py
import os
import argparse
def launch_training(**kwargs):
# Launch training
train_GAN.train(**kwargs)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Train model')
parser.add_argument('--backend', type=str, default="theano", help="theano or tensorflow")
parser.add_argument('--generator', type=str, default="upsampling", help="upsampling or deconv")
parser.add_argument('--dset', type=str, default="mnist", help="mnist or celebA")
parser.add_argument('--batch_size', default=32, type=int, help='Batch size')
parser.add_argument('--n_batch_per_epoch', default=200, type=int, help="Number of training epochs")
parser.add_argument('--nb_epoch', default=400, type=int, help="Number of batches per epoch")
parser.add_argument('--epoch', default=10, type=int, help="Epoch at which weights were saved for evaluation")
parser.add_argument('--nb_classes', default=2, type=int, help="Number of classes")
parser.add_argument('--do_plot', default=False, type=bool, help="Debugging plot")
parser.add_argument('--bn_mode', default=2, type=int, help="Batch norm mode")
parser.add_argument('--img_dim', default=64, type=int, help="Image width == height")
parser.add_argument('--noise_scale', default=0.5, type=float, help="variance of the normal from which we sample the noise")
parser.add_argument('--label_smoothing', action="store_true", help="smooth the positive labels when training D")
parser.add_argument('--use_mbd', action="store_true", help="use mini batch disc")
parser.add_argument('--label_flipping', default=0, type=float, help="Probability (0 to 1.) to flip the labels when training D")
args = parser.parse_args()
assert args.dset in ["mnist", "celebA"]
# Set the backend by modifying the env variable
if args.backend == "theano":
os.environ["KERAS_BACKEND"] = "theano"
elif args.backend == "tensorflow":
os.environ["KERAS_BACKEND"] = "tensorflow"
# Import the backend
import keras.backend as K
# manually set dim ordering otherwise it is not changed
if args.backend == "theano":
image_dim_ordering = "th"
K.set_image_dim_ordering(image_dim_ordering)
elif args.backend == "tensorflow":
image_dim_ordering = "tf"
K.set_image_dim_ordering(image_dim_ordering)
import train_GAN
# Set default params
d_params = {"mode": "train_GAN",
"dset": args.dset,
"generator": args.generator,
"batch_size": args.batch_size,
"n_batch_per_epoch": args.n_batch_per_epoch,
"nb_epoch": args.nb_epoch,
"model_name": "CNN",
"epoch": args.epoch,
"nb_classes": args.nb_classes,
"do_plot": args.do_plot,
"image_dim_ordering": image_dim_ordering,
"bn_mode": args.bn_mode,
"img_dim": args.img_dim,
"label_smoothing": args.label_smoothing,
"label_flipping": args.label_flipping,
"noise_scale": args.noise_scale,
"use_mbd": args.use_mbd,
}
# Launch training
launch_training(**d_params)
Since the model is called in train, let's look at the model first. Since upsampling is selected by default, look at upsampling.
models_GAN.py
def generator_upsampling(noise_dim, img_dim, bn_mode, model_name="generator_upsampling", dset="mnist"):
"""
Generator model of the DCGAN
args : img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net model
"""
s = img_dim[1]
f = 512
if dset == "mnist":
start_dim = int(s / 4)
nb_upconv = 2
else:
start_dim = int(s / 16)
nb_upconv = 4
if K.image_dim_ordering() == "th":
bn_axis = 1
reshape_shape = (f, start_dim, start_dim)
output_channels = img_dim[0]
else:
reshape_shape = (start_dim, start_dim, f)
bn_axis = -1
output_channels = img_dim[-1]
gen_input = Input(shape=noise_dim, name="generator_input")
x = Dense(f * start_dim * start_dim, input_dim=noise_dim)(gen_input)
x = Reshape(reshape_shape)(x)
x = BatchNormalization(mode=bn_mode, axis=bn_axis)(x)
x = Activation("relu")(x)
# Upscaling blocks
for i in range(nb_upconv):
x = UpSampling2D(size=(2, 2))(x)
nb_filters = int(f / (2 ** (i + 1)))
x = Convolution2D(nb_filters, 3, 3, border_mode="same")(x)
x = BatchNormalization(mode=bn_mode, axis=1)(x)
x = Activation("relu")(x)
x = Convolution2D(nb_filters, 3, 3, border_mode="same")(x)
x = Activation("relu")(x)
x = Convolution2D(output_channels, 3, 3, name="gen_convolution2d_final", border_mode="same", activation='tanh')(x)
generator_model = Model(input=[gen_input], output=[x], name=model_name)
return generator_model
Discriminator.
models_GAN.py
def DCGAN_discriminator(noise_dim, img_dim, bn_mode, model_name="DCGAN_discriminator", dset="mnist", use_mbd=False):
"""
Discriminator model of the DCGAN
args : img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net model
"""
if K.image_dim_ordering() == "th":
bn_axis = 1
else:
bn_axis = -1
disc_input = Input(shape=img_dim, name="discriminator_input")
if dset == "mnist":
list_f = [128]
else:
list_f = [64, 128, 256]
# First conv
x = Convolution2D(32, 3, 3, subsample=(2, 2), name="disc_convolution2d_1", border_mode="same")(disc_input)
x = BatchNormalization(mode=bn_mode, axis=bn_axis)(x)
x = LeakyReLU(0.2)(x)
# Next convs
for i, f in enumerate(list_f):
name = "disc_convolution2d_%s" % (i + 2)
x = Convolution2D(f, 3, 3, subsample=(2, 2), name=name, border_mode="same")(x)
x = BatchNormalization(mode=bn_mode, axis=bn_axis)(x)
x = LeakyReLU(0.2)(x)
x = Flatten()(x)
def minb_disc(x):
diffs = K.expand_dims(x, 3) - K.expand_dims(K.permute_dimensions(x, [1, 2, 0]), 0)
abs_diffs = K.sum(K.abs(diffs), 2)
x = K.sum(K.exp(-abs_diffs), 2)
return x
def lambda_output(input_shape):
return input_shape[:2]
num_kernels = 100
dim_per_kernel = 5
M = Dense(num_kernels * dim_per_kernel, bias=False, activation=None)
MBD = Lambda(minb_disc, output_shape=lambda_output)
if use_mbd:
x_mbd = M(x)
x_mbd = Reshape((num_kernels, dim_per_kernel))(x_mbd)
x_mbd = MBD(x_mbd)
x = merge([x, x_mbd], mode='concat')
x = Dense(2, activation='softmax', name="disc_dense_2")(x)
discriminator_model = Model(input=[disc_input], output=[x], name=model_name)
return discriminator_model
The two models were combined.
models_GAN.py
def DCGAN(generator, discriminator_model, noise_dim, img_dim):
noise_input = Input(shape=noise_dim, name="noise_input")
generated_image = generator(noise_input)
DCGAN_output = discriminator_model(generated_image)
DCGAN = Model(input=[noise_input],
output=[DCGAN_output],
name="DCGAN")
return DCGAN
It can be called with load.
models_GAN.py
def load(model_name, noise_dim, img_dim, bn_mode, batch_size, dset="mnist", use_mbd=False):
if model_name == "generator_upsampling":
model = generator_upsampling(noise_dim, img_dim, bn_mode, model_name=model_name, dset=dset)
print model.summary()
from keras.utils.visualize_util import plot
plot(model, to_file='../../figures/%s.png' % model_name, show_shapes=True, show_layer_names=True)
return model
if model_name == "generator_deconv":
model = generator_deconv(noise_dim, img_dim, bn_mode, batch_size, model_name=model_name, dset=dset)
print model.summary()
from keras.utils.visualize_util import plot
plot(model, to_file='../../figures/%s.png' % model_name, show_shapes=True, show_layer_names=True)
return model
if model_name == "DCGAN_discriminator":
model = DCGAN_discriminator(noise_dim, img_dim, bn_mode, model_name=model_name, dset=dset, use_mbd=use_mbd)
model.summary()
from keras.utils.visualize_util import plot
plot(model, to_file='../../figures/%s.png' % model_name, show_shapes=True, show_layer_names=True)
return model
Let's look at learning. The train was called from main.py, but all the processing is written in the train. It is almost the same as the implementation of Part 1.
import models_GAN Bring DCGAN from as models models. The two models were combined. DCGAN_model = models.DCGAN(generator_model, discriminator_model, noise_dim, img_dim) Learn discriminators. disc_loss = discriminator_model.train_on_batch(X_disc, y_disc) Learn a model that combines two. gen_loss = DCGAN_model.train_on_batch(X_gen, y_gen)
train_GAN.py
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
generator = kwargs["generator"]
model_name = kwargs["model_name"]
image_dim_ordering = kwargs["image_dim_ordering"]
img_dim = kwargs["img_dim"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["label_smoothing"]
label_flipping = kwargs["label_flipping"]
noise_scale = kwargs["noise_scale"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(model_name)
# Load and rescale data
if dset == "celebA":
X_real_train = data_utils.load_celebA(img_dim, image_dim_ordering)
if dset == "mnist":
X_real_train, _, _, _ = data_utils.load_mnist(image_dim_ordering)
img_dim = X_real_train.shape[-3:]
noise_dim = (100,)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.5, beta_2=0.999, epsilon=1e-08)
opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
# Load generator model
generator_model = models.load("generator_%s" % generator,
noise_dim,
img_dim,
bn_mode,
batch_size,
dset=dset,
use_mbd=use_mbd)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator",
noise_dim,
img_dim,
bn_mode,
batch_size,
dset=dset,
use_mbd=use_mbd)
generator_model.compile(loss='mse', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model,
discriminator_model,
noise_dim,
img_dim)
loss = ['binary_crossentropy']
loss_weights = [1]
DCGAN_model.compile(loss=loss, loss_weights=loss_weights, optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy', optimizer=opt_discriminator)
gen_loss = 100
disc_loss = 100
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_real_batch in data_utils.gen_batch(X_real_train, batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(X_real_batch,
generator_model,
batch_counter,
batch_size,
noise_dim,
noise_scale=noise_scale,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen, y_gen = data_utils.get_gen_batch(batch_size, noise_dim, noise_scale=noise_scale)
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen, y_gen)
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size, values=[("D logloss", disc_loss),
("G logloss", gen_loss)])
# Save images for visualization
if batch_counter % 100 == 0:
data_utils.plot_generated_batch(X_real_batch, generator_model,
batch_size, noise_dim, image_dim_ordering)
if batch_counter >= n_batch_per_epoch:
break
print("")
print('Epoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
if e % 5 == 0:
gen_weights_path = os.path.join('../../models/%s/gen_weights_epoch%s.h5' % (model_name, e))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join('../../models/%s/disc_weights_epoch%s.h5' % (model_name, e))
discriminator_model.save_weights(disc_weights_path, overwrite=True)
DCGAN_weights_path = os.path.join('../../models/%s/DCGAN_weights_epoch%s.h5' % (model_name, e))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
except KeyboardInterrupt:
pass
pix2pix
pix2pix architecture
Put an image in the generator instead of noise. Make noise by adding dropouts during learning and testing.
The generator is called u-net, and the encoder / decoder is skipped and combined.
pix2 pix implementation in keras
Source https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/pix2pix
It is written with almost the same configuration as DCGAN implementation # 2. main.py is called train of train.py.
main.py
import os
import argparse
def launch_training(**kwargs):
# Launch training
train.train(**kwargs)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Train model')
parser.add_argument('patch_size', type=int, nargs=2, action="store", help="Patch size for D")
parser.add_argument('--backend', type=str, default="theano", help="theano or tensorflow")
parser.add_argument('--generator', type=str, default="upsampling", help="upsampling or deconv")
parser.add_argument('--dset', type=str, default="facades", help="facades")
parser.add_argument('--batch_size', default=4, type=int, help='Batch size')
parser.add_argument('--n_batch_per_epoch', default=100, type=int, help="Number of training epochs")
parser.add_argument('--nb_epoch', default=400, type=int, help="Number of batches per epoch")
parser.add_argument('--epoch', default=10, type=int, help="Epoch at which weights were saved for evaluation")
parser.add_argument('--nb_classes', default=2, type=int, help="Number of classes")
parser.add_argument('--do_plot', action="store_true", help="Debugging plot")
parser.add_argument('--bn_mode', default=2, type=int, help="Batch norm mode")
parser.add_argument('--img_dim', default=64, type=int, help="Image width == height")
parser.add_argument('--use_mbd', action="store_true", help="Whether to use minibatch discrimination")
parser.add_argument('--use_label_smoothing', action="store_true", help="Whether to smooth the positive labels when training D")
parser.add_argument('--label_flipping', default=0, type=float, help="Probability (0 to 1.) to flip the labels when training D")
args = parser.parse_args()
# Set the backend by modifying the env variable
if args.backend == "theano":
os.environ["KERAS_BACKEND"] = "theano"
elif args.backend == "tensorflow":
os.environ["KERAS_BACKEND"] = "tensorflow"
# Import the backend
import keras.backend as K
# manually set dim ordering otherwise it is not changed
if args.backend == "theano":
image_dim_ordering = "th"
K.set_image_dim_ordering(image_dim_ordering)
elif args.backend == "tensorflow":
image_dim_ordering = "tf"
K.set_image_dim_ordering(image_dim_ordering)
import train
# Set default params
d_params = {"dset": args.dset,
"generator": args.generator,
"batch_size": args.batch_size,
"n_batch_per_epoch": args.n_batch_per_epoch,
"nb_epoch": args.nb_epoch,
"model_name": "CNN",
"epoch": args.epoch,
"nb_classes": args.nb_classes,
"do_plot": args.do_plot,
"image_dim_ordering": image_dim_ordering,
"bn_mode": args.bn_mode,
"img_dim": args.img_dim,
"use_label_smoothing": args.use_label_smoothing,
"label_flipping": args.label_flipping,
"patch_size": args.patch_size,
"use_mbd": args.use_mbd
}
# Launch training
launch_training(**d_params)
Take a look at the model. generator. It has changed to u-net compared to DCGAN.
models.py
def generator_unet_upsampling(img_dim, bn_mode, model_name="generator_unet_upsampling"):
nb_filters = 64
if K.image_dim_ordering() == "th":
bn_axis = 1
nb_channels = img_dim[0]
min_s = min(img_dim[1:])
else:
bn_axis = -1
nb_channels = img_dim[-1]
min_s = min(img_dim[:-1])
unet_input = Input(shape=img_dim, name="unet_input")
# Prepare encoder filters
nb_conv = int(np.floor(np.log(min_s) / np.log(2)))
list_nb_filters = [nb_filters * min(8, (2 ** i)) for i in range(nb_conv)]
# Encoder
list_encoder = [Convolution2D(list_nb_filters[0], 3, 3,
subsample=(2, 2), name="unet_conv2D_1", border_mode="same")(unet_input)]
for i, f in enumerate(list_nb_filters[1:]):
name = "unet_conv2D_%s" % (i + 2)
conv = conv_block_unet(list_encoder[-1], f, name, bn_mode, bn_axis)
list_encoder.append(conv)
# Prepare decoder filters
list_nb_filters = list_nb_filters[:-2][::-1]
if len(list_nb_filters) < nb_conv - 1:
list_nb_filters.append(nb_filters)
# Decoder
list_decoder = [up_conv_block_unet(list_encoder[-1], list_encoder[-2],
list_nb_filters[0], "unet_upconv2D_1", bn_mode, bn_axis, dropout=True)]
for i, f in enumerate(list_nb_filters[1:]):
name = "unet_upconv2D_%s" % (i + 2)
# Dropout only on first few layers
if i < 2:
d = True
else:
d = False
conv = up_conv_block_unet(list_decoder[-1], list_encoder[-(i + 3)], f, name, bn_mode, bn_axis, dropout=d)
list_decoder.append(conv)
x = Activation("relu")(list_decoder[-1])
x = UpSampling2D(size=(2, 2))(x)
x = Convolution2D(nb_channels, 3, 3, name="last_conv", border_mode="same")(x)
x = Activation("tanh")(x)
generator_unet = Model(input=[unet_input], output=[x])
return generator_unet
Discriminator.
models.py
def DCGAN_discriminator(img_dim, nb_patch, bn_mode, model_name="DCGAN_discriminator", use_mbd=True):
"""
Discriminator model of the DCGAN
args : img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net model
"""
list_input = [Input(shape=img_dim, name="disc_input_%s" % i) for i in range(nb_patch)]
if K.image_dim_ordering() == "th":
bn_axis = 1
else:
bn_axis = -1
nb_filters = 64
nb_conv = int(np.floor(np.log(img_dim[1]) / np.log(2)))
list_filters = [nb_filters * min(8, (2 ** i)) for i in range(nb_conv)]
# First conv
x_input = Input(shape=img_dim, name="discriminator_input")
x = Convolution2D(list_filters[0], 3, 3, subsample=(2, 2), name="disc_conv2d_1", border_mode="same")(x_input)
x = BatchNormalization(mode=bn_mode, axis=bn_axis)(x)
x = LeakyReLU(0.2)(x)
# Next convs
for i, f in enumerate(list_filters[1:]):
name = "disc_conv2d_%s" % (i + 2)
x = Convolution2D(f, 3, 3, subsample=(2, 2), name=name, border_mode="same")(x)
x = BatchNormalization(mode=bn_mode, axis=bn_axis)(x)
x = LeakyReLU(0.2)(x)
x_flat = Flatten()(x)
x = Dense(2, activation='softmax', name="disc_dense")(x_flat)
PatchGAN = Model(input=[x_input], output=[x, x_flat], name="PatchGAN")
print("PatchGAN summary")
PatchGAN.summary()
x = [PatchGAN(patch)[0] for patch in list_input]
x_mbd = [PatchGAN(patch)[1] for patch in list_input]
if len(x) > 1:
x = merge(x, mode="concat", name="merge_feat")
else:
x = x[0]
if use_mbd:
if len(x_mbd) > 1:
x_mbd = merge(x_mbd, mode="concat", name="merge_feat_mbd")
else:
x_mbd = x_mbd[0]
num_kernels = 100
dim_per_kernel = 5
M = Dense(num_kernels * dim_per_kernel, bias=False, activation=None)
MBD = Lambda(minb_disc, output_shape=lambda_output)
x_mbd = M(x_mbd)
x_mbd = Reshape((num_kernels, dim_per_kernel))(x_mbd)
x_mbd = MBD(x_mbd)
x = merge([x, x_mbd], mode='concat')
x_out = Dense(2, activation="softmax", name="disc_output")(x)
discriminator_model = Model(input=list_input, output=[x_out], name=model_name)
return discriminator_model
Combining two models.
models.py
def DCGAN(generator, discriminator_model, img_dim, patch_size, image_dim_ordering):
gen_input = Input(shape=img_dim, name="DCGAN_input")
generated_image = generator(gen_input)
if image_dim_ordering == "th":
h, w = img_dim[1:]
else:
h, w = img_dim[:-1]
ph, pw = patch_size
list_row_idx = [(i * ph, (i + 1) * ph) for i in range(h / ph)]
list_col_idx = [(i * pw, (i + 1) * pw) for i in range(w / pw)]
list_gen_patch = []
for row_idx in list_row_idx:
for col_idx in list_col_idx:
if image_dim_ordering == "tf":
x_patch = Lambda(lambda z: z[:, row_idx[0]:row_idx[1], col_idx[0]:col_idx[1], :])(generated_image)
else:
x_patch = Lambda(lambda z: z[:, :, row_idx[0]:row_idx[1], col_idx[0]:col_idx[1]])(generated_image)
list_gen_patch.append(x_patch)
DCGAN_output = discriminator_model(list_gen_patch)
DCGAN = Model(input=[gen_input],
output=[generated_image, DCGAN_output],
name="DCGAN")
return DCGAN
Load for calling from main.py.
models.py
def load(model_name, img_dim, nb_patch, bn_mode, use_mbd, batch_size):
if model_name == "generator_unet_upsampling":
model = generator_unet_upsampling(img_dim, bn_mode, model_name=model_name)
print model.summary()
from keras.utils.visualize_util import plot
plot(model, to_file='../../figures/%s.png' % model_name, show_shapes=True, show_layer_names=True)
return model
if model_name == "generator_unet_deconv":
model = generator_unet_deconv(img_dim, bn_mode, batch_size, model_name=model_name)
print model.summary()
from keras.utils.visualize_util import plot
plot(model, to_file='../../figures/%s.png' % model_name, show_shapes=True, show_layer_names=True)
return model
if model_name == "DCGAN_discriminator":
model = DCGAN_discriminator(img_dim, nb_patch, bn_mode, model_name=model_name, use_mbd=use_mbd)
model.summary()
from keras.utils.visualize_util import plot
plot(model, to_file='../../figures/%s.png' % model_name, show_shapes=True, show_layer_names=True)
return model
if __name__ == '__main__':
# load("generator_unet_deconv", (256, 256, 3), 16, 2, False, 32)
load("generator_unet_upsampling", (256, 256, 3), 16, 2, False, 32)
Combine the two. DCGAN_model = models.DCGAN(generator_model, discriminator_model, img_dim, patch_size, image_dim_ordering) Learn discriminators. disc_loss = discriminator_model.train_on_batch(X_disc, y_disc) Learn a model that combines two. gen_loss = DCGAN_model.train_on_batch(X_gen, [X_gen_target, y_gen])
train.py
import os
import sys
import time
import numpy as np
import models
from keras.utils import generic_utils
from keras.optimizers import Adam, SGD
import keras.backend as K
# Utils
sys.path.append("../utils")
import general_utils
import data_utils
def l1_loss(y_true, y_pred):
return K.sum(K.abs(y_pred - y_true), axis=-1)
def train(**kwargs):
"""
Train model
Load the whole train data in memory for faster operations
args: **kwargs (dict) keyword arguments that specify the model hyperparameters
"""
# Roll out the parameters
batch_size = kwargs["batch_size"]
n_batch_per_epoch = kwargs["n_batch_per_epoch"]
nb_epoch = kwargs["nb_epoch"]
model_name = kwargs["model_name"]
generator = kwargs["generator"]
image_dim_ordering = kwargs["image_dim_ordering"]
img_dim = kwargs["img_dim"]
patch_size = kwargs["patch_size"]
bn_mode = kwargs["bn_mode"]
label_smoothing = kwargs["use_label_smoothing"]
label_flipping = kwargs["label_flipping"]
dset = kwargs["dset"]
use_mbd = kwargs["use_mbd"]
epoch_size = n_batch_per_epoch * batch_size
# Setup environment (logging directory etc)
general_utils.setup_logging(model_name)
# Load and rescale data
X_full_train, X_sketch_train, X_full_val, X_sketch_val = data_utils.load_data(dset, image_dim_ordering)
img_dim = X_full_train.shape[-3:]
# Get the number of non overlapping patch and the size of input image to the discriminator
nb_patch, img_dim_disc = data_utils.get_nb_patch(img_dim, patch_size, image_dim_ordering)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# opt_discriminator = SGD(lr=1E-3, momentum=0.9, nesterov=True)
opt_discriminator = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# Load generator model
generator_model = models.load("generator_unet_%s" % generator,
img_dim,
nb_patch,
bn_mode,
use_mbd,
batch_size)
# Load discriminator model
discriminator_model = models.load("DCGAN_discriminator",
img_dim_disc,
nb_patch,
bn_mode,
use_mbd,
batch_size)
generator_model.compile(loss='mae', optimizer=opt_discriminator)
discriminator_model.trainable = False
DCGAN_model = models.DCGAN(generator_model,
discriminator_model,
img_dim,
patch_size,
image_dim_ordering)
loss = [l1_loss, 'binary_crossentropy']
loss_weights = [1E1, 1]
DCGAN_model.compile(loss=loss, loss_weights=loss_weights, optimizer=opt_dcgan)
discriminator_model.trainable = True
discriminator_model.compile(loss='binary_crossentropy', optimizer=opt_discriminator)
gen_loss = 100
disc_loss = 100
# Start training
print("Start training")
for e in range(nb_epoch):
# Initialize progbar and batch counter
progbar = generic_utils.Progbar(epoch_size)
batch_counter = 1
start = time.time()
for X_full_batch, X_sketch_batch in data_utils.gen_batch(X_full_train, X_sketch_train, batch_size):
# Create a batch to feed the discriminator model
X_disc, y_disc = data_utils.get_disc_batch(X_full_batch,
X_sketch_batch,
generator_model,
batch_counter,
patch_size,
image_dim_ordering,
label_smoothing=label_smoothing,
label_flipping=label_flipping)
# Update the discriminator
disc_loss = discriminator_model.train_on_batch(X_disc, y_disc)
# Create a batch to feed the generator model
X_gen_target, X_gen = next(data_utils.gen_batch(X_full_train, X_sketch_train, batch_size))
y_gen = np.zeros((X_gen.shape[0], 2), dtype=np.uint8)
y_gen[:, 1] = 1
# Freeze the discriminator
discriminator_model.trainable = False
gen_loss = DCGAN_model.train_on_batch(X_gen, [X_gen_target, y_gen])
# Unfreeze the discriminator
discriminator_model.trainable = True
batch_counter += 1
progbar.add(batch_size, values=[("D logloss", disc_loss),
("G tot", gen_loss[0]),
("G L1", gen_loss[1]),
("G logloss", gen_loss[2])])
# Save images for visualization
if batch_counter % (n_batch_per_epoch / 2) == 0:
# Get new images from validation
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch, generator_model,
batch_size, image_dim_ordering, "training")
X_full_batch, X_sketch_batch = next(data_utils.gen_batch(X_full_val, X_sketch_val, batch_size))
data_utils.plot_generated_batch(X_full_batch, X_sketch_batch, generator_model,
batch_size, image_dim_ordering, "validation")
if batch_counter >= n_batch_per_epoch:
break
print("")
print('Epoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
if e % 5 == 0:
gen_weights_path = os.path.join('../../models/%s/gen_weights_epoch%s.h5' % (model_name, e))
generator_model.save_weights(gen_weights_path, overwrite=True)
disc_weights_path = os.path.join('../../models/%s/disc_weights_epoch%s.h5' % (model_name, e))
discriminator_model.save_weights(disc_weights_path, overwrite=True)
DCGAN_weights_path = os.path.join('../../models/%s/DCGAN_weights_epoch%s.h5' % (model_name, e))
DCGAN_model.save_weights(DCGAN_weights_path, overwrite=True)
except KeyboardInterrupt:
pass
The place to get the data is converted to hdf5.
data_utils.py
chainer code
https://github.com/pfnet-research/chainer-pix2pix
git clone https://github.com/pfnet-research/chainer-pix2pix.git
cd chainer-pix2pix
Drop the dataset and perform learning
python train_facade.py -g 0 -i CMP_facade_DB_base/base --out image_out --snapshot_interval 10000
Load the trained model and fine-tune
python train_facade.py -g 0 -i CMP_facade_DB_base/base --out image_out --snapshot_interval 10000 -r image_out/snapshot_iter_30000.npz
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