from __future__ import print_function, division
import scipy
from keras.datasets import mnist
from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
import datetime
import matplotlib.pyplot as plt
import sys
from data_loader import DataLoader
import numpy as np
import os
class DiscoGAN():
def __init__(self):
# Input shape
self.img_rows = 128
self.img_cols = 128
self.channels = 3
self.img_shape = (self.img_rows, self.img_cols, self.channels)
# Configure data loader
self.dataset_name = 'edges2shoes'
self.data_loader = DataLoader(dataset_name=self.dataset_name,
img_res=(self.img_rows, self.img_cols))
# Calculate output shape of D (PatchGAN)
patch = int(self.img_rows / 2**4)
self.disc_patch = (patch, patch, 1)
# Number of filters in the first layer of G and D
self.gf = 64
self.df = 64
optimizer = Adam(0.0002, 0.5)
# Build and compile the discriminators
self.d_A = self.build_discriminator()
self.d_B = self.build_discriminator()
self.d_A.compile(loss='mse',
optimizer=optimizer,
metrics=['accuracy'])
self.d_B.compile(loss='mse',
optimizer=optimizer,
metrics=['accuracy'])
#-------------------------
# Construct Computational
# Graph of Generators
#-------------------------
# Build the generators
self.g_AB = self.build_generator()
self.g_BA = self.build_generator()
# Input images from both domains
img_A = Input(shape=self.img_shape)
img_B = Input(shape=self.img_shape)
# Translate images to the other domain
fake_B = self.g_AB(img_A)
fake_A = self.g_BA(img_B)
# Translate images back to original domain
reconstr_A = self.g_BA(fake_B)
reconstr_B = self.g_AB(fake_A)
# For the combined model we will only train the generators
self.d_A.trainable = False
self.d_B.trainable = False
# Discriminators determines validity of translated images
valid_A = self.d_A(fake_A)
valid_B = self.d_B(fake_B)
# Objectives
# + Adversarial: Fool domain discriminators
# + Translation: Minimize MAE between e.g. fake B and true B
# + Cycle-consistency: Minimize MAE between reconstructed images and original
self.combined = Model(inputs=[img_A, img_B],
outputs=[ valid_A, valid_B,
fake_B, fake_A,
reconstr_A, reconstr_B ])
self.combined.compile(loss=['mse', 'mse',
'mae', 'mae',
'mae', 'mae'],
optimizer=optimizer)
def build_generator(self):
"""U-Net Generator"""
def conv2d(layer_input, filters, f_size=4, normalize=True):
"""Layers used during downsampling"""
d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
d = LeakyReLU(alpha=0.2)(d)
if normalize:
d = InstanceNormalization()(d)
return d
def deconv2d(layer_input, skip_input, filters, f_size=4, dropout_rate=0):
"""Layers used during upsampling"""
u = UpSampling2D(size=2)(layer_input)
u = Conv2D(filters, kernel_size=f_size, strides=1, padding='same', activation='relu')(u)
if dropout_rate:
u = Dropout(dropout_rate)(u)
u = InstanceNormalization()(u)
u = Concatenate()([u, skip_input])
return u
# Image input
d0 = Input(shape=self.img_shape)
# Downsampling
d1 = conv2d(d0, self.gf, normalize=False)
d2 = conv2d(d1, self.gf*2)
d3 = conv2d(d2, self.gf*4)
d4 = conv2d(d3, self.gf*8)
d5 = conv2d(d4, self.gf*8)
d6 = conv2d(d5, self.gf*8)
d7 = conv2d(d6, self.gf*8)
# Upsampling
u1 = deconv2d(d7, d6, self.gf*8)
u2 = deconv2d(u1, d5, self.gf*8)
u3 = deconv2d(u2, d4, self.gf*8)
u4 = deconv2d(u3, d3, self.gf*4)
u5 = deconv2d(u4, d2, self.gf*2)
u6 = deconv2d(u5, d1, self.gf)
u7 = UpSampling2D(size=2)(u6)
output_img = Conv2D(self.channels, kernel_size=4, strides=1,
padding='same', activation='tanh')(u7)
return Model(d0, output_img)
def build_discriminator(self):
def d_layer(layer_input, filters, f_size=4, normalization=True):
"""Discriminator layer"""
d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
d = LeakyReLU(alpha=0.2)(d)
if normalization:
d = InstanceNormalization()(d)
return d
img = Input(shape=self.img_shape)
d1 = d_layer(img, self.df, normalization=False)
d2 = d_layer(d1, self.df*2)
d3 = d_layer(d2, self.df*4)
d4 = d_layer(d3, self.df*8)
validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
return Model(img, validity)
def train(self, epochs, batch_size=128, sample_interval=50):
start_time = datetime.datetime.now()
# Adversarial loss ground truths
valid = np.ones((batch_size,) + self.disc_patch)
fake = np.zeros((batch_size,) + self.disc_patch)
for epoch in range(epochs):
for batch_i, (imgs_A, imgs_B) in enumerate(self.data_loader.load_batch(batch_size)):
# ----------------------
# Train Discriminators
# ----------------------
# Translate images to opposite domain
fake_B = self.g_AB.predict(imgs_A)
fake_A = self.g_BA.predict(imgs_B)
# Train the discriminators (original images = real / translated = Fake)
dA_loss_real = self.d_A.train_on_batch(imgs_A, valid)
dA_loss_fake = self.d_A.train_on_batch(fake_A, fake)
dA_loss = 0.5 * np.add(dA_loss_real, dA_loss_fake)
dB_loss_real = self.d_B.train_on_batch(imgs_B, valid)
dB_loss_fake = self.d_B.train_on_batch(fake_B, fake)
dB_loss = 0.5 * np.add(dB_loss_real, dB_loss_fake)
# Total disciminator loss
d_loss = 0.5 * np.add(dA_loss, dB_loss)
# ------------------
# Train Generators
# ------------------
# Train the generators
g_loss = self.combined.train_on_batch([imgs_A, imgs_B], [valid, valid, \
imgs_B, imgs_A, \
imgs_A, imgs_B])
elapsed_time = datetime.datetime.now() - start_time
# Plot the progress
print ("[%d] [%d/%d] time: %s, [d_loss: %f, g_loss: %f]" % (epoch, batch_i,
self.data_loader.n_batches,
elapsed_time,
d_loss[0], g_loss[0]))
# If at save interval => save generated image samples
if batch_i % sample_interval == 0:
self.sample_images(epoch, batch_i)
def sample_images(self, epoch, batch_i):
os.makedirs('images/%s' % self.dataset_name, exist_ok=True)
r, c = 2, 3
imgs_A, imgs_B = self.data_loader.load_data(batch_size=1, is_testing=True)
# Translate images to the other domain
fake_B = self.g_AB.predict(imgs_A)
fake_A = self.g_BA.predict(imgs_B)
# Translate back to original domain
reconstr_A = self.g_BA.predict(fake_B)
reconstr_B = self.g_AB.predict(fake_A)
gen_imgs = np.concatenate([imgs_A, fake_B, reconstr_A, imgs_B, fake_A, reconstr_B])
# Rescale images 0 - 1
gen_imgs = 0.5 * gen_imgs + 0.5
titles = ['Original', 'Translated', 'Reconstructed']
fig, axs = plt.subplots(r, c)
cnt = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(gen_imgs[cnt])
axs[i, j].set_title(titles[j])
axs[i,j].axis('off')
cnt += 1
fig.savefig("images/%s/%d_%d.png" % (self.dataset_name, epoch, batch_i))
plt.close()
if __name__ == '__main__':
gan = DiscoGAN()
gan.train(epochs=20, batch_size=1, sample_interval=200)
