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RaGAN_CustomLayers.py
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RaGAN_CustomLayers.py
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import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Input, Dense, Lambda, Conv2D, Conv2DTranspose, Activation, LeakyReLU, Concatenate
from keras.layers import BatchNormalization, GlobalAveragePooling2D, Reshape
import keras.backend as K
from keras.models import Model
from keras.utils import plot_model
from keras.optimizers import *
from keras.utils.generic_utils import Progbar
from time import time
import sys
import os
import pickle
import os
import pickle
import argparse
plt.switch_backend('agg')
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--training_ratio', type=int, default=1)
parser.add_argument('--lr', type=float, default=2e-4, help='Learning rate')
parser.add_argument('--beta_1', type=float, default=0.5)
parser.add_argument('--beta_2', type=float, default=0.999)
parser.add_argument('--loss', type=str, default='BXE', help='Choose Loss: BXE for binary cross entropy, LS for least square')
parser.add_argument('--dataset', type=str, default='fashion_mnist', help='Choose dataset: mnist, fashion_mnist, cifar10')
args = parser.parse_args()
EPOCHS = args.epochs
BATCHSIZE = args.batch_size
TRAINING_RATIO =args.training_ratio
DATASET = args.dataset
LOSS = args.loss
GENERATE_ROW_NUM = 10
OPT = Adam(lr=args.lr, beta_1=args.beta_1, beta_2=args.beta_2)
STAMP = '{}_{}'.format(DATASET, LOSS)
print(STAMP)
if not os.path.isdir('result/'+STAMP):
print('mkdir result/{}'.format(STAMP))
os.mkdir('result/'+STAMP)
from keras.datasets import mnist, fashion_mnist, cifar10
exec('(X_train, y_train), (X_test, y_test) = {}.load_data()'.format(DATASET))
X = np.concatenate((X_train, X_test))
if len(X.shape)==3:
X = np.expand_dims(X, axis=-1)
X = X/255*2-1
def DC_Generator(input_shape=(128,) ,output_shape=(28,28,1), dc_shape=(7,7,128), name='Generator'):
layer_num=int(np.log2(output_shape[1]/dc_shape[1]))
z = Input(shape=input_shape)
h = Dense(dc_shape[0]*dc_shape[1]*dc_shape[2], activation='relu',kernel_initializer='glorot_uniform')(z)
h = Reshape(dc_shape)(h)
for i in range(layer_num):
h = Conv2DTranspose(int(dc_shape[2]/(2**(i+1))), kernel_size=4, strides=2, padding='same', activation='relu',kernel_initializer='glorot_uniform')(h)
h = BatchNormalization(momentum=0.9, epsilon=0.00002)(h)
x = Conv2DTranspose(output_shape[-1], kernel_size=3, strides=1, padding='same', activation='tanh',kernel_initializer='glorot_uniform')(h)
model = Model(z,x, name=name)
model.summary()
return model
def DC_Discriminator(input_shape=(28,28,1),layer_num=2, start_dim=64, name='Discriminator'):
x = Input(shape=input_shape)
h = x
for i in range(layer_num):
h = Conv2D(start_dim*(2**i), kernel_size=4, strides=2, padding='same',kernel_initializer='glorot_uniform')(h)
h = LeakyReLU(0.1)(h)
h = GlobalAveragePooling2D()(h)
y = Dense(1,kernel_initializer='glorot_uniform' )(h)
model = Model(x,y, name=name)
model.summary()
return model
if X.shape[2] == 28:
dc_shape = (7,7,128)
dis_layer_num = 2
else:
dc_shape = (4,4,512)
dis_layer_num = 4
generator = DC_Generator(output_shape=X.shape[1:], dc_shape=dc_shape)
discriminator = DC_Discriminator(input_shape=X.shape[1:], layer_num=dis_layer_num)
def relativistic_average(input_):
x_0 = input_[0]
x_1 = input_[1]
return x_0 - K.mean(x_1, axis=0)
Real_image = Input(shape=X.shape[1:])
Noise_input = Input(shape=(128,))
Fake_image = generator(Noise_input)
Discriminator_real_out = discriminator(Real_image)
Discriminator_fake_out = discriminator(Fake_image)
Real_Fake_relativistic_average_out = Lambda(relativistic_average, name='Real_minus_mean_fake')([Discriminator_real_out, Discriminator_fake_out])
Fake_Real_relativistic_average_out = Lambda(relativistic_average, name='Fake_minus_mean_real')([Discriminator_fake_out, Discriminator_real_out])
if LOSS=='BXE':
Real_Fake_relativistic_average_out = Activation('sigmoid')(Real_Fake_relativistic_average_out)
Fake_Real_relativistic_average_out = Activation('sigmoid')(Fake_Real_relativistic_average_out)
Discriminator_Relativistic_out = Concatenate()([Real_Fake_relativistic_average_out, Fake_Real_relativistic_average_out])
epsilon=0.000001
if LOSS=='BXE':
LOSS='binary_crossentropy'
elif LOSS=='LS':
LOSS='mean_squared_error'
generator_train = Model([Noise_input, Real_image], Discriminator_Relativistic_out)
discriminator.trainable=False
generator_train.compile(OPT, loss=LOSS)
generator_train.summary()
discriminator_train = Model([Noise_input, Real_image],Discriminator_Relativistic_out)
generator.trainable = False
discriminator.trainable=True
discriminator_train.summary()
discriminator_train.compile(OPT, loss=LOSS)
if LOSS=='binary_crossentropy':
true_y = np.ones((BATCHSIZE, 1), dtype=np.float32)
fake_y = np.zeros((BATCHSIZE, 1), dtype=np.float32)
y_for_dis = np.concatenate((true_y, fake_y), axis=1)
y_for_gen = np.concatenate((fake_y, true_y), axis=1)
if LOSS=='mean_squared_error':
true_y = np.ones((BATCHSIZE, 1), dtype=np.float32)
fake_y = -np.ones((BATCHSIZE, 1), dtype=np.float32)
y_for_dis = np.concatenate((true_y, fake_y), axis=1)
y_for_gen = np.concatenate((fake_y, true_y), axis=1)
GENERATE_BATCHSIZE = GENERATE_ROW_NUM*GENERATE_ROW_NUM
test_noise = np.random.randn(GENERATE_BATCHSIZE, 128)
discriminator_loss = list()
generator_loss = list()
for epoch in range(EPOCHS):
np.random.shuffle(X)
print("epoch {} of {}".format(epoch+1, EPOCHS))
num_batches = int(X.shape[0] // BATCHSIZE)
minibatches_size = BATCHSIZE * (TRAINING_RATIO+1)
print("number of batches: {}".format(int(X.shape[0] // (minibatches_size))))
progress_bar = Progbar(target=int(X.shape[0] // minibatches_size))
plt.clf()
start_time = time()
for index in range(int(X.shape[0] // (minibatches_size))):
progress_bar.update(index)
itreation_minibatches = X[index * minibatches_size:(index + 1) * minibatches_size]
for j in range(TRAINING_RATIO):
image_batch = itreation_minibatches[j * BATCHSIZE : (j + 1) * BATCHSIZE]
noise = np.random.randn(BATCHSIZE, 128).astype(np.float32)
discriminator.trainable = True
generator.trainable = False
discriminator_loss.append(discriminator_train.train_on_batch([noise, image_batch],y_for_dis))
image_batch = itreation_minibatches[TRAINING_RATIO*BATCHSIZE : (TRAINING_RATIO + 1) * BATCHSIZE]
noise = np.random.randn(BATCHSIZE, 128).astype(np.float32)
discriminator.trainable = False
generator.trainable = True
generator_loss.append(generator_train.train_on_batch([noise, image_batch], y_for_gen))
print('\nepoch time: {}'.format(time()-start_time))
generated_image = generator.predict(test_noise)
generated_image = (generated_image+1)/2
for i in range(GENERATE_ROW_NUM):
if X.shape[3]==1:
new = generated_image[i*GENERATE_ROW_NUM:i*GENERATE_ROW_NUM+GENERATE_ROW_NUM].reshape(X.shape[2]*GENERATE_ROW_NUM,X.shape[2])
else:
new = generated_image[i*GENERATE_ROW_NUM:i*GENERATE_ROW_NUM+GENERATE_ROW_NUM].reshape(X.shape[2]*GENERATE_ROW_NUM,X.shape[2], 3)
if i!=0:
old = np.concatenate((old,new),axis=1)
else:
old = new
print('plot generated_image')
if X.shape[-1]==1:
plt.imsave('result/{}/epoch_{:03}.png'.format(STAMP, epoch), old, cmap='gray')
else:
plt.imsave('result/{}/epoch_{:03}.png'.format(STAMP, epoch), old)
print('plot Loss')
plt.plot(discriminator_loss)
plt.plot(generator_loss)
plt.legend(['discriminator', 'generator'])
plt.savefig('result/{}/loss.png'.format(STAMP))
plt.clf()
pickle.dump({'discriminator_loss': discriminator_loss,
'generator_loss': generator_loss},
open('result/{}/loss-history.pkl'.format(STAMP), 'wb'))