def train(epochs, batch_size):
downscale_factor = 4
batch_count = int(x_train_hr.shape[0] / batch_size)
shape = (image_shape[0] // downscale_factor,
image_shape[1] // downscale_factor, image_shape[2])
generator = Generator(shape).generator()
discriminator = Discriminator(image_shape).discriminator()
adam = Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
generator.compile(loss=vgg_loss, optimizer=adam)
discriminator.compile(loss="binary_crossentropy", optimizer=adam)
shape = (image_shape[0] // downscale_factor,
image_shape[1] // downscale_factor, 3)
gan = get_gan_network(discriminator, shape, generator, adam)
for e in range(1, epochs + 1):
print('-' * 15, 'Epoch %d' % e, '-' * 15)
for _ in range(batch_count):
rand_nums = np.random.randint(0,
x_train_hr.shape[0],
size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
generated_images_sr = generator.predict(image_batch_lr)
real_data_Y = np.ones(
batch_size) - np.random.random_sample(batch_size) * 0.2
fake_data_Y = np.random.random_sample(batch_size) * 0.2
discriminator.trainable = True
d_loss_real = discriminator.train_on_batch(image_batch_hr,
real_data_Y)
d_loss_fake = discriminator.train_on_batch(generated_images_sr,
fake_data_Y)
d_loss = 0.5 * np.add(d_loss_fake, d_loss_real)
rand_nums = np.random.randint(0,
x_train_hr.shape[0],
size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
gan_Y = np.ones(
batch_size) - np.random.random_sample(batch_size) * 0.2
discriminator.trainable = False
loss_gan = gan.train_on_batch(image_batch_lr,
[image_batch_hr, gan_Y])
print("Loss HR , Loss LR, Loss GAN")
print(d_loss_real, d_loss_fake, loss_gan)
if e % 300 == 0:
generator.save('./output/gen_model%d.h5' % e)
def train(epochs, batch_size):
psnr_v=[]
epochs_v = []
downscale_factor = 4
batch_count = int(x_train_hr.shape[0] / batch_size)
shape = (image_shape[0]//downscale_factor, image_shape[1]//downscale_factor, image_shape[2])
generator = Generator(shape).generator()
discriminator = Discriminator(image_shape).discriminator()
adam = Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
generator.compile(loss=vgg_loss, optimizer=adam)
discriminator.compile(loss="binary_crossentropy", optimizer=adam)
shape = (image_shape[0]//downscale_factor, image_shape[1]//downscale_factor, 3)
gan = get_gan_network(discriminator, shape, generator, adam)
for e in range(1, epochs+1):
print ('-'*15, 'Epoch %d' % e, '-'*15)
for _ in range(batch_count):
rand_nums = np.random.randint(0, x_train_hr.shape[0], size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
generated_images_sr = generator.predict(image_batch_lr)
real_data_Y = np.ones(batch_size) - np.random.random_sample(batch_size)*0.2
fake_data_Y = np.random.random_sample(batch_size)*0.2
discriminator.trainable = True
d_loss_real = discriminator.train_on_batch(image_batch_hr, real_data_Y)
d_loss_fake = discriminator.train_on_batch(generated_images_sr, fake_data_Y)
d_loss = 0.5 * np.add(d_loss_fake, d_loss_real)
rand_nums = np.random.randint(0, x_train_hr.shape[0], size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
gan_Y = np.ones(batch_size) - np.random.random_sample(batch_size)*0.2
discriminator.trainable = False
loss_gan = gan.train_on_batch(image_batch_lr, [image_batch_hr,gan_Y])
print("Loss HR , Loss LR, Loss GAN")
print(d_loss_real, d_loss_fake, loss_gan)
if e % 300 == 0:
generator.save('./model/gen_model%d.h5' % e)
model = load_model('./model/gen_model%d.h5'%e , custom_objects={'vgg_loss': loss.vgg_loss})
model.predict(x_train_lr[rand_nums[0]])
generated_image = denormalize(gen_img)
psnr_v.append(sewar.psnr(x_train_hr[rand_nums[0]],generated_image,MAX=None))
epochs_v.append(e)
plt.plot(psnr_v,epochs_v)
plt.savefig('./model' + 'progress%d.png'%e)
def train(epochs, batch_size):
data= []
batch_count = int(images_hr_train.shape[0] / batch_size)
shape = image_shape_LR
generator = Generator(shape).generator()
discriminator = Discriminator(image_shape_HR).discriminator()
adam = Adam(lr=1E-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
generator.compile(loss=vgg_loss, optimizer=adam)
discriminator.compile(loss="binary_crossentropy", optimizer=adam)
gan = get_gan_network(discriminator, shape, generator, adam)
for e in range(1, epochs+1):
print ('-'*15, 'Epoch %d' % e, '-'*15)
for _ in range(batch_count):
rand_nums = np.random.randint(0, images_hr_train.shape[0], size=batch_size)
image_batch_hr = images_hr_train[rand_nums]
image_batch_lr = images_lr_train[rand_nums]
generated_images_sr = generator.predict(image_batch_lr)
real_data_Y = np.ones(batch_size) - np.random.random_sample(batch_size)*0.2
fake_data_Y = np.random.random_sample(batch_size)*0.2
discriminator.trainable = True
d_loss_real = discriminator.train_on_batch(image_batch_hr, real_data_Y)
d_loss_fake = discriminator.train_on_batch(generated_images_sr, fake_data_Y)
#d_loss = 0.5 * np.add(d_loss_fake, d_loss_real)
rand_nums = np.random.randint(0, images_hr_train.shape[0], size=batch_size)
image_batch_hr = images_hr_train[rand_nums]
image_batch_lr = images_lr_train[rand_nums]
gan_Y = np.ones(batch_size) - np.random.random_sample(batch_size)*0.2
discriminator.trainable = False
loss_gan = gan.train_on_batch(image_batch_lr, [image_batch_hr,gan_Y])
print("Loss HR , Loss LR, Loss GAN")
print(d_loss_real, d_loss_fake, loss_gan)
data= data + [(e,d_loss_real, d_loss_fake, loss_gan)]
if e == 1 or e % 10 == 0:
plot_generated_images(e, generator)
if e % 100 == 0:
generator.save('./output/gen_model%d.h5' % e)
discriminator.save('./output/dis_model%d.h5' % e)
gan.save('./output/gan_model%d.h5' % e)
with open('LossData.csv','w') as out:
csv_out=csv.writer(out)
csv_out.writerow(['epoch','Loss HR' , 'Loss LR', 'Loss GAN'])
for row in data:
csv_out.writerow(row)
def train(epochs, batch_size, input_dir, output_dir, model_save_dir,
number_of_images, train_test_ratio):
x_train_lr, x_train_hr, x_test_lr, x_test_hr = Utils.load_training_data(
input_dir, '.png', number_of_images, train_test_ratio)
loss = VGG_LOSS(image_shape)
batch_count = int(x_train_hr.shape[0] / batch_size)
shape = (image_shape[0] // downscale_factor,
image_shape[1] // downscale_factor, image_shape[2]) # Not good
generator, _ = Generator(shape).generator()
discriminator = Discriminator(image_shape).discriminator()
optimizer = Utils_model.get_optimizer()
generator.compile(loss=loss.vgg_loss, optimizer=optimizer)
discriminator.compile(loss="binary_crossentropy", optimizer=optimizer)
gan = get_gan_network(discriminator, shape, generator, optimizer,
loss.vgg_loss)
loss_file = open(model_save_dir + 'losses.txt', 'w+')
loss_file.close()
for e in range(1, epochs + 1):
print('-' * 15, 'Epoch %d' % e, '-' * 15)
for _ in tqdm(range(batch_count)):
rand_nums = np.random.randint(0,
x_train_hr.shape[0],
size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
generated_images_sr = generator.predict(image_batch_lr)
real_data_Y = np.ones(
batch_size) - np.random.random_sample(batch_size) * 0.2
fake_data_Y = np.random.random_sample(batch_size) * 0.2
discriminator.trainable = True
d_loss_real = discriminator.train_on_batch(image_batch_hr,
real_data_Y)
d_loss_fake = discriminator.train_on_batch(generated_images_sr,
fake_data_Y)
discriminator_loss = 0.5 * np.add(d_loss_fake, d_loss_real)
rand_nums = np.random.randint(0,
x_train_hr.shape[0],
size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
gan_Y = np.ones(
batch_size) - np.random.random_sample(batch_size) * 0.2
discriminator.trainable = False
gan_loss = gan.train_on_batch(image_batch_lr,
[image_batch_hr, gan_Y])
print("discriminator_loss : %f" % discriminator_loss)
print("gan_loss :", gan_loss)
gan_loss = str(gan_loss)
loss_file = open(model_save_dir + 'losses.txt', 'a')
loss_file.write('epoch%d : gan_loss = %s ; discriminator_loss = %f\n' %
(e, gan_loss, discriminator_loss))
loss_file.close()
if e == 1 or e % 5 == 0:
Utils.plot_generated_images(output_dir, e, generator, x_test_hr,
x_test_lr)
# generator.save(model_save_dir + 'gen_model%d.h5' % e)
# generator.save_weights(model_save_dir + 'gen_w%d.h5' % e)
# exit()
if e % 500 == 0:
# generator.save(model_save_dir + 'gen_model%d.h5' % e)
generator.save_weights(model_save_dir + 'gen_w%d.h5' % e)
def train(epochs, batch_size, input_dir, output_dir, model_save_dir, number_of_images, train_test_ratio, image_extension):
# Loading images
x_train_lr, x_train_hr, x_test_lr, x_test_hr = \
Utils.load_training_data(input_dir, image_extension, image_shape, number_of_images, train_test_ratio)
print('======= Loading VGG_loss ========')
# Loading VGG loss
loss = VGG_LOSS(image_shape)
loss2 = VGG_LOSS(image_shape)
print('====== VGG_LOSS =======', loss)
batch_count = int(x_train_hr.shape[0] / batch_size)
print('====== Batch_count =======', batch_count)
shape = (image_shape[0] // downscale_factor, image_shape[1] // downscale_factor, image_shape[2])
print('====== Shape =======', shape)
# Generator description
generator = Generator(shape).generator()
complex_generator = complex_Generator(shape).generator()
# Discriminator description
discriminator = Discriminator(image_shape).discriminator()
discriminator2 = Discriminator(image_shape).discriminator()
optimizer = Utils_model.get_optimizer()
generator.compile(loss=loss.vgg_loss, optimizer=optimizer)
complex_generator.compile(loss=loss2.vgg_loss, optimizer=optimizer)
discriminator.compile(loss="binary_crossentropy", optimizer=optimizer)
discriminator2.compile(loss="binary_crossentropy", optimizer=optimizer)
gan = get_gan_network(discriminator, shape, generator, optimizer, loss.vgg_loss)
complex_gan = get_gan_network(discriminator2, shape, complex_generator, optimizer, loss2.vgg_loss)
loss_file = open(model_save_dir + 'losses.txt', 'w+')
loss_file.close()
for e in range(1, epochs + 1):
print('-' * 15, 'Epoch %d' % e, '-' * 15)
for _ in tqdm(range(batch_count)):
rand_nums = np.random.randint(0, x_train_hr.shape[0], size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
generated_images_sr = generator.predict(image_batch_lr)
generated_images_csr = complex_generator.predict(image_batch_lr)
real_data_Y = np.ones(batch_size) - np.random.random_sample(batch_size) * 0.2
fake_data_Y = np.random.random_sample(batch_size) * 0.2
discriminator.trainable = True
discriminator2.trainable = True
d_loss_real = discriminator.train_on_batch(image_batch_hr, real_data_Y)
d_loss_fake = discriminator.train_on_batch(generated_images_sr, fake_data_Y)
discriminator_loss = 0.5 * np.add(d_loss_fake, d_loss_real)
d_loss_creal = discriminator2.train_on_batch(image_batch_hr, real_data_Y)
d_loss_cfake = discriminator2.train_on_batch(generated_images_csr, fake_data_Y)
discriminator_c_loss = 0.5 * np.add(d_loss_cfake, d_loss_creal)
########
rand_nums = np.random.randint(0, x_train_hr.shape[0], size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
gan_Y = np.ones(batch_size) - np.random.random_sample(batch_size) * 0.2
discriminator.trainable = False
discriminator2.trainable = False
gan_loss = gan.train_on_batch(image_batch_lr, [image_batch_hr, gan_Y])
gan_c_loss = complex_gan.train_on_batch(image_batch_lr, [image_batch_hr, gan_Y])
print("discriminator_loss : %f" % discriminator_loss)
print("gan_loss :", gan_loss)
print("gan_c_loss :", gan_c_loss)
gan_loss = str(gan_loss)
loss_file = open(model_save_dir + 'losses.txt', 'a')
loss_file.write('epoch%d : gan_loss = %s ; discriminator_loss = %f\n' % (e, gan_loss, discriminator_loss))
loss_file.close()
if e % 1 == 0:
Utils.plot_generated_images(output_dir, e, generator,complex_generator, x_test_hr, x_test_lr)
if e % 50 == 0:
generator.save(model_save_dir + 'gen_model%d.h5' % e)
discriminator.save(model_save_dir + 'dis_model%d.h5' % e)
def train(epochs, batch_size, input_dir, output_dir, model_save_dir,
number_of_images, train_test_ratio, image_extension):
# Loading images
x_train_lr, x_train_hr, x_test_lr, x_test_hr = \
Utils.load_training_data(input_dir, image_extension, image_shape, number_of_images, train_test_ratio)
# convert to loading PATCHES
#num_samples = dataset_info['num_samples'][1]
print('======= Loading VGG_loss ========')
# Loading VGG loss
# convert to 3 channels
#img_input = Input(shape=original_image_shape)
#image_shape_gray = Concatenate()([img_input, img_input, img_input])
#image_shape_gray = Concatenate()([original_image_shape, original_image_shape])
#image_shape_gray = Concatenate()([image_shape_gray,original_image_shape])
#image_shape = patch_shape
#experimental_run_tf_function=False
loss = VGG_LOSS(image_shape) # was image_shape
print('====== VGG_LOSS =======', loss)
# 1 channel
#image_shape= original_image_shape
batch_count = int(x_train_hr.shape[0] / batch_size)
#batch_count = int(x_train_hr_patch.shape[0] / batch_size) # for patch
print('====== Batch_count =======', batch_count)
shape = (image_shape[0] // downscale_factor,
image_shape[1] // downscale_factor, image_shape[2]
) # commented by Furat
#shape = (image_shape[0] // downscale_factor, image_shape[1] // downscale_factor)
print('====== Shape =======', shape)
# Generator description
generator = Generator(shape).generator()
# Discriminator description
discriminator = Discriminator(image_shape).discriminator()
optimizer = Utils_model.get_optimizer()
generator.compile(loss=loss.vgg_loss, optimizer=optimizer)
discriminator.compile(loss="binary_crossentropy", optimizer=optimizer)
gan = get_gan_network(discriminator, shape, generator, optimizer,
loss.vgg_loss)
loss_file = open(model_save_dir + 'losses.txt', 'w+')
loss_file.close()
## restore the patches into 1 image:
# x_train_hr should have a whole image insted of patches?
######
# input_data= x_train_hr
# patch_shape = train_conf['patch_shape']
# output_shape = train_conf['output_shape']
# num_chs = num_modalities * dataset_info['RGBch'] # number of total channels
# if input_data.ndim == 6: # augmentation case
# num_samples = dataset_info['num_samples'][1]
#num_samples = 3
######
#lr_data= x_train_lr
#hr_data= x_train_hr
#for patch_idx in range (num_patches):
#this_input_data = np.reshape(input_data[:,:,patch_idx], input_data.shape[:2]+input_data.shape[3:])
#this_hr_patch, this_lr_patch = overlap_patching(gen_conf, train_conf, x_train_hr)
#this_output_patch, out = overlap_patching(gen_conf, train_conf, output_data)
# take patches:
this_hr_patch, = extract_2d(x_train_hr, (32, 32))
this_lr_patch = extract_2d(x_train_lr, (32, 32))
x_train_lr = this_lr_patch
x_train_hr = this_hr_patch
#convert to grayscale
#x_train_hr= tf.image.rgb_to_grayscale(x_train_hr)
#x_train_hr= rgb2gray(x_train_hr)
#x_train_hr= np.concatenate(x_train_hr,1)
#x_train_hr= np.array(x_train_hr)
#x_train_lr= tf.image.rgb_to_grayscale(x_train_lr)
#x_train_lr= np.array(x_train_lr)
#x_train_lr= rgb2gray(x_train_lr)
#x_train_lr= np.concatenate(x_train_lr,1)
for e in range(1, epochs + 1):
print('-' * 15, 'Epoch %d' % e, '-' * 15)
for _ in tqdm(range(batch_count)):
rand_nums = np.random.randint(0,
x_train_hr.shape[0],
size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
generated_images_sr = generator.predict(image_batch_lr)
real_data_Y = np.ones(
batch_size) - np.random.random_sample(batch_size) * 0.2
fake_data_Y = np.random.random_sample(batch_size) * 0.2
discriminator.trainable = True
d_loss_real = discriminator.train_on_batch(image_batch_hr,
real_data_Y)
d_loss_fake = discriminator.train_on_batch(generated_images_sr,
fake_data_Y)
discriminator_loss = 0.5 * np.add(d_loss_fake, d_loss_real)
rand_nums = np.random.randint(0,
x_train_hr.shape[0],
size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
gan_Y = np.ones(
batch_size) - np.random.random_sample(batch_size) * 0.2
discriminator.trainable = False
gan_loss = gan.train_on_batch(image_batch_lr,
[image_batch_hr, gan_Y])
print("discriminator_loss : %f" % discriminator_loss)
print("gan_loss :", gan_loss)
gan_loss = str(gan_loss)
loss_file = open(model_save_dir + 'losses.txt', 'a')
loss_file.write('epoch%d : gan_loss = %s ; discriminator_loss = %f\n' %
(e, gan_loss, discriminator_loss))
loss_file.close()
if e == 1 or e % 5 == 0:
Utils.plot_generated_images(output_dir, e, generator, x_test_hr,
x_test_lr)
if e % 200 == 0:
generator.save(model_save_dir + 'gen_model%d.h5' % e)
discriminator.save(model_save_dir + 'dis_model%d.h5' % e)
for e in range(1, epochs+1):
print ('-'*15, 'Epoch %d' % e, '-'*15)
for _ in range(batch_count):
rand_nums = np.random.randint(0, x_train_hr.shape[0], size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
generated_images_sr = generator.predict(image_batch_lr)
real_data_Y = np.ones(batch_size) - np.random.random_sample(batch_size)*0.2
fake_data_Y = np.random.random_sample(batch_size)*0.2
discriminator.trainable = True
d_loss_real = discriminator.train_on_batch(image_batch_hr, real_data_Y)
d_loss_fake = discriminator.train_on_batch(generated_images_sr, fake_data_Y)
#d_loss = 0.5 * np.add(d_loss_fake, d_loss_real)
rand_nums = np.random.randint(0, x_train_hr.shape[0], size=batch_size)
image_batch_hr = x_train_hr[rand_nums]
image_batch_lr = x_train_lr[rand_nums]
gan_Y = np.ones(batch_size) - np.random.random_sample(batch_size)*0.2
discriminator.trainable = False
loss_gan = gan.train_on_batch(image_batch_lr, [image_batch_hr,gan_Y])
print("Loss HR , Loss LR, Loss GAN")
print(d_loss_real, d_loss_fake, loss_gan)
temp_loss = [d_loss_real, d_loss_fake, loss_gan]
losses_to_file.append(temp_loss)
def train(epochs, batch_size, input_dir, output_dir, model_save_dir,
number_of_images, train_test_ratio, resume_train, downscale_factor,
arch):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
set_session(sess)
hr_images, hr_label, lr_images, lr_label = Utils.load_training_data(
input_dir, number_of_images)
print(hr_images)
loss = VGG_LOSS(IMAGE_SHAPE)
lr_shape = (IMAGE_SHAPE[0] // downscale_factor,
IMAGE_SHAPE[1] // downscale_factor, IMAGE_SHAPE[2])
print(lr_shape)
generator = Generator(lr_shape, downscale_factor, arch).generator()
discriminator = Discriminator(IMAGE_SHAPE).discriminator()
optimizer = Utils_model.get_optimizer()
if (resume_train == True):
last_epoch_number = Utils.get_last_epoch_number(model_save_dir +
'last_model_epoch.txt')
gen_model = model_save_dir + arch + "_gen_model" + str(
last_epoch_number) + ".h5"
dis_model = model_save_dir + arch + "_dis_model" + str(
last_epoch_number) + ".h5"
generator.load_weights(gen_model)
discriminator.load_weights(dis_model)
else:
last_epoch_number = 1
generator.compile(loss=loss.vgg_loss, optimizer=optimizer)
discriminator.compile(loss="binary_crossentropy", optimizer=optimizer)
gan = gan_network(discriminator, lr_shape, generator, optimizer,
loss.vgg_loss)
for e in range(last_epoch_number, last_epoch_number + epochs):
print('-' * 15, 'Epoch %d' % e, '-' * 15)
for _ in tqdm(range(1)):
rand_nums = np.random.randint(0,
hr_images.shape[0],
size=batch_size)
image_batch_hr = hr_images[rand_nums]
image_batch_lr = lr_images[rand_nums]
# video_images = lr_images[0]
generated_images = generator.predict(
image_batch_lr) #array of generated images
real_data = np.ones(
batch_size) - np.random.random_sample(batch_size) * 0.2
fake_data = np.random.random_sample(batch_size) * 0.2
discriminator.trainable = True
discriminator_loss_real = discriminator.train_on_batch(
image_batch_hr, real_data)
discriminator_loss_fake = discriminator.train_on_batch(
generated_images, fake_data)
discriminator_loss = 0.5 * np.add(
discriminator_loss_fake,
discriminator_loss_real) #Mean Of Discriminator Loss
rand_nums = np.random.randint(0,
hr_images.shape[0],
size=batch_size)
discriminator.trainable = False
gan_loss = gan.train_on_batch(image_batch_lr,
[image_batch_hr, real_data])
print("discriminator_loss : %f" % discriminator_loss)
print("gan_loss : ", gan_loss)
gan_loss = str(gan_loss)
# generated_video_image = generator.predict(np.expand_dims(video_images,axis=0))
Utils.save_losses_file(model_save_dir, e, gan_loss, discriminator_loss,
arch + '_losses.txt')
# image_array.append(cv2.cvtColor(denormalize(generated_video_image[0]),cv2.COLOR_BGR2RGB))
if e % EPOCHS_CHECKPOINT == 0:
Utils.save_losses_file(model_save_dir, e, gan_loss,
discriminator_loss, 'last_model_epoch.txt')
generator.save(model_save_dir + arch + '_gen_model%d.h5' % e)
discriminator.save(model_save_dir + arch + '_dis_model%d.h5' % e)