def train_cycle_gan(data_root, semi_supervised=False):
opt = get_opts()
ensure_dir(models_prefix)
ensure_dir(images_prefix)
cycle_gan = CycleGAN(device,
models_prefix,
opt["lr"],
opt["b1"],
train=True,
semi_supervised=semi_supervised)
data = DataLoader(data_root=data_root,
image_size=(opt['img_height'], opt['img_width']),
batch_size=opt['batch_size'])
total_images = len(data.names)
print("Total Training Images", total_images)
total_batches = int(ceil(total_images / opt['batch_size']))
for epoch in range(cycle_gan.epoch_tracker.epoch, opt['n_epochs']):
for iteration in range(total_batches):
if (epoch == cycle_gan.epoch_tracker.epoch
and iteration < cycle_gan.epoch_tracker.iter):
continue
y, x = next(data.data_generator(iteration))
real_A = Variable(x.type(Tensor))
real_B = Variable(y.type(Tensor))
cycle_gan.set_input(real_A, real_B)
cycle_gan.train()
message = (
"\r[Epoch {}/{}] [Batch {}/{}] [DA:{}, DB:{}] [GA:{}, GB:{}, cycleA:{}, cycleB:{}, G:{}]"
.format(epoch, opt["n_epochs"], iteration, total_batches,
cycle_gan.loss_disA.item(), cycle_gan.loss_disB.item(),
cycle_gan.loss_genA.item(), cycle_gan.loss_genB.item(),
cycle_gan.loss_cycle_A.item(),
cycle_gan.loss_cycle_B.item(), cycle_gan.loss_G))
print(message)
logger.info(message)
if iteration % opt['sample_interval'] == 0:
cycle_gan.save_progress(images_prefix, epoch, iteration)
cycle_gan.save_progress(images_prefix,
epoch,
total_batches,
save_epoch=True)
def train_cycle_gan(data_root, semi_supervised=False):
opt = get_opts()
ensure_dir(models_prefix)
ensure_dir(images_prefix)
cycle_gan = CycleGAN(device, models_prefix, opt["lr"], opt["b1"],
train=True, semi_supervised=semi_supervised)
# data = DataLoader(data_root=data_root,
# image_size=(opt['img_height'], opt['img_width']),
# batch_size=opt['batch_size'])
dataset = TrainDataSet(data_root=data_root, image_size=(opt['img_height'], opt['img_width']))
print("dataset : ", dataset)
dataLoader = DataLoader(dataset, batch_size=1)
total_images = len(dataset.names)
print("Total Training Images", total_images)
total_batches = int(ceil(total_images / opt['batch_size']))
for epoch in range(5):
for i, data in enumerate(dataLoader):
real_A, real_B = data
real_A = Variable(real_A.type(Tensor))
real_B = Variable(real_B.type(Tensor))
cycle_gan.set_input(real_A, real_B)
cycle_gan.train()
message = (
"\r[Epoch {}/{}] [Batch {}/{}] [DA:{}, DB:{}] [GA:{}, GB:{}, cycleA:{}, cycleB:{}, G:{}]"
.format(epoch, opt["n_epochs"], iteration, total_batches,
cycle_gan.loss_disA.item(),
cycle_gan.loss_disB.item(),
cycle_gan.loss_genA.item(),
cycle_gan.loss_genB.item(),
cycle_gan.loss_cycle_A.item(),
cycle_gan.loss_cycle_B.item(),
cycle_gan.loss_G))
print(message)
logger.info(message)
import numpy as np
from glob import glob
from image_helper_cycle_gan import ImageHelper
from cycle_gan import CycleGAN
image_helper = ImageHelper()
dataset_folder = "datasets/monet2photo" #modify this folder
print("Ploting the images...")
filenames = np.array(glob(dataset_folder + '/testA/*.jpg'))
image_helper.plot20(filenames)
generative_advarsial_network = CycleGAN((128, 128, 3), 10.0, image_helper)
generative_advarsial_network.train(100, 1, dataset_folder)