def main(_):
with tf.Session() as sess:
rdn = RDN(sess,
image_size=FLAGS.image_size,
is_train=FLAGS.is_train,
scale=FLAGS.scale,
c_dim=FLAGS.c_dim,
batch_size=FLAGS.batch_size,
test_img=FLAGS.test_img,
D=FLAGS.D,
C=FLAGS.C,
G=FLAGS.G,
G0=FLAGS.G0,
kernel_size=FLAGS.kernel_size)
rdn.train(FLAGS)
def main(_):
rdn = RDN(tf.Session(),
image_size=FLAGS.image_size,
is_train=FLAGS.is_train,
scale=FLAGS.scale,
c_dim=FLAGS.c_dim,
batch_size=FLAGS.batch_size,
test_img=FLAGS.test_img,
D=FLAGS.D,
C=FLAGS.C,
G=FLAGS.G,
G0=FLAGS.G0,
kernel_size=FLAGS.kernel_size)
if rdn.is_train:
rdn.train(FLAGS)
else:
rdn.test(FLAGS)
if torch.cuda.is_available():
netG.cuda()
netD.cuda()
generator_criterion.cuda()
optimizerG = optim.Adam(netG.parameters())
optimizerD = optim.Adam(netD.parameters())
results = {'d_loss': [], 'g_loss': [], 'd_score': [], 'g_score': []}
for epoch in range(1, NUM_EPOCHS + 1):
train_bar = tqdm(train_loader)
running_results = {'batch_sizes': 0, 'd_loss': 0, 'g_loss': 0, 'd_score': 0, 'g_score': 0}
netG.train()
netD.train()
for data, target in train_bar:
g_update_first = True
batch_size = data.size(0)
running_results['batch_sizes'] += batch_size
############################
# (1) Update D network: maximize D(x)-1-D(G(z))
###########################
real_img = Variable(target)
if torch.cuda.is_available():
real_img = real_img.cuda()
z = Variable(data)
if torch.cuda.is_available():
z = z.cuda()
import data
import argparse
from model import RDN
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", default="data/General-100")
parser.add_argument("--testset", default="")
parser.add_argument("--imgsize", default=128, type=int)
parser.add_argument("--scale", default=4, type=int)
parser.add_argument("--globallayers", default=16, type=int)
parser.add_argument("--locallayers", default=8, type=int)
parser.add_argument("--featuresize", default=64, type=int)
parser.add_argument("--batchsize", default=16, type=int)
parser.add_argument("--savedir", default='saved_models')
parser.add_argument("--iterations", default=10000, type=int)
parser.add_argument("--usepre", default=0, type=int)
args = parser.parse_args()
data.load_dataset(args.dataset, args.testset, args.imgsize)
down_size = args.imgsize // args.scale
network = RDN(down_size, args.globallayers, args.locallayers, args.featuresize,
args.scale)
network.set_data_fn(data.get_batch, (args.batchsize, args.imgsize, down_size),
data.get_test_set, (args.imgsize, args.scale))
network.train(args.iterations, args.savedir, args.usepre)