def main():
# parse arguments
args = parse_args()
if args is None:
exit()
# open session
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
gan = GAN(sess, args)
# build graph
gan.build_model()
if args.phase == 'train' :
gan.train()
print(" [*] Training finished!")
if args.phase == 'test' :
gan.test()
print(" [*] Test finished!")
def main(argv):
# Load configs from file
config = json.load(open(FLAGS.config))
# set_backend()
# Set name
#name = '{}_{}_'.format(config['INPUT_NAME'], config['TARGET_NAME'])
#for l in config['LABELS']:
# name += str(l)
#config['NAME'] += '_' + name
if FLAGS.use_wandb:
import wandb
resume_wandb = True if FLAGS.wandb_resume_id is not None else False
wandb.init(config=config,
resume=resume_wandb,
id=FLAGS.wandb_resume_id,
project='EchoGen',
name=FLAGS.wandb_run_name)
# Initialize GAN
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
model = GAN(config, FLAGS.use_wandb, device, FLAGS.dataset_path)
# load trained models if they exist
if FLAGS.ckpt_load is not None:
model.load(f'{FLAGS.ckpt_load}/generator_last_checkpoint.bin',
model='generator')
model.load(f'{FLAGS.ckpt_load}/discriminator_last_checkpoint.bin',
model='discriminator')
if FLAGS.test:
model.test()
else:
model.train()
(FLAGS.updates_per_epoch * FLAGS.batch_size)
#every 10th batch, write summaries to disk
if (i % 10) == 0:
model.write_summaries(sum_epoch=i +
(num_batches * epoch))
model.calculate_accuracy(nova_params,
epoch=i +
(num_batches * epoch))
del real_images, nova_params
#calculate testing loss and accuracy
inputs = novaSet.test_info(which_config=[config, 'data'],
which_type=config_type)
gen_tst_loss, gen_tst_accur, disc_tst_loss, disc_tst_accur = model.test(
imgs=inputs[0], data=inputs[1], epoch=epoch)
print("Epoch %d : Gen Test Loss %f, Gen Test Accur %f, Disc Test Loss %f, Disc Test Accur %f, Gen Train Loss %f"\
% (epoch, gen_tst_loss, gen_tst_accur, disc_tst_loss, disc_tst_accur, training_loss))
del inputs
#every 10 epochs, write the models' parameters to the disk an save images
if (epoch % 10) == 0:
model.save_model(epoch)
#save images
print("Saving Image Summaries")
images = novaSet.next_batch(which_config=[config, 'data'],
which_type=config_type,
batch_size=5)
model.save_image_summary(r_inputs=images[0],
f_inputs=images[1],
