def main():
if not os.path.exists(os.path.join(config.tensorboard_dir, config.name)):
os.makedirs(os.path.join(config.tensorboard_dir, config.name))
if not os.path.exists(os.path.join(config.checkpoint_dir, config.name)):
os.makedirs(os.path.join(config.checkpoint_dir, config.name))
device = torch.device('cuda:0' if config.use_cuda else 'cpu')
models = GAN(config).to(device)
if config.load_epoch != 0:
load_checkpoints(models, config.checkpoint_dir, config.name,
config.load_epoch)
if config.is_train:
models.train()
writer = SummaryWriter(
log_dir=os.path.join(config.tensorboard_dir, config.name))
train(models, writer, device)
else:
models.eval()
test(models, device)
if __name__ == '__main__':
imageDir = '...celeb_images/Part 1'
image_path_list = []
for file in os.listdir(imageDir):
image_path_list.append(os.path.join(imageDir, file))
image = np.empty([len(image_path_list), 64, 64, 3])
for indx, imagePath in enumerate(image_path_list):
if(indx>5000):
break
im = Image.open(imagePath).convert('RGB')
im = im.resize((64, 64))
im=np.array(im,dtype=np.float32)
im=im/255
im=Normalize(im, [0.5,0.5,0.5], [0.5,0.5,0.5])
image[indx,:,:,:] = im
image = image[:5000,:,:,:]
gan = GAN()
gen, dis = gan.train(image,200)
noise = Variable(torch.randn(1, 100, 1, 1)).float().cuda()
im = gen.feed_forward(noise)
im = im.permute(0, 2, 3, 1)
im = im.squeeze(0).cpu().detach().numpy()*255
im = DeNormalize(im,[0.5,0.5,0.5],[0.5,0.5,0.5])
plt.imshow(im)