model = Generator(128, [1024, 512, 256, 128, 64, 32], 3)
ckpt = torch.load(args.cp_path, map_location=lambda storage, loc: storage)
model.load_state_dict(ckpt['model_state'])
if args.cuda:
model = model.cuda()
print('Cuda Mode is: {}'.format(args.cuda))
history = ckpt['history']
if not args.no_plots:
plot_learningcurves(history, 'gen_loss')
plot_learningcurves(history, 'disc_loss')
plot_learningcurves(history, 'gen_loss_minibatch')
plot_learningcurves(history, 'disc_loss_minibatch')
test_model(model=model,
n_tests=args.n_tests,
cuda_mode=args.cuda,
SNGAN=True)
save_samples(prefix='cats',
generator=model,
cp_name=args.cp_path.split('/')[-1].split('.')[0],
cuda_mode=args.cuda,
enhance=False,
im_size=256,
fig_size=(4, 8),
SNGAN=True)
if args.cuda:
model = model.cuda()
print('Cuda Mode is: {}'.format(args.cuda))
history = ckpt['history']
print('Min FID:', np.min(history['FID-c']))
print('Epoch with min FID:', np.argmin(history['FID-c']))
if not args.no_plots:
plot_learningcurves(history, 'gen_loss')
plot_learningcurves(history, 'disc_loss')
plot_learningcurves(history, 'gen_loss_minibatch')
plot_learningcurves(history, 'disc_loss_minibatch')
plot_learningcurves(history, 'FID-c')
test_model(model=model, n_tests=args.n_tests, cuda_mode=args.cuda)
save_samples(prefix='CIFAR10_DCGAN',
generator=model,
cp_name=args.cp_path.split('/')[-1].split('.')[0],
cuda_mode=args.cuda)
if args.inception:
print(
inception_score(model,
N=args.n_inception,
cuda=args.cuda,
resize=True,
splits=10))