runner = Trainer(default_save_path=f"{tt_logger.save_dir}",
min_nb_epochs=1,
logger=tt_logger,
log_save_interval=100,
train_percent_check=1.,
val_percent_check=1.,
num_sanity_val_steps=5,
early_stop_callback=False,
**config['trainer_params'])
print(f"======= Training {config['model_params']['name']} =======")
load_dict = torch.load(config.ckpt_path)
experiment.load_state_dict(load_dict['state_dict'])
experiment.cuda()
experiment.eval()
sample_dataloader = experiment.train_dataloader()
test_input, test_label = next(iter(sample_dataloader))
#test_input = test_input.to('cuda')
test_label = test_label.to('cuda')
#imgs = experiment.model.sample(num_samples=64, current_device=0)
test_input = scio.loadmat('./cifar10_index.mat')
test_input = torch.Tensor(test_input['data']).to('cuda')
imgs_recon = experiment.model.generate(test_input, labels=test_label)
FID_IS_tf = build_GAN_metric(config.GAN_metric)
class SampleFunc(object):
def __init__(self, model):
self.model = model
pass
model_save_path = os.getcwd(
) #'{}/{}/version_{}'.format(config['logging_params']['save_dir'], config['logging_params']['name'], tt_logger.version)
parent = '/'.join(model_save_path.split('/')[:-3])
config['logging_params']['save_dir'] = os.path.join(
parent, config['logging_params']['save_dir'])
config['exp_params']['data_path'] = os.path.join(
parent, config['exp_params']['data_path'])
print(parent, config['exp_params']['data_path'])
model = vae_models[config['model_params']['name']](
imsize=config['exp_params']['img_size'], **config['model_params'])
experiment = VAEXperiment(model, config['exp_params'])
weights = [x for x in os.listdir(model_save_path) if '.ckpt' in x]
weights.sort(key=lambda x: os.path.getmtime(x))
load_weight = weights[-1]
print('loading: ', load_weight)
checkpoint = torch.load(load_weight)
experiment.load_state_dict(checkpoint['state_dict'])
_ = experiment.train_dataloader()
experiment.eval()
experiment.freeze()
experiment.sample_interpolate(
save_dir=config['logging_params']['save_dir'],
name=config['logging_params']['name'],
version=config['logging_params']['version'],
save_svg=True,
other_interpolations=config['logging_params']['other_interpolations'])
