def load_model(path, model): for file in os.listdir(path): if model in file: print(f"Loading the following model for sampling: {file}") try: ckpt = torchlib.load_checkpoint(os.path.join(path, file)) if model == "(1)": G_normal.load_state_dict(ckpt['G']) return G_normal elif model == "spectral": G_spectral.load_state_dict(ckpt['G']) return G_spectral except Exception as e: print(str(e)) return None
@torch.no_grad() def sample(z): G.eval() return G(z) # ============================================================================== # = run = # ============================================================================== # load checkpoint if exists ckpt_dir = py.join(output_dir, 'checkpoints') py.mkdir(ckpt_dir) try: ckpt = torchlib.load_checkpoint(ckpt_dir) ep, it_d, it_g = ckpt['ep'], ckpt['it_d'], ckpt['it_g'] D.load_state_dict(ckpt['D']) G.load_state_dict(ckpt['G']) D_optimizer.load_state_dict(ckpt['D_optimizer']) G_optimizer.load_state_dict(ckpt['G_optimizer']) except: ep, it_d, it_g = 0, 0, 0 # sample sample_dir = py.join(output_dir, 'samples_training') py.mkdir(sample_dir) # main loop writer = tensorboardX.SummaryWriter(py.join(output_dir, 'summaries')) z = torch.randn(100, args.z_dim, 1, 1).to(device) # a fixed noise for sampling
@torch.no_grad() def sample(z): G.eval() return G(z) # ============================================================================== # = run = # ============================================================================== # load checkpoint if exists ckpt_dir = py.join(output_dir, 'checkpoints') py.mkdir(ckpt_dir) try: ckpt = torchlib.load_checkpoint(py.join(ckpt_dir, 'Last.ckpt')) ep, it_d, it_g = ckpt['ep'], ckpt['it_d'], ckpt['it_g'] D.load_state_dict(ckpt['D']) G.load_state_dict(ckpt['G']) D_optimizer.load_state_dict(ckpt['D_optimizer']) G_optimizer.load_state_dict(ckpt['G_optimizer']) print('loading was successful. Starting at epoch: ', ep) except Exception as e: print(e) ep, it_d, it_g = 0, 0, 0 # sample sample_dir = py.join(output_dir, 'samples_training') py.mkdir(sample_dir) # main loop