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save_step = step // config.training.snapshot_freq
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save_checkpoint(os.path.join(checkpoint_dir, f'checkpoint_{save_step}.pth'), state)
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# Generate and save samples
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if config.training.snapshot_sampling:
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ema.store(score_model.parameters())
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ema.copy_to(score_model.parameters())
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sample, sample_steps, sample_nodes = sampling_fn(score_model, n_node_pmf)
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sample_list = adj2graph(sample, sample_nodes)
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ema.restore(score_model.parameters())
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this_sample_dir = os.path.join(sample_dir, "iter_{}".format(step))
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if not os.path.exists(this_sample_dir):
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os.makedirs(this_sample_dir)
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# graph visualization and save figs
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visualize.visualize_graphs(sample_list, this_sample_dir, config)
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def sde_evaluate(config, workdir, eval_folder="eval"):
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"""Evaluate trained models.
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Args:
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config: Configuration to use.
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workdir: Working directory for checkpoints.
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eval_folder: The subfolder for storing evaluation results. Default to "eval".
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"""
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# Create directory to eval_folder
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eval_dir = os.path.join(workdir, eval_folder)
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if not os.path.exists(eval_dir):
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os.makedirs(eval_dir)
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# Build data pipeline
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train_ds, _, test_ds, n_node_pmf = datasets.get_dataset(config)
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n_node_pmf = torch.from_numpy(n_node_pmf).to(config.device)
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# Creat data normalizer and its inverse
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scaler = datasets.get_data_scaler(config)
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inverse_scaler = datasets.get_data_inverse_scaler(config)
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# Initialize model
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score_model = mutils.create_model(config)
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optimizer = losses.get_optimizer(config, score_model.parameters())
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ema = ExponentialMovingAverage(score_model.parameters(), decay=config.model.ema_rate)
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state = dict(optimizer=optimizer, model=score_model, ema=ema, step=0)
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checkpoint_dir = os.path.join(workdir, "checkpoints")
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# Setup SDEs
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if config.training.sde.lower() == 'vpsde':
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sde = sde_lib.VPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max, N=config.model.num_scales)
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sampling_eps = 1e-3
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elif config.training.sde.lower() == 'subvpsde':
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sde = sde_lib.subVPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max,
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N=config.model.num_scales)
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sampling_eps = 1e-3
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elif config.training.sde.lower() == 'vesde':
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sde = sde_lib.VESDE(sigma_min=config.model.sigma_min, sigma_max=config.model.sigma_max,
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N=config.model.num_scales)
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sampling_eps = 1e-5
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else:
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raise NotImplementedError(f"SDE {config.training.sde} unknown.")
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# Build the sampling function when sampling is enabled
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if config.eval.enable_sampling:
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sampling_shape = (config.eval.batch_size,
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config.data.num_channels,
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config.data.max_node, config.data.max_node)
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sampling_fn = sampling.get_sampling_fn(config, sde, sampling_shape, inverse_scaler, sampling_eps)
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eval_stats_fn = get_stats_eval(config)
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nn_eval_fn = get_nn_eval(config)
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# Begin evaluation
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begin_ckpt = config.eval.begin_ckpt
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logging.info("begin checkpoint: %d" % (begin_ckpt,))
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for ckpt in range(begin_ckpt, config.eval.end_ckpt + 1):
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# Wait if the target checkpoint doesn't exist yet
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waiting_message_printed = False
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ckpt_filename = os.path.join(checkpoint_dir, "checkpoint_{}.pth".format(ckpt))
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while not os.path.exists(ckpt_filename):
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if not waiting_message_printed:
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logging.warning("Waiting for the arrival of checkpoint_%d" % (ckpt,))
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waiting_message_printed = True
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time.sleep(60)
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# Wait for 2 additional mins in case the file exists but is not ready for reading
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ckpt_path = os.path.join(checkpoint_dir, f'checkpoint_{ckpt}.pth')
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try:
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state = restore_checkpoint(ckpt_path, state, device=config.device)
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except:
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time.sleep(60)
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try:
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state = restore_checkpoint(ckpt_path, state, device=config.device)
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except:
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time.sleep(120)
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state = restore_checkpoint(ckpt_path, state, device=config.device)
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ema.copy_to(score_model.parameters())
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# Generate samples and compute MMD stats
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if config.eval.enable_sampling:
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