def get_spacegan_config(training_step, prob_config, check_config, cond_input, target):
model_save_prefix = 'saved_models/'
# load generator
gen_model = torch.load(model_save_prefix+"gen_iter " + str(training_step) + ".pkl.gz")
# load discrimininator
disc_model = torch.load(model_save_prefix+"disc_iter " + str(training_step) + ".pkl.gz")
# create a cgan object
spacegan_i = spacegan_method.SpaceGAN(prob_config, check_config, disc_model, gen_model)
# get scaling method
spacegan_i.fit_scaling_method(cond_input, target)
return spacegan_i
"input_labels"] = spacegan_config.cond_vars + neighbour_list
# pre-instantiation
disc_method = spacegan_config.Discriminator(
spacegan_config.prob_config["output_dim"],
spacegan_config.prob_config["cond_dim"])
disc_method.to(spacegan_config.prob_config["device"])
gen_method = spacegan_config.Generator(
spacegan_config.prob_config["cond_dim"],
spacegan_config.prob_config["noise_dim"],
spacegan_config.prob_config["output_dim"])
gen_method.to(spacegan_config.prob_config["device"])
# training SpaceGAN
spacegan = spacegan_method.SpaceGAN(spacegan_config.prob_config,
spacegan_config.check_config,
disc_method, gen_method)
spacegan.train(x_train=cond_input, y_train=target, coords=coord_input)
spacegan.checkpoint_model(spacegan.epochs) # export final model and data
# computing metrics
gan_metrics = spacegan_selection.compute_metrics(
target, cond_input, spacegan_config.prob_config,
spacegan_config.check_config, coord_input, spacegan_config.neighbours)
# selecting and sampling gan
for criteria in list(spacegan_config.check_config["perf_metrics"].keys()):
# find best config
criteria_info = spacegan_config.check_config["pf_metrics_setting"][
criteria]
perf_metrics = gan_metrics[criteria_info["metric_level"]]