def main(args):
""" main() driver function """
# Parameters parsing
if filepath_is_not_valid(args.config):
logging.error("The path {} is not a file. Aborting..".format(args.config))
exit()
configuration, architecture, hyperparameters = parse_config_file(args.config, args.variation)
dataset_info = prepare_dataset(configuration)
if (dataset_info is None):
exit()
# Initialization
model = None
if (args.variation == "VAE"):
model = VAE(architecture, hyperparameters, dataset_info)
elif (args.variation == "B-VAE"):
model = betaVAE(architecture, hyperparameters, dataset_info)
# here you can change the gpus parameter into the amount of gpus you want the model to use
trainer = Trainer(max_epochs = hyperparameters["epochs"], gpus=None, fast_dev_run=False)
# Training and testing
trainer.fit(model)
result = trainer.test(model)
# Model needs to be transferred to the cpu as sample and reconstruct are custom methods
model = model.cpu()
model.sample(5)
model.reconstruct(5)
train(epoch)
evaluate()
evaluate('test')
# test()
# _, idx = torch.sort(score, 1, True)
# y_score = idx[:, :args.N]
# test = T.tocsr()
# evaluator = Evaluator({'recall'})
# evaluator.evaluate(run, test)
# result = evaluator.show(
# ['recall_5', 'recall_10', 'recall_15', 'recall_20'])
# print(result)
# line = 'cVAE\t{}\t{}\t{}\t{}\t0'.format(args.data, args.alpha, args.beta, len(args.layer))
# for _, value in result.items():
# line += '\t{:.5f}'.format(value)
# line += '\r\n'
# file = open('result', 'a')
# file.write(line)
# file.close()
#
if args.save:
name = 'cvae' if args.rating else 'fvae'
path = directory + '/model/' + name
for l in args.layer:
path += '_' + str(l)
model.cpu()
torch.save(model.state_dict(), path)
