device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('device: {}'.format(device))
train = Train(device)
train.train_model(is_resume, epoch, file)
if args.t: # testing
if args.f:
file = args.f
else:
file = config.best_model
torch.cuda.empty_cache()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('device: {}'.format(device))
test = Test(device)
test.test_model(file)
if args.e: # rouge evaluation
s_file = args.sf
r_file = args.rf
torch.cuda.empty_cache()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('device: {}'.format(device))
test = Evaluate(device) # files to be pass here
final = test.evaluate_summaries(config.s_summaries)
# this gives size in kbs -- have to convert in bytes
usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss * 1024
memory = get_size(usage)
