INNER CODE UNIT · Python
exp
graphdeeplearning/benchmarking-gnns · main_CYCLES_graph_classification.py:390
exp=torch.mean(torch.exp(torch.div(1, D)) - 1),
log=torch.mean(torch.log(D + 1)))
out_dir = out_dir + '_samples_' + str(net_params['num_train_data']) + '/'
root_log_dir = out_dir + 'logs/' + MODEL_NAME + "_" + DATASET_NAME + "_GPU" + str(config['gpu']['id']) + "_" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')
root_ckpt_dir = out_dir + 'checkpoints/' + MODEL_NAME + "_" + DATASET_NAME + "_GPU" + str(config['gpu']['id']) + "_" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')
write_file_name = out_dir + 'results/result_' + MODEL_NAME + "_" + DATASET_NAME + "_GPU" + str(config['gpu']['id']) + "_" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')
write_config_file = out_dir + 'configs/config_' + MODEL_NAME + "_" + DATASET_NAME + "_GPU" + str(config['gpu']['id']) + "_" + time.strftime('%Hh%Mm%Ss_on_%b_%d_%Y')
dirs = root_log_dir, root_ckpt_dir, write_file_name, write_config_file
if not os.path.exists(out_dir + 'results'):
os.makedirs(out_dir + 'results')
if not os.path.exists(out_dir + 'configs'):
os.makedirs(out_dir + 'configs')
net_params['total_param'] = view_model_param(MODEL_NAME, net_params)
train_val_pipeline(MODEL_NAME, dataset, params, net_params, dirs)